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PythagorasGoal/scripts/analysis/partial_tp_ladder.py
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Adriano Dal Pastro 3accc91f84 docs(diary): exit ladder 80/20 con runner+stop dinamico SCARTATO (il runner non corre: TP=media, ~3% oltre TP, −38% ret su MR02 ETH)
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>
2026-06-04 19:48:45 +00:00

165 lines
6.8 KiB
Python

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