Adriano Dal Pastro
90c4726a31
- CLAUDE.md: default PORT06 (FULL 7.20/OOS 9.66, 18 sleeve), paragrafo BLEND 15m flat-skip (origine gioco Blind Traders, gate, validazione, caveat slippage), copertura reale ~83% (6 pairs), scripts/games/ + pairs15m_* nella struttura. - make_strategy_doc.py: header dinamico dal backtest, colonna ETH/BTC·15m nella tabella pairs, card PR01 col blend, conteggi sleeve aggiornati -> rigenerato HTML. - pairs_sim_flat: ritorna yearly_n (parita' con pairs_sim, usato dal doc). 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.
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Famiglia nuova market-neutral (distinta da tutto l'esistente, single-asset).
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Idea: il log-ratio di due cripto oscilla attorno alla media; z-score estremo -> rientra.
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Engine ONESTO (no look-ahead, verificato):
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- r[i] = log(closeA[i]/closeB[i]); ma/sd = rolling(n) su r -> usano solo r[<=i].
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- z[i] = (r[i]-ma[i])/sd[i]. ENTRY a close[i] (eseguibile):
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z<=-z_in -> LONG ratio (long A / short B); z>=+z_in -> SHORT ratio.
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- EXIT quando |z[j]| <= z_exit (rientro) o time-limit max_bars, a close[j].
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- pairs = 2 GAMBE -> fee = 2*fee_rt*lev (0.20% RT/coppia a fee_rt=0.001), il doppio
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del single-asset. Rendimento neutral = retA*d - retB*d (notional uguale per gamba).
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- non-overlap, capitale composto. Filtro candele sporche: salta salti |dr|>jump_max.
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- Ritorno riportato come CAGR e Sharpe ANNUALIZZATO sul tempo reale (no sqrt(n_trade)).
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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
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FEE_RT, LEV, POS, OOS_FRAC = 0.001, 3.0, 0.15, 0.30
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BARS_YEAR = 8760 # 1h
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def aligned(a: str, b: str, tf: str = "1h"):
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da = load_data(a, tf)[["timestamp", "open", "high", "low", "close"]].rename(columns=lambda x: x + "_a" if x != "timestamp" else x)
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db = load_data(b, tf)[["timestamp", "close"]].rename(columns={"close": "close_b"})
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m = da.merge(db, on="timestamp", how="inner").reset_index(drop=True)
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m["dt"] = pd.to_datetime(m["timestamp"], unit="ms", utc=True)
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return m
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def pairs_sim(a, b, tf="1h", n=50, z_in=2.0, z_exit=0.5, max_bars=72,
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jump_max=0.08, fee_rt=FEE_RT, lev=LEV, pos=POS, split_frac=0.0):
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m = aligned(a, b, tf)
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ca, cb = m["close_a"].values, m["close_b"].values
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r = np.log(ca / cb)
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dr = np.abs(np.diff(r, prepend=r[0])) # salto 1-bar del log-ratio
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ma = pd.Series(r).rolling(n).mean().values
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sd = pd.Series(r).rolling(n).std().values
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z = (r - ma) / np.where(sd == 0, np.nan, sd) # causale: usa r[<=i]
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ts = m["dt"]; N = len(r)
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split = int(N * split_frac)
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fee = 2 * fee_rt * lev # 2 gambe
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cap = peak = 1000.0; dd = 0.0; last = -1
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trades = wins = 0; rets = []; yearly = {}; yearly_n = {}
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eq_ts: list = []; eq_v: list = []
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for i in range(n + 1, N - 1):
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if i < split or np.isnan(z[i]) or dr[i] > jump_max:
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continue
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if i <= last:
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continue
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if z[i] <= -z_in:
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d = 1
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elif z[i] >= z_in:
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d = -1
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else:
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continue
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# exit: |z|<=z_exit o max_bars
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j = min(i + max_bars, N - 1)
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for k in range(1, max_bars + 1):
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jj = i + k
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if jj >= N:
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j = N - 1; break
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if abs(z[jj]) <= z_exit:
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j = jj; break
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j = jj
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retA = (ca[j] - ca[i]) / ca[i]
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retB = (cb[j] - cb[i]) / cb[i]
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ret = (retA - retB) * d * lev - fee # long A / short B (o viceversa)
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cap = max(cap + cap * pos * ret, 10.0)
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peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
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trades += 1; wins += ret > 0; rets.append(ret * pos); last = j
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eq_ts.append(ts.iloc[j]); eq_v.append(cap)
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yearly[ts.iloc[i].year] = yearly.get(ts.iloc[i].year, 0.0) + ret * 100
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yearly_n[ts.iloc[i].year] = yearly_n.get(ts.iloc[i].year, 0) + 1
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yrs_span = (ts.iloc[-1] - ts.iloc[max(split, 0)]).days / 365.25 or 1
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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
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# Sharpe annualizzato sul tempo reale: usa rendimenti per-trade scalati alla frequenza media
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if len(rets) > 1 and np.std(rets) > 0:
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trades_per_year = trades / yrs_span
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sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(trades_per_year))
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ret_tot = (cap / 1000 - 1) * 100
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cagr = ((cap / 1000) ** (1 / yrs_span) - 1) * 100 if cap > 0 else -100
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return dict(trades=trades, win=wins / trades * 100 if trades else 0, ret=ret_tot, cagr=cagr,
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dd=dd * 100, sharpe=sharpe, yearly=yearly, yearly_n=yearly_n,
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eq_ts=eq_ts, eq_v=eq_v)
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def aligned_ohlc(a: str, b: str, tf: str = "1h"):
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"""Come aligned ma con OHLC di ENTRAMBE le gambe (serve a rilevare candele flat)."""
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da = load_data(a, tf)[["timestamp", "open", "high", "low", "close"]].rename(
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columns=lambda x: x + "_a" if x != "timestamp" else x)
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db = load_data(b, tf)[["timestamp", "open", "high", "low", "close"]].rename(
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columns=lambda x: x + "_b" if x != "timestamp" else x)
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m = da.merge(db, on="timestamp", how="inner").reset_index(drop=True)
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m["dt"] = pd.to_datetime(m["timestamp"], unit="ms", utc=True)
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return m
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def is_flat_ohlc(o, h, l, c):
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"""Candela flat (O=H=L=C): prezzo fermo / liquidita' zero -> fill non eseguibile."""
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return (o == h) & (h == l) & (l == c)
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def pairs_sim_flat(a, b, tf="1h", n=50, z_in=2.0, z_exit=0.5, max_bars=72,
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jump_max=0.08, fee_rt=FEE_RT, lev=LEV, pos=POS, split_frac=0.0,
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flat_skip=False, scan_buffer=192):
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"""Engine pairs GENERALIZZATO con opzione flat-skip LIVE-REALIZABLE.
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Identico a pairs_sim quando flat_skip=False (regression-lock verificato).
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Con flat_skip=True:
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- ENTRY: saltata se la barra d'ingresso e' flat in UNA delle due gambe (prezzo stale).
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- EXIT: la condizione di uscita (|z|<=z_exit O bars>=max_bars) arma 'exit_ready';
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si esce al CLOSE della PRIMA barra PULITA successiva (mai a un prezzo passato).
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scan_buffer = barre extra oltre max_bars concesse per trovare la barra pulita.
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Questa e' la stessa regola implementata nel PairsWorker live (flat_skip) -> parita'.
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"""
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m = aligned_ohlc(a, b, tf)
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ca, cb = m["close_a"].values, m["close_b"].values
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N = len(ca)
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if flat_skip:
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flat = (is_flat_ohlc(m["open_a"].values, m["high_a"].values, m["low_a"].values, ca)
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| is_flat_ohlc(m["open_b"].values, m["high_b"].values, m["low_b"].values, cb))
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else:
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flat = np.zeros(N, dtype=bool)
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r = np.log(ca / cb)
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dr = np.abs(np.diff(r, prepend=r[0]))
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ma = pd.Series(r).rolling(n).mean().values
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sd = pd.Series(r).rolling(n).std().values
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z = (r - ma) / np.where(sd == 0, np.nan, sd)
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ts = m["dt"]
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split = int(N * split_frac)
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fee = 2 * fee_rt * lev
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cap = peak = 1000.0; dd = 0.0; last = -1
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trades = wins = 0; rets = []; yearly = {}; yearly_n = {}
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eq_ts, eq_v = [], []
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n_skip_entry = 0
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kmax = max_bars + (scan_buffer if flat_skip else 0)
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for i in range(n + 1, N - 1):
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if i < split or np.isnan(z[i]) or dr[i] > jump_max or i <= last:
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continue
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if z[i] <= -z_in:
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d = 1
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elif z[i] >= z_in:
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d = -1
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else:
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continue
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if flat[i]:
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n_skip_entry += 1
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continue # niente ingresso su barra stale
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# uscita live-realizable: arma a |z|<=z_exit o max_bars, esci alla prima barra pulita
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exit_ready = False; j = i
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for k in range(1, kmax + 1):
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jj = i + k
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if jj >= N:
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j = N - 1; break
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if not exit_ready and (abs(z[jj]) <= z_exit or k >= max_bars):
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exit_ready = True
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if exit_ready and not flat[jj]:
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j = jj; break
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j = jj
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retA = (ca[j] - ca[i]) / ca[i]
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retB = (cb[j] - cb[i]) / cb[i]
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ret = (retA - retB) * d * lev - fee
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cap = max(cap + cap * pos * ret, 10.0)
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peak = max(peak, cap); dd = max(dd, (peak - cap) / peak)
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trades += 1; wins += ret > 0; rets.append(ret * pos); last = j
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eq_ts.append(ts.iloc[j]); eq_v.append(cap)
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yearly[ts.iloc[i].year] = yearly.get(ts.iloc[i].year, 0.0) + ret * 100
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yearly_n[ts.iloc[i].year] = yearly_n.get(ts.iloc[i].year, 0) + 1
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yrs_span = (ts.iloc[-1] - ts.iloc[max(split, 0)]).days / 365.25 or 1
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sharpe = 0.0
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if len(rets) > 1 and np.std(rets) > 0:
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sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(trades / yrs_span))
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ret_tot = (cap / 1000 - 1) * 100
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cagr = ((cap / 1000) ** (1 / yrs_span) - 1) * 100 if cap > 0 else -100
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return dict(trades=trades, win=wins / trades * 100 if trades else 0, ret=ret_tot,
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cagr=cagr, dd=dd * 100, sharpe=sharpe, yearly=yearly, yearly_n=yearly_n,
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eq_ts=eq_ts, eq_v=eq_v, n_skip_entry=n_skip_entry)
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def check_no_lookahead():
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"""Perturba il FUTURO del ratio e verifica che z[i] non cambi (causalita')."""
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m = aligned("ETH", "BTC")
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r = np.log(m["close_a"].values / m["close_b"].values)
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n = 50; i = 1000
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z_i = (r[i] - pd.Series(r).rolling(n).mean().values[i]) / pd.Series(r).rolling(n).std().values[i]
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r2 = r.copy(); r2[i + 1:] += 0.5 # stravolge il futuro
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z_i2 = (r2[i] - pd.Series(r2).rolling(n).mean().values[i]) / pd.Series(r2).rolling(n).std().values[i]
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print(f" no-look-ahead: z[i]={z_i:.6f} vs z[i] con futuro perturbato={z_i2:.6f} -> "
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f"{'OK (identico)' if abs(z_i - z_i2) < 1e-9 else 'VIOLAZIONE!'}")
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def main():
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print("=" * 104)
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print(f" PAIRS spread reversion — NETTO fee 0.20% RT/coppia (2 gambe), leva {LEV:.0f}x, OOS ultimo {int(OOS_FRAC*100)}%")
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print("=" * 104)
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check_no_lookahead()
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pairs = [("ETH", "BTC"), ("LTC", "ETH"), ("ADA", "ETH"), ("SOL", "ETH"),
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("BNB", "BTC"), ("XRP", "BTC"), ("SOL", "BTC"), ("DOGE", "BTC")]
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print(f"\n {'coppia':<10s}{'trd':>5s}{'win%':>6s}{'FULL%':>8s}{'OOS%':>8s}{'CAGR%':>7s}"
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f"{'DD%':>6s}{'oDD%':>6s}{'Shrp':>6s}{'anni+':>7s}{'fee0.4%RT':>11s}")
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print(" " + "-" * 96)
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for a, b in pairs:
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f = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72)
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o = pairs_sim(a, b, n=50, z_in=2.0, z_exit=0.5, max_bars=72, split_frac=1 - OOS_FRAC)
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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
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yrs = f["yearly"]; pos_y = sum(1 for v in yrs.values() if v > 0)
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print(f" {a+'/'+b:<10s}{f['trades']:>5d}{f['win']:>6.1f}{f['ret']:>+8.0f}{o['ret']:>+8.0f}"
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f"{f['cagr']:>7.0f}{f['dd']:>6.0f}{o['dd']:>6.0f}{f['sharpe']:>6.2f}{f'{pos_y}/{len(yrs)}':>7s}"
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f"{hi['ret']:>+11.0f}")
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# correlazione con BTC daily (market-neutrality) sulla coppia migliore
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print("\n Verifica market-neutrality ETH/BTC: per-anno")
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f = pairs_sim("ETH", "BTC", n=50, z_in=2.0, z_exit=0.5, max_bars=72)
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print(" " + " ".join(f"{y}:{v:+.0f}%" for y, v in sorted(f["yearly"].items())))
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if __name__ == "__main__":
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main()
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