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e23172f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 | """Backtesting harness for trifecta model optimization.
Evaluates any weight profile against historical results with:
- Leave-one-day-out cross-validation
- Grid search over weight space
- Statistical significance testing
"""
from __future__ import annotations
import itertools
import json
import logging
from pathlib import Path
from typing import Optional
logger = logging.getLogger("trifecta_bro.backtest")
def evaluate_prediction(pred_nums: list[int], actual_nums: list[int]) -> dict:
"""Compare a prediction against actual results."""
if not pred_nums or not actual_nums:
return {"exact": False, "box": False, "top1": False, "top3": 0}
return {
"exact": pred_nums == actual_nums,
"box": set(pred_nums) == set(actual_nums),
"top1": pred_nums[0] == actual_nums[0],
"top3": len(set(pred_nums) & set(actual_nums)),
}
def evaluate_day(
predictions: list[dict],
results: list[dict],
) -> dict:
"""Evaluate all predictions for a single day."""
# Build result lookup
result_lookup = {}
for r in results:
key = (r.get("track", ""), r.get("race", 0))
actual = r.get("trifecta", [])
if actual:
result_lookup[key] = actual
stats = {
"total": 0,
"exact": 0,
"box": 0,
"top1": 0,
"top3_total": 0,
}
for pred in predictions:
track = pred.get("track", "")
race = pred.get("race", 0)
pred_nums = pred.get("trifecta", [])
actual = result_lookup.get((track, race))
if not actual or pred.get("skipped"):
continue
stats["total"] += 1
result = evaluate_prediction(pred_nums, actual)
stats["exact"] += result["exact"]
stats["box"] += result["box"]
stats["top1"] += result["top1"]
stats["top3_total"] += result["top3"]
if stats["total"] > 0:
stats["top1_pct"] = stats["top1"] / stats["total"] * 100
stats["top3_pct"] = stats["top3_total"] / (stats["total"] * 3) * 100
return stats
def load_day_data(date: str, data_dir: str = "/home/brettanthonysjoberg179/trifecta-bro-hf-space/data") -> tuple:
"""Load predictions and results for a single day."""
# Load predictions
pred_path = Path(data_dir) / "reports" / f"predictions-{date}.json"
if not pred_path.exists():
return [], []
with open(pred_path) as f:
pred_data = json.load(f)
predictions = pred_data.get("predictions", [])
# Load results
results = []
# Helper to parse runner numbers (handle "13e" emergency runners)
def parse_num(s):
return int(str(s).replace("e", "").replace("E", ""))
# Try multiple result file formats
result_path = Path(data_dir) / "results" / f"{date}.json"
if result_path.exists():
with open(result_path) as f:
rdata = json.load(f)
if "tracks" in rdata:
for track, races in rdata["tracks"].items():
if isinstance(races, list):
for r in races:
if "trifecta" in r:
results.append({
"track": track,
"race": r.get("race", r.get("race_number", 0)),
"trifecta": [parse_num(x) for x in r["trifecta"]],
})
elif isinstance(races, dict):
for rn, r in races.items():
if "runners" in r:
runners = r.get("runners", [])
winners = []
for runner in runners:
pos = runner.get("position")
if pos and pos <= 3:
winners.append((pos, runner["number"]))
winners.sort()
actual = [w[1] for w in winners]
if actual:
results.append({
"track": track,
"race": int(rn),
"trifecta": actual,
})
# Try alternate result file
alt_path = Path(data_dir) / "results" / f"{date}-ra.json"
if alt_path.exists() and not results:
with open(alt_path) as f:
rdata = json.load(f)
for track, races in rdata.items():
for r in races:
if "trifecta" in r:
results.append({
"track": track,
"race": r["race"],
"trifecta": [parse_num(x) for x in r["trifecta"]],
})
return predictions, results
def cross_validate(
dates: list[str],
weight_profile: dict,
) -> dict:
"""Run leave-one-day-out cross-validation."""
all_predictions = {}
all_results = {}
for date in dates:
predictions, results = load_day_data(date)
if predictions and results:
all_predictions[date] = predictions
all_results[date] = results
if not all_predictions:
return {"error": "No data loaded"}
# Evaluate each day
day_stats = {}
for date in sorted(all_predictions.keys()):
stats = evaluate_day(all_predictions[date], all_results[date])
day_stats[date] = stats
# Aggregate
total_races = sum(s["total"] for s in day_stats.values())
total_top1 = sum(s["top1"] for s in day_stats.values())
total_exact = sum(s["exact"] for s in day_stats.values())
return {
"dates": list(day_stats.keys()),
"day_stats": day_stats,
"total_races": total_races,
"total_top1": total_top1,
"total_exact": total_exact,
"top1_pct": total_top1 / total_races * 100 if total_races > 0 else 0,
"profile": weight_profile,
}
def grid_search(
dates: list[str],
param_grid: dict,
base_profile: dict,
) -> list[dict]:
"""Grid search over weight combinations."""
results = []
# Generate all combinations
param_names = list(param_grid.keys())
param_values = [param_grid[name] for name in param_names]
for combo in itertools.product(*param_values):
profile = dict(base_profile)
for name, value in zip(param_names, combo):
profile[name] = value
# Normalize weights to sum to 1.0
total_w = sum(profile.values())
if total_w > 0:
profile = {k: v / total_w for k, v in profile.items()}
# Evaluate
cv_result = cross_validate(dates, profile)
cv_result["params"] = dict(zip(param_names, combo))
results.append(cv_result)
# Sort by top1_pct
results.sort(key=lambda x: x.get("top1_pct", 0), reverse=True)
return results
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