Adriano Dal Pastro
14522262e6
Reset del progetto su fondamenta verificate dopo la scoperta che l'intera libreria "validata OOS" era artefatto di feed contaminato (print fantasma del feed Cerbero TESTNET + storico Binance/USDT). - Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE 50-82% barre flat; XRP/BNB non certificabili). - Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST con segnale residuo, da ri-validare in isolamento. - Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio, runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/ portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/ (preservati, non cancellati). Diario consolidato in un unico documento. - Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal + src/backtest/engine + load_data; tool dati certificati (rebuild_history, certify_feed, audit_feed, multi_source_check). - Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
229 lines
11 KiB
Python
229 lines
11 KiB
Python
"""Verifica indipendente + ricerca PAIRS / SPREAD MEAN-REVERSION fra cripto.
|
|
|
|
Famiglia nuova market-neutral (distinta da tutto l'esistente, single-asset).
|
|
Idea: il log-ratio di due cripto oscilla attorno alla media; z-score estremo -> rientra.
|
|
|
|
Engine ONESTO (no look-ahead, verificato):
|
|
- r[i] = log(closeA[i]/closeB[i]); ma/sd = rolling(n) su r -> usano solo r[<=i].
|
|
- z[i] = (r[i]-ma[i])/sd[i]. ENTRY a close[i] (eseguibile):
|
|
z<=-z_in -> LONG ratio (long A / short B); z>=+z_in -> SHORT ratio.
|
|
- EXIT quando |z[j]| <= z_exit (rientro) o time-limit max_bars, a close[j].
|
|
- pairs = 2 GAMBE -> fee = 2*fee_rt*lev (0.20% RT/coppia a fee_rt=0.001), il doppio
|
|
del single-asset. Rendimento neutral = retA*d - retB*d (notional uguale per gamba).
|
|
- non-overlap, capitale composto. Filtro candele sporche: salta salti |dr|>jump_max.
|
|
- Ritorno riportato come CAGR e Sharpe ANNUALIZZATO sul tempo reale (no sqrt(n_trade)).
|
|
"""
|
|
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
|
|
|
|
FEE_RT, LEV, POS, OOS_FRAC = 0.001, 3.0, 0.15, 0.30
|
|
BARS_YEAR = 8760 # 1h
|
|
|
|
|
|
def aligned(a: str, b: str, tf: str = "1h"):
|
|
da = load_data(a, tf)[["timestamp", "open", "high", "low", "close"]].rename(columns=lambda x: x + "_a" if x != "timestamp" else x)
|
|
db = load_data(b, tf)[["timestamp", "close"]].rename(columns={"close": "close_b"})
|
|
m = da.merge(db, on="timestamp", how="inner").reset_index(drop=True)
|
|
m["dt"] = pd.to_datetime(m["timestamp"], unit="ms", utc=True)
|
|
return m
|
|
|
|
|
|
def pairs_sim(a, b, tf="1h", n=50, z_in=2.0, z_exit=0.5, max_bars=72,
|
|
jump_max=0.08, fee_rt=FEE_RT, lev=LEV, pos=POS, split_frac=0.0):
|
|
m = aligned(a, b, tf)
|
|
ca, cb = m["close_a"].values, m["close_b"].values
|
|
r = np.log(ca / cb)
|
|
dr = np.abs(np.diff(r, prepend=r[0])) # salto 1-bar del log-ratio
|
|
ma = pd.Series(r).rolling(n).mean().values
|
|
sd = pd.Series(r).rolling(n).std().values
|
|
z = (r - ma) / np.where(sd == 0, np.nan, sd) # causale: usa r[<=i]
|
|
ts = m["dt"]; N = len(r)
|
|
split = int(N * split_frac)
|
|
fee = 2 * fee_rt * lev # 2 gambe
|
|
cap = peak = 1000.0; dd = 0.0; last = -1
|
|
trades = wins = 0; rets = []; yearly = {}; yearly_n = {}
|
|
eq_ts: list = []; eq_v: list = []
|
|
for i in range(n + 1, N - 1):
|
|
if i < split or np.isnan(z[i]) or dr[i] > jump_max:
|
|
continue
|
|
if i <= last:
|
|
continue
|
|
if z[i] <= -z_in:
|
|
d = 1
|
|
elif z[i] >= z_in:
|
|
d = -1
|
|
else:
|
|
continue
|
|
# exit: |z|<=z_exit o max_bars
|
|
j = min(i + max_bars, N - 1)
|
|
for k in range(1, max_bars + 1):
|
|
jj = i + k
|
|
if jj >= N:
|
|
j = N - 1; break
|
|
if abs(z[jj]) <= z_exit:
|
|
j = jj; break
|
|
j = jj
|
|
retA = (ca[j] - ca[i]) / ca[i]
|
|
retB = (cb[j] - cb[i]) / cb[i]
|
|
ret = (retA - retB) * d * lev - fee # long A / short B (o viceversa)
|
|
cap = max(cap + cap * pos * ret, 10.0)
|
|
peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
|
|
trades += 1; wins += ret > 0; rets.append(ret * pos); last = j
|
|
eq_ts.append(ts.iloc[j]); eq_v.append(cap)
|
|
yearly[ts.iloc[i].year] = yearly.get(ts.iloc[i].year, 0.0) + ret * 100
|
|
yearly_n[ts.iloc[i].year] = yearly_n.get(ts.iloc[i].year, 0) + 1
|
|
yrs_span = (ts.iloc[-1] - ts.iloc[max(split, 0)]).days / 365.25 or 1
|
|
sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(BARS_YEAR / np.mean([max_bars])) ) if len(rets) > 1 and np.std(rets) > 0 else 0.0
|
|
# Sharpe annualizzato sul tempo reale: usa rendimenti per-trade scalati alla frequenza media
|
|
if len(rets) > 1 and np.std(rets) > 0:
|
|
trades_per_year = trades / yrs_span
|
|
sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(trades_per_year))
|
|
ret_tot = (cap / 1000 - 1) * 100
|
|
cagr = ((cap / 1000) ** (1 / yrs_span) - 1) * 100 if cap > 0 else -100
|
|
return dict(trades=trades, win=wins / trades * 100 if trades else 0, ret=ret_tot, cagr=cagr,
|
|
dd=dd * 100, sharpe=sharpe, yearly=yearly, yearly_n=yearly_n,
|
|
eq_ts=eq_ts, eq_v=eq_v)
|
|
|
|
|
|
def aligned_ohlc(a: str, b: str, tf: str = "1h"):
|
|
"""Come aligned ma con OHLC di ENTRAMBE le gambe (serve a rilevare candele flat)."""
|
|
da = load_data(a, tf)[["timestamp", "open", "high", "low", "close"]].rename(
|
|
columns=lambda x: x + "_a" if x != "timestamp" else x)
|
|
db = load_data(b, tf)[["timestamp", "open", "high", "low", "close"]].rename(
|
|
columns=lambda x: x + "_b" if x != "timestamp" else x)
|
|
m = da.merge(db, on="timestamp", how="inner").reset_index(drop=True)
|
|
m["dt"] = pd.to_datetime(m["timestamp"], unit="ms", utc=True)
|
|
return m
|
|
|
|
|
|
def is_flat_ohlc(o, h, l, c):
|
|
"""Candela flat (O=H=L=C): prezzo fermo / liquidita' zero -> fill non eseguibile."""
|
|
return (o == h) & (h == l) & (l == c)
|
|
|
|
|
|
def pairs_sim_flat(a, b, tf="1h", n=50, z_in=2.0, z_exit=0.5, max_bars=72,
|
|
jump_max=0.08, fee_rt=FEE_RT, lev=LEV, pos=POS, split_frac=0.0,
|
|
flat_skip=False, scan_buffer=192):
|
|
"""Engine pairs GENERALIZZATO con opzione flat-skip LIVE-REALIZABLE.
|
|
|
|
Identico a pairs_sim quando flat_skip=False (regression-lock verificato).
|
|
Con flat_skip=True:
|
|
- ENTRY: saltata se la barra d'ingresso e' flat in UNA delle due gambe (prezzo stale).
|
|
- EXIT: la condizione di uscita (|z|<=z_exit O bars>=max_bars) arma 'exit_ready';
|
|
si esce al CLOSE della PRIMA barra PULITA successiva (mai a un prezzo passato).
|
|
scan_buffer = barre extra oltre max_bars concesse per trovare la barra pulita.
|
|
Questa e' la stessa regola implementata nel PairsWorker live (flat_skip) -> parita'.
|
|
"""
|
|
m = aligned_ohlc(a, b, tf)
|
|
ca, cb = m["close_a"].values, m["close_b"].values
|
|
N = len(ca)
|
|
if flat_skip:
|
|
flat = (is_flat_ohlc(m["open_a"].values, m["high_a"].values, m["low_a"].values, ca)
|
|
| is_flat_ohlc(m["open_b"].values, m["high_b"].values, m["low_b"].values, cb))
|
|
else:
|
|
flat = np.zeros(N, dtype=bool)
|
|
r = np.log(ca / cb)
|
|
dr = np.abs(np.diff(r, prepend=r[0]))
|
|
ma = pd.Series(r).rolling(n).mean().values
|
|
sd = pd.Series(r).rolling(n).std().values
|
|
z = (r - ma) / np.where(sd == 0, np.nan, sd)
|
|
ts = m["dt"]
|
|
split = int(N * split_frac)
|
|
fee = 2 * fee_rt * lev
|
|
cap = peak = 1000.0; dd = 0.0; last = -1
|
|
trades = wins = 0; rets = []; yearly = {}; yearly_n = {}
|
|
eq_ts, eq_v = [], []
|
|
n_skip_entry = 0
|
|
kmax = max_bars + (scan_buffer if flat_skip else 0)
|
|
for i in range(n + 1, N - 1):
|
|
if i < split or np.isnan(z[i]) or dr[i] > jump_max or i <= last:
|
|
continue
|
|
if z[i] <= -z_in:
|
|
d = 1
|
|
elif z[i] >= z_in:
|
|
d = -1
|
|
else:
|
|
continue
|
|
if flat[i]:
|
|
n_skip_entry += 1
|
|
continue # niente ingresso su barra stale
|
|
# uscita live-realizable: arma a |z|<=z_exit o max_bars, esci alla prima barra pulita
|
|
exit_ready = False; j = i
|
|
for k in range(1, kmax + 1):
|
|
jj = i + k
|
|
if jj >= N:
|
|
j = N - 1; break
|
|
if not exit_ready and (abs(z[jj]) <= z_exit or k >= max_bars):
|
|
exit_ready = True
|
|
if exit_ready and not flat[jj]:
|
|
j = jj; break
|
|
j = jj
|
|
retA = (ca[j] - ca[i]) / ca[i]
|
|
retB = (cb[j] - cb[i]) / cb[i]
|
|
ret = (retA - retB) * d * lev - fee
|
|
cap = max(cap + cap * pos * ret, 10.0)
|
|
peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
|
|
trades += 1; wins += ret > 0; rets.append(ret * pos); last = j
|
|
eq_ts.append(ts.iloc[j]); eq_v.append(cap)
|
|
yearly[ts.iloc[i].year] = yearly.get(ts.iloc[i].year, 0.0) + ret * 100
|
|
yearly_n[ts.iloc[i].year] = yearly_n.get(ts.iloc[i].year, 0) + 1
|
|
yrs_span = (ts.iloc[-1] - ts.iloc[max(split, 0)]).days / 365.25 or 1
|
|
sharpe = 0.0
|
|
if len(rets) > 1 and np.std(rets) > 0:
|
|
sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(trades / yrs_span))
|
|
ret_tot = (cap / 1000 - 1) * 100
|
|
cagr = ((cap / 1000) ** (1 / yrs_span) - 1) * 100 if cap > 0 else -100
|
|
return dict(trades=trades, win=wins / trades * 100 if trades else 0, ret=ret_tot,
|
|
cagr=cagr, dd=dd * 100, sharpe=sharpe, yearly=yearly, yearly_n=yearly_n,
|
|
eq_ts=eq_ts, eq_v=eq_v, n_skip_entry=n_skip_entry)
|
|
|
|
|
|
def check_no_lookahead():
|
|
"""Perturba il FUTURO del ratio e verifica che z[i] non cambi (causalita')."""
|
|
m = aligned("ETH", "BTC")
|
|
r = np.log(m["close_a"].values / m["close_b"].values)
|
|
n = 50; i = 1000
|
|
z_i = (r[i] - pd.Series(r).rolling(n).mean().values[i]) / pd.Series(r).rolling(n).std().values[i]
|
|
r2 = r.copy(); r2[i + 1:] += 0.5 # stravolge il futuro
|
|
z_i2 = (r2[i] - pd.Series(r2).rolling(n).mean().values[i]) / pd.Series(r2).rolling(n).std().values[i]
|
|
print(f" no-look-ahead: z[i]={z_i:.6f} vs z[i] con futuro perturbato={z_i2:.6f} -> "
|
|
f"{'OK (identico)' if abs(z_i - z_i2) < 1e-9 else 'VIOLAZIONE!'}")
|
|
|
|
|
|
def main():
|
|
print("=" * 104)
|
|
print(f" PAIRS spread reversion — NETTO fee 0.20% RT/coppia (2 gambe), leva {LEV:.0f}x, OOS ultimo {int(OOS_FRAC*100)}%")
|
|
print("=" * 104)
|
|
check_no_lookahead()
|
|
pairs = [("ETH", "BTC"), ("LTC", "ETH"), ("ADA", "ETH"), ("SOL", "ETH"),
|
|
("BNB", "BTC"), ("XRP", "BTC"), ("SOL", "BTC"), ("DOGE", "BTC")]
|
|
print(f"\n {'coppia':<10s}{'trd':>5s}{'win%':>6s}{'FULL%':>8s}{'OOS%':>8s}{'CAGR%':>7s}"
|
|
f"{'DD%':>6s}{'oDD%':>6s}{'Shrp':>6s}{'anni+':>7s}{'fee0.4%RT':>11s}")
|
|
print(" " + "-" * 96)
|
|
for a, b in pairs:
|
|
f = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72)
|
|
o = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72, split_frac=1 - OOS_FRAC)
|
|
hi = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72, fee_rt=0.002) # 0.4% RT/coppia
|
|
yrs = f["yearly"]; pos_y = sum(1 for v in yrs.values() if v > 0)
|
|
print(f" {a+'/'+b:<10s}{f['trades']:>5d}{f['win']:>6.1f}{f['ret']:>+8.0f}{o['ret']:>+8.0f}"
|
|
f"{f['cagr']:>7.0f}{f['dd']:>6.0f}{o['dd']:>6.0f}{f['sharpe']:>6.2f}{f'{pos_y}/{len(yrs)}':>7s}"
|
|
f"{hi['ret']:>+11.0f}")
|
|
# correlazione con BTC daily (market-neutrality) sulla coppia migliore
|
|
print("\n Verifica market-neutrality ETH/BTC: per-anno")
|
|
f = pairs_sim("ETH", "BTC", n=50, z_in=2.0, z_exit=0.5, max_bars=72)
|
|
print(" " + " ".join(f"{y}:{v:+.0f}%" for y, v in sorted(f["yearly"].items())))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|