Gahow Wang
722cade974
Share point-in-time signals and a cash-funded order ledger across backtests and trading. Correct eligibility, missing-price fills, quality factors, regime inputs, warmup windows, and cash-injection accounting. Add reproducible registered-strategy comparisons and paired forward candidates with a 20% resize band. Validation: 125 Python tests, 23 web tests, TypeScript/Vite build, 87 strategy causality checks, and real-data target/order/equity parity for 10 strategies.
112 lines
6.7 KiB
Python
112 lines
6.7 KiB
Python
"""Fixed-budget validation of registered strategies on the shared execution engine."""
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from __future__ import annotations
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import copy
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import hashlib
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import json
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from datetime import datetime, timezone
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from pathlib import Path
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import pandas as pd
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import metrics
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import data_manager
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from backtest_runner import (ENGINE_VERSION, load_stock_panels, prepare_strategy, replay_prepared)
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from trader import make_strategy
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from strategies.robust_momentum import RobustMomentumStrategy
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from research.lab.trials import append_trials
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def _stats(equity):
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return {k: v for k, v in metrics.raw_summary(equity).items()
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if k in ('annualizedReturn', 'maxDrawdown', 'sharpeRatio', 'annualizedVolatility')}
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def evaluate_registered(args):
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market = 'cn' if args.market.startswith('cn') else 'us'
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destination = Path(args.output or f'data/research/results/registered_{market}')
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destination.mkdir(parents=True, exist_ok=True)
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panels = load_stock_panels(market, args.registered, None, refresh=False)
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end = args.end or str(panels['panels']['close'].index[-1].date())
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start = args.start or '2018-01-01'
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profiles = ['baseline', 'resize20', 'buffer3'] + (['cash_shy'] if market == 'us' else [])
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protocol = {
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'created_at': datetime.now(timezone.utc).isoformat(), 'engine': ENGINE_VERSION,
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'market': market, 'strategies': args.registered, 'start': start, 'end': end,
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'profiles': profiles, 'one_way_costs': [.001, .0025], 'capital': 10000 if market == 'us' else 70000,
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'selection_rule': 'CAGR improves at both costs; drawdown deterioration <= 2pp; improves in >=2/3 time blocks',
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'role': 'historical development comparison; not independent OOS; do not auto-promote',
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'source_hashes': {str(p): hashlib.sha256(p.read_bytes()).hexdigest()
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for p in [Path('execution.py'), Path('signals.py'), Path('backtest_runner.py')]
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+ sorted(Path('strategies').glob('*.py'))},
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'data_hashes': {str(p): hashlib.sha256(p.read_bytes()).hexdigest()
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for p in [Path('data') / f'{market}.csv', Path('data') / f'{market}_open.csv',
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Path('data/sp500_history.json' if market == 'us' else 'data/csi300_history.json')]},
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}
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protocol_path = destination / 'protocol.json'
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if protocol_path.exists():
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raise FileExistsError(f'Choose a new --output to preserve the completed study: {destination}')
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protocol_path.write_text(json.dumps(protocol, indent=2))
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rows, verdicts = [], []
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fees = {'tx_cost': .001, 'fixed_fee': 2 if market == 'us' else 5,
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'fee_base': 1.88 if market == 'us' else 0, 'fee_per_share': .009 if market == 'us' else 0}
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for name in args.registered:
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base_strategy = make_strategy(name, market)
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base_plan = prepare_strategy(name, market, panels['panels'], panels['stocks'], member=panels['member'],
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start=start, end=end, strategy=base_strategy)
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if base_plan['warmup_rows'] < 315:
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raise ValueError('Registered comparison requires >=315 pre-window sessions; move --start or add history')
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dates = base_plan['prices'].index
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cuts = [0, len(dates)//3, 2*len(dates)//3, len(dates)]
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for profile in profiles:
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plan, band = base_plan, .1
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if profile == 'resize20':
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band = .2
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elif profile == 'buffer3':
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if not isinstance(base_strategy, RobustMomentumStrategy):
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continue
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changed = copy.deepcopy(base_strategy); changed.buffer = 3.0
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plan = prepare_strategy(name, market, panels['panels'], panels['stocks'], member=panels['member'],
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start=start, end=end, strategy=changed)
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elif profile == 'cash_shy':
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if 'SHY' not in panels['panels']['close']:
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raise ValueError('SHY bars are required for a traded cash sleeve')
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plan = dict(base_plan)
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plan['prices'] = base_plan['prices'].copy()
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plan['prices']['SHY'] = panels['panels']['close']['SHY'].reindex(dates)
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if base_plan.get('volume') is not None:
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plan['volume'] = base_plan['volume'].copy()
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plan['volume']['SHY'] = panels['panels']['volume']['SHY'].reindex(dates)
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target = base_plan['targets'].reindex(columns=plan['prices'].columns, fill_value=0).copy()
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target['SHY'] += (1-target.sum(axis=1)).clip(lower=0)
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plan['targets'] = target
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for cost in protocol['one_way_costs']:
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run = replay_prepared(plan, protocol['capital'], **{**fees, 'tx_cost': cost}, resize_band=band)
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row = {'strategy': name, 'profile': profile, 'cost': cost, **_stats(run['equity']),
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'orders': len(run['trade_log']), 'fees': sum(t['commission'] for t in run['trade_log']),
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'minimum_cash': float(run['cash'].min()), 'blocks': []}
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for a, b in zip(cuts[:-1], cuts[1:]):
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row['blocks'].append(_stats(run['equity'].iloc[a:b+1]))
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rows.append(row)
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append_trials(destination/'trials.jsonl', [row], protocol)
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print(name, profile, cost, f"CAGR {row['annualizedReturn']:.2%}",
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f"DD {row['maxDrawdown']:.2%}", flush=True)
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baseline = {r['cost']: r for r in rows if r['strategy'] == name and r['profile'] == 'baseline'}
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for profile in profiles[1:]:
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candidates = [r for r in rows if r['strategy'] == name and r['profile'] == profile]
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if not candidates:
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continue
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passed = all(r['annualizedReturn'] > baseline[r['cost']]['annualizedReturn']
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and r['maxDrawdown'] >= baseline[r['cost']]['maxDrawdown']-.02
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and sum(a['annualizedReturn'] > b['annualizedReturn']
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for a,b in zip(r['blocks'],baseline[r['cost']]['blocks'])) >= 2
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for r in candidates)
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verdicts.append({'strategy': name, 'profile': profile,
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'verdict': 'candidate_for_forward_only' if passed else 'retain_baseline'})
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summary = {'protocol': protocol, 'comparisons': rows, 'decisions': verdicts,
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'boundary': 'Missing historical constituents and adjusted-share execution remain limitations.'}
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(destination/'results.json').write_text(json.dumps(summary, indent=2, allow_nan=False))
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pd.DataFrame([{k:v for k,v in r.items() if k!='blocks'} for r in rows]).to_csv(destination/'results.csv',index=False)
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print('Saved',destination/'results.json',flush=True)
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return summary
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