Adriano Dal Pastro
ca88e62a11
oos_validation.py: backtest OOS fedele al worker live (non-overlap, hold, stop, fee, leva) su finestra held-out. Mostra che l'edge storico 76-79% e' un artefatto di look-ahead (ingresso a close[i-1]) e che nessuna regola di direzione onesta supera il lancio di moneta; le fee sono secondarie (4/6 config perdono anche a fee zero). intrabar_test.py: ingresso intra-barra su 5m vs close 15m a parita' di exit. Lo "scatto" del breakout e' avverso (rientro immediato alla media), quindi la granularita' piu' fine non recupera edge. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
260 lines
9.9 KiB
Python
260 lines
9.9 KiB
Python
"""Validazione out-of-sample fee-aware di tutte le strategie live.
|
|
|
|
Per ognuna delle 6 config in strategies.yml:
|
|
- split temporale held-out (train = primi (1-test_frac), test = ultimo test_frac)
|
|
- ML01 (SignalEngine): allena sul train, predice sul test (come il worker live)
|
|
- rule-based: i segnali sono causali, si valutano quelli nella finestra test
|
|
- simulazione fedele al worker live: una posizione per volta (non-overlap),
|
|
uscita a `hold` barre o stop a -2%, fee round-trip e leva inclusi
|
|
|
|
Stampa, per ogni config: numero trade nel test, win% lordo e netto, return netto,
|
|
costo commissioni, e confronto lordo-vs-netto per isolare l'impatto delle fee.
|
|
Usa i parquet locali (data/raw), nessuna chiamata di rete.
|
|
"""
|
|
from __future__ import annotations
|
|
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import yaml
|
|
|
|
PROJECT_ROOT = Path(__file__).resolve().parents[2]
|
|
sys.path.insert(0, str(PROJECT_ROOT))
|
|
|
|
from src.data.downloader import load_data
|
|
from src.live.strategy_loader import load_strategy
|
|
from src.live.signal_engine import SignalEngine, keltner_ratio, build_features
|
|
|
|
TEST_FRAC = 0.30
|
|
STOP_PCT = -0.02
|
|
|
|
|
|
def simulate(entries: list[tuple[int, int]], close: np.ndarray, hold: int,
|
|
fee_rt: float, lev: float, pos: float,
|
|
initial: float = 1000.0, entry_offset: int = 0) -> dict:
|
|
"""FSM fedele al worker live: non-overlap, hold N barre o stop -2%.
|
|
|
|
entry_offset: 0 = ingresso a close[i] (worker live); 1 = close[i-1]
|
|
(convenzione del backtest storico, che conosce la direzione di barra i).
|
|
"""
|
|
n = len(close)
|
|
capital = peak = initial
|
|
max_dd = 0.0
|
|
fees_eur = gross_eur = 0.0
|
|
wins_gross = wins_net = n_trades = 0
|
|
last_exit = -1
|
|
|
|
for i, d in entries:
|
|
e = i - entry_offset
|
|
if e <= last_exit or e < 0 or e + 1 >= n:
|
|
continue
|
|
entry = close[e]
|
|
exit_price = close[min(e + hold, n - 1)]
|
|
for k in range(1, hold + 1):
|
|
j = e + k
|
|
if j >= n:
|
|
exit_price = close[n - 1]
|
|
break
|
|
if k < hold and (close[j] - entry) / entry * d <= STOP_PCT:
|
|
exit_price = close[j]
|
|
break
|
|
if k == hold:
|
|
exit_price = close[j]
|
|
|
|
actual = (exit_price - entry) / entry * d # movimento prezzo * direzione (no leva)
|
|
gross = actual * lev
|
|
fee = fee_rt * lev
|
|
net = gross - fee
|
|
|
|
cap_before = capital
|
|
capital = max(cap_before + cap_before * pos * net, 10.0)
|
|
gross_eur += cap_before * pos * gross
|
|
fees_eur += cap_before * pos * fee
|
|
peak = max(peak, capital)
|
|
max_dd = max(max_dd, (peak - capital) / peak)
|
|
|
|
n_trades += 1
|
|
wins_gross += actual > 0
|
|
wins_net += net > 0
|
|
last_exit = e + hold
|
|
|
|
return {
|
|
"trades": n_trades,
|
|
"win_gross": wins_gross / n_trades * 100 if n_trades else 0.0,
|
|
"win_net": wins_net / n_trades * 100 if n_trades else 0.0,
|
|
"net_return_pct": (capital / initial - 1) * 100,
|
|
"net_eur": capital - initial,
|
|
"gross_eur": gross_eur,
|
|
"fees_eur": fees_eur,
|
|
"final_capital": capital,
|
|
"max_dd": max_dd * 100,
|
|
}
|
|
|
|
|
|
def rule_entries(name: str, df: pd.DataFrame, params: dict, split: int) -> list[tuple[int, int]]:
|
|
strat = load_strategy(name)
|
|
ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
|
sigs = strat.generate_signals(df, ts, **params)
|
|
return [(s.idx, s.direction) for s in sigs if s.idx >= split]
|
|
|
|
|
|
def ml_entries(df: pd.DataFrame, params: dict, split: int, hold: int) -> tuple[list[tuple[int, int]], dict]:
|
|
bb_w = params.get("bb_window", 14)
|
|
sq_thr = params.get("sq_threshold", 0.8)
|
|
ml_thr = params.get("ml_threshold", 0.70)
|
|
|
|
eng = SignalEngine(bb_w=bb_w, sq_thr=sq_thr, ml_thr=ml_thr)
|
|
train_res = eng.train(df.iloc[:split].reset_index(drop=True), lookahead=hold)
|
|
if not eng.trained:
|
|
return [], train_res
|
|
|
|
close = df["close"].values
|
|
high = df["high"].values
|
|
low = df["low"].values
|
|
volume = df["volume"].values
|
|
n = len(df)
|
|
kcr = keltner_ratio(close, high, low, bb_w)
|
|
up_idx = list(eng.model.classes_).index(1)
|
|
|
|
entries: list[tuple[int, int]] = []
|
|
in_sq = False
|
|
sq_start = 0
|
|
for i in range(bb_w + 1, n):
|
|
if np.isnan(kcr[i]):
|
|
continue
|
|
is_sq = kcr[i] < sq_thr
|
|
if is_sq and not in_sq:
|
|
in_sq, sq_start = True, i
|
|
elif not is_sq and in_sq:
|
|
in_sq = False
|
|
dur = i - sq_start
|
|
if dur < eng.min_squeeze_bars or i < split or i + hold >= n:
|
|
continue
|
|
avg_vol = float(np.mean(volume[sq_start:i]))
|
|
feats = build_features(df, i, dur, avg_vol, kcr[i])
|
|
if feats is None:
|
|
continue
|
|
p_up = eng.model.predict_proba(eng.scaler.transform(feats.reshape(1, -1)))[0][up_idx]
|
|
if p_up >= ml_thr:
|
|
entries.append((i, 1))
|
|
elif p_up <= (1 - ml_thr):
|
|
entries.append((i, -1))
|
|
return entries, train_res
|
|
|
|
|
|
def squeeze_releases(df: pd.DataFrame, bb_w: int, sq_thr: float, min_dur: int,
|
|
split: int) -> list[int]:
|
|
"""Indici delle barre di rilascio squeeze nella finestra test (idx >= split)."""
|
|
close = df["close"].values
|
|
high = df["high"].values
|
|
low = df["low"].values
|
|
kcr = keltner_ratio(close, high, low, bb_w)
|
|
rels: list[int] = []
|
|
in_sq = False
|
|
sq_start = 0
|
|
for i in range(bb_w + 1, len(df)):
|
|
if np.isnan(kcr[i]):
|
|
continue
|
|
is_sq = kcr[i] < sq_thr
|
|
if is_sq and not in_sq:
|
|
in_sq, sq_start = True, i
|
|
elif not is_sq and in_sq:
|
|
in_sq = False
|
|
if i - sq_start >= min_dur and i >= split:
|
|
rels.append(i)
|
|
return rels
|
|
|
|
|
|
def honest_entries(df: pd.DataFrame, rels: list[int], rule: str, mom: int = 4) -> list[tuple[int, int]]:
|
|
"""Direzione da regole honest (solo dati <= i-1) o baseline breakout.
|
|
|
|
breakout: sign(close[i]-close[i-1]) -> conoscibile solo a close[i] (= live attuale)
|
|
premom: sign(close[i-1]-close[i-1-mom]) -> trend pre-release, 100% honest
|
|
fade: -sign(close[i]-close[i-1]) -> mean-reversion del breakout
|
|
"""
|
|
close = df["close"].values
|
|
out: list[tuple[int, int]] = []
|
|
for i in rels:
|
|
if i - 1 - mom < 0:
|
|
continue
|
|
if rule == "premom":
|
|
d = np.sign(close[i - 1] - close[i - 1 - mom])
|
|
elif rule == "fade":
|
|
d = -np.sign(close[i] - close[i - 1])
|
|
else: # breakout
|
|
d = np.sign(close[i] - close[i - 1])
|
|
if d != 0:
|
|
out.append((i, int(d)))
|
|
return out
|
|
|
|
|
|
def main():
|
|
cfg = yaml.safe_load((PROJECT_ROOT / "strategies.yml").read_text())
|
|
defaults = cfg.get("defaults", {})
|
|
hold = defaults.get("hold_bars", 3)
|
|
lev = defaults.get("leverage", 3)
|
|
fee_rt = 0.002
|
|
|
|
fee_grid = [0.0, 0.0005, 0.001, 0.0015, 0.002]
|
|
|
|
# ---- (b) SENSIBILITA' ALLE FEE (config live, ingresso close[i]) ----
|
|
print("=" * 104)
|
|
print(f" (b) SENSIBILITA' ALLE FEE — config live, ingresso close[i] | OOS {int(TEST_FRAC*100)}% | hold={hold} leva={lev}x")
|
|
print("=" * 104)
|
|
print(f" {'Strategia':<26s}{'Asset':>5s}{'Trd':>5s}{'Lordo€':>9s}"
|
|
+ "".join(f"{f'{f*100:.2f}%':>10s}" for f in fee_grid))
|
|
print(" " + "-" * 100)
|
|
|
|
for entry in cfg.get("strategies", []):
|
|
if not entry.get("enabled", True):
|
|
continue
|
|
name, asset, tf = entry["name"], entry["asset"], entry["tf"]
|
|
pos = entry.get("position_size", defaults.get("position_size", 0.15))
|
|
params = dict(entry.get("params", {}))
|
|
params["asset"], params["tf"] = asset, tf
|
|
|
|
df = load_data(asset, tf).reset_index(drop=True)
|
|
split = int(len(df) * (1 - TEST_FRAC))
|
|
close = df["close"].values
|
|
entries = (ml_entries(df, params, split, hold)[0] if name.startswith("ML01")
|
|
else rule_entries(name, df, params, split))
|
|
|
|
gross = simulate(entries, close, hold, 0.0, lev, pos)["net_eur"]
|
|
rets = [simulate(entries, close, hold, f, lev, pos)["net_return_pct"] for f in fee_grid]
|
|
print(f" {name:<26s}{asset:>5s}{len(entries):>5d}{gross:>+9.0f}"
|
|
+ "".join(f"{r:>+10.1f}" for r in rets))
|
|
|
|
print(" " + "-" * 100)
|
|
print(" Colonne = Ret% netto al variare della fee RT. 0.00% isola l'edge puro (senza costi).")
|
|
print(" Deribit perp reale: taker ~0.10% RT, maker ~0%. Il modello live usa 0.20% RT.")
|
|
|
|
# ---- (a) HONEST-ENTRY squeeze: direzione decisa <= i-1, ingresso close[i] ----
|
|
print("\n" + "=" * 104)
|
|
print(f" (a) HONEST-ENTRY squeeze (bb14 sq0.8 dur>=5) — ingresso close[i], fee={fee_rt*100:.1f}% RT")
|
|
print("=" * 104)
|
|
print(f" {'Asset':>5s}{'Regola direzione':>20s}{'Trd':>6s}{'Win%g':>8s}{'Win%n':>8s}{'Netto€':>9s}{'Ret%':>9s}{'DD%':>7s}")
|
|
print(" " + "-" * 100)
|
|
|
|
rules = [("breakout (=live)", "breakout"), ("pre-trend mom4", "premom"),
|
|
("pre-trend mom8", "premom8"), ("fade breakout", "fade")]
|
|
for asset in ["BTC", "ETH"]:
|
|
df = load_data(asset, "15m").reset_index(drop=True)
|
|
split = int(len(df) * (1 - TEST_FRAC))
|
|
close = df["close"].values
|
|
rels = squeeze_releases(df, 14, 0.8, 5, split)
|
|
for label, rule in rules:
|
|
mom = 8 if rule == "premom8" else 4
|
|
ents = honest_entries(df, rels, "premom" if rule == "premom8" else rule, mom=mom)
|
|
r = simulate(ents, close, hold, fee_rt, lev, 0.15)
|
|
print(f" {asset:>5s}{label:>20s}{r['trades']:>6d}{r['win_gross']:>8.1f}"
|
|
f"{r['win_net']:>8.1f}{r['net_eur']:>+9.0f}{r['net_return_pct']:>+9.1f}{r['max_dd']:>7.1f}")
|
|
print(" " + "-" * 100)
|
|
print(" pre-trend = direzione dal trend PRIMA del rilascio (solo dati <= i-1): 100% honest.")
|
|
print(" Se nessuna regola honest batte ~breakeven, non esiste edge direzionale tradeable.")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|