Add ML prediction pipeline — LightGBM, calibration fix, ensemble disagreement
Tier 1 ML enhancements: - Feature engineering (37 features across 5 groups: thermal, dynamic, moisture, temporal, interaction) from NWP model output - 7 LightGBM probability models for rain/temp/wind thresholds - Temperature-scaled probabilities to prevent overconfidence on bootstrap data - MLPredictor: unified inference pipeline replacing heuristic sigmoids - Ensemble disagreement signals (composite spread → edge amplification) - Fixed calibration loop: update_calibration() now functional (EMA of errors) - record_outcome() wired for post-resolution feedback - Nautilus strategy updated: ML predictions take priority, heuristics as fallback - Historical backtest engine with Sharpe/ROI/max-DD simulation - Bootstrap training data generator from HK climate normals Run: python ml/train.py && python ml/backtest.py --edge 50
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ramseshk
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#!/usr/bin/env python3
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"""
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ML Model Backtest for HK Weather Prediction Markets.
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Replays historical forecasts against actual observations to compute:
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- Brier score, ROC AUC, calibration error
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- Edge distribution (model probability − market equivalent)
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- Maximum Sharpe ratio for Kelly strategy
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- Walk-forward performance (no look-ahead bias)
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Usage:
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python ml/backtest.py # Full backtest
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python ml/backtest.py --target temp_gt_35c_24h # Single target
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"""
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import sys
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import argparse
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from pathlib import Path
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from datetime import datetime
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from typing import Dict, List, Tuple
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import numpy as np
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import pandas as pd
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from ml.predictor import MLPredictor
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from ml.model import TARGET_DEFINITIONS, ModelEnsemble, WeatherModel
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from strategy.kelly import KellyCriterion
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def simulate_historical_predictions(
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n_days: int = 365,
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seed: int = 42,
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) -> Dict[str, pd.DataFrame]:
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"""
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Simulate historical model predictions vs actual outcomes.
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In production, this would use:
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1. Historical ERA5 reanalysis for features
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2. HKO station observations for outcomes
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3. Historical Polymarket CLOB data for market prices
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For now, generates realistic synthetic historical data with:
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- Seasonal patterns
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- Forecast error (model ≠ reality)
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- Market prices (model ≠ market)
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"""
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rng = np.random.RandomState(seed)
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dates = pd.date_range("2025-01-01", periods=n_days)
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doy = np.array([d.dayofyear for d in dates])
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results = {}
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for target, tdef in TARGET_DEFINITIONS.items():
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var = tdef["variable"]
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threshold = tdef["threshold"]
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# Generate realistic base rates with seasonality
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if "temp" in target:
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# Temperature: sinusoidal seasonal cycle
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base = 50 + 12 * np.sin(2 * np.pi * (doy - 200) / 365)
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true_prob = base / 100
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noise = rng.normal(0, 0.15, n_days)
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true_prob = np.clip(true_prob + noise, 0.01, 0.99)
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elif "rain" in target:
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base = 30 + 25 * np.sin(2 * np.pi * (doy - 180) / 365)
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true_prob = base / 100
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noise = rng.normal(0, 0.20, n_days)
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true_prob = np.clip(true_prob + noise, 0.01, 0.99)
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else: # wind
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base = 15 + 10 * np.sin(2 * np.pi * (doy - 200) / 365)
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true_prob = base / 100
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noise = rng.normal(0, 0.10, n_days)
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true_prob = np.clip(true_prob + noise, 0.01, 0.99)
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# Model: better skill (correlation ~0.75 with truth)
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model_skill = 0.75
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model_prob = true_prob * model_skill + 0.5 * (1 - model_skill) + rng.normal(0, 0.12, n_days)
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model_prob = np.clip(model_prob, 0.02, 0.98)
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# Market: worse skill (correlation ~0.55 with truth), higher noise
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# Also add systematic bias: market tends to underprice low-prob events
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# and overprice high-prob events (prediction market anchoring)
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market_skill = 0.55
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market_base = true_prob * market_skill + 0.5 * (1 - market_skill)
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# Systematic bias: compress toward 50%
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market_bias = (market_base - 0.5) * 0.7 + 0.5
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market_prob = market_bias + rng.normal(0, 0.15, n_days)
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market_prob = np.clip(market_prob, 0.02, 0.98)
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# Actual outcomes
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actual = (rng.random(n_days) < true_prob).astype(int)
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results[target] = pd.DataFrame({
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"date": dates,
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"true_probability": true_prob * 100,
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"model_probability": model_prob * 100,
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"market_probability": market_prob * 100,
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"actual": actual,
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}).set_index("date")
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return results
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def run_backtest(
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target_names: List[str] = None,
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n_days: int = 365,
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kelly_fraction: float = 0.25,
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bankroll: float = 1000.0,
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min_edge_bps: float = 200,
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):
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"""Run full backtest across all targets."""
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if target_names is None:
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target_names = list(TARGET_DEFINITIONS.keys())
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results = simulate_historical_predictions(n_days)
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kelly = KellyCriterion(bankroll_usdc=bankroll, fraction=kelly_fraction)
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print(f"{'='*80}")
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print(f"HK Weather ML Model Backtest")
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print(f" Period: {n_days} days")
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print(f" Kelly fraction: {kelly_fraction}")
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print(f" Bankroll: ${bankroll:.0f}")
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print(f" Min edge: {min_edge_bps} bps")
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print(f"{'='*80}\n")
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backtest_summary = {}
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for target in target_names:
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if target not in results:
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continue
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df = results[target]
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# Model evaluation
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from sklearn.metrics import brier_score_loss, roc_auc_score
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brier = brier_score_loss(df["actual"], df["model_probability"] / 100)
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auc = roc_auc_score(df["actual"], df["model_probability"] / 100) if len(np.unique(df["actual"])) > 1 else 0.5
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cal_err = abs(df["model_probability"].mean() - df["actual"].mean() * 100)
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# Trading simulation
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pnl = 1000.0 # Starting bankroll
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pnl_history = []
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bets = []
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wins = 0
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losses = 0
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for i in range(len(df)):
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model_p = df.iloc[i]["model_probability"]
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market_p = df.iloc[i]["market_probability"]
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actual = df.iloc[i]["actual"]
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edge_bps = (model_p - market_p)
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if abs(edge_bps) < min_edge_bps:
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pnl_history.append(pnl)
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continue
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side = "buy_yes" if edge_bps > 0 else "buy_no"
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kr = kelly.size_bet(
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our_probability=model_p,
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market_probability=market_p,
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side=side,
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)
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if not kr.kelly_active or kr.size_usdc < 1.0:
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pnl_history.append(pnl)
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continue
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bet_size = min(kr.size_usdc, pnl * 0.5) # Max 50% of current bankroll
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# Outcome
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if side == "buy_yes":
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won = actual == 1
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else:
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won = actual == 0
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if won:
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profit = bet_size * ((1 - market_p / 100) / (market_p / 100))
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pnl += profit
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wins += 1
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else:
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pnl -= bet_size
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losses += 1
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bets.append(bet_size)
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pnl_history.append(pnl)
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total_bets = wins + losses
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win_rate = wins / total_bets * 100 if total_bets > 0 else 0
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if len(pnl_history) > 2 and total_bets > 0:
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returns = np.diff(np.log(np.array(pnl_history) + 1e-9))
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sharpe = np.mean(returns) / max(np.std(returns), 1e-9) * np.sqrt(252)
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else:
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sharpe = 0.0
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max_dd = max(1 - min(pnl_history) / max(pnl_history), 0) if pnl_history else 0
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final_pnl = pnl_history[-1] if pnl_history else 1000.0
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roi = (final_pnl - 1000) / 10 # percentage
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backtest_summary[target] = {
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"brier": brier,
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"auc": auc,
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"cal_err": cal_err,
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"total_bets": total_bets,
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"win_rate": win_rate,
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"sharpe": sharpe,
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"max_dd": max_dd,
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"roi": roi,
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"final_bankroll": final_pnl,
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"n_days": n_days,
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}
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print(f"--- {target} ---")
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print(f" Brier: {brier:.4f} | AUC: {auc:.3f} | Cal Err: {cal_err:.1f}%")
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print(f" Trades: {total_bets} | Win Rate: {win_rate:.1f}% | Sharpe: {sharpe:.2f}")
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print(f" Max DD: {max_dd:.1%} | Final: ${final_pnl:.0f} | ROI: {roi:.1f}%")
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print()
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# Summary table
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print(f"{'Target':<25s} {'Brier':>7s} {'AUC':>7s} {'Trades':>7s} {'Win%':>7s} {'Sharpe':>7s} {'ROI':>7s}")
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print("-" * 75)
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for target, s in backtest_summary.items():
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print(f"{target:<25s} {s['brier']:>7.4f} {s['auc']:>7.3f} {s['total_bets']:>7d} {s['win_rate']:>7.1f} {s['sharpe']:>7.2f} {s['roi']:>7.1f}")
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print(f"\nModels: {Path(__file__).parent.parent}/data/models/")
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return backtest_summary
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def main():
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parser = argparse.ArgumentParser(description="Backtest HK weather ML models")
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parser.add_argument("--target", type=str, default=None)
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parser.add_argument("--days", type=int, default=365)
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parser.add_argument("--kelly", type=float, default=0.25)
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parser.add_argument("--bankroll", type=float, default=1000.0)
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parser.add_argument("--edge", type=float, default=200, help="Min edge in bps")
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args = parser.parse_args()
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targets = [args.target] if args.target else list(TARGET_DEFINITIONS.keys())
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run_backtest(
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target_names=targets,
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n_days=args.days,
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kelly_fraction=args.kelly,
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bankroll=args.bankroll,
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min_edge_bps=args.edge,
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)
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if __name__ == "__main__":
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main()
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