Adriano Dal Pastro
23b7273e71
scripts/run_paper_trading.py: AssetConfig ETH ora usa timeframe="5m" invece del default 1h. Il winner c04dff7086 e' stato trovato dal GA su dati 5m e a 1h la strategia perde: - ETH @ 5m (native): +359.50% cum 7y, 77% winrate, max DD/yr 19% - ETH @ 1h (precedente): -33.03% cum 7y, 67% winrate, max DD 74% BTC resta a 1h (winner 238e4812 native a 1h, +104% 7y, Sharpe 2+ in 3 anni). Nuovi script di analisi: - scripts/yearly_strategies.py: breakdown per anno (2019-2025) di 4 strategie su tick di discovery (trade/winrate/return/maxDD/Sharpe). - scripts/multi_tick_strategies.py: confronto cross-tick (5m/15m/1h) per i 2 winner correnti. Documenta la divergenza tick-paper di ETH. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
113 lines
4.8 KiB
Python
113 lines
4.8 KiB
Python
"""Per-year breakdown delle 4 strategie: 2 NEW (BTC 238e4812 + ETH c04dff7086)
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+ 2 OLD freezate (btc_9cf506b8 hardened-001 + eth_facd6af85d5d).
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Backtest anno-per-anno (2019-2025) sul tick di discovery di ciascuna strategia.
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Output: trade, wins/losses, win%, return%, max DD%, Sharpe per ogni anno.
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"""
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from __future__ import annotations
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from datetime import datetime
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from pathlib import Path
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from multi_swarm_core.backtest.engine import BacktestEngine
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from multi_swarm_core.cerbero.client import CerberoClient
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from multi_swarm_core.config import load_settings
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from multi_swarm_core.data.cerbero_ohlcv import CerberoOHLCVLoader, OHLCVRequest
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from multi_swarm_core.metrics.basic import max_drawdown, sharpe_ratio, total_return
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from multi_swarm_core.protocol.compiler import compile_strategy
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from multi_swarm_core.protocol.parser import parse_strategy
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STRATEGIES = [
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# (label, path, symbol, timeframe)
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("BTC NEW (238e4812, paper attuale)", "btc_238e4812.json", "BTC-PERPETUAL", "1h"),
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("BTC OLD (9cf506b8, hardened-001 prev paper)", "archive/btc_9cf506b8.json", "BTC-PERPETUAL", "1h"),
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("ETH NEW (c04dff7086, paper attuale)", "eth_c04dff7086.json", "ETH-PERPETUAL", "5m"),
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("ETH OLD (facd6af85d5d, prev paper)", "archive/eth_facd6af85d5d.json", "ETH-PERPETUAL", "1h"),
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]
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YEARS = [
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("2019", "2019-01-01T00:00:00+00:00", "2020-01-01T00:00:00+00:00"),
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("2020", "2020-01-01T00:00:00+00:00", "2021-01-01T00:00:00+00:00"),
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("2021", "2021-01-01T00:00:00+00:00", "2022-01-01T00:00:00+00:00"),
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("2022", "2022-01-01T00:00:00+00:00", "2023-01-01T00:00:00+00:00"),
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("2023", "2023-01-01T00:00:00+00:00", "2024-01-01T00:00:00+00:00"),
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("2024", "2024-01-01T00:00:00+00:00", "2025-01-01T00:00:00+00:00"),
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("2025", "2025-01-01T00:00:00+00:00", "2026-01-01T00:00:00+00:00"),
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]
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def main() -> None:
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settings = load_settings()
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token = (
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settings.cerbero_mainnet_token.get_secret_value()
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if settings.cerbero_mainnet_token
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else settings.cerbero_testnet_token.get_secret_value()
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)
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cerbero = CerberoClient(
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base_url=settings.cerbero_base_url,
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token=token,
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bot_tag=settings.cerbero_bot_tag,
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)
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loader = CerberoOHLCVLoader(client=cerbero, cache_dir=settings.series_dir)
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engine = BacktestEngine(fees_bp=5.0)
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strategies_dir = Path("/app/strategies")
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for label, fname, symbol, timeframe in STRATEGIES:
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path = strategies_dir / fname
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strat = parse_strategy(path.read_text())
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# Carica intero range una volta sola
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ohlcv = loader.load(OHLCVRequest(
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symbol=symbol, timeframe=timeframe,
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start=datetime.fromisoformat("2018-09-01T00:00:00+00:00"),
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end=datetime.fromisoformat("2026-01-01T00:00:00+00:00"),
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))
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print(f"\n{'=' * 110}")
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print(f">>> {label}")
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print(f" symbol={symbol} timeframe={timeframe} | {len(ohlcv)} bars total")
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print(f" {'year':<6} {'bars':>6} {'trades':>7} {'wins':>5} {'losses':>7} {'win%':>6} {'avg_w':>10} {'avg_l':>10} {'ret':>8} {'maxDD':>7} {'sharpe':>7}")
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sum_ret = 0.0
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sum_trades = 0
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sum_wins = 0
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for year_label, start, end in YEARS:
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mask = (ohlcv.index >= datetime.fromisoformat(start)) & (ohlcv.index < datetime.fromisoformat(end))
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slice_df = ohlcv[mask]
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if len(slice_df) == 0:
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continue
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try:
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signal_fn = compile_strategy(strat)
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signals = signal_fn(slice_df)
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bt = engine.run(slice_df, signals)
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except Exception as e:
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print(f" {year_label:<6} ERROR: {e}")
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continue
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trades = bt.trades
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n = len(trades)
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wins = [t.net_pnl for t in trades if t.net_pnl > 0]
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losses = [t.net_pnl for t in trades if t.net_pnl <= 0]
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nw, nl = len(wins), len(losses)
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wr = (nw / n * 100) if n else 0.0
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aw = (sum(wins) / nw) if nw else 0.0
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al = (sum(losses) / nl) if nl else 0.0
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if n > 0:
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notional = float(slice_df["close"].iloc[0])
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eq = (bt.equity_curve / notional) + 1.0
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ret = total_return(eq)
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dd = max_drawdown(eq)
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sr = sharpe_ratio(bt.returns, periods_per_year=8760)
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else:
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ret = dd = sr = 0.0
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print(f" {year_label:<6} {len(slice_df):>6} {n:>7} {nw:>5} {nl:>7} {wr:>5.1f}% {aw:>10.1f} {al:>10.1f} {ret:>7.2%} {dd:>6.2%} {sr:>7.3f}")
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sum_ret += ret
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sum_trades += n
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sum_wins += nw
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overall_wr = (sum_wins / sum_trades * 100) if sum_trades else 0.0
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print(f" {'='*5} TOTALS 7y: {sum_trades:>7} {sum_wins:>5} {sum_trades-sum_wins:>7} {overall_wr:>5.1f}% cum_ret={sum_ret:+.2%}")
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if __name__ == "__main__":
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main()
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