Implement AGI Trading Platform foundation (partial)

[Copilot]

Bootstraps a comprehensive AI-powered trading platform with a hierarchical AI architecture (AGI → AI Brain → Vertical/Synthetic/Quantum AI) layered over an ops framework (LLMOps, AIOps, EdgeOps, DevSecOps).

Added

Shared Layer (shared/)

• Pydantic v2 models: OHLCV, OrderBook, Order, Position, Portfolio, TradingSignal, AGIDecision, RiskAssessment
• Centralized loguru logger with correlation ID propagation, rotating file sink
• pydantic-settings config with env overlay; custom exception hierarchy; retry/rate-limiter/timer utilities
• gRPC proto definitions: trading.proto, agi.proto, monitoring.proto

AI Components

• AGI Orchestrator (agi-orchestrator/) — multi-level decision engine (strategic/tactical/operational), global state pub-sub, goal priority queue, self-improvement loop, causal inference, strategic planner, meta-cognitive reflection, agent coordinator, resource allocator, conflict resolver
• AI Brain Orchestrator (ai-brain-orchestrator/) — versioned model registry, weighted ensemble inference, regime-aware model selector, context build/enrich/compress pipeline, bounded short/long-term memory, softmax attention, distributed async inference, chain-of-thought executor, reflection/self-correction loops
• Vertical AI (vertical-ai/) — technical indicators (SMA/EMA/RSI/MACD/BBands/ATR), fundamental ratios, lexicon sentiment with negation, order-book imbalance; historical/parametric VaR/CVaR, Kelly/volatility-targeting sizer, Almgren-Chriss impact model, FINRA PDT + wash-trade compliance
• Synthetic AI (synthetic-ai/) — GBM + jump-diffusion simulator, 6-scenario generator, adversarial edge-case generator, vectorised backtester (Sharpe/Sortino/Calmar), Monte Carlo VaR, multi-agent market sim, KS/moment distribution validation
• Quantum AI (quantum-ai/) — classical simulations of QAOA, VQE, simulated quantum annealing, Grover amplitude amplification; state-vector simulator; depolarising/bit-flip/phase-flip noise models; hybrid quantum-classical portfolio optimiser

Ops Framework

• LLMOps (llmops/) — fine-tune/RLHF/continual-learning pipeline stubs; async model server; A/B + canary deployment; PSI/KS drift detection; prompt template library + auto-optimizer + context injector
• Agentic AIOps (agentic-aiops/) — monitoring/healing/optimization/security agents; Z-score + IQR anomaly detection; incident response with severity classification; capacity forecasting; chaos fault injection
• EdgeOps (edgeops/) — model compression (quantization/pruning), federated averaging, async low-latency inference with timeout, TTL+LRU edge cache, multi-edge topology coordinator
• DevSecOps (devsecops/) — env-only secret manager, Fernet encryption, HMAC-signed audit log, rate-limit threat detection, bandit SAST wrapper, FINRA/GDPR/SOX compliance stubs, canary deployment gates, rollback mechanism

Not Yet Implemented

• trading-engine/ (exchange connectors, order manager, risk engine, portfolio manager)
• dataops/ (market data ingestion, feature store, time-series DB wrappers)
• Infrastructure files (Docker, Kubernetes, Terraform)
• Root config files (main.py, config.yaml, requirements.txt, pyproject.toml)
• Tests and CI/CD workflows
• Documentation

Original prompt

AGI Trading Platform - Complete Implementation

Overview

Implement a comprehensive AI-powered trading platform that integrates AGI (Artificial General Intelligence) orchestration with multiple operational frameworks to trade stocks and cryptocurrencies. The platform uses a hierarchical AI architecture with Vertical AI, Synthetic AI, and Quantum AI working together under AGI control.

System Architecture

Core AI Components

1. AGI Master Orchestrator

Create the highest-level intelligence layer that controls all platform operations:

Directory Structure:

agi-orchestrator/
├── __init__.py
├── core/
│   ├── __init__.py
│   ├── decision_engine.py          # Meta-learning decision maker
│   ├── global_state_manager.py     # System-wide state tracking
│   ├── goal_hierarchy.py           # Multi-objective optimization
│   └── self_improvement.py         # Autonomous learning loops
├── reasoning/
│   ├── __init__.py
│   ├── causal_inference.py         # Causal reasoning engine
│   ├── strategic_planner.py        # Long-term strategy
│   └── meta_cognitive.py           # Self-awareness & reflection
└── coordination/
    ├── __init__.py
    ├── agent_coordinator.py        # Multi-agent orchestration
    ├── resource_allocator.py       # Dynamic resource management
    └── conflict_resolver.py        # Inter-system conflict handling

Key Features:

• Multi-level decision making (strategic → tactical → operational)
• Self-optimization and meta-learning capabilities
• Autonomous goal setting and adjustment
• Cross-system coordination and conflict resolution

2. AI Brain Orchestrator

Implement multi-model coordination system:

Directory Structure:

ai-brain-orchestrator/
├── __init__.py
├── model_hub/
│   ├── __init__.py
│   ├── model_registry.py           # Model versioning & management
│   ├── ensemble_manager.py         # Multi-model coordination
│   └── model_selector.py           # Dynamic model selection
├── context/
│   ├── __init__.py
│   ├── context_engine.py           # Contextual awareness
│   ├── memory_manager.py           # Short/long-term memory
│   └── attention_mechanism.py      # Focus prioritization
└── inference/
    ├── __init__.py
    ├── distributed_inference.py    # Parallel model execution
    ├── chain_of_thought.py         # Reasoning chains
    └── reflection_loops.py         # Self-correction

Integration Pattern Example:

class AIBrainOrchestrator:
    def process_trading_signal(self, market_data):
        # Step 1: Context gathering
        context = self.context_engine.build_context(market_data)
        
        # Step 2: Multi-model analysis
        vertical_analysis = self.vertical_ai.analyze(context)
        synthetic_scenarios = self.synthetic_ai.generate(context)
        quantum_predictions = self.quantum_ai.compute(context)
        
        # Step 3: AGI synthesis
        decision = self.agi_orchestrator.synthesize(
            vertical_analysis,
            synthetic_scenarios,
            quantum_predictions
        )
        
        return decision

3. Vertical AI Modules

Implement specialized domain-specific AI agents:

Directory Structure:

vertical-ai/
├── __init__.py
├── market_analysis/
│   ├── __init__.py
│   ├── technical_analyzer.py       # Chart patterns, indicators
│   ├── fundamental_analyzer.py     # Financial statements, ratios
│   ├── sentiment_analyzer.py       # News, social media sentiment
│   └── orderbook_analyzer.py       # Market depth analysis
├── risk_management/
│   ├── __init__.py
│   ├── portfolio_risk.py           # VaR, CVaR, drawdown
│   ├── position_sizer.py           # Kelly criterion, optimal sizing
│   └── correlation_analyzer.py     # Asset correlation tracking
├── execution/
│   ├── __init__.py
│   ├── smart_order_router.py      # Optimal execution paths
│   ├── slippage_predictor.py      # Cost prediction
│   └── market_impact_model.py     # Impact estimation
└── compliance/
    ├── __init__.py
    ├── regulatory_checker.py       # Rule compliance
    └── audit_logger.py             # Comprehensive logging

4. Synthetic AI Generator

Create synthetic data and scenario generation:

Directory Structure:

synthetic-ai/
├── __init__.py
├── generators/
│   ├── __init__.py
│   ├── market_simulator.py         # Synthetic market conditions
│   ├── scenario_generator.py       # "What-if" scenarios
│   ├── adversarial_generator.py    # Edge case generation
│   └── synthetic_data_forge.py     # Training data augmentation
├── simulation/
│   ├── __init__.py
│   ├── backtesting_engine.py       # Historical simulation
│   ├── monte_carlo.py              # Probabilistic modeling
│   └── agent_simulation.py         # Multi-agent market sim
└── validation/
    ├── __init__.py
    ├── reality_checker.py          # Synthetic vs real validation
    └── distribution_matcher.py     # Statistical validation

5. Quantum AI Processor

Implement quantum-inspired algori...

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