3accc91f84
Test onesto scripts/analysis/partial_tp_ladder.py: stessi segnali, engine intrabar fade_base, fee identiche. Il ladder compra win-rate/DD pagando i winner migliori — stesso profilo del vol-target gia' scartato. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
165 lines
6.8 KiB
Python
165 lines
6.8 KiB
Python
"""Exit 'ladder' per le fade: al tocco di una frazione f del TP esce la quota q,
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il runner (1-q) corre con STOP DINAMICO bloccato alla soglia (profitto lockato).
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Proposta 2026-06-04 ("e se all'80% del TP usciamo con 80% e mettiamo un SL
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dinamico su quella soglia e lo lasciamo correre?"). Confronto ONESTO con l'exit
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canonico (TP pieno al livello) sugli STESSI segnali, stesso engine intrabar di
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fade_base (ingresso a close[i], SL prioritario nello stesso bar, fee 0.10% RT
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x leva su tutto il notional — il ladder NON paga fee extra: due uscite ma
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stesso notional totale).
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Convenzioni intrabar del ladder (oneste/conservative):
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- SL pieno prioritario sulla soglia nello stesso bar;
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- se il bar che tocca la soglia CHIUDE oltre la soglia (rientro), il runner
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si considera stoppato subito alla soglia (non gli si regala il bar);
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- il runner non ha TP (corre), esce su ri-tocco della soglia o a max_bars.
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Gate (metodologia repo): il ladder deve migliorare ret E DD (o chiaramente il
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rischio/rendimento) su ENTRAMBI gli asset, full E OOS, per tutte e 3 le fade.
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uv run python scripts/analysis/partial_tp_ladder.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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from src.data.downloader import load_data # noqa: E402
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from src.live.strategy_loader import load_strategy # noqa: E402
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LIVE_PARAMS = dict(trend_max=3.0, ema_long=200, hurst_max=0.55, min_tp_frac=0.0015)
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OOS_START = pd.Timestamp("2023-11-01", tz="UTC")
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LEV, POS, FEE_RT = 3.0, 0.15, 0.001
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CODES = ["MR01_bollinger_fade", "MR02_donchian_fade", "MR07_return_reversal"]
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def simulate(signals, df, ts, policy: str, f: float = 0.8, q: float = 0.8,
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start=None) -> dict:
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"""Replay intrabar degli stessi segnali con exit 'base' o 'ladder'."""
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h, l, c = df["high"].values, df["low"].values, df["close"].values
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n = len(c)
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fee = FEE_RT * LEV
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capital = peak = 1000.0
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max_dd = 0.0
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last_exit = -1
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trades = wins = 0
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runner_beyond_tp = runner_stopped = 0
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rets = []
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for sig in signals:
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i, d = sig.idx, sig.direction
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if start is not None and ts.iloc[i] < start:
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continue
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if i <= last_exit or i + 1 >= n:
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continue
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entry = c[i]
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tp, sl, mb = sig.metadata["tp"], sig.metadata["sl"], sig.metadata["max_bars"]
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j = i
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if policy == "base":
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exit_p = c[min(i + mb, n - 1)]
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for step in range(1, mb + 1):
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j = i + step
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if j >= n:
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j = n - 1; exit_p = c[j]; break
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hit_sl = (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl)
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hit_tp = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp)
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if hit_sl:
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exit_p = sl; break
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if hit_tp:
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exit_p = tp; break
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if step == mb:
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exit_p = c[j]
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ret = (exit_p - entry) / entry * d * LEV - fee
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else: # ladder
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th = entry + f * (tp - entry) # soglia f del percorso verso il TP
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p1 = p2 = None # fill quota q / runner (1-q)
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state = "full"
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for step in range(1, mb + 1):
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j = i + step
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if j >= n:
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j = n - 1
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if p1 is None:
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p1 = c[j]
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p2 = c[j]
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break
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if state == "full":
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hit_sl = (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl)
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hit_th = (d == 1 and h[j] >= th) or (d == -1 and l[j] <= th)
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if hit_sl: # SL pieno prioritario (conservativo)
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p1 = p2 = sl; break
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if hit_th:
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p1 = th; state = "runner"
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# il bar chiude oltre la soglia -> runner stoppato subito
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if (d == 1 and c[j] < th) or (d == -1 and c[j] > th):
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p2 = th; runner_stopped += 1; break
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if step == mb:
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p2 = c[j]; break
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continue
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if step == mb:
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p1 = p2 = c[j]; break
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else: # runner: stop dinamico = soglia
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hit_stop = (d == 1 and l[j] <= th) or (d == -1 and h[j] >= th)
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if hit_stop:
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p2 = th; runner_stopped += 1; break
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if step == mb:
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p2 = c[j]; break
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if p2 is not None and (p2 - tp) * d > 0:
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runner_beyond_tp += 1
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ret = (q * (p1 - entry) + (1 - q) * (p2 - entry)) / entry * d * LEV - fee
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capital = max(capital + capital * POS * ret, 10.0)
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peak = max(peak, capital)
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max_dd = max(max_dd, (peak - capital) / peak)
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last_exit = j
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trades += 1
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wins += ret > 0
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rets.append(ret)
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if trades == 0:
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return {}
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rets = np.array(rets)
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return {
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"ret_pct": (capital / 1000.0 - 1) * 100,
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"dd_pct": max_dd * 100,
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"trades": trades,
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"win_pct": wins / trades * 100,
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"avg_ret_bps": rets.mean() * 1e4,
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"sharpe_t": rets.mean() / rets.std() * np.sqrt(len(rets)) if rets.std() else 0,
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"runner_beyond_tp": runner_beyond_tp,
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"runner_stopped": runner_stopped,
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}
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def main() -> None:
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variants = [("base", None, None), ("ladder", 0.8, 0.8),
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("ladder", 0.8, 0.5), ("ladder", 0.5, 0.5)]
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for code in CODES:
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strat = load_strategy(code)
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for asset in ("BTC", "ETH"):
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df = load_data(asset, "1h")
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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signals = strat.generate_signals(df, ts, **LIVE_PARAMS)
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print(f"\n=== {code} {asset} 1h — {len(signals)} segnali (params live) ===")
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print(f"{'policy':<16}{'periodo':<6}{'ret%':>10}{'DD%':>8}{'win%':>7}"
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f"{'avg bps':>9}{'Sh(t)':>7}{'n':>6}{'run>TP':>8}{'run-stop':>9}")
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for policy, f, q in variants:
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tag = "base" if policy == "base" else f"ladder f{f} q{q}"
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for label, start in (("FULL", None), ("OOS", OOS_START)):
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r = simulate(signals, df, ts, policy, f or 0.8, q or 0.8, start)
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if not r:
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continue
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print(f"{tag:<16}{label:<6}{r['ret_pct']:>10.0f}{r['dd_pct']:>8.1f}"
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f"{r['win_pct']:>7.1f}{r['avg_ret_bps']:>9.1f}{r['sharpe_t']:>7.2f}"
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f"{r['trades']:>6}{r['runner_beyond_tp']:>8}{r['runner_stopped']:>9}")
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if __name__ == "__main__":
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main()
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