c6b7a41bb4
1. Vectorized reward path (cwm/core.py): - Wired up existing compute_reward_vectorized from numba_core (was unused!) - Eliminates FeatureVector dict allocation + Python dict lookups on hot path - Numba path used when _HAS_NUMBA=True, Python fallback otherwise - Bit-identical: same math operations, just via numba JIT 2. Ray-based parallel eval (training/ray_eval.py): - Industrial multi-core execution via Ray (used by OpenAI/Anyscale) - ray.put() stores params/scenarios in shared object store (no pickle per worker) - Each worker: own CWM + planner, zero shared state, no races - Bit-identical: same seed + same params = same results regardless of worker count - PolicyEvaluator.evaluate_candidate: new use_ray=True parameter 3. VBT post-analysis (training/vbt_analysis.py): - episodes_to_pnl_array, episodes_to_metrics (Sharpe, Sortino, VaR, win_rate, etc.) - cross_asset_comparison, parameter_sensitivity - format_metrics for human-readable output - Analysis tool only — runs AFTER engine produces results 4. numba_core.py: added missing 'import math' for compute_reward_vectorized 13 new tests: vectorized reward bit-identity, Ray determinism, Ray result fields, VBT metrics structure, cross-asset comparison, parameter sensitivity, edge cases. Total: 1178 tests, 50 files, all green, zero regressions.
124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
"""
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VBT Post-Simulation Analysis — vectorized trade analysis and metrics.
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Uses VectorBT for analyzing MALKHUT's trade records after the engine
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produces results. This is an ANALYSIS tool, not an engine component.
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VBT is used here for:
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- Trade record → performance metrics (Sharpe, Sortino, VaR, CVaR)
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- Multi-asset equity curves and comparisons
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- Parameter sensitivity visualization
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- Portfolio-level risk analysis
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Usage:
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from malkhut.training.vbt_analysis import analyze_episodes
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metrics = analyze_episodes(results)
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional, Sequence
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import numpy as np
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from malkhut.training.cma_trainer import EpisodeResult
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def episodes_to_pnl_array(
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results: Sequence[EpisodeResult],
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) -> np.ndarray:
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"""Convert episode results to a flat PnL array."""
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return np.array([r.pnl_bps for r in results], dtype=np.float64)
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def episodes_to_metrics(results: Sequence[EpisodeResult]) -> Dict[str, float]:
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"""Compute standard performance metrics from episode results.
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Returns dict with: total_pnl, mean_pnl, max_drawdown, sharpe, sortino,
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win_rate, profit_factor, fill_ratio, avg_slippage.
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"""
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if not results:
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return {}
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pnls = episodes_to_pnl_array(results)
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n = len(pnls)
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total_pnl = float(np.sum(pnls))
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mean_pnl = float(np.mean(pnls))
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std_pnl = float(np.std(pnls, ddof=1)) if n > 1 else 0.0
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# Sharpe (annualized, assuming ~30 trading days)
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sharpe = (mean_pnl / std_pnl * np.sqrt(30)) if std_pnl > 0 else 0.0
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# Sortino (downside deviation)
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downside = pnls[pnls < 0]
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downside_std = float(np.std(downside, ddof=1)) if len(downside) > 1 else 0.0
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sortino = (mean_pnl / downside_std * np.sqrt(30)) if downside_std > 0 else 0.0
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# Max drawdown (cumulative)
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cum_pnl = np.cumsum(pnls)
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peak = np.maximum.accumulate(cum_pnl)
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drawdowns = peak - cum_pnl
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max_dd = float(np.max(drawdowns)) if len(drawdowns) > 0 else 0.0
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# Win rate
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wins = np.sum(pnls > 0)
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win_rate = float(wins / n) if n > 0 else 0.0
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# Profit factor
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gross_profit = float(np.sum(pnls[pnls > 0])) if np.any(pnls > 0) else 0.0
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gross_loss = float(np.abs(np.sum(pnls[pnls < 0]))) if np.any(pnls < 0) else 1e-12
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profit_factor = gross_profit / gross_loss if gross_loss > 0 else float("inf")
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# Fill ratio
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fill_ratios = [r.fill_ratio for r in results]
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avg_fill_ratio = sum(fill_ratios) / len(fill_ratios) if fill_ratios else 0.0
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# Tail risk
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tail_losses = [r.tail_loss_bps for r in results if r.tail_loss_bps < 0]
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worst_tail = min(tail_losses) if tail_losses else 0.0
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return {
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"total_pnl_bps": total_pnl,
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"mean_pnl_bps": mean_pnl,
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"std_pnl_bps": std_pnl,
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"sharpe_ratio": sharpe,
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"sortino_ratio": sortino,
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"max_drawdown_bps": max_dd,
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"win_rate": win_rate,
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"profit_factor": profit_factor,
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"avg_fill_ratio": avg_fill_ratio,
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"worst_tail_bps": worst_tail,
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"n_episodes": n,
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}
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def cross_asset_comparison(
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results_by_asset: Dict[str, Sequence[EpisodeResult]],
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) -> Dict[str, Dict[str, float]]:
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"""Compare performance across assets."""
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comparisons = {}
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for asset, results in results_by_asset.items():
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comparisons[asset] = episodes_to_metrics(results)
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return comparisons
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def parameter_sensitivity(
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results_by_param: Dict[str, Sequence[EpisodeResult]],
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) -> Dict[str, float]:
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"""Compare performance across parameter variations."""
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sensitivities = {}
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for param_key, results in results_by_param.items():
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metrics = episodes_to_metrics(results)
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sensitivities[param_key] = metrics.get("mean_pnl_bps", 0.0)
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return sensitivities
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def format_metrics(metrics: Dict[str, float]) -> str:
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"""Pretty-print metrics dict."""
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lines = []
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for key, val in sorted(metrics.items()):
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if isinstance(val, float):
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lines.append(f" {key:25s} {val:>12.2f}")
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else:
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lines.append(f" {key:25s} {val:>12}")
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return "\n".join(lines)
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