863a4cc8c9
Strategy DSL v2 (dsl.py): 40+ action primitives, 40+ market sensors, 12 comparison operators, 16 builtins, full parser. Strategy Generator (generator.py): genetic programming evolution — crossover, mutation, tournament selection, pool management. Supporting: discrepancy tracking, execution quality, hooks, feature importance, observability, parallel eval, auto-rollback, stress testing, structured observations, trajectory recording.
101 lines
3.1 KiB
Python
101 lines
3.1 KiB
Python
"""
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Structured Observability — compact JSONL logging for every decision.
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Records:
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- Every planner decision with features and diagnostics
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- Every risk gate decision
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- Every venue action
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- Every fill/discrepancy
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- Replayable audit trail
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"""
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from __future__ import annotations
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import json
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import time
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from dataclasses import dataclass, field
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from typing import Any, Dict, List, Mapping, Optional
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from malkhut.state import MarketWorldState
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from malkhut.actions import FulfilmentAction, PlannedPolicy, RiskDecision
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@dataclass(frozen=True, slots=True)
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class DecisionRecord:
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"""One decision in the audit trail."""
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ts_ns: int
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symbol: str
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action_kind: str
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action_side: Optional[str]
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action_price: Optional[float]
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approved: bool
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risk_reason: str
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plan_latency_ns: int
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policy_version: str
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root_entropy: float
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sims: int
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features: Mapping[str, float] = field(default_factory=dict)
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diagnostics: Mapping[str, Any] = field(default_factory=dict)
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class ObservabilityLogger:
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"""
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Compact JSONL logger for every decision.
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One line per decision. Replayable audit trail.
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"""
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def __init__(self, log_path: str = "decisions.log") -> None:
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self._log_path = log_path
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self._decisions: list[DecisionRecord] = []
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self._total_decisions = 0
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def log_decision(
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self,
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state: MarketWorldState,
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planned: PlannedPolicy,
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decision: RiskDecision,
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plan_ns: int,
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features: Optional[Mapping[str, float]] = None,
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) -> None:
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"""Log a decision to the audit trail."""
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record = DecisionRecord(
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ts_ns=state.ts_ns,
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symbol=state.venue.symbol,
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action_kind=planned.selected_action.kind.value,
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action_side=planned.selected_action.side.value if planned.selected_action.side else None,
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action_price=planned.selected_action.price_ticks_from_best if planned.selected_action else None,
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approved=decision.approved,
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risk_reason=decision.reason,
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plan_latency_ns=plan_ns,
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policy_version="live",
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root_entropy=planned.diagnostics.get("entropy", 0.0),
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sims=planned.diagnostics.get("sims", 0),
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features=features or {},
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)
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self._decisions.append(record)
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self._total_decisions += 1
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# Write to JSONL
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try:
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with open(self._log_path, "a") as f:
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f.write(json.dumps({
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"ts": record.ts_ns,
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"sym": record.symbol,
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"act": record.action_kind,
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"side": record.action_side,
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"app": record.approved,
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"risk": record.risk_reason,
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"lat_ns": record.plan_latency_ns,
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"entropy": round(record.root_entropy, 4),
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"sims": record.sims,
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}, separators=(",", ":")) + "\n")
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except OSError:
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pass
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@property
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def total_decisions(self) -> int:
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return self._total_decisions
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def get_recent(self, n: int = 10) -> List[DecisionRecord]:
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return self._decisions[-n:]
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