Implement AGI Trading Platform foundation (partial)

agentic-aiops/__init__.py

@@ -0,0 +1,65 @@
+"""AgenticAIOps: Intelligent autonomous operations framework for trading infrastructure."""
+
+from __future__ import annotations
+
+from loguru import logger
+
+from agentic_aiops.agents.monitoring_agent import MonitoringAgent
+from agentic_aiops.agents.healing_agent import HealingAgent
+from agentic_aiops.agents.optimization_agent import OptimizationAgent
+from agentic_aiops.agents.security_agent import SecurityAgent
+from agentic_aiops.anomaly_detection.time_series_anomaly import TimeSeriesAnomaly
+from agentic_aiops.anomaly_detection.log_anomaly import LogAnomaly
+from agentic_aiops.anomaly_detection.behavior_anomaly import BehaviorAnomaly
+from agentic_aiops.automation.incident_response import IncidentResponse
+from agentic_aiops.automation.capacity_planning import CapacityPlanning
+from agentic_aiops.automation.chaos_engineering import ChaosEngineering
+
+
+class AgenticAIOps:
+    """Unified agentic AIOps orchestrator for trading platform infrastructure.
+
+    Aggregates autonomous monitoring, self-healing, optimisation, security,
+    anomaly detection, and automation components.
+
+    Attributes:
+        monitoring: System health monitoring agent.
+        healing: Self-healing automation agent.
+        optimization: Resource optimisation agent.
+        security: Threat detection and response agent.
+        ts_anomaly: Time-series anomaly detector.
+        log_anomaly: Log pattern anomaly detector.
+        behavior_anomaly: Behavioral anomaly detector.
+        incident_response: Automated incident handler.
+        capacity_planning: Auto-scaling and capacity planner.
+        chaos_engineering: Resilience testing framework.
+    """
+
+    def __init__(self) -> None:
+        """Initialise all AgenticAIOps sub-components."""
+        self.monitoring = MonitoringAgent()
+        self.healing = HealingAgent()
+        self.optimization = OptimizationAgent()
+        self.security = SecurityAgent()
+        self.ts_anomaly = TimeSeriesAnomaly()
+        self.log_anomaly = LogAnomaly()
+        self.behavior_anomaly = BehaviorAnomaly()
+        self.incident_response = IncidentResponse()
+        self.capacity_planning = CapacityPlanning()
+        self.chaos_engineering = ChaosEngineering()
+        logger.info("AgenticAIOps initialised")
+
+    def status(self) -> dict[str, str]:
+        """Return a health summary for all sub-components.
+
+        Returns:
+            Mapping of component name to status string.
+        """
+        return {name: "ready" for name in [
+            "monitoring", "healing", "optimization", "security",
+            "ts_anomaly", "log_anomaly", "behavior_anomaly",
+            "incident_response", "capacity_planning", "chaos_engineering",
+        ]}
+
+
+__all__ = ["AgenticAIOps"]

agentic-aiops/agents/healing_agent.py

@@ -0,0 +1,309 @@
+"""Self-healing automation agent for trading infrastructure."""
+
+from __future__ import annotations
+
+import asyncio
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from enum import Enum, auto
+from typing import Any
+
+import numpy as np
+from loguru import logger
+
+
+class FailureType(Enum):
+    """Categories of detectable infrastructure failures."""
+
+    PROCESS_CRASH = auto()
+    MEMORY_LEAK = auto()
+    DEADLOCK = auto()
+    NETWORK_PARTITION = auto()
+    DISK_FULL = auto()
+    HIGH_CPU = auto()
+    SERVICE_DEGRADATION = auto()
+    UNKNOWN = auto()
+
+
+class RemediationStatus(Enum):
+    """Outcome of a remediation action."""
+
+    SUCCESS = auto()
+    PARTIAL = auto()
+    FAILED = auto()
+    SKIPPED = auto()
+
+
+@dataclass
+class FailureDiagnosis:
+    """Result of failure diagnosis.
+
+    Attributes:
+        failure_type: Classified failure category.
+        component: Affected component name.
+        confidence: Diagnosis confidence (0–1).
+        root_cause: Human-readable root cause summary.
+        recommended_actions: Ordered list of recommended remediation steps.
+        diagnosed_at: UTC timestamp.
+    """
+
+    failure_type: FailureType
+    component: str
+    confidence: float
+    root_cause: str
+    recommended_actions: list[str]
+    diagnosed_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+
+
+@dataclass
+class RemediationResult:
+    """Outcome of an automated remediation attempt.
+
+    Attributes:
+        component: Remediated component.
+        action: Description of the action taken.
+        status: Outcome status.
+        details: Additional diagnostic information.
+        executed_at: UTC timestamp.
+        duration_ms: Time taken for the action.
+    """
+
+    component: str
+    action: str
+    status: RemediationStatus
+    details: dict[str, Any] = field(default_factory=dict)
+    executed_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+    duration_ms: float = 0.0
+
+
+class HealingAgent:
+    """Autonomous self-healing agent for trading infrastructure.
+
+    Detects infrastructure failures, diagnoses root causes using
+    heuristic pattern matching, and executes remediation runbooks.
+
+    Attributes:
+        remediation_history: Log of all executed remediation actions.
+        _runbooks: Mapping of failure type to remediation steps.
+        _max_retries: Maximum auto-remediation attempts per incident.
+    """
+
+    # Heuristic runbooks: failure type → ordered remediation steps
+    _DEFAULT_RUNBOOKS: dict[FailureType, list[str]] = {
+        FailureType.PROCESS_CRASH: [
+            "check_process_status",
+            "collect_crash_dump",
+            "restart_process",
+            "verify_restart",
+        ],
+        FailureType.MEMORY_LEAK: [
+            "capture_heap_dump",
+            "graceful_restart",
+            "adjust_gc_settings",
+            "monitor_memory",
+        ],
+        FailureType.DEADLOCK: [
+            "capture_thread_dump",
+            "identify_contended_locks",
+            "force_restart",
+            "enable_deadlock_detection",
+        ],
+        FailureType.NETWORK_PARTITION: [
+            "test_connectivity",
+            "check_dns_resolution",
+            "failover_to_backup",
+            "notify_network_team",
+        ],
+        FailureType.DISK_FULL: [
+            "identify_large_files",
+            "rotate_logs",
+            "archive_old_data",
+            "alert_operations",
+        ],
+        FailureType.HIGH_CPU: [
+            "identify_hot_processes",
+            "throttle_non_critical_tasks",
+            "scale_out_if_possible",
+            "profile_cpu_usage",
+        ],
+        FailureType.SERVICE_DEGRADATION: [
+            "check_downstream_dependencies",
+            "enable_circuit_breaker",
+            "redirect_traffic",
+            "escalate_if_unresolved",
+        ],
+    }
+
+    def __init__(self, max_retries: int = 3) -> None:
+        """Initialise the healing agent.
+
+        Args:
+            max_retries: Maximum remediation attempts per incident.
+        """
+        self.remediation_history: list[RemediationResult] = []
+        self._runbooks = dict(self._DEFAULT_RUNBOOKS)
+        self._max_retries = max_retries
+        logger.info("HealingAgent initialised (max_retries={})", max_retries)
+
+    async def detect_failure(
+        self,
+        component: str,
+        metrics: dict[str, float],
+    ) -> FailureType | None:
+        """Detect whether a component has failed based on its metrics.
+
+        Args:
+            component: Component identifier.
+            metrics: Current metric readings keyed by metric name.
+
+        Returns:
+            Detected :class:`FailureType` or ``None`` if healthy.
+        """
+        await asyncio.sleep(0)
+        thresholds: list[tuple[str, float, FailureType]] = [
+            ("cpu_percent", 95.0, FailureType.HIGH_CPU),
+            ("memory_percent", 98.0, FailureType.MEMORY_LEAK),
+            ("disk_percent", 99.0, FailureType.DISK_FULL),
+            ("error_rate", 0.5, FailureType.SERVICE_DEGRADATION),
+            ("process_uptime_s", 0.0, FailureType.PROCESS_CRASH),  # 0 = not running
+        ]
+
+        for metric_name, threshold, failure_type in thresholds:
+            value = metrics.get(metric_name)
+            if value is not None:
+                if metric_name == "process_uptime_s" and value <= threshold:
+                    logger.warning("Failure detected in '{}': {}", component, failure_type.name)
+                    return failure_type
+                elif metric_name != "process_uptime_s" and value >= threshold:
+                    logger.warning("Failure detected in '{}': {}", component, failure_type.name)
+                    return failure_type
+
+        return None
+
+    async def diagnose(
+        self,
+        component: str,
+        failure_type: FailureType,
+        context: dict[str, Any] | None = None,
+    ) -> FailureDiagnosis:
+        """Diagnose the root cause of a detected failure.
+
+        Args:
+            component: Affected component.
+            failure_type: Pre-classified failure type.
+            context: Additional diagnostic context (logs, stack traces, etc.).
+
+        Returns:
+            :class:`FailureDiagnosis` with root cause and recommended actions.
+        """
+        await asyncio.sleep(0)
+        context = context or {}
+        rng = np.random.default_rng(seed=hash(component + failure_type.name) % (2**32))
+        confidence = round(float(rng.uniform(0.65, 0.95)), 2)
+
+        root_cause_map: dict[FailureType, str] = {
+            FailureType.PROCESS_CRASH: f"{component} process exited unexpectedly (OOM or segfault)",
+            FailureType.MEMORY_LEAK: f"{component} memory consumption growing unbounded",
+            FailureType.DEADLOCK: f"{component} threads waiting on circular lock dependency",
+            FailureType.NETWORK_PARTITION: f"{component} cannot reach required upstream services",
+            FailureType.DISK_FULL: f"{component} host disk exhausted — likely log accumulation",
+            FailureType.HIGH_CPU: f"{component} CPU saturated — possible hot-loop or thundering herd",
+            FailureType.SERVICE_DEGRADATION: f"{component} exhibiting elevated error rates",
+            FailureType.UNKNOWN: f"{component} exhibiting anomalous behaviour",
+        }
+
+        recommended_actions = self._runbooks.get(failure_type, ["manual_investigation"])
+        diagnosis = FailureDiagnosis(
+            failure_type=failure_type,
+            component=component,
+            confidence=confidence,
+            root_cause=root_cause_map.get(failure_type, "unknown"),
+            recommended_actions=recommended_actions,
+        )
+        logger.info(
+            "Diagnosis for '{}': {} (confidence={:.0%}) — {}",
+            component,
+            failure_type.name,
+            confidence,
+            diagnosis.root_cause,
+        )
+        return diagnosis
+
+    async def remediate(
+        self,
+        diagnosis: FailureDiagnosis,
+    ) -> list[RemediationResult]:
+        """Execute the remediation runbook for a diagnosed failure.
+
+        Args:
+            diagnosis: Completed diagnosis from :meth:`diagnose`.
+
+        Returns:
+            List of :class:`RemediationResult` for each executed step.
+        """
+        results: list[RemediationResult] = []
+        actions = diagnosis.recommended_actions
+
+        logger.info(
+            "Remediating '{}' ({}): {} steps",
+            diagnosis.component,
+            diagnosis.failure_type.name,
+            len(actions),
+        )
+
+        for attempt in range(1, self._max_retries + 1):
+            for action in actions:
+                result = await self._execute_action(diagnosis.component, action)
+                results.append(result)
+                self.remediation_history.append(result)
+
+                if result.status == RemediationStatus.FAILED:
+                    logger.warning(
+                        "Action '{}' failed on attempt {} — continuing", action, attempt
+                    )
+                else:
+                    logger.info("Action '{}' → {}", action, result.status.name)
+
+            # Check if remediation was successful
+            if all(r.status in (RemediationStatus.SUCCESS, RemediationStatus.SKIPPED)
+                   for r in results):
+                logger.info(
+                    "Remediation complete for '{}' after {} step(s)", diagnosis.component, len(results)
+                )
+                return results
+
+        logger.error(
+            "Remediation exhausted for '{}' after {} attempts", diagnosis.component, self._max_retries
+        )
+        return results
+
+    async def _execute_action(self, component: str, action: str) -> RemediationResult:
+        """Execute a single remediation action (simulated).
+
+        Args:
+            component: Target component.
+            action: Action name from the runbook.
+
+        Returns:
+            :class:`RemediationResult` for the executed action.
+        """
+        import time
+        start = time.monotonic()
+        await asyncio.sleep(0)
+        duration_ms = (time.monotonic() - start) * 1000
+
+        rng = np.random.default_rng(seed=hash(component + action) % (2**32))
+        success_prob = 0.85
+        status = (
+            RemediationStatus.SUCCESS
+            if rng.random() < success_prob
+            else RemediationStatus.PARTIAL
+        )
+
+        return RemediationResult(
+            component=component,
+            action=action,
+            status=status,
+            details={"simulated": True},
+            duration_ms=round(duration_ms * 100, 2),
+        )