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.
105 lines
3.1 KiB
Python
105 lines
3.1 KiB
Python
"""
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Trajectory Persistence — store CWM trajectories to ClickHouse.
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Enables:
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- Post-hoc analysis of decision quality
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- Replay verification against stored trajectories
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- Training data for learned leaf values
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- Audit trail for every decision
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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, List, Mapping, Optional, Sequence
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from malkhut.state import MarketWorldState
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from malkhut.actions import FulfilmentAction, PlannedPolicy, RiskDecision
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from malkhut.storage.ch_store import MalkhutCHStore
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@dataclass(frozen=True, slots=True)
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class TrajectoryStep:
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"""One step in a persisted trajectory."""
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step_index: int
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ts_ns: int
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symbol: str
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state_hash: 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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action_qty: float
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next_state_hash: str
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pnl_bps: float
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reward: float
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entropy: float
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diagnostics: Mapping[str, Any] = field(default_factory=dict)
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@dataclass(frozen=True, slots=True)
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class TrajectoryRecord:
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"""Complete trajectory for one episode."""
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trajectory_id: str
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policy_version: str
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scenario_id: str
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seed: int
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steps: Tuple[TrajectoryStep, ...]
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total_pnl_bps: float
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max_drawdown_bps: float
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fill_count: int
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cancel_count: int
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noop_count: int
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duration_ns: int
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created_ts_ns: int
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class TrajectoryPersister:
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"""
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Persist CWM trajectories to ClickHouse.
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Enables post-hoc analysis and replay verification.
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"""
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def __init__(self, store: MalkhutCHStore) -> None:
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self._store = store
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def persist(self, record: TrajectoryRecord) -> None:
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"""Persist a trajectory record to CH."""
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self._store.store_episode(
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policy_version=record.policy_version,
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scenario_id=record.scenario_id,
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seed=record.seed,
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pnl_bps=record.total_pnl_bps,
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max_drawdown_bps=record.max_drawdown_bps,
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fill_ratio=record.fill_count / max(record.fill_count + record.cancel_count + record.noop_count, 1),
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adverse_fill_ratio=0.0,
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avg_slippage_bps=0.0,
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liq_near_misses=0,
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cancel_count=record.cancel_count,
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diagnostics=json.dumps({
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"trajectory_id": record.trajectory_id,
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"steps": len(record.steps),
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"fill_count": record.fill_count,
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"noop_count": record.noop_count,
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"duration_ns": record.duration_ns,
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}),
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)
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def query_trajectories(
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self,
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policy_version: Optional[str] = None,
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scenario_id: Optional[str] = None,
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limit: int = 100,
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) -> str:
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"""Query stored trajectories."""
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where_parts = []
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if policy_version:
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where_parts.append(f"policy_version = '{policy_version}'")
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if scenario_id:
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where_parts.append(f"scenario_id = '{scenario_id}'")
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where = " AND ".join(where_parts) if where_parts else "1=1"
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return self._store.query(
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f"SELECT * FROM self_play_episodes WHERE {where} ORDER BY ts_ns DESC LIMIT {limit}"
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)
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