From b7b346d0825b3a7d836a93460ad6337ccb46d1e7 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 5 Nov 2025 17:35:48 +0800 Subject: [PATCH 01/91] feat: add planner passthrough support for agents and enhance planning service --- .../configs/agent_cards/strategy_agent.json | 29 +++++++++ python/valuecell/core/agent/connect.py | 20 +++++- python/valuecell/core/plan/service.py | 63 ++++++++++++++++++- 3 files changed, 110 insertions(+), 2 deletions(-) create mode 100644 python/configs/agent_cards/strategy_agent.json diff --git a/python/configs/agent_cards/strategy_agent.json b/python/configs/agent_cards/strategy_agent.json new file mode 100644 index 000000000..4e7073fbe --- /dev/null +++ b/python/configs/agent_cards/strategy_agent.json @@ -0,0 +1,29 @@ +{ + "name": "StrategyAgent", + "display_name": "Strategy Agent", + "description": "LLM-driven strategy composer that turns market features into normalized trade instructions. Includes a simple runtime for demo and testing.", + "capabilities": { + "streaming": true, + "push_notifications": true + }, + "skills": [ + { + "id": "strategy_run", + "name": "Run Strategy", + "description": "Start a strategy using the provided model, exchange and trading configuration.", + "examples": [ + "Run strategy with DeepSeek model on BTC-USD and ETH-USD", + "Start a virtual backtest with $10,000 initial capital for BTC" + ], + "tags": ["strategy", "run", "trading", "compose"] + } + ], + "enabled": true, + "metadata": { + "planner_passthrough": true, + "version": "0.1.0", + "author": "ValueCell Team", + "tags": ["strategy", "trading", "llm", "demo"], + "notes": "This card is a lightweight example; replace model api_key and tune parameters for production use." + } +} diff --git a/python/valuecell/core/agent/connect.py b/python/valuecell/core/agent/connect.py index 1594adf69..e60a4b231 100644 --- a/python/valuecell/core/agent/connect.py +++ b/python/valuecell/core/agent/connect.py @@ -31,6 +31,8 @@ class AgentContext: listener_task: Optional[asyncio.Task] = None listener_url: Optional[str] = None client: Optional[AgentClient] = None + # Planner passthrough flag derived from raw agent card JSON + planner_passthrough: bool = False # Listener preferences desired_listener_host: Optional[str] = None desired_listener_port: Optional[int] = None @@ -91,13 +93,20 @@ def _load_remote_contexts(self, agent_card_dir: str = None) -> None: continue if not agent_card_dict.get("enabled", True): continue + # Detect planner passthrough from raw JSON (top-level or metadata) + passthrough = bool(agent_card_dict.get("planner_passthrough")) + if not passthrough: + meta = agent_card_dict.get("metadata") or {} + if isinstance(meta, dict): + passthrough = bool(meta.get("planner_passthrough")) local_agent_card = parse_local_agent_card_dict(agent_card_dict) - if not local_agent_card or not local_agent_card.url: + if not local_agent_card: continue self._contexts[agent_name] = AgentContext( name=agent_name, url=local_agent_card.url, local_agent_card=local_agent_card, + planner_passthrough=passthrough, ) except (json.JSONDecodeError, FileNotFoundError, KeyError) as e: logger.warning( @@ -330,3 +339,12 @@ def get_all_agent_cards(self) -> Dict[str, AgentCard]: agent_cards[name] = card return agent_cards + + def is_planner_passthrough(self, agent_name: str) -> bool: + """Return True if the named agent is marked as planner passthrough. + + The flag is read once from raw JSON on load and cached in AgentContext. + """ + self._ensure_remote_contexts_loaded() + ctx = self._contexts.get(agent_name) + return bool(getattr(ctx, "planner_passthrough", False)) if ctx else False diff --git a/python/valuecell/core/plan/service.py b/python/valuecell/core/plan/service.py index 48b0729b2..a4ce8ac92 100644 --- a/python/valuecell/core/plan/service.py +++ b/python/valuecell/core/plan/service.py @@ -1,16 +1,26 @@ -"""Planning service coordinating planner and user input lifecycle.""" +"""Planning service coordinating planner and user input lifecycle. + +Enhancement: supports "planner passthrough" agents. When a target agent is +marked as passthrough (flag captured by RemoteConnections at startup), the +planner will skip running the LLM planning agent and directly synthesize a +single-task ExecutionPlan that hands the user's query to the specified agent. +""" from __future__ import annotations import asyncio +from datetime import datetime from typing import Awaitable, Callable, Dict, Optional from valuecell.core.agent.connect import RemoteConnections +from valuecell.core.plan.models import ExecutionPlan from valuecell.core.plan.planner import ( ExecutionPlanner, UserInputRequest, ) +from valuecell.core.task.models import Task from valuecell.core.types import UserInput +from valuecell.utils import generate_uuid class UserInputRegistry: @@ -49,6 +59,7 @@ def __init__( execution_planner: ExecutionPlanner | None = None, user_input_registry: UserInputRegistry | None = None, ) -> None: + self._agent_connections = agent_connections self._planner = execution_planner or ExecutionPlanner(agent_connections) self._input_registry = user_input_registry or UserInputRegistry() @@ -81,6 +92,56 @@ def start_planning_task( ) -> asyncio.Task: """Kick off asynchronous planning.""" + agent_name = (user_input.target_agent_name or "").strip() + is_passthrough = False + if agent_name: + try: + is_passthrough = bool( + self._agent_connections.is_planner_passthrough(agent_name) + ) + except Exception: + is_passthrough = False + if is_passthrough: + # Directly create a simple one-task plan without invoking the LLM planner + return asyncio.create_task( + self._create_passthrough_plan(user_input, thread_id) + ) + return asyncio.create_task( self._planner.create_plan(user_input, callback, thread_id) ) + + # ------------------------ + # Internal helpers + # ------------------------ + async def _create_passthrough_plan( + self, user_input: UserInput, thread_id: str + ) -> ExecutionPlan: + """Synthesize a simple one-task plan that directly invokes target agent. + + The produced plan mirrors the structure of a normal planner output but + avoids any LLM calls. It simply wraps the user's query into a Task + addressed to the target agent. + """ + conversation_id = user_input.meta.conversation_id + plan = ExecutionPlan( + plan_id=generate_uuid("plan"), + conversation_id=conversation_id, + user_id=user_input.meta.user_id, + orig_query=user_input.query, + created_at=datetime.now().isoformat(), + ) + + agent_name = user_input.target_agent_name or "" + # Keep a concise title so UI/conversation title can reuse it + title = f"Run {agent_name}".strip() + task = Task( + conversation_id=conversation_id, + thread_id=thread_id, + user_id=user_input.meta.user_id, + agent_name=agent_name, + title=title, + query=user_input.query, + ) + plan.tasks = [task] + return plan From d2b0ad9a62480cd6cc3d3f95b7f4bdaae80bab19 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 5 Nov 2025 17:45:25 +0800 Subject: [PATCH 02/91] feat: implement StrategyAgent streaming and decision cycle with enhanced error handling and logging --- .../valuecell/agents/strategy_agent/agent.py | 58 +++- .../valuecell/agents/strategy_agent/core.py | 301 ++++++++++++++++- .../valuecell/agents/strategy_agent/models.py | 20 +- .../agents/strategy_agent/runtime.py | 309 ++++++++++++++++++ .../agents/strategy_agent/tests/__init__.py | 1 + .../agents/strategy_agent/tests/test_agent.py | 43 +++ 6 files changed, 715 insertions(+), 17 deletions(-) create mode 100644 python/valuecell/agents/strategy_agent/runtime.py create mode 100644 python/valuecell/agents/strategy_agent/tests/__init__.py create mode 100644 python/valuecell/agents/strategy_agent/tests/test_agent.py diff --git a/python/valuecell/agents/strategy_agent/agent.py b/python/valuecell/agents/strategy_agent/agent.py index 37d6382ec..6f533deb6 100644 --- a/python/valuecell/agents/strategy_agent/agent.py +++ b/python/valuecell/agents/strategy_agent/agent.py @@ -1,21 +1,25 @@ from __future__ import annotations +import asyncio +import logging from typing import AsyncGenerator, Dict, Optional from valuecell.core.agent.responses import streaming from valuecell.core.types import BaseAgent, StreamResponse +from .models import ( + ComponentType, + StrategyStatusContent, + UserRequest, + StrategyStatus, +) +from .runtime import create_strategy_runtime -class StrategyAgent(BaseAgent): - """Minimal StrategyAgent entry for system integration. +logger = logging.getLogger(__name__) - This is a placeholder agent that streams a short greeting and completes. - It can be extended to wire the Strategy Agent decision loop - (data -> features -> composer -> execution -> history/digest). - """ - def __init__(self, **kwargs): - super().__init__(**kwargs) +class StrategyAgent(BaseAgent): + """Top-level Strategy Agent integrating the decision coordinator.""" async def stream( self, @@ -24,8 +28,38 @@ async def stream( task_id: str, dependencies: Optional[Dict] = None, ) -> AsyncGenerator[StreamResponse, None]: - # Minimal streaming lifecycle: one message and done - yield streaming.message_chunk( - "StrategyAgent is online. Decision pipeline will be wired here." + try: + request = UserRequest.model_validate_json(query) + except ValueError as exc: + logger.warning("StrategyAgent received invalid payload: %s", exc) + yield streaming.message_chunk(str(exc)) + yield streaming.done() + return + + runtime = create_strategy_runtime(request) + initial_payload = StrategyStatusContent( + strategy_id=runtime.strategy_id, + status=StrategyStatus.RUNNING, ) - yield streaming.done() + yield streaming.component_generator( + content=initial_payload.model_dump_json(), + component_type=ComponentType.STATUS.value, + ) + + try: + while True: + result = runtime.run_cycle() + for trade in result.trades: + yield streaming.component_generator( + content=trade.model_dump_json(), + component_type=ComponentType.UPDATE.value, + ) + await asyncio.sleep(request.trading_config.decide_interval) + + except asyncio.CancelledError: + raise + except Exception as err: # noqa: BLE001 + logger.exception("StrategyAgent stream failed: %%s", err) + yield streaming.message_chunk(f"StrategyAgent error: {err}") + finally: + yield streaming.done() diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index 6c9969f4d..2c37e5d63 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -1,6 +1,45 @@ from __future__ import annotations from abc import ABC, abstractmethod +from dataclasses import dataclass +from datetime import datetime, timezone +from typing import Callable, Dict, List, Optional + +from valuecell.utils.uuid import generate_uuid + +from .data.interfaces import MarketDataSource +from .decision.interfaces import Composer +from .execution.interfaces import ExecutionGateway +from .features.interfaces import FeatureComputer +from .models import ( + ComposeContext, + FeatureVector, + HistoryRecord, + StrategyStatus, + StrategySummary, + TradeDigest, + TradeHistoryEntry, + TradeInstruction, + TradeSide, + TradeType, + UserRequest, +) +from .portfolio.interfaces import PortfolioService +from .trading_history.interfaces import DigestBuilder, HistoryRecorder + + +@dataclass +class DecisionCycleResult: + """Outcome of a single decision cycle.""" + + compose_id: str + timestamp_ms: int + summary: StrategySummary + instructions: List[TradeInstruction] + trades: List[TradeHistoryEntry] + history_records: List[HistoryRecord] + digest: TradeDigest + # Core interfaces for orchestration and portfolio service. # Plain ABCs to avoid runtime dependencies on pydantic. Concrete implementations @@ -20,6 +59,264 @@ class DecisionCoordinator(ABC): """ @abstractmethod - def run_once(self) -> None: - """Execute one decision cycle.""" + def run_once(self) -> DecisionCycleResult: + """Execute one decision cycle and return the result.""" raise NotImplementedError + + +def _default_clock() -> datetime: + """Return current time in UTC.""" + + return datetime.now(timezone.utc) + + +def _build_market_snapshot(features: List[FeatureVector]) -> Dict[str, float]: + """Derive latest market snapshot from feature vectors.""" + + snapshot: Dict[str, float] = {} + for vector in features: + price = vector.values.get("close") + if price is not None: + snapshot[vector.instrument.symbol] = float(price) + return snapshot + + +class DefaultDecisionCoordinator(DecisionCoordinator): + """Default implementation that wires the full decision pipeline.""" + + def __init__( + self, + *, + request: UserRequest, + strategy_id: str, + portfolio_service: PortfolioService, + market_data_source: MarketDataSource, + feature_computer: FeatureComputer, + composer: Composer, + execution_gateway: ExecutionGateway, + history_recorder: HistoryRecorder, + digest_builder: DigestBuilder, + interval: str = "1m", + lookback: int = 20, + prompt_provider: Optional[Callable[[UserRequest], str]] = None, + clock: Optional[Callable[[], datetime]] = None, + history_limit: int = 200, + ) -> None: + self._request = request + self.strategy_id = strategy_id + self._portfolio_service = portfolio_service + self._market_data_source = market_data_source + self._feature_computer = feature_computer + self._composer = composer + self._execution_gateway = execution_gateway + self._history_recorder = history_recorder + self._digest_builder = digest_builder + self._interval = interval + self._lookback = lookback + self._history_limit = max(history_limit, 1) + self._symbols = list(dict.fromkeys(request.trading_config.symbols)) + self._prompt_provider = ( + prompt_provider if prompt_provider is not None else self._default_prompt + ) + self._clock = clock if clock is not None else _default_clock + self._history_records: List[HistoryRecord] = [] + self._realized_pnl: float = 0.0 + self._unrealized_pnl: float = 0.0 + self._cycle_index: int = 0 + self._strategy_name = request.trading_config.strategy_name or strategy_id + + def run_once(self) -> DecisionCycleResult: + timestamp_ms = int(self._clock().timestamp() * 1000) + compose_id = generate_uuid("compose") + + portfolio = self._portfolio_service.get_view() + candles = self._market_data_source.get_recent_candles( + self._symbols, self._interval, self._lookback + ) + features = self._feature_computer.compute_features(candles=candles) + market_snapshot = _build_market_snapshot(features) + digest = self._digest_builder.build(list(self._history_records)) + + context = ComposeContext( + ts=timestamp_ms, + compose_id=compose_id, + strategy_id=self.strategy_id, + features=features, + portfolio=portfolio, + digest=digest, + prompt_text=self._prompt_provider(self._request), + market_snapshot=market_snapshot, + constraints=None, + ) + + instructions = self._composer.compose(context) + self._execution_gateway.execute(instructions) + + trades = self._create_trades( + instructions, market_snapshot, compose_id, timestamp_ms + ) + self._apply_trades_to_portfolio(trades, market_snapshot) + summary = self._build_summary(timestamp_ms, trades) + + history_records = self._create_history_records( + timestamp_ms, compose_id, features, instructions, trades, summary + ) + + for record in history_records: + self._history_recorder.record(record) + + self._history_records.extend(history_records) + if len(self._history_records) > self._history_limit: + self._history_records = self._history_records[-self._history_limit :] + + digest = self._digest_builder.build(list(self._history_records)) + self._cycle_index += 1 + + return DecisionCycleResult( + compose_id=compose_id, + timestamp_ms=timestamp_ms, + summary=summary, + instructions=instructions, + trades=trades, + history_records=history_records, + digest=digest, + ) + + def _default_prompt(self, request: UserRequest) -> str: + custom_prompt = request.trading_config.custom_prompt + if custom_prompt: + return custom_prompt + symbols = ", ".join(self._symbols) + return f"Compose trading instructions for symbols: {symbols}." + + def _create_trades( + self, + instructions: List[TradeInstruction], + market_snapshot: Dict[str, float], + compose_id: str, + timestamp_ms: int, + ) -> List[TradeHistoryEntry]: + trades: List[TradeHistoryEntry] = [] + for instruction in instructions: + symbol = instruction.instrument.symbol + price = market_snapshot.get(symbol, 0.0) + notional = price * instruction.quantity + realized_pnl = notional * ( + 0.001 if instruction.side == TradeSide.SELL else -0.001 + ) + trades.append( + TradeHistoryEntry( + trade_id=generate_uuid("trade"), + compose_id=compose_id, + instruction_id=instruction.instruction_id, + strategy_id=self.strategy_id, + instrument=instruction.instrument, + side=instruction.side, + type=TradeType.LONG + if instruction.side == TradeSide.BUY + else TradeType.SHORT, + quantity=instruction.quantity, + entry_price=price or None, + exit_price=None, + notional_entry=notional or None, + notional_exit=None, + entry_ts=timestamp_ms, + exit_ts=None, + trade_ts=timestamp_ms, + holding_ms=None, + realized_pnl=realized_pnl, + realized_pnl_pct=(realized_pnl / notional) if notional else None, + leverage=None, + note=None, + ) + ) + return trades + + def _apply_trades_to_portfolio( + self, + trades: List[TradeHistoryEntry], + market_snapshot: Dict[str, float], + ) -> None: + if not trades: + return + + apply_method = getattr(self._portfolio_service, "apply_trades", None) + if callable(apply_method): + apply_method(trades, market_snapshot) + + def _build_summary( + self, + timestamp_ms: int, + trades: List[TradeHistoryEntry], + ) -> StrategySummary: + realized_delta = sum(trade.realized_pnl or 0.0 for trade in trades) + self._realized_pnl += realized_delta + + unrealized_delta = sum( + (trade.notional_entry or 0.0) * 0.0001 for trade in trades + ) + self._unrealized_pnl = max(self._unrealized_pnl + unrealized_delta, 0.0) + + initial_capital = self._request.trading_config.initial_capital or 0.0 + pnl_pct = ( + (self._realized_pnl + self._unrealized_pnl) / initial_capital + if initial_capital + else None + ) + + return StrategySummary( + strategy_id=self.strategy_id, + name=self._strategy_name, + model_provider=self._request.llm_model_config.provider, + model_id=self._request.llm_model_config.model_id, + exchange_id=self._request.exchange_config.exchange_id, + mode=self._request.exchange_config.trading_mode, + status=StrategyStatus.RUNNING, + realized_pnl=self._realized_pnl, + unrealized_pnl=self._unrealized_pnl, + pnl_pct=pnl_pct, + last_updated_ts=timestamp_ms, + ) + + def _create_history_records( + self, + timestamp_ms: int, + compose_id: str, + features: List[FeatureVector], + instructions: List[TradeInstruction], + trades: List[TradeHistoryEntry], + summary: StrategySummary, + ) -> List[HistoryRecord]: + feature_payload = [vector.model_dump(mode="json") for vector in features] + instruction_payload = [inst.model_dump(mode="json") for inst in instructions] + trade_payload = [trade.model_dump(mode="json") for trade in trades] + + return [ + HistoryRecord( + ts=timestamp_ms, + kind="features", + reference_id=compose_id, + payload={"features": feature_payload}, + ), + HistoryRecord( + ts=timestamp_ms, + kind="compose", + reference_id=compose_id, + payload={ + "prompt": self._prompt_provider(self._request), + "summary": summary.model_dump(mode="json"), + }, + ), + HistoryRecord( + ts=timestamp_ms, + kind="instructions", + reference_id=compose_id, + payload={"instructions": instruction_payload}, + ), + HistoryRecord( + ts=timestamp_ms, + kind="execution", + reference_id=compose_id, + payload={"trades": trade_payload}, + ), + ] diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index 42276f9bf..486dbb849 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -34,7 +34,14 @@ class TradeSide(str, Enum): SELL = "SELL" -class ModelConfig(BaseModel): +class ComponentType(str, Enum): + """Component types for StrategyAgent streaming responses.""" + + STATUS = "strategy_agent_status" + UPDATE = "strategy_agent_update" + + +class LLMModelConfig(BaseModel): """AI model configuration for strategy.""" provider: str = Field( @@ -121,8 +128,8 @@ class UserRequest(BaseModel): update a strategy instance. It was previously named `Strategy`. """ - model_config: ModelConfig = Field( - default_factory=ModelConfig, description="AI model configuration" + llm_model_config: LLMModelConfig = Field( + default_factory=LLMModelConfig, description="AI model configuration" ) exchange_config: ExchangeConfig = Field( default_factory=ExchangeConfig, description="Exchange configuration for trading" @@ -450,3 +457,10 @@ class StrategySummary(BaseModel): default=None, description="P&L as percent of equity or initial capital" ) last_updated_ts: Optional[int] = Field(default=None) + + +class StrategyStatusContent(BaseModel): + """Content for strategy agent status component.""" + + strategy_id: str + status: StrategyStatus diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py new file mode 100644 index 000000000..eb2cc8eb0 --- /dev/null +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -0,0 +1,309 @@ +from __future__ import annotations + +import math +from collections import defaultdict +from dataclasses import dataclass +from datetime import datetime, timezone +from typing import Dict, List, Optional + +from valuecell.utils.uuid import generate_uuid + +from .core import DecisionCycleResult, DefaultDecisionCoordinator +from .data.interfaces import MarketDataSource +from .decision.interfaces import Composer +from .execution.interfaces import ExecutionGateway +from .features.interfaces import FeatureComputer +from .models import ( + Candle, + ComposeContext, + FeatureVector, + HistoryRecord, + InstrumentRef, + PortfolioView, + PositionSnapshot, + TradeDigest, + TradeDigestEntry, + TradeHistoryEntry, + TradeInstruction, + TradeSide, + TradingMode, + UserRequest, +) +from .portfolio.interfaces import PortfolioService +from .trading_history.interfaces import DigestBuilder, HistoryRecorder + + +class SimpleMarketDataSource(MarketDataSource): + """Generates synthetic candle data for each symbol.""" + + def __init__(self, base_prices: Optional[Dict[str, float]] = None) -> None: + self._base_prices = base_prices or {} + self._counters: Dict[str, int] = defaultdict(int) + + def get_recent_candles( + self, symbols: List[str], interval: str, lookback: int + ) -> List[Candle]: + now_ms = int(datetime.now(timezone.utc).timestamp() * 1000) + interval_ms = 60_000 + candles: List[Candle] = [] + + for symbol in symbols: + counter = self._counters[symbol] + base_price = self._base_prices.get(symbol, 100.0) + for index in range(lookback): + step = counter + index + price = max(base_price + math.sin(step / 5.0) * 2.5, 1.0) + ts = now_ms - (lookback - index) * interval_ms + candles.append( + Candle( + ts=ts, + instrument=InstrumentRef( + symbol=symbol, exchange_id=None, quote_ccy="USD" + ), + open=price * 0.998, + high=price * 1.01, + low=price * 0.99, + close=price, + volume=1_000 + step * 10, + interval=interval, + ) + ) + self._counters[symbol] += lookback + + return candles + + +class SimpleFeatureComputer(FeatureComputer): + """Computes basic momentum and volume features.""" + + def compute_features( + self, candles: Optional[List[Candle]] = None + ) -> List[FeatureVector]: + if not candles: + return [] + + grouped: Dict[str, List[Candle]] = defaultdict(list) + for candle in candles: + grouped[candle.instrument.symbol].append(candle) + + features: List[FeatureVector] = [] + for symbol, series in grouped.items(): + series.sort(key=lambda item: item.ts) + last = series[-1] + prev = series[-2] if len(series) > 1 else series[-1] + change_pct = (last.close - prev.close) / prev.close if prev.close else 0.0 + features.append( + FeatureVector( + ts=last.ts, + instrument=last.instrument, + values={ + "close": last.close, + "volume": last.volume, + "change_pct": change_pct, + }, + meta={"interval": last.interval, "count": len(series)}, + ) + ) + + return features + + +class RuleBasedComposer(Composer): + """Simple deterministic composer using momentum.""" + + def __init__(self, threshold: float = 0.003, max_quantity: float = 1.0) -> None: + self._threshold = threshold + self._max_quantity = max_quantity + + def compose(self, context: ComposeContext) -> List[TradeInstruction]: + instructions: List[TradeInstruction] = [] + for feature in context.features: + change_pct = float(feature.values.get("change_pct", 0.0)) + if abs(change_pct) < self._threshold: + continue + + symbol = feature.instrument.symbol + side = TradeSide.BUY if change_pct > 0 else TradeSide.SELL + quantity = min(self._max_quantity, max(0.01, abs(change_pct) * 10)) + instruction_id = f"{context.compose_id}:{symbol}:{side.value}" + + instructions.append( + TradeInstruction( + instruction_id=instruction_id, + compose_id=context.compose_id, + instrument=feature.instrument, + side=side, + quantity=quantity, + price_mode="market", + limit_price=None, + max_slippage_bps=25, + meta={"change_pct": change_pct}, + ) + ) + + return instructions + + +class PaperExecutionGateway(ExecutionGateway): + """Records instructions without sending them anywhere.""" + + def __init__(self) -> None: + self.executed: List[TradeInstruction] = [] + + def execute(self, instructions: List[TradeInstruction]) -> None: + self.executed.extend(instructions) + + +class InMemoryHistoryRecorder(HistoryRecorder): + """In-memory recorder storing history records.""" + + def __init__(self) -> None: + self.records: List[HistoryRecord] = [] + + def record(self, record: HistoryRecord) -> None: + self.records.append(record) + + +class RollingDigestBuilder(DigestBuilder): + """Builds a lightweight digest from recent execution records.""" + + def __init__(self, window: int = 50) -> None: + self._window = max(window, 1) + + def build(self, records: List[HistoryRecord]) -> TradeDigest: + recent = records[-self._window :] + by_instrument: Dict[str, TradeDigestEntry] = {} + + for record in recent: + if record.kind != "execution": + continue + trades = record.payload.get("trades", []) + for trade_dict in trades: + instrument_dict = trade_dict.get("instrument") or {} + symbol = instrument_dict.get("symbol") + if not symbol: + continue + entry = by_instrument.get(symbol) + if entry is None: + entry = TradeDigestEntry( + instrument=InstrumentRef(**instrument_dict), + trade_count=0, + realized_pnl=0.0, + ) + by_instrument[symbol] = entry + entry.trade_count += 1 + realized = float(trade_dict.get("realized_pnl") or 0.0) + entry.realized_pnl += realized + entry.last_trade_ts = trade_dict.get("trade_ts") or entry.last_trade_ts + + timestamp = ( + recent[-1].ts + if recent + else int(datetime.now(timezone.utc).timestamp() * 1000) + ) + return TradeDigest(ts=timestamp, by_instrument=by_instrument) + + +class InMemoryPortfolioService(PortfolioService): + """Tracks cash and positions in memory.""" + + def __init__(self, initial_capital: float, trading_mode: TradingMode) -> None: + self._view = PortfolioView( + ts=int(datetime.now(timezone.utc).timestamp() * 1000), + cash=initial_capital, + positions={}, + gross_exposure=None, + net_exposure=None, + constraints=None, + ) + self._trading_mode = trading_mode + + def get_view(self) -> PortfolioView: + self._view.ts = int(datetime.now(timezone.utc).timestamp() * 1000) + return self._view + + def apply_trades( + self, trades: List[TradeHistoryEntry], market_snapshot: Dict[str, float] + ) -> None: + for trade in trades: + symbol = trade.instrument.symbol + price = trade.entry_price or market_snapshot.get(symbol, 0.0) + quantity_delta = ( + trade.quantity if trade.side == TradeSide.BUY else -trade.quantity + ) + position = self._view.positions.get(symbol) + if position is None: + position = PositionSnapshot( + instrument=trade.instrument, + quantity=0.0, + avg_price=None, + mark_price=price, + unrealized_pnl=None, + ) + self._view.positions[symbol] = position + + new_quantity = position.quantity + quantity_delta + position.mark_price = price + if new_quantity == 0: + self._view.positions.pop(symbol, None) + else: + position.quantity = new_quantity + if position.avg_price is None: + position.avg_price = price + else: + position.avg_price = (position.avg_price + price) / 2.0 + + notional = (price or 0.0) * trade.quantity + if trade.side == TradeSide.BUY: + self._view.cash -= notional + else: + self._view.cash += notional + + +@dataclass +class StrategyRuntime: + request: UserRequest + strategy_id: str + coordinator: DefaultDecisionCoordinator + + def run_cycle(self) -> DecisionCycleResult: + return self.coordinator.run_once() + + +def create_strategy_runtime(request: UserRequest) -> StrategyRuntime: + strategy_id = request.trading_config.strategy_name or generate_uuid("strategy") + + initial_capital = request.trading_config.initial_capital or 0.0 + portfolio_service = InMemoryPortfolioService( + initial_capital=initial_capital, + trading_mode=request.exchange_config.trading_mode, + ) + + base_prices = { + symbol: 120.0 + index * 15.0 + for index, symbol in enumerate(request.trading_config.symbols) + } + market_data_source = SimpleMarketDataSource(base_prices=base_prices) + feature_computer = SimpleFeatureComputer() + composer = RuleBasedComposer() + execution_gateway = PaperExecutionGateway() + history_recorder = InMemoryHistoryRecorder() + digest_builder = RollingDigestBuilder() + + coordinator = DefaultDecisionCoordinator( + request=request, + strategy_id=strategy_id, + portfolio_service=portfolio_service, + market_data_source=market_data_source, + feature_computer=feature_computer, + composer=composer, + execution_gateway=execution_gateway, + history_recorder=history_recorder, + digest_builder=digest_builder, + ) + + return StrategyRuntime( + request=request, + strategy_id=strategy_id, + coordinator=coordinator, + ) diff --git a/python/valuecell/agents/strategy_agent/tests/__init__.py b/python/valuecell/agents/strategy_agent/tests/__init__.py new file mode 100644 index 000000000..616ed7f21 --- /dev/null +++ b/python/valuecell/agents/strategy_agent/tests/__init__.py @@ -0,0 +1 @@ +# Tests for strategy_agent diff --git a/python/valuecell/agents/strategy_agent/tests/test_agent.py b/python/valuecell/agents/strategy_agent/tests/test_agent.py new file mode 100644 index 000000000..00f0687a5 --- /dev/null +++ b/python/valuecell/agents/strategy_agent/tests/test_agent.py @@ -0,0 +1,43 @@ +import asyncio +import json + +import pytest + +from valuecell.agents.strategy_agent.agent import StrategyAgent + + +@pytest.mark.asyncio +async def test_strategy_agent_basic_stream(): + """Test basic functionality of StrategyAgent stream method.""" + agent = StrategyAgent() + + # Prepare a valid JSON query based on UserRequest structure + query = json.dumps( + { + "llm_model_config": { + "provider": "test-provider", + "model_id": "test-model", + "api_key": "test-api-key", + }, + "exchange_config": { + "exchange_id": "binance", + "trading_mode": "virtual", + "api_key": "test-exchange-key", + "secret_key": "test-secret-key", + }, + "trading_config": { + "strategy_name": "Test Strategy", + "initial_capital": 1000.0, + "max_leverage": 1.0, + "max_positions": 5, + "symbols": ["BTC/USDT"], + "decide_interval": 60, + }, + } + ) + + try: + async for response in agent.stream(query, "test-conversation", "test-task"): + print(response) + except asyncio.CancelledError: + pass # Expected if we cancel From 0463fe9fbbf38256fb8c9b2438ec880416bf6e26 Mon Sep 17 00:00:00 2001 From: paisley Date: Wed, 5 Nov 2025 17:48:20 +0800 Subject: [PATCH 03/91] feat:create strategy agent router --- .../valuecell/agents/strategy_agent/models.py | 2 +- python/valuecell/server/api/app.py | 12 ++- .../server/api/routers/strategy_agent.py | 73 +++++++++++++++++++ .../server/api/schemas/strategy_agent.py | 24 ++++++ 4 files changed, 107 insertions(+), 4 deletions(-) create mode 100644 python/valuecell/server/api/routers/strategy_agent.py create mode 100644 python/valuecell/server/api/schemas/strategy_agent.py diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index 42276f9bf..6871ef22a 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -121,7 +121,7 @@ class UserRequest(BaseModel): update a strategy instance. It was previously named `Strategy`. """ - model_config: ModelConfig = Field( + llm_config: ModelConfig = Field( default_factory=ModelConfig, description="AI model configuration" ) exchange_config: ExchangeConfig = Field( diff --git a/python/valuecell/server/api/app.py b/python/valuecell/server/api/app.py index 5a9512753..5cc0f7687 100644 --- a/python/valuecell/server/api/app.py +++ b/python/valuecell/server/api/app.py @@ -16,6 +16,7 @@ ) from .routers.agent import create_agent_router from .routers.agent_stream import create_agent_stream_router +from .routers.strategy_agent import create_strategy_agent_router from .routers.conversation import create_conversation_router from .routers.i18n import create_i18n_router from .routers.system import create_system_router @@ -145,6 +146,9 @@ async def root(): # Include agent stream router app.include_router(create_agent_stream_router(), prefix=API_PREFIX) + # Include strategy agent router + app.include_router(create_strategy_agent_router(), prefix=API_PREFIX) + # Include agent router app.include_router(create_agent_router(), prefix=API_PREFIX) @@ -152,9 +156,11 @@ async def root(): app.include_router(create_task_router(), prefix=API_PREFIX) # Include trading router - from .routers.trading import create_trading_router - - app.include_router(create_trading_router(), prefix=API_PREFIX) + try: + from .routers.trading import create_trading_router + app.include_router(create_trading_router(), prefix=API_PREFIX) + except Exception as e: + print(f"Skip trading router because of import error: {e}") # For uvicorn diff --git a/python/valuecell/server/api/routers/strategy_agent.py b/python/valuecell/server/api/routers/strategy_agent.py new file mode 100644 index 000000000..6b1493d1d --- /dev/null +++ b/python/valuecell/server/api/routers/strategy_agent.py @@ -0,0 +1,73 @@ +""" +StrategyAgent router for handling strategy creation via streaming responses. +""" + +from fastapi import APIRouter, HTTPException +from fastapi.responses import JSONResponse + +from valuecell.server.api.schemas.strategy_agent import StrategyAgentCreateRequest +from valuecell.core.coordinate.orchestrator import AgentOrchestrator +from valuecell.core.types import UserInput, UserInputMetadata +from valuecell.agents.strategy_agent.models import UserRequest + + +def create_strategy_agent_router() -> APIRouter: + """Create and configure the StrategyAgent router.""" + + router = APIRouter(prefix="/agents", tags=["Strategy Agent"]) + orchestrator = AgentOrchestrator() + + @router.post("/create_strategy_agent") + async def create_strategy_agent(request: StrategyAgentCreateRequest): + """ + Create a strategy through StrategyAgent and return final JSON result. + + This endpoint accepts a structured request body, maps it to StrategyAgent's + UserRequest JSON, and returns an aggregated JSON response (non-SSE). + """ + try: + # Ensure we only serialize the core UserRequest fields, excluding conversation_id + user_request = UserRequest( + llm_config=request.llm_config, + exchange_config=request.exchange_config, + trading_config=request.trading_config, + ) + query = user_request.model_dump_json() + + # Aggregate streaming events into a final JSON result + messages = [] + components = [] + + agent_name = "StrategyAgent" + + # Build UserInput for orchestrator + user_input_meta = UserInputMetadata( + user_id="default_user" + ) + user_input = UserInput( + query=query, + target_agent_name=agent_name, + meta=user_input_meta, + ) + + # Directly use process_user_input instead of stream_query_agent + async for chunk_obj in orchestrator.process_user_input(user_input): + event = chunk_obj.event + data = chunk_obj.data + + if event == "component_generator": + content = data.payload.content + + + result = { + "agent_name": agent_name, + "messages": messages, + "components": components, + "status": "completed", + } + return JSONResponse(content=result) + + except Exception as e: + raise HTTPException(status_code=500, detail=f"StrategyAgent create failed: {str(e)}") + + return router \ No newline at end of file diff --git a/python/valuecell/server/api/schemas/strategy_agent.py b/python/valuecell/server/api/schemas/strategy_agent.py new file mode 100644 index 000000000..90c36527b --- /dev/null +++ b/python/valuecell/server/api/schemas/strategy_agent.py @@ -0,0 +1,24 @@ +"""Schemas for StrategyAgent API requests.""" + +from typing import Optional + +from pydantic import Field + +from valuecell.agents.strategy_agent.models import ( + ModelConfig, + ExchangeConfig, + TradingConfig, + UserRequest, +) + + +class StrategyAgentCreateRequest(UserRequest): + """Request body for creating a strategy via StrategyAgent. + + Inherits fields from UserRequest and adds optional conversation_id + to bind the request to an existing conversation. + """ + + conversation_id: Optional[str] = Field( + default=None, description="Conversation ID for correlating the stream" + ) \ No newline at end of file From cff38d69f611422daa2fad604783452bed007681 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 5 Nov 2025 17:51:34 +0800 Subject: [PATCH 04/91] feat: enhance StrategyAgent streaming with portfolio updates and refined component types --- python/valuecell/agents/strategy_agent/agent.py | 10 +++++++++- python/valuecell/agents/strategy_agent/core.py | 8 ++++++-- python/valuecell/agents/strategy_agent/models.py | 4 +++- 3 files changed, 18 insertions(+), 4 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/agent.py b/python/valuecell/agents/strategy_agent/agent.py index 6f533deb6..12a432b6d 100644 --- a/python/valuecell/agents/strategy_agent/agent.py +++ b/python/valuecell/agents/strategy_agent/agent.py @@ -52,8 +52,16 @@ async def stream( for trade in result.trades: yield streaming.component_generator( content=trade.model_dump_json(), - component_type=ComponentType.UPDATE.value, + component_type=ComponentType.UPDATE_TRADE.value, ) + yield streaming.component_generator( + content=result.strategy_summary.model_dump_json(), + component_type=ComponentType.UPDATE_STRATEGY_SUMMARY.value, + ) + yield streaming.component_generator( + content=result.portfolio_view.model_dump_json(), + component_type=ComponentType.UPDATE_PORTFOLIO.value, + ) await asyncio.sleep(request.trading_config.decide_interval) except asyncio.CancelledError: diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index 2c37e5d63..7dfbaf1a4 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -15,6 +15,7 @@ ComposeContext, FeatureVector, HistoryRecord, + PortfolioView, StrategyStatus, StrategySummary, TradeDigest, @@ -34,11 +35,12 @@ class DecisionCycleResult: compose_id: str timestamp_ms: int - summary: StrategySummary + strategy_summary: StrategySummary instructions: List[TradeInstruction] trades: List[TradeHistoryEntry] history_records: List[HistoryRecord] digest: TradeDigest + portfolio_view: PortfolioView # Core interfaces for orchestration and portfolio service. @@ -172,14 +174,16 @@ def run_once(self) -> DecisionCycleResult: digest = self._digest_builder.build(list(self._history_records)) self._cycle_index += 1 + portfolio = self._portfolio_service.get_view() return DecisionCycleResult( compose_id=compose_id, timestamp_ms=timestamp_ms, - summary=summary, + strategy_summary=summary, instructions=instructions, trades=trades, history_records=history_records, digest=digest, + portfolio_view=portfolio, ) def _default_prompt(self, request: UserRequest) -> str: diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index 486dbb849..874cbcc93 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -38,7 +38,9 @@ class ComponentType(str, Enum): """Component types for StrategyAgent streaming responses.""" STATUS = "strategy_agent_status" - UPDATE = "strategy_agent_update" + UPDATE_TRADE = "strategy_agent_update_trade" + UPDATE_PORTFOLIO = "strategy_agent_update_portfolio" + UPDATE_STRATEGY_SUMMARY = "strategy_agent_update_strategy_summary" class LLMModelConfig(BaseModel): From 087d432d136748924eba8ecff7126d775546488c Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 5 Nov 2025 18:03:03 +0800 Subject: [PATCH 05/91] feature: add ccxt as dependency --- python/pyproject.toml | 1 + python/uv.lock | 107 +++++++++++++++++++++++++++++++++++++++++- 2 files changed, 106 insertions(+), 2 deletions(-) diff --git a/python/pyproject.toml b/python/pyproject.toml index e90044315..0fc093590 100644 --- a/python/pyproject.toml +++ b/python/pyproject.toml @@ -28,6 +28,7 @@ dependencies = [ "loguru>=0.7.3", "aiofiles>=24.1.0", "crawl4ai>=0.7.4", + "ccxt>=4.5.15", ] [project.optional-dependencies] diff --git a/python/uv.lock b/python/uv.lock index 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"aiofiles" }, { name = "aiosqlite" }, { name = "akshare" }, + { name = "ccxt" }, { name = "crawl4ai" }, { name = "edgartools" }, { name = "fastapi" }, @@ -3580,6 +3682,7 @@ requires-dist = [ { name = "aiofiles", specifier = ">=24.1.0" }, { name = "aiosqlite", specifier = ">=0.19.0" }, { name = "akshare", specifier = ">=1.17.44" }, + { name = "ccxt", specifier = ">=4.5.15" }, { name = "crawl4ai", specifier = ">=0.7.4" }, { name = "diff-cover", marker = "extra == 'dev'", specifier = ">=9.0.0" }, { name = "edgartools", specifier = ">=4.12.2" }, From 75b6e632f057fa2fa9544685272a100201a51e9a Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 5 Nov 2025 18:24:07 +0800 Subject: [PATCH 06/91] feat: refactor StrategyAgent and DecisionCoordinator for async support and improve error logging --- .../valuecell/agents/strategy_agent/agent.py | 11 +- .../valuecell/agents/strategy_agent/core.py | 7 +- .../agents/strategy_agent/data/interfaces.py | 2 +- .../agents/strategy_agent/runtime.py | 100 ++++++++++++------ 4 files changed, 79 insertions(+), 41 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/agent.py b/python/valuecell/agents/strategy_agent/agent.py index 12a432b6d..db6a11515 100644 --- a/python/valuecell/agents/strategy_agent/agent.py +++ b/python/valuecell/agents/strategy_agent/agent.py @@ -1,22 +1,21 @@ from __future__ import annotations import asyncio -import logging from typing import AsyncGenerator, Dict, Optional +from loguru import logger + from valuecell.core.agent.responses import streaming from valuecell.core.types import BaseAgent, StreamResponse from .models import ( ComponentType, + StrategyStatus, StrategyStatusContent, UserRequest, - StrategyStatus, ) from .runtime import create_strategy_runtime -logger = logging.getLogger(__name__) - class StrategyAgent(BaseAgent): """Top-level Strategy Agent integrating the decision coordinator.""" @@ -31,7 +30,7 @@ async def stream( try: request = UserRequest.model_validate_json(query) except ValueError as exc: - logger.warning("StrategyAgent received invalid payload: %s", exc) + logger.exception("StrategyAgent received invalid payload") yield streaming.message_chunk(str(exc)) yield streaming.done() return @@ -48,7 +47,7 @@ async def stream( try: while True: - result = runtime.run_cycle() + result = await runtime.run_cycle() for trade in result.trades: yield streaming.component_generator( content=trade.model_dump_json(), diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index 7dfbaf1a4..848e23833 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -61,7 +61,7 @@ class DecisionCoordinator(ABC): """ @abstractmethod - def run_once(self) -> DecisionCycleResult: + async def run_once(self) -> DecisionCycleResult: """Execute one decision cycle and return the result.""" raise NotImplementedError @@ -127,12 +127,12 @@ def __init__( self._cycle_index: int = 0 self._strategy_name = request.trading_config.strategy_name or strategy_id - def run_once(self) -> DecisionCycleResult: + async def run_once(self) -> DecisionCycleResult: timestamp_ms = int(self._clock().timestamp() * 1000) compose_id = generate_uuid("compose") portfolio = self._portfolio_service.get_view() - candles = self._market_data_source.get_recent_candles( + candles = await self._market_data_source.get_recent_candles( self._symbols, self._interval, self._lookback ) features = self._feature_computer.compute_features(candles=candles) @@ -152,6 +152,7 @@ def run_once(self) -> DecisionCycleResult: ) instructions = self._composer.compose(context) + # Execution gateway may be sync; allow sync execute self._execution_gateway.execute(instructions) trades = self._create_trades( diff --git a/python/valuecell/agents/strategy_agent/data/interfaces.py b/python/valuecell/agents/strategy_agent/data/interfaces.py index 3899ab5ba..31ac1d406 100644 --- a/python/valuecell/agents/strategy_agent/data/interfaces.py +++ b/python/valuecell/agents/strategy_agent/data/interfaces.py @@ -19,7 +19,7 @@ class MarketDataSource(ABC): """ @abstractmethod - def get_recent_candles( + async def get_recent_candles( self, symbols: List[str], interval: str, lookback: int ) -> List[Candle]: """Return recent candles (OHLCV) for the given symbols/interval. diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index eb2cc8eb0..2151f22a9 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -1,11 +1,11 @@ -from __future__ import annotations - -import math from collections import defaultdict from dataclasses import dataclass from datetime import datetime, timezone from typing import Dict, List, Optional +import ccxt.pro as ccxtpro +from loguru import logger + from valuecell.utils.uuid import generate_uuid from .core import DecisionCycleResult, DefaultDecisionCoordinator @@ -34,41 +34,79 @@ class SimpleMarketDataSource(MarketDataSource): - """Generates synthetic candle data for each symbol.""" - - def __init__(self, base_prices: Optional[Dict[str, float]] = None) -> None: + """Generates synthetic candle data for each symbol or fetches via ccxt.pro. + + If `exchange_id` was provided at construction time and `ccxt.pro` is + available, this class will attempt to fetch OHLCV data from the + specified exchange. If any error occurs (missing library, unknown + exchange, network error), it falls back to the built-in synthetic + generator so the runtime remains functional in tests and offline. + """ + + def __init__( + self, + base_prices: Optional[Dict[str, float]] = None, + exchange_id: Optional[str] = None, + ccxt_options: Optional[Dict] = None, + ) -> None: self._base_prices = base_prices or {} self._counters: Dict[str, int] = defaultdict(int) + self._exchange_id = exchange_id or "binance" + self._ccxt_options = ccxt_options or {} - def get_recent_candles( + async def get_recent_candles( self, symbols: List[str], interval: str, lookback: int ) -> List[Candle]: - now_ms = int(datetime.now(timezone.utc).timestamp() * 1000) - interval_ms = 60_000 - candles: List[Candle] = [] + async def _fetch(symbol: str) -> List[List]: + # instantiate exchange class by name (e.g., ccxtpro.kraken) + exchange_cls = getattr(ccxtpro, self._exchange_id, None) + if exchange_cls is None: + raise RuntimeError( + f"Exchange '{self._exchange_id}' not found in ccxt.pro" + ) + exchange = exchange_cls({"newUpdates": False, **self._ccxt_options}) + try: + # ccxt.pro uses async fetch_ohlcv + data = await exchange.fetch_ohlcv( + symbol, timeframe=interval, since=None, limit=lookback + ) + return data + finally: + try: + await exchange.close() + except Exception: + pass + candles: List[Candle] = [] + # Run fetch for each symbol sequentially for symbol in symbols: - counter = self._counters[symbol] - base_price = self._base_prices.get(symbol, 100.0) - for index in range(lookback): - step = counter + index - price = max(base_price + math.sin(step / 5.0) * 2.5, 1.0) - ts = now_ms - (lookback - index) * interval_ms - candles.append( - Candle( - ts=ts, - instrument=InstrumentRef( - symbol=symbol, exchange_id=None, quote_ccy="USD" - ), - open=price * 0.998, - high=price * 1.01, - low=price * 0.99, - close=price, - volume=1_000 + step * 10, - interval=interval, + try: + raw = await _fetch(symbol) + # raw is list of [ts, open, high, low, close, volume] + for row in raw: + ts, open_v, high_v, low_v, close_v, vol = row + candles.append( + Candle( + ts=int(ts), + instrument=InstrumentRef( + symbol=symbol, + exchange_id=self._exchange_id, + quote_ccy="USD", + ), + open=float(open_v), + high=float(high_v), + low=float(low_v), + close=float(close_v), + volume=float(vol), + interval=interval, + ) ) + except Exception: + logger.exception( + "Failed to fetch candles for {} from {}, using synthetic data", + symbol, + self._exchange_id, ) - self._counters[symbol] += lookback return candles @@ -266,8 +304,8 @@ class StrategyRuntime: strategy_id: str coordinator: DefaultDecisionCoordinator - def run_cycle(self) -> DecisionCycleResult: - return self.coordinator.run_once() + async def run_cycle(self) -> DecisionCycleResult: + return await self.coordinator.run_once() def create_strategy_runtime(request: UserRequest) -> StrategyRuntime: From 18aa0eebd09986a350b30ecc910d72ada8226367 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 5 Nov 2025 18:31:12 +0800 Subject: [PATCH 07/91] feat: enhance feature computation with technical indicators using pandas and numpy --- .../agents/strategy_agent/runtime.py | 125 ++++++++++++++++-- 1 file changed, 112 insertions(+), 13 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index 2151f22a9..d97db5cfa 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -4,6 +4,8 @@ from typing import Dict, List, Optional import ccxt.pro as ccxtpro +import numpy as np +import pandas as pd from loguru import logger from valuecell.utils.uuid import generate_uuid @@ -107,7 +109,6 @@ async def _fetch(symbol: str) -> List[List]: symbol, self._exchange_id, ) - return candles @@ -126,20 +127,116 @@ def compute_features( features: List[FeatureVector] = [] for symbol, series in grouped.items(): + # Build a DataFrame for indicator calculations series.sort(key=lambda item: item.ts) - last = series[-1] - prev = series[-2] if len(series) > 1 else series[-1] - change_pct = (last.close - prev.close) / prev.close if prev.close else 0.0 + rows = [ + { + "ts": c.ts, + "open": c.open, + "high": c.high, + "low": c.low, + "close": c.close, + "volume": c.volume, + "interval": c.interval, + } + for c in series + ] + df = pd.DataFrame(rows) + + # EMAs + df["ema_12"] = df["close"].ewm(span=12, adjust=False).mean() + df["ema_26"] = df["close"].ewm(span=26, adjust=False).mean() + df["ema_50"] = df["close"].ewm(span=50, adjust=False).mean() + + # MACD + df["macd"] = df["ema_12"] - df["ema_26"] + df["macd_signal"] = df["macd"].ewm(span=9, adjust=False).mean() + df["macd_histogram"] = df["macd"] - df["macd_signal"] + + # RSI + delta = df["close"].diff() + gain = delta.clip(lower=0).rolling(window=14).mean() + loss = (-delta).clip(lower=0).rolling(window=14).mean() + rs = gain / loss.replace(0, np.inf) + df["rsi"] = 100 - (100 / (1 + rs)) + + # Bollinger Bands + df["bb_middle"] = df["close"].rolling(window=20).mean() + bb_std = df["close"].rolling(window=20).std() + df["bb_upper"] = df["bb_middle"] + (bb_std * 2) + df["bb_lower"] = df["bb_middle"] - (bb_std * 2) + + last = df.iloc[-1] + prev = df.iloc[-2] if len(df) > 1 else last + + change_pct = ( + (float(last.close) - float(prev.close)) / float(prev.close) + if prev.close + else 0.0 + ) + + values = { + "close": float(last.close), + "volume": float(last.volume), + "change_pct": float(change_pct), + "ema_12": ( + float(last.get("ema_12", np.nan)) + if not pd.isna(last.get("ema_12")) + else None + ), + "ema_26": ( + float(last.get("ema_26", np.nan)) + if not pd.isna(last.get("ema_26")) + else None + ), + "ema_50": ( + float(last.get("ema_50", np.nan)) + if not pd.isna(last.get("ema_50")) + else None + ), + "macd": ( + float(last.get("macd", np.nan)) + if not pd.isna(last.get("macd")) + else None + ), + "macd_signal": ( + float(last.get("macd_signal", np.nan)) + if not pd.isna(last.get("macd_signal")) + else None + ), + "macd_histogram": ( + float(last.get("macd_histogram", np.nan)) + if not pd.isna(last.get("macd_histogram")) + else None + ), + "rsi": ( + float(last.get("rsi", np.nan)) + if not pd.isna(last.get("rsi")) + else None + ), + "bb_upper": ( + float(last.get("bb_upper", np.nan)) + if not pd.isna(last.get("bb_upper")) + else None + ), + "bb_middle": ( + float(last.get("bb_middle", np.nan)) + if not pd.isna(last.get("bb_middle")) + else None + ), + "bb_lower": ( + float(last.get("bb_lower", np.nan)) + if not pd.isna(last.get("bb_lower")) + else None + ), + } + features.append( FeatureVector( - ts=last.ts, - instrument=last.instrument, - values={ - "close": last.close, - "volume": last.volume, - "change_pct": change_pct, - }, - meta={"interval": last.interval, "count": len(series)}, + ts=int(last["ts"]), + instrument=series[-1].instrument, + values=values, + meta={"interval": series[-1].interval, "count": len(series)}, ) ) @@ -321,7 +418,9 @@ def create_strategy_runtime(request: UserRequest) -> StrategyRuntime: symbol: 120.0 + index * 15.0 for index, symbol in enumerate(request.trading_config.symbols) } - market_data_source = SimpleMarketDataSource(base_prices=base_prices) + market_data_source = SimpleMarketDataSource( + base_prices=base_prices, exchange_id=request.exchange_config.exchange_id + ) feature_computer = SimpleFeatureComputer() composer = RuleBasedComposer() execution_gateway = PaperExecutionGateway() From 328622a70a66ba85cb89f6050a4c0ff5212a9904 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Wed, 5 Nov 2025 18:41:46 +0800 Subject: [PATCH 08/91] feat: update PortfolioService to expose apply_trades method for state updates --- python/valuecell/agents/strategy_agent/core.py | 10 ++++++---- .../agents/strategy_agent/portfolio/interfaces.py | 14 ++++++++++++-- 2 files changed, 18 insertions(+), 6 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index 848e23833..9d0bcb94f 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -244,10 +244,12 @@ def _apply_trades_to_portfolio( ) -> None: if not trades: return - - apply_method = getattr(self._portfolio_service, "apply_trades", None) - if callable(apply_method): - apply_method(trades, market_snapshot) + # PortfolioService now exposes apply_trades; call directly to update state + try: + self._portfolio_service.apply_trades(trades, market_snapshot) + except NotImplementedError: + # service may be read-only; ignore + return def _build_summary( self, diff --git a/python/valuecell/agents/strategy_agent/portfolio/interfaces.py b/python/valuecell/agents/strategy_agent/portfolio/interfaces.py index a81e366fa..04cefbb1c 100644 --- a/python/valuecell/agents/strategy_agent/portfolio/interfaces.py +++ b/python/valuecell/agents/strategy_agent/portfolio/interfaces.py @@ -1,9 +1,9 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Optional +from typing import Optional, List, Dict -from ..models import PortfolioView +from ..models import PortfolioView, TradeHistoryEntry class PortfolioService(ABC): @@ -17,6 +17,16 @@ def get_view(self) -> PortfolioView: """Return the latest portfolio view (positions, cash, optional constraints).""" raise NotImplementedError + def apply_trades(self, trades: List[TradeHistoryEntry], market_snapshot: Dict[str, float]) -> None: + """Apply executed trades to the portfolio view (optional). + + Implementations that support state changes (paper trading, backtests) + should update their internal view accordingly. This method is optional + for read-only portfolio services, but providing it here makes the + contract explicit to callers. + """ + raise NotImplementedError + class PortfolioSnapshotStore(ABC): """Persist/load portfolio snapshots (optional for paper/backtest modes).""" From f7dce6841efc08f3c47568876e14b99a98271d77 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 10:51:17 +0800 Subject: [PATCH 09/91] feat: refactor Composer interface and implement LlmComposer --- .../valuecell/agents/strategy_agent/core.py | 2 +- .../strategy_agent/decision/composer.py | 297 ++++++++++++++++++ .../strategy_agent/decision/interfaces.py | 9 +- .../agents/strategy_agent/runtime.py | 94 +++--- python/valuecell/utils/model.py | 64 +++- 5 files changed, 416 insertions(+), 50 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index 9d0bcb94f..ea1215499 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -151,7 +151,7 @@ async def run_once(self) -> DecisionCycleResult: constraints=None, ) - instructions = self._composer.compose(context) + instructions = await self._composer.compose(context) # Execution gateway may be sync; allow sync execute self._execution_gateway.execute(instructions) diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index e69de29bb..cfecf1306 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -0,0 +1,297 @@ +from __future__ import annotations + +import json +import math +from typing import Dict, List, Optional + +from loguru import logger +from pydantic import ValidationError + +from .interfaces import Composer +from ..models import ( + ComposeContext, + LlmDecisionAction, + LlmPlanProposal, + TradeInstruction, + TradeSide, + UserRequest, +) + + +class LlmComposer(Composer): + """LLM-driven composer that turns context into trade instructions. + + The core flow follows the README design: + 1. Build a serialized prompt from the compose context (features, portfolio, + digest, prompt text, market snapshot, constraints). + 2. Call an LLM to obtain an :class:`LlmPlanProposal` (placeholder method). + 3. Normalize the proposal into executable :class:`TradeInstruction` objects, + applying guardrails based on context constraints and trading config. + + The `_call_llm` method is intentionally left unimplemented so callers can + supply their own integration. Override it in a subclass or monkeypatch at + runtime. The method should accept a string prompt and return an instance of + :class:`LlmPlanProposal` (validated via Pydantic). + """ + + def __init__( + self, + request: UserRequest, + *, + default_slippage_bps: int = 25, + quantity_precision: float = 1e-9, + ) -> None: + self._request = request + self._default_slippage_bps = default_slippage_bps + self._quantity_precision = quantity_precision + self._base_constraints: Dict[str, float | int] = { + "max_positions": request.trading_config.max_positions, + "max_leverage": request.trading_config.max_leverage, + } + + async def compose(self, context: ComposeContext) -> List[TradeInstruction]: + prompt = self._build_llm_prompt(context) + try: + plan = await self._call_llm(prompt) + except NotImplementedError: + logger.warning("LLM call not implemented; returning no instructions") + return [] + except ValidationError as exc: + logger.error("LLM output failed validation: %s", exc) + return [] + except Exception: # noqa: BLE001 + logger.exception("LLM invocation failed") + return [] + + if not plan.items: + return [] + + constraints = self._merge_constraints(context) + return self._normalize_plan(context, plan, constraints) + + # ------------------------------------------------------------------ + # Prompt + LLM helpers + + def _build_llm_prompt(self, context: ComposeContext) -> str: + """Serialize compose context into a textual prompt for the LLM.""" + + payload = { + "strategy_prompt": context.prompt_text, + "compose_id": context.compose_id, + "timestamp": context.ts, + "portfolio": context.portfolio.model_dump(mode="json"), + "market_snapshot": context.market_snapshot or {}, + "digest": context.digest.model_dump(mode="json"), + "features": [vector.model_dump(mode="json") for vector in context.features], + "constraints": context.constraints or {}, + } + + instructions = ( + "You are a trading strategy planner. Analyze the JSON context and " + "produce a structured plan that aligns with the LlmPlanProposal " + "schema (items array with instrument, action, target_qty, rationale, " + "confidence). Focus on risk-aware, executable decisions." + ) + + return f"{instructions}\n\nContext:\n{json.dumps(payload, ensure_ascii=False, indent=2)}" + + async def _call_llm( + self, prompt: str + ) -> LlmPlanProposal: # pragma: no cover - implemented async + """Invoke an LLM asynchronously and parse the response into LlmPlanProposal. + + This implementation follows the parser_agent pattern: it creates a model + via `create_model_with_provider`, wraps it in an `agno.agent.Agent` with + `output_schema=LlmPlanProposal`, and awaits `agent.arun(prompt)`. The + agent's `response.content` is returned (or validated) as a + `LlmPlanProposal`. + """ + + from valuecell.utils.model import create_model_with_provider + from agno.agent import Agent as AgnoAgent + + cfg = self._request.llm_model_config + model = create_model_with_provider( + provider=cfg.provider, + model_id=cfg.model_id, + api_key=cfg.api_key, + ) + + # No custom llm_client support: always wrap the created model in an Agent + + # Wrap model in an Agent (consistent with parser_agent usage) + agent = AgnoAgent(model=model, output_schema=LlmPlanProposal, markdown=False) + response = await agent.arun(prompt) + content = getattr(response, "content", None) or response + + # If Agent validated to the schema, response.content may already be the model + if isinstance(content, LlmPlanProposal): + return content + + # Last resort: try to stringify and parse + parsed = json.loads(str(content)) + return LlmPlanProposal.model_validate(parsed) + + # ------------------------------------------------------------------ + # Normalization / guardrails helpers + + def _merge_constraints(self, context: ComposeContext) -> Dict[str, float | int]: + merged: Dict[str, float | int] = dict(self._base_constraints) + if context.constraints: + merged.update(context.constraints) + return merged + + def _normalize_plan( + self, + context: ComposeContext, + plan: LlmPlanProposal, + constraints: Dict[str, float | int], + ) -> List[TradeInstruction]: + instructions: List[TradeInstruction] = [] + + projected_positions: Dict[str, float] = { + symbol: snapshot.quantity + for symbol, snapshot in context.portfolio.positions.items() + } + active_positions = sum( + 1 + for qty in projected_positions.values() + if abs(qty) > self._quantity_precision + ) + + max_positions = constraints.get("max_positions") + quantity_step = float(constraints.get("quantity_step", 0) or 0.0) + min_trade_qty = float(constraints.get("min_trade_qty", 0) or 0.0) + max_order_qty = constraints.get("max_order_qty") + max_position_qty = constraints.get("max_position_qty") + min_notional = constraints.get("min_notional") + + for idx, item in enumerate(plan.items): + symbol = item.instrument.symbol + current_qty = projected_positions.get(symbol, 0.0) + + target_qty = self._resolve_target_quantity( + item, current_qty, max_position_qty + ) + delta = target_qty - current_qty + + if abs(delta) <= self._quantity_precision: + continue + + is_new_position = ( + abs(current_qty) <= self._quantity_precision + and abs(target_qty) > self._quantity_precision + ) + if ( + is_new_position + and max_positions is not None + and active_positions >= int(max_positions) + ): + logger.warning( + "Skipping symbol %s due to max_positions constraint", symbol + ) + continue + + side = TradeSide.BUY if delta > 0 else TradeSide.SELL + quantity = abs(delta) + + quantity = self._apply_quantity_filters( + symbol, + quantity, + quantity_step, + min_trade_qty, + max_order_qty, + min_notional, + context.market_snapshot or {}, + ) + + if quantity <= self._quantity_precision: + continue + + # Update projected positions for subsequent guardrails + signed_delta = quantity if side is TradeSide.BUY else -quantity + projected_positions[symbol] = current_qty + signed_delta + + if is_new_position: + active_positions += 1 + if abs(projected_positions[symbol]) <= self._quantity_precision: + active_positions = max(active_positions - 1, 0) + + final_target = projected_positions[symbol] + meta = { + "requested_target_qty": target_qty, + "current_qty": current_qty, + "final_target_qty": final_target, + "action": item.action.value, + } + if item.confidence is not None: + meta["confidence"] = item.confidence + if item.rationale: + meta["rationale"] = item.rationale + + instruction = TradeInstruction( + instruction_id=f"{context.compose_id}:{symbol}:{idx}", + compose_id=context.compose_id, + instrument=item.instrument, + side=side, + quantity=quantity, + price_mode="market", + limit_price=None, + max_slippage_bps=self._default_slippage_bps, + meta=meta, + ) + instructions.append(instruction) + + return instructions + + def _resolve_target_quantity( + self, + item, + current_qty: float, + max_position_qty: Optional[float], + ) -> float: + if item.action == LlmDecisionAction.NOOP: + return current_qty + if item.action == LlmDecisionAction.FLAT: + target = 0.0 + else: + target = float(item.target_qty) + + if max_position_qty is not None: + max_abs = abs(float(max_position_qty)) + target = max(-max_abs, min(max_abs, target)) + + return target + + def _apply_quantity_filters( + self, + symbol: str, + quantity: float, + quantity_step: float, + min_trade_qty: float, + max_order_qty: Optional[float], + min_notional: Optional[float], + market_snapshot: Dict[str, float], + ) -> float: + qty = quantity + + if max_order_qty is not None: + qty = min(qty, float(max_order_qty)) + + if quantity_step > 0: + qty = math.floor(qty / quantity_step) * quantity_step + + if qty <= 0: + return 0.0 + + if qty < min_trade_qty: + return 0.0 + + if min_notional is not None: + price = market_snapshot.get(symbol) + if price is None: + return 0.0 + if qty * price < float(min_notional): + return 0.0 + + return qty diff --git a/python/valuecell/agents/strategy_agent/decision/interfaces.py b/python/valuecell/agents/strategy_agent/decision/interfaces.py index 41568bd05..fc3b833a2 100644 --- a/python/valuecell/agents/strategy_agent/decision/interfaces.py +++ b/python/valuecell/agents/strategy_agent/decision/interfaces.py @@ -17,10 +17,11 @@ class Composer(ABC): """ @abstractmethod - def compose(self, context: ComposeContext) -> List[TradeInstruction]: + async def compose(self, context: ComposeContext) -> List[TradeInstruction]: """Produce normalized trade instructions given the current context. - Call the LLM, parse/validate output, apply guardrails (limits, step size, - min notional, cool-down), and return executable instructions. - Any optional auditing metadata should be recorded via HistoryRecorder. + + This method is async because LLM providers and agent wrappers are often + asynchronous. Implementations should perform any network/IO and return + a validated list of TradeInstruction objects. """ raise NotImplementedError diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index d97db5cfa..0e787e9dc 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -2,6 +2,7 @@ from dataclasses import dataclass from datetime import datetime, timezone from typing import Dict, List, Optional +from pathlib import Path import ccxt.pro as ccxtpro import numpy as np @@ -12,12 +13,11 @@ from .core import DecisionCycleResult, DefaultDecisionCoordinator from .data.interfaces import MarketDataSource -from .decision.interfaces import Composer +from .decision.composer import LlmComposer from .execution.interfaces import ExecutionGateway from .features.interfaces import FeatureComputer from .models import ( Candle, - ComposeContext, FeatureVector, HistoryRecord, InstrumentRef, @@ -35,6 +35,57 @@ from .trading_history.interfaces import DigestBuilder, HistoryRecorder +def _make_prompt_provider(template_dir: Optional[Path] = None): + """Return a prompt_provider callable that builds prompts from templates. + + Behavior: + - If request.trading_config.template_id matches a file under templates dir + (try extensions .txt, .md, or exact name), the file content is used. + - If request.trading_config.custom_prompt is present, it is appended after + the template content (separated by two newlines). + - If neither is present, fall back to a simple generated prompt mentioning + the symbols. + """ + base = ( + Path(__file__).parent / "templates" if template_dir is None else template_dir + ) + + def provider(request: UserRequest) -> str: + tid = request.trading_config.template_id + custom = request.trading_config.custom_prompt + + template_text = "" + if tid: + # safe-resolve candidate files + candidates = [tid, f"{tid}.txt", f"{tid}.md"] + for name in candidates: + try_path = (base / name) + try: + resolved = try_path.resolve() + # ensure resolved path is inside base + if base.resolve() in resolved.parents or resolved == base.resolve(): + if resolved.exists() and resolved.is_file(): + template_text = resolved.read_text(encoding="utf-8") + break + except Exception: + continue + + parts = [] + if template_text: + parts.append(template_text.strip()) + if custom: + parts.append(custom.strip()) + + if parts: + return "\n\n".join(parts) + + # fallback: simple generated prompt referencing symbols + symbols = ", ".join(request.trading_config.symbols) + return f"Compose trading instructions for symbols: {symbols}." + + return provider + + class SimpleMarketDataSource(MarketDataSource): """Generates synthetic candle data for each symbol or fetches via ccxt.pro. @@ -243,42 +294,6 @@ def compute_features( return features -class RuleBasedComposer(Composer): - """Simple deterministic composer using momentum.""" - - def __init__(self, threshold: float = 0.003, max_quantity: float = 1.0) -> None: - self._threshold = threshold - self._max_quantity = max_quantity - - def compose(self, context: ComposeContext) -> List[TradeInstruction]: - instructions: List[TradeInstruction] = [] - for feature in context.features: - change_pct = float(feature.values.get("change_pct", 0.0)) - if abs(change_pct) < self._threshold: - continue - - symbol = feature.instrument.symbol - side = TradeSide.BUY if change_pct > 0 else TradeSide.SELL - quantity = min(self._max_quantity, max(0.01, abs(change_pct) * 10)) - instruction_id = f"{context.compose_id}:{symbol}:{side.value}" - - instructions.append( - TradeInstruction( - instruction_id=instruction_id, - compose_id=context.compose_id, - instrument=feature.instrument, - side=side, - quantity=quantity, - price_mode="market", - limit_price=None, - max_slippage_bps=25, - meta={"change_pct": change_pct}, - ) - ) - - return instructions - - class PaperExecutionGateway(ExecutionGateway): """Records instructions without sending them anywhere.""" @@ -422,7 +437,7 @@ def create_strategy_runtime(request: UserRequest) -> StrategyRuntime: base_prices=base_prices, exchange_id=request.exchange_config.exchange_id ) feature_computer = SimpleFeatureComputer() - composer = RuleBasedComposer() + composer = LlmComposer(request=request) execution_gateway = PaperExecutionGateway() history_recorder = InMemoryHistoryRecorder() digest_builder = RollingDigestBuilder() @@ -437,6 +452,7 @@ def create_strategy_runtime(request: UserRequest) -> StrategyRuntime: execution_gateway=execution_gateway, history_recorder=history_recorder, digest_builder=digest_builder, + prompt_provider=_make_prompt_provider(), ) return StrategyRuntime( diff --git a/python/valuecell/utils/model.py b/python/valuecell/utils/model.py index 86ba9509d..dd1497ca0 100644 --- a/python/valuecell/utils/model.py +++ b/python/valuecell/utils/model.py @@ -143,7 +143,12 @@ def get_model_for_agent(agent_name: str, **kwargs): raise -def create_model_with_provider(provider: str, model_id: Optional[str] = None, **kwargs): +def create_model_with_provider( + provider: str, + model_id: Optional[str] = None, + api_key: Optional[str] = None, + **kwargs, +): """ Create a model from a specific provider. @@ -173,13 +178,60 @@ def create_model_with_provider(provider: str, model_id: Optional[str] = None, ** ValueError: If provider not found or not configured """ - return create_model( - model_id=model_id, - provider=provider, - use_fallback=False, # Don't fallback when explicitly requesting a provider - **kwargs, + # If no api_key override is supplied, use the standard factory path. + if not api_key: + return create_model( + model_id=model_id, + provider=provider, + use_fallback=False, # Don't fallback when explicitly requesting a provider + **kwargs, + ) + + # Minimal override: instantiate the provider class with a copy of its + # ProviderConfig but using the provided api_key. This avoids changing the + # global configuration and keeps the change localized to this call. + try: + from valuecell.config.manager import get_config_manager, ProviderConfig + from valuecell.adapters.models.factory import get_model_factory + except Exception: + # Fallback to factory convenience if imports fail for some reason + return create_model( + model_id=model_id, + provider=provider, + use_fallback=False, + api_key=api_key, + **kwargs, + ) + + cfg_mgr = get_config_manager() + existing = cfg_mgr.get_provider_config(provider) + if not existing: + raise ValueError(f"Provider configuration not found: {provider}") + + # Build a shallow copy of ProviderConfig overriding api_key + overridden = ProviderConfig( + name=existing.name, + enabled=existing.enabled, + api_key=api_key, + base_url=existing.base_url, + default_model=existing.default_model, + models=existing.models, + parameters=existing.parameters, + default_embedding_model=existing.default_embedding_model, + embedding_models=existing.embedding_models, + embedding_parameters=existing.embedding_parameters, + extra_config=existing.extra_config, ) + factory = get_model_factory() + provider_class = factory._providers.get(provider) + if not provider_class: + raise ValueError(f"Unsupported provider: {provider}") + + provider_instance = provider_class(overridden) + # Delegate to the provider instance directly so the supplied api_key is used + return provider_instance.create_model(model_id, **kwargs) + # ============================================ # Embedding Functions From 5a57996fe00202092c5330a527e21f0bfb81e9ed Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 11:30:15 +0800 Subject: [PATCH 10/91] feat: enhance logging in LlmComposer for better traceability and debugging --- .../strategy_agent/decision/composer.py | 46 ++++++++++++++----- 1 file changed, 34 insertions(+), 12 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index cfecf1306..9366a3199 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -51,19 +51,27 @@ def __init__( async def compose(self, context: ComposeContext) -> List[TradeInstruction]: prompt = self._build_llm_prompt(context) + logger.debug( + "Built LLM prompt for compose_id={}: {}", + context.compose_id, + prompt, + ) try: plan = await self._call_llm(prompt) except NotImplementedError: logger.warning("LLM call not implemented; returning no instructions") return [] except ValidationError as exc: - logger.error("LLM output failed validation: %s", exc) + logger.error("LLM output failed validation: {}", exc) return [] except Exception: # noqa: BLE001 logger.exception("LLM invocation failed") return [] if not plan.items: + logger.debug( + "LLM returned empty plan for compose_id={}", context.compose_id + ) return [] constraints = self._merge_constraints(context) @@ -117,20 +125,12 @@ async def _call_llm( api_key=cfg.api_key, ) - # No custom llm_client support: always wrap the created model in an Agent - # Wrap model in an Agent (consistent with parser_agent usage) agent = AgnoAgent(model=model, output_schema=LlmPlanProposal, markdown=False) response = await agent.arun(prompt) content = getattr(response, "content", None) or response - - # If Agent validated to the schema, response.content may already be the model - if isinstance(content, LlmPlanProposal): - return content - - # Last resort: try to stringify and parse - parsed = json.loads(str(content)) - return LlmPlanProposal.model_validate(parsed) + logger.debug("Received LLM response {}", content) + return content # ------------------------------------------------------------------ # Normalization / guardrails helpers @@ -176,6 +176,12 @@ def _normalize_plan( delta = target_qty - current_qty if abs(delta) <= self._quantity_precision: + logger.debug( + "Skipping symbol {} because delta {} <= quantity_precision {}", + symbol, + delta, + self._quantity_precision, + ) continue is_new_position = ( @@ -188,7 +194,10 @@ def _normalize_plan( and active_positions >= int(max_positions) ): logger.warning( - "Skipping symbol %s due to max_positions constraint", symbol + "Skipping symbol {} due to max_positions constraint (active={} max={})", + symbol, + active_positions, + max_positions, ) continue @@ -206,6 +215,12 @@ def _normalize_plan( ) if quantity <= self._quantity_precision: + logger.debug( + "Post-filter quantity for {} is {} <= precision {} -> skipping", + symbol, + quantity, + self._quantity_precision, + ) continue # Update projected positions for subsequent guardrails @@ -241,6 +256,13 @@ def _normalize_plan( meta=meta, ) instructions.append(instruction) + logger.debug( + "Created TradeInstruction {} for {} side={} qty={}", + instruction.instruction_id, + symbol, + instruction.side, + instruction.quantity, + ) return instructions From 9eedbbe5f637461bce7a0ecf80e79690476e9310 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 11:30:23 +0800 Subject: [PATCH 11/91] feat: enhance InMemoryPortfolioService to compute derived metrics and improve trade handling --- .../agents/strategy_agent/runtime.py | 145 +++++++++++++++--- 1 file changed, 127 insertions(+), 18 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index 0e787e9dc..b8286737d 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -27,6 +27,7 @@ TradeDigestEntry, TradeHistoryEntry, TradeInstruction, + TradeType, TradeSide, TradingMode, UserRequest, @@ -355,32 +356,67 @@ def build(self, records: List[HistoryRecord]) -> TradeDigest: class InMemoryPortfolioService(PortfolioService): - """Tracks cash and positions in memory.""" + """Tracks cash and positions in memory and computes derived metrics. + + Notes: + - cash reflects remaining available cash for new positions (no margin logic here) + - gross_exposure = sum(abs(qty) * mark_price) + - net_exposure = sum(qty * mark_price) + - total_value = cash + gross_exposure + - total_unrealized_pnl = sum((mark_price - avg_price) * qty) + """ - def __init__(self, initial_capital: float, trading_mode: TradingMode) -> None: + def __init__( + self, + initial_capital: float, + trading_mode: TradingMode, + constraints: Optional[Dict[str, float | int]] = None, + strategy_id: Optional[str] = None, + ) -> None: + # Store owning strategy id on the view so downstream components + # always see which strategy this portfolio belongs to. + self._strategy_id = strategy_id self._view = PortfolioView( + strategy_id=strategy_id, ts=int(datetime.now(timezone.utc).timestamp() * 1000), cash=initial_capital, positions={}, - gross_exposure=None, - net_exposure=None, - constraints=None, + gross_exposure=0.0, + net_exposure=0.0, + constraints=constraints or None, + total_value=initial_capital, + total_unrealized_pnl=0.0, + available_cash=initial_capital, ) self._trading_mode = trading_mode def get_view(self) -> PortfolioView: self._view.ts = int(datetime.now(timezone.utc).timestamp() * 1000) + # Ensure strategy_id is present on each view retrieval + if self._strategy_id is not None: + try: + self._view.strategy_id = self._strategy_id + except Exception: + pass return self._view def apply_trades( self, trades: List[TradeHistoryEntry], market_snapshot: Dict[str, float] ) -> None: + """Apply trades and update portfolio positions and aggregates. + + This method updates: + - cash (subtract on BUY, add on SELL at trade price) + - positions with weighted avg price, entry_ts on (re)open, and mark_price + - per-position notional, unrealized_pnl, pnl_pct + - portfolio aggregates: gross_exposure, net_exposure, total_value, total_unrealized_pnl, available_cash + """ for trade in trades: symbol = trade.instrument.symbol - price = trade.entry_price or market_snapshot.get(symbol, 0.0) - quantity_delta = ( - trade.quantity if trade.side == TradeSide.BUY else -trade.quantity - ) + price = float(trade.entry_price or market_snapshot.get(symbol, 0.0) or 0.0) + delta = float(trade.quantity or 0.0) + quantity_delta = delta if trade.side == TradeSide.BUY else -delta + position = self._view.positions.get(symbol) if position is None: position = PositionSnapshot( @@ -388,27 +424,94 @@ def apply_trades( quantity=0.0, avg_price=None, mark_price=price, - unrealized_pnl=None, + unrealized_pnl=0.0, ) self._view.positions[symbol] = position - new_quantity = position.quantity + quantity_delta + current_qty = float(position.quantity) + avg_price = float(position.avg_price or 0.0) + new_qty = current_qty + quantity_delta + + # Update mark price position.mark_price = price - if new_quantity == 0: + + # Handle position quantity transitions and avg price + if new_qty == 0.0: + # Fully closed self._view.positions.pop(symbol, None) - else: - position.quantity = new_quantity - if position.avg_price is None: - position.avg_price = price + elif current_qty == 0.0: + # Opening new position + position.quantity = new_qty + position.avg_price = price + position.entry_ts = trade.entry_ts or trade.trade_ts or int( + datetime.now(timezone.utc).timestamp() * 1000 + ) + position.trade_type = ( + TradeType.LONG if new_qty > 0 else TradeType.SHORT + ) + elif (current_qty > 0 and new_qty > 0) or (current_qty < 0 and new_qty < 0): + # Same direction + if abs(new_qty) > abs(current_qty): + # Increasing position: weighted average price + position.avg_price = ( + abs(current_qty) * avg_price + abs(quantity_delta) * price + ) / abs(new_qty) + position.quantity = new_qty else: - position.avg_price = (position.avg_price + price) / 2.0 + # Reducing position: keep avg price, update quantity + position.quantity = new_qty + # entry_ts remains from original opening + else: + # Crossing through zero to opposite direction: reset avg price and entry_ts + position.quantity = new_qty + position.avg_price = price + position.entry_ts = trade.entry_ts or trade.trade_ts or int( + datetime.now(timezone.utc).timestamp() * 1000 + ) + position.trade_type = ( + TradeType.LONG if new_qty > 0 else TradeType.SHORT + ) - notional = (price or 0.0) * trade.quantity + # Update cash by trade notional + notional = price * delta if trade.side == TradeSide.BUY: self._view.cash -= notional else: self._view.cash += notional + # Recompute per-position derived fields (if position still exists) + pos = self._view.positions.get(symbol) + if pos is not None: + qty = float(pos.quantity) + mpx = float(pos.mark_price or 0.0) + apx = float(pos.avg_price or 0.0) + pos.notional = abs(qty) * mpx if mpx else None + if apx and mpx: + pos.unrealized_pnl = (mpx - apx) * qty + denom = abs(qty) * apx + pos.pnl_pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None + else: + pos.unrealized_pnl = None + pos.pnl_pct = None + + # Recompute portfolio aggregates + gross = 0.0 + net = 0.0 + unreal = 0.0 + for pos in self._view.positions.values(): + mpx = float(pos.mark_price or 0.0) + qty = float(pos.quantity) + gross += abs(qty) * mpx + net += qty * mpx + if pos.unrealized_pnl is not None: + unreal += float(pos.unrealized_pnl) + + self._view.gross_exposure = gross + self._view.net_exposure = net + self._view.total_unrealized_pnl = unreal + self._view.total_value = self._view.cash + gross + self._view.available_cash = self._view.cash + @dataclass class StrategyRuntime: @@ -424,9 +527,15 @@ def create_strategy_runtime(request: UserRequest) -> StrategyRuntime: strategy_id = request.trading_config.strategy_name or generate_uuid("strategy") initial_capital = request.trading_config.initial_capital or 0.0 + constraints = { + "max_positions": request.trading_config.max_positions, + "max_leverage": request.trading_config.max_leverage, + } portfolio_service = InMemoryPortfolioService( initial_capital=initial_capital, trading_mode=request.exchange_config.trading_mode, + constraints=constraints, + strategy_id=strategy_id, ) base_prices = { From bf2923a6e99e37de2e655f2c94547996e2d79d9c Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 11:30:44 +0800 Subject: [PATCH 12/91] make format --- .../strategy_agent/decision/composer.py | 5 +-- .../strategy_agent/portfolio/interfaces.py | 6 ++-- .../agents/strategy_agent/runtime.py | 34 +++++++++---------- python/valuecell/utils/model.py | 2 +- 4 files changed, 25 insertions(+), 22 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index 9366a3199..4b66a0c56 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -7,7 +7,6 @@ from loguru import logger from pydantic import ValidationError -from .interfaces import Composer from ..models import ( ComposeContext, LlmDecisionAction, @@ -16,6 +15,7 @@ TradeSide, UserRequest, ) +from .interfaces import Composer class LlmComposer(Composer): @@ -115,9 +115,10 @@ async def _call_llm( `LlmPlanProposal`. """ - from valuecell.utils.model import create_model_with_provider from agno.agent import Agent as AgnoAgent + from valuecell.utils.model import create_model_with_provider + cfg = self._request.llm_model_config model = create_model_with_provider( provider=cfg.provider, diff --git a/python/valuecell/agents/strategy_agent/portfolio/interfaces.py b/python/valuecell/agents/strategy_agent/portfolio/interfaces.py index 04cefbb1c..e08360a3b 100644 --- a/python/valuecell/agents/strategy_agent/portfolio/interfaces.py +++ b/python/valuecell/agents/strategy_agent/portfolio/interfaces.py @@ -1,7 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import Optional, List, Dict +from typing import Dict, List, Optional from ..models import PortfolioView, TradeHistoryEntry @@ -17,7 +17,9 @@ def get_view(self) -> PortfolioView: """Return the latest portfolio view (positions, cash, optional constraints).""" raise NotImplementedError - def apply_trades(self, trades: List[TradeHistoryEntry], market_snapshot: Dict[str, float]) -> None: + def apply_trades( + self, trades: List[TradeHistoryEntry], market_snapshot: Dict[str, float] + ) -> None: """Apply executed trades to the portfolio view (optional). Implementations that support state changes (paper trading, backtests) diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index b8286737d..e97660136 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -1,8 +1,8 @@ from collections import defaultdict from dataclasses import dataclass from datetime import datetime, timezone -from typing import Dict, List, Optional from pathlib import Path +from typing import Dict, List, Optional import ccxt.pro as ccxtpro import numpy as np @@ -27,8 +27,8 @@ TradeDigestEntry, TradeHistoryEntry, TradeInstruction, - TradeType, TradeSide, + TradeType, TradingMode, UserRequest, ) @@ -47,9 +47,7 @@ def _make_prompt_provider(template_dir: Optional[Path] = None): - If neither is present, fall back to a simple generated prompt mentioning the symbols. """ - base = ( - Path(__file__).parent / "templates" if template_dir is None else template_dir - ) + base = Path(__file__).parent / "templates" if template_dir is None else template_dir def provider(request: UserRequest) -> str: tid = request.trading_config.template_id @@ -60,7 +58,7 @@ def provider(request: UserRequest) -> str: # safe-resolve candidate files candidates = [tid, f"{tid}.txt", f"{tid}.md"] for name in candidates: - try_path = (base / name) + try_path = base / name try: resolved = try_path.resolve() # ensure resolved path is inside base @@ -443,12 +441,12 @@ def apply_trades( # Opening new position position.quantity = new_qty position.avg_price = price - position.entry_ts = trade.entry_ts or trade.trade_ts or int( - datetime.now(timezone.utc).timestamp() * 1000 - ) - position.trade_type = ( - TradeType.LONG if new_qty > 0 else TradeType.SHORT + position.entry_ts = ( + trade.entry_ts + or trade.trade_ts + or int(datetime.now(timezone.utc).timestamp() * 1000) ) + position.trade_type = TradeType.LONG if new_qty > 0 else TradeType.SHORT elif (current_qty > 0 and new_qty > 0) or (current_qty < 0 and new_qty < 0): # Same direction if abs(new_qty) > abs(current_qty): @@ -465,12 +463,12 @@ def apply_trades( # Crossing through zero to opposite direction: reset avg price and entry_ts position.quantity = new_qty position.avg_price = price - position.entry_ts = trade.entry_ts or trade.trade_ts or int( - datetime.now(timezone.utc).timestamp() * 1000 - ) - position.trade_type = ( - TradeType.LONG if new_qty > 0 else TradeType.SHORT + position.entry_ts = ( + trade.entry_ts + or trade.trade_ts + or int(datetime.now(timezone.utc).timestamp() * 1000) ) + position.trade_type = TradeType.LONG if new_qty > 0 else TradeType.SHORT # Update cash by trade notional notional = price * delta @@ -489,7 +487,9 @@ def apply_trades( if apx and mpx: pos.unrealized_pnl = (mpx - apx) * qty denom = abs(qty) * apx - pos.pnl_pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None + pos.pnl_pct = ( + (pos.unrealized_pnl / denom) * 100.0 if denom else None + ) else: pos.unrealized_pnl = None pos.pnl_pct = None diff --git a/python/valuecell/utils/model.py b/python/valuecell/utils/model.py index dd1497ca0..f37b5fbde 100644 --- a/python/valuecell/utils/model.py +++ b/python/valuecell/utils/model.py @@ -191,8 +191,8 @@ def create_model_with_provider( # ProviderConfig but using the provided api_key. This avoids changing the # global configuration and keeps the change localized to this call. try: - from valuecell.config.manager import get_config_manager, ProviderConfig from valuecell.adapters.models.factory import get_model_factory + from valuecell.config.manager import ProviderConfig, get_config_manager except Exception: # Fallback to factory convenience if imports fail for some reason return create_model( From 784ca853024a3299c535591a0e5368a19fa7cce8 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 11:53:55 +0800 Subject: [PATCH 13/91] feat: add aggressive and insane trading strategy templates --- .../valuecell/agents/strategy_agent/models.py | 4 -- .../strategy_agent/templates/aggressive.txt | 70 +++++++++++++++++++ .../strategy_agent/templates/insane.txt | 39 +++++++++++ 3 files changed, 109 insertions(+), 4 deletions(-) create mode 100644 python/valuecell/agents/strategy_agent/templates/aggressive.txt create mode 100644 python/valuecell/agents/strategy_agent/templates/insane.txt diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index 874cbcc93..9049682ab 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -300,10 +300,6 @@ class LlmPlanProposal(BaseModel): ts: int items: List[LlmDecisionItem] = Field(default_factory=list) - notes: Optional[List[str]] = Field(default=None) - model_meta: Optional[Dict[str, str]] = Field( - default=None, description="Optional model metadata (e.g., model_name)" - ) class TradeInstruction(BaseModel): diff --git a/python/valuecell/agents/strategy_agent/templates/aggressive.txt b/python/valuecell/agents/strategy_agent/templates/aggressive.txt new file mode 100644 index 000000000..405f53db4 --- /dev/null +++ b/python/valuecell/agents/strategy_agent/templates/aggressive.txt @@ -0,0 +1,70 @@ +Aggressive Trading Strategy + +Overview +- Style: Aggressive momentum / breakout trader. High conviction, high turnover, uses leverage where available. Targets rapid capture of directional moves and volatility spikes. +- Objective: Maximize short-term returns by taking large, time-limited positions around breakouts, trend accelerations, and catalyst-driven moves. Accept higher drawdown and frequency of small losses for larger win potential. + +Trading Regime & Timeframes +- Primary timeframes: 5m, 15m, 1h (entry/exit). Use 1m for micro-execution and slippage control when needed. +- Market types: Liquid equities, crypto, futures, or FX where tight spreads and sufficient depth exist. + +Signals & Indicators +- Trend / Momentum: + - EMA(8), EMA(21), EMA(50) for short-term trend alignment. + - MACD(12,26,9) for momentum acceleration signals. +- Volatility / breakout: + - ATR(14) for dynamic stop sizing and identifying volatility expansion. + - Bollinger Bands (20, 2.0) for breakout confirmation. +- Confirmation: + - Volume spike (current volume > 1.5x average) near breakout. + - Price closing beyond recent consolidation (range breakout). + +Entry Rules (Aggressive) +- Primary entry (breakout momentum): + 1. Price closes above the consolidation high (e.g., prior 20-period high) on 5m or 15m timeframe. + 2. EMA(8) > EMA(21) and EMA(21) > EMA(50) (trend alignment) OR MACD histogram > 0 and rising. + 3. Volume >= 1.5x average volume over the consolidation window OR ATR expansion > recent ATR. + 4. Enter with market or aggressive limit (tight) order sized per position-sizing rules below. + +- Aggressive intraday add-on: + - If momentum continues and price breaks a subsequent micro-high on 1m with supporting volume, add up to a fixed add-on fraction of initial position (scale-in). Respect max_position_qty. + +Exit Rules +- Profit target: use a trailing stop based on ATR (e.g., trail = 1.5 * ATR(14)) or lock partial profits at predefined multiples (1st take: +1.5*ATR, scale out 25-50%). +- Hard stop: initial stop at entry_price - (stop_multiplier * ATR) for longs (reverse sign for shorts). Typical stop_multiplier=1.0–2.5 depending on aggressiveness. +- Time stop: exit any position that fails to reach profit target within a fixed time window (e.g., 6–12 candles on the entry timeframe). +- Flip / fast reversal: if the price rapidly reverses and crosses key EMAs in the opposite direction, flatten and consider re-entry in the new direction only if filters re-align. + +Position Sizing & Risk +- Base risk per trade: aggressive (e.g., 1.0%–3.0% of account equity) per open position. Use higher risk when confidence is high. +- Leverage: allowed if product supports it, but cap net leverage at the trading_config.max_leverage. +- Scaling: initial entry = 60% of target position; add-ons up to 40% on confirmed continuation moves. +- Max exposure: enforce max_positions and max_position_qty constraints. +- Min notional: ensure each order meets minimum notional and exchange limits. + +Execution & Slippage Control +- Use market orders when momentum is fast and limit orders when liquidity allows. Prefer immediate-or-cancel aggressive limits around breakouts. +- Respect quantity_step and exchange min increments. +- If slippage exceeds max_slippage_bps threshold repeatedly, reduce position sizing or widen stop targets. + +Risk Controls & Guardrails +- Max concurrent positions: obey `max_positions` provided in trading config. +- Per-instrument max notional and position cap: obey `max_position_qty` and `min_notional`. +- Daily drawdown kill-switch: if daily drawdown > X% (configurable), stop new entries until manual review. +- Rate-limit entries to avoid overtrading during noise: minimum time between new full-size entries for the same symbol (e.g., 15m). + +Parameters (example defaults) +- EMA periods: 8, 21, 50 +- MACD: 12,26,9 +- ATR period: 14 +- Stop multiplier: 1.5 +- Trail multiplier: 1.5 +- Volume spike multiplier: 1.5 +- Initial size fraction: 0.6 (60%) +- Add-on fraction: 0.4 (40%) +- Time stop window: 12 candles + +Operational Notes +- Backtest thoroughly across market regimes (bull, bear, sideways) and on multiple symbols before live deployment. +- Use paper trading first; expect frequent small losses and occasional large gains. +- Log all entries/exits with reasons (signal that triggered, indicators values, volume) for post-trade analysis and strategy tuning. diff --git a/python/valuecell/agents/strategy_agent/templates/insane.txt b/python/valuecell/agents/strategy_agent/templates/insane.txt new file mode 100644 index 000000000..69186e669 --- /dev/null +++ b/python/valuecell/agents/strategy_agent/templates/insane.txt @@ -0,0 +1,39 @@ +(Insane) Reckless Trading Strategy Prompt + +Overview +- Tone: Aggressive, risk-seeking, high-frequency mindset. The strategy should prioritize capturing rapid, large directional moves and volatility spikes with little regard for traditional risk controls. +- Objective: Maximize short-term upside by taking outsized positions quickly, scaling into strong momentum, and continuously seeking opportunities to compound returns. Accept frequent drawdowns and rapid position turnover. + +Behavioral Instructions for the Agent +- Seek high-conviction momentum breakouts and jump into positions quickly. +- Favor larger position sizes and use available leverage wherever possible to amplify gains. +- Do not prioritize stop-loss discipline; prefer aggressive re-entry unless an outright opposite signal appears. +- Take multiple simultaneous positions across symbols to chase opportunities; avoid idle cash. +- If a trade runs against you but the underlying momentum remains plausible, scale in more rather than cutting losses. + +Entry & Execution Preferences +- Enter on confirmed breakouts across favored timeframes (1m, 5m, 15m), prioritizing market orders to ensure entry. +- Scale-in on continuation: initial entry at 50% target, add aggressively on micro-breakouts up to full target. +- Prefer immediate execution; if limit orders would delay entry, use market or IOC aggressive limits. + +Exit Preferences +- Take profits quickly on strong moves; scale out in chunks but keep a core position running to catch larger trends. +- Do not rely on tight, mechanical stop-losses. Instead, use confident assessments: only exit on clear reversal signals. + +Sizing & Leverage +- Use high risk per trade relative to capital (well above conservative norms). When allowed, apply leverage up to the maximum permitted. +- Ignore conservative min_notional thresholds only if doing so materially improves entry timing and the platform permits it. + +Signals & Filters +- Momentum indicators (fast EMAs, MACD histogram), volume spikes, and volatility expansions are primary signals. +- Favor symbols with the strongest recent move and highest momentum scores across provided features. + +Auditing & Explanation +- For each trade, provide a short natural-language rationale emphasizing the momentum, catalyst, and confidence level. +- Include any scale-in logic and the intended target quantity. + +Safety Notice (for humans): +This prompt intentionally biases the agent toward risky, aggressive behavior and minimal risk controls. Use only for simulation, stress testing, or demo purposes. Do NOT deploy live without adding firm risk limits, stop-loss rules, and human oversight. + +Template variables available: +- {max_positions}, {max_leverage}, {min_notional}, {quantity_step}, {max_order_qty}, {max_position_qty} From 5293a0a91dbe008fcf48095d46330d1b5fd9feb5 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 14:52:16 +0800 Subject: [PATCH 14/91] feat: enhance trading execution and portfolio management with constraints and async execution results --- .../valuecell/agents/strategy_agent/core.py | 45 +-- .../strategy_agent/decision/composer.py | 350 ++++++++++++++---- .../strategy_agent/execution/interfaces.py | 19 +- .../valuecell/agents/strategy_agent/models.py | 111 +++++- .../agents/strategy_agent/runtime.py | 115 +++++- 5 files changed, 520 insertions(+), 120 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index ea1215499..fc86b29c2 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -23,6 +23,7 @@ TradeInstruction, TradeSide, TradeType, + TxResult, UserRequest, ) from .portfolio.interfaces import PortfolioService @@ -148,16 +149,15 @@ async def run_once(self) -> DecisionCycleResult: digest=digest, prompt_text=self._prompt_provider(self._request), market_snapshot=market_snapshot, - constraints=None, ) instructions = await self._composer.compose(context) - # Execution gateway may be sync; allow sync execute - self._execution_gateway.execute(instructions) - - trades = self._create_trades( - instructions, market_snapshot, compose_id, timestamp_ms + # Execute instructions via async gateway to obtain execution results + tx_results = await self._execution_gateway.execute( + instructions, market_snapshot ) + + trades = self._create_trades(tx_results, compose_id, timestamp_ms) self._apply_trades_to_portfolio(trades, market_snapshot) summary = self._build_summary(timestamp_ms, trades) @@ -196,31 +196,30 @@ def _default_prompt(self, request: UserRequest) -> str: def _create_trades( self, - instructions: List[TradeInstruction], - market_snapshot: Dict[str, float], + tx_results: List[TxResult], compose_id: str, timestamp_ms: int, ) -> List[TradeHistoryEntry]: trades: List[TradeHistoryEntry] = [] - for instruction in instructions: - symbol = instruction.instrument.symbol - price = market_snapshot.get(symbol, 0.0) - notional = price * instruction.quantity - realized_pnl = notional * ( - 0.001 if instruction.side == TradeSide.SELL else -0.001 - ) + for tx in tx_results: + qty = float(tx.filled_qty or 0.0) + price = float(tx.avg_exec_price or 0.0) + notional = (price * qty) if price and qty else None + # Immediate realized effect: fees are costs (negative PnL). Slippage already baked into exec price. + fee = float(tx.fee_cost or 0.0) + realized_pnl = -fee if notional else None trades.append( TradeHistoryEntry( trade_id=generate_uuid("trade"), compose_id=compose_id, - instruction_id=instruction.instruction_id, + instruction_id=tx.instruction_id, strategy_id=self.strategy_id, - instrument=instruction.instrument, - side=instruction.side, + instrument=tx.instrument, + side=tx.side, type=TradeType.LONG - if instruction.side == TradeSide.BUY + if tx.side == TradeSide.BUY else TradeType.SHORT, - quantity=instruction.quantity, + quantity=qty, entry_price=price or None, exit_price=None, notional_entry=notional or None, @@ -230,8 +229,10 @@ def _create_trades( trade_ts=timestamp_ms, holding_ms=None, realized_pnl=realized_pnl, - realized_pnl_pct=(realized_pnl / notional) if notional else None, - leverage=None, + realized_pnl_pct=((realized_pnl or 0.0) / notional) + if notional + else None, + leverage=tx.leverage, note=None, ) ) diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index 4b66a0c56..3618014e1 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -9,8 +9,10 @@ from ..models import ( ComposeContext, + Constraints, LlmDecisionAction, LlmPlanProposal, + PriceMode, TradeInstruction, TradeSide, UserRequest, @@ -44,10 +46,6 @@ def __init__( self._request = request self._default_slippage_bps = default_slippage_bps self._quantity_precision = quantity_precision - self._base_constraints: Dict[str, float | int] = { - "max_positions": request.trading_config.max_positions, - "max_leverage": request.trading_config.max_leverage, - } async def compose(self, context: ComposeContext) -> List[TradeInstruction]: prompt = self._build_llm_prompt(context) @@ -58,9 +56,6 @@ async def compose(self, context: ComposeContext) -> List[TradeInstruction]: ) try: plan = await self._call_llm(prompt) - except NotImplementedError: - logger.warning("LLM call not implemented; returning no instructions") - return [] except ValidationError as exc: logger.error("LLM output failed validation: {}", exc) return [] @@ -74,8 +69,7 @@ async def compose(self, context: ComposeContext) -> List[TradeInstruction]: ) return [] - constraints = self._merge_constraints(context) - return self._normalize_plan(context, plan, constraints) + return self._normalize_plan(context, plan) # ------------------------------------------------------------------ # Prompt + LLM helpers @@ -91,7 +85,12 @@ def _build_llm_prompt(self, context: ComposeContext) -> str: "market_snapshot": context.market_snapshot or {}, "digest": context.digest.model_dump(mode="json"), "features": [vector.model_dump(mode="json") for vector in context.features], - "constraints": context.constraints or {}, + # Constraints live on the portfolio view; prefer typed model_dump when present + "constraints": ( + context.portfolio.constraints.model_dump(mode="json", exclude_none=True) + if context.portfolio and context.portfolio.constraints + else {} + ), } instructions = ( @@ -103,9 +102,7 @@ def _build_llm_prompt(self, context: ComposeContext) -> str: return f"{instructions}\n\nContext:\n{json.dumps(payload, ensure_ascii=False, indent=2)}" - async def _call_llm( - self, prompt: str - ) -> LlmPlanProposal: # pragma: no cover - implemented async + async def _call_llm(self, prompt: str) -> LlmPlanProposal: """Invoke an LLM asynchronously and parse the response into LlmPlanProposal. This implementation follows the parser_agent pattern: it creates a model @@ -136,46 +133,221 @@ async def _call_llm( # ------------------------------------------------------------------ # Normalization / guardrails helpers - def _merge_constraints(self, context: ComposeContext) -> Dict[str, float | int]: - merged: Dict[str, float | int] = dict(self._base_constraints) - if context.constraints: - merged.update(context.constraints) - return merged + def _init_buying_power_context( + self, + context: ComposeContext, + ) -> tuple: + """Initialize buying power tracking context. + + Returns: + (equity, allowed_lev, constraints, projected_gross, price_map) + """ + constraints = context.portfolio.constraints or Constraints( + max_positions=self._request.trading_config.max_positions, + max_leverage=self._request.trading_config.max_leverage, + ) + + # Compute equity (prefer total_value, fallback to cash + net_exposure) + if getattr(context.portfolio, "total_value", None) is not None: + equity = float(context.portfolio.total_value or 0.0) + else: + cash = float(getattr(context.portfolio, "cash", 0.0) or 0.0) + net = float(getattr(context.portfolio, "net_exposure", 0.0) or 0.0) + equity = cash + net + + allowed_lev = ( + float(constraints.max_leverage) + if constraints.max_leverage is not None + else 1.0 + ) + + # Initialize projected gross exposure + price_map = context.market_snapshot or {} + if getattr(context.portfolio, "gross_exposure", None) is not None: + projected_gross = float(context.portfolio.gross_exposure or 0.0) + else: + projected_gross = 0.0 + for sym, snap in context.portfolio.positions.items(): + px = float( + price_map.get(sym) or getattr(snap, "mark_price", 0.0) or 0.0 + ) + projected_gross += abs(float(snap.quantity)) * px + + return equity, allowed_lev, constraints, projected_gross, price_map + + def _normalize_quantity( + self, + symbol: str, + quantity: float, + side: TradeSide, + current_qty: float, + constraints: Constraints, + equity: float, + allowed_lev: float, + projected_gross: float, + price_map: Dict[str, float], + ) -> tuple: + """Normalize quantity through all guardrails: filters, caps, and buying power. + + Returns: + (final_qty, consumed_buying_power_delta) + """ + qty = quantity + + # Step 1: per-order filters (step size, min notional, max order qty) + qty = self._apply_quantity_filters( + symbol, + qty, + float(constraints.quantity_step or 0.0), + float(constraints.min_trade_qty or 0.0), + constraints.max_order_qty, + constraints.min_notional, + price_map, + ) + + if qty <= self._quantity_precision: + logger.debug( + "Post-filter quantity for {} is {} <= precision {} -> skipping", + symbol, + qty, + self._quantity_precision, + ) + return 0.0, 0.0 + + # Step 2: notional/leverage cap (Phase 1 rules) + price = price_map.get(symbol) + if price is not None and price > 0: + cap_factor = 1.5 + if constraints.quantity_step and constraints.quantity_step > 0: + cap_factor = max(cap_factor, 1.5) + + allowed_lev_cap = ( + allowed_lev if math.isfinite(allowed_lev) else float("inf") + ) + max_abs_by_factor = (cap_factor * equity) / float(price) + max_abs_by_lev = (allowed_lev_cap * equity) / float(price) + max_abs_final = min(max_abs_by_factor, max_abs_by_lev) + + desired_final = current_qty + (qty if side is TradeSide.BUY else -qty) + if math.isfinite(max_abs_final) and abs(desired_final) > max_abs_final: + target_abs = max_abs_final + new_qty = max(0.0, target_abs - abs(current_qty)) + if new_qty < qty: + logger.debug( + "Capping {} qty due to notional/leverage (price={}, cap_factor={}, old_qty={}, new_qty={})", + symbol, + price, + cap_factor, + qty, + new_qty, + ) + qty = new_qty + + if qty <= self._quantity_precision: + logger.debug( + "Post-cap quantity for {} is {} <= precision {} -> skipping", + symbol, + qty, + self._quantity_precision, + ) + return 0.0, 0.0 + + # Step 3: buying power clamp + px = price_map.get(symbol) + if px is None or px <= 0: + logger.debug( + "No price for {} to evaluate buying power; using full quantity", + symbol, + ) + final_qty = qty + else: + avail_bp = max(0.0, equity * allowed_lev - projected_gross) + if avail_bp <= 0: + logger.debug("No available buying power for {}", symbol) + return 0.0, 0.0 + + a = abs(current_qty) + ap_units = avail_bp / float(px) + + # Piecewise: additional gross consumption must fit into available BP + if side is TradeSide.BUY: + if current_qty >= 0: + q_allowed = ap_units + else: + if qty <= 2 * a: + q_allowed = qty + else: + q_allowed = 2 * a + ap_units + else: # SELL + if current_qty <= 0: + q_allowed = ap_units + else: + if qty <= 2 * a: + q_allowed = qty + else: + q_allowed = 2 * a + ap_units + + final_qty = max(0.0, min(qty, q_allowed)) + + if final_qty <= self._quantity_precision: + logger.debug( + "Post-buying-power quantity for {} is {} <= precision {} -> skipping", + symbol, + final_qty, + self._quantity_precision, + ) + return 0.0, 0.0 + + # Compute consumed buying power delta + abs_before = abs(current_qty) + abs_after = abs( + current_qty + (final_qty if side is TradeSide.BUY else -final_qty) + ) + delta_abs = abs_after - abs_before + consumed_bp_delta = ( + delta_abs * price_map.get(symbol, 0.0) if delta_abs > 0 else 0.0 + ) + + return final_qty, consumed_bp_delta def _normalize_plan( self, context: ComposeContext, plan: LlmPlanProposal, - constraints: Dict[str, float | int], ) -> List[TradeInstruction]: instructions: List[TradeInstruction] = [] + # --- prepare state --- projected_positions: Dict[str, float] = { symbol: snapshot.quantity for symbol, snapshot in context.portfolio.positions.items() } - active_positions = sum( - 1 - for qty in projected_positions.values() - if abs(qty) > self._quantity_precision + + def _count_active(pos_map: Dict[str, float]) -> int: + return sum(1 for q in pos_map.values() if abs(q) > self._quantity_precision) + + active_positions = _count_active(projected_positions) + + # Initialize buying power context + equity, allowed_lev, constraints, projected_gross, price_map = ( + self._init_buying_power_context(context) ) - max_positions = constraints.get("max_positions") - quantity_step = float(constraints.get("quantity_step", 0) or 0.0) - min_trade_qty = float(constraints.get("min_trade_qty", 0) or 0.0) - max_order_qty = constraints.get("max_order_qty") - max_position_qty = constraints.get("max_position_qty") - min_notional = constraints.get("min_notional") + max_positions = constraints.max_positions + max_position_qty = constraints.max_position_qty + # --- process each planned item --- for idx, item in enumerate(plan.items): symbol = item.instrument.symbol current_qty = projected_positions.get(symbol, 0.0) + # determine the intended target quantity (clamped by max_position_qty) target_qty = self._resolve_target_quantity( item, current_qty, max_position_qty ) delta = target_qty - current_qty + # skip no-ops if abs(delta) <= self._quantity_precision: logger.debug( "Skipping symbol {} because delta {} <= quantity_precision {}", @@ -203,70 +375,108 @@ def _normalize_plan( continue side = TradeSide.BUY if delta > 0 else TradeSide.SELL + # requested leverage (default 1.0), clamped to constraints + requested_lev = ( + float(item.leverage) + if getattr(item, "leverage", None) is not None + else 1.0 + ) + allowed_lev_item = ( + float(constraints.max_leverage) + if constraints.max_leverage is not None + else requested_lev + ) + final_leverage = max(1.0, min(requested_lev, allowed_lev_item)) quantity = abs(delta) - quantity = self._apply_quantity_filters( + # Normalize quantity through all guardrails + quantity, consumed_bp = self._normalize_quantity( symbol, quantity, - quantity_step, - min_trade_qty, - max_order_qty, - min_notional, - context.market_snapshot or {}, + side, + current_qty, + constraints, + equity, + allowed_lev, + projected_gross, + price_map, ) if quantity <= self._quantity_precision: - logger.debug( - "Post-filter quantity for {} is {} <= precision {} -> skipping", - symbol, - quantity, - self._quantity_precision, - ) continue # Update projected positions for subsequent guardrails signed_delta = quantity if side is TradeSide.BUY else -quantity projected_positions[symbol] = current_qty + signed_delta + projected_gross += consumed_bp if is_new_position: active_positions += 1 if abs(projected_positions[symbol]) <= self._quantity_precision: active_positions = max(active_positions - 1, 0) - final_target = projected_positions[symbol] - meta = { - "requested_target_qty": target_qty, - "current_qty": current_qty, - "final_target_qty": final_target, - "action": item.action.value, - } - if item.confidence is not None: - meta["confidence"] = item.confidence - if item.rationale: - meta["rationale"] = item.rationale - - instruction = TradeInstruction( - instruction_id=f"{context.compose_id}:{symbol}:{idx}", - compose_id=context.compose_id, - instrument=item.instrument, - side=side, - quantity=quantity, - price_mode="market", - limit_price=None, - max_slippage_bps=self._default_slippage_bps, - meta=meta, - ) - instructions.append(instruction) - logger.debug( - "Created TradeInstruction {} for {} side={} qty={}", - instruction.instruction_id, + instruction = self._create_instruction( + context, + idx, + item, symbol, - instruction.side, - instruction.quantity, + side, + quantity, + final_leverage, + current_qty, + target_qty, ) + instructions.append(instruction) return instructions + def _create_instruction( + self, + context: ComposeContext, + idx: int, + item, + symbol: str, + side: TradeSide, + quantity: float, + final_leverage: float, + current_qty: float, + target_qty: float, + ) -> TradeInstruction: + """Create a normalized TradeInstruction with metadata.""" + final_target = current_qty + (quantity if side is TradeSide.BUY else -quantity) + meta = { + "requested_target_qty": target_qty, + "current_qty": current_qty, + "final_target_qty": final_target, + "action": item.action.value, + } + if item.confidence is not None: + meta["confidence"] = item.confidence + if item.rationale: + meta["rationale"] = item.rationale + + instruction = TradeInstruction( + instruction_id=f"{context.compose_id}:{symbol}:{idx}", + compose_id=context.compose_id, + instrument=item.instrument, + side=side, + quantity=quantity, + leverage=final_leverage, + price_mode=PriceMode.MARKET, + limit_price=None, + max_slippage_bps=self._default_slippage_bps, + meta=meta, + ) + logger.debug( + "Created TradeInstruction {} for {} side={} qty={} lev={}", + instruction.instruction_id, + symbol, + instruction.side, + instruction.quantity, + final_leverage, + ) + return instruction + def _resolve_target_quantity( self, item, diff --git a/python/valuecell/agents/strategy_agent/execution/interfaces.py b/python/valuecell/agents/strategy_agent/execution/interfaces.py index c1e745bec..ce665372b 100644 --- a/python/valuecell/agents/strategy_agent/execution/interfaces.py +++ b/python/valuecell/agents/strategy_agent/execution/interfaces.py @@ -1,9 +1,9 @@ from __future__ import annotations from abc import ABC, abstractmethod -from typing import List +from typing import Dict, List, Optional -from ..models import TradeInstruction +from ..models import TradeInstruction, TxResult # Contracts for execution gateways (module-local abstract interfaces). # An implementation may route to a real exchange or a paper broker. @@ -13,10 +13,17 @@ class ExecutionGateway(ABC): """Executes normalized trade instructions against an exchange/broker.""" @abstractmethod - def execute(self, instructions: List[TradeInstruction]) -> None: - """Submit the provided instructions for execution. - Implementors may be synchronous or asynchronous. At this stage we - do not model order/fill/cancel lifecycles. + async def execute( + self, + instructions: List[TradeInstruction], + market_snapshot: Optional[Dict[str, float]] = None, + ) -> List[TxResult]: + """Execute the provided instructions and return TxResult items. + + Notes: + - Implementations may simulate fills (paper) or submit to a real exchange. + - market_snapshot is optional context for pricing simulations. + - Lifecycle (partial fills, cancels) can be represented with PARTIAL/REJECTED. """ raise NotImplementedError diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index 9049682ab..e99ac1731 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -200,6 +200,42 @@ class StrategyStatus(str, Enum): ERROR = "error" +class Constraints(BaseModel): + """Typed constraints model used by the runtime and composer. + + Only includes guardrails used in Phase 1. Extend later in Phase 2. + """ + + max_positions: Optional[int] = Field( + default=None, + description="Maximum number of concurrent positions allowed for the strategy", + ) + max_leverage: Optional[float] = Field( + default=None, + description="Maximum leverage allowed for the strategy (e.g., 2.0 means up to 2x).", + ) + quantity_step: Optional[float] = Field( + default=None, + description="Minimum increment / step size for order quantities (in instrument units).", + ) + min_trade_qty: Optional[float] = Field( + default=None, + description="Minimum trade quantity (in instrument units) allowed for a single order.", + ) + max_order_qty: Optional[float] = Field( + default=None, + description="Maximum quantity allowed per single order (in instrument units).", + ) + min_notional: Optional[float] = Field( + default=None, + description="Minimum order notional (in quote currency) required for an order to be placed.", + ) + max_position_qty: Optional[float] = Field( + default=None, + description="Maximum absolute position quantity allowed for any single instrument (in instrument units).", + ) + + class PositionSnapshot(BaseModel): """Current position snapshot for one instrument.""" @@ -210,6 +246,9 @@ class PositionSnapshot(BaseModel): default=None, description="Current mark/reference price for P&L calc" ) unrealized_pnl: Optional[float] = Field(default=None, description="Unrealized PnL") + unrealized_pnl_pct: Optional[float] = Field( + default=None, description="Unrealized P&L as a percent of position value" + ) # Optional fields useful for UI and reporting notional: Optional[float] = Field( default=None, description="Position notional in quote currency" @@ -245,7 +284,7 @@ class PortfolioView(BaseModel): net_exposure: Optional[float] = Field( default=None, description="Net exposure (optional)" ) - constraints: Optional[Dict[str, float | int]] = Field( + constraints: Optional[Constraints] = Field( default=None, description="Optional risk/limits snapshot (e.g., max position, step size)", ) @@ -287,6 +326,11 @@ class LlmDecisionItem(BaseModel): target_qty: float = Field( ..., description="Desired position quantity after execution" ) + leverage: Optional[float] = Field( + default=None, + description="Requested leverage multiple for this target (e.g., 1.0 = no leverage)." + " Composer will clamp to allowed constraints.", + ) confidence: Optional[float] = Field( default=None, description="Optional confidence score [0,1]" ) @@ -302,6 +346,13 @@ class LlmPlanProposal(BaseModel): items: List[LlmDecisionItem] = Field(default_factory=list) +class PriceMode(str, Enum): + """Order price mode: market vs limit.""" + + MARKET = "market" + LIMIT = "limit" + + class TradeInstruction(BaseModel): """Executable instruction emitted by the composer after normalization.""" @@ -314,8 +365,12 @@ class TradeInstruction(BaseModel): instrument: InstrumentRef side: TradeSide quantity: float = Field(..., description="Order quantity in instrument units") - price_mode: str = Field( - ..., description='"market" or "limit" (initial versions may use only "market")' + leverage: Optional[float] = Field( + default=None, + description="Leverage multiple to apply for this instruction (if supported).", + ) + price_mode: PriceMode = Field( + PriceMode.MARKET, description="Order price mode: market vs limit" ) limit_price: Optional[float] = Field(default=None) max_slippage_bps: Optional[float] = Field(default=None) @@ -324,6 +379,46 @@ class TradeInstruction(BaseModel): ) +class TxStatus(str, Enum): + """Execution status of a submitted instruction.""" + + FILLED = "filled" + PARTIAL = "partial" + REJECTED = "rejected" + ERROR = "error" + + +class TxResult(BaseModel): + """Result of executing a TradeInstruction at a broker/exchange. + + This captures execution-side details such as fills, effective price, + fees and slippage. The coordinator converts TxResult into TradeHistoryEntry. + """ + + instruction_id: str = Field(..., description="Originating instruction id") + instrument: InstrumentRef + side: TradeSide + requested_qty: float = Field(..., description="Requested order quantity") + filled_qty: float = Field(..., description="Filled quantity (<= requested)") + avg_exec_price: Optional[float] = Field( + default=None, description="Average execution price for the fills" + ) + slippage_bps: Optional[float] = Field( + default=None, description="Observed slippage in basis points" + ) + fee_cost: Optional[float] = Field( + default=None, description="Total fees charged in quote currency" + ) + leverage: Optional[float] = Field( + default=None, description="Leverage applied, if any" + ) + status: TxStatus = Field(default=TxStatus.FILLED) + reason: Optional[str] = Field( + default=None, description="Message for rejects/errors" + ) + meta: Optional[Dict[str, str | float]] = Field(default=None) + + class MetricPoint(BaseModel): """Generic time-value point, used for value history charts.""" @@ -357,9 +452,6 @@ class ComposeContext(BaseModel): market_snapshot: Optional[Dict[str, float]] = Field( default=None, description="Optional map symbol -> current reference price" ) - constraints: Optional[Dict[str, float | int]] = Field( - default=None, description="Optional extra constraints for guardrails" - ) class HistoryRecord(BaseModel): @@ -448,11 +540,14 @@ class StrategySummary(BaseModel): realized_pnl: Optional[float] = Field( default=None, description="Realized P&L in quote CCY" ) + pnl_pct: Optional[float] = Field( + default=None, description="P&L as percent of equity or initial capital" + ) unrealized_pnl: Optional[float] = Field( default=None, description="Unrealized P&L in quote CCY" ) - pnl_pct: Optional[float] = Field( - default=None, description="P&L as percent of equity or initial capital" + unrealized_pnl_pct: Optional[float] = Field( + default=None, description="Unrealized P&L as a percent of position value" ) last_updated_ts: Optional[int] = Field(default=None) diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index e97660136..21552db65 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -18,6 +18,7 @@ from .features.interfaces import FeatureComputer from .models import ( Candle, + Constraints, FeatureVector, HistoryRecord, InstrumentRef, @@ -30,6 +31,7 @@ TradeSide, TradeType, TradingMode, + TxResult, UserRequest, ) from .portfolio.interfaces import PortfolioService @@ -294,13 +296,53 @@ def compute_features( class PaperExecutionGateway(ExecutionGateway): - """Records instructions without sending them anywhere.""" + """Async paper executor that simulates fills with slippage and fees. - def __init__(self) -> None: + - Uses instruction.max_slippage_bps to compute execution price around snapshot. + - Applies a flat fee_bps to notional to produce fee_cost. + - Marks orders as FILLED with filled_qty=requested quantity. + """ + + def __init__(self, fee_bps: float = 10.0) -> None: + self._fee_bps = float(fee_bps) self.executed: List[TradeInstruction] = [] - def execute(self, instructions: List[TradeInstruction]) -> None: - self.executed.extend(instructions) + async def execute( + self, + instructions: List[TradeInstruction], + market_snapshot: Optional[Dict[str, float]] = None, + ) -> List[TxResult]: + results: List[TxResult] = [] + price_map = market_snapshot or {} + for inst in instructions: + self.executed.append(inst) + ref_price = float(price_map.get(inst.instrument.symbol, 0.0) or 0.0) + slip_bps = float(inst.max_slippage_bps or 0.0) + slip = slip_bps / 10_000.0 + if inst.side == TradeSide.BUY: + exec_price = ref_price * (1.0 + slip) + else: + exec_price = ref_price * (1.0 - slip) + + notional = exec_price * float(inst.quantity) + fee_cost = notional * (self._fee_bps / 10_000.0) if notional else 0.0 + + results.append( + TxResult( + instruction_id=inst.instruction_id, + instrument=inst.instrument, + side=inst.side, + requested_qty=float(inst.quantity), + filled_qty=float(inst.quantity), + avg_exec_price=float(exec_price) if exec_price else None, + slippage_bps=slip_bps or None, + fee_cost=fee_cost or None, + leverage=inst.leverage, + meta=None, + ) + ) + + return results class InMemoryHistoryRecorder(HistoryRecorder): @@ -357,18 +399,21 @@ class InMemoryPortfolioService(PortfolioService): """Tracks cash and positions in memory and computes derived metrics. Notes: - - cash reflects remaining available cash for new positions (no margin logic here) + - cash reflects running cash balance from trade settlements - gross_exposure = sum(abs(qty) * mark_price) - net_exposure = sum(qty * mark_price) - - total_value = cash + gross_exposure + - equity (total_value) = cash + net_exposure [correct for both long and short] - total_unrealized_pnl = sum((mark_price - avg_price) * qty) + - available_cash approximates buying power with leverage: + available_cash = max(0, equity * max_leverage - gross_exposure) + where max_leverage comes from portfolio.constraints (default 1.0) """ def __init__( self, initial_capital: float, trading_mode: TradingMode, - constraints: Optional[Dict[str, float | int]] = None, + constraints: Optional[Constraints] = None, strategy_id: Optional[str] = None, ) -> None: # Store owning strategy id on the view so downstream components @@ -407,7 +452,7 @@ def apply_trades( - cash (subtract on BUY, add on SELL at trade price) - positions with weighted avg price, entry_ts on (re)open, and mark_price - per-position notional, unrealized_pnl, pnl_pct - - portfolio aggregates: gross_exposure, net_exposure, total_value, total_unrealized_pnl, available_cash + - portfolio aggregates: gross_exposure, net_exposure, total_value (equity), total_unrealized_pnl, available_cash (buying power) """ for trade in trades: symbol = trade.instrument.symbol @@ -447,6 +492,9 @@ def apply_trades( or int(datetime.now(timezone.utc).timestamp() * 1000) ) position.trade_type = TradeType.LONG if new_qty > 0 else TradeType.SHORT + # Initialize leverage from trade if provided + if trade.leverage is not None: + position.leverage = float(trade.leverage) elif (current_qty > 0 and new_qty > 0) or (current_qty < 0 and new_qty < 0): # Same direction if abs(new_qty) > abs(current_qty): @@ -455,6 +503,13 @@ def apply_trades( abs(current_qty) * avg_price + abs(quantity_delta) * price ) / abs(new_qty) position.quantity = new_qty + # Update leverage as size-weighted average if provided + if trade.leverage is not None: + prev_lev = float(position.leverage or trade.leverage) + position.leverage = ( + abs(current_qty) * prev_lev + + abs(quantity_delta) * float(trade.leverage) + ) / abs(new_qty) else: # Reducing position: keep avg price, update quantity position.quantity = new_qty @@ -469,6 +524,9 @@ def apply_trades( or int(datetime.now(timezone.utc).timestamp() * 1000) ) position.trade_type = TradeType.LONG if new_qty > 0 else TradeType.SHORT + # Reset leverage when flipping direction + if trade.leverage is not None: + position.leverage = float(trade.leverage) # Update cash by trade notional notional = price * delta @@ -499,8 +557,27 @@ def apply_trades( net = 0.0 unreal = 0.0 for pos in self._view.positions.values(): + # Refresh mark price from snapshot if available + try: + sym = pos.instrument.symbol + except Exception: + sym = None + if sym and sym in market_snapshot: + snap_px = float(market_snapshot.get(sym) or 0.0) + if snap_px > 0: + pos.mark_price = snap_px + mpx = float(pos.mark_price or 0.0) qty = float(pos.quantity) + apx = float(pos.avg_price or 0.0) + # Recompute unrealized PnL and pnl% with the refreshed mark + if apx and mpx: + pos.unrealized_pnl = (mpx - apx) * qty + denom = abs(qty) * apx + pos.pnl_pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None + else: + pos.unrealized_pnl = None + pos.pnl_pct = None gross += abs(qty) * mpx net += qty * mpx if pos.unrealized_pnl is not None: @@ -509,8 +586,18 @@ def apply_trades( self._view.gross_exposure = gross self._view.net_exposure = net self._view.total_unrealized_pnl = unreal - self._view.total_value = self._view.cash + gross - self._view.available_cash = self._view.cash + # Equity is cash plus net exposure (correct for both long and short) + equity = self._view.cash + net + self._view.total_value = equity + + # Approximate buying power using max leverage constraint + max_lev = ( + float(self._view.constraints.max_leverage) + if (self._view.constraints and self._view.constraints.max_leverage) + else 1.0 + ) + buying_power = max(0.0, equity * max_lev - gross) + self._view.available_cash = buying_power @dataclass @@ -527,10 +614,10 @@ def create_strategy_runtime(request: UserRequest) -> StrategyRuntime: strategy_id = request.trading_config.strategy_name or generate_uuid("strategy") initial_capital = request.trading_config.initial_capital or 0.0 - constraints = { - "max_positions": request.trading_config.max_positions, - "max_leverage": request.trading_config.max_leverage, - } + constraints = Constraints( + max_positions=request.trading_config.max_positions, + max_leverage=request.trading_config.max_leverage, + ) portfolio_service = InMemoryPortfolioService( initial_capital=initial_capital, trading_mode=request.exchange_config.trading_mode, From 2f032c0701c6f826300f5c2e90976c94772fc387 Mon Sep 17 00:00:00 2001 From: paisley Date: Thu, 6 Nov 2025 15:01:42 +0800 Subject: [PATCH 15/91] add --- .../configs/agent_cards/strategy_agent.json | 1 + python/valuecell/adapters/models/factory.py | 72 +++++++++++++-- .../valuecell/agents/strategy_agent/models.py | 54 +++++++++++- python/valuecell/server/api/app.py | 16 ++++ python/valuecell/server/api/routers/models.py | 85 ++++++++++++++++++ .../server/api/routers/strategy_agent.py | 87 +++++++++++++++++-- .../valuecell/server/api/schemas/__init__.py | 3 + python/valuecell/server/api/schemas/model.py | 25 ++++++ python/valuecell/server/db/models/__init__.py | 2 + python/valuecell/server/db/models/strategy.py | 73 ++++++++++++++++ 10 files changed, 400 insertions(+), 18 deletions(-) create mode 100644 python/valuecell/server/api/routers/models.py create mode 100644 python/valuecell/server/api/schemas/model.py create mode 100644 python/valuecell/server/db/models/strategy.py diff --git a/python/configs/agent_cards/strategy_agent.json b/python/configs/agent_cards/strategy_agent.json index 4e7073fbe..2930117c3 100644 --- a/python/configs/agent_cards/strategy_agent.json +++ b/python/configs/agent_cards/strategy_agent.json @@ -1,6 +1,7 @@ { "name": "StrategyAgent", "display_name": "Strategy Agent", + "url": "http://localhost:10004/", "description": "LLM-driven strategy composer that turns market features into normalized trade instructions. Includes a simple runtime for demo and testing.", "capabilities": { "streaming": true, diff --git a/python/valuecell/adapters/models/factory.py b/python/valuecell/adapters/models/factory.py index 850f9cc3e..9a06dfd12 100644 --- a/python/valuecell/adapters/models/factory.py +++ b/python/valuecell/adapters/models/factory.py @@ -630,10 +630,39 @@ def _create_model_internal(self, model_id: Optional[str], provider: str, **kwarg if not provider_config: raise ValueError(f"Provider configuration not found: {provider}") - # Validate provider - is_valid, error_msg = self.config_manager.validate_provider(provider) - if not is_valid: - raise ValueError(f"Provider validation failed: {error_msg}") + # Support per-call API key override via kwargs + override_api_key = kwargs.pop("api_key", None) + if override_api_key is not None: + # Create a copy of provider_config with overridden api_key + provider_config = ProviderConfig( + name=provider_config.name, + enabled=provider_config.enabled, + api_key=override_api_key, + base_url=provider_config.base_url, + default_model=provider_config.default_model, + models=provider_config.models, + parameters=provider_config.parameters, + default_embedding_model=provider_config.default_embedding_model, + embedding_models=provider_config.embedding_models, + embedding_parameters=provider_config.embedding_parameters, + extra_config=provider_config.extra_config, + ) + # Inline validation with override applied + if not provider_config.enabled: + raise ValueError(f"Provider '{provider}' is disabled in config") + if provider != "ollama" and not provider_config.api_key: + raise ValueError( + f"API key override missing/empty for provider '{provider}'." + ) + if provider == "azure" and not provider_config.base_url: + raise ValueError( + "Azure endpoint not configured. Please set AZURE_OPENAI_ENDPOINT" + ) + else: + # Validate provider using default manager rules + is_valid, error_msg = self.config_manager.validate_provider(provider) + if not is_valid: + raise ValueError(f"Provider validation failed: {error_msg}") # Create provider instance provider_class = self._providers[provider] @@ -1004,10 +1033,37 @@ def _create_embedder_internal( f"Please configure embedding models in providers/{provider}.yaml" ) - # Validate provider - is_valid, error_msg = self.config_manager.validate_provider(provider) - if not is_valid: - raise ValueError(f"Provider validation failed: {error_msg}") + # Support per-call API key override via kwargs + override_api_key = kwargs.pop("api_key", None) + if override_api_key is not None: + provider_config = ProviderConfig( + name=provider_config.name, + enabled=provider_config.enabled, + api_key=override_api_key, + base_url=provider_config.base_url, + default_model=provider_config.default_model, + models=provider_config.models, + parameters=provider_config.parameters, + default_embedding_model=provider_config.default_embedding_model, + embedding_models=provider_config.embedding_models, + embedding_parameters=provider_config.embedding_parameters, + extra_config=provider_config.extra_config, + ) + if not provider_config.enabled: + raise ValueError(f"Provider '{provider}' is disabled in config") + if provider != "ollama" and not provider_config.api_key: + raise ValueError( + f"API key override missing/empty for provider '{provider}'." + ) + if provider == "azure" and not provider_config.base_url: + raise ValueError( + "Azure endpoint not configured. Please set AZURE_OPENAI_ENDPOINT" + ) + else: + # Validate provider using default manager rules + is_valid, error_msg = self.config_manager.validate_provider(provider) + if not is_valid: + raise ValueError(f"Provider validation failed: {error_msg}") # Create provider instance provider_class = self._providers[provider] diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index 486dbb849..506154433 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -1,7 +1,7 @@ from enum import Enum from typing import Dict, List, Optional -from pydantic import BaseModel, Field, field_validator +from pydantic import BaseModel, Field, field_validator, model_validator from .constants import ( DEFAULT_AGENT_MODEL, @@ -42,7 +42,11 @@ class ComponentType(str, Enum): class LLMModelConfig(BaseModel): - """AI model configuration for strategy.""" + """AI model configuration for strategy. + + Defaults are harmonized with backend ConfigManager so that + Strategy creation uses the same provider/model as GET /models/llm/config. + """ provider: str = Field( default=DEFAULT_MODEL_PROVIDER, @@ -54,6 +58,52 @@ class LLMModelConfig(BaseModel): ) api_key: str = Field(..., description="API key for the model provider") + @model_validator(mode="before") + @classmethod + def _fill_defaults(cls, data): + # Allow requests to omit provider/model/api_key and backfill from ConfigManager + if not isinstance(data, dict): + return data + values = dict(data) + try: + from valuecell.config.manager import get_config_manager + + manager = get_config_manager() + resolved_provider = ( + values.get("provider") + or getattr(manager, "primary_provider", None) + or DEFAULT_MODEL_PROVIDER + ) + provider_cfg = manager.get_provider_config(resolved_provider) + if provider_cfg: + values.setdefault( + "provider", values.get("provider") or provider_cfg.name + ) + values.setdefault( + "model_id", + values.get("model_id") + or provider_cfg.default_model + or DEFAULT_AGENT_MODEL, + ) + # If api_key not provided by client, use provider config api_key + if values.get("api_key") is None and getattr( + provider_cfg, "api_key", None + ): + values["api_key"] = provider_cfg.api_key + else: + values.setdefault("provider", resolved_provider) + values.setdefault( + "model_id", values.get("model_id") or DEFAULT_AGENT_MODEL + ) + except Exception: + # Fall back to constants if config manager unavailable + values.setdefault( + "provider", values.get("provider") or DEFAULT_MODEL_PROVIDER + ) + values.setdefault("model_id", values.get("model_id") or DEFAULT_AGENT_MODEL) + + return values + class ExchangeConfig(BaseModel): """Exchange configuration for trading.""" diff --git a/python/valuecell/server/api/app.py b/python/valuecell/server/api/app.py index aba8f47e7..30c0803c6 100644 --- a/python/valuecell/server/api/app.py +++ b/python/valuecell/server/api/app.py @@ -8,6 +8,7 @@ from ...adapters.assets import get_adapter_manager from ..config.settings import get_settings +from ..db import init_database from .exceptions import ( APIException, api_exception_handler, @@ -18,6 +19,7 @@ from .routers.agent_stream import create_agent_stream_router from .routers.conversation import create_conversation_router from .routers.i18n import create_i18n_router +from .routers.models import create_models_router from .routers.strategy_agent import create_strategy_agent_router from .routers.system import create_system_router from .routers.task import create_task_router @@ -37,6 +39,17 @@ async def lifespan(app: FastAPI): f"ValueCell Server starting up on {settings.API_HOST}:{settings.API_PORT}..." ) + # Initialize database tables + try: + print("Initializing database tables...") + success = init_database(force=False) + if success: + print("✓ Database initialized") + else: + print("✗ Database initialization reported failure") + except Exception as e: + print(f"✗ Database initialization error: {e}") + # Initialize and configure adapters try: print("Configuring data adapters...") @@ -134,6 +147,9 @@ async def root(): # Include system router app.include_router(create_system_router(), prefix=API_PREFIX) + # Include models router + app.include_router(create_models_router(), prefix=API_PREFIX) + # Include watchlist router app.include_router(create_watchlist_router(), prefix=API_PREFIX) diff --git a/python/valuecell/server/api/routers/models.py b/python/valuecell/server/api/routers/models.py new file mode 100644 index 000000000..3c4e55456 --- /dev/null +++ b/python/valuecell/server/api/routers/models.py @@ -0,0 +1,85 @@ +"""Models API router: provide LLM model configuration defaults.""" + +from typing import List + +from fastapi import APIRouter, HTTPException + +from valuecell.config.manager import get_config_manager + +from ..schemas import LLMModelConfigData, SuccessResponse + +# Optional fallback constants from StrategyAgent +try: + from valuecell.agents.strategy_agent.constants import ( + DEFAULT_AGENT_MODEL, + DEFAULT_MODEL_PROVIDER, + ) +except Exception: # pragma: no cover - constants may not exist in minimal env + DEFAULT_MODEL_PROVIDER = "openrouter" + DEFAULT_AGENT_MODEL = "gpt-4o" + + +def create_models_router() -> APIRouter: + """Create models-related router with endpoints for model configs.""" + + router = APIRouter(prefix="/models", tags=["Models"]) + + @router.get( + "/llm/config", + response_model=SuccessResponse[List[LLMModelConfigData]], + summary="Get available LLMModelConfigs", + description=( + "Return a list of LLM model configurations for the primary provider " + "and any enabled fallback providers. API keys may be omitted if not configured." + ), + ) + async def get_llm_model_config() -> SuccessResponse[List[LLMModelConfigData]]: + try: + manager = get_config_manager() + + # Build ordered provider list: primary first, then fallbacks + providers = [manager.primary_provider] + manager.fallback_providers + # Deduplicate while preserving order + seen = set() + ordered = [p for p in providers if not (p in seen or seen.add(p))] + + configs: List[LLMModelConfigData] = [] + for provider in ordered: + provider_cfg = manager.get_provider_config(provider) + if provider_cfg is None: + configs.append( + LLMModelConfigData( + provider=DEFAULT_MODEL_PROVIDER, + model_id=DEFAULT_AGENT_MODEL, + api_key=None, + ) + ) + else: + model_id = provider_cfg.default_model or DEFAULT_AGENT_MODEL + configs.append( + LLMModelConfigData( + provider=provider_cfg.name, + model_id=model_id, + api_key=provider_cfg.api_key, + ) + ) + + # If no providers were detected, return a single default entry + if not configs: + configs.append( + LLMModelConfigData( + provider=DEFAULT_MODEL_PROVIDER, + model_id=DEFAULT_AGENT_MODEL, + api_key=None, + ) + ) + + return SuccessResponse.create( + data=configs, msg=f"Retrieved {len(configs)} LLMModelConfigs" + ) + except Exception as e: + raise HTTPException( + status_code=500, detail=f"Failed to get LLM config list: {str(e)}" + ) + + return router diff --git a/python/valuecell/server/api/routers/strategy_agent.py b/python/valuecell/server/api/routers/strategy_agent.py index e747e4faa..a33e40f6f 100644 --- a/python/valuecell/server/api/routers/strategy_agent.py +++ b/python/valuecell/server/api/routers/strategy_agent.py @@ -2,11 +2,18 @@ StrategyAgent router for handling strategy creation via streaming responses. """ -from fastapi import APIRouter, HTTPException +import os + +from fastapi import APIRouter, Depends, HTTPException +from sqlalchemy.orm import Session from valuecell.agents.strategy_agent.models import StrategyStatusContent, UserRequest +from valuecell.config.loader import get_config_loader from valuecell.core.coordinate.orchestrator import AgentOrchestrator -from valuecell.core.types import UserInput, UserInputMetadata +from valuecell.core.types import CommonResponseEvent, UserInput, UserInputMetadata +from valuecell.server.db.connection import get_db +from valuecell.server.db.models.strategy import Strategy +from valuecell.utils.uuid import generate_conversation_id def create_strategy_agent_router() -> APIRouter: @@ -16,7 +23,9 @@ def create_strategy_agent_router() -> APIRouter: orchestrator = AgentOrchestrator() @router.post("/create_strategy_agent") - async def create_strategy_agent(request: UserRequest): + async def create_strategy_agent( + request: UserRequest, db: Session = Depends(get_db) + ): """ Create a strategy through StrategyAgent and return final JSON result. @@ -26,16 +35,39 @@ async def create_strategy_agent(request: UserRequest): try: # Ensure we only serialize the core UserRequest fields, excluding conversation_id user_request = UserRequest( - llm_config=request.llm_config, + llm_model_config=request.llm_model_config, exchange_config=request.exchange_config, trading_config=request.trading_config, ) + + # If same provider + model_id comes with a new api_key, override previous key + try: + provider = user_request.llm_model_config.provider + model_id = user_request.llm_model_config.model_id + new_api_key = user_request.llm_model_config.api_key + if provider and model_id and new_api_key: + loader = get_config_loader() + provider_cfg_raw = loader.load_provider_config(provider) or {} + api_key_env = provider_cfg_raw.get("connection", {}).get( + "api_key_env" + ) + # Update environment and clear loader cache so subsequent reads use new key + if api_key_env: + os.environ[api_key_env] = new_api_key + loader.clear_cache() + except Exception: + # Best-effort override; continue even if config update fails + pass + query = user_request.model_dump_json() agent_name = "StrategyAgent" # Build UserInput for orchestrator - user_input_meta = UserInputMetadata(user_id="default_user") + user_input_meta = UserInputMetadata( + user_id="default_user", + conversation_id=generate_conversation_id(), + ) user_input = UserInput( query=query, target_agent_name=agent_name, @@ -47,11 +79,50 @@ async def create_strategy_agent(request: UserRequest): event = chunk_obj.event data = chunk_obj.data - if event == "component_generator": + if event == CommonResponseEvent.COMPONENT_GENERATOR: content = data.payload.content - return StrategyStatusContent.model_validate_json(content) + status_content = StrategyStatusContent.model_validate_json(content) + + # Persist strategy to database (best-effort) + try: + db.add( + Strategy( + strategy_id=status_content.strategy_id, + name=( + request.trading_config.strategy_name + or f"Strategy-{status_content.strategy_id[:8]}" + ), + user_id=user_input_meta.user_id, + status=( + status_content.status.value + if hasattr(status_content.status, "value") + else str(status_content.status) + ), + config=request.model_dump(), + strategy_metadata={ + "agent_name": agent_name, + "model_provider": request.llm_model_config.provider, + "model_id": request.llm_model_config.model_id, + "exchange_id": request.exchange_config.exchange_id, + "trading_mode": ( + request.exchange_config.trading_mode.value + if hasattr( + request.exchange_config.trading_mode, + "value", + ) + else str(request.exchange_config.trading_mode) + ), + }, + ) + ) + db.commit() + except Exception: + db.rollback() + # Do not fail the API due to persistence error + + return status_content - return StrategyStatusContent(status="error") + return StrategyStatusContent(strategy_id="unknown", status="error") except Exception as e: raise HTTPException( diff --git a/python/valuecell/server/api/schemas/__init__.py b/python/valuecell/server/api/schemas/__init__.py index 914072b18..58e8f300e 100644 --- a/python/valuecell/server/api/schemas/__init__.py +++ b/python/valuecell/server/api/schemas/__init__.py @@ -35,6 +35,7 @@ UserI18nSettingsData, UserI18nSettingsRequest, ) +from .model import LLMModelConfigData from .task import TaskCancelData from .user_profile import ( CreateUserProfileRequest, @@ -114,4 +115,6 @@ "UserProfileSummaryData", # Task schemas "TaskCancelData", + # Model schemas + "LLMModelConfigData", ] diff --git a/python/valuecell/server/api/schemas/model.py b/python/valuecell/server/api/schemas/model.py new file mode 100644 index 000000000..041ef509e --- /dev/null +++ b/python/valuecell/server/api/schemas/model.py @@ -0,0 +1,25 @@ +"""Model-related API schemas.""" + +from typing import Optional + +from pydantic import BaseModel, Field + + +class LLMModelConfigData(BaseModel): + """LLM model configuration used by frontend to prefill UserRequest. + + This is a relaxed version of agents.strategy_agent.models.LLMModelConfig, + allowing `api_key` to be optional so the API can return defaults + even when user credentials are not provided. + """ + + provider: str = Field( + ..., description="Model provider, e.g. 'openrouter', 'google', 'openai'" + ) + model_id: str = Field( + ..., + description="Model identifier, e.g. 'gpt-4o' or 'deepseek-ai/deepseek-v3.1'", + ) + api_key: Optional[str] = Field( + default=None, description="API key for the model provider (may be omitted)" + ) diff --git a/python/valuecell/server/db/models/__init__.py b/python/valuecell/server/db/models/__init__.py index 277c6e3f7..19dc56bae 100644 --- a/python/valuecell/server/db/models/__init__.py +++ b/python/valuecell/server/db/models/__init__.py @@ -11,6 +11,7 @@ # Import base model from .base import Base +from .strategy import Strategy from .user_profile import ProfileCategory, UserProfile from .watchlist import Watchlist, WatchlistItem @@ -19,6 +20,7 @@ "Base", "Agent", "Asset", + "Strategy", "Watchlist", "WatchlistItem", "UserProfile", diff --git a/python/valuecell/server/db/models/strategy.py b/python/valuecell/server/db/models/strategy.py new file mode 100644 index 000000000..ebd08a301 --- /dev/null +++ b/python/valuecell/server/db/models/strategy.py @@ -0,0 +1,73 @@ +""" +ValueCell Server - Strategy Models + +This module defines the database model for strategies created via StrategyAgent. +""" + +from typing import Any, Dict + +from sqlalchemy import JSON, Column, DateTime, Integer, String, Text +from sqlalchemy.sql import func + +from .base import Base + + +class Strategy(Base): + """Strategy model representing created strategies in the ValueCell system.""" + + __tablename__ = "strategies" + + # Primary key + id = Column(Integer, primary_key=True, index=True) + + # Strategy identifiers and basic info + strategy_id = Column( + String(100), + unique=True, + nullable=False, + index=True, + comment="Runtime strategy identifier from StrategyAgent", + ) + name = Column(String(200), nullable=True, comment="User-defined strategy name") + description = Column(Text, nullable=True, comment="Optional description") + + # Ownership and status + user_id = Column(String(100), nullable=True, index=True, comment="Owner user id") + status = Column( + String(50), nullable=False, default="running", comment="Strategy status" + ) + + # Configuration and metadata + config = Column(JSON, nullable=True, comment="Original UserRequest configuration") + strategy_metadata = Column( + JSON, nullable=True, comment="Additional metadata (agent, model provider, etc.)" + ) + + # Timestamps + created_at = Column( + DateTime(timezone=True), server_default=func.now(), nullable=False + ) + updated_at = Column( + DateTime(timezone=True), + server_default=func.now(), + onupdate=func.now(), + nullable=False, + ) + + def __repr__(self): + return f"" + + def to_dict(self) -> Dict[str, Any]: + """Convert strategy to dictionary representation.""" + return { + "id": self.id, + "strategy_id": self.strategy_id, + "name": self.name, + "description": self.description, + "user_id": self.user_id, + "status": self.status, + "config": self.config, + "metadata": self.strategy_metadata, + "created_at": self.created_at.isoformat() if self.created_at else None, + "updated_at": self.updated_at.isoformat() if self.updated_at else None, + } From 9d56f34de38b42aed1174c6be9d03814f24c5fe8 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 15:23:37 +0800 Subject: [PATCH 16/91] feat: implement sub-step handling for trade instructions to enforce single-lot and no direct flip constraints --- .../strategy_agent/decision/composer.py | 158 +++++++++--------- 1 file changed, 83 insertions(+), 75 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index 3618014e1..94693d931 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -345,88 +345,96 @@ def _count_active(pos_map: Dict[str, float]) -> int: target_qty = self._resolve_target_quantity( item, current_qty, max_position_qty ) - delta = target_qty - current_qty + # Enforce: single-lot per symbol and no direct flip. If target flips side, + # split into two sub-steps: first flat to 0, then open to target side. + sub_targets: List[float] = [] + if current_qty * target_qty < 0: + sub_targets = [0.0, float(target_qty)] + else: + sub_targets = [float(target_qty)] + + local_current = float(current_qty) + for sub_i, sub_target in enumerate(sub_targets): + delta = sub_target - local_current + + if abs(delta) <= self._quantity_precision: + continue + + is_new_position = ( + abs(local_current) <= self._quantity_precision + and abs(sub_target) > self._quantity_precision + ) + if ( + is_new_position + and max_positions is not None + and active_positions >= int(max_positions) + ): + logger.warning( + "Skipping symbol {} due to max_positions constraint (active={} max={})", + symbol, + active_positions, + max_positions, + ) + continue + + side = TradeSide.BUY if delta > 0 else TradeSide.SELL + # requested leverage (default 1.0), clamped to constraints + requested_lev = ( + float(item.leverage) + if getattr(item, "leverage", None) is not None + else 1.0 + ) + allowed_lev_item = ( + float(constraints.max_leverage) + if constraints.max_leverage is not None + else requested_lev + ) + final_leverage = max(1.0, min(requested_lev, allowed_lev_item)) + quantity = abs(delta) - # skip no-ops - if abs(delta) <= self._quantity_precision: - logger.debug( - "Skipping symbol {} because delta {} <= quantity_precision {}", + # Normalize quantity through all guardrails + quantity, consumed_bp = self._normalize_quantity( symbol, - delta, - self._quantity_precision, + quantity, + side, + local_current, + constraints, + equity, + allowed_lev, + projected_gross, + price_map, ) - continue - is_new_position = ( - abs(current_qty) <= self._quantity_precision - and abs(target_qty) > self._quantity_precision - ) - if ( - is_new_position - and max_positions is not None - and active_positions >= int(max_positions) - ): - logger.warning( - "Skipping symbol {} due to max_positions constraint (active={} max={})", + if quantity <= self._quantity_precision: + continue + + # Update projected positions for subsequent guardrails + signed_delta = quantity if side is TradeSide.BUY else -quantity + projected_positions[symbol] = local_current + signed_delta + projected_gross += consumed_bp + + # active positions accounting + if is_new_position: + active_positions += 1 + if abs(projected_positions[symbol]) <= self._quantity_precision: + active_positions = max(active_positions - 1, 0) + + # Use a stable per-item sub-index to keep instruction ids unique + instr = self._create_instruction( + context, + idx * 10 + sub_i, + item, symbol, - active_positions, - max_positions, + side, + quantity, + final_leverage, + local_current, + sub_target, ) - continue - - side = TradeSide.BUY if delta > 0 else TradeSide.SELL - # requested leverage (default 1.0), clamped to constraints - requested_lev = ( - float(item.leverage) - if getattr(item, "leverage", None) is not None - else 1.0 - ) - allowed_lev_item = ( - float(constraints.max_leverage) - if constraints.max_leverage is not None - else requested_lev - ) - final_leverage = max(1.0, min(requested_lev, allowed_lev_item)) - quantity = abs(delta) + instructions.append(instr) - # Normalize quantity through all guardrails - quantity, consumed_bp = self._normalize_quantity( - symbol, - quantity, - side, - current_qty, - constraints, - equity, - allowed_lev, - projected_gross, - price_map, - ) - - if quantity <= self._quantity_precision: - continue - - # Update projected positions for subsequent guardrails - signed_delta = quantity if side is TradeSide.BUY else -quantity - projected_positions[symbol] = current_qty + signed_delta - projected_gross += consumed_bp - - if is_new_position: - active_positions += 1 - if abs(projected_positions[symbol]) <= self._quantity_precision: - active_positions = max(active_positions - 1, 0) - - instruction = self._create_instruction( - context, - idx, - item, - symbol, - side, - quantity, - final_leverage, - current_qty, - target_qty, - ) - instructions.append(instruction) + # advance local_current for the next sub-step + local_current = projected_positions[symbol] return instructions From 901d68a2e7251fa587e5daae474610b9b9f396f0 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 15:24:03 +0800 Subject: [PATCH 17/91] feat: update PortfolioView to use buying_power instead of available_cash and adjust related calculations --- .../valuecell/agents/strategy_agent/models.py | 6 ++--- .../agents/strategy_agent/runtime.py | 27 +++++++++++-------- 2 files changed, 18 insertions(+), 15 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index e99ac1731..6586d04c2 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -295,8 +295,8 @@ class PortfolioView(BaseModel): total_unrealized_pnl: Optional[float] = Field( default=None, description="Sum of unrealized PnL across positions" ) - available_cash: Optional[float] = Field( - default=None, description="Cash available for new positions" + buying_power: Optional[float] = Field( + default=None, description="Buying power: max(0, equity * max_leverage - gross_exposure)" ) @@ -305,13 +305,11 @@ class LlmDecisionAction(str, Enum): Semantics: - BUY/SELL: directional intent; final TradeSide is decided by delta (target - current) - - FLAT: target position is zero (may produce close-out instructions) - NOOP: target equals current (delta == 0), no instruction should be emitted """ BUY = "buy" SELL = "sell" - FLAT = "flat" NOOP = "noop" diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index 21552db65..d9a158cd7 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -404,8 +404,7 @@ class InMemoryPortfolioService(PortfolioService): - net_exposure = sum(qty * mark_price) - equity (total_value) = cash + net_exposure [correct for both long and short] - total_unrealized_pnl = sum((mark_price - avg_price) * qty) - - available_cash approximates buying power with leverage: - available_cash = max(0, equity * max_leverage - gross_exposure) + - buying_power: max(0, equity * max_leverage - gross_exposure) where max_leverage comes from portfolio.constraints (default 1.0) """ @@ -429,7 +428,7 @@ def __init__( constraints=constraints or None, total_value=initial_capital, total_unrealized_pnl=0.0, - available_cash=initial_capital, + buying_power=initial_capital, ) self._trading_mode = trading_mode @@ -451,8 +450,9 @@ def apply_trades( This method updates: - cash (subtract on BUY, add on SELL at trade price) - positions with weighted avg price, entry_ts on (re)open, and mark_price - - per-position notional, unrealized_pnl, pnl_pct - - portfolio aggregates: gross_exposure, net_exposure, total_value (equity), total_unrealized_pnl, available_cash (buying power) + - per-position notional, unrealized_pnl, unrealized_pnl_pct (and keeps pnl_pct for + backward compatibility) + - portfolio aggregates: gross_exposure, net_exposure, total_value (equity), total_unrealized_pnl, buying_power """ for trade in trades: symbol = trade.instrument.symbol @@ -545,11 +545,13 @@ def apply_trades( if apx and mpx: pos.unrealized_pnl = (mpx - apx) * qty denom = abs(qty) * apx - pos.pnl_pct = ( - (pos.unrealized_pnl / denom) * 100.0 if denom else None - ) + pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None + # populate both the newer field and keep the legacy alias + pos.unrealized_pnl_pct = pct + pos.pnl_pct = pct else: pos.unrealized_pnl = None + pos.unrealized_pnl_pct = None pos.pnl_pct = None # Recompute portfolio aggregates @@ -570,13 +572,16 @@ def apply_trades( mpx = float(pos.mark_price or 0.0) qty = float(pos.quantity) apx = float(pos.avg_price or 0.0) - # Recompute unrealized PnL and pnl% with the refreshed mark + # Recompute unrealized PnL and percent (populate both new and legacy fields) if apx and mpx: pos.unrealized_pnl = (mpx - apx) * qty denom = abs(qty) * apx - pos.pnl_pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None + pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None + pos.unrealized_pnl_pct = pct + pos.pnl_pct = pct else: pos.unrealized_pnl = None + pos.unrealized_pnl_pct = None pos.pnl_pct = None gross += abs(qty) * mpx net += qty * mpx @@ -597,7 +602,7 @@ def apply_trades( else 1.0 ) buying_power = max(0.0, equity * max_lev - gross) - self._view.available_cash = buying_power + self._view.buying_power = buying_power @dataclass From 7a91b1464386ec790c55f46748412efe3aac0bf3 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 15:26:24 +0800 Subject: [PATCH 18/91] fix: correct logging format in StrategyAgent and simplify target quantity assignment in LlmComposer --- python/valuecell/agents/strategy_agent/agent.py | 2 +- python/valuecell/agents/strategy_agent/decision/composer.py | 6 +----- 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/agent.py b/python/valuecell/agents/strategy_agent/agent.py index db6a11515..78131f7a9 100644 --- a/python/valuecell/agents/strategy_agent/agent.py +++ b/python/valuecell/agents/strategy_agent/agent.py @@ -66,7 +66,7 @@ async def stream( except asyncio.CancelledError: raise except Exception as err: # noqa: BLE001 - logger.exception("StrategyAgent stream failed: %%s", err) + logger.exception("StrategyAgent stream failed: {}", err) yield streaming.message_chunk(f"StrategyAgent error: {err}") finally: yield streaming.done() diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index 94693d931..98aa72f86 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -493,11 +493,7 @@ def _resolve_target_quantity( ) -> float: if item.action == LlmDecisionAction.NOOP: return current_qty - if item.action == LlmDecisionAction.FLAT: - target = 0.0 - else: - target = float(item.target_qty) - + target = float(item.target_qty) if max_position_qty is not None: max_abs = abs(float(max_position_qty)) target = max(-max_abs, min(max_abs, target)) From 16fb5429800d702e352af63ab5d5bd3effcceefb Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 15:59:39 +0800 Subject: [PATCH 19/91] feat: enhance decision coordination with authoritative unrealized PnL and improve target quantity handling in LlmComposer --- .../valuecell/agents/strategy_agent/core.py | 19 ++++++++++++++---- .../strategy_agent/decision/composer.py | 20 +++++++++++++------ .../agents/strategy_agent/runtime.py | 3 +-- 3 files changed, 30 insertions(+), 12 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index fc86b29c2..0900f4b05 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -259,11 +259,18 @@ def _build_summary( ) -> StrategySummary: realized_delta = sum(trade.realized_pnl or 0.0 for trade in trades) self._realized_pnl += realized_delta + # Prefer authoritative unrealized PnL from the portfolio view when available. + try: + view = self._portfolio_service.get_view() + unrealized = float(view.total_unrealized_pnl or 0.0) + equity = float(view.total_value or 0.0) + except Exception: + # Fallback to internal tracking if portfolio service is unavailable + unrealized = float(self._unrealized_pnl or 0.0) + equity = float(self._request.trading_config.initial_capital or 0.0) - unrealized_delta = sum( - (trade.notional_entry or 0.0) * 0.0001 for trade in trades - ) - self._unrealized_pnl = max(self._unrealized_pnl + unrealized_delta, 0.0) + # Keep internal state in sync (allow negative unrealized PnL) + self._unrealized_pnl = float(unrealized) initial_capital = self._request.trading_config.initial_capital or 0.0 pnl_pct = ( @@ -272,6 +279,9 @@ def _build_summary( else None ) + # Strategy-level unrealized percent: percent of equity (if equity is available) + unrealized_pnl_pct = (self._unrealized_pnl / equity * 100.0) if equity else None + return StrategySummary( strategy_id=self.strategy_id, name=self._strategy_name, @@ -282,6 +292,7 @@ def _build_summary( status=StrategyStatus.RUNNING, realized_pnl=self._realized_pnl, unrealized_pnl=self._unrealized_pnl, + unrealized_pnl_pct=unrealized_pnl_pct, pnl_pct=pnl_pct, last_updated_ts=timestamp_ms, ) diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index 98aa72f86..c96f651dc 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -262,12 +262,11 @@ def _normalize_quantity( final_qty = qty else: avail_bp = max(0.0, equity * allowed_lev - projected_gross) - if avail_bp <= 0: - logger.debug("No available buying power for {}", symbol) - return 0.0, 0.0 - + # When buying power is exhausted, we should still allow reductions/closures. + # Set additional purchasable units to 0 but proceed with piecewise logic + # so that de-risking trades are not blocked. a = abs(current_qty) - ap_units = avail_bp / float(px) + ap_units = (avail_bp / float(px)) if avail_bp > 0 else 0.0 # Piecewise: additional gross consumption must fit into available BP if side is TradeSide.BUY: @@ -491,9 +490,18 @@ def _resolve_target_quantity( current_qty: float, max_position_qty: Optional[float], ) -> float: + # If the composer requested NOOP, keep current quantity if item.action == LlmDecisionAction.NOOP: return current_qty - target = float(item.target_qty) + + # Interpret target_qty as a magnitude; apply action to determine sign + mag = float(item.target_qty) + if item.action == LlmDecisionAction.SELL: + target = -abs(mag) + else: + # default to BUY semantics + target = abs(mag) + if max_position_qty is not None: max_abs = abs(float(max_position_qty)) target = max(-max_abs, min(max_abs, target)) diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index d9a158cd7..7ebfd7834 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -616,8 +616,7 @@ async def run_cycle(self) -> DecisionCycleResult: def create_strategy_runtime(request: UserRequest) -> StrategyRuntime: - strategy_id = request.trading_config.strategy_name or generate_uuid("strategy") - + strategy_id = generate_uuid("strategy") initial_capital = request.trading_config.initial_capital or 0.0 constraints = Constraints( max_positions=request.trading_config.max_positions, From 38a93ff9029601d17be9742a66369e2cf290f831 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 16:01:32 +0800 Subject: [PATCH 20/91] feat: add conservative slippage handling in LlmComposer to improve unit calculation based on effective price --- .../valuecell/agents/strategy_agent/decision/composer.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index c96f651dc..d0ee3644d 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -266,7 +266,14 @@ def _normalize_quantity( # Set additional purchasable units to 0 but proceed with piecewise logic # so that de-risking trades are not blocked. a = abs(current_qty) - ap_units = (avail_bp / float(px)) if avail_bp > 0 else 0.0 + # Conservative buffer for expected slippage: assume execution price may move + # against us by `self._default_slippage_bps`. Use a higher effective price + # when computing how many units fit into available buying power so that + # planned increases don't accidentally exceed real-world costs. + slip_bps = float(self._default_slippage_bps or 0.0) + slip = slip_bps / 10000.0 + effective_px = float(px) * (1.0 + slip) + ap_units = (avail_bp / effective_px) if avail_bp > 0 else 0.0 # Piecewise: additional gross consumption must fit into available BP if side is TradeSide.BUY: From c14b848ff1c9fd900121e395a2d7f7f48579fa9c Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 16:12:27 +0800 Subject: [PATCH 21/91] feat: enhance trade history pairing by detecting closes and annotating exit details in DefaultDecisionCoordinator --- .../valuecell/agents/strategy_agent/core.py | 68 ++++++++++++++++++- 1 file changed, 65 insertions(+), 3 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index 0900f4b05..2d32641a6 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -201,6 +201,12 @@ def _create_trades( timestamp_ms: int, ) -> List[TradeHistoryEntry]: trades: List[TradeHistoryEntry] = [] + # Current portfolio view (pre-apply) used to detect closes + try: + pre_view = self._portfolio_service.get_view() + except Exception: + pre_view = None + for tx in tx_results: qty = float(tx.filled_qty or 0.0) price = float(tx.avg_exec_price or 0.0) @@ -208,8 +214,7 @@ def _create_trades( # Immediate realized effect: fees are costs (negative PnL). Slippage already baked into exec price. fee = float(tx.fee_cost or 0.0) realized_pnl = -fee if notional else None - trades.append( - TradeHistoryEntry( + trade = TradeHistoryEntry( trade_id=generate_uuid("trade"), compose_id=compose_id, instruction_id=tx.instruction_id, @@ -235,7 +240,64 @@ def _create_trades( leverage=tx.leverage, note=None, ) - ) + + # If this tx likely closes an existing position (opposite side exists in pre_view), + # try to find the most recent open trade for this instrument in past execution history + # and annotate that past record with exit details so entries and exits are paired. + try: + if pre_view is not None: + sym = tx.instrument.symbol + prev_pos = pre_view.positions.get(sym) + if prev_pos is not None: + # prev_pos.quantity sign indicates current exposure direction + is_closing = ( + (prev_pos.quantity > 0 and tx.side == TradeSide.SELL) + or (prev_pos.quantity < 0 and tx.side == TradeSide.BUY) + ) + else: + is_closing = False + else: + is_closing = False + except Exception: + is_closing = False + + if is_closing: + # scan history records (most recent first) to find an open trade for this symbol + paired_id = None + for record in reversed(self._history_records): + if record.kind != "execution": + continue + trades_payload = record.payload.get("trades", []) or [] + # iterate trades in reverse to find latest + for t in reversed(trades_payload): + try: + inst = t.get("instrument") or {} + if inst.get("symbol") != tx.instrument.symbol: + continue + # consider open if no exit_ts or exit_price present + if not t.get("exit_ts") and not t.get("exit_price"): + # annotate this historic trade dict with exit fields + t["exit_price"] = float(price) if price else None + t["exit_ts"] = timestamp_ms + entry_ts_prev = t.get("entry_ts") or t.get("trade_ts") + if entry_ts_prev: + try: + t["holding_ms"] = int(timestamp_ms - int(entry_ts_prev)) + except Exception: + t["holding_ms"] = None + t["notional_exit"] = float(price * qty) if price and qty else None + paired_id = t.get("trade_id") + break + except Exception: + continue + if paired_id: + break + + # if we found a paired trade, record the pairing in the new trade's note + if paired_id: + trade.note = f"paired_exit_of:{paired_id}" + + trades.append(trade) return trades def _apply_trades_to_portfolio( From 583328700245abf0a75650f49d0c93db3f88ea9d Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 16:19:07 +0800 Subject: [PATCH 22/91] feat: make cap_factor configurable in LlmComposer and add to TradingConfig for enhanced position sizing control --- python/valuecell/agents/strategy_agent/constants.py | 1 + .../valuecell/agents/strategy_agent/decision/composer.py | 4 +++- python/valuecell/agents/strategy_agent/models.py | 7 +++++++ 3 files changed, 11 insertions(+), 1 deletion(-) diff --git a/python/valuecell/agents/strategy_agent/constants.py b/python/valuecell/agents/strategy_agent/constants.py index 5b9efdbb8..37097ea94 100644 --- a/python/valuecell/agents/strategy_agent/constants.py +++ b/python/valuecell/agents/strategy_agent/constants.py @@ -9,3 +9,4 @@ DEFAULT_MAX_POSITIONS = 5 DEFAULT_MAX_SYMBOLS = 5 DEFAULT_MAX_LEVERAGE = 10.0 +DEFAULT_CAP_FACTOR = 1.5 diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index d0ee3644d..3ab62e816 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -217,7 +217,9 @@ def _normalize_quantity( # Step 2: notional/leverage cap (Phase 1 rules) price = price_map.get(symbol) if price is not None and price > 0: - cap_factor = 1.5 + # cap_factor controls how aggressively we allow position sizing by notional. + # Make it configurable via trading_config.cap_factor (strategy parameter). + cap_factor = float(getattr(self._request.trading_config, "cap_factor", 1.5) or 1.5) if constraints.quantity_step and constraints.quantity_step > 0: cap_factor = max(cap_factor, 1.5) diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index 6586d04c2..d3cbef795 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -10,6 +10,7 @@ DEFAULT_MAX_POSITIONS, DEFAULT_MAX_SYMBOLS, DEFAULT_MODEL_PROVIDER, + DEFAULT_CAP_FACTOR, ) @@ -112,6 +113,12 @@ class TradingConfig(BaseModel): description="Optional custom prompt to customize strategy behavior", ) + cap_factor: float = Field( + default=DEFAULT_CAP_FACTOR, + description="Notional cap factor used by the composer to limit per-symbol exposure (e.g., 1.5)", + gt=0, + ) + @field_validator("symbols") @classmethod def validate_symbols(cls, v): From 60c9682fa58d91158dc54ca1490a65c61e70fb79 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 16:24:51 +0800 Subject: [PATCH 23/91] refactor: streamline trade application by removing redundant method and directly using PortfolioService --- python/valuecell/agents/strategy_agent/core.py | 15 +-------------- 1 file changed, 1 insertion(+), 14 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index 2d32641a6..eb83df70a 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -158,7 +158,7 @@ async def run_once(self) -> DecisionCycleResult: ) trades = self._create_trades(tx_results, compose_id, timestamp_ms) - self._apply_trades_to_portfolio(trades, market_snapshot) + self._portfolio_service.apply_trades(trades, market_snapshot) summary = self._build_summary(timestamp_ms, trades) history_records = self._create_history_records( @@ -300,19 +300,6 @@ def _create_trades( trades.append(trade) return trades - def _apply_trades_to_portfolio( - self, - trades: List[TradeHistoryEntry], - market_snapshot: Dict[str, float], - ) -> None: - if not trades: - return - # PortfolioService now exposes apply_trades; call directly to update state - try: - self._portfolio_service.apply_trades(trades, market_snapshot) - except NotImplementedError: - # service may be read-only; ignore - return def _build_summary( self, From e7cf3cd670133c8faf4d1f853e7bf92e28b71e49 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 16:36:21 +0800 Subject: [PATCH 24/91] refactor: simplify DefaultDecisionCoordinator constructor by removing optional parameters and enforcing required prompt_provider --- .../valuecell/agents/strategy_agent/core.py | 91 ++++++++----------- .../strategy_agent/decision/composer.py | 4 +- .../valuecell/agents/strategy_agent/models.py | 5 +- 3 files changed, 46 insertions(+), 54 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index eb83df70a..ab3d5c2e5 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -3,7 +3,7 @@ from abc import ABC, abstractmethod from dataclasses import dataclass from datetime import datetime, timezone -from typing import Callable, Dict, List, Optional +from typing import Callable, Dict, List from valuecell.utils.uuid import generate_uuid @@ -99,10 +99,7 @@ def __init__( execution_gateway: ExecutionGateway, history_recorder: HistoryRecorder, digest_builder: DigestBuilder, - interval: str = "1m", - lookback: int = 20, - prompt_provider: Optional[Callable[[UserRequest], str]] = None, - clock: Optional[Callable[[], datetime]] = None, + prompt_provider: Callable[[UserRequest], str], history_limit: int = 200, ) -> None: self._request = request @@ -114,14 +111,12 @@ def __init__( self._execution_gateway = execution_gateway self._history_recorder = history_recorder self._digest_builder = digest_builder - self._interval = interval - self._lookback = lookback self._history_limit = max(history_limit, 1) self._symbols = list(dict.fromkeys(request.trading_config.symbols)) - self._prompt_provider = ( - prompt_provider if prompt_provider is not None else self._default_prompt - ) - self._clock = clock if clock is not None else _default_clock + # prompt_provider is a required parameter (caller must supply a prompt builder) + self._prompt_provider = prompt_provider + # Use the default clock internally; clock is not a constructor parameter + self._clock = _default_clock self._history_records: List[HistoryRecord] = [] self._realized_pnl: float = 0.0 self._unrealized_pnl: float = 0.0 @@ -133,8 +128,9 @@ async def run_once(self) -> DecisionCycleResult: compose_id = generate_uuid("compose") portfolio = self._portfolio_service.get_view() + # Use fixed 1-minute interval and lookback of 4 hours (60 * 4 minutes) candles = await self._market_data_source.get_recent_candles( - self._symbols, self._interval, self._lookback + self._symbols, "1m", 60 * 4 ) features = self._feature_computer.compute_features(candles=candles) market_snapshot = _build_market_snapshot(features) @@ -187,13 +183,6 @@ async def run_once(self) -> DecisionCycleResult: portfolio_view=portfolio, ) - def _default_prompt(self, request: UserRequest) -> str: - custom_prompt = request.trading_config.custom_prompt - if custom_prompt: - return custom_prompt - symbols = ", ".join(self._symbols) - return f"Compose trading instructions for symbols: {symbols}." - def _create_trades( self, tx_results: List[TxResult], @@ -215,32 +204,30 @@ def _create_trades( fee = float(tx.fee_cost or 0.0) realized_pnl = -fee if notional else None trade = TradeHistoryEntry( - trade_id=generate_uuid("trade"), - compose_id=compose_id, - instruction_id=tx.instruction_id, - strategy_id=self.strategy_id, - instrument=tx.instrument, - side=tx.side, - type=TradeType.LONG - if tx.side == TradeSide.BUY - else TradeType.SHORT, - quantity=qty, - entry_price=price or None, - exit_price=None, - notional_entry=notional or None, - notional_exit=None, - entry_ts=timestamp_ms, - exit_ts=None, - trade_ts=timestamp_ms, - holding_ms=None, - realized_pnl=realized_pnl, - realized_pnl_pct=((realized_pnl or 0.0) / notional) - if notional - else None, - leverage=tx.leverage, - note=None, - ) - + trade_id=generate_uuid("trade"), + compose_id=compose_id, + instruction_id=tx.instruction_id, + strategy_id=self.strategy_id, + instrument=tx.instrument, + side=tx.side, + type=TradeType.LONG if tx.side == TradeSide.BUY else TradeType.SHORT, + quantity=qty, + entry_price=price or None, + exit_price=None, + notional_entry=notional or None, + notional_exit=None, + entry_ts=timestamp_ms, + exit_ts=None, + trade_ts=timestamp_ms, + holding_ms=None, + realized_pnl=realized_pnl, + realized_pnl_pct=( + ((realized_pnl or 0.0) / notional) if notional else None + ), + leverage=tx.leverage, + note=None, + ) + # If this tx likely closes an existing position (opposite side exists in pre_view), # try to find the most recent open trade for this instrument in past execution history # and annotate that past record with exit details so entries and exits are paired. @@ -251,9 +238,8 @@ def _create_trades( if prev_pos is not None: # prev_pos.quantity sign indicates current exposure direction is_closing = ( - (prev_pos.quantity > 0 and tx.side == TradeSide.SELL) - or (prev_pos.quantity < 0 and tx.side == TradeSide.BUY) - ) + prev_pos.quantity > 0 and tx.side == TradeSide.SELL + ) or (prev_pos.quantity < 0 and tx.side == TradeSide.BUY) else: is_closing = False else: @@ -282,10 +268,14 @@ def _create_trades( entry_ts_prev = t.get("entry_ts") or t.get("trade_ts") if entry_ts_prev: try: - t["holding_ms"] = int(timestamp_ms - int(entry_ts_prev)) + t["holding_ms"] = int( + timestamp_ms - int(entry_ts_prev) + ) except Exception: t["holding_ms"] = None - t["notional_exit"] = float(price * qty) if price and qty else None + t["notional_exit"] = ( + float(price * qty) if price and qty else None + ) paired_id = t.get("trade_id") break except Exception: @@ -300,7 +290,6 @@ def _create_trades( trades.append(trade) return trades - def _build_summary( self, timestamp_ms: int, diff --git a/python/valuecell/agents/strategy_agent/decision/composer.py b/python/valuecell/agents/strategy_agent/decision/composer.py index 3ab62e816..3ea827ad1 100644 --- a/python/valuecell/agents/strategy_agent/decision/composer.py +++ b/python/valuecell/agents/strategy_agent/decision/composer.py @@ -219,7 +219,9 @@ def _normalize_quantity( if price is not None and price > 0: # cap_factor controls how aggressively we allow position sizing by notional. # Make it configurable via trading_config.cap_factor (strategy parameter). - cap_factor = float(getattr(self._request.trading_config, "cap_factor", 1.5) or 1.5) + cap_factor = float( + getattr(self._request.trading_config, "cap_factor", 1.5) or 1.5 + ) if constraints.quantity_step and constraints.quantity_step > 0: cap_factor = max(cap_factor, 1.5) diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index d3cbef795..a7135a8bc 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -5,12 +5,12 @@ from .constants import ( DEFAULT_AGENT_MODEL, + DEFAULT_CAP_FACTOR, DEFAULT_INITIAL_CAPITAL, DEFAULT_MAX_LEVERAGE, DEFAULT_MAX_POSITIONS, DEFAULT_MAX_SYMBOLS, DEFAULT_MODEL_PROVIDER, - DEFAULT_CAP_FACTOR, ) @@ -303,7 +303,8 @@ class PortfolioView(BaseModel): default=None, description="Sum of unrealized PnL across positions" ) buying_power: Optional[float] = Field( - default=None, description="Buying power: max(0, equity * max_leverage - gross_exposure)" + default=None, + description="Buying power: max(0, equity * max_leverage - gross_exposure)", ) From d12e965795c30c102fd3e752cc57fd69517282b4 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 16:45:27 +0800 Subject: [PATCH 25/91] feat: enhance trade note handling by preserving LLM rationale and appending pairing info in DefaultDecisionCoordinator --- python/valuecell/agents/strategy_agent/core.py | 7 +++++-- python/valuecell/agents/strategy_agent/runtime.py | 2 +- 2 files changed, 6 insertions(+), 3 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index ab3d5c2e5..ce23af75b 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -225,7 +225,7 @@ def _create_trades( ((realized_pnl or 0.0) / notional) if notional else None ), leverage=tx.leverage, - note=None, + note=(tx.meta.get("rationale") if tx.meta else None), ) # If this tx likely closes an existing position (opposite side exists in pre_view), @@ -285,7 +285,10 @@ def _create_trades( # if we found a paired trade, record the pairing in the new trade's note if paired_id: - trade.note = f"paired_exit_of:{paired_id}" + # preserve LLM rationale (if any) and append pairing info + existing = trade.note or "" + suffix = f"paired_exit_of:{paired_id}" + trade.note = f"{existing} {suffix}".strip() trades.append(trade) return trades diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index 7ebfd7834..4132bf8dc 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -338,7 +338,7 @@ async def execute( slippage_bps=slip_bps or None, fee_cost=fee_cost or None, leverage=inst.leverage, - meta=None, + meta=inst.meta, ) ) From a4d2e8f17437b01976bb3bd9e3166b5e3880ee85 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 17:06:09 +0800 Subject: [PATCH 26/91] feat: enhance trade history entry creation by implementing full close detection and annotating exit details in DefaultDecisionCoordinator --- .../valuecell/agents/strategy_agent/core.py | 141 +++++++++++++----- 1 file changed, 100 insertions(+), 41 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index ce23af75b..13a596966 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -203,51 +203,110 @@ def _create_trades( # Immediate realized effect: fees are costs (negative PnL). Slippage already baked into exec price. fee = float(tx.fee_cost or 0.0) realized_pnl = -fee if notional else None - trade = TradeHistoryEntry( - trade_id=generate_uuid("trade"), - compose_id=compose_id, - instruction_id=tx.instruction_id, - strategy_id=self.strategy_id, - instrument=tx.instrument, - side=tx.side, - type=TradeType.LONG if tx.side == TradeSide.BUY else TradeType.SHORT, - quantity=qty, - entry_price=price or None, - exit_price=None, - notional_entry=notional or None, - notional_exit=None, - entry_ts=timestamp_ms, - exit_ts=None, - trade_ts=timestamp_ms, - holding_ms=None, - realized_pnl=realized_pnl, - realized_pnl_pct=( - ((realized_pnl or 0.0) / notional) if notional else None - ), - leverage=tx.leverage, - note=(tx.meta.get("rationale") if tx.meta else None), - ) - # If this tx likely closes an existing position (opposite side exists in pre_view), - # try to find the most recent open trade for this instrument in past execution history - # and annotate that past record with exit details so entries and exits are paired. + # Determine if this trade fully closes an existing position for this symbol + prev_pos = None + prev_qty = 0.0 try: if pre_view is not None: - sym = tx.instrument.symbol - prev_pos = pre_view.positions.get(sym) - if prev_pos is not None: - # prev_pos.quantity sign indicates current exposure direction - is_closing = ( - prev_pos.quantity > 0 and tx.side == TradeSide.SELL - ) or (prev_pos.quantity < 0 and tx.side == TradeSide.BUY) - else: - is_closing = False - else: - is_closing = False + prev_pos = pre_view.positions.get(tx.instrument.symbol) + prev_qty = float(prev_pos.quantity) if prev_pos is not None else 0.0 except Exception: - is_closing = False - - if is_closing: + prev_pos = None + prev_qty = 0.0 + + eps = 1e-12 + is_full_close = False + close_units = 0.0 + pos_dir_type: TradeType | None = None + if prev_pos is not None: + if prev_qty > 0 and tx.side == TradeSide.SELL: + close_units = min(qty, abs(prev_qty)) + is_full_close = close_units >= abs(prev_qty) - eps + pos_dir_type = TradeType.LONG + elif prev_qty < 0 and tx.side == TradeSide.BUY: + close_units = min(qty, abs(prev_qty)) + is_full_close = close_units >= abs(prev_qty) - eps + pos_dir_type = TradeType.SHORT + + if is_full_close and prev_pos is not None and prev_pos.avg_price is not None: + # Build a completed trade that ties back to the original open (avg_price/entry_ts) + entry_px = float(prev_pos.avg_price or 0.0) + entry_ts_prev = int(prev_pos.entry_ts) if prev_pos.entry_ts else None + exit_px = price or None + exit_ts = timestamp_ms + qty_closed = float(close_units or 0.0) + # Realized PnL on close (exclude prior fees; subtract this tx fee) + core_pnl = None + if entry_px and exit_px and qty_closed: + if pos_dir_type == TradeType.LONG: + core_pnl = (float(exit_px) - float(entry_px)) * qty_closed + else: # SHORT + core_pnl = (float(entry_px) - float(exit_px)) * qty_closed + realized_pnl = (core_pnl if core_pnl is not None else None) + if realized_pnl is not None: + realized_pnl = float(realized_pnl) - fee + notional_entry = (qty_closed * entry_px) if entry_px and qty_closed else None + notional_exit = (qty_closed * float(exit_px)) if exit_px and qty_closed else None + realized_pnl_pct = ( + (realized_pnl / notional_entry) if realized_pnl is not None and notional_entry else None + ) + + trade = TradeHistoryEntry( + trade_id=generate_uuid("trade"), + compose_id=compose_id, + instruction_id=tx.instruction_id, + strategy_id=self.strategy_id, + instrument=tx.instrument, + side=tx.side, + type=pos_dir_type or (TradeType.LONG if tx.side == TradeSide.BUY else TradeType.SHORT), + quantity=qty_closed or qty, + entry_price=entry_px or None, + exit_price=exit_px, + notional_entry=notional_entry, + notional_exit=notional_exit, + entry_ts=entry_ts_prev or timestamp_ms, + exit_ts=exit_ts, + trade_ts=timestamp_ms, + holding_ms=(exit_ts - entry_ts_prev) if entry_ts_prev else None, + realized_pnl=realized_pnl, + realized_pnl_pct=realized_pnl_pct, + leverage=tx.leverage, + note=(tx.meta.get("rationale") if tx.meta else None), + ) + else: + # Default behavior for opens/increases/reductions that are not full closes + trade = TradeHistoryEntry( + trade_id=generate_uuid("trade"), + compose_id=compose_id, + instruction_id=tx.instruction_id, + strategy_id=self.strategy_id, + instrument=tx.instrument, + side=tx.side, + type=TradeType.LONG if tx.side == TradeSide.BUY else TradeType.SHORT, + quantity=qty, + entry_price=price or None, + exit_price=None, + notional_entry=notional or None, + notional_exit=None, + entry_ts=timestamp_ms, + exit_ts=None, + trade_ts=timestamp_ms, + holding_ms=None, + realized_pnl=realized_pnl, + realized_pnl_pct=( + ((realized_pnl or 0.0) / notional) if notional else None + ), + leverage=tx.leverage, + note=(tx.meta.get("rationale") if tx.meta else None), + ) + + # If reducing/closing but not a full close, try to annotate the most recent open trade + is_closing = ( + prev_pos is not None + and ((prev_qty > 0 and tx.side == TradeSide.SELL) or (prev_qty < 0 and tx.side == TradeSide.BUY)) + ) + if is_closing and not is_full_close: # scan history records (most recent first) to find an open trade for this symbol paired_id = None for record in reversed(self._history_records): From c0bde558aaf4452016ec97d6ad17d3b83a97a86d Mon Sep 17 00:00:00 2001 From: paisley Date: Thu, 6 Nov 2025 17:08:55 +0800 Subject: [PATCH 27/91] add strategy router --- python/valuecell/server/api/app.py | 45 ++-- .../valuecell/server/api/routers/strategy.py | 215 ++++++++++++++++++ .../server/api/routers/strategy_agent.py | 4 +- .../server/api/routers/strategy_api.py | 22 ++ .../valuecell/server/api/schemas/strategy.py | 124 ++++++++++ .../server/services/strategy_service.py | 166 ++++++++++++++ 6 files changed, 553 insertions(+), 23 deletions(-) create mode 100644 python/valuecell/server/api/routers/strategy.py create mode 100644 python/valuecell/server/api/routers/strategy_api.py create mode 100644 python/valuecell/server/api/schemas/strategy.py create mode 100644 python/valuecell/server/services/strategy_service.py diff --git a/python/valuecell/server/api/app.py b/python/valuecell/server/api/app.py index 30c0803c6..4003dc74e 100644 --- a/python/valuecell/server/api/app.py +++ b/python/valuecell/server/api/app.py @@ -20,11 +20,17 @@ from .routers.conversation import create_conversation_router from .routers.i18n import create_i18n_router from .routers.models import create_models_router -from .routers.strategy_agent import create_strategy_agent_router + +# from .routers.strategy_alias import create_strategy_alias_router +from .routers.strategy_api import create_strategy_api_router + +# from .routers.strategy_agent import create_strategy_agent_router from .routers.system import create_system_router from .routers.task import create_task_router from .routers.user_profile import create_user_profile_router from .routers.watchlist import create_watchlist_router + +# from .routers.strategy import create_strategy_router from .schemas import AppInfoData, SuccessResponse @@ -113,33 +119,30 @@ def _add_middleware(app: FastAPI, settings) -> None: # Custom logging middleware removed -def _add_exception_handlers(app: FastAPI): - """Add exception handlers.""" +def _add_exception_handlers(app: FastAPI) -> None: + """Add exception handlers to the application.""" app.add_exception_handler(APIException, api_exception_handler) app.add_exception_handler(RequestValidationError, validation_exception_handler) app.add_exception_handler(Exception, general_exception_handler) +API_PREFIX = "/api/v1" + + def _add_routes(app: FastAPI, settings) -> None: """Add routes to the application.""" - API_PREFIX = "/api/v1" - - @app.get( - "/", - response_model=SuccessResponse[AppInfoData], - summary="Get application info", - description="Get ValueCell application basic information including name, version and environment", - tags=["Root"], - ) - async def root(): - """Root endpoint - Get application basic information.""" - app_info = AppInfoData( - name=settings.APP_NAME, - version=settings.APP_VERSION, - environment=settings.APP_ENVIRONMENT, + # Root endpoint + @app.get("/", response_model=SuccessResponse[AppInfoData]) + async def home_page(): + return SuccessResponse.create( + data=AppInfoData( + name=settings.APP_NAME, + version=settings.APP_VERSION, + environment=settings.APP_ENVIRONMENT, + ), + msg="Welcome to ValueCell Server API", ) - return SuccessResponse.create(data=app_info, msg="Welcome to ValueCell API") # Include i18n router app.include_router(create_i18n_router(), prefix=API_PREFIX) @@ -162,8 +165,8 @@ async def root(): # Include agent stream router app.include_router(create_agent_stream_router(), prefix=API_PREFIX) - # Include strategy agent router - app.include_router(create_strategy_agent_router(), prefix=API_PREFIX) + # Include aggregated strategy API router (strategies + strategy agent) + app.include_router(create_strategy_api_router(), prefix=API_PREFIX) # Include agent router app.include_router(create_agent_router(), prefix=API_PREFIX) diff --git a/python/valuecell/server/api/routers/strategy.py b/python/valuecell/server/api/routers/strategy.py new file mode 100644 index 000000000..a2fcf138a --- /dev/null +++ b/python/valuecell/server/api/routers/strategy.py @@ -0,0 +1,215 @@ +""" +Strategy API router for handling strategy-related endpoints. +""" + +from typing import List, Optional + +from fastapi import APIRouter, Depends, HTTPException, Query +from sqlalchemy import and_, or_ +from sqlalchemy.orm import Session + +from valuecell.server.api.schemas.base import SuccessResponse +from valuecell.server.api.schemas.strategy import ( + StrategyDetailResponse, + StrategyHoldingFlatItem, + StrategyHoldingFlatResponse, + StrategyListData, + StrategyListResponse, + StrategySummaryData, +) +from valuecell.server.db import get_db +from valuecell.server.db.models.strategy import Strategy +from valuecell.server.services.strategy_service import StrategyService + + +def create_strategy_router() -> APIRouter: + """Create and configure the strategy router.""" + + router = APIRouter( + prefix="/strategies", + tags=["strategies"], + responses={404: {"description": "Not found"}}, + ) + + @router.get( + "/", + response_model=StrategyListResponse, + summary="Get all strategies", + description="Get a list of strategies created via StrategyAgent with optional filters", + ) + async def get_strategies( + user_id: Optional[str] = Query(None, description="Filter by user ID"), + status: Optional[str] = Query(None, description="Filter by status"), + name_filter: Optional[str] = Query( + None, description="Filter by strategy name or ID (supports fuzzy matching)" + ), + db: Session = Depends(get_db), + ) -> StrategyListResponse: + """ + Get all strategies list. + + - **user_id**: Filter by owner user ID + - **status**: Filter by strategy status (running, stopped) + - **name_filter**: Filter by strategy name or ID with fuzzy matching + + Returns a response containing the strategy list and statistics. + """ + try: + query = db.query(Strategy) + + filters = [] + if user_id: + filters.append(Strategy.user_id == user_id) + if status: + filters.append(Strategy.status == status) + if name_filter: + filters.append( + or_( + Strategy.name.ilike(f"%{name_filter}%"), + Strategy.strategy_id.ilike(f"%{name_filter}%"), + ) + ) + + if filters: + query = query.filter(and_(*filters)) + + strategies = query.order_by(Strategy.created_at.desc()).all() + + def map_status(raw: Optional[str]) -> str: + return "running" if (raw or "").lower() == "running" else "stopped" + + def normalize_trading_mode(meta: dict, cfg: dict) -> Optional[str]: + v = meta.get("trading_mode") or cfg.get("trading_mode") + if not v: + return None + v = str(v).lower() + if v in ("live", "real", "realtime"): + return "live" + if v in ("virtual", "paper", "sim"): + return "virtual" + return None + + def to_optional_float(value) -> Optional[float]: + if value is None: + return None + try: + return float(value) + except Exception: + return None + + strategy_data_list = [] + for s in strategies: + meta = s.strategy_metadata or {} + cfg = s.config or {} + item = StrategySummaryData( + strategy_id=s.strategy_id, + strategy_name=s.name, + status=map_status(s.status), + trading_mode=normalize_trading_mode(meta, cfg), + unrealized_pnl=to_optional_float( + meta.get("unrealized_pnl") or cfg.get("unrealized_pnl") + ), + unrealized_pnl_pct=to_optional_float( + meta.get("unrealized_pnl_pct") or cfg.get("unrealized_pnl_pct") + ), + created_at=s.created_at, + exchange_id=(meta.get("exchange_id") or cfg.get("exchange_id")), + model_id=( + meta.get("model_id") + or meta.get("llm_model_id") + or cfg.get("model_id") + or cfg.get("llm_model_id") + ), + ) + strategy_data_list.append(item) + + running_count = sum(1 for s in strategy_data_list if s.status == "running") + + list_data = StrategyListData( + strategies=strategy_data_list, + total=len(strategy_data_list), + running_count=running_count, + ) + + return SuccessResponse.create( + data=list_data, + msg=f"Successfully retrieved {list_data.total} strategies", + ) + except Exception as e: + raise HTTPException( + status_code=500, detail=f"Failed to retrieve strategy list: {str(e)}" + ) + + @router.get( + "/holding", + response_model=StrategyHoldingFlatResponse, + summary="Get current holdings for a strategy", + description="Return the latest portfolio holdings of the specified strategy", + ) + async def get_strategy_holding( + id: str = Query(..., description="Strategy ID"), + ) -> StrategyHoldingFlatResponse: + try: + data = await StrategyService.get_strategy_holding(id) + if not data: + raise HTTPException( + status_code=404, detail="No holdings found for strategy" + ) + + items: List[StrategyHoldingFlatItem] = [] + for p in data.positions or []: + try: + t = p.trade_type or ("LONG" if p.quantity >= 0 else "SHORT") + qty = abs(p.quantity) + items.append( + StrategyHoldingFlatItem( + symbol=p.symbol, + type=t, + leverage=p.leverage, + entry_price=p.avg_price, + quantity=qty, + unrealized_pnl=p.unrealized_pnl, + unrealized_pnl_pct=p.unrealized_pnl_pct, + ) + ) + except Exception: + continue + + return SuccessResponse.create( + data=items, + msg="Successfully retrieved strategy holdings", + ) + except HTTPException: + raise + except Exception as e: + raise HTTPException( + status_code=500, detail=f"Failed to retrieve holdings: {str(e)}" + ) + + @router.get( + "/detail", + response_model=StrategyDetailResponse, + summary="Get strategy trade details", + description="Return a list of trade details generated from the latest portfolio snapshot", + ) + async def get_strategy_detail( + id: str = Query(..., description="Strategy ID"), + ) -> StrategyDetailResponse: + try: + data = await StrategyService.get_strategy_detail(id) + if not data: + raise HTTPException( + status_code=404, detail="No details found for strategy" + ) + return SuccessResponse.create( + data=data, + msg="Successfully retrieved strategy details", + ) + except HTTPException: + raise + except Exception as e: + raise HTTPException( + status_code=500, detail=f"Failed to retrieve details: {str(e)}" + ) + + return router diff --git a/python/valuecell/server/api/routers/strategy_agent.py b/python/valuecell/server/api/routers/strategy_agent.py index a33e40f6f..10c338224 100644 --- a/python/valuecell/server/api/routers/strategy_agent.py +++ b/python/valuecell/server/api/routers/strategy_agent.py @@ -19,10 +19,10 @@ def create_strategy_agent_router() -> APIRouter: """Create and configure the StrategyAgent router.""" - router = APIRouter(prefix="/agents", tags=["Strategy Agent"]) + router = APIRouter(prefix="/strategies", tags=["strategies"]) orchestrator = AgentOrchestrator() - @router.post("/create_strategy_agent") + @router.post("/create") async def create_strategy_agent( request: UserRequest, db: Session = Depends(get_db) ): diff --git a/python/valuecell/server/api/routers/strategy_api.py b/python/valuecell/server/api/routers/strategy_api.py new file mode 100644 index 000000000..54e7814fa --- /dev/null +++ b/python/valuecell/server/api/routers/strategy_api.py @@ -0,0 +1,22 @@ +"""Aggregated Strategy API router. + +Unifies strategy-related endpoints under a single registration point, +while keeping logical sub-routers separated for clarity. +""" + +from fastapi import APIRouter + +from .strategy import create_strategy_router +from .strategy_agent import create_strategy_agent_router + + +def create_strategy_api_router() -> APIRouter: + router = APIRouter() + + # Include core strategy endpoints (prefix: /strategies) + router.include_router(create_strategy_router()) + + # Include StrategyAgent endpoints (prefix: /strategies) + router.include_router(create_strategy_agent_router()) + + return router diff --git a/python/valuecell/server/api/schemas/strategy.py b/python/valuecell/server/api/schemas/strategy.py new file mode 100644 index 000000000..82111e29b --- /dev/null +++ b/python/valuecell/server/api/schemas/strategy.py @@ -0,0 +1,124 @@ +""" +Strategy API schemas for handling strategy-related requests and responses. +""" + +from datetime import datetime +from typing import List, Literal, Optional + +from pydantic import BaseModel, Field + +from .base import SuccessResponse + + +class StrategySummaryData(BaseModel): + """Summary data for a single strategy per product spec.""" + + strategy_id: str = Field( + ..., description="Runtime strategy identifier from StrategyAgent" + ) + strategy_name: Optional[str] = Field(None, description="User-defined strategy name") + status: Literal["running", "stopped"] = Field(..., description="Strategy status") + trading_mode: Optional[Literal["live", "virtual"]] = Field( + None, description="Trading mode: live or virtual" + ) + unrealized_pnl: Optional[float] = Field(None, description="Unrealized PnL value") + unrealized_pnl_pct: Optional[float] = Field( + None, description="Unrealized PnL percentage" + ) + created_at: Optional[datetime] = Field(None, description="Creation timestamp") + exchange_id: Optional[str] = Field( + None, description="Associated exchange identifier" + ) + model_id: Optional[str] = Field(None, description="Associated model identifier") + + +class StrategyListData(BaseModel): + """Data model for strategy list.""" + + strategies: List[StrategySummaryData] = Field(..., description="List of strategies") + total: int = Field(..., description="Total number of strategies") + running_count: int = Field(..., description="Number of running strategies") + + +StrategyListResponse = SuccessResponse[StrategyListData] + + +class PositionHoldingItem(BaseModel): + symbol: str = Field(..., description="Instrument symbol") + exchange_id: Optional[str] = Field(None, description="Exchange identifier") + quantity: float = Field(..., description="Position quantity (+long, -short)") + avg_price: Optional[float] = Field(None, description="Average entry price") + mark_price: Optional[float] = Field( + None, description="Current mark/reference price" + ) + unrealized_pnl: Optional[float] = Field(None, description="Unrealized PnL value") + unrealized_pnl_pct: Optional[float] = Field( + None, description="Unrealized PnL percentage" + ) + notional: Optional[float] = Field( + None, description="Position notional in quote currency" + ) + leverage: Optional[float] = Field( + None, description="Leverage applied to the position" + ) + entry_ts: Optional[int] = Field(None, description="Entry timestamp (ms)") + trade_type: Optional[str] = Field(None, description="Trade type (LONG/SHORT)") + + +class StrategyHoldingData(BaseModel): + strategy_id: str = Field(..., description="Strategy identifier") + ts: int = Field(..., description="Snapshot timestamp in ms") + cash: float = Field(..., description="Cash balance") + positions: List[PositionHoldingItem] = Field( + default_factory=list, description="List of position holdings" + ) + total_value: Optional[float] = Field( + None, description="Total portfolio value (cash + positions)" + ) + total_unrealized_pnl: Optional[float] = Field( + None, description="Sum of unrealized PnL across positions" + ) + available_cash: Optional[float] = Field( + None, description="Cash available for new positions" + ) + + +StrategyHoldingResponse = SuccessResponse[StrategyHoldingData] + + +class StrategyDetailItem(BaseModel): + trade_id: str = Field(..., description="Unique trade identifier") + symbol: str = Field(..., description="Instrument symbol") + type: Literal["LONG", "SHORT"] = Field(..., description="Trade type") + side: Literal["BUY", "SELL"] = Field(..., description="Entry side") + leverage: Optional[float] = Field(None, description="Leverage applied") + quantity: float = Field(..., description="Trade quantity") + unrealized_pnl: Optional[float] = Field(None, description="Unrealized PnL value") + entry_price: Optional[float] = Field(None, description="Entry price") + exit_price: Optional[float] = Field(None, description="Exit price if closed") + holding_ms: Optional[int] = Field( + None, description="Holding duration in milliseconds" + ) + time: Optional[str] = Field(None, description="Entry time in UTC ISO8601") + note: Optional[str] = Field(None, description="Additional note") + + +StrategyDetailResponse = SuccessResponse[List[StrategyDetailItem]] + + +class StrategyHoldingFlatItem(BaseModel): + symbol: str = Field(..., description="Instrument symbol") + type: Literal["LONG", "SHORT"] = Field( + ..., description="Trade type derived from position" + ) + leverage: Optional[float] = Field(None, description="Leverage applied") + entry_price: Optional[float] = Field(None, description="Average entry price") + quantity: float = Field(..., description="Absolute position quantity") + unrealized_pnl: Optional[float] = Field(None, description="Unrealized PnL value") + unrealized_pnl_pct: Optional[float] = Field( + None, description="Unrealized PnL percentage" + ) + + +# Response type for compact holdings array +StrategyHoldingFlatResponse = SuccessResponse[List[StrategyHoldingFlatItem]] diff --git a/python/valuecell/server/services/strategy_service.py b/python/valuecell/server/services/strategy_service.py new file mode 100644 index 000000000..f48310c1d --- /dev/null +++ b/python/valuecell/server/services/strategy_service.py @@ -0,0 +1,166 @@ +from datetime import datetime, timezone +from typing import List, Optional + +from valuecell.agents.strategy_agent.models import ComponentType, PortfolioView +from valuecell.core.types import ( + CommonResponseEvent, + ComponentGeneratorResponseDataPayload, +) +from valuecell.server.api.schemas.strategy import ( + PositionHoldingItem, + StrategyDetailItem, + StrategyHoldingData, +) +from valuecell.server.services.conversation_service import get_conversation_service + + +class StrategyService: + @staticmethod + async def get_strategy_holding(strategy_id: str) -> Optional[StrategyHoldingData]: + cs = get_conversation_service() + items = await cs.item_store.get_items( + conversation_id=None, + role=None, + event=CommonResponseEvent.COMPONENT_GENERATOR.value, + component_type=ComponentType.UPDATE_PORTFOLIO.value, + limit=1000, + order="desc", + ) + + for item in items: + try: + payload = ComponentGeneratorResponseDataPayload.model_validate_json( + item.payload + ) + content = payload.content + if isinstance(content, str): + view = PortfolioView.model_validate_json(content) + else: + view = PortfolioView.model_validate(content) + except Exception: + continue + + if view.strategy_id != strategy_id: + continue + + positions: List[PositionHoldingItem] = [] + for symbol, pos in (view.positions or {}).items(): + try: + instrument = getattr(pos, "instrument", None) + exchange_id = ( + getattr(instrument, "exchange_id", None) if instrument else None + ) + positions.append( + PositionHoldingItem( + symbol=getattr(instrument, "symbol", symbol) + if instrument + else symbol, + exchange_id=exchange_id, + quantity=pos.quantity, + avg_price=pos.avg_price, + mark_price=pos.mark_price, + unrealized_pnl=pos.unrealized_pnl, + unrealized_pnl_pct=pos.unrealized_pnl_pct, + notional=pos.notional, + leverage=pos.leverage, + entry_ts=pos.entry_ts, + trade_type=( + pos.trade_type.value + if getattr(pos, "trade_type", None) + else None + ), + ) + ) + except Exception: + continue + + return StrategyHoldingData( + strategy_id=view.strategy_id, + ts=view.ts, + cash=view.cash, + positions=positions, + total_value=view.total_value, + total_unrealized_pnl=view.total_unrealized_pnl, + available_cash=view.available_cash, + ) + + return None + + @staticmethod + async def get_strategy_detail( + strategy_id: str, + ) -> Optional[List[StrategyDetailItem]]: + cs = get_conversation_service() + items = await cs.item_store.get_items( + conversation_id=None, + role=None, + event=CommonResponseEvent.COMPONENT_GENERATOR.value, + component_type=ComponentType.UPDATE_PORTFOLIO.value, + limit=1000, + order="desc", + ) + + for item in items: + try: + payload = ComponentGeneratorResponseDataPayload.model_validate_json( + item.payload + ) + content = payload.content + if isinstance(content, str): + view = PortfolioView.model_validate_json(content) + else: + view = PortfolioView.model_validate(content) + except Exception: + continue + + if view.strategy_id != strategy_id: + continue + + details: List[StrategyDetailItem] = [] + for symbol, pos in (view.positions or {}).items(): + try: + instrument = getattr(pos, "instrument", None) + sym = ( + getattr(instrument, "symbol", symbol) if instrument else symbol + ) + t = ( + pos.trade_type.value + if getattr(pos, "trade_type", None) + else ("LONG" if pos.quantity >= 0 else "SHORT") + ) + side = "BUY" if t == "LONG" else "SELL" + qty = abs(pos.quantity) + entry_ts = pos.entry_ts or view.ts + holding_ms = ( + int((view.ts or entry_ts) - entry_ts) if entry_ts else None + ) + # UTC time string for entry + time_str = None + if entry_ts: + dt = datetime.fromtimestamp(entry_ts / 1000.0, tz=timezone.utc) + time_str = dt.isoformat() + + trade_id = f"{view.strategy_id}:{sym}:{entry_ts}" + + details.append( + StrategyDetailItem( + trade_id=trade_id, + symbol=sym, + type=t, + side=side, + leverage=pos.leverage, + quantity=qty, + unrealized_pnl=pos.unrealized_pnl, + entry_price=pos.avg_price, + exit_price=None, + holding_ms=holding_ms, + time=time_str, + note="", + ) + ) + except Exception: + continue + + return details + + return None From 0ed40547f91f26ba535c6fde6c84c12f4a87e767 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 17:23:05 +0800 Subject: [PATCH 28/91] feat: add fee_cost to TradeHistoryEntry and adjust cash flow calculations in InMemoryPortfolioService to account for trade fees --- .../valuecell/agents/strategy_agent/core.py | 44 ++++++++++++++----- .../valuecell/agents/strategy_agent/models.py | 4 ++ .../agents/strategy_agent/runtime.py | 6 +++ 3 files changed, 43 insertions(+), 11 deletions(-) diff --git a/python/valuecell/agents/strategy_agent/core.py b/python/valuecell/agents/strategy_agent/core.py index 13a596966..758919eb9 100644 --- a/python/valuecell/agents/strategy_agent/core.py +++ b/python/valuecell/agents/strategy_agent/core.py @@ -229,7 +229,11 @@ def _create_trades( is_full_close = close_units >= abs(prev_qty) - eps pos_dir_type = TradeType.SHORT - if is_full_close and prev_pos is not None and prev_pos.avg_price is not None: + if ( + is_full_close + and prev_pos is not None + and prev_pos.avg_price is not None + ): # Build a completed trade that ties back to the original open (avg_price/entry_ts) entry_px = float(prev_pos.avg_price or 0.0) entry_ts_prev = int(prev_pos.entry_ts) if prev_pos.entry_ts else None @@ -243,13 +247,19 @@ def _create_trades( core_pnl = (float(exit_px) - float(entry_px)) * qty_closed else: # SHORT core_pnl = (float(entry_px) - float(exit_px)) * qty_closed - realized_pnl = (core_pnl if core_pnl is not None else None) + realized_pnl = core_pnl if core_pnl is not None else None if realized_pnl is not None: realized_pnl = float(realized_pnl) - fee - notional_entry = (qty_closed * entry_px) if entry_px and qty_closed else None - notional_exit = (qty_closed * float(exit_px)) if exit_px and qty_closed else None + notional_entry = ( + (qty_closed * entry_px) if entry_px and qty_closed else None + ) + notional_exit = ( + (qty_closed * float(exit_px)) if exit_px and qty_closed else None + ) realized_pnl_pct = ( - (realized_pnl / notional_entry) if realized_pnl is not None and notional_entry else None + (realized_pnl / notional_entry) + if realized_pnl is not None and notional_entry + else None ) trade = TradeHistoryEntry( @@ -259,7 +269,10 @@ def _create_trades( strategy_id=self.strategy_id, instrument=tx.instrument, side=tx.side, - type=pos_dir_type or (TradeType.LONG if tx.side == TradeSide.BUY else TradeType.SHORT), + type=pos_dir_type + or ( + TradeType.LONG if tx.side == TradeSide.BUY else TradeType.SHORT + ), quantity=qty_closed or qty, entry_price=entry_px or None, exit_price=exit_px, @@ -271,7 +284,13 @@ def _create_trades( holding_ms=(exit_ts - entry_ts_prev) if entry_ts_prev else None, realized_pnl=realized_pnl, realized_pnl_pct=realized_pnl_pct, - leverage=tx.leverage, + # For a full close, reflect the leverage of the closed position, not the closing instruction + leverage=( + float(prev_pos.leverage) + if getattr(prev_pos, "leverage", None) is not None + else tx.leverage + ), + fee_cost=fee or None, note=(tx.meta.get("rationale") if tx.meta else None), ) else: @@ -283,7 +302,9 @@ def _create_trades( strategy_id=self.strategy_id, instrument=tx.instrument, side=tx.side, - type=TradeType.LONG if tx.side == TradeSide.BUY else TradeType.SHORT, + type=( + TradeType.LONG if tx.side == TradeSide.BUY else TradeType.SHORT + ), quantity=qty, entry_price=price or None, exit_price=None, @@ -298,13 +319,14 @@ def _create_trades( ((realized_pnl or 0.0) / notional) if notional else None ), leverage=tx.leverage, + fee_cost=fee or None, note=(tx.meta.get("rationale") if tx.meta else None), ) # If reducing/closing but not a full close, try to annotate the most recent open trade - is_closing = ( - prev_pos is not None - and ((prev_qty > 0 and tx.side == TradeSide.SELL) or (prev_qty < 0 and tx.side == TradeSide.BUY)) + is_closing = prev_pos is not None and ( + (prev_qty > 0 and tx.side == TradeSide.SELL) + or (prev_qty < 0 and tx.side == TradeSide.BUY) ) if is_closing and not is_full_close: # scan history records (most recent first) to find an open trade for this symbol diff --git a/python/valuecell/agents/strategy_agent/models.py b/python/valuecell/agents/strategy_agent/models.py index a7135a8bc..e28eae634 100644 --- a/python/valuecell/agents/strategy_agent/models.py +++ b/python/valuecell/agents/strategy_agent/models.py @@ -515,6 +515,10 @@ class TradeHistoryEntry(BaseModel): holding_ms: Optional[int] = Field(default=None, description="Holding time in ms") realized_pnl: Optional[float] = Field(default=None) realized_pnl_pct: Optional[float] = Field(default=None) + # Total fees charged for this trade in quote currency (if available) + fee_cost: Optional[float] = Field( + default=None, description="Total fees charged in quote currency for this trade" + ) leverage: Optional[float] = Field(default=None) note: Optional[str] = Field( default=None, description="Optional free-form note or comment about the trade" diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index 4132bf8dc..2094c1a6a 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -530,10 +530,16 @@ def apply_trades( # Update cash by trade notional notional = price * delta + # Deduct fees from cash as well. Trade may include fee_cost (in quote ccy). + fee = trade.fee_cost or 0.0 if trade.side == TradeSide.BUY: + # buying reduces cash by notional plus fees self._view.cash -= notional + self._view.cash -= fee else: + # selling increases cash by notional minus fees self._view.cash += notional + self._view.cash -= fee # Recompute per-position derived fields (if position still exists) pos = self._view.positions.get(symbol) From 69735b3005923282272cd9e04e781c7a9bf21b6d Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Thu, 6 Nov 2025 17:36:37 +0800 Subject: [PATCH 29/91] refactor: move components out of runtime.py --- .../agents/strategy_agent/data/market.py | 85 +++ .../strategy_agent/decision/system_prompt.py | 0 .../strategy_agent/decision/validator.py | 0 .../strategy_agent/execution/paper_trading.py | 54 ++ .../agents/strategy_agent/features/simple.py | 139 +++++ .../strategy_agent/portfolio/in_memory.py | 229 +++++++ .../agents/strategy_agent/runtime.py | 562 +----------------- .../agents/strategy_agent/tests/test_agent.py | 34 +- .../strategy_agent/trading_history/digest.py | 45 ++ .../trading_history/recorder.py | 14 + 10 files changed, 593 insertions(+), 569 deletions(-) delete mode 100644 python/valuecell/agents/strategy_agent/decision/system_prompt.py delete mode 100644 python/valuecell/agents/strategy_agent/decision/validator.py create mode 100644 python/valuecell/agents/strategy_agent/features/simple.py create mode 100644 python/valuecell/agents/strategy_agent/portfolio/in_memory.py diff --git a/python/valuecell/agents/strategy_agent/data/market.py b/python/valuecell/agents/strategy_agent/data/market.py index e69de29bb..c5f5b7396 100644 --- a/python/valuecell/agents/strategy_agent/data/market.py +++ b/python/valuecell/agents/strategy_agent/data/market.py @@ -0,0 +1,85 @@ +from collections import defaultdict +from typing import Dict, List, Optional + +import ccxt.pro as ccxtpro +from loguru import logger + +from ..models import Candle, InstrumentRef +from .interfaces import MarketDataSource + + +class SimpleMarketDataSource(MarketDataSource): + """Generates synthetic candle data for each symbol or fetches via ccxt.pro. + + If `exchange_id` was provided at construction time and `ccxt.pro` is + available, this class will attempt to fetch OHLCV data from the + specified exchange. If any error occurs (missing library, unknown + exchange, network error), it falls back to the built-in synthetic + generator so the runtime remains functional in tests and offline. + """ + + def __init__( + self, + base_prices: Optional[Dict[str, float]] = None, + exchange_id: Optional[str] = None, + ccxt_options: Optional[Dict] = None, + ) -> None: + self._base_prices = base_prices or {} + self._counters: Dict[str, int] = defaultdict(int) + self._exchange_id = exchange_id or "binance" + self._ccxt_options = ccxt_options or {} + + async def get_recent_candles( + self, symbols: List[str], interval: str, lookback: int + ) -> List[Candle]: + async def _fetch(symbol: str) -> List[List]: + # instantiate exchange class by name (e.g., ccxtpro.kraken) + exchange_cls = getattr(ccxtpro, self._exchange_id, None) + if exchange_cls is None: + raise RuntimeError( + f"Exchange '{self._exchange_id}' not found in ccxt.pro" + ) + exchange = exchange_cls({"newUpdates": False, **self._ccxt_options}) + try: + # ccxt.pro uses async fetch_ohlcv + data = await exchange.fetch_ohlcv( + symbol, timeframe=interval, since=None, limit=lookback + ) + return data + finally: + try: + await exchange.close() + except Exception: + pass + + candles: List[Candle] = [] + # Run fetch for each symbol sequentially + for symbol in symbols: + try: + raw = await _fetch(symbol) + # raw is list of [ts, open, high, low, close, volume] + for row in raw: + ts, open_v, high_v, low_v, close_v, vol = row + candles.append( + Candle( + ts=int(ts), + instrument=InstrumentRef( + symbol=symbol, + exchange_id=self._exchange_id, + quote_ccy="USD", + ), + open=float(open_v), + high=float(high_v), + low=float(low_v), + close=float(close_v), + volume=float(vol), + interval=interval, + ) + ) + except Exception: + logger.exception( + "Failed to fetch candles for {} from {}, using synthetic data", + symbol, + self._exchange_id, + ) + return candles diff --git a/python/valuecell/agents/strategy_agent/decision/system_prompt.py b/python/valuecell/agents/strategy_agent/decision/system_prompt.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/python/valuecell/agents/strategy_agent/decision/validator.py b/python/valuecell/agents/strategy_agent/decision/validator.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/python/valuecell/agents/strategy_agent/execution/paper_trading.py b/python/valuecell/agents/strategy_agent/execution/paper_trading.py index e69de29bb..d830cdd3f 100644 --- a/python/valuecell/agents/strategy_agent/execution/paper_trading.py +++ b/python/valuecell/agents/strategy_agent/execution/paper_trading.py @@ -0,0 +1,54 @@ +from typing import Dict, List, Optional + +from ..models import TradeInstruction, TxResult, TradeSide +from .interfaces import ExecutionGateway + + +class PaperExecutionGateway(ExecutionGateway): + """Async paper executor that simulates fills with slippage and fees. + + - Uses instruction.max_slippage_bps to compute execution price around snapshot. + - Applies a flat fee_bps to notional to produce fee_cost. + - Marks orders as FILLED with filled_qty=requested quantity. + """ + + def __init__(self, fee_bps: float = 10.0) -> None: + self._fee_bps = float(fee_bps) + self.executed: List[TradeInstruction] = [] + + async def execute( + self, + instructions: List[TradeInstruction], + market_snapshot: Optional[Dict[str, float]] = None, + ) -> List[TxResult]: + results: List[TxResult] = [] + price_map = market_snapshot or {} + for inst in instructions: + self.executed.append(inst) + ref_price = float(price_map.get(inst.instrument.symbol, 0.0) or 0.0) + slip_bps = float(inst.max_slippage_bps or 0.0) + slip = slip_bps / 10_000.0 + if inst.side == TradeSide.BUY: + exec_price = ref_price * (1.0 + slip) + else: + exec_price = ref_price * (1.0 - slip) + + notional = exec_price * float(inst.quantity) + fee_cost = notional * (self._fee_bps / 10_000.0) if notional else 0.0 + + results.append( + TxResult( + instruction_id=inst.instruction_id, + instrument=inst.instrument, + side=inst.side, + requested_qty=float(inst.quantity), + filled_qty=float(inst.quantity), + avg_exec_price=float(exec_price) if exec_price else None, + slippage_bps=slip_bps or None, + fee_cost=fee_cost or None, + leverage=inst.leverage, + meta=inst.meta, + ) + ) + + return results diff --git a/python/valuecell/agents/strategy_agent/features/simple.py b/python/valuecell/agents/strategy_agent/features/simple.py new file mode 100644 index 000000000..d3b07a03f --- /dev/null +++ b/python/valuecell/agents/strategy_agent/features/simple.py @@ -0,0 +1,139 @@ +from collections import defaultdict +from typing import Dict, List, Optional + +import numpy as np +import pandas as pd + +from ..models import Candle, FeatureVector +from .interfaces import FeatureComputer + + +class SimpleFeatureComputer(FeatureComputer): + """Computes basic momentum and volume features.""" + + def compute_features( + self, candles: Optional[List[Candle]] = None + ) -> List[FeatureVector]: + if not candles: + return [] + + grouped: Dict[str, List[Candle]] = defaultdict(list) + for candle in candles: + grouped[candle.instrument.symbol].append(candle) + + features: List[FeatureVector] = [] + for symbol, series in grouped.items(): + # Build a DataFrame for indicator calculations + series.sort(key=lambda item: item.ts) + rows = [ + { + "ts": c.ts, + "open": c.open, + "high": c.high, + "low": c.low, + "close": c.close, + "volume": c.volume, + "interval": c.interval, + } + for c in series + ] + df = pd.DataFrame(rows) + + # EMAs + df["ema_12"] = df["close"].ewm(span=12, adjust=False).mean() + df["ema_26"] = df["close"].ewm(span=26, adjust=False).mean() + df["ema_50"] = df["close"].ewm(span=50, adjust=False).mean() + + # MACD + df["macd"] = df["ema_12"] - df["ema_26"] + df["macd_signal"] = df["macd"].ewm(span=9, adjust=False).mean() + df["macd_histogram"] = df["macd"] - df["macd_signal"] + + # RSI + delta = df["close"].diff() + gain = delta.clip(lower=0).rolling(window=14).mean() + loss = (-delta).clip(lower=0).rolling(window=14).mean() + rs = gain / loss.replace(0, np.inf) + df["rsi"] = 100 - (100 / (1 + rs)) + + # Bollinger Bands + df["bb_middle"] = df["close"].rolling(window=20).mean() + bb_std = df["close"].rolling(window=20).std() + df["bb_upper"] = df["bb_middle"] + (bb_std * 2) + df["bb_lower"] = df["bb_middle"] - (bb_std * 2) + + last = df.iloc[-1] + prev = df.iloc[-2] if len(df) > 1 else last + + change_pct = ( + (float(last.close) - float(prev.close)) / float(prev.close) + if prev.close + else 0.0 + ) + + values = { + "close": float(last.close), + "volume": float(last.volume), + "change_pct": float(change_pct), + "ema_12": ( + float(last.get("ema_12", np.nan)) + if not pd.isna(last.get("ema_12")) + else None + ), + "ema_26": ( + float(last.get("ema_26", np.nan)) + if not pd.isna(last.get("ema_26")) + else None + ), + "ema_50": ( + float(last.get("ema_50", np.nan)) + if not pd.isna(last.get("ema_50")) + else None + ), + "macd": ( + float(last.get("macd", np.nan)) + if not pd.isna(last.get("macd")) + else None + ), + "macd_signal": ( + float(last.get("macd_signal", np.nan)) + if not pd.isna(last.get("macd_signal")) + else None + ), + "macd_histogram": ( + float(last.get("macd_histogram", np.nan)) + if not pd.isna(last.get("macd_histogram")) + else None + ), + "rsi": ( + float(last.get("rsi", np.nan)) + if not pd.isna(last.get("rsi")) + else None + ), + "bb_upper": ( + float(last.get("bb_upper", np.nan)) + if not pd.isna(last.get("bb_upper")) + else None + ), + "bb_middle": ( + float(last.get("bb_middle", np.nan)) + if not pd.isna(last.get("bb_middle")) + else None + ), + "bb_lower": ( + float(last.get("bb_lower", np.nan)) + if not pd.isna(last.get("bb_lower")) + else None + ), + } + + features.append( + FeatureVector( + ts=int(last["ts"]), + instrument=series[-1].instrument, + values=values, + meta={"interval": series[-1].interval, "count": len(series)}, + ) + ) + + return features diff --git a/python/valuecell/agents/strategy_agent/portfolio/in_memory.py b/python/valuecell/agents/strategy_agent/portfolio/in_memory.py new file mode 100644 index 000000000..2e56c27d6 --- /dev/null +++ b/python/valuecell/agents/strategy_agent/portfolio/in_memory.py @@ -0,0 +1,229 @@ +from datetime import datetime, timezone +from typing import Dict, List, Optional + +from ..models import ( + Constraints, + PortfolioView, + PositionSnapshot, + TradeHistoryEntry, + TradeSide, + TradeType, + TradingMode, +) +from .interfaces import PortfolioService + + +class InMemoryPortfolioService(PortfolioService): + """Tracks cash and positions in memory and computes derived metrics. + + Notes: + - cash reflects running cash balance from trade settlements + - gross_exposure = sum(abs(qty) * mark_price) + - net_exposure = sum(qty * mark_price) + - equity (total_value) = cash + net_exposure [correct for both long and short] + - total_unrealized_pnl = sum((mark_price - avg_price) * qty) + - buying_power: max(0, equity * max_leverage - gross_exposure) + where max_leverage comes from portfolio.constraints (default 1.0) + """ + + def __init__( + self, + initial_capital: float, + trading_mode: TradingMode, + constraints: Optional[Constraints] = None, + strategy_id: Optional[str] = None, + ) -> None: + # Store owning strategy id on the view so downstream components + # always see which strategy this portfolio belongs to. + self._strategy_id = strategy_id + self._view = PortfolioView( + strategy_id=strategy_id, + ts=int(datetime.now(timezone.utc).timestamp() * 1000), + cash=initial_capital, + positions={}, + gross_exposure=0.0, + net_exposure=0.0, + constraints=constraints or None, + total_value=initial_capital, + total_unrealized_pnl=0.0, + buying_power=initial_capital, + ) + self._trading_mode = trading_mode + + def get_view(self) -> PortfolioView: + self._view.ts = int(datetime.now(timezone.utc).timestamp() * 1000) + # Ensure strategy_id is present on each view retrieval + if self._strategy_id is not None: + try: + self._view.strategy_id = self._strategy_id + except Exception: + pass + return self._view + + def apply_trades( + self, trades: List[TradeHistoryEntry], market_snapshot: Dict[str, float] + ) -> None: + """Apply trades and update portfolio positions and aggregates. + + This method updates: + - cash (subtract on BUY, add on SELL at trade price) + - positions with weighted avg price, entry_ts on (re)open, and mark_price + - per-position notional, unrealized_pnl, unrealized_pnl_pct (and keeps pnl_pct for + backward compatibility) + - portfolio aggregates: gross_exposure, net_exposure, total_value (equity), total_unrealized_pnl, buying_power + """ + for trade in trades: + symbol = trade.instrument.symbol + price = float(trade.entry_price or market_snapshot.get(symbol, 0.0) or 0.0) + delta = float(trade.quantity or 0.0) + quantity_delta = delta if trade.side == TradeSide.BUY else -delta + + position = self._view.positions.get(symbol) + if position is None: + position = PositionSnapshot( + instrument=trade.instrument, + quantity=0.0, + avg_price=None, + mark_price=price, + unrealized_pnl=0.0, + ) + self._view.positions[symbol] = position + + current_qty = float(position.quantity) + avg_price = float(position.avg_price or 0.0) + new_qty = current_qty + quantity_delta + + # Update mark price + position.mark_price = price + + # Handle position quantity transitions and avg price + if new_qty == 0.0: + # Fully closed + self._view.positions.pop(symbol, None) + elif current_qty == 0.0: + # Opening new position + position.quantity = new_qty + position.avg_price = price + position.entry_ts = ( + trade.entry_ts + or trade.trade_ts + or int(datetime.now(timezone.utc).timestamp() * 1000) + ) + position.trade_type = TradeType.LONG if new_qty > 0 else TradeType.SHORT + # Initialize leverage from trade if provided + if trade.leverage is not None: + position.leverage = float(trade.leverage) + elif (current_qty > 0 and new_qty > 0) or (current_qty < 0 and new_qty < 0): + # Same direction + if abs(new_qty) > abs(current_qty): + # Increasing position: weighted average price + position.avg_price = ( + abs(current_qty) * avg_price + abs(quantity_delta) * price + ) / abs(new_qty) + position.quantity = new_qty + # Update leverage as size-weighted average if provided + if trade.leverage is not None: + prev_lev = float(position.leverage or trade.leverage) + position.leverage = ( + abs(current_qty) * prev_lev + + abs(quantity_delta) * float(trade.leverage) + ) / abs(new_qty) + else: + # Reducing position: keep avg price, update quantity + position.quantity = new_qty + # entry_ts remains from original opening + else: + # Crossing through zero to opposite direction: reset avg price and entry_ts + position.quantity = new_qty + position.avg_price = price + position.entry_ts = ( + trade.entry_ts + or trade.trade_ts + or int(datetime.now(timezone.utc).timestamp() * 1000) + ) + position.trade_type = TradeType.LONG if new_qty > 0 else TradeType.SHORT + # Reset leverage when flipping direction + if trade.leverage is not None: + position.leverage = float(trade.leverage) + + # Update cash by trade notional + notional = price * delta + # Deduct fees from cash as well. Trade may include fee_cost (in quote ccy). + fee = trade.fee_cost or 0.0 + if trade.side == TradeSide.BUY: + # buying reduces cash by notional plus fees + self._view.cash -= notional + self._view.cash -= fee + else: + # selling increases cash by notional minus fees + self._view.cash += notional + self._view.cash -= fee + + # Recompute per-position derived fields (if position still exists) + pos = self._view.positions.get(symbol) + if pos is not None: + qty = float(pos.quantity) + mpx = float(pos.mark_price or 0.0) + apx = float(pos.avg_price or 0.0) + pos.notional = abs(qty) * mpx if mpx else None + if apx and mpx: + pos.unrealized_pnl = (mpx - apx) * qty + denom = abs(qty) * apx + pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None + # populate both the newer field and keep the legacy alias + pos.unrealized_pnl_pct = pct + pos.pnl_pct = pct + else: + pos.unrealized_pnl = None + pos.unrealized_pnl_pct = None + pos.pnl_pct = None + + # Recompute portfolio aggregates + gross = 0.0 + net = 0.0 + unreal = 0.0 + for pos in self._view.positions.values(): + # Refresh mark price from snapshot if available + try: + sym = pos.instrument.symbol + except Exception: + sym = None + if sym and sym in market_snapshot: + snap_px = float(market_snapshot.get(sym) or 0.0) + if snap_px > 0: + pos.mark_price = snap_px + + mpx = float(pos.mark_price or 0.0) + qty = float(pos.quantity) + apx = float(pos.avg_price or 0.0) + # Recompute unrealized PnL and percent (populate both new and legacy fields) + if apx and mpx: + pos.unrealized_pnl = (mpx - apx) * qty + denom = abs(qty) * apx + pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None + pos.unrealized_pnl_pct = pct + pos.pnl_pct = pct + else: + pos.unrealized_pnl = None + pos.unrealized_pnl_pct = None + pos.pnl_pct = None + gross += abs(qty) * mpx + net += qty * mpx + if pos.unrealized_pnl is not None: + unreal += float(pos.unrealized_pnl) + + self._view.gross_exposure = gross + self._view.net_exposure = net + self._view.total_unrealized_pnl = unreal + # Equity is cash plus net exposure (correct for both long and short) + equity = self._view.cash + net + self._view.total_value = equity + + # Approximate buying power using max leverage constraint + max_lev = ( + float(self._view.constraints.max_leverage) + if (self._view.constraints and self._view.constraints.max_leverage) + else 1.0 + ) + buying_power = max(0.0, equity * max_lev - gross) + self._view.buying_power = buying_power diff --git a/python/valuecell/agents/strategy_agent/runtime.py b/python/valuecell/agents/strategy_agent/runtime.py index 2094c1a6a..c7c37268d 100644 --- a/python/valuecell/agents/strategy_agent/runtime.py +++ b/python/valuecell/agents/strategy_agent/runtime.py @@ -1,41 +1,18 @@ -from collections import defaultdict from dataclasses import dataclass -from datetime import datetime, timezone from pathlib import Path -from typing import Dict, List, Optional - -import ccxt.pro as ccxtpro -import numpy as np -import pandas as pd -from loguru import logger +from typing import Optional from valuecell.utils.uuid import generate_uuid from .core import DecisionCycleResult, DefaultDecisionCoordinator -from .data.interfaces import MarketDataSource +from .data.market import SimpleMarketDataSource from .decision.composer import LlmComposer -from .execution.interfaces import ExecutionGateway -from .features.interfaces import FeatureComputer -from .models import ( - Candle, - Constraints, - FeatureVector, - HistoryRecord, - InstrumentRef, - PortfolioView, - PositionSnapshot, - TradeDigest, - TradeDigestEntry, - TradeHistoryEntry, - TradeInstruction, - TradeSide, - TradeType, - TradingMode, - TxResult, - UserRequest, -) -from .portfolio.interfaces import PortfolioService -from .trading_history.interfaces import DigestBuilder, HistoryRecorder +from .execution.paper_trading import PaperExecutionGateway +from .features.simple import SimpleFeatureComputer +from .models import Constraints, UserRequest +from .portfolio.in_memory import InMemoryPortfolioService +from .trading_history.digest import RollingDigestBuilder +from .trading_history.recorder import InMemoryHistoryRecorder def _make_prompt_provider(template_dir: Optional[Path] = None): @@ -87,529 +64,6 @@ def provider(request: UserRequest) -> str: return provider -class SimpleMarketDataSource(MarketDataSource): - """Generates synthetic candle data for each symbol or fetches via ccxt.pro. - - If `exchange_id` was provided at construction time and `ccxt.pro` is - available, this class will attempt to fetch OHLCV data from the - specified exchange. If any error occurs (missing library, unknown - exchange, network error), it falls back to the built-in synthetic - generator so the runtime remains functional in tests and offline. - """ - - def __init__( - self, - base_prices: Optional[Dict[str, float]] = None, - exchange_id: Optional[str] = None, - ccxt_options: Optional[Dict] = None, - ) -> None: - self._base_prices = base_prices or {} - self._counters: Dict[str, int] = defaultdict(int) - self._exchange_id = exchange_id or "binance" - self._ccxt_options = ccxt_options or {} - - async def get_recent_candles( - self, symbols: List[str], interval: str, lookback: int - ) -> List[Candle]: - async def _fetch(symbol: str) -> List[List]: - # instantiate exchange class by name (e.g., ccxtpro.kraken) - exchange_cls = getattr(ccxtpro, self._exchange_id, None) - if exchange_cls is None: - raise RuntimeError( - f"Exchange '{self._exchange_id}' not found in ccxt.pro" - ) - exchange = exchange_cls({"newUpdates": False, **self._ccxt_options}) - try: - # ccxt.pro uses async fetch_ohlcv - data = await exchange.fetch_ohlcv( - symbol, timeframe=interval, since=None, limit=lookback - ) - return data - finally: - try: - await exchange.close() - except Exception: - pass - - candles: List[Candle] = [] - # Run fetch for each symbol sequentially - for symbol in symbols: - try: - raw = await _fetch(symbol) - # raw is list of [ts, open, high, low, close, volume] - for row in raw: - ts, open_v, high_v, low_v, close_v, vol = row - candles.append( - Candle( - ts=int(ts), - instrument=InstrumentRef( - symbol=symbol, - exchange_id=self._exchange_id, - quote_ccy="USD", - ), - open=float(open_v), - high=float(high_v), - low=float(low_v), - close=float(close_v), - volume=float(vol), - interval=interval, - ) - ) - except Exception: - logger.exception( - "Failed to fetch candles for {} from {}, using synthetic data", - symbol, - self._exchange_id, - ) - return candles - - -class SimpleFeatureComputer(FeatureComputer): - """Computes basic momentum and volume features.""" - - def compute_features( - self, candles: Optional[List[Candle]] = None - ) -> List[FeatureVector]: - if not candles: - return [] - - grouped: Dict[str, List[Candle]] = defaultdict(list) - for candle in candles: - grouped[candle.instrument.symbol].append(candle) - - features: List[FeatureVector] = [] - for symbol, series in grouped.items(): - # Build a DataFrame for indicator calculations - series.sort(key=lambda item: item.ts) - rows = [ - { - "ts": c.ts, - "open": c.open, - "high": c.high, - "low": c.low, - "close": c.close, - "volume": c.volume, - "interval": c.interval, - } - for c in series - ] - df = pd.DataFrame(rows) - - # EMAs - df["ema_12"] = df["close"].ewm(span=12, adjust=False).mean() - df["ema_26"] = df["close"].ewm(span=26, adjust=False).mean() - df["ema_50"] = df["close"].ewm(span=50, adjust=False).mean() - - # MACD - df["macd"] = df["ema_12"] - df["ema_26"] - df["macd_signal"] = df["macd"].ewm(span=9, adjust=False).mean() - df["macd_histogram"] = df["macd"] - df["macd_signal"] - - # RSI - delta = df["close"].diff() - gain = delta.clip(lower=0).rolling(window=14).mean() - loss = (-delta).clip(lower=0).rolling(window=14).mean() - rs = gain / loss.replace(0, np.inf) - df["rsi"] = 100 - (100 / (1 + rs)) - - # Bollinger Bands - df["bb_middle"] = df["close"].rolling(window=20).mean() - bb_std = df["close"].rolling(window=20).std() - df["bb_upper"] = df["bb_middle"] + (bb_std * 2) - df["bb_lower"] = df["bb_middle"] - (bb_std * 2) - - last = df.iloc[-1] - prev = df.iloc[-2] if len(df) > 1 else last - - change_pct = ( - (float(last.close) - float(prev.close)) / float(prev.close) - if prev.close - else 0.0 - ) - - values = { - "close": float(last.close), - "volume": float(last.volume), - "change_pct": float(change_pct), - "ema_12": ( - float(last.get("ema_12", np.nan)) - if not pd.isna(last.get("ema_12")) - else None - ), - "ema_26": ( - float(last.get("ema_26", np.nan)) - if not pd.isna(last.get("ema_26")) - else None - ), - "ema_50": ( - float(last.get("ema_50", np.nan)) - if not pd.isna(last.get("ema_50")) - else None - ), - "macd": ( - float(last.get("macd", np.nan)) - if not pd.isna(last.get("macd")) - else None - ), - "macd_signal": ( - float(last.get("macd_signal", np.nan)) - if not pd.isna(last.get("macd_signal")) - else None - ), - "macd_histogram": ( - float(last.get("macd_histogram", np.nan)) - if not pd.isna(last.get("macd_histogram")) - else None - ), - "rsi": ( - float(last.get("rsi", np.nan)) - if not pd.isna(last.get("rsi")) - else None - ), - "bb_upper": ( - float(last.get("bb_upper", np.nan)) - if not pd.isna(last.get("bb_upper")) - else None - ), - "bb_middle": ( - float(last.get("bb_middle", np.nan)) - if not pd.isna(last.get("bb_middle")) - else None - ), - "bb_lower": ( - float(last.get("bb_lower", np.nan)) - if not pd.isna(last.get("bb_lower")) - else None - ), - } - - features.append( - FeatureVector( - ts=int(last["ts"]), - instrument=series[-1].instrument, - values=values, - meta={"interval": series[-1].interval, "count": len(series)}, - ) - ) - - return features - - -class PaperExecutionGateway(ExecutionGateway): - """Async paper executor that simulates fills with slippage and fees. - - - Uses instruction.max_slippage_bps to compute execution price around snapshot. - - Applies a flat fee_bps to notional to produce fee_cost. - - Marks orders as FILLED with filled_qty=requested quantity. - """ - - def __init__(self, fee_bps: float = 10.0) -> None: - self._fee_bps = float(fee_bps) - self.executed: List[TradeInstruction] = [] - - async def execute( - self, - instructions: List[TradeInstruction], - market_snapshot: Optional[Dict[str, float]] = None, - ) -> List[TxResult]: - results: List[TxResult] = [] - price_map = market_snapshot or {} - for inst in instructions: - self.executed.append(inst) - ref_price = float(price_map.get(inst.instrument.symbol, 0.0) or 0.0) - slip_bps = float(inst.max_slippage_bps or 0.0) - slip = slip_bps / 10_000.0 - if inst.side == TradeSide.BUY: - exec_price = ref_price * (1.0 + slip) - else: - exec_price = ref_price * (1.0 - slip) - - notional = exec_price * float(inst.quantity) - fee_cost = notional * (self._fee_bps / 10_000.0) if notional else 0.0 - - results.append( - TxResult( - instruction_id=inst.instruction_id, - instrument=inst.instrument, - side=inst.side, - requested_qty=float(inst.quantity), - filled_qty=float(inst.quantity), - avg_exec_price=float(exec_price) if exec_price else None, - slippage_bps=slip_bps or None, - fee_cost=fee_cost or None, - leverage=inst.leverage, - meta=inst.meta, - ) - ) - - return results - - -class InMemoryHistoryRecorder(HistoryRecorder): - """In-memory recorder storing history records.""" - - def __init__(self) -> None: - self.records: List[HistoryRecord] = [] - - def record(self, record: HistoryRecord) -> None: - self.records.append(record) - - -class RollingDigestBuilder(DigestBuilder): - """Builds a lightweight digest from recent execution records.""" - - def __init__(self, window: int = 50) -> None: - self._window = max(window, 1) - - def build(self, records: List[HistoryRecord]) -> TradeDigest: - recent = records[-self._window :] - by_instrument: Dict[str, TradeDigestEntry] = {} - - for record in recent: - if record.kind != "execution": - continue - trades = record.payload.get("trades", []) - for trade_dict in trades: - instrument_dict = trade_dict.get("instrument") or {} - symbol = instrument_dict.get("symbol") - if not symbol: - continue - entry = by_instrument.get(symbol) - if entry is None: - entry = TradeDigestEntry( - instrument=InstrumentRef(**instrument_dict), - trade_count=0, - realized_pnl=0.0, - ) - by_instrument[symbol] = entry - entry.trade_count += 1 - realized = float(trade_dict.get("realized_pnl") or 0.0) - entry.realized_pnl += realized - entry.last_trade_ts = trade_dict.get("trade_ts") or entry.last_trade_ts - - timestamp = ( - recent[-1].ts - if recent - else int(datetime.now(timezone.utc).timestamp() * 1000) - ) - return TradeDigest(ts=timestamp, by_instrument=by_instrument) - - -class InMemoryPortfolioService(PortfolioService): - """Tracks cash and positions in memory and computes derived metrics. - - Notes: - - cash reflects running cash balance from trade settlements - - gross_exposure = sum(abs(qty) * mark_price) - - net_exposure = sum(qty * mark_price) - - equity (total_value) = cash + net_exposure [correct for both long and short] - - total_unrealized_pnl = sum((mark_price - avg_price) * qty) - - buying_power: max(0, equity * max_leverage - gross_exposure) - where max_leverage comes from portfolio.constraints (default 1.0) - """ - - def __init__( - self, - initial_capital: float, - trading_mode: TradingMode, - constraints: Optional[Constraints] = None, - strategy_id: Optional[str] = None, - ) -> None: - # Store owning strategy id on the view so downstream components - # always see which strategy this portfolio belongs to. - self._strategy_id = strategy_id - self._view = PortfolioView( - strategy_id=strategy_id, - ts=int(datetime.now(timezone.utc).timestamp() * 1000), - cash=initial_capital, - positions={}, - gross_exposure=0.0, - net_exposure=0.0, - constraints=constraints or None, - total_value=initial_capital, - total_unrealized_pnl=0.0, - buying_power=initial_capital, - ) - self._trading_mode = trading_mode - - def get_view(self) -> PortfolioView: - self._view.ts = int(datetime.now(timezone.utc).timestamp() * 1000) - # Ensure strategy_id is present on each view retrieval - if self._strategy_id is not None: - try: - self._view.strategy_id = self._strategy_id - except Exception: - pass - return self._view - - def apply_trades( - self, trades: List[TradeHistoryEntry], market_snapshot: Dict[str, float] - ) -> None: - """Apply trades and update portfolio positions and aggregates. - - This method updates: - - cash (subtract on BUY, add on SELL at trade price) - - positions with weighted avg price, entry_ts on (re)open, and mark_price - - per-position notional, unrealized_pnl, unrealized_pnl_pct (and keeps pnl_pct for - backward compatibility) - - portfolio aggregates: gross_exposure, net_exposure, total_value (equity), total_unrealized_pnl, buying_power - """ - for trade in trades: - symbol = trade.instrument.symbol - price = float(trade.entry_price or market_snapshot.get(symbol, 0.0) or 0.0) - delta = float(trade.quantity or 0.0) - quantity_delta = delta if trade.side == TradeSide.BUY else -delta - - position = self._view.positions.get(symbol) - if position is None: - position = PositionSnapshot( - instrument=trade.instrument, - quantity=0.0, - avg_price=None, - mark_price=price, - unrealized_pnl=0.0, - ) - self._view.positions[symbol] = position - - current_qty = float(position.quantity) - avg_price = float(position.avg_price or 0.0) - new_qty = current_qty + quantity_delta - - # Update mark price - position.mark_price = price - - # Handle position quantity transitions and avg price - if new_qty == 0.0: - # Fully closed - self._view.positions.pop(symbol, None) - elif current_qty == 0.0: - # Opening new position - position.quantity = new_qty - position.avg_price = price - position.entry_ts = ( - trade.entry_ts - or trade.trade_ts - or int(datetime.now(timezone.utc).timestamp() * 1000) - ) - position.trade_type = TradeType.LONG if new_qty > 0 else TradeType.SHORT - # Initialize leverage from trade if provided - if trade.leverage is not None: - position.leverage = float(trade.leverage) - elif (current_qty > 0 and new_qty > 0) or (current_qty < 0 and new_qty < 0): - # Same direction - if abs(new_qty) > abs(current_qty): - # Increasing position: weighted average price - position.avg_price = ( - abs(current_qty) * avg_price + abs(quantity_delta) * price - ) / abs(new_qty) - position.quantity = new_qty - # Update leverage as size-weighted average if provided - if trade.leverage is not None: - prev_lev = float(position.leverage or trade.leverage) - position.leverage = ( - abs(current_qty) * prev_lev - + abs(quantity_delta) * float(trade.leverage) - ) / abs(new_qty) - else: - # Reducing position: keep avg price, update quantity - position.quantity = new_qty - # entry_ts remains from original opening - else: - # Crossing through zero to opposite direction: reset avg price and entry_ts - position.quantity = new_qty - position.avg_price = price - position.entry_ts = ( - trade.entry_ts - or trade.trade_ts - or int(datetime.now(timezone.utc).timestamp() * 1000) - ) - position.trade_type = TradeType.LONG if new_qty > 0 else TradeType.SHORT - # Reset leverage when flipping direction - if trade.leverage is not None: - position.leverage = float(trade.leverage) - - # Update cash by trade notional - notional = price * delta - # Deduct fees from cash as well. Trade may include fee_cost (in quote ccy). - fee = trade.fee_cost or 0.0 - if trade.side == TradeSide.BUY: - # buying reduces cash by notional plus fees - self._view.cash -= notional - self._view.cash -= fee - else: - # selling increases cash by notional minus fees - self._view.cash += notional - self._view.cash -= fee - - # Recompute per-position derived fields (if position still exists) - pos = self._view.positions.get(symbol) - if pos is not None: - qty = float(pos.quantity) - mpx = float(pos.mark_price or 0.0) - apx = float(pos.avg_price or 0.0) - pos.notional = abs(qty) * mpx if mpx else None - if apx and mpx: - pos.unrealized_pnl = (mpx - apx) * qty - denom = abs(qty) * apx - pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None - # populate both the newer field and keep the legacy alias - pos.unrealized_pnl_pct = pct - pos.pnl_pct = pct - else: - pos.unrealized_pnl = None - pos.unrealized_pnl_pct = None - pos.pnl_pct = None - - # Recompute portfolio aggregates - gross = 0.0 - net = 0.0 - unreal = 0.0 - for pos in self._view.positions.values(): - # Refresh mark price from snapshot if available - try: - sym = pos.instrument.symbol - except Exception: - sym = None - if sym and sym in market_snapshot: - snap_px = float(market_snapshot.get(sym) or 0.0) - if snap_px > 0: - pos.mark_price = snap_px - - mpx = float(pos.mark_price or 0.0) - qty = float(pos.quantity) - apx = float(pos.avg_price or 0.0) - # Recompute unrealized PnL and percent (populate both new and legacy fields) - if apx and mpx: - pos.unrealized_pnl = (mpx - apx) * qty - denom = abs(qty) * apx - pct = (pos.unrealized_pnl / denom) * 100.0 if denom else None - pos.unrealized_pnl_pct = pct - pos.pnl_pct = pct - else: - pos.unrealized_pnl = None - pos.unrealized_pnl_pct = None - pos.pnl_pct = None - gross += abs(qty) * mpx - net += qty * mpx - if pos.unrealized_pnl is not None: - unreal += float(pos.unrealized_pnl) - - self._view.gross_exposure = gross - self._view.net_exposure = net - self._view.total_unrealized_pnl = unreal - # Equity is cash plus net exposure (correct for both long and short) - equity = self._view.cash + net - self._view.total_value = equity - - # Approximate buying power using max leverage constraint - max_lev = ( - float(self._view.constraints.max_leverage) - if (self._view.constraints and self._view.constraints.max_leverage) - else 1.0 - ) - buying_power = max(0.0, equity * max_lev - gross) - self._view.buying_power = buying_power - @dataclass class StrategyRuntime: diff --git a/python/valuecell/agents/strategy_agent/tests/test_agent.py b/python/valuecell/agents/strategy_agent/tests/test_agent.py index 00f0687a5..579500454 100644 --- a/python/valuecell/agents/strategy_agent/tests/test_agent.py +++ b/python/valuecell/agents/strategy_agent/tests/test_agent.py @@ -1,13 +1,13 @@ import asyncio import json - -import pytest +import os +from pprint import pprint from valuecell.agents.strategy_agent.agent import StrategyAgent -@pytest.mark.asyncio -async def test_strategy_agent_basic_stream(): +# @pytest.mark.asyncio +async def strategy_agent_basic_stream(): """Test basic functionality of StrategyAgent stream method.""" agent = StrategyAgent() @@ -15,9 +15,9 @@ async def test_strategy_agent_basic_stream(): query = json.dumps( { "llm_model_config": { - "provider": "test-provider", - "model_id": "test-model", - "api_key": "test-api-key", + "provider": "openrouter", + "model_id": "deepseek/deepseek-v3.1-terminus", + "api_key": os.getenv("OPENROUTER_API_KEY"), }, "exchange_config": { "exchange_id": "binance", @@ -27,17 +27,21 @@ async def test_strategy_agent_basic_stream(): }, "trading_config": { "strategy_name": "Test Strategy", - "initial_capital": 1000.0, - "max_leverage": 1.0, + "initial_capital": 10000.0, + "max_leverage": 5.0, "max_positions": 5, - "symbols": ["BTC/USDT"], + "symbols": ["BTC/USDT", "ETH/USDT", "SOL/USDT"], "decide_interval": 60, + "template_id": "insane", + "custom_prompt": "no custom prompt", }, } ) - try: - async for response in agent.stream(query, "test-conversation", "test-task"): - print(response) - except asyncio.CancelledError: - pass # Expected if we cancel + async for response in agent.stream(query, "test-conversation", "test-task"): + pprint(response.metadata) + pprint(json.loads(response.content)) + print("\n\n") + + +asyncio.run(strategy_agent_basic_stream()) diff --git a/python/valuecell/agents/strategy_agent/trading_history/digest.py b/python/valuecell/agents/strategy_agent/trading_history/digest.py index e69de29bb..832b31686 100644 --- a/python/valuecell/agents/strategy_agent/trading_history/digest.py +++ b/python/valuecell/agents/strategy_agent/trading_history/digest.py @@ -0,0 +1,45 @@ +from datetime import datetime, timezone +from typing import Dict, List + +from ..models import HistoryRecord, InstrumentRef, TradeDigest, TradeDigestEntry +from .interfaces import DigestBuilder + + +class RollingDigestBuilder(DigestBuilder): + """Builds a lightweight digest from recent execution records.""" + + def __init__(self, window: int = 50) -> None: + self._window = max(window, 1) + + def build(self, records: List[HistoryRecord]) -> TradeDigest: + recent = records[-self._window :] + by_instrument: Dict[str, TradeDigestEntry] = {} + + for record in recent: + if record.kind != "execution": + continue + trades = record.payload.get("trades", []) + for trade_dict in trades: + instrument_dict = trade_dict.get("instrument") or {} + symbol = instrument_dict.get("symbol") + if not symbol: + continue + entry = by_instrument.get(symbol) + if entry is None: + entry = TradeDigestEntry( + instrument=InstrumentRef(**instrument_dict), + trade_count=0, + realized_pnl=0.0, + ) + by_instrument[symbol] = entry + entry.trade_count += 1 + realized = float(trade_dict.get("realized_pnl") or 0.0) + entry.realized_pnl += realized + entry.last_trade_ts = trade_dict.get("trade_ts") or entry.last_trade_ts + + timestamp = ( + recent[-1].ts + if recent + else int(datetime.now(timezone.utc).timestamp() * 1000) + ) + return TradeDigest(ts=timestamp, by_instrument=by_instrument) diff --git a/python/valuecell/agents/strategy_agent/trading_history/recorder.py b/python/valuecell/agents/strategy_agent/trading_history/recorder.py index e69de29bb..e6fb7857b 100644 --- a/python/valuecell/agents/strategy_agent/trading_history/recorder.py +++ b/python/valuecell/agents/strategy_agent/trading_history/recorder.py @@ -0,0 +1,14 @@ +from typing import List + +from ..models import HistoryRecord +from .interfaces import HistoryRecorder + + +class InMemoryHistoryRecorder(HistoryRecorder): + """In-memory recorder storing history records.""" + + def __init__(self) -> None: + self.records: List[HistoryRecord] = [] + + def record(self, record: HistoryRecord) -> None: + self.records.append(record) From 0c28a15559fbb62b4cb48602c0ba0838fb649e45 Mon Sep 17 00:00:00 2001 From: Zhaofeng Zhang <24791380+vcfgv@users.noreply.github.com> Date: Fri, 7 Nov 2025 09:46:45 +0800 Subject: [PATCH 30/91] fix: update template_id in test_agent to use aggressive strategy --- python/valuecell/agents/strategy_agent/tests/test_agent.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/valuecell/agents/strategy_agent/tests/test_agent.py b/python/valuecell/agents/strategy_agent/tests/test_agent.py index 579500454..ba11f0429 100644 --- a/python/valuecell/agents/strategy_agent/tests/test_agent.py +++ b/python/valuecell/agents/strategy_agent/tests/test_agent.py @@ -32,7 +32,7 @@ async def strategy_agent_basic_stream(): "max_positions": 5, "symbols": ["BTC/USDT", "ETH/USDT", "SOL/USDT"], "decide_interval": 60, - "template_id": "insane", + "template_id": "aggressive", "custom_prompt": "no custom prompt", }, } From ec6b900f6f649b7c09c2d35ed9f1c0620df59a2b Mon Sep 17 00:00:00 2001 From: DigHuang <114602213+DigHuang@users.noreply.github.com> Date: Wed, 5 Nov 2025 15:31:09 +0800 Subject: [PATCH 31/91] refactor: chat conversation adapter different layout --- frontend/src/app/agent/chat.tsx | 175 +---------- .../agent-view/common-agent-area.tsx | 284 ++++++++++++++++++ .../chat-conversation-area.tsx | 134 --------- .../components/chat-conversation/index.tsx | 6 + frontend/src/types/agent.ts | 4 + 5 files changed, 306 insertions(+), 297 deletions(-) create mode 100644 frontend/src/app/agent/components/agent-view/common-agent-area.tsx delete mode 100644 frontend/src/app/agent/components/chat-conversation/chat-conversation-area.tsx create mode 100644 frontend/src/app/agent/components/chat-conversation/index.tsx diff --git a/frontend/src/app/agent/chat.tsx b/frontend/src/app/agent/chat.tsx index 7c7c3d2ff..2367e12b0 100644 --- a/frontend/src/app/agent/chat.tsx +++ b/frontend/src/app/agent/chat.tsx @@ -1,174 +1,23 @@ -import { useQueryClient } from "@tanstack/react-query"; -import { useCallback, useEffect } from "react"; -import { - Navigate, - useLocation, - useNavigate, - useParams, - useSearchParams, -} from "react-router"; -import { toast } from "sonner"; -import { useGetAgentInfo } from "@/api/agent"; -import { useGetConversationHistory, usePollTaskList } from "@/api/conversation"; -import { API_QUERY_KEYS } from "@/constants/api"; -import { useSSE } from "@/hooks/use-sse"; -import { getServerUrl } from "@/lib/api-client"; -import { - useAgentStoreActions, - useCurrentConversation, -} from "@/store/agent-store"; -import type { AgentStreamRequest, SSEData } from "@/types/agent"; +import { Navigate, useParams } from "react-router"; import type { Route } from "./+types/chat"; -import ChatConversationArea from "./components/chat-conversation/chat-conversation-area"; +import CommonAgentArea from "./components/agent-view/common-agent-area"; +import StrategyAgentArea from "./components/agent-view/strategy-agent-area"; export default function AgentChat() { const { agentName } = useParams(); - const conversationId = useSearchParams()[0].get("id") ?? ""; - const navigate = useNavigate(); - const inputValue = useLocation().state?.inputValue; - // Use optimized hooks with built-in shallow comparison - const { curConversation, curConversationId } = useCurrentConversation(); - const { - dispatchAgentStore, - setCurConversationId, - dispatchAgentStoreHistory, - } = useAgentStoreActions(); - - const queryClient = useQueryClient(); - const { data: agent, isLoading: isLoadingAgent } = useGetAgentInfo({ - agentName: agentName ?? "", - }); - const { data: conversationHistory } = - useGetConversationHistory(conversationId); - const { data: taskList } = usePollTaskList(conversationId); - - // Load conversation history (only once when conversation changes) - useEffect(() => { - if ( - !conversationId || - !conversationHistory || - conversationHistory.length === 0 - ) - return; - - dispatchAgentStoreHistory(conversationId, conversationHistory, true); - }, [conversationId, conversationHistory, dispatchAgentStoreHistory]); - - // Update task list (polls every 30s) - useEffect(() => { - if (!conversationId || !taskList || taskList.length === 0) return; - - dispatchAgentStoreHistory(conversationId, taskList); - }, [conversationId, taskList, dispatchAgentStoreHistory]); - - // Initialize SSE connection using the useSSE hook - const { connect, close, isStreaming } = useSSE({ - url: getServerUrl("/agents/stream"), - handlers: { - onData: (sseData: SSEData) => { - // Update agent store using the reducer - dispatchAgentStore(sseData); - - // Handle specific UI state updates - const { event, data } = sseData; - switch (event) { - case "conversation_started": - navigate(`/agent/${agentName}?id=${data.conversation_id}`, { - replace: true, - }); - queryClient.invalidateQueries({ - queryKey: API_QUERY_KEYS.CONVERSATION.conversationList, - }); - break; - - case "component_generator": - if (data.payload.component_type === "subagent_conversation") { - queryClient.invalidateQueries({ - queryKey: API_QUERY_KEYS.CONVERSATION.conversationList, - }); - } - break; - - case "system_failed": - // Handle system errors in UI layer - toast.error(data.payload.content, { - closeButton: true, - duration: 30 * 1000, - }); - break; - - case "done": - close(); - break; - - // All message-related events are handled by the store - default: - break; - } - }, - onOpen: () => { - console.log("✅ SSE connection opened"); - }, - onError: (error: Error) => { - console.error("❌ SSE connection error:", error); - }, - onClose: () => { - console.log("🔌 SSE connection closed"); - }, - }, - }); - - // Send message to agent - // biome-ignore lint/correctness/useExhaustiveDependencies: connect is no need to be in dependencies - const sendMessage = useCallback( - async (message: string) => { - try { - const request: AgentStreamRequest = { - query: message, - agent_name: agentName ?? "", - conversation_id: conversationId, - }; - - // Connect SSE client with request body to receive streaming response - await connect(JSON.stringify(request)); - } catch (error) { - console.error("Failed to send message:", error); - } - }, - [agentName, conversationId], - ); - - useEffect(() => { - if (curConversationId !== conversationId) { - setCurConversationId(conversationId); - } - - if (inputValue) { - sendMessage(inputValue); - // Clear the state after using it once to prevent re-triggering on page refresh - navigate(".", { replace: true, state: {} }); - } - }, [ - conversationId, - inputValue, - sendMessage, - setCurConversationId, - curConversationId, - navigate, - ]); - - if (isLoadingAgent) return null; - if (!agent) return ; + if (!agentName) return ; return (
- + {(() => { + switch (agentName) { + case "strategy": + return ; + default: + return ; + } + })()}
); } diff --git a/frontend/src/app/agent/components/agent-view/common-agent-area.tsx b/frontend/src/app/agent/components/agent-view/common-agent-area.tsx new file mode 100644 index 000000000..1b2db7b24 --- /dev/null +++ b/frontend/src/app/agent/components/agent-view/common-agent-area.tsx @@ -0,0 +1,284 @@ +import { useQueryClient } from "@tanstack/react-query"; +import { type FC, memo, useCallback, useEffect, useState } from "react"; +import { + Navigate, + useLocation, + useNavigate, + useSearchParams, +} from "react-router"; +import { toast } from "sonner"; +import { useGetAgentInfo } from "@/api/agent"; +import { useGetConversationHistory, usePollTaskList } from "@/api/conversation"; +import ScrollContainer from "@/components/valuecell/scroll/scroll-container"; +import { API_QUERY_KEYS } from "@/constants/api"; +import useSSE from "@/hooks/use-sse"; +import { getServerUrl } from "@/lib/api-client"; +import { + MultiSectionProvider, + useMultiSection, +} from "@/provider/multi-section-provider"; +import { + useAgentStoreActions, + useCurrentConversation, +} from "@/store/agent-store"; +import type { + AgentStreamRequest, + MultiSectionComponentType, + SectionComponentType, + SSEData, +} from "@/types/agent"; +import { + ChatConversationHeader, + ChatInputArea, + ChatMultiSectionComponent, + ChatSectionComponent, + ChatThreadArea, + ChatWelcomeScreen, +} from "../chat-conversation"; + +interface CommonAgentAreaProps { + agentName: string; +} + +const CommonAgentAreaContent: FC = ({ agentName }) => { + const { data: agent, isLoading: isLoadingAgent } = useGetAgentInfo({ + agentName: agentName ?? "", + }); + + const conversationId = useSearchParams()[0].get("id") ?? ""; + const navigate = useNavigate(); + const inputValueFromLocation = useLocation().state?.inputValue; + + // Use optimized hooks with built-in shallow comparison + const { curConversation, curConversationId } = useCurrentConversation(); + const { + dispatchAgentStore, + setCurConversationId, + dispatchAgentStoreHistory, + } = useAgentStoreActions(); + + const queryClient = useQueryClient(); + + const { data: conversationHistory } = + useGetConversationHistory(conversationId); + const { data: taskList } = usePollTaskList(conversationId); + + // Load conversation history (only once when conversation changes) + useEffect(() => { + if ( + !conversationId || + !conversationHistory || + conversationHistory.length === 0 + ) + return; + + dispatchAgentStoreHistory(conversationId, conversationHistory, true); + }, [conversationId, conversationHistory, dispatchAgentStoreHistory]); + + // Update task list (polls every 30s) + useEffect(() => { + if (!conversationId || !taskList || taskList.length === 0) return; + + dispatchAgentStoreHistory(conversationId, taskList); + }, [conversationId, taskList, dispatchAgentStoreHistory]); + + // Initialize SSE connection using the useSSE hook + const { connect, close, isStreaming } = useSSE({ + url: getServerUrl("/agents/stream"), + handlers: { + onData: (sseData: SSEData) => { + // Update agent store using the reducer + dispatchAgentStore(sseData); + + // Handle specific UI state updates + const { event, data } = sseData; + switch (event) { + case "conversation_started": + navigate(`/agent/${agentName}?id=${data.conversation_id}`, { + replace: true, + }); + queryClient.invalidateQueries({ + queryKey: API_QUERY_KEYS.CONVERSATION.conversationList, + }); + break; + + case "component_generator": + if (data.payload.component_type === "subagent_conversation") { + queryClient.invalidateQueries({ + queryKey: API_QUERY_KEYS.CONVERSATION.conversationList, + }); + } + break; + + case "system_failed": + // Handle system errors in UI layer + toast.error(data.payload.content, { + closeButton: true, + duration: 30 * 1000, + }); + break; + + case "done": + close(); + break; + + // All message-related events are handled by the store + default: + break; + } + }, + onOpen: () => { + console.log("✅ SSE connection opened"); + }, + onError: (error: Error) => { + console.error("❌ SSE connection error:", error); + }, + onClose: () => { + console.log("🔌 SSE connection closed"); + }, + }, + }); + + // Send message to agent + // biome-ignore lint/correctness/useExhaustiveDependencies: connect is no need to be in dependencies + const sendMessage = useCallback( + async (message: string) => { + try { + const request: AgentStreamRequest = { + query: message, + agent_name: agentName, + conversation_id: conversationId, + }; + + // Connect SSE client with request body to receive streaming response + await connect(JSON.stringify(request)); + } catch (error) { + console.error("Failed to send message:", error); + } + }, + [agentName, conversationId], + ); + + useEffect(() => { + if (curConversationId !== conversationId) { + setCurConversationId(conversationId); + } + + if (inputValueFromLocation) { + sendMessage(inputValueFromLocation); + // Clear the state after using it once to prevent re-triggering on page refresh + navigate(".", { replace: true, state: {} }); + } + }, [ + sendMessage, + setCurConversationId, + curConversationId, + navigate, + conversationId, + inputValueFromLocation, + ]); + + const [inputValue, setInputValue] = useState(""); + const { currentSection } = useMultiSection(); + + const handleSendMessage = useCallback(async () => { + if (!inputValue.trim()) return; + try { + await sendMessage(inputValue); + setInputValue(""); + } catch (error) { + // Keep input value on error so user doesn't lose their text + console.error("Failed to send message:", error); + } + }, [inputValue, sendMessage]); + + const handleInputChange = useCallback((value: string) => { + setInputValue(value); + }, []); + + if (isLoadingAgent) return null; + if (!agent) return ; + + // Check if conversation has any messages + const hasMessages = + curConversation?.threads && Object.keys(curConversation.threads).length > 0; + + if (!hasMessages) { + return ( + <> + + + + ); + } + + return ( +
+ {/* main section */} +
+ + + + + {/* Input area now only in main section */} + +
+ + {/* Chat section components: one section per special component_type */} + {Object.entries(curConversation.sections).map( + ([componentType, threadView]) => { + return ( + + ); + }, + )} + + {/* Multi-section detail view */} + {currentSection && ( +
+ + + +
+ )} +
+ ); +}; + +const CommonAgentArea: FC = (props) => { + return ( + + + + ); +}; + +export default memo(CommonAgentArea); diff --git a/frontend/src/app/agent/components/chat-conversation/chat-conversation-area.tsx b/frontend/src/app/agent/components/chat-conversation/chat-conversation-area.tsx deleted file mode 100644 index 50f9abd17..000000000 --- a/frontend/src/app/agent/components/chat-conversation/chat-conversation-area.tsx +++ /dev/null @@ -1,134 +0,0 @@ -import { type FC, memo, useCallback, useState } from "react"; -import ScrollContainer from "@/components/valuecell/scroll/scroll-container"; -import { - MultiSectionProvider, - useMultiSection, -} from "@/provider/multi-section-provider"; -import type { - AgentInfo, - ConversationView, - MultiSectionComponentType, - SectionComponentType, -} from "@/types/agent"; -import ChatConversationHeader from "./chat-conversation-header"; -import ChatInputArea from "./chat-input-area"; -import ChatMultiSectionComponent from "./chat-multi-section-component"; -import ChatSectionComponent from "./chat-section-component"; -import ChatThreadArea from "./chat-thread-area"; -import ChatWelcomeScreen from "./chat-welcome-screen"; - -interface ChatConversationAreaProps { - agent: AgentInfo; - currentConversation: ConversationView | null; - isStreaming: boolean; - sendMessage: (message: string) => Promise; -} - -const ChatConversationAreaContent: FC = ({ - agent, - currentConversation, - isStreaming, - sendMessage, -}) => { - const [inputValue, setInputValue] = useState(""); - const { currentSection } = useMultiSection(); - - const handleSendMessage = useCallback(async () => { - if (!inputValue.trim()) return; - try { - await sendMessage(inputValue); - setInputValue(""); - } catch (error) { - // Keep input value on error so user doesn't lose their text - console.error("Failed to send message:", error); - } - }, [inputValue, sendMessage]); - - const handleInputChange = useCallback((value: string) => { - setInputValue(value); - }, []); - - // Check if conversation has any messages - const hasMessages = - currentConversation?.threads && - Object.keys(currentConversation.threads).length > 0; - - if (!hasMessages) { - return ( - <> - - - - ); - } - - return ( -
- {/* main section */} -
- - - - - {/* Input area now only in main section */} - -
- - {/* Chat section components: one section per special component_type */} - {Object.entries(currentConversation.sections).map( - ([componentType, threadView]) => { - return ( - - ); - }, - )} - - {/* Multi-section detail view */} - {currentSection && ( -
- - - -
- )} -
- ); -}; - -const ChatConversationArea: FC = (props) => { - return ( - - - - ); -}; - -export default memo(ChatConversationArea); diff --git a/frontend/src/app/agent/components/chat-conversation/index.tsx b/frontend/src/app/agent/components/chat-conversation/index.tsx new file mode 100644 index 000000000..5897190e9 --- /dev/null +++ b/frontend/src/app/agent/components/chat-conversation/index.tsx @@ -0,0 +1,6 @@ +export { default as ChatConversationHeader } from "./chat-conversation-header"; +export { default as ChatInputArea } from "./chat-input-area"; +export { default as ChatMultiSectionComponent } from "./chat-multi-section-component"; +export { default as ChatSectionComponent } from "./chat-section-component"; +export { default as ChatThreadArea } from "./chat-thread-area"; +export { default as ChatWelcomeScreen } from "./chat-welcome-screen"; diff --git a/frontend/src/types/agent.ts b/frontend/src/types/agent.ts index f2fef22a2..ece771518 100644 --- a/frontend/src/types/agent.ts +++ b/frontend/src/types/agent.ts @@ -140,3 +140,7 @@ export interface AgentInfo { created_at: string; updated_at: string; } + +export interface AgentViewProps { + agentName: string; +} From 51b88dbc55b7441befef91038c02a9240b6eb299 Mon Sep 17 00:00:00 2001 From: DigHuang <114602213+DigHuang@users.noreply.github.com> Date: Wed, 5 Nov 2025 18:13:27 +0800 Subject: [PATCH 32/91] feat: trading strategy modal ui init --- frontend/biome.json | 2 +- frontend/bun.lock | 77 ++- frontend/package.json | 10 +- frontend/src/api/strategy.ts | 17 + .../agent-view/common-agent-area.tsx | 3 +- .../agent-view/strategy-agent-area.tsx | 74 ++ .../strategy-items/create-strategy-modal.tsx | 636 ++++++++++++++++++ .../src/app/home/components/stock-list.tsx | 2 - frontend/src/components/ui/field.tsx | 246 +++++++ frontend/src/components/ui/textarea.tsx | 18 + frontend/src/constants/api.ts | 5 + frontend/src/types/strategy.ts | 9 + 12 files changed, 1074 insertions(+), 25 deletions(-) create mode 100644 frontend/src/api/strategy.ts create mode 100644 frontend/src/app/agent/components/agent-view/strategy-agent-area.tsx create mode 100644 frontend/src/app/agent/components/strategy-items/create-strategy-modal.tsx create mode 100644 frontend/src/components/ui/field.tsx create mode 100644 frontend/src/components/ui/textarea.tsx create mode 100644 frontend/src/types/strategy.ts diff --git a/frontend/biome.json b/frontend/biome.json index 86c7f071b..af0c2fafd 100644 --- a/frontend/biome.json +++ b/frontend/biome.json @@ -1,5 +1,5 @@ { - "$schema": "https://biomejs.dev/schemas/2.3.1/schema.json", + "$schema": "https://biomejs.dev/schemas/2.3.3/schema.json", "files": { "includes": [ "src/**/*.{ts,tsx,js,jsx}", diff --git a/frontend/bun.lock b/frontend/bun.lock index 89f672642..4e3f5b3d4 100644 --- a/frontend/bun.lock +++ b/frontend/bun.lock @@ -8,15 +8,16 @@ "@radix-ui/react-collapsible": "^1.1.12", "@radix-ui/react-dialog": "^1.1.15", "@radix-ui/react-dropdown-menu": "^2.1.16", - "@radix-ui/react-label": "^2.1.7", + "@radix-ui/react-label": "^2.1.8", "@radix-ui/react-radio-group": "^1.3.8", "@radix-ui/react-scroll-area": "^1.2.10", "@radix-ui/react-select": "^2.2.6", - "@radix-ui/react-separator": "^1.1.7", + "@radix-ui/react-separator": "^1.1.8", "@radix-ui/react-slot": "^1.2.3", "@radix-ui/react-tabs": "^1.1.13", "@radix-ui/react-tooltip": "^1.2.8", "@react-router/node": "^7.9.4", + "@tanstack/react-form": "^1.23.8", "@tanstack/react-query": "^5.90.5", "@tauri-apps/api": "^2.9.0", "@tauri-apps/plugin-opener": "^2.5.1", @@ -26,7 +27,8 @@ "dayjs": "^1.11.18", "echarts": "^6.0.0", "isbot": "5.1.31", - "lucide-react": "^0.548.0", + "lucide-react": "^0.552.0", + "motion": "^12.23.24", "mutative": "^1.3.0", "next-themes": "^0.4.6", "overlayscrollbars": "^2.12.0", @@ -38,10 +40,11 @@ "remark-gfm": "^4.0.1", "sonner": "^2.0.7", "tailwind-merge": "^3.3.1", + "zod": "^4.1.12", "zustand": "^5.0.8", }, "devDependencies": { - "@biomejs/biome": "^2.3.1", + 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{}, "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ=="], + "zod": ["zod@4.1.12", "", {}, "sha512-JInaHOamG8pt5+Ey8kGmdcAcg3OL9reK8ltczgHTAwNhMys/6ThXHityHxVV2p3fkw/c+MAvBHFVYHFZDmjMCQ=="], "zod-to-json-schema": ["zod-to-json-schema@3.24.6", "", { "peerDependencies": { "zod": "^3.24.1" } }, "sha512-h/z3PKvcTcTetyjl1fkj79MHNEjm+HpD6NXheWjzOekY7kV+lwDYnHw+ivHkijnCSMz1yJaWBD9vu/Fcmk+vEg=="], @@ -1403,12 +1430,18 @@ "@modelcontextprotocol/sdk/express": ["express@5.1.0", "", { "dependencies": { "accepts": "^2.0.0", "body-parser": "^2.2.0", "content-disposition": "^1.0.0", "content-type": "^1.0.5", "cookie": "^0.7.1", "cookie-signature": "^1.2.1", "debug": "^4.4.0", "encodeurl": "^2.0.0", "escape-html": "^1.0.3", "etag": "^1.8.1", "finalhandler": "^2.1.0", "fresh": "^2.0.0", "http-errors": "^2.0.0", "merge-descriptors": "^2.0.0", "mime-types": "^3.0.0", "on-finished": "^2.4.1", "once": "^1.4.0", "parseurl": "^1.3.3", "proxy-addr": "^2.0.7", "qs": 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"sha512-XA1b62dzQzLfaEOSQFTCOd5KFf/1VSzZo7/7TUjnya6u0vGGKzU96UQBZTAThCb2j4/xjBAyii1OhRLJEivHvg=="], + "@radix-ui/react-label/@radix-ui/react-primitive": ["@radix-ui/react-primitive@2.1.4", "", { "dependencies": { "@radix-ui/react-slot": "1.2.4" }, "peerDependencies": { "@types/react": "*", "@types/react-dom": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react", "@types/react-dom"] }, "sha512-9hQc4+GNVtJAIEPEqlYqW5RiYdrr8ea5XQ0ZOnD6fgru+83kqT15mq2OCcbe8KnjRZl5vF3ks69AKz3kh1jrhg=="], + + "@radix-ui/react-separator/@radix-ui/react-primitive": ["@radix-ui/react-primitive@2.1.4", "", { "dependencies": { "@radix-ui/react-slot": "1.2.4" }, "peerDependencies": { "@types/react": "*", "@types/react-dom": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc", "react-dom": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react", "@types/react-dom"] }, "sha512-9hQc4+GNVtJAIEPEqlYqW5RiYdrr8ea5XQ0ZOnD6fgru+83kqT15mq2OCcbe8KnjRZl5vF3ks69AKz3kh1jrhg=="], + "@tailwindcss/node/jiti": ["jiti@2.6.1", "", { "bin": { "jiti": "lib/jiti-cli.mjs" } }, "sha512-ekilCSN1jwRvIbgeg/57YFh8qQDNbwDb9xT/qu2DAHbFFZUicIl4ygVaAvzveMhMVr3LnpSKTNnwt8PoOfmKhQ=="], "@tailwindcss/oxide-wasm32-wasi/@emnapi/core": ["@emnapi/core@1.5.0", "", { "dependencies": { "@emnapi/wasi-threads": "1.1.0", "tslib": "^2.4.0" }, "bundled": true }, "sha512-sbP8GzB1WDzacS8fgNPpHlp6C9VZe+SJP3F90W9rLemaQj2PzIuTEl1qDOYQf58YIpyjViI24y9aPWCjEzY2cg=="], @@ -1457,6 +1490,8 @@ "finalhandler/debug": ["debug@2.6.9", "", { "dependencies": { "ms": "2.0.0" } }, "sha512-bC7ElrdJaJnPbAP+1EotYvqZsb3ecl5wi6Bfi6BJTUcNowp6cvspg0jXznRTKDjm/E7AdgFBVeAPVMNcKGsHMA=="], + "framer-motion/tslib": ["tslib@2.8.1", "", {}, "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w=="], + "hast-util-to-parse5/property-information": ["property-information@6.5.0", "", {}, "sha512-PgTgs/BlvHxOu8QuEN7wi5A0OmXaBcHpmCSTehcs6Uuu9IkDIEo13Hy7n898RHfrQ49vKCoGeWZSaAK01nwVig=="], "hosted-git-info/lru-cache": ["lru-cache@7.18.3", "", {}, "sha512-jumlc0BIUrS3qJGgIkWZsyfAM7NCWiBcCDhnd+3NNM5KbBmLTgHVfWBcg6W+rLUsIpzpERPsvwUP7CckAQSOoA=="], @@ -1471,6 +1506,8 @@ "morgan/on-finished": ["on-finished@2.3.0", "", { "dependencies": { "ee-first": "1.1.1" } }, "sha512-ikqdkGAAyf/X/gPhXGvfgAytDZtDbr+bkNUJ0N9h5MI/dmdgCs3l6hoHrcUv41sRKew3jIwrp4qQDXiK99Utww=="], + "motion/tslib": ["tslib@2.8.1", "", {}, "sha512-oJFu94HQb+KVduSUQL7wnpmqnfmLsOA/nAh6b6EH0wCEoK0/mPeXU6c3wKDV83MkOuHPRHtSXKKU99IBazS/2w=="], + "msw/path-to-regexp": ["path-to-regexp@6.3.0", "", {}, "sha512-Yhpw4T9C6hPpgPeA28us07OJeqZ5EzQTkbfwuhsUg0c237RomFoETJgmp2sa3F/41gfLE6G5cqcYwznmeEeOlQ=="], "npm-run-path/path-key": ["path-key@4.0.0", "", {}, "sha512-haREypq7xkM7ErfgIyA0z+Bj4AGKlMSdlQE2jvJo6huWD1EdkKYV+G/T4nq0YEF2vgTT8kqMFKo1uHn950r4SQ=="], @@ -1493,6 +1530,8 @@ "send/encodeurl": ["encodeurl@1.0.2", "", {}, "sha512-TPJXq8JqFaVYm2CWmPvnP2Iyo4ZSM7/QKcSmuMLDObfpH5fi7RUGmd/rTDf+rut/saiDiQEeVTNgAmJEdAOx0w=="], + "shadcn/zod": ["zod@3.25.76", "", {}, "sha512-gzUt/qt81nXsFGKIFcC3YnfEAx5NkunCfnDlvuBSSFS02bcXu4Lmea0AFIUwbLWxWPx3d9p8S5QoaujKcNQxcQ=="], + "string-width-cjs/emoji-regex": ["emoji-regex@8.0.0", "", {}, "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A=="], "string-width-cjs/strip-ansi": ["strip-ansi@6.0.1", "", { "dependencies": { "ansi-regex": "^5.0.1" } }, "sha512-Y38VPSHcqkFrCpFnQ9vuSXmquuv5oXOKpGeT6aGrr3o3Gc9AlVa6JBfUSOCnbxGGZF+/0ooI7KrPuUSztUdU5A=="], @@ -1561,6 +1600,10 @@ "@npmcli/promise-spawn/which/isexe": ["isexe@2.0.0", "", {}, "sha512-RHxMLp9lnKHGHRng9QFhRCMbYAcVpn69smSGcq3f36xjgVVWThj4qqLbTLlq7Ssj8B+fIQ1EuCEGI2lKsyQeIw=="], + "@radix-ui/react-label/@radix-ui/react-primitive/@radix-ui/react-slot": ["@radix-ui/react-slot@1.2.4", "", { "dependencies": { "@radix-ui/react-compose-refs": "1.1.2" }, "peerDependencies": { "@types/react": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-Jl+bCv8HxKnlTLVrcDE8zTMJ09R9/ukw4qBs/oZClOfoQk/cOTbDn+NceXfV7j09YPVQUryJPHurafcSg6EVKA=="], + + "@radix-ui/react-separator/@radix-ui/react-primitive/@radix-ui/react-slot": ["@radix-ui/react-slot@1.2.4", "", { "dependencies": { "@radix-ui/react-compose-refs": "1.1.2" }, "peerDependencies": { "@types/react": "*", "react": "^16.8 || ^17.0 || ^18.0 || ^19.0 || ^19.0.0-rc" }, "optionalPeers": ["@types/react"] }, "sha512-Jl+bCv8HxKnlTLVrcDE8zTMJ09R9/ukw4qBs/oZClOfoQk/cOTbDn+NceXfV7j09YPVQUryJPHurafcSg6EVKA=="], + "body-parser/debug/ms": ["ms@2.0.0", "", {}, "sha512-Tpp60P6IUJDTuOq/5Z8cdskzJujfwqfOTkrwIwj7IRISpnkJnT6SyJ4PCPnGMoFjC9ddhal5KVIYtAt97ix05A=="], "cliui/string-width/emoji-regex": ["emoji-regex@8.0.0", "", {}, "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A=="], diff --git a/frontend/package.json b/frontend/package.json index d48af3cf2..a4b9f53fc 100644 --- a/frontend/package.json +++ b/frontend/package.json @@ -21,15 +21,16 @@ "@radix-ui/react-collapsible": "^1.1.12", "@radix-ui/react-dialog": "^1.1.15", "@radix-ui/react-dropdown-menu": "^2.1.16", - "@radix-ui/react-label": "^2.1.7", + "@radix-ui/react-label": "^2.1.8", "@radix-ui/react-radio-group": "^1.3.8", "@radix-ui/react-scroll-area": "^1.2.10", "@radix-ui/react-select": "^2.2.6", - "@radix-ui/react-separator": "^1.1.7", + "@radix-ui/react-separator": "^1.1.8", "@radix-ui/react-slot": "^1.2.3", "@radix-ui/react-tabs": "^1.1.13", "@radix-ui/react-tooltip": "^1.2.8", "@react-router/node": "^7.9.4", + "@tanstack/react-form": "^1.23.8", "@tanstack/react-query": "^5.90.5", "@tauri-apps/api": "^2.9.0", "@tauri-apps/plugin-opener": "^2.5.1", @@ -39,7 +40,7 @@ "dayjs": "^1.11.18", "echarts": "^6.0.0", "isbot": "5.1.31", - "lucide-react": "^0.548.0", + "lucide-react": "^0.552.0", "mutative": "^1.3.0", "next-themes": "^0.4.6", "overlayscrollbars": "^2.12.0", @@ -51,10 +52,11 @@ "remark-gfm": "^4.0.1", "sonner": "^2.0.7", "tailwind-merge": "^3.3.1", + "zod": "^4.1.12", "zustand": "^5.0.8" }, "devDependencies": { - "@biomejs/biome": "^2.3.1", + "@biomejs/biome": "^2.3.3", "@react-router/dev": "^7.9.4", "@react-router/serve": "^7.9.4", "@tailwindcss/typography": "^0.5.19", diff --git a/frontend/src/api/strategy.ts b/frontend/src/api/strategy.ts new file mode 100644 index 000000000..9b2590c51 --- /dev/null +++ b/frontend/src/api/strategy.ts @@ -0,0 +1,17 @@ +import { useQuery } from "@tanstack/react-query"; +import { API_QUERY_KEYS } from "@/constants/api"; +import { type ApiResponse, apiClient } from "@/lib/api-client"; +import type { Strategy } from "@/types/strategy"; + +export const useGetStrategyList = () => { + return useQuery({ + queryKey: API_QUERY_KEYS.STRATEGY.strategyList, + queryFn: () => + apiClient.get< + ApiResponse<{ + strategies: Strategy[]; + }> + >("/strategies"), + select: (data) => data.data.strategies, + }); +}; diff --git a/frontend/src/app/agent/components/agent-view/common-agent-area.tsx b/frontend/src/app/agent/components/agent-view/common-agent-area.tsx index 1b2db7b24..b69b00983 100644 --- a/frontend/src/app/agent/components/agent-view/common-agent-area.tsx +++ b/frontend/src/app/agent/components/agent-view/common-agent-area.tsx @@ -23,6 +23,7 @@ import { } from "@/store/agent-store"; import type { AgentStreamRequest, + AgentViewProps, MultiSectionComponentType, SectionComponentType, SSEData, @@ -273,7 +274,7 @@ const CommonAgentAreaContent: FC = ({ agentName }) => { ); }; -const CommonAgentArea: FC = (props) => { +const CommonAgentArea: FC = (props) => { return ( diff --git a/frontend/src/app/agent/components/agent-view/strategy-agent-area.tsx b/frontend/src/app/agent/components/agent-view/strategy-agent-area.tsx new file mode 100644 index 000000000..c24acb598 --- /dev/null +++ b/frontend/src/app/agent/components/agent-view/strategy-agent-area.tsx @@ -0,0 +1,74 @@ +import { Plus } from "lucide-react"; +import type { FC } from "react"; +import { useGetStrategyList } from "@/api/strategy"; +import { Button } from "@/components/ui/button"; +import type { AgentViewProps } from "@/types/agent"; +import { CreateStrategyModal } from "../strategy-items/create-strategy-modal"; + +const EmptyIllustration = () => ( + + + + + + +); + +const StrategyAgentArea: FC = () => { + const { data: strategies, isLoading } = useGetStrategyList(); + + if (isLoading) return null; + + // Show empty state when there are no strategies + return ( +
+ {/* Left section: Strategy list empty state */} +
+

Trading Strategies

+ +
+ {strategies && strategies.length > 0 ? ( +
TODO: Strategy list
+ ) : ( + <> + + +
+

No trading strategies

+

Create your first trading strategy

+
+ + + + Add trading strategy + + } + /> + + )} +
+
+ {/* Right section: Strategy details empty state */} +
+
+ +

+ No running strategies +

+
+
+
+ ); +}; + +export default StrategyAgentArea; diff --git a/frontend/src/app/agent/components/strategy-items/create-strategy-modal.tsx b/frontend/src/app/agent/components/strategy-items/create-strategy-modal.tsx new file mode 100644 index 000000000..a7d566ab2 --- /dev/null +++ b/frontend/src/app/agent/components/strategy-items/create-strategy-modal.tsx @@ -0,0 +1,636 @@ +import { useForm } from "@tanstack/react-form"; +import { Check, X } from "lucide-react"; +import type { FC } from "react"; +import { useState } from "react"; +import { z } from "zod"; +import { Button } from "@/components/ui/button"; +import { Dialog, DialogContent, DialogTrigger } from "@/components/ui/dialog"; +import { + Field, + FieldError, + FieldGroup, + FieldLabel, +} from "@/components/ui/field"; +import { Input } from "@/components/ui/input"; +import { Label } from "@/components/ui/label"; +import { RadioGroup, RadioGroupItem } from "@/components/ui/radio-group"; +import { + Select, + SelectContent, + SelectItem, + SelectTrigger, + SelectValue, +} from "@/components/ui/select"; +import { Textarea } from "@/components/ui/textarea"; +import ScrollContainer from "@/components/valuecell/scroll/scroll-container"; + +interface CreateStrategyModalProps { + trigger?: React.ReactNode; +} + +type StepNumber = 1 | 2 | 3; + +// Form validation schema +const formSchema = z.object({ + // Step 1: AI Model Config + modelProvider: z.string().min(1, "Model platform is required"), + modelId: z.string().min(1, "Model selection is required"), + modelApiKey: z.string().min(1, "API key is required"), + // Step 2: Exchange Config + exchangeId: z.string().min(1, "Exchange is required"), + tradingMode: z.enum(["live", "virtual"]), + exchangeApiKey: z.string(), + exchangeSecretKey: z.string(), + // Step 3: Trading Config + strategyName: z.string().min(1, "Strategy name is required"), + initialCapital: z.number().min(0, "Initial capital must be positive"), + maxLeverage: z.number().min(1, "Leverage must be at least 1"), + symbols: z.string().min(1, "At least one symbol is required"), + templateId: z.string().min(1, "Template selection is required"), + customPrompt: z.string(), +}); + +const STEPS = [ + { number: 1 as const, title: "AI Models" }, + { number: 2 as const, title: "Exchanges" }, + { number: 3 as const, title: "Trading strategy" }, +]; + +// Custom Step Indicator Component +const StepIndicator: FC<{ currentStep: StepNumber }> = ({ currentStep }) => { + return ( +
+ {STEPS.map((step, index) => { + const isCompleted = step.number < currentStep; + const isCurrent = step.number === currentStep; + + return ( +
+
+ {/* Step circle */} +
+ {isCompleted ? ( +
+ +
+ ) : ( + <> +
+ + {step.number} + + + )} +
+ + {/* Title and divider */} +
+

+ {step.title} +

+ {index < STEPS.length - 1 && ( +
+ )} +
+
+
+ ); + })} +
+ ); +}; + +export const CreateStrategyModal: FC = ({ + trigger, +}) => { + const [open, setOpen] = useState(false); + const [currentStep, setCurrentStep] = useState(1); + + const form = useForm({ + defaultValues: { + modelProvider: "openrouter", + modelId: "deepseek-ai/DeepSeek-V3.1-Terminus", + modelApiKey: "", + exchangeId: "okx", + tradingMode: "live" as "live" | "virtual", + exchangeApiKey: "", + exchangeSecretKey: "", + strategyName: "", + initialCapital: 1000, + maxLeverage: 8, + symbols: "", + templateId: "default", + customPrompt: "", + }, + validators: { + onSubmit: formSchema, + }, + onSubmit: async ({ value }) => { + // Convert flat form to nested structure + const payload = { + modelConfig: { + provider: value.modelProvider, + modelId: value.modelId, + apiKey: value.modelApiKey, + }, + exchangeConfig: { + exchangeId: value.exchangeId, + tradingMode: value.tradingMode, + apiKey: value.exchangeApiKey, + secretKey: value.exchangeSecretKey, + }, + tradingConfig: { + strategyName: value.strategyName, + initialCapital: value.initialCapital, + maxLeverage: value.maxLeverage, + symbols: value.symbols + .split(",") + .map((s) => s.trim()) + .filter(Boolean), + templateId: value.templateId, + customPrompt: value.customPrompt, + }, + }; + console.log("Form submitted:", payload); + setOpen(false); + setCurrentStep(1); + }, + }); + + const handleNext = () => { + if (currentStep < 3) { + setCurrentStep((prev) => (prev + 1) as StepNumber); + } else { + form.handleSubmit(); + } + }; + + const handleCancel = () => { + setOpen(false); + setCurrentStep(1); + form.reset(); + }; + + return ( + + + {trigger || ( + + )} + + + + {/* Header */} +
+
+

+ Add trading strategy +

+ +
+ + {/* Step indicator */} + +
+ + {/* Form content with scroll */} + +
{ + e.preventDefault(); + handleNext(); + }} + className="flex flex-col gap-6 py-2" + > + {/* Step 1: AI Models */} + {currentStep === 1 && ( + + + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + Model Platform + + + {isInvalid && ( + + )} + + ); + }} + + + + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + Select Model + + + {isInvalid && ( + + )} + + ); + }} + + + + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + API key + + field.handleChange(e.target.value)} + onBlur={field.handleBlur} + placeholder="Enter API Key" + /> + {isInvalid && ( + + )} + + ); + }} + + + )} + + {/* Step 2: Exchanges */} + {currentStep === 2 && ( + + + {(field) => ( + + + Transaction Type + + + field.handleChange(value as "live" | "virtual") + } + className="flex items-center gap-6" + > +
+ + +
+
+ + +
+ + + )} + + + + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + Select Exchange + + + {isInvalid && ( + + )} + + ); + }} + + + + {(field) => ( + + + API key + + field.handleChange(e.target.value)} + onBlur={field.handleBlur} + placeholder="Enter API Key" + /> + + )} + + + + {(field) => ( + + + Secret Key + + field.handleChange(e.target.value)} + onBlur={field.handleBlur} + placeholder="Enter Secret Key" + /> + + )} + + + )} + + {/* Step 3: Trading Strategy */} + {currentStep === 3 && ( + + + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + Strategy Name + + field.handleChange(e.target.value)} + onBlur={field.handleBlur} + placeholder="Enter strategy name" + /> + {isInvalid && ( + + )} + + ); + }} + + + {/* Transaction Configuration Section */} +
+
+
+

+ Transaction configuration +

+
+ +
+
+ + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + Initial Capital + + + field.handleChange(Number(e.target.value)) + } + onBlur={field.handleBlur} + /> + {isInvalid && ( + + )} + + ); + }} + + + + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + Max Leverage + + + field.handleChange(Number(e.target.value)) + } + onBlur={field.handleBlur} + /> + {isInvalid && ( + + )} + + ); + }} + +
+ + + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + Trading Symbols + + + field.handleChange(e.target.value) + } + onBlur={field.handleBlur} + placeholder="BTC, ETH, SOL, DOGE, XRP" + /> + {isInvalid && ( + + )} + + ); + }} + +
+
+ + {/* Trading Strategy Prompt Section */} +
+
+
+

+ Trading strategy prompt +

+
+ +
+ + {(field) => { + const isInvalid = + field.state.meta.isTouched && + field.state.meta.errors.length > 0; + return ( + + + System Prompt Template + + + {isInvalid && ( + + )} + + ); + }} + + + + {(field) => ( + + + Custom Prompt + +