102 lines
4.2 KiB
Python
102 lines
4.2 KiB
Python
from __future__ import annotations
|
|
|
|
import json
|
|
import multiprocessing as mp
|
|
import sys
|
|
from concurrent.futures import ProcessPoolExecutor, as_completed
|
|
from dataclasses import asdict
|
|
from pathlib import Path
|
|
|
|
import pandas as pd
|
|
|
|
PACKAGE_PARENT = Path(__file__).resolve().parents[2]
|
|
if str(PACKAGE_PARENT) not in sys.path:
|
|
sys.path.insert(0, str(PACKAGE_PARENT))
|
|
|
|
from strategy32.research.soft_router import load_component_bundle, score_candidate
|
|
from strategy32.scripts.run_current_cash_learned_blocker import CACHE_PATH, LearnedBlockerCandidate
|
|
from strategy32.scripts.run_current_cash_learned_blocker_exact import _exact_period_worker
|
|
from strategy32.scripts.run_current_relaxed_hybrid_experiment import WINDOWS, YEAR_PERIODS, YTD_START
|
|
|
|
|
|
OUT_JSON = Path("/tmp/strategy32_current_cash_blocker_exact_candidates.json")
|
|
BASELINE_JSON = Path("/tmp/strategy32_live_combo_backtest.json")
|
|
def _candidate_space() -> list[LearnedBlockerCandidate]:
|
|
return [
|
|
LearnedBlockerCandidate(42, 8, 24, 1.0, -0.0025, 0.0),
|
|
LearnedBlockerCandidate(42, 12, 24, 1.0, -0.0025, 0.0),
|
|
LearnedBlockerCandidate(42, 18, 24, 1.0, -0.0025, 0.0),
|
|
LearnedBlockerCandidate(42, 12, 24, 20.0, -0.0025, 0.0),
|
|
LearnedBlockerCandidate(42, 18, 24, 20.0, -0.0025, 0.0),
|
|
LearnedBlockerCandidate(42, 12, 24, 1.0, -0.0025, 0.25),
|
|
LearnedBlockerCandidate(42, 12, 24, 1.0, -0.0025, 0.50),
|
|
]
|
|
|
|
|
|
def main() -> None:
|
|
_, latest_bar = load_component_bundle(CACHE_PATH)
|
|
period_specs: list[tuple[str, str, pd.Timestamp, pd.Timestamp]] = []
|
|
for days, label in WINDOWS:
|
|
period_specs.append(("window", label, latest_bar - pd.Timedelta(days=days), latest_bar))
|
|
for label, start, end_exclusive in YEAR_PERIODS:
|
|
period_specs.append(("year", label, start, min(latest_bar, end_exclusive - pd.Timedelta(seconds=1))))
|
|
period_specs.append(("year", "2026_YTD", YTD_START, latest_bar))
|
|
|
|
ctx = mp.get_context("fork")
|
|
results: list[dict[str, object]] = []
|
|
candidates = _candidate_space()
|
|
for idx, candidate in enumerate(candidates, start=1):
|
|
print(f"[candidate] {idx}/{len(candidates)} {candidate.name}", flush=True)
|
|
window_results: dict[str, dict[str, float]] = {}
|
|
year_results: dict[str, dict[str, float]] = {}
|
|
with ProcessPoolExecutor(max_workers=min(6, len(period_specs)), mp_context=ctx) as executor:
|
|
future_map = {
|
|
executor.submit(
|
|
_exact_period_worker,
|
|
CACHE_PATH,
|
|
candidate_payload=asdict(candidate),
|
|
kind=kind,
|
|
label=label,
|
|
start_text=str(start),
|
|
end_text=str(end),
|
|
): (kind, label)
|
|
for kind, label, start, end in period_specs
|
|
}
|
|
for future in as_completed(future_map):
|
|
kind, label, metrics = future.result()
|
|
if kind == "window":
|
|
window_results[label] = metrics
|
|
else:
|
|
year_results[label] = metrics
|
|
|
|
score, negative_years, mdd_violations = score_candidate(
|
|
{label: window_results[label] for _, label in WINDOWS},
|
|
{k: year_results[k] for k, _, _ in YEAR_PERIODS},
|
|
)
|
|
results.append(
|
|
{
|
|
"candidate": asdict(candidate),
|
|
"name": candidate.name,
|
|
"score": score,
|
|
"negative_years": negative_years,
|
|
"mdd_violations": mdd_violations,
|
|
"windows": {label: window_results[label] for _, label in WINDOWS},
|
|
"years": year_results,
|
|
}
|
|
)
|
|
|
|
results.sort(key=lambda item: float(item["score"]), reverse=True)
|
|
output = {
|
|
"analysis": "current_cash_learned_blocker_exact_candidates",
|
|
"latest_bar": str(latest_bar),
|
|
"results": results,
|
|
"baseline_exact": json.loads(BASELINE_JSON.read_text(encoding="utf-8")) if BASELINE_JSON.exists() else None,
|
|
}
|
|
OUT_JSON.write_text(json.dumps(output, indent=2), encoding="utf-8")
|
|
print(json.dumps(output["results"], indent=2))
|
|
print(f"[saved] {OUT_JSON}", flush=True)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|