"""Validazione DURA del solo edge sopravvissuto alla ricerca shape: ML walk-forward (LogisticRegression) sulle feature di FORMA. Tutto il resto della famiglia shape e' rumore. Candidato: BTC logit W24H12 th0.58 (FULL +219% / OOS +42% / Sharpe 2.72 / 8-9 anni+, regge fee 0.20% RT). Prima di promuoverlo a strategia serve (metodologia obbligatoria): 1. ROBUSTEZZA MULTI-ASSET: stessa config su BTC/ETH/LTC/SOL/ADA/XRP 1h. 2. WALK-FORWARD ROLLING (train fisso 2y) oltre all'expanding -> niente "memoria infinita". 3. STRESS leva 2x + slippage doppio (fee 0.20% RT) -> regge in condizioni realistiche? 4. ROBUSTEZZA SU GRIGLIA (th, W, H) -> plateau, non picco. 5. CORRELAZIONE col MASTER + integrazione -> e' un diversificatore (free-lunch)? Tutto netto-fee, OOS = ultimo 30%. Conta il PnL netto, non l'accuracy (lezione squeeze). Run: uv run python scripts/analysis/shape_ml_validate.py """ from __future__ import annotations import sys import time import warnings 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)) warnings.filterwarnings("ignore") from scripts.analysis.explore_lab import get_df, evaluate, robust, FEE_RT, LEV, POS, OOS_FRAC from scripts.analysis.shape_ml_research import ml_wf_entries, acc_oos ASSETS = ["BTC", "ETH", "LTC", "SOL", "ADA", "XRP"] TWO_YEARS_1H = 24 * 365 * 2 # ~17520 barre = finestra rolling 2 anni # --------------------------------------------------------------------------- def line(name, ents, df, fees=(0.0, 0.001, 0.002)): """Riga evaluate + accuracy OOS, ritorna (res, robusto?).""" res = evaluate(name, ents, df) ac = acc_oos(ents, df) rb = (res["full"]["ret"] > 0 and res["oos"]["ret"] > 0 and res["sweep"][0.002] > 0 and res["sweep_oos"][0.002] > 0) print(f" ^ accOOS={ac:4.1f}% {'[ROBUST fee0.2%]' if rb else ''}") return res, rb # --------------------------------------------------------------------------- def sec_multi_asset(W=24, H=12, th=0.58): print("\n[1] MULTI-ASSET — logit W%dH%d th%.2f, walk-forward EXPANDING (1h):" % (W, H, th)) ok = [] dfs = {} for a in ASSETS: df = get_df(a, "1h"); dfs[a] = df ents = ml_wf_entries(df, W=W, H=H, model="logit", thresh=th) _, rb = line(f"{a} exp", ents, df) if rb: ok.append(a) print(f" -> EXPANDING robusti (fee0.2%): {ok if ok else 'NESSUNO'}") return dfs, ok def sec_rolling(dfs, W=24, H=12, th=0.58, tw=TWO_YEARS_1H): print("\n[2] WALK-FORWARD ROLLING — train fisso ~2 anni (%d barre), stessa config:" % tw) ok = [] for a in ASSETS: ents = ml_wf_entries(dfs[a], W=W, H=H, model="logit", thresh=th, train_window=tw) _, rb = line(f"{a} roll2y", ents, dfs[a]) if rb: ok.append(a) print(f" -> ROLLING robusti (fee0.2%): {ok if ok else 'NESSUNO'}") return ok def sec_stress(dfs, W=24, H=12, th=0.58): print("\n[3] STRESS — leva 2x + slippage doppio (fee 0.20% RT) su BTC/ETH:") print(" (la config nominale e' leva 3x fee 0.10%; qui peggioro entrambe)") from scripts.analysis.explore_lab import simulate for a in ["BTC", "ETH"]: ents = ml_wf_entries(dfs[a], W=W, H=H, model="logit", thresh=th) df = dfs[a] split = int(len(df) * (1 - OOS_FRAC)) base = simulate(ents, df, fee_rt=0.001, lev=3.0) stress_f = simulate(ents, df, fee_rt=0.002, lev=2.0) stress_o = simulate(ents, df, fee_rt=0.002, lev=2.0, split=split) print(f" {a}: base(3x,0.1%) FULL={base['ret']:+.0f}% Shrp={base['sharpe']:.2f} | " f"STRESS(2x,0.2%) FULL={stress_f['ret']:+.0f}% OOS={stress_o['ret']:+.0f}% " f"DD={stress_f['dd']:.0f}% Shrp={stress_f['sharpe']:.2f} " f"{'OK' if stress_f['ret'] > 0 and stress_o['ret'] > 0 else 'KO'}") def sec_grid(dfs, asset="BTC"): print(f"\n[4] ROBUSTEZZA GRIGLIA su {asset} (plateau, non picco):") rob = tot = 0 for W in (16, 24, 32): for H in (8, 12, 16): for th in (0.56, 0.58, 0.60): ents = ml_wf_entries(dfs[asset], W=W, H=H, model="logit", thresh=th) _, rb = line(f"{asset} W{W}H{H}th{th}", ents, dfs[asset]) tot += 1; rob += rb print(f" -> {asset}: {rob}/{tot} celle robuste a fee 0.2% (plateau se alta frazione)") # --------------------------------------------------------------------------- def shape_daily_equity(asset, IDX, W=24, H=12, th=0.58): """Equity giornaliera dello sleeve shape-ML (time-exit a H, non-overlap, pos 0.15, leva 3x, fee 0.10% RT), normalizzata sull'indice comune dei portafogli.""" from src.data.downloader import load_data df = get_df(asset, "1h") c = df["close"].values ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True) ents = ml_wf_entries(df, W=W, H=H, model="logit", thresh=th) n = len(c); eq = np.full(n, 1000.0); cap = 1000.0; last_exit = -1 fee = FEE_RT * LEV for e in sorted(ents, key=lambda x: x["i"]): i, d, mb = e["i"], e["d"], e["max_bars"] if i <= last_exit or i + mb >= n: continue j = i + mb ret = (c[j] - c[i]) / c[i] * d * LEV - fee cap = max(cap + cap * POS * ret, 10.0) eq[j:] = cap; last_exit = j s = pd.Series(eq, index=ts).resample("1D").last().reindex(IDX).ffill().bfill() return s / s.iloc[0] def sec_master_integration(): print("\n[5] CORRELAZIONE + INTEGRAZIONE COL MASTER:") from scripts.analysis.combine_portfolio import ( build_all_sleeves, port_returns, metrics, IDX, SPLIT, ) sleeves = build_all_sleeves() # sleeve shape: BTC + ETH (i due con piu' storia/edge) shape = {} for a in ("BTC", "ETH"): shape[f"SH_{a}"] = shape_daily_equity(a, IDX) dr_master = port_returns(sleeves) # MASTER equal-weight attuale dr_shape = port_returns(shape) corr = float(dr_master.corr(dr_shape)) print(f" correlazione daily MASTER vs sleeve-shape: {corr:+.3f}") # correlazione media shape vs ogni sleeve esistente cs = {k: float(port_returns({k: v}).corr(dr_shape)) for k, v in sleeves.items()} print(" corr shape vs singole sleeve: " + ", ".join(f"{k}={v:+.2f}" for k, v in cs.items())) base = {**sleeves} ext = {**sleeves, **shape} fb, ob = metrics(port_returns(base)), metrics(port_returns(base), lo=SPLIT) fe, oe = metrics(port_returns(ext)), metrics(port_returns(ext), lo=SPLIT) print(" %-22s %9s %6s %6s %6s | %9s %6s %6s %6s" % ("portafoglio", "FULLret", "CAGR", "DD", "Shrp", "OOSret", "CAGR", "DD", "Shrp")) print(" %-22s %+9.0f %6.0f %6.1f %6.2f | %+9.0f %6.0f %6.1f %6.2f" % ("MASTER (9 sleeve)", fb["ret"], fb["cagr"], fb["dd"], fb["sharpe"], ob["ret"], ob["cagr"], ob["dd"], ob["sharpe"])) print(" %-22s %+9.0f %6.0f %6.1f %6.2f | %+9.0f %6.0f %6.1f %6.2f" % ("MASTER + shape", fe["ret"], fe["cagr"], fe["dd"], fe["sharpe"], oe["ret"], oe["cagr"], oe["dd"], oe["sharpe"])) better = oe["sharpe"] > ob["sharpe"] and oe["dd"] <= ob["dd"] + 1 print(f" -> aggiungere shape MIGLIORA il MASTER OOS (Sharpe up, DD ~stabile)? " f"{'SI' if better else 'NO'}") def run(): t0 = time.time() print("=" * 100) print(" VALIDAZIONE DURA — shape-ML (LogisticRegression walk-forward sulle feature di forma)") print("=" * 100) dfs, _ = sec_multi_asset() sec_rolling(dfs) sec_stress(dfs) sec_grid(dfs, "BTC") sec_master_integration() print(f"\n tempo totale: {time.time() - t0:.0f}s") print("=" * 100) if __name__ == "__main__": run()