"""Gestione del rischio sulle fade (MR01/MR02/MR03/MR07): alzare Acc, ridurre DD. Due analisi, ognuna misurata FULL e OOS (ultimo 30%) per non illudersi: (A) SCREENING LEVE — confronta su ogni strategia le leve di rischio: - vol-target sizing (size ~ 1/distanza-SL) -> SCARTATA (peggiora) - skip alta volatilita' (ATR% in coda alta) -> SCARTATA (peggiora) - filtro trend (|close-EMA200|/ATR oltre soglia) -> ADOTTATA (Acc+ DD-) - combinazione di tutte (B) FILTRO TREND + PORTAFOGLIO: - sweep della soglia trend (assoluta in ATR, regola unica = no overfit) - portafoglio equipesato su sotto-conti indipendenti: curve poco correlate -> DD aggregato << DD del singolo sleeve (vera leva anti-drawdown) Engine fedele: ingresso close[i], exit TP/SL intrabar (high/low) o time-limit, non-overlap, capitale composto. Numeri NETTI fee 0.10% RT, leva 3x. """ 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 from scripts.analysis.strategy_research import bollinger_fade, atr from scripts.analysis.strategy_research_v2 import donchian_fade, keltner_fade, return_reversal FEE_RT, LEV, POS, INIT, OOS_FRAC = 0.001, 3.0, 0.15, 1000.0, 0.30 # config base di ogni strategia (come strategies.yml); su ETH MR03 usa n=50 STRATS = { "MR01": (bollinger_fade, dict(n=50, k=2.5, sl_atr=2.0, max_bars=24)), "MR02": (donchian_fade, dict(n=20, sl_atr=2.0, max_bars=24)), "MR03": (keltner_fade, dict(n=30, k=2.0, sl_atr=2.0, max_bars=24)), "MR07": (return_reversal,dict(n=50, k=3.5, tp_atr=2.0, sl_atr=1.5, max_bars=24)), } STRATS_ETH = dict(STRATS); STRATS_ETH["MR03"] = (keltner_fade, dict(n=50, k=2.0, sl_atr=2.0, max_bars=24)) def strats_for(asset: str) -> dict: return STRATS_ETH if asset == "ETH" else STRATS # ============================ (A) SCREENING LEVE ============================ def add_context(ents, df, ema_long=200): """Aggiunge a ogni entry: sl_dist, atr_pct, trend_dist (|close-EMA|/ATR).""" c = df["close"].values a = atr(df, 14) el = pd.Series(c).ewm(span=ema_long, adjust=False).mean().values apct = a / c for e in ents: i = e["i"] e["sl_dist"] = abs(c[i] - e["sl"]) / c[i] e["atr_pct"] = apct[i] e["trend_dist"] = abs(c[i] - el[i]) / a[i] if a[i] else 0.0 return ents def simulate(ents, df, fee_rt=FEE_RT, lev=LEV, split=-1, sizer=None, vol_skip=None, trend_skip=None, max_size=0.30): """sizer: funzione(entry)->frazione capitale; default POS fisso. vol_skip: soglia atr_pct sopra cui salto. trend_skip: soglia trend_dist sopra cui salto.""" h, l, c = df["high"].values, df["low"].values, df["close"].values n = len(c) ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True) cap = peak = INIT dd = 0.0; last = -1; trd = wins = 0 fee = fee_rt * lev yearly = {}; rets = [] for e in ents: i, d = e["i"], e["d"] if i <= last or i + 1 >= n or i < split: continue if vol_skip is not None and e["atr_pct"] > vol_skip: continue if trend_skip is not None and e["trend_dist"] > trend_skip: continue entry = c[i]; tp, sl, mb = e["tp"], e["sl"], e["max_bars"] exit_p = c[min(i + mb, n - 1)]; j = min(i + mb, n - 1) for k in range(1, mb + 1): j = i + k if j >= n: exit_p = c[n - 1]; break hs = (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl) ht = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp) if hs: exit_p = sl; break if ht: exit_p = tp; break if k == mb: exit_p = c[j] ret = (exit_p - entry) / entry * d * lev - fee size = POS if sizer is None else min(sizer(e), max_size) cap = max(cap + cap * size * ret, 10.0) peak = max(peak, cap); dd = max(dd, (peak - cap) / peak) trd += 1; wins += ret > 0; last = j; rets.append(ret * size) y = ts.iloc[i].year; yearly[y] = yearly.get(y, 0.0) + ret * size * INIT sharpe = float(np.mean(rets) / np.std(rets) * np.sqrt(len(rets))) if len(rets) > 1 and np.std(rets) > 0 else 0.0 return dict(trades=trd, acc=wins / trd * 100 if trd else 0.0, ret=(cap / INIT - 1) * 100, dd=dd * 100, yearly=yearly, sharpe=sharpe) def vol_target_sizer(target=0.015): """size t.c. rischio (size*lev*sl_dist) ~ target; piu' largo lo stop, meno size.""" return lambda e: target / (LEV * max(e["sl_dist"], 1e-4)) def _line(label, full, oos): print(f" {label:<28s}{full['trades']:>6d}{full['acc']:>7.1f}{full['ret']:>+10.0f}{full['dd']:>7.1f}{full['sharpe']:>7.2f}" f" | {oos['trades']:>5d}{oos['acc']:>7.1f}{oos['ret']:>+9.0f}{oos['dd']:>7.1f}{oos['sharpe']:>7.2f}") def screen_levers(): print("=" * 110) print(" (A) SCREENING LEVE — vol-target / vol-skip / filtro-trend | NETTO fee 0.10% RT, leva 3x") print("=" * 110) for asset in ["BTC", "ETH"]: df = load_data(asset, "1h") split = int(len(df) * (1 - OOS_FRAC)) print(f"\n {asset} 1h") print(f" {'config':<28s}{'Trd':>6s}{'Acc%':>7s}{'Ret%':>10s}{'DD%':>7s}{'Shrp':>7s}" f" | {'oTrd':>5s}{'oAcc':>7s}{'oRet':>9s}{'oDD':>7s}{'oShrp':>7s}") print(" " + "-" * 106) for nm, (fn, params) in strats_for(asset).items(): ents = add_context(fn(df, **params), df) p85 = float(np.quantile([e["atr_pct"] for e in ents], 0.85)) t90 = float(np.quantile([e["trend_dist"] for e in ents], 0.90)) _line(f"{nm} base", simulate(ents, df), simulate(ents, df, split=split)) _line(f"{nm} +volTarget", simulate(ents, df, sizer=vol_target_sizer()), simulate(ents, df, split=split, sizer=vol_target_sizer())) _line(f"{nm} +volSkip(p85)", simulate(ents, df, vol_skip=p85), simulate(ents, df, split=split, vol_skip=p85)) _line(f"{nm} +trendSkip(p90)", simulate(ents, df, trend_skip=t90), simulate(ents, df, split=split, trend_skip=t90)) _line(f"{nm} +ALL", simulate(ents, df, sizer=vol_target_sizer(), vol_skip=p85, trend_skip=t90), simulate(ents, df, split=split, sizer=vol_target_sizer(), vol_skip=p85, trend_skip=t90)) print(" " + "-" * 106) print("\n Esito: vol-target e vol-skip PEGGIORANO; il filtro trend e' l'unica leva utile.") # ===================== (B) FILTRO TREND + PORTAFOGLIO ===================== def build_trades(ents, df, lev=LEV, fee_rt=FEE_RT, trend_max=None, ema_long=200): """Lista trade non-overlap: (entry_idx, exit_idx, ret_netto). Filtro trend opzionale.""" h, l, c = df["high"].values, df["low"].values, df["close"].values n = len(c); a = atr(df, 14) el = pd.Series(c).ewm(span=ema_long, adjust=False).mean().values fee = fee_rt * lev out = []; last = -1 for e in ents: i, d = e["i"], e["d"] if i <= last or i + 1 >= n: continue if trend_max is not None and a[i] and abs(c[i] - el[i]) / a[i] > trend_max: continue entry = c[i]; tp, sl, mb = e["tp"], e["sl"], e["max_bars"] exit_p = c[min(i + mb, n - 1)]; j = min(i + mb, n - 1) for k in range(1, mb + 1): j = i + k if j >= n: exit_p = c[n - 1]; break hs = (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl) ht = (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp) if hs: exit_p = sl; break if ht: exit_p = tp; break if k == mb: exit_p = c[j] ret = (exit_p - entry) / entry * d * lev - fee out.append((i, j, ret)); last = j return out def metrics_single(trades, pos=POS, split=-1): cap = peak = INIT; dd = 0.0; trd = wins = 0; rets = [] for i, j, ret in trades: if i < split: continue cap = max(cap + cap * pos * ret, 10.0) peak = max(peak, cap); dd = max(dd, (peak - cap) / peak) trd += 1; wins += ret > 0; rets.append(ret * pos) sh = float(np.mean(rets) / np.std(rets) * np.sqrt(len(rets))) if len(rets) > 1 and np.std(rets) > 0 else 0.0 return dict(trades=trd, acc=wins / trd * 100 if trd else 0.0, ret=(cap / INIT - 1) * 100, dd=dd * 100, sharpe=sh) def sleeve_equity(trades, n_bars, pos=POS, split=-1): """Equity di uno sleeve su sotto-conto indipendente (capitale INIT, pos fissa).""" eq = np.full(n_bars, INIT, dtype=float) cap = INIT for i, j, ret in sorted(trades, key=lambda t: t[1]): if i < split: continue cap = max(cap + cap * pos * ret, 10.0) eq[j:] = cap return eq def metrics_portfolio(strat_trades, n_bars, pos=POS, split=-1): """Portafoglio equipesato: media di N sotto-conti indipendenti. DD sull'aggregata.""" sleeves = [sleeve_equity(tr, n_bars, pos=pos, split=split) for tr in strat_trades.values()] agg = np.mean(sleeves, axis=0) agg = agg[max(split, 0):] peak = np.maximum.accumulate(agg) dd = float(np.max((peak - agg) / peak) * 100) trd = sum(1 for tr in strat_trades.values() for i, _, _ in tr if i >= split) wins = sum(1 for tr in strat_trades.values() for i, _, r in tr if i >= split and r > 0) return dict(trades=trd, acc=wins / trd * 100 if trd else 0.0, ret=(agg[-1] / INIT - 1) * 100, dd=dd) def trend_and_portfolio(): # --- sweep soglia trend --- print("\n" + "=" * 104) print(" (B1) FILTRO TREND |close-EMA200|/ATR > soglia -> SALTA | NETTO fee 0.10% RT, leva 3x") print("=" * 104) print(f" {'Strat/Asset':<14s}{'soglia':>8s}{'Trd':>6s}{'Acc%':>7s}{'Ret%':>9s}{'DD%':>7s}" f" | {'oAcc':>6s}{'oRet':>9s}{'oDD':>7s}{'oShrp':>7s}") print(" " + "-" * 100) for asset in ["BTC", "ETH"]: df = load_data(asset, "1h"); split = int(len(df) * (1 - OOS_FRAC)) for nm, (fn, params) in strats_for(asset).items(): ents = fn(df, **params) for thr in [None, 4.0, 3.0, 2.5, 2.0]: tr = build_trades(ents, df, trend_max=thr) f = metrics_single(tr); o = metrics_single(tr, split=split) lab = "base" if thr is None else f"{thr}ATR" print(f" {nm+' '+asset:<14s}{lab:>8s}{f['trades']:>6d}{f['acc']:>7.1f}{f['ret']:>+9.0f}{f['dd']:>7.1f}" f" | {o['acc']:>6.1f}{o['ret']:>+9.0f}{o['dd']:>7.1f}{o['sharpe']:>7.2f}") print(" " + "-" * 100) # --- portafoglio equipesato (filtro trend 3.0 ATR) --- print("\n" + "=" * 104) print(" (B2) PORTAFOGLIO equipesato: N sotto-conti indipendenti (pos 0.15, filtro trend 3.0 ATR)") print("=" * 104) print(f" {'Universo':<26s}{'Trd':>6s}{'Acc%':>7s}{'Ret%':>10s}{'DD%':>7s}" f" | {'oAcc':>6s}{'oRet':>9s}{'oDD':>7s}") print(" " + "-" * 100) all_trades = {} for asset in ["BTC", "ETH"]: df = load_data(asset, "1h"); split = int(len(df) * (1 - OOS_FRAC)); n = len(df) st = {f"{nm}_{asset}": build_trades(fn(df, **p), df, trend_max=3.0) for nm, (fn, p) in strats_for(asset).items()} all_trades.update(st) f = metrics_portfolio(st, n); o = metrics_portfolio(st, n, split=split) print(f" {'Portafoglio '+asset+' (4 strat)':<26s}{f['trades']:>6d}{f['acc']:>7.1f}{f['ret']:>+10.0f}{f['dd']:>7.1f}" f" | {o['acc']:>6.1f}{o['ret']:>+9.0f}{o['dd']:>7.1f}") df0 = load_data("BTC", "1h"); split0 = int(len(df0) * (1 - OOS_FRAC)) f = metrics_portfolio(all_trades, len(df0)); o = metrics_portfolio(all_trades, len(df0), split=split0) print(" " + "-" * 100) print(f" {'GLOBALE BTC+ETH (8 sleeve)':<26s}{f['trades']:>6d}{f['acc']:>7.1f}{f['ret']:>+10.0f}{f['dd']:>7.1f}" f" | {o['acc']:>6.1f}{o['ret']:>+9.0f}{o['dd']:>7.1f}") print("\n Curve poco correlate => DD aggregato molto piu' basso del singolo sleeve.") def main(): screen_levers() trend_and_portfolio() if __name__ == "__main__": main()