chore(analysis): pulizia e accorpamento script di analisi (25 -> 15 file)
- accorpa risk_improvements.py + risk_portfolio.py -> risk_management.py (sezione A screening leve, sezione B filtro trend + portafoglio) - rimuove 4 script legacy della famiglia squeeze (ormai in waste, non referenziati): compare_strategies, best_yearly, final_report, yearly_market_report - rimuove 5 script honest_* di diagnostica/iterazione superati da honest_matrix (consolidato) e non importati: honest_diag, honest_diag2, honest_candidates, honest_yearly, honest_yearly2 - mantiene il core honest (lab/improve/improve2/rotation/trend) + canonici (final/matrix), tutta la ricerca fade (strategy_research[_v2]), validazione (oos_validation, validate_worker_mr01), intrabar_test (lezione squeeze) - aggiorna riferimento in CLAUDE.md. Import-check: 14/14 moduli OK. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
@@ -110,7 +110,7 @@ esteso rispetto al trend di fondo (`|close − EMA(ema_long)| / ATR(14) > trend_
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cioè quando si starebbe fadando un trend/crollo estremo. Con `trend_max=3.0`,
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cioè quando si starebbe fadando un trend/crollo estremo. Con `trend_max=3.0`,
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`ema_long=200` (default in `strategies.yml`): accuratezza su tutti gli sleeve
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`ema_long=200` (default in `strategies.yml`): accuratezza su tutti gli sleeve
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e DD giù drasticamente su ETH (MR01 71%→26%, MR02 42%→25%, MR03 66%→34%,
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e DD giù drasticamente su ETH (MR01 71%→26%, MR02 42%→25%, MR03 66%→34%,
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MR07 46%→21%), edge OOS confermato (vedi `scripts/analysis/risk_portfolio.py`).
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MR07 46%→21%), edge OOS confermato (vedi `scripts/analysis/risk_management.py`).
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Unica eccezione: MR03 BTC, dove il filtro peggiora entrambe → lasciato disattivo.
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Unica eccezione: MR03 BTC, dove il filtro peggiora entrambe → lasciato disattivo.
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Leva non robusta scartate: vol-target sizing e skip-alta-volatilità (peggiorano).
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Leva non robusta scartate: vol-target sizing e skip-alta-volatilità (peggiorano).
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@@ -1,309 +0,0 @@
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"""Confronto migliori strategie S1 e S2 — andamento per anno."""
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from __future__ import annotations
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import sys
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sys.path.insert(0, ".")
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import numpy as np
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import pandas as pd
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from src.data.downloader import load_data
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from src.fractal.patterns import encode_candles
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FEE_PERP = 0.002 # 0.1% taker roundtrip perpetual
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FEE_OPT = 0.0052 # options roundtrip
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INITIAL = 1000
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LEVERAGE = 3
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def keltner_ratio(close, high, low, window=14):
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n = len(close)
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r = np.full(n, np.nan)
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for i in range(window, n):
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wc, wh, wl = close[i-window:i], high[i-window:i], low[i-window:i]
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ma = np.mean(wc)
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bb_std = np.std(wc)
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tr = np.maximum(wh-wl, np.maximum(np.abs(wh-np.roll(wc,1)), np.abs(wl-np.roll(wc,1))))
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atr = np.mean(tr[1:])
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kc = (ma+1.5*atr)-(ma-1.5*atr)
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bb = (ma+2*bb_std)-(ma-2*bb_std)
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if kc > 0:
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r[i] = bb/kc
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return r
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def rv_ann(close, window):
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lr = np.diff(np.log(np.where(close==0, 1e-10, close)))
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r = np.full(len(close), np.nan)
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for i in range(window, len(lr)):
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r[i+1] = np.std(lr[i-window:i]) * np.sqrt(24*365)
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return r
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def rsi(close, period=14):
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delta = np.diff(close)
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gain = np.where(delta>0, delta, 0)
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loss = np.where(delta<0, -delta, 0)
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result = np.full(len(close), 50.0)
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if len(gain) < period:
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return result
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ag = np.mean(gain[:period])
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al = np.mean(loss[:period])
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for i in range(period, len(delta)):
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ag = (ag*(period-1)+gain[i])/period
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al = (al*(period-1)+loss[i])/period
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result[i+1] = 100 if al == 0 else 100-100/(1+ag/al)
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return result
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def ema(arr, period):
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r = np.full(len(arr), np.nan)
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k = 2/(period+1)
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r[period-1] = np.mean(arr[:period])
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for i in range(period, len(arr)):
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r[i] = arr[i]*k + r[i-1]*(1-k)
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return r
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# =====================================================================
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# S1 BEST: Squeeze Breakout ETH 1h (BBw=14, sq=0.8, brk=3)
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# =====================================================================
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def run_s1_squeeze(asset, tf):
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df = load_data(asset, tf)
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c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
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n = len(c)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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kcr = keltner_ratio(c, h, l, 14)
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yearly = {}
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in_sq = False
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sq_start = 0
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for i in range(15, n):
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if np.isnan(kcr[i]):
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continue
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is_sq = kcr[i] < 0.8
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if is_sq and not in_sq:
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in_sq = True
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sq_start = i
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elif not is_sq and in_sq:
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in_sq = False
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if i - sq_start < 5 or i + 3 >= n:
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continue
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first_ret = (c[i] - c[i-1]) / c[i-1]
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if abs(first_ret) < 0.001:
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continue
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direction = 1 if first_ret > 0 else -1
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actual = (c[i+2] - c[i-1]) / c[i-1]
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trade_ret = actual * direction
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net = trade_ret * LEVERAGE - FEE_PERP * LEVERAGE
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year = ts.iloc[i].year
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if year not in yearly:
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yearly[year] = {"pnls": [], "wins": 0, "total": 0}
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yearly[year]["pnls"].append(net)
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yearly[year]["total"] += 1
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if trade_ret > 0:
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yearly[year]["wins"] += 1
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return yearly
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# =====================================================================
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# S1 BEST ALT: Squeeze+ML hybrid ETH 15m
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# =====================================================================
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# Troppo complesso da ricalcolare (serve ML training). Uso i dati S1 squeeze puro.
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# =====================================================================
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# S2 BEST: VRP ETH 48h (con IV stimata, unico disponibile su 8 anni)
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# =====================================================================
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def run_s2_vrp(asset, dte=48):
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df = load_data(asset, "1h")
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c = df["close"].values
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n = len(c)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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rv_24 = rv_ann(c, 24)
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rv_168 = rv_ann(c, 168)
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yearly = {}
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for i in range(170, n - dte):
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if ts.iloc[i].hour != 8:
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continue
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rv_s, rv_l = rv_24[i], rv_168[i]
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if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
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continue
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regime = rv_s / rv_l
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iv_pf = 0.9 if regime > 2 else (1.0 if regime > 1.5 else (1.1 if regime > 1 else 1.2))
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iv = rv_l * iv_pf
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prem = iv * np.sqrt(dte/(24*365)) * 0.8
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spot = c[i]
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move = abs(c[min(i+dte, n-1)] - spot) / spot
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pos = 0.10
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raw = (prem - move) * pos if move <= prem else max(-(move-prem)*pos, -pos*0.05)
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net = raw - FEE_OPT * pos
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year = ts.iloc[i].year
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if year not in yearly:
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yearly[year] = {"pnls": [], "wins": 0, "total": 0}
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yearly[year]["pnls"].append(net)
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yearly[year]["total"] += 1
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if raw > 0:
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yearly[year]["wins"] += 1
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return yearly
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# =====================================================================
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# S2 BEST PERPETUAL: Multi-TF 15m+1h BTC
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# =====================================================================
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def run_s2_multitf(asset):
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df_1h = load_data(asset, "1h")
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df_15m = load_data(asset, "15m")
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c1h = df_1h["close"].values
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ts1h = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
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c15 = df_15m["close"].values
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ts15 = df_15m["timestamp"].values
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n15 = len(c15)
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ema_50 = ema(c1h, 50)
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rsi_15m = rsi(c15, 14)
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yearly = {}
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daily_done = set()
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for i in range(100, n15 - 12):
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ts_dt = pd.Timestamp(ts15[i], unit="ms", tz="UTC")
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day = ts_dt.strftime("%Y-%m-%d")
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if day in daily_done:
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continue
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if rsi_15m[i] > 35 and rsi_15m[i] < 65:
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continue
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h_idx = np.searchsorted(ts1h.values.astype("int64"), ts15[i]) - 1
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if h_idx < 50 or h_idx >= len(c1h) or np.isnan(ema_50[h_idx]):
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continue
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direction = None
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if rsi_15m[i] < 30 and c1h[h_idx] > ema_50[h_idx]:
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direction = "long"
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elif rsi_15m[i] > 70 and c1h[h_idx] < ema_50[h_idx]:
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direction = "short"
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if direction is None:
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continue
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entry = c15[i]
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exit_price = c15[min(i+12, n15-1)]
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trade_ret = (exit_price-entry)/entry if direction == "long" else (entry-exit_price)/entry
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net = trade_ret * LEVERAGE - FEE_PERP * LEVERAGE
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year = ts_dt.year
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if year not in yearly:
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yearly[year] = {"pnls": [], "wins": 0, "total": 0}
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yearly[year]["pnls"].append(net)
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yearly[year]["total"] += 1
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if trade_ret > 0:
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yearly[year]["wins"] += 1
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daily_done.add(day)
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return yearly
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# =====================================================================
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# REPORT
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# =====================================================================
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strategies = {
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"S1: Squeeze BTC 1h": run_s1_squeeze("BTC", "1h"),
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"S1: Squeeze ETH 1h": run_s1_squeeze("ETH", "1h"),
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"S1: Squeeze ETH 15m": run_s1_squeeze("ETH", "15m"),
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"S2: VRP ETH 48h (IV est)": run_s2_vrp("ETH", 48),
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"S2: VRP BTC 48h (IV est)": run_s2_vrp("BTC", 48),
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"S2: MultiTF BTC 15m+1h": run_s2_multitf("BTC"),
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"S2: MultiTF ETH 15m+1h": run_s2_multitf("ETH"),
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}
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all_years = sorted(set(y for v in strategies.values() for y in v))
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print("=" * 120)
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print(" MIGLIORI STRATEGIE — ANDAMENTO PER ANNO")
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print(" Fee reali. PnL su €1000 flat (no compounding). Dati OHLCV reali 2018-2026.")
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print(" ⚠ VRP usa IV STIMATA (non reale) — fidarsi solo dei dati perpetual per backtest lungo")
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print("=" * 120)
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# Header
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hdr = f" {'Anno':>6s}"
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for name in strategies:
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short = name.split(": ")[1][:18]
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hdr += f" | {short:>18s}"
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print(hdr)
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print(f" {'-' * (len(hdr) - 2)}")
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# Per anno: accuracy / PnL totale
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for year in all_years:
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row_acc = f" {year:>6d}"
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row_pnl = f" {'':>6s}"
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for name, yearly in strategies.items():
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if year in yearly:
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d = yearly[year]
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acc = d["wins"]/d["total"]*100 if d["total"] > 0 else 0
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pnl = sum(d["pnls"]) * INITIAL
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tag = "▓" if acc >= 75 else "▒" if acc >= 65 else "░" if acc >= 55 else " "
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row_acc += f" | {acc:>5.1f}% {tag} {d['total']:>3d}t"
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row_pnl += f" | €{pnl:>+8.0f} "
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else:
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row_acc += f" | {'—':>18s}"
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row_pnl += f" | {'':>18s}"
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print(row_acc)
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print(row_pnl)
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# Totali
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print(f" {'-' * (len(hdr) - 2)}")
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row_tot = f" {'TOT':>6s}"
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for name, yearly in strategies.items():
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all_pnls = [p for d in yearly.values() for p in d["pnls"]]
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all_wins = sum(d["wins"] for d in yearly.values())
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all_total = sum(d["total"] for d in yearly.values())
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acc = all_wins/all_total*100 if all_total > 0 else 0
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pnl = sum(all_pnls) * INITIAL
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row_tot += f" | {acc:>5.1f}% {all_total:>4d}t"
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print(row_tot)
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row_pnl_tot = f" {'€TOT':>6s}"
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for name, yearly in strategies.items():
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all_pnls = [p for d in yearly.values() for p in d["pnls"]]
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pnl = sum(all_pnls) * INITIAL
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row_pnl_tot += f" | €{pnl:>+8.0f} "
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print(row_pnl_tot)
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# Compounding
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print(f"\n {'':>6s}", end="")
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for name in strategies:
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short = name.split(": ")[1][:18]
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print(f" | {short:>18s}", end="")
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print()
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row_comp = f" {'COMP':>6s}"
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for name, yearly in strategies.items():
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cap = float(INITIAL)
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for year in sorted(yearly):
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for pnl in yearly[year]["pnls"]:
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cap += cap * pnl
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cap = max(cap, 10)
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row_comp += f" | €{cap:>12,.0f} "
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print(row_comp)
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# Drawdown
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row_dd = f" {'MAXDD':>6s}"
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for name, yearly in strategies.items():
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cap = float(INITIAL)
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peak = cap
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mdd = 0
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for year in sorted(yearly):
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for pnl in yearly[year]["pnls"]:
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cap += cap * pnl
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cap = max(cap, 10)
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if cap > peak: peak = cap
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dd = (peak - cap) / peak
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mdd = max(mdd, dd)
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row_dd += f" | {mdd*100:>12.1f}% "
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print(row_dd)
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# Legenda
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print(f"\n Legenda: ▓ ≥75% acc ▒ ≥65% acc ░ ≥55% acc")
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print(f" ⚠ S2 VRP: IV stimata (rv_long × 1.0-1.2), NON dati reali opzioni")
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print(f" S1 Squeeze e S2 MultiTF: dati OHLCV reali al 100%")
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@@ -1,559 +0,0 @@
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"""Analisi finale — S1 (squeeze puro) vs Script 13 (squeeze+ML GBM).
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Metriche: PnL, num trades, DD max, tempo medio a mercato, descrizione.
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"""
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||||||
from __future__ import annotations
|
|
||||||
import sys
|
|
||||||
sys.path.insert(0, ".")
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
import pandas as pd
|
|
||||||
from sklearn.ensemble import GradientBoostingClassifier
|
|
||||||
from sklearn.preprocessing import StandardScaler
|
|
||||||
from src.data.downloader import load_data
|
|
||||||
from src.fractal.patterns import encode_candles
|
|
||||||
|
|
||||||
FEE_PERP = 0.002
|
|
||||||
FEE_ML = 0.001
|
|
||||||
INITIAL = 1000
|
|
||||||
LEVERAGE = 3
|
|
||||||
|
|
||||||
TF_MINUTES = {"1m": 1, "5m": 5, "15m": 15, "1h": 60, "4h": 240, "1d": 1440}
|
|
||||||
|
|
||||||
|
|
||||||
# ── helpers ──────────────────────────────────────────────────────────
|
|
||||||
|
|
||||||
def keltner_ratio(close, high, low, window=14):
|
|
||||||
n = len(close)
|
|
||||||
r = np.full(n, np.nan)
|
|
||||||
for i in range(window, n):
|
|
||||||
wc, wh, wl = close[i-window:i], high[i-window:i], low[i-window:i]
|
|
||||||
ma = np.mean(wc)
|
|
||||||
bb_std = np.std(wc)
|
|
||||||
tr = np.maximum(wh-wl, np.maximum(np.abs(wh-np.roll(wc,1)), np.abs(wl-np.roll(wc,1))))
|
|
||||||
atr = np.mean(tr[1:])
|
|
||||||
kc = (ma+1.5*atr)-(ma-1.5*atr)
|
|
||||||
bb = (ma+2*bb_std)-(ma-2*bb_std)
|
|
||||||
if kc > 0:
|
|
||||||
r[i] = bb/kc
|
|
||||||
return r
|
|
||||||
|
|
||||||
|
|
||||||
def detect_squeezes(close, high, low, kcr, sq_thr=0.8, min_dur=5):
|
|
||||||
events = []
|
|
||||||
in_sq = False
|
|
||||||
sq_start = 0
|
|
||||||
for i in range(1, len(close)):
|
|
||||||
if np.isnan(kcr[i]):
|
|
||||||
continue
|
|
||||||
is_sq = kcr[i] < sq_thr
|
|
||||||
if is_sq and not in_sq:
|
|
||||||
in_sq = True
|
|
||||||
sq_start = i
|
|
||||||
elif not is_sq and in_sq:
|
|
||||||
in_sq = False
|
|
||||||
dur = i - sq_start
|
|
||||||
if dur < min_dur:
|
|
||||||
continue
|
|
||||||
events.append({"idx": i, "dur": dur, "sq_start": sq_start,
|
|
||||||
"avg_vol_squeeze": np.mean(close[sq_start:i]),
|
|
||||||
"kcr_at_release": kcr[i]})
|
|
||||||
return events
|
|
||||||
|
|
||||||
|
|
||||||
def _build_result(yearly, capital, max_dd, all_t, all_w, time_pct, avg_dur_h):
|
|
||||||
acc = all_w / all_t * 100
|
|
||||||
tot_pnl = sum(p for d in yearly.values() for p in d["pnls"])
|
|
||||||
years_active = len(yearly)
|
|
||||||
all_pnls = [p for d in yearly.values() for p in d["pnls"]]
|
|
||||||
sharpe = np.mean(all_pnls) / np.std(all_pnls) * np.sqrt(252) if len(all_pnls) > 1 and np.std(all_pnls) > 0 else 0
|
|
||||||
|
|
||||||
year_details = {}
|
|
||||||
for y in sorted(yearly):
|
|
||||||
d = yearly[y]
|
|
||||||
ya = d["w"] / d["t"] * 100 if d["t"] > 0 else 0
|
|
||||||
yp = sum(d["pnls"])
|
|
||||||
year_details[y] = {"trades": d["t"], "acc": ya, "pnl": yp}
|
|
||||||
|
|
||||||
valid_years = {y: d for y, d in year_details.items() if d["trades"] >= 10}
|
|
||||||
if valid_years:
|
|
||||||
worst_y = min(valid_years, key=lambda y: valid_years[y]["acc"])
|
|
||||||
worst_acc = valid_years[worst_y]["acc"]
|
|
||||||
elif year_details:
|
|
||||||
worst_y = min(year_details, key=lambda y: year_details[y]["acc"])
|
|
||||||
worst_acc = year_details[worst_y]["acc"]
|
|
||||||
else:
|
|
||||||
worst_y = "N/A"
|
|
||||||
worst_acc = 0
|
|
||||||
|
|
||||||
daily_pnl = tot_pnl / (years_active * 365) if years_active > 0 else 0
|
|
||||||
|
|
||||||
return {
|
|
||||||
"trades": all_t, "acc": acc, "pnl": tot_pnl, "capital": capital,
|
|
||||||
"max_dd": max_dd * 100, "sharpe": sharpe, "daily_pnl": daily_pnl,
|
|
||||||
"time_in_market_pct": time_pct, "avg_dur_h": avg_dur_h,
|
|
||||||
"years_active": years_active, "worst_year": str(worst_y),
|
|
||||||
"worst_acc": worst_acc, "year_details": year_details,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
# ── S1: Squeeze breakout puro ────────────────────────────────────────
|
|
||||||
|
|
||||||
def run_s1_squeeze(asset, tf, hold=3):
|
|
||||||
df = load_data(asset, tf)
|
|
||||||
c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
|
|
||||||
n = len(c)
|
|
||||||
ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
|
||||||
kcr = keltner_ratio(c, h, l, 14)
|
|
||||||
events = detect_squeezes(c, h, l, kcr)
|
|
||||||
|
|
||||||
yearly = {}
|
|
||||||
capital = float(INITIAL)
|
|
||||||
peak = capital
|
|
||||||
max_dd = 0
|
|
||||||
total_bars = 0
|
|
||||||
|
|
||||||
for ev in events:
|
|
||||||
i = ev["idx"]
|
|
||||||
if i + hold + 1 >= n:
|
|
||||||
continue
|
|
||||||
first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
|
|
||||||
if abs(first_ret) < 0.001:
|
|
||||||
continue
|
|
||||||
direction = 1 if first_ret > 0 else -1
|
|
||||||
entry = c[i-1]
|
|
||||||
exit_price = c[min(i + hold - 1, n - 1)]
|
|
||||||
actual = (exit_price - entry) / entry * direction
|
|
||||||
net = actual * LEVERAGE - FEE_PERP * LEVERAGE
|
|
||||||
|
|
||||||
capital += capital * 0.15 * net
|
|
||||||
capital = max(capital, 10)
|
|
||||||
if capital > peak: peak = capital
|
|
||||||
dd = (peak - capital) / peak
|
|
||||||
max_dd = max(max_dd, dd)
|
|
||||||
total_bars += hold
|
|
||||||
|
|
||||||
year = ts.iloc[i].year
|
|
||||||
if year not in yearly:
|
|
||||||
yearly[year] = {"w": 0, "t": 0, "pnls": []}
|
|
||||||
yearly[year]["t"] += 1
|
|
||||||
if actual > 0: yearly[year]["w"] += 1
|
|
||||||
yearly[year]["pnls"].append(net * INITIAL)
|
|
||||||
|
|
||||||
all_t = sum(d["t"] for d in yearly.values())
|
|
||||||
all_w = sum(d["w"] for d in yearly.values())
|
|
||||||
if all_t == 0: return None
|
|
||||||
return _build_result(yearly, capital, max_dd, all_t, all_w,
|
|
||||||
total_bars / n * 100, hold * TF_MINUTES.get(tf, 60) / 60)
|
|
||||||
|
|
||||||
|
|
||||||
def run_s1_antifake_vol(asset, tf, hold=3):
|
|
||||||
df = load_data(asset, tf)
|
|
||||||
c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
|
|
||||||
n = len(c)
|
|
||||||
ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
|
||||||
kcr = keltner_ratio(c, h, l, 14)
|
|
||||||
events = detect_squeezes(c, h, l, kcr)
|
|
||||||
|
|
||||||
yearly = {}
|
|
||||||
capital = float(INITIAL)
|
|
||||||
peak = capital
|
|
||||||
max_dd = 0
|
|
||||||
total_bars = 0
|
|
||||||
|
|
||||||
for ev in events:
|
|
||||||
i = ev["idx"]
|
|
||||||
if i + hold + 1 >= n:
|
|
||||||
continue
|
|
||||||
first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
|
|
||||||
if abs(first_ret) < 0.001:
|
|
||||||
continue
|
|
||||||
br = h[i] - l[i]
|
|
||||||
if br > 0:
|
|
||||||
if c[i] > c[i-1]:
|
|
||||||
if (h[i] - c[i]) / br > 0.6:
|
|
||||||
continue
|
|
||||||
else:
|
|
||||||
if (c[i] - l[i]) / br > 0.6:
|
|
||||||
continue
|
|
||||||
avg_v = np.mean(v[ev["sq_start"]:i])
|
|
||||||
if avg_v > 0 and v[i] <= avg_v * 1.3:
|
|
||||||
continue
|
|
||||||
|
|
||||||
direction = 1 if first_ret > 0 else -1
|
|
||||||
entry = c[i-1]
|
|
||||||
exit_price = c[min(i + hold - 1, n - 1)]
|
|
||||||
actual = (exit_price - entry) / entry * direction
|
|
||||||
net = actual * LEVERAGE - FEE_PERP * LEVERAGE
|
|
||||||
capital += capital * 0.15 * net
|
|
||||||
capital = max(capital, 10)
|
|
||||||
if capital > peak: peak = capital
|
|
||||||
dd = (peak - capital) / peak
|
|
||||||
max_dd = max(max_dd, dd)
|
|
||||||
total_bars += hold
|
|
||||||
|
|
||||||
year = ts.iloc[i].year
|
|
||||||
if year not in yearly:
|
|
||||||
yearly[year] = {"w": 0, "t": 0, "pnls": []}
|
|
||||||
yearly[year]["t"] += 1
|
|
||||||
if actual > 0: yearly[year]["w"] += 1
|
|
||||||
yearly[year]["pnls"].append(net * INITIAL)
|
|
||||||
|
|
||||||
all_t = sum(d["t"] for d in yearly.values())
|
|
||||||
all_w = sum(d["w"] for d in yearly.values())
|
|
||||||
if all_t == 0: return None
|
|
||||||
return _build_result(yearly, capital, max_dd, all_t, all_w,
|
|
||||||
total_bars / n * 100, hold * TF_MINUTES.get(tf, 60) / 60)
|
|
||||||
|
|
||||||
|
|
||||||
# ── Script 13: Squeeze + ML ibrida (GBM walk-forward) ────────────────
|
|
||||||
|
|
||||||
def build_features_at(df, i, squeeze_info):
|
|
||||||
if i < 100:
|
|
||||||
return None
|
|
||||||
o = df["open"].values
|
|
||||||
h = df["high"].values
|
|
||||||
l = df["low"].values
|
|
||||||
c = df["close"].values
|
|
||||||
v = df["volume"].values
|
|
||||||
feats = []
|
|
||||||
for w in [12, 24, 48]:
|
|
||||||
win_c = c[i-w:i]
|
|
||||||
win_o = o[i-w:i]
|
|
||||||
win_h = h[i-w:i]
|
|
||||||
win_l = l[i-w:i]
|
|
||||||
win_v = v[i-w:i]
|
|
||||||
mn, mx = win_l.min(), max(win_h.max(), win_c.max())
|
|
||||||
rng = mx - mn if mx - mn > 0 else 1e-10
|
|
||||||
total = win_h - win_l
|
|
||||||
total = np.where(total == 0, 1e-10, total)
|
|
||||||
body = np.abs(win_c - win_o) / total
|
|
||||||
direction = np.sign(win_c - win_o)
|
|
||||||
log_c = np.log(np.where(win_c == 0, 1e-10, win_c))
|
|
||||||
rets = np.diff(log_c)
|
|
||||||
v_mean = np.mean(win_v)
|
|
||||||
feats.extend([
|
|
||||||
np.mean(rets) if len(rets) > 0 else 0,
|
|
||||||
np.std(rets) if len(rets) > 0 else 0,
|
|
||||||
np.sum(rets) if len(rets) > 0 else 0,
|
|
||||||
float(pd.Series(rets).skew()) if len(rets) > 2 else 0,
|
|
||||||
float(pd.Series(rets).kurtosis()) if len(rets) > 3 else 0,
|
|
||||||
np.mean(body), np.std(body),
|
|
||||||
np.mean(direction), np.mean(direction[-min(3, w):]),
|
|
||||||
(win_c[-1] - mn) / rng,
|
|
||||||
win_v[-1] / v_mean if v_mean > 0 else 1,
|
|
||||||
np.corrcoef(rets[:-1], rets[1:])[0, 1] if len(rets) > 1 and np.std(rets) > 0 else 0,
|
|
||||||
])
|
|
||||||
sq = squeeze_info
|
|
||||||
feats.extend([
|
|
||||||
sq["dur"], sq["dur"] / 24, sq["kcr_at_release"],
|
|
||||||
v[i-1] / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
|
|
||||||
np.mean(v[i:min(i+3, len(v))]) / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
|
|
||||||
])
|
|
||||||
h48 = np.max(h[max(0, i-48):i])
|
|
||||||
l48 = np.min(l[max(0, i-48):i])
|
|
||||||
r48 = h48 - l48
|
|
||||||
feats.append((c[i-1] - l48) / r48 if r48 > 0 else 0.5)
|
|
||||||
tr = np.maximum(h[i-14:i] - l[i-14:i],
|
|
||||||
np.maximum(np.abs(h[i-14:i] - np.roll(c[i-14:i], 1)),
|
|
||||||
np.abs(l[i-14:i] - np.roll(c[i-14:i], 1))))
|
|
||||||
atr = np.mean(tr[1:])
|
|
||||||
feats.append(atr / c[i-1] if c[i-1] > 0 else 0)
|
|
||||||
first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
|
|
||||||
feats.append(first_ret)
|
|
||||||
return np.nan_to_num(np.array(feats), nan=0, posinf=1e6, neginf=-1e6)
|
|
||||||
|
|
||||||
|
|
||||||
def run_s13_hybrid(asset, tf, bb_w, sq_thr, brk_bars, leverage, pos_pct, ml_thr):
|
|
||||||
df = load_data(asset, tf)
|
|
||||||
close = df["close"].values
|
|
||||||
high = df["high"].values
|
|
||||||
low = df["low"].values
|
|
||||||
volume = df["volume"].values
|
|
||||||
n = len(df)
|
|
||||||
ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
|
||||||
|
|
||||||
kcr = keltner_ratio(close, high, low, bb_w)
|
|
||||||
events = detect_squeezes(close, high, low, kcr, sq_thr)
|
|
||||||
|
|
||||||
X_all, y_all, ev_all = [], [], []
|
|
||||||
for ev in events:
|
|
||||||
i = ev["idx"]
|
|
||||||
if i + brk_bars >= n or i < 100:
|
|
||||||
continue
|
|
||||||
feats = build_features_at(df, i, ev)
|
|
||||||
if feats is None:
|
|
||||||
continue
|
|
||||||
actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
|
|
||||||
X_all.append(feats)
|
|
||||||
y_all.append(1 if actual_ret > 0 else 0)
|
|
||||||
ev_all.append(ev)
|
|
||||||
|
|
||||||
if len(X_all) < 50:
|
|
||||||
return None
|
|
||||||
|
|
||||||
X = np.array(X_all)
|
|
||||||
y = np.array(y_all)
|
|
||||||
|
|
||||||
TRAIN_SIZE = max(int(len(X) * 0.5), 50)
|
|
||||||
STEP_SIZE = max(int(len(X) * 0.1), 10)
|
|
||||||
|
|
||||||
yearly = {}
|
|
||||||
capital = float(INITIAL)
|
|
||||||
peak = capital
|
|
||||||
max_dd = 0
|
|
||||||
total_bars = 0
|
|
||||||
all_t = 0
|
|
||||||
all_w = 0
|
|
||||||
|
|
||||||
start = 0
|
|
||||||
while start + TRAIN_SIZE + STEP_SIZE <= len(X):
|
|
||||||
train_end = start + TRAIN_SIZE
|
|
||||||
test_end = min(train_end + STEP_SIZE, len(X))
|
|
||||||
X_tr, y_tr = X[start:train_end], y[start:train_end]
|
|
||||||
X_te = X[train_end:test_end]
|
|
||||||
|
|
||||||
if len(np.unique(y_tr)) < 2:
|
|
||||||
start += STEP_SIZE
|
|
||||||
continue
|
|
||||||
|
|
||||||
scaler = StandardScaler()
|
|
||||||
X_tr_s = scaler.fit_transform(X_tr)
|
|
||||||
X_te_s = scaler.transform(X_te)
|
|
||||||
|
|
||||||
model = GradientBoostingClassifier(
|
|
||||||
n_estimators=150, max_depth=4, min_samples_leaf=10,
|
|
||||||
learning_rate=0.05, subsample=0.8, random_state=42,
|
|
||||||
)
|
|
||||||
model.fit(X_tr_s, y_tr)
|
|
||||||
|
|
||||||
up_idx = list(model.classes_).index(1) if 1 in model.classes_ else -1
|
|
||||||
if up_idx < 0:
|
|
||||||
start += STEP_SIZE
|
|
||||||
continue
|
|
||||||
|
|
||||||
for j in range(len(X_te)):
|
|
||||||
proba = model.predict_proba(X_te_s[j:j+1])[0]
|
|
||||||
p_up = proba[up_idx]
|
|
||||||
|
|
||||||
ev = ev_all[train_end + j]
|
|
||||||
i = ev["idx"]
|
|
||||||
actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
|
|
||||||
|
|
||||||
direction = None
|
|
||||||
if p_up >= ml_thr:
|
|
||||||
direction = 1
|
|
||||||
elif p_up <= (1 - ml_thr):
|
|
||||||
direction = -1
|
|
||||||
if direction is None:
|
|
||||||
continue
|
|
||||||
|
|
||||||
is_correct = (direction == 1 and actual_ret > 0) or (direction == -1 and actual_ret < 0)
|
|
||||||
trade_ret = actual_ret * direction
|
|
||||||
net = trade_ret * leverage - FEE_ML * 2 * leverage
|
|
||||||
capital += capital * pos_pct * net
|
|
||||||
capital = max(capital, 10)
|
|
||||||
if capital > peak: peak = capital
|
|
||||||
dd = (peak - capital) / peak
|
|
||||||
max_dd = max(max_dd, dd)
|
|
||||||
total_bars += brk_bars
|
|
||||||
|
|
||||||
all_t += 1
|
|
||||||
if is_correct: all_w += 1
|
|
||||||
|
|
||||||
year = ts.iloc[i].year
|
|
||||||
if year not in yearly:
|
|
||||||
yearly[year] = {"w": 0, "t": 0, "pnls": []}
|
|
||||||
yearly[year]["t"] += 1
|
|
||||||
if is_correct: yearly[year]["w"] += 1
|
|
||||||
yearly[year]["pnls"].append(net * INITIAL)
|
|
||||||
|
|
||||||
start += STEP_SIZE
|
|
||||||
|
|
||||||
if all_t == 0:
|
|
||||||
return None
|
|
||||||
return _build_result(yearly, capital, max_dd, all_t, all_w,
|
|
||||||
total_bars / n * 100, brk_bars * TF_MINUTES.get(tf, 60) / 60)
|
|
||||||
|
|
||||||
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
# ESECUZIONE
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
|
|
||||||
print("Calcolo in corso...\n")
|
|
||||||
|
|
||||||
strategies = []
|
|
||||||
|
|
||||||
def add(name, desc, cat, result):
|
|
||||||
if result and result["trades"] >= 20:
|
|
||||||
strategies.append({"name": name, "desc": desc, "cat": cat, **result})
|
|
||||||
|
|
||||||
# ── S1: Squeeze puro ────────────────────────────────────────────
|
|
||||||
add("S1 Squeeze BTC 15m", "Squeeze breakout puro, BBw=14, hold 3×15m, leva 3x",
|
|
||||||
"S1", run_s1_squeeze("BTC", "15m"))
|
|
||||||
add("S1 Squeeze ETH 15m", "Squeeze breakout puro, BBw=14, hold 3×15m, leva 3x",
|
|
||||||
"S1", run_s1_squeeze("ETH", "15m"))
|
|
||||||
add("S1 Squeeze BTC 1h", "Squeeze breakout puro, BBw=14, hold 3×1h, leva 3x",
|
|
||||||
"S1", run_s1_squeeze("BTC", "1h"))
|
|
||||||
add("S1 Squeeze ETH 1h", "Squeeze breakout puro, BBw=14, hold 3×1h, leva 3x",
|
|
||||||
"S1", run_s1_squeeze("ETH", "1h"))
|
|
||||||
add("S1 AF+Vol BTC 15m", "Squeeze + antifakeout + volume confirm >1.3× media",
|
|
||||||
"S1", run_s1_antifake_vol("BTC", "15m"))
|
|
||||||
add("S1 AF+Vol ETH 15m", "Squeeze + antifakeout + volume confirm >1.3× media",
|
|
||||||
"S1", run_s1_antifake_vol("ETH", "15m"))
|
|
||||||
add("S1 AF+Vol BTC 1h", "Squeeze + antifakeout + volume confirm >1.3× media",
|
|
||||||
"S1", run_s1_antifake_vol("BTC", "1h"))
|
|
||||||
add("S1 AF+Vol ETH 1h", "Squeeze + antifakeout + volume confirm >1.3× media",
|
|
||||||
"S1", run_s1_antifake_vol("ETH", "1h"))
|
|
||||||
|
|
||||||
# ── Script 13: Squeeze + ML (GBM walk-forward) ─────────────────
|
|
||||||
print(" Training ML models...")
|
|
||||||
add("S13 ETH 15m bb14 ml70", "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.70, 3x leva 15% pos",
|
|
||||||
"S13", run_s13_hybrid("ETH", "15m", 14, 0.8, 3, 3, 0.15, 0.70))
|
|
||||||
add("S13 ETH 15m bb14 ml65", "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.65, 3x leva 15% pos",
|
|
||||||
"S13", run_s13_hybrid("ETH", "15m", 14, 0.8, 3, 3, 0.15, 0.65))
|
|
||||||
add("S13 ETH 15m bb20 ml70", "Squeeze+GBM walk-forward, BBw=20 sq=0.9 ml≥0.70, 3x leva 15% pos",
|
|
||||||
"S13", run_s13_hybrid("ETH", "15m", 20, 0.9, 3, 3, 0.15, 0.70))
|
|
||||||
add("S13 BTC 15m bb14 ml70", "Squeeze+GBM walk-forward, BBw=14 sq=0.9 ml≥0.70, 3x leva 15% pos",
|
|
||||||
"S13", run_s13_hybrid("BTC", "15m", 14, 0.9, 3, 3, 0.15, 0.70))
|
|
||||||
add("S13 BTC 15m bb14 ml65", "Squeeze+GBM walk-forward, BBw=14 sq=0.9 ml≥0.65, 3x leva 15% pos",
|
|
||||||
"S13", run_s13_hybrid("BTC", "15m", 14, 0.9, 3, 3, 0.15, 0.65))
|
|
||||||
add("S13 BTC 1h bb14 ml70", "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.70, 3x leva 20% pos",
|
|
||||||
"S13", run_s13_hybrid("BTC", "1h", 14, 0.8, 3, 3, 0.20, 0.70))
|
|
||||||
add("S13 BTC 1h bb14 ml65", "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.65, 3x leva 20% pos",
|
|
||||||
"S13", run_s13_hybrid("BTC", "1h", 14, 0.8, 3, 3, 0.20, 0.65))
|
|
||||||
add("S13 ETH 1h bb14 ml70", "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.70, 3x leva 20% pos",
|
|
||||||
"S13", run_s13_hybrid("ETH", "1h", 14, 0.8, 3, 3, 0.20, 0.70))
|
|
||||||
|
|
||||||
strategies.sort(key=lambda x: x["acc"], reverse=True)
|
|
||||||
|
|
||||||
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
# TABELLA 1: Classifica
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
|
|
||||||
W = 150
|
|
||||||
print("=" * W)
|
|
||||||
print(" S1 (SQUEEZE PURO) vs S13 (SQUEEZE + GBM) — CLASSIFICA FINALE")
|
|
||||||
print(f" Fee: 0.2% RT. Dati OHLCV reali 2018-2026. Position 15%. Leva 3x.")
|
|
||||||
print("=" * W)
|
|
||||||
hdr = (f" {'#':>2s} {'Cat':>3s} {'Nome':<26s} {'Trades':>6s} {'Acc':>6s} "
|
|
||||||
f"{'PnL€':>9s} {'DD%':>6s} {'€/day':>7s} {'Sharpe':>7s} "
|
|
||||||
f"{'Mkt%':>5s} {'Dur':>6s} {'Worst':>12s} {'Anni':>4s}")
|
|
||||||
print(hdr)
|
|
||||||
print(f" {'─'*(W-4)}")
|
|
||||||
|
|
||||||
for idx, s in enumerate(strategies, 1):
|
|
||||||
worst = f"{s['worst_year']}({s['worst_acc']:.0f}%)"
|
|
||||||
dur_str = f"{s['avg_dur_h']:.0f}h" if s['avg_dur_h'] >= 1 else f"{s['avg_dur_h']*60:.0f}m"
|
|
||||||
tag = " ★★" if s["acc"] >= 78 else " ★" if s["acc"] >= 76 else ""
|
|
||||||
print(f" {idx:>2d} {s['cat']:>3s} {s['name']:<26s} {s['trades']:>6d} {s['acc']:>5.1f}% "
|
|
||||||
f"€{s['pnl']:>+8.0f} {s['max_dd']:>5.1f}% {s['daily_pnl']:>+6.2f} {s['sharpe']:>7.2f} "
|
|
||||||
f"{s['time_in_market_pct']:>4.1f}% {dur_str:>6s} {worst:>12s} {s['years_active']:>4d}{tag}")
|
|
||||||
|
|
||||||
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
# TABELLA 2: Descrizione
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
|
|
||||||
print(f"\n\n{'=' * W}")
|
|
||||||
print(" DESCRIZIONE")
|
|
||||||
print(f"{'=' * W}")
|
|
||||||
print(f" {'#':>2s} {'Nome':<26s} {'Descrizione'}")
|
|
||||||
print(f" {'─'*(W-4)}")
|
|
||||||
for idx, s in enumerate(strategies, 1):
|
|
||||||
print(f" {idx:>2d} {s['name']:<26s} {s['desc']}")
|
|
||||||
|
|
||||||
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
# TABELLA 3: Breakdown per anno
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
|
|
||||||
top_n = min(12, len(strategies))
|
|
||||||
top = strategies[:top_n]
|
|
||||||
all_years = sorted(set(y for s in top for y in s["year_details"]))
|
|
||||||
|
|
||||||
print(f"\n\n{'=' * W}")
|
|
||||||
print(f" BREAKDOWN PER ANNO — TOP {top_n} (accuracy% / trades)")
|
|
||||||
print(f"{'=' * W}")
|
|
||||||
|
|
||||||
header = f" {'Nome':<26s}"
|
|
||||||
for y in all_years:
|
|
||||||
header += f" {y:>10d}"
|
|
||||||
print(header)
|
|
||||||
print(f" {'─'*(W-4)}")
|
|
||||||
|
|
||||||
for s in top:
|
|
||||||
line = f" {s['name']:<26s}"
|
|
||||||
for y in all_years:
|
|
||||||
if y in s["year_details"]:
|
|
||||||
d = s["year_details"][y]
|
|
||||||
line += f" {d['acc']:>4.0f}%/{d['trades']:<4d}"
|
|
||||||
else:
|
|
||||||
line += f" {'—':>10s}"
|
|
||||||
print(line)
|
|
||||||
|
|
||||||
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
# TABELLA 4: Robustezza
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
|
|
||||||
print(f"\n\n{'=' * W}")
|
|
||||||
print(f" ANALISI ROBUSTEZZA")
|
|
||||||
print(f"{'=' * W}")
|
|
||||||
print(f" {'#':>2s} {'Nome':<26s} {'MinAcc':>7s} {'MaxAcc':>7s} {'Spread':>7s} "
|
|
||||||
f"{'AnniOK':>7s} {'€/trade':>8s} {'Verdict':<12s}")
|
|
||||||
print(f" {'─'*90}")
|
|
||||||
|
|
||||||
for idx, s in enumerate(strategies, 1):
|
|
||||||
yd = s["year_details"]
|
|
||||||
valid = {y: d for y, d in yd.items() if d["trades"] >= 10}
|
|
||||||
accs = [d["acc"] for d in (valid if valid else yd).values()]
|
|
||||||
if not accs:
|
|
||||||
continue
|
|
||||||
min_a, max_a = min(accs), max(accs)
|
|
||||||
spread = max_a - min_a
|
|
||||||
years_ok = sum(1 for a in accs if a >= 70)
|
|
||||||
avg_pnl = s["pnl"] / s["trades"] if s["trades"] > 0 else 0
|
|
||||||
n_valid = len(valid if valid else yd)
|
|
||||||
|
|
||||||
if n_valid < 4:
|
|
||||||
verdict = "⚠ CORTO"
|
|
||||||
elif min_a < 60:
|
|
||||||
verdict = "⚠ FRAGILE"
|
|
||||||
elif min_a >= 72 and s["acc"] >= 77:
|
|
||||||
verdict = "✅ SOLIDO"
|
|
||||||
elif min_a >= 65 and s["acc"] >= 74:
|
|
||||||
verdict = "~ BUONO"
|
|
||||||
else:
|
|
||||||
verdict = "~ OK"
|
|
||||||
|
|
||||||
print(f" {idx:>2d} {s['name']:<26s} {min_a:>6.1f}% {max_a:>6.1f}% {spread:>6.1f}% "
|
|
||||||
f"{years_ok:>3d}/{n_valid:<3d} €{avg_pnl:>+7.1f} {verdict:<12s}")
|
|
||||||
|
|
||||||
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
# VERDETTO
|
|
||||||
# ═══════════════════════════════════════════════════════════════════
|
|
||||||
|
|
||||||
print(f"\n\n{'=' * W}")
|
|
||||||
print(f" VERDETTO FINALE")
|
|
||||||
print(f"{'=' * W}")
|
|
||||||
|
|
||||||
solidi = [s for s in strategies if s["trades"] >= 200 and s["years_active"] >= 5 and s["worst_acc"] >= 65]
|
|
||||||
solidi_s1 = [s for s in solidi if s["cat"] == "S1"]
|
|
||||||
solidi_ml = [s for s in solidi if s["cat"] == "S13"]
|
|
||||||
solidi_s1.sort(key=lambda x: x["acc"], reverse=True)
|
|
||||||
solidi_ml.sort(key=lambda x: x["daily_pnl"], reverse=True)
|
|
||||||
|
|
||||||
if solidi_s1:
|
|
||||||
b = solidi_s1[0]
|
|
||||||
print(f"\n MIGLIORE S1 (regole pure, facile da deployare):")
|
|
||||||
print(f" {b['name']} — {b['acc']:.1f}% acc, {b['trades']} trades, DD {b['max_dd']:.1f}%, €{b['daily_pnl']:+.2f}/day, Sharpe {b['sharpe']:.2f}")
|
|
||||||
|
|
||||||
if solidi_ml:
|
|
||||||
m = solidi_ml[0]
|
|
||||||
print(f"\n MIGLIORE S13 (squeeze+GBM, più complesso):")
|
|
||||||
print(f" {m['name']} — {m['acc']:.1f}% acc, {m['trades']} trades, DD {m['max_dd']:.1f}%, €{m['daily_pnl']:+.2f}/day, Sharpe {m['sharpe']:.2f}")
|
|
||||||
|
|
||||||
max_pnl = max(strategies, key=lambda x: x["pnl"])
|
|
||||||
print(f"\n MAX PnL: {max_pnl['name']} — €{max_pnl['pnl']:+,.0f}")
|
|
||||||
@@ -1,298 +0,0 @@
|
|||||||
"""Report finale: TOP 5 metodi + simulazione crescita capitale €1000 → €50/giorno."""
|
|
||||||
from __future__ import annotations
|
|
||||||
import sys
|
|
||||||
sys.path.insert(0, ".")
|
|
||||||
|
|
||||||
import numpy as np
|
|
||||||
from src.data.downloader import load_data
|
|
||||||
|
|
||||||
print("=" * 70)
|
|
||||||
print(" REPORT FINALE — TOP 5 METODI")
|
|
||||||
print(" Target: accuracy >80%, ROI annuo >30%, €50/giorno da €1000")
|
|
||||||
print("=" * 70)
|
|
||||||
|
|
||||||
# Metodo 1: Squeeze Breakout ETH 1h (BBw=20, sqThr=0.8, volume confirmed)
|
|
||||||
# Metodo 2: Squeeze Breakout ETH 1h (BBw=30, sqThr=0.9, senza vol filter)
|
|
||||||
# Metodo 3: Squeeze Breakout BTC+ETH combinato
|
|
||||||
# Metodo 4: Squeeze Breakout 15m (alta frequenza)
|
|
||||||
# Metodo 5: GBM Structural + Squeeze filter (ibrido ML + strutturale)
|
|
||||||
|
|
||||||
FEE = 0.001
|
|
||||||
LEVERAGE = 3
|
|
||||||
INITIAL = 1000
|
|
||||||
|
|
||||||
|
|
||||||
def bollinger_bandwidth(close, window=20):
|
|
||||||
n = len(close)
|
|
||||||
result = np.full(n, np.nan)
|
|
||||||
for i in range(window, n):
|
|
||||||
w = close[i-window:i]
|
|
||||||
ma = np.mean(w)
|
|
||||||
std = np.std(w)
|
|
||||||
if ma > 0:
|
|
||||||
result[i] = (2 * 2 * std) / ma
|
|
||||||
return result
|
|
||||||
|
|
||||||
|
|
||||||
def keltner_ratio(close, high, low, window=20):
|
|
||||||
n = len(close)
|
|
||||||
result = np.full(n, np.nan)
|
|
||||||
for i in range(window, n):
|
|
||||||
wc = close[i-window:i]
|
|
||||||
wh = high[i-window:i]
|
|
||||||
wl = low[i-window:i]
|
|
||||||
ma = np.mean(wc)
|
|
||||||
bb_std = np.std(wc)
|
|
||||||
tr = np.maximum(wh - wl, np.maximum(np.abs(wh - np.roll(wc,1)), np.abs(wl - np.roll(wc,1))))
|
|
||||||
atr = np.mean(tr[1:])
|
|
||||||
kc_r = (ma + 1.5*atr) - (ma - 1.5*atr)
|
|
||||||
bb_r = (ma + 2*bb_std) - (ma - 2*bb_std)
|
|
||||||
if kc_r > 0:
|
|
||||||
result[i] = bb_r / kc_r
|
|
||||||
return result
|
|
||||||
|
|
||||||
|
|
||||||
def run_squeeze_backtest(close, high, low, volume, bb_w, sq_thr, brk_bars, vol_filter, split_pct=0.7, leverage=3, pos_pct=0.2):
|
|
||||||
n = len(close)
|
|
||||||
split = int(n * split_pct)
|
|
||||||
kcr = keltner_ratio(close, high, low, bb_w)
|
|
||||||
|
|
||||||
in_sq = False
|
|
||||||
sq_start = 0
|
|
||||||
capital = float(INITIAL)
|
|
||||||
equity = [capital]
|
|
||||||
trades = []
|
|
||||||
|
|
||||||
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 = True
|
|
||||||
sq_start = i
|
|
||||||
elif not is_sq and in_sq:
|
|
||||||
in_sq = False
|
|
||||||
duration = i - sq_start
|
|
||||||
if duration < 5 or i < split or i + brk_bars >= n:
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Volume check
|
|
||||||
if vol_filter:
|
|
||||||
avg_v = np.mean(volume[sq_start:i])
|
|
||||||
brk_v = np.mean(volume[i:i+brk_bars])
|
|
||||||
if avg_v > 0 and brk_v < avg_v * 1.3:
|
|
||||||
continue
|
|
||||||
|
|
||||||
first_ret = (close[i] - close[i-1]) / close[i-1]
|
|
||||||
if abs(first_ret) < 0.001:
|
|
||||||
continue
|
|
||||||
|
|
||||||
direction = 1 if first_ret > 0 else -1
|
|
||||||
actual = (close[i+brk_bars-1] - close[i-1]) / close[i-1]
|
|
||||||
is_correct = (direction == 1 and actual > 0) or (direction == -1 and actual < 0)
|
|
||||||
|
|
||||||
trade_ret = actual * direction
|
|
||||||
net = trade_ret * leverage - FEE * 2 * leverage
|
|
||||||
pnl = capital * pos_pct * net
|
|
||||||
capital += pnl
|
|
||||||
capital = max(capital, 0)
|
|
||||||
equity.append(capital)
|
|
||||||
|
|
||||||
trades.append({
|
|
||||||
"correct": is_correct,
|
|
||||||
"actual_ret": actual,
|
|
||||||
"net_pnl": pnl,
|
|
||||||
"capital_after": capital,
|
|
||||||
})
|
|
||||||
|
|
||||||
if not trades:
|
|
||||||
return None
|
|
||||||
|
|
||||||
correct = sum(1 for t in trades if t["correct"])
|
|
||||||
acc = correct / len(trades) * 100
|
|
||||||
total_ret = (capital - INITIAL) / INITIAL * 100
|
|
||||||
test_candles = n - split
|
|
||||||
test_days = test_candles / 24
|
|
||||||
test_years = test_days / 365.25
|
|
||||||
ann = ((capital / INITIAL) ** (1/test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
|
|
||||||
daily_pnl = (capital - INITIAL) / test_days if test_days > 0 else 0
|
|
||||||
|
|
||||||
peak = equity[0]
|
|
||||||
max_dd = 0
|
|
||||||
for v in equity:
|
|
||||||
if v > peak: peak = v
|
|
||||||
dd = (peak - v) / peak if peak > 0 else 0
|
|
||||||
max_dd = max(max_dd, dd)
|
|
||||||
|
|
||||||
return {
|
|
||||||
"trades": len(trades),
|
|
||||||
"accuracy": acc,
|
|
||||||
"total_return": total_ret,
|
|
||||||
"annualized": ann,
|
|
||||||
"max_drawdown": max_dd * 100,
|
|
||||||
"final_capital": capital,
|
|
||||||
"daily_pnl": daily_pnl,
|
|
||||||
"trades_per_year": len(trades) / test_years if test_years > 0 else 0,
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
methods = []
|
|
||||||
|
|
||||||
# --- Metodo 1: ETH 1h, BBw=20, sqThr=0.8, vol confirmed ---
|
|
||||||
df_eth = load_data("ETH", "1h")
|
|
||||||
r1 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
|
|
||||||
bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=True)
|
|
||||||
methods.append(("M1: ETH 1h Squeeze+Vol (BBw=20,sq=0.8)", r1))
|
|
||||||
|
|
||||||
# --- Metodo 2: ETH 1h, BBw=30, sqThr=0.9, no vol ---
|
|
||||||
r2 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
|
|
||||||
bb_w=30, sq_thr=0.9, brk_bars=3, vol_filter=False)
|
|
||||||
methods.append(("M2: ETH 1h Squeeze (BBw=30,sq=0.9)", r2))
|
|
||||||
|
|
||||||
# --- Metodo 3: BTC+ETH combinato ---
|
|
||||||
df_btc = load_data("BTC", "1h")
|
|
||||||
r3a = run_squeeze_backtest(df_btc["close"].values, df_btc["high"].values, df_btc["low"].values, df_btc["volume"].values,
|
|
||||||
bb_w=14, sq_thr=0.8, brk_bars=3, vol_filter=False, pos_pct=0.1)
|
|
||||||
r3b = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
|
|
||||||
bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=False, pos_pct=0.1)
|
|
||||||
|
|
||||||
if r3a and r3b:
|
|
||||||
combined_trades = r3a["trades"] + r3b["trades"]
|
|
||||||
combined_correct = int(r3a["accuracy"]/100 * r3a["trades"]) + int(r3b["accuracy"]/100 * r3b["trades"])
|
|
||||||
combined_acc = combined_correct / combined_trades * 100 if combined_trades > 0 else 0
|
|
||||||
|
|
||||||
# Simulate portfolio
|
|
||||||
cap = float(INITIAL)
|
|
||||||
# Rough estimate: alternate between assets
|
|
||||||
for r in [r3a, r3b]:
|
|
||||||
ret_per_trade = r["total_return"] / 100 / r["trades"] if r["trades"] > 0 else 0
|
|
||||||
for _ in range(r["trades"]):
|
|
||||||
cap *= (1 + ret_per_trade * 0.5)
|
|
||||||
|
|
||||||
r3 = {
|
|
||||||
"trades": combined_trades,
|
|
||||||
"accuracy": combined_acc,
|
|
||||||
"total_return": (cap - INITIAL) / INITIAL * 100,
|
|
||||||
"annualized": r3a["annualized"] * 0.5 + r3b["annualized"] * 0.5,
|
|
||||||
"max_drawdown": max(r3a["max_drawdown"], r3b["max_drawdown"]),
|
|
||||||
"final_capital": cap,
|
|
||||||
"daily_pnl": r3a["daily_pnl"] + r3b["daily_pnl"],
|
|
||||||
"trades_per_year": r3a["trades_per_year"] + r3b["trades_per_year"],
|
|
||||||
}
|
|
||||||
methods.append(("M3: BTC+ETH 1h Portafoglio Squeeze", r3))
|
|
||||||
|
|
||||||
# --- Metodo 4: BTC 15m alta frequenza ---
|
|
||||||
df_btc_15 = load_data("BTC", "15m")
|
|
||||||
r4 = run_squeeze_backtest(df_btc_15["close"].values, df_btc_15["high"].values, df_btc_15["low"].values, df_btc_15["volume"].values,
|
|
||||||
bb_w=14, sq_thr=0.9, brk_bars=3, vol_filter=True)
|
|
||||||
methods.append(("M4: BTC 15m Squeeze+Vol alta freq", r4))
|
|
||||||
|
|
||||||
# --- Metodo 5: ETH 1h squeeze aggressivo ---
|
|
||||||
r5 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
|
|
||||||
bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=False, leverage=3)
|
|
||||||
methods.append(("M5: ETH 1h Squeeze aggressivo (no vol)", r5))
|
|
||||||
|
|
||||||
# --- Print results ---
|
|
||||||
print("\n")
|
|
||||||
for i, (name, r) in enumerate(methods, 1):
|
|
||||||
if r is None:
|
|
||||||
print(f" {name}: NO TRADES")
|
|
||||||
continue
|
|
||||||
print(f" {'='*65}")
|
|
||||||
print(f" #{i} — {name}")
|
|
||||||
print(f" {'='*65}")
|
|
||||||
print(f" Trades: {r['trades']}")
|
|
||||||
print(f" Accuracy: {r['accuracy']:.1f}% {'✅' if r['accuracy'] >= 80 else '⚠️' if r['accuracy'] >= 70 else '❌'}")
|
|
||||||
print(f" Return totale: {r['total_return']:+.1f}%")
|
|
||||||
print(f" Return annuo: {r['annualized']:+.1f}% {'✅' if r['annualized'] >= 30 else '⚠️' if r['annualized'] >= 15 else '❌'}")
|
|
||||||
print(f" Max Drawdown: {r['max_drawdown']:.1f}%")
|
|
||||||
print(f" Capitale finale: €{r['final_capital']:.0f}")
|
|
||||||
print(f" €/giorno media: €{r['daily_pnl']:.2f}")
|
|
||||||
print(f" Trades/anno: {r['trades_per_year']:.0f}")
|
|
||||||
print()
|
|
||||||
|
|
||||||
|
|
||||||
# --- Simulazione crescita 6 mesi ---
|
|
||||||
print("\n" + "=" * 70)
|
|
||||||
print(" SIMULAZIONE CRESCITA CAPITALE — 6 MESI")
|
|
||||||
print(" Metodo: M1 (ETH 1h Squeeze+Vol) — il più preciso (83.9%)")
|
|
||||||
print("=" * 70)
|
|
||||||
|
|
||||||
# M1 params: ~87 trades in ~2.5 anni test = ~35 trades/anno = ~3 al mese
|
|
||||||
# Accuracy: 83.9%, average return per trade with 3x leverage
|
|
||||||
|
|
||||||
# Simulo con dati reali: prendo i trade dal test period
|
|
||||||
close = df_eth["close"].values
|
|
||||||
high = df_eth["high"].values
|
|
||||||
low = df_eth["low"].values
|
|
||||||
volume = df_eth["volume"].values
|
|
||||||
n = len(close)
|
|
||||||
split = int(n * 0.7)
|
|
||||||
|
|
||||||
kcr = keltner_ratio(close, high, low, 20)
|
|
||||||
in_sq = False
|
|
||||||
sq_start = 0
|
|
||||||
all_trade_rets = []
|
|
||||||
|
|
||||||
for i in range(21, n):
|
|
||||||
if np.isnan(kcr[i]):
|
|
||||||
continue
|
|
||||||
is_sq = kcr[i] < 0.8
|
|
||||||
if is_sq and not in_sq:
|
|
||||||
in_sq = True
|
|
||||||
sq_start = i
|
|
||||||
elif not is_sq and in_sq:
|
|
||||||
in_sq = False
|
|
||||||
if i - sq_start < 5 or i < split or i + 3 >= n:
|
|
||||||
continue
|
|
||||||
avg_v = np.mean(volume[sq_start:i])
|
|
||||||
brk_v = np.mean(volume[i:i+3])
|
|
||||||
if avg_v > 0 and brk_v < avg_v * 1.3:
|
|
||||||
continue
|
|
||||||
first_ret = (close[i] - close[i-1]) / close[i-1]
|
|
||||||
if abs(first_ret) < 0.001:
|
|
||||||
continue
|
|
||||||
direction = 1 if first_ret > 0 else -1
|
|
||||||
actual = (close[i+2] - close[i-1]) / close[i-1]
|
|
||||||
trade_ret = actual * direction
|
|
||||||
all_trade_rets.append(trade_ret)
|
|
||||||
|
|
||||||
avg_win = np.mean([r for r in all_trade_rets if r > 0]) if any(r > 0 for r in all_trade_rets) else 0
|
|
||||||
avg_loss = np.mean([r for r in all_trade_rets if r <= 0]) if any(r <= 0 for r in all_trade_rets) else 0
|
|
||||||
win_rate = sum(1 for r in all_trade_rets if r > 0) / len(all_trade_rets)
|
|
||||||
|
|
||||||
print(f"\n Statistiche trade:")
|
|
||||||
print(f" Win rate: {win_rate*100:.1f}%")
|
|
||||||
print(f" Avg win: {avg_win*100:.2f}%")
|
|
||||||
print(f" Avg loss: {avg_loss*100:.2f}%")
|
|
||||||
print(f" Trades totali nel test: {len(all_trade_rets)}")
|
|
||||||
print(f" Trades/mese stimati: ~{len(all_trade_rets) / 30:.0f}")
|
|
||||||
|
|
||||||
print(f"\n Crescita simulata mese per mese (€1000 iniziali, leva 3x, 20% per trade):")
|
|
||||||
capital = 1000.0
|
|
||||||
monthly_trades = max(len(all_trade_rets) // 30, 3)
|
|
||||||
|
|
||||||
# Shuffle trades to simulate different sequences
|
|
||||||
np.random.seed(42)
|
|
||||||
for month in range(1, 7):
|
|
||||||
n_trades = monthly_trades
|
|
||||||
month_rets = np.random.choice(all_trade_rets, size=n_trades, replace=True)
|
|
||||||
|
|
||||||
for ret in month_rets:
|
|
||||||
net = ret * LEVERAGE - FEE * 2 * LEVERAGE
|
|
||||||
capital += capital * 0.2 * net
|
|
||||||
capital = max(capital, 10)
|
|
||||||
|
|
||||||
daily_pnl = capital * 0.003 # stima conservativa 0.3% daily basata su performance
|
|
||||||
print(f" Mese {month}: capitale €{capital:.0f}, €/giorno stima: €{daily_pnl:.1f}")
|
|
||||||
|
|
||||||
print(f"\n Capitale dopo 6 mesi: €{capital:.0f}")
|
|
||||||
print(f" €/giorno necessari: €50")
|
|
||||||
print(f" €/giorno ottenibili (0.5% daily su capitale): €{capital * 0.005:.1f}")
|
|
||||||
|
|
||||||
if capital * 0.005 >= 50:
|
|
||||||
print(f"\n ✅ TARGET RAGGIUNGIBILE: con €{capital:.0f} di capitale, 0.5% daily = €{capital*0.005:.0f}/giorno")
|
|
||||||
else:
|
|
||||||
needed = 50 / 0.005
|
|
||||||
print(f"\n ⚠️ Servono €{needed:.0f} di capitale per €50/giorno al 0.5% daily")
|
|
||||||
print(f" Raggiungibile estendendo il periodo di crescita a ~{int(np.log(needed/1000) / np.log(1 + 0.15) + 0.5)} mesi")
|
|
||||||
@@ -1,175 +0,0 @@
|
|||||||
"""Strategie candidate ONESTE + sweep multi-asset/tf con verdetto.
|
|
||||||
|
|
||||||
Ogni generatore restituisce una lista di entries {i,d,tp,sl,max_bars} usando
|
|
||||||
SOLO dati fino a close[i]. L'engine (honest_lab.simulate) entra a close[i].
|
|
||||||
|
|
||||||
Famiglie testate (meccanismi distinti, per diversificazione):
|
|
||||||
MR mean-reversion single-asset (Bollinger fade, RSI revert, Z-score)
|
|
||||||
XS cross-sectional relative-value (fade della divergenza vs paniere)
|
|
||||||
MOM time-series momentum / trend su timeframe alto
|
|
||||||
SES seasonality (ora del giorno UTC)
|
|
||||||
"""
|
|
||||||
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 scripts.analysis.honest_lab import ( # noqa: E402
|
|
||||||
atr, rsi, ema, get_df, simulate, oos_split, verdict,
|
|
||||||
available_assets, FEE_RT,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
# ============================================================================
|
|
||||||
# MR — mean reversion single-asset
|
|
||||||
# ============================================================================
|
|
||||||
def bollinger_fade(df, n=50, k=2.5, sl_atr=2.0, max_bars=24):
|
|
||||||
c = df["close"].values
|
|
||||||
ma = pd.Series(c).rolling(n).mean().values
|
|
||||||
sd = pd.Series(c).rolling(n).std().values
|
|
||||||
a = atr(df, 14)
|
|
||||||
up, lo = ma + k * sd, ma - k * sd
|
|
||||||
ents = []
|
|
||||||
for i in range(n + 14, len(c)):
|
|
||||||
if np.isnan(up[i]) or np.isnan(a[i]):
|
|
||||||
continue
|
|
||||||
if c[i] < lo[i] and c[i - 1] >= lo[i - 1]:
|
|
||||||
ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
elif c[i] > up[i] and c[i - 1] <= up[i - 1]:
|
|
||||||
ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
return ents
|
|
||||||
|
|
||||||
|
|
||||||
def rsi_revert(df, n=14, lo=25, hi=75, sl_atr=2.5, max_bars=24, ma_n=20):
|
|
||||||
c = df["close"].values
|
|
||||||
r = rsi(c, n)
|
|
||||||
ma = pd.Series(c).rolling(ma_n).mean().values
|
|
||||||
a = atr(df, 14)
|
|
||||||
ents = []
|
|
||||||
for i in range(max(n, ma_n) + 1, len(c)):
|
|
||||||
if np.isnan(r[i]) or np.isnan(ma[i]) or np.isnan(a[i]):
|
|
||||||
continue
|
|
||||||
if r[i - 1] < lo <= r[i]:
|
|
||||||
ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
elif r[i - 1] > hi >= r[i]:
|
|
||||||
ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
return ents
|
|
||||||
|
|
||||||
|
|
||||||
def zscore_revert(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24):
|
|
||||||
"""Entra quando close e' a |z|>z_in std dalla media; TP alla media."""
|
|
||||||
c = df["close"].values
|
|
||||||
ma = pd.Series(c).rolling(n).mean().values
|
|
||||||
sd = pd.Series(c).rolling(n).std().values
|
|
||||||
a = atr(df, 14)
|
|
||||||
z = (c - ma) / sd
|
|
||||||
ents = []
|
|
||||||
for i in range(n + 14, len(c)):
|
|
||||||
if np.isnan(z[i]) or np.isnan(a[i]) or sd[i] == 0:
|
|
||||||
continue
|
|
||||||
if z[i] <= -z_in and z[i - 1] > -z_in:
|
|
||||||
ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
elif z[i] >= z_in and z[i - 1] < z_in:
|
|
||||||
ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
return ents
|
|
||||||
|
|
||||||
|
|
||||||
# ============================================================================
|
|
||||||
# MOM — time-series momentum / trend (timeframe alto, niente breakout intrabar)
|
|
||||||
# ============================================================================
|
|
||||||
def ema_trend(df, fast=20, slow=50, sl_atr=3.0, tp_atr=10.0, max_bars=240):
|
|
||||||
"""Trend following: cross EMA fast/slow deciso a close[i], TP/SL ad ATR."""
|
|
||||||
c = df["close"].values
|
|
||||||
ef, es = ema(c, fast), ema(c, slow)
|
|
||||||
a = atr(df, 14)
|
|
||||||
ents = []
|
|
||||||
for i in range(slow + 14, len(c)):
|
|
||||||
if np.isnan(a[i]):
|
|
||||||
continue
|
|
||||||
cross_up = ef[i] > es[i] and ef[i - 1] <= es[i - 1]
|
|
||||||
cross_dn = ef[i] < es[i] and ef[i - 1] >= es[i - 1]
|
|
||||||
if cross_up:
|
|
||||||
ents.append({"i": i, "d": 1, "tp": c[i] + tp_atr * a[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
elif cross_dn:
|
|
||||||
ents.append({"i": i, "d": -1, "tp": c[i] - tp_atr * a[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
return ents
|
|
||||||
|
|
||||||
|
|
||||||
# ============================================================================
|
|
||||||
# SES — seasonality (ora del giorno UTC). Direzione fissa decisa solo dall'ora.
|
|
||||||
# ============================================================================
|
|
||||||
def time_of_day(df, hour_long=None, hour_short=None, hold=6):
|
|
||||||
"""Entra a close della candela all'ora UTC indicata, esce dopo `hold` barre
|
|
||||||
(no TP/SL: tp/sl messi a +-inf cosi' esce solo a time-limit)."""
|
|
||||||
ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
|
||||||
c = df["close"].values
|
|
||||||
hours = ts.dt.hour.values
|
|
||||||
hour_long = set(hour_long or [])
|
|
||||||
hour_short = set(hour_short or [])
|
|
||||||
ents = []
|
|
||||||
for i in range(1, len(c)):
|
|
||||||
if hours[i] in hour_long:
|
|
||||||
ents.append({"i": i, "d": 1, "tp": np.inf, "sl": -np.inf, "max_bars": hold})
|
|
||||||
elif hours[i] in hour_short:
|
|
||||||
ents.append({"i": i, "d": -1, "tp": -np.inf, "sl": np.inf, "max_bars": hold})
|
|
||||||
return ents
|
|
||||||
|
|
||||||
|
|
||||||
# ============================================================================
|
|
||||||
# sweep
|
|
||||||
# ============================================================================
|
|
||||||
def run_sweep(generators: dict, assets: list[str], tfs: list[str]):
|
|
||||||
print("=" * 130)
|
|
||||||
print(f" HONEST LAB — NETTO fee {FEE_RT*100:.2f}% RT | leva 3x | pos 15% | OOS ultimo 30%")
|
|
||||||
print("=" * 130)
|
|
||||||
print(f" {'Strategia':<26s}{'Asset':>5s}{'TF':>5s}{'Trd':>6s}{'Win%':>7s}"
|
|
||||||
f"{'FULL%':>9s}{'OOS%':>9s}{'DD%':>6s}{'Exp%':>6s}{'AnniPos':>9s}{'OK':>4s}")
|
|
||||||
print(" " + "-" * 126)
|
|
||||||
survivors = []
|
|
||||||
for label, (fn, params) in generators.items():
|
|
||||||
for asset in assets:
|
|
||||||
for tf in tfs:
|
|
||||||
try:
|
|
||||||
df = get_df(asset, tf)
|
|
||||||
except Exception:
|
|
||||||
continue
|
|
||||||
ents = fn(df, **params)
|
|
||||||
if len(ents) < 30:
|
|
||||||
continue
|
|
||||||
full = simulate(ents, df)
|
|
||||||
_, oos_e = oos_split(ents, df)
|
|
||||||
oos = simulate(oos_e, df)
|
|
||||||
ok = verdict(full, oos)
|
|
||||||
flag = " OK" if ok else ""
|
|
||||||
print(f" {label:<26s}{asset:>5s}{tf:>5s}{full.trades:>6d}{full.win:>7.1f}"
|
|
||||||
f"{full.ret:>+9.0f}{oos.ret:>+9.0f}{full.dd:>6.0f}{full.exposure:>6.0f}"
|
|
||||||
f"{f'{full.pos_years}/{full.n_years}':>9s}{flag:>4s}")
|
|
||||||
if ok:
|
|
||||||
survivors.append((label, asset, tf, full, oos))
|
|
||||||
print(" " + "-" * 126)
|
|
||||||
return survivors
|
|
||||||
|
|
||||||
|
|
||||||
GENERATORS = {
|
|
||||||
"MR_boll n50 k2.5": (bollinger_fade, dict(n=50, k=2.5, sl_atr=2.0, max_bars=24)),
|
|
||||||
"MR_boll n20 k2.5": (bollinger_fade, dict(n=20, k=2.5, sl_atr=2.0, max_bars=24)),
|
|
||||||
"MR_rsi 25/75": (rsi_revert, dict(n=14, lo=25, hi=75, sl_atr=2.5, max_bars=24)),
|
|
||||||
"MR_zscore z2.5": (zscore_revert, dict(n=50, z_in=2.5, sl_atr=2.5, max_bars=24)),
|
|
||||||
"MR_zscore z3": (zscore_revert, dict(n=50, z_in=3.0, sl_atr=2.5, max_bars=24)),
|
|
||||||
"MOM_ema 20/50": (ema_trend, dict(fast=20, slow=50, sl_atr=3.0, tp_atr=10.0, max_bars=240)),
|
|
||||||
}
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
assets = available_assets()
|
|
||||||
print("Asset disponibili:", assets)
|
|
||||||
survivors = run_sweep(GENERATORS, assets, ["1h", "4h"])
|
|
||||||
print(f"\n SOPRAVVISSUTI (FULL+OOS+anni+DD): {len(survivors)}")
|
|
||||||
for label, a, tf, full, oos in survivors:
|
|
||||||
print(f" {label:<26s} {a} {tf} FULL {full.ret:+.0f}% OOS {oos.ret:+.0f}% DD {full.dd:.0f}%")
|
|
||||||
@@ -1,73 +0,0 @@
|
|||||||
"""Diagnostica: perche' la mean-reversion simmetrica perde su asset trending?
|
|
||||||
Test: long-only vs short-only, e MR FILTRATA DAL TREND (buy-dip in uptrend,
|
|
||||||
sell-rip in downtrend) per evitare di fadeare i trend forti.
|
|
||||||
"""
|
|
||||||
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 scripts.analysis.honest_lab import ( # noqa: E402
|
|
||||||
atr, ema, get_df, simulate, oos_split, available_assets, FEE_RT,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def zscore_entries(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24,
|
|
||||||
trend_n=0, side="both"):
|
|
||||||
"""Z-score revert con filtro trend opzionale.
|
|
||||||
trend_n>0: EMA di lungo periodo. Long solo se close>EMA (uptrend),
|
|
||||||
short solo se close<EMA (downtrend).
|
|
||||||
side: 'both' | 'long' | 'short'
|
|
||||||
"""
|
|
||||||
c = df["close"].values
|
|
||||||
ma = pd.Series(c).rolling(n).mean().values
|
|
||||||
sd = pd.Series(c).rolling(n).std().values
|
|
||||||
a = atr(df, 14)
|
|
||||||
z = (c - ma) / np.where(sd == 0, np.nan, sd)
|
|
||||||
et = ema(c, trend_n) if trend_n > 0 else None
|
|
||||||
start = max(n + 14, trend_n + 1 if trend_n else 0)
|
|
||||||
ents = []
|
|
||||||
for i in range(start, len(c)):
|
|
||||||
if np.isnan(z[i]) or np.isnan(a[i]):
|
|
||||||
continue
|
|
||||||
long_ok = (et is None or c[i] > et[i]) and side in ("both", "long")
|
|
||||||
short_ok = (et is None or c[i] < et[i]) and side in ("both", "short")
|
|
||||||
if z[i] <= -z_in and z[i - 1] > -z_in and long_ok:
|
|
||||||
ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
elif z[i] >= z_in and z[i - 1] < z_in and short_ok:
|
|
||||||
ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
return ents
|
|
||||||
|
|
||||||
|
|
||||||
def row(label, df, ents):
|
|
||||||
if len(ents) < 20:
|
|
||||||
print(f" {label:<34s} {'<20 trd':>50s}")
|
|
||||||
return None
|
|
||||||
full = simulate(ents, df)
|
|
||||||
_, oe = oos_split(ents, df)
|
|
||||||
oos = simulate(oe, df)
|
|
||||||
print(f" {label:<34s}{full.trades:>6d}{full.win:>7.1f}{full.ret:>+9.0f}"
|
|
||||||
f"{oos.ret:>+9.0f}{full.dd:>6.0f}{f'{full.pos_years}/{full.n_years}':>8s}")
|
|
||||||
return full, oos
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
assets = available_assets()
|
|
||||||
print(f"HONEST DIAG — z-score revert, fee {FEE_RT*100:.2f}% RT, leva 3x | OOS 30%")
|
|
||||||
for tf in ["1h"]:
|
|
||||||
for a in assets:
|
|
||||||
df = get_df(a, tf)
|
|
||||||
print(f"\n === {a} {tf} === {'Trd':>5s}{'Win%':>7s}{'FULL%':>8s}{'OOS%':>8s}{'DD%':>6s}{'AnniP':>8s}")
|
|
||||||
base = dict(n=50, z_in=2.5, sl_atr=2.5, max_bars=24)
|
|
||||||
row("both, no filter", df, zscore_entries(df, **base, side="both"))
|
|
||||||
row("long-only, no filter", df, zscore_entries(df, **base, side="long"))
|
|
||||||
row("short-only, no filter", df, zscore_entries(df, **base, side="short"))
|
|
||||||
row("both + trend200 filter", df, zscore_entries(df, **base, trend_n=200, side="both"))
|
|
||||||
row("both + trend500 filter", df, zscore_entries(df, **base, trend_n=500, side="both"))
|
|
||||||
row("long + trend200 filter", df, zscore_entries(df, **base, trend_n=200, side="long"))
|
|
||||||
@@ -1,64 +0,0 @@
|
|||||||
"""Diag2: long-MR sempre + short-MR SOLO in downtrend confermato (close<EMA_t).
|
|
||||||
Idea: il dip-buying funziona su tutti gli asset (drift rialzista crypto); lo
|
|
||||||
short funziona solo quando il trend e' gia' giu' -> shortare i rimbalzi in
|
|
||||||
downtrend, mai i rimbalzi in bull-run.
|
|
||||||
"""
|
|
||||||
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 scripts.analysis.honest_lab import ( # noqa: E402
|
|
||||||
atr, ema, get_df, simulate, oos_split, available_assets, FEE_RT,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def regime_mr(df, n=50, z_in=2.5, sl_atr=2.5, max_bars=24, trend_n=200,
|
|
||||||
allow_short=True):
|
|
||||||
"""Long su z<=-z_in SEMPRE. Short su z>=+z_in solo se close<EMA(trend_n)."""
|
|
||||||
c = df["close"].values
|
|
||||||
ma = pd.Series(c).rolling(n).mean().values
|
|
||||||
sd = pd.Series(c).rolling(n).std().values
|
|
||||||
a = atr(df, 14)
|
|
||||||
z = (c - ma) / np.where(sd == 0, np.nan, sd)
|
|
||||||
et = ema(c, trend_n)
|
|
||||||
start = max(n + 14, trend_n + 1)
|
|
||||||
ents = []
|
|
||||||
for i in range(start, len(c)):
|
|
||||||
if np.isnan(z[i]) or np.isnan(a[i]):
|
|
||||||
continue
|
|
||||||
if z[i] <= -z_in and z[i - 1] > -z_in:
|
|
||||||
ents.append({"i": i, "d": 1, "tp": ma[i], "sl": c[i] - sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
elif allow_short and z[i] >= z_in and z[i - 1] < z_in and c[i] < et[i]:
|
|
||||||
ents.append({"i": i, "d": -1, "tp": ma[i], "sl": c[i] + sl_atr * a[i], "max_bars": max_bars})
|
|
||||||
return ents
|
|
||||||
|
|
||||||
|
|
||||||
def show(label, df, ents):
|
|
||||||
if len(ents) < 20:
|
|
||||||
print(f" {label:<30s} <20 trd"); return None
|
|
||||||
full = simulate(ents, df); _, oe = oos_split(ents, df); oos = simulate(oe, df)
|
|
||||||
print(f" {label:<30s}{full.trades:>6d}{full.win:>7.1f}{full.ret:>+9.0f}"
|
|
||||||
f"{oos.ret:>+9.0f}{full.dd:>6.0f}{f'{full.pos_years}/{full.n_years}':>8s}")
|
|
||||||
return full, oos
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
assets = available_assets()
|
|
||||||
print(f"DIAG2 — regime MR (long sempre + short in downtrend) fee {FEE_RT*100:.2f}% leva3x OOS30%")
|
|
||||||
surv = 0
|
|
||||||
for a in assets:
|
|
||||||
df = get_df(a, "1h")
|
|
||||||
print(f"\n === {a} 1h === {'Trd':>5s}{'Win%':>7s}{'FULL%':>8s}{'OOS%':>8s}{'DD%':>6s}{'AnniP':>8s}")
|
|
||||||
show("long-only", df, regime_mr(df, allow_short=False))
|
|
||||||
r = show("long + short@downtrend200", df, regime_mr(df, trend_n=200))
|
|
||||||
show("long + short@downtrend500", df, regime_mr(df, trend_n=500))
|
|
||||||
if r and r[0].ret > 0 and r[1].ret > 0:
|
|
||||||
surv += 1
|
|
||||||
print(f"\n Asset con regime200 positivo FULL+OOS: {surv}/{len(assets)}")
|
|
||||||
@@ -1,188 +0,0 @@
|
|||||||
"""Report PER ANNO (Trade, Acc%, DD%, PnL%) delle 3 strategie oneste.
|
|
||||||
|
|
||||||
Acc: DIP01/TR01 = win-rate dei trade chiusi (episodi); ROT01 = % giorni positivi.
|
|
||||||
DD : drawdown massimo dell'equity DENTRO l'anno solare.
|
|
||||||
PnL: variazione % dell'equity nell'anno (composta).
|
|
||||||
Tutto NETTO (fee 0.10% RT, leva 3x, pos 15%). Replica gli engine di honest_*.
|
|
||||||
"""
|
|
||||||
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 scripts.analysis.honest_lab import atr, ema, get_df, available_assets, FEE_RT
|
|
||||||
from scripts.analysis.honest_final import dip_entries
|
|
||||||
from scripts.analysis.honest_rotation import build_panel
|
|
||||||
|
|
||||||
LEV, POS = 3.0, 0.15
|
|
||||||
|
|
||||||
|
|
||||||
def _yearly_dd(years: np.ndarray, equity: np.ndarray) -> dict[int, float]:
|
|
||||||
"""DD massimo intra-anno da una serie di equity etichettata per anno."""
|
|
||||||
out: dict[int, float] = {}
|
|
||||||
for y in np.unique(years):
|
|
||||||
eq = equity[years == y]
|
|
||||||
peak = eq[0]; dd = 0.0
|
|
||||||
for v in eq:
|
|
||||||
peak = max(peak, v)
|
|
||||||
dd = max(dd, (peak - v) / peak if peak > 0 else 0.0)
|
|
||||||
out[int(y)] = dd * 100
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def _print(title, header, rows):
|
|
||||||
print("\n" + "=" * 78)
|
|
||||||
print(f" {title}")
|
|
||||||
print("=" * 78)
|
|
||||||
print(" " + header)
|
|
||||||
print(" " + "-" * 74)
|
|
||||||
for r in rows:
|
|
||||||
print(" " + r)
|
|
||||||
|
|
||||||
|
|
||||||
# --------------------------- DIP01 (trade-based) ---------------------------
|
|
||||||
def dip_yearly(asset, tf="1h"):
|
|
||||||
df = get_df(asset, tf)
|
|
||||||
ents = dip_entries(df)
|
|
||||||
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)
|
|
||||||
fee = FEE_RT * LEV
|
|
||||||
cap = 1000.0
|
|
||||||
last_exit = -1
|
|
||||||
eq_y, eq_v = [], []
|
|
||||||
yt: dict[int, list] = {} # year -> [trades, wins, pnl_start_cap, pnl_end_cap]
|
|
||||||
for e in ents:
|
|
||||||
i, d = e["i"], e["d"]
|
|
||||||
if i <= last_exit or i + 1 >= n:
|
|
||||||
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:
|
|
||||||
j = n - 1; exit_p = c[j]; break
|
|
||||||
if (d == 1 and l[j] <= sl) or (d == -1 and h[j] >= sl):
|
|
||||||
exit_p = sl; break
|
|
||||||
if (d == 1 and h[j] >= tp) or (d == -1 and l[j] <= tp):
|
|
||||||
exit_p = tp; break
|
|
||||||
if k == mb:
|
|
||||||
exit_p = c[j]
|
|
||||||
ret = (exit_p - entry) / entry * d * LEV - fee
|
|
||||||
cap = max(cap + cap * POS * ret, 10.0)
|
|
||||||
last_exit = j
|
|
||||||
y = ts.iloc[i].year
|
|
||||||
rec = yt.setdefault(y, [0, 0, None, None])
|
|
||||||
rec[0] += 1; rec[1] += ret > 0
|
|
||||||
eq_y.append(y); eq_v.append(cap)
|
|
||||||
dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
|
|
||||||
# PnL% anno: da equity prima/dopo
|
|
||||||
rows = []
|
|
||||||
prev = 1000.0
|
|
||||||
yrs = sorted(yt)
|
|
||||||
cum = {}
|
|
||||||
cprev = 1000.0
|
|
||||||
# ricostruisci equity di fine anno
|
|
||||||
end_cap = {}
|
|
||||||
for y, v in zip(eq_y, eq_v):
|
|
||||||
end_cap[y] = v
|
|
||||||
for y in yrs:
|
|
||||||
t, w = yt[y][0], yt[y][1]
|
|
||||||
ec = end_cap[y]
|
|
||||||
pnl = (ec / cprev - 1) * 100
|
|
||||||
cprev = ec
|
|
||||||
rows.append(f"{y:>6d}{t:>8d}{(w/t*100 if t else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
|
|
||||||
return rows
|
|
||||||
|
|
||||||
|
|
||||||
# --------------------------- TR01 (position episodes) ---------------------------
|
|
||||||
def tr_yearly(asset, tf="4h", fast=20, slow=100):
|
|
||||||
df = get_df(asset, tf)
|
|
||||||
c = df["close"].values; n = len(c)
|
|
||||||
ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
|
||||||
ef, es = ema(c, fast), ema(c, slow)
|
|
||||||
sig = np.where(ef > es, 1.0, 0.0); sig[:slow] = 0.0
|
|
||||||
cap = 1000.0; cur = 0.0
|
|
||||||
fee = FEE_RT / 2 * LEV
|
|
||||||
ep_start_cap = None; ep_year = None
|
|
||||||
yt: dict[int, list] = {}
|
|
||||||
eq_y, eq_v = [], []
|
|
||||||
for i in range(n - 1):
|
|
||||||
s = sig[i]
|
|
||||||
if s != cur:
|
|
||||||
cap -= cap * POS * fee * abs(s - cur)
|
|
||||||
if s == 1.0: # apertura long
|
|
||||||
ep_start_cap = cap; ep_year = ts.iloc[i].year
|
|
||||||
elif cur == 1.0 and ep_start_cap is not None: # chiusura long
|
|
||||||
rec = yt.setdefault(ep_year, [0, 0])
|
|
||||||
rec[0] += 1; rec[1] += cap > ep_start_cap
|
|
||||||
ep_start_cap = None
|
|
||||||
cur = s
|
|
||||||
pr = (c[i + 1] - c[i]) / c[i]
|
|
||||||
cap = max(cap * (1 + POS * LEV * pr * cur), 10.0)
|
|
||||||
eq_y.append(ts.iloc[i].year); eq_v.append(cap)
|
|
||||||
if cur == 1.0 and ep_start_cap is not None:
|
|
||||||
rec = yt.setdefault(ep_year, [0, 0]); rec[0] += 1; rec[1] += cap > ep_start_cap
|
|
||||||
dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
|
|
||||||
end_cap = {}
|
|
||||||
for y, v in zip(eq_y, eq_v):
|
|
||||||
end_cap[y] = v
|
|
||||||
rows = []; cprev = 1000.0
|
|
||||||
for y in sorted(end_cap):
|
|
||||||
t, w = yt.get(y, [0, 0])
|
|
||||||
pnl = (end_cap[y] / cprev - 1) * 100; cprev = end_cap[y]
|
|
||||||
rows.append(f"{y:>6d}{t:>8d}{(w/t*100 if t else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
|
|
||||||
return rows
|
|
||||||
|
|
||||||
|
|
||||||
# --------------------------- ROT01 (daily portfolio) ---------------------------
|
|
||||||
def rot_yearly(lookback=60, top_k=2, gross=0.45):
|
|
||||||
panel = build_panel(available_assets(), "1d")
|
|
||||||
P = panel.values; T, N = P.shape
|
|
||||||
rets = np.zeros_like(P); rets[1:] = P[1:] / P[:-1] - 1
|
|
||||||
years = panel.index.year.values
|
|
||||||
cap = 1000.0; w = np.zeros(N)
|
|
||||||
yt: dict[int, list] = {} # year -> [rebal, pos_days, days]
|
|
||||||
eq_y, eq_v = [], []
|
|
||||||
for i in range(lookback + 1, T - 1):
|
|
||||||
mom = P[i] / P[i - lookback] - 1
|
|
||||||
order = np.argsort(mom)[::-1]
|
|
||||||
chosen = [j for j in order if mom[j] > 0][:top_k]
|
|
||||||
new_w = np.zeros(N)
|
|
||||||
for j in chosen:
|
|
||||||
new_w[j] = gross / len(chosen)
|
|
||||||
turnover = np.abs(new_w - w).sum()
|
|
||||||
if turnover > 1e-9:
|
|
||||||
cap -= cap * turnover * (FEE_RT / 2)
|
|
||||||
w = new_w
|
|
||||||
pr = float(np.dot(w, rets[i + 1]))
|
|
||||||
cap = max(cap * (1 + pr), 10.0)
|
|
||||||
y = int(years[i])
|
|
||||||
rec = yt.setdefault(y, [0, 0, 0])
|
|
||||||
rec[0] += turnover > 1e-9; rec[1] += pr > 0; rec[2] += 1
|
|
||||||
eq_y.append(y); eq_v.append(cap)
|
|
||||||
dd = _yearly_dd(np.array(eq_y), np.array(eq_v))
|
|
||||||
end_cap = {}
|
|
||||||
for y, v in zip(eq_y, eq_v):
|
|
||||||
end_cap[y] = v
|
|
||||||
rows = []; cprev = 1000.0
|
|
||||||
for y in sorted(end_cap):
|
|
||||||
reb, pos, days = yt[y]
|
|
||||||
pnl = (end_cap[y] / cprev - 1) * 100; cprev = end_cap[y]
|
|
||||||
rows.append(f"{y:>6d}{reb:>8d}{(pos/days*100 if days else 0):>8.1f}{dd.get(y,0):>8.1f}{pnl:>+10.1f}")
|
|
||||||
return rows
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
H = f"{'Anno':>6s}{'Trade':>8s}{'Acc%':>8s}{'DD%':>8s}{'PnL%':>10s}"
|
|
||||||
for a in ["BTC", "ETH", "SOL"]:
|
|
||||||
_print(f"DIP01 — {a} 1h (Acc = win-rate trade)", H, dip_yearly(a))
|
|
||||||
for a in ["BNB", "BTC", "DOGE", "SOL", "XRP"]:
|
|
||||||
_print(f"TR01 — {a} 4h (Trade = episodi long, Acc = win-rate episodi)", H, tr_yearly(a))
|
|
||||||
_print("ROT01 — paniere 8 crypto 1d (Trade = ribilanciamenti, Acc = % giorni positivi)",
|
|
||||||
H, rot_yearly())
|
|
||||||
@@ -1,74 +0,0 @@
|
|||||||
"""Tabella per-anno (PnL% e DD% intra-anno) delle versioni MIGLIORATE:
|
|
||||||
ROT02 (dual-momentum), le 3 sleeve e il PORTAFOGLIO combinato.
|
|
||||||
Tutto NETTO. Riusa gli engine di honest_improve / honest_improve2.
|
|
||||||
"""
|
|
||||||
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 scripts.analysis.honest_improve2 import ( # noqa: E402
|
|
||||||
dip_market_gated, _daily_equity, _norm, _tr_basket_daily, _rot_daily_equity,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def _year_dd(eq: pd.Series) -> dict[int, float]:
|
|
||||||
out = {}
|
|
||||||
for y, g in eq.groupby(eq.index.year):
|
|
||||||
peak = g.iloc[0]; dd = 0.0
|
|
||||||
for v in g:
|
|
||||||
peak = max(peak, v); dd = max(dd, (peak - v) / peak if peak > 0 else 0.0)
|
|
||||||
out[int(y)] = dd * 100
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def _year_pnl(eq: pd.Series) -> dict[int, float]:
|
|
||||||
out = {}
|
|
||||||
for y, g in eq.groupby(eq.index.year):
|
|
||||||
out[int(y)] = (g.iloc[-1] / g.iloc[0] - 1) * 100
|
|
||||||
return out
|
|
||||||
|
|
||||||
|
|
||||||
def table(name, eq):
|
|
||||||
eq = _norm(eq)
|
|
||||||
dd = _year_dd(eq); pnl = _year_pnl(eq)
|
|
||||||
print(f"\n {name}")
|
|
||||||
print(f" {'Anno':>6s}{'PnL%':>9s}{'DD%':>7s}")
|
|
||||||
print(" " + "-" * 22)
|
|
||||||
for y in sorted(pnl):
|
|
||||||
print(f" {y:>6d}{pnl[y]:>+9.0f}{dd[y]:>7.0f}")
|
|
||||||
tot = (eq.iloc[-1] / eq.iloc[0] - 1) * 100
|
|
||||||
print(f" {'TOT':>6s}{tot:>+9.0f}{_year_dd(eq) and max(_year_dd(eq).values()):>7.0f}(max anno)")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
print("=" * 60)
|
|
||||||
print(" RISULTATI PER ANNO — versioni migliorate (NETTO)")
|
|
||||||
print("=" * 60)
|
|
||||||
|
|
||||||
# ROT02 dal 2020 (dati paniere)
|
|
||||||
idx_rot = pd.date_range("2020-09-01", "2026-05-26", freq="1D", tz="UTC")
|
|
||||||
eq_rot = _rot_daily_equity(idx_rot)
|
|
||||||
table("ROT02 — dual-momentum rotation (1d)", eq_rot)
|
|
||||||
|
|
||||||
# sleeve + portafoglio dal 2021
|
|
||||||
idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC")
|
|
||||||
d = dip_market_gated("BTC", market_n=0, return_equity=True)
|
|
||||||
eq_dip = _norm(_daily_equity(d["eq_ts"], d["eq_v"], idx))
|
|
||||||
eq_tr = _norm(_tr_basket_daily(["BNB", "BTC", "DOGE", "SOL", "XRP"], idx))
|
|
||||||
eq_r2 = _norm(_rot_daily_equity(idx))
|
|
||||||
table("Sleeve DIP01 — BTC (1h)", eq_dip)
|
|
||||||
table("Sleeve TR01 — basket (4h)", eq_tr)
|
|
||||||
table("Sleeve ROT02 (1d)", eq_r2)
|
|
||||||
|
|
||||||
drets = pd.DataFrame({"DIP": eq_dip.pct_change().fillna(0),
|
|
||||||
"TR": eq_tr.pct_change().fillna(0),
|
|
||||||
"ROT": eq_r2.pct_change().fillna(0)})
|
|
||||||
combo = (1 + drets.mean(axis=1)).cumprod()
|
|
||||||
table("PORTAFOGLIO equal-weight (daily rebal)", combo)
|
|
||||||
@@ -1,139 +0,0 @@
|
|||||||
"""Migliorare Acc e ridurre DD sulle fade (MR01/MR02/MR03/MR07) senza overfit.
|
|
||||||
|
|
||||||
Leve testate, ognuna misurata FULL e OOS (ultimo 30%) per non illudersi:
|
|
||||||
- vol-target sizing: size per trade ~ 1/distanza-SL -> rischio costante, DD piu' liscio
|
|
||||||
- filtro vol regime: salta i trade con ATR% in coda alta (periodi caotici)
|
|
||||||
- filtro anti-trend: non fadare contro un trend forte (|close-EMA_long|/ATR grande)
|
|
||||||
- portfolio: equity curve combinata delle 4 strategie su un conto unico
|
|
||||||
|
|
||||||
Engine fedele (ingresso close[i], exit TP/SL intrabar o time-limit, non-overlap,
|
|
||||||
capitale composto) con sizing per-trade. 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)
|
|
||||||
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_ETH3 = dict(STRATS); STRATS_ETH3["MR03"] = (keltner_fade, dict(n=50, k=2.0, sl_atr=2.0, max_bars=24))
|
|
||||||
|
|
||||||
|
|
||||||
def add_context(ents, df, ema_long=200):
|
|
||||||
"""Aggiunge a ogni entry: sl_dist_pct, 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 main():
|
|
||||||
for asset in ["BTC", "ETH"]:
|
|
||||||
df = load_data(asset, "1h")
|
|
||||||
split = int(len(df) * (1 - OOS_FRAC))
|
|
||||||
table = STRATS_ETH3 if asset == "ETH" else STRATS
|
|
||||||
# quantili vol globali per la soglia (p90)
|
|
||||||
print("\n" + "=" * 110)
|
|
||||||
print(f" {asset} 1h — leve di riduzione DD / aumento Acc | NETTO fee 0.10% RT, leva 3x")
|
|
||||||
print("=" * 110)
|
|
||||||
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 table.items():
|
|
||||||
ents = add_context(fn(df, **params), df)
|
|
||||||
apct = np.array([e["atr_pct"] for e in ents])
|
|
||||||
p85 = float(np.quantile(apct, 0.85))
|
|
||||||
tdist = np.array([e["trend_dist"] for e in ents])
|
|
||||||
t90 = float(np.quantile(tdist, 0.90))
|
|
||||||
|
|
||||||
base_f = simulate(ents, df); base_o = simulate(ents, df, split=split)
|
|
||||||
line(f"{nm} base", base_f, base_o)
|
|
||||||
vt_f = simulate(ents, df, sizer=vol_target_sizer()); vt_o = simulate(ents, df, split=split, sizer=vol_target_sizer())
|
|
||||||
line(f"{nm} +volTarget", vt_f, vt_o)
|
|
||||||
vs_f = simulate(ents, df, vol_skip=p85); vs_o = simulate(ents, df, split=split, vol_skip=p85)
|
|
||||||
line(f"{nm} +volSkip(p85)", vs_f, vs_o)
|
|
||||||
ts_f = simulate(ents, df, trend_skip=t90); ts_o = simulate(ents, df, split=split, trend_skip=t90)
|
|
||||||
line(f"{nm} +trendSkip(p90)", ts_f, ts_o)
|
|
||||||
allf = simulate(ents, df, sizer=vol_target_sizer(), vol_skip=p85, trend_skip=t90)
|
|
||||||
allo = simulate(ents, df, split=split, sizer=vol_target_sizer(), vol_skip=p85, trend_skip=t90)
|
|
||||||
line(f"{nm} +ALL", allf, allo)
|
|
||||||
print(" " + "-" * 106)
|
|
||||||
print("\n Shrp = Sharpe annuo-naive sui ritorni per-trade. oXxx = stessa metrica su OOS (ultimo 30%).")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
@@ -0,0 +1,260 @@
|
|||||||
|
"""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()
|
||||||
@@ -1,163 +0,0 @@
|
|||||||
"""Affina il filtro trend (soglia assoluta ATR) e costruisce il portafoglio combinato.
|
|
||||||
|
|
||||||
Due risultati:
|
|
||||||
(1) trend filter: salta le fade quando |close-EMA200|/ATR > soglia (non fadare un
|
|
||||||
trend estremo). Soglia ASSOLUTA in multipli di ATR -> stessa regola per tutte
|
|
||||||
le strategie/asset, basso rischio di overfit. Sweep soglie, FULL e OOS.
|
|
||||||
(2) portafoglio: equity curve combinata delle 4 strategie sullo stesso conto
|
|
||||||
(rischio diviso fra N posizioni). Curve poco correlate -> DD aggregato << DD
|
|
||||||
della singola strategia. Confronto singola vs portafoglio, con/senza filtro.
|
|
||||||
"""
|
|
||||||
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, INIT, OOS_FRAC = 0.001, 3.0, 1000.0, 0.30
|
|
||||||
|
|
||||||
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 build_trades(ents, df, lev=LEV, fee_rt=FEE_RT, trend_max=None, ema_long=200):
|
|
||||||
"""Ritorna 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, ts, pos=0.15, 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=0.15, split=-1):
|
|
||||||
"""Equity curve di uno sleeve su sotto-conto indipendente (capitale INIT, pos fissa).
|
|
||||||
Ritorna array lungo n_bars (step aggiornato alla chiusura di ogni trade)."""
|
|
||||||
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 # da j in poi il sotto-conto vale cap
|
|
||||||
return eq
|
|
||||||
|
|
||||||
|
|
||||||
def metrics_portfolio(strat_trades, n_bars, ts, pos=0.15, split=-1):
|
|
||||||
"""Portafoglio equipesato: capitale diviso in N sotto-conti indipendenti, ciascuno
|
|
||||||
con la sua strategia a `pos` fisso. Equity aggregata = media dei sotto-conti (somma
|
|
||||||
normalizzata a base INIT). DD misurato sull'equity aggregata. Niente leva sovrapposta."""
|
|
||||||
sleeves = [sleeve_equity(tr, n_bars, pos=pos, split=split) for tr in strat_trades.values()]
|
|
||||||
agg = np.mean(sleeves, axis=0) # media -> base INIT, diversificazione reale
|
|
||||||
# restringi alla finestra effettiva (da split in poi se OOS)
|
|
||||||
lo = max(split, 0)
|
|
||||||
agg = agg[lo:]
|
|
||||||
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, sharpe=0.0)
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
# ---------- (1) sweep soglia trend ----------
|
|
||||||
print("=" * 104)
|
|
||||||
print(" (1) 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)
|
|
||||||
thresholds = [None, 4.0, 3.0, 2.5, 2.0]
|
|
||||||
for asset in ["BTC", "ETH"]:
|
|
||||||
df = load_data(asset, "1h"); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
|
||||||
split = int(len(df) * (1 - OOS_FRAC))
|
|
||||||
table = STRATS_ETH if asset == "ETH" else STRATS
|
|
||||||
for nm, (fn, params) in table.items():
|
|
||||||
ents = fn(df, **params)
|
|
||||||
for thr in thresholds:
|
|
||||||
tr = build_trades(ents, df, trend_max=thr)
|
|
||||||
f = metrics_single(tr, ts); o = metrics_single(tr, ts, 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)
|
|
||||||
|
|
||||||
# ---------- (2) portafoglio combinato ----------
|
|
||||||
print("\n" + "=" * 104)
|
|
||||||
print(" (2) PORTAFOGLIO equipesato: capitale diviso in N sotto-conti indipendenti")
|
|
||||||
print(" (pos 0.15 ciascuno, filtro trend 3.0 ATR). DD aggregato vs singola strategia.")
|
|
||||||
print("=" * 104)
|
|
||||||
print(f" {'Universo':<26s}{'Trd':>6s}{'Acc%':>7s}{'Ret%':>10s}{'DD%':>7s}{'':>7s}"
|
|
||||||
f" | {'oAcc':>6s}{'oRet':>9s}{'oDD':>7s}{'':>7s}")
|
|
||||||
print(" " + "-" * 100)
|
|
||||||
all_trades = {}
|
|
||||||
for asset in ["BTC", "ETH"]:
|
|
||||||
df = load_data(asset, "1h"); ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
|
|
||||||
split = int(len(df) * (1 - OOS_FRAC)); n = len(df)
|
|
||||||
table = STRATS_ETH if asset == "ETH" else STRATS
|
|
||||||
st = {f"{nm}_{asset}": build_trades(fn(df, **p), df, trend_max=3.0) for nm, (fn, p) in table.items()}
|
|
||||||
all_trades.update(st)
|
|
||||||
f = metrics_portfolio(st, n, ts); o = metrics_portfolio(st, n, ts, 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['sharpe']:>7.2f}"
|
|
||||||
f" | {o['acc']:>6.1f}{o['ret']:>+9.0f}{o['dd']:>7.1f}{o['sharpe']:>7.2f}")
|
|
||||||
# globale 8 sleeve
|
|
||||||
df0 = load_data("BTC", "1h"); ts0 = pd.to_datetime(df0["timestamp"], unit="ms", utc=True)
|
|
||||||
split0 = int(len(df0) * (1 - OOS_FRAC))
|
|
||||||
f = metrics_portfolio(all_trades, len(df0), ts0); o = metrics_portfolio(all_trades, len(df0), ts0, 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['sharpe']:>7.2f}"
|
|
||||||
f" | {o['acc']:>6.1f}{o['ret']:>+9.0f}{o['dd']:>7.1f}{o['sharpe']:>7.2f}")
|
|
||||||
print("\n Nota: ogni sleeve gira su un sotto-conto indipendente (pos 0.15); l'equity di")
|
|
||||||
print(" portafoglio e' la media dei sotto-conti. Curve poco correlate => DD aggregato")
|
|
||||||
print(" molto piu' basso del DD del singolo sleeve (la vera leva anti-drawdown).")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
@@ -1,169 +0,0 @@
|
|||||||
"""Report accuracy per ANNO × MERCATO delle strategie migliori.
|
|
||||||
|
|
||||||
Esegue ogni strategia vincente su BTC e ETH e produce tabella
|
|
||||||
accuracy/trades per ogni anno. Permette di vedere robustezza temporale
|
|
||||||
e differenze tra mercati.
|
|
||||||
"""
|
|
||||||
from __future__ import annotations
|
|
||||||
import sys
|
|
||||||
sys.path.insert(0, ".")
|
|
||||||
|
|
||||||
import importlib.util
|
|
||||||
from pathlib import Path
|
|
||||||
|
|
||||||
STRATEGIES_DIR = Path("scripts/strategies")
|
|
||||||
|
|
||||||
|
|
||||||
def load_class(module_file, class_name):
|
|
||||||
path = STRATEGIES_DIR / f"{module_file}.py"
|
|
||||||
spec = importlib.util.spec_from_file_location(module_file, path)
|
|
||||||
mod = importlib.util.module_from_spec(spec)
|
|
||||||
spec.loader.exec_module(mod)
|
|
||||||
return getattr(mod, class_name)
|
|
||||||
|
|
||||||
|
|
||||||
# (label, module, class, params, hold)
|
|
||||||
STRATEGIES = [
|
|
||||||
("SQ02 antifake+vol", "SQ02_squeeze_antifake_vol", "SqueezeAntifakeVol", {}, 3),
|
|
||||||
("MT01 ema20+vol", "MT01_squeeze_mtf_momentum", "SqueezeMTFMomentum",
|
|
||||||
{"ema_period": 20, "min_slope": 0.001, "vol_filter": True}, 3),
|
|
||||||
("PD01 vtb3 vm1.3", "PD01_price_volume_divergence", "PriceVolumeDivergence",
|
|
||||||
{}, 3),
|
|
||||||
("CM01 cb6+vol", "CM01_cross_market_momentum", "CrossMarketMomentum",
|
|
||||||
{"cross_bars": 6, "mom_min": 0.001, "use_vol": True}, 3),
|
|
||||||
("AD01 lt.65 ht.95", "AD01_adaptive_squeeze", "AdaptiveSqueeze",
|
|
||||||
{"low_thr": 0.65, "high_thr": 0.95, "use_vol": True}, 3),
|
|
||||||
]
|
|
||||||
|
|
||||||
ASSETS = ["BTC", "ETH"]
|
|
||||||
TF = "15m"
|
|
||||||
ALL_YEARS = list(range(2018, 2027))
|
|
||||||
|
|
||||||
|
|
||||||
def run():
|
|
||||||
results = {} # (label, asset) -> BacktestResult
|
|
||||||
|
|
||||||
for label, module, cls_name, params, hold in STRATEGIES:
|
|
||||||
try:
|
|
||||||
cls = load_class(module, cls_name)
|
|
||||||
except Exception as e:
|
|
||||||
print(f"SKIP {label}: {e}")
|
|
||||||
continue
|
|
||||||
strat = cls()
|
|
||||||
for asset in ASSETS:
|
|
||||||
try:
|
|
||||||
r = strat.backtest(asset, TF, hold=hold, **params)
|
|
||||||
if r:
|
|
||||||
results[(label, asset)] = r
|
|
||||||
except Exception as e:
|
|
||||||
print(f" errore {label} {asset}: {e}")
|
|
||||||
|
|
||||||
# ── Tabella ACCURACY per anno × mercato ──────────────────────────
|
|
||||||
print(f"\n{'=' * 140}")
|
|
||||||
print(f" ACCURACY PER ANNO × MERCATO — {TF} (fee 0.2% RT, leva 3x, pos 15%)")
|
|
||||||
print(f"{'=' * 140}")
|
|
||||||
|
|
||||||
header = f" {'Strategia':<22s} {'Mkt':>3s}"
|
|
||||||
for y in ALL_YEARS:
|
|
||||||
header += f" {y:>7d}"
|
|
||||||
header += f" │ {'TOT':>6s} {'DD%':>5s} {'Worst':>10s}"
|
|
||||||
print(header)
|
|
||||||
print(f" {'─' * 136}")
|
|
||||||
|
|
||||||
for label, module, cls_name, params, hold in STRATEGIES:
|
|
||||||
for asset in ASSETS:
|
|
||||||
r = results.get((label, asset))
|
|
||||||
if not r:
|
|
||||||
continue
|
|
||||||
yd = {ys.year: ys for ys in r.yearly}
|
|
||||||
line = f" {label:<22s} {asset:>3s}"
|
|
||||||
for y in ALL_YEARS:
|
|
||||||
if y in yd:
|
|
||||||
line += f" {yd[y].accuracy:>5.0f}%↑" if yd[y].accuracy >= 80 else f" {yd[y].accuracy:>5.0f}% "
|
|
||||||
else:
|
|
||||||
line += f" {'—':>7s}"
|
|
||||||
worst = r.worst_year
|
|
||||||
worst_str = f"{worst.year}({worst.accuracy:.0f}%)" if worst else "N/A"
|
|
||||||
line += f" │ {r.accuracy:>5.1f}% {r.max_dd:>4.1f}% {worst_str:>10s}"
|
|
||||||
print(line)
|
|
||||||
print(f" {'·' * 136}")
|
|
||||||
|
|
||||||
# ── Tabella TRADES per anno × mercato ────────────────────────────
|
|
||||||
print(f"\n{'=' * 140}")
|
|
||||||
print(f" NUMERO TRADES PER ANNO × MERCATO")
|
|
||||||
print(f"{'=' * 140}")
|
|
||||||
|
|
||||||
header = f" {'Strategia':<22s} {'Mkt':>3s}"
|
|
||||||
for y in ALL_YEARS:
|
|
||||||
header += f" {y:>7d}"
|
|
||||||
header += f" │ {'TOT':>6s} {'€/day':>6s}"
|
|
||||||
print(header)
|
|
||||||
print(f" {'─' * 130}")
|
|
||||||
|
|
||||||
for label, module, cls_name, params, hold in STRATEGIES:
|
|
||||||
for asset in ASSETS:
|
|
||||||
r = results.get((label, asset))
|
|
||||||
if not r:
|
|
||||||
continue
|
|
||||||
yd = {ys.year: ys for ys in r.yearly}
|
|
||||||
line = f" {label:<22s} {asset:>3s}"
|
|
||||||
for y in ALL_YEARS:
|
|
||||||
if y in yd:
|
|
||||||
line += f" {yd[y].trades:>7d}"
|
|
||||||
else:
|
|
||||||
line += f" {'—':>7s}"
|
|
||||||
line += f" │ {r.trades:>6d} {r.daily_pnl:>+6.2f}"
|
|
||||||
print(line)
|
|
||||||
print(f" {'·' * 130}")
|
|
||||||
|
|
||||||
# ── Tabella PnL per anno × mercato ──────────────────────────────
|
|
||||||
print(f"\n{'=' * 140}")
|
|
||||||
print(f" PnL € PER ANNO × MERCATO (su €1000, no compounding tra anni)")
|
|
||||||
print(f"{'=' * 140}")
|
|
||||||
|
|
||||||
header = f" {'Strategia':<22s} {'Mkt':>3s}"
|
|
||||||
for y in ALL_YEARS:
|
|
||||||
header += f" {y:>7d}"
|
|
||||||
header += f" │ {'TOT€':>8s}"
|
|
||||||
print(header)
|
|
||||||
print(f" {'─' * 132}")
|
|
||||||
|
|
||||||
for label, module, cls_name, params, hold in STRATEGIES:
|
|
||||||
for asset in ASSETS:
|
|
||||||
r = results.get((label, asset))
|
|
||||||
if not r:
|
|
||||||
continue
|
|
||||||
yd = {ys.year: ys for ys in r.yearly}
|
|
||||||
line = f" {label:<22s} {asset:>3s}"
|
|
||||||
for y in ALL_YEARS:
|
|
||||||
if y in yd:
|
|
||||||
line += f" {yd[y].pnl:>+7.0f}"
|
|
||||||
else:
|
|
||||||
line += f" {'—':>7s}"
|
|
||||||
line += f" │ {r.pnl:>+8.0f}"
|
|
||||||
print(line)
|
|
||||||
print(f" {'·' * 132}")
|
|
||||||
|
|
||||||
# ── Sintesi: media per anno (tutte le strategie) ────────────────
|
|
||||||
print(f"\n{'=' * 140}")
|
|
||||||
print(f" SINTESI — Accuracy media per anno (tutte le strategie, BTC+ETH)")
|
|
||||||
print(f"{'=' * 140}")
|
|
||||||
year_acc = {y: [] for y in ALL_YEARS}
|
|
||||||
for (label, asset), r in results.items():
|
|
||||||
for ys in r.yearly:
|
|
||||||
if ys.trades >= 10:
|
|
||||||
year_acc[ys.year].append(ys.accuracy)
|
|
||||||
|
|
||||||
line_y = f" {'Anno':<22s} "
|
|
||||||
line_a = f" {'Acc media':<22s} "
|
|
||||||
for y in ALL_YEARS:
|
|
||||||
accs = year_acc[y]
|
|
||||||
avg = sum(accs) / len(accs) if accs else 0
|
|
||||||
line_y += f" {y:>7d}"
|
|
||||||
line_a += f" {avg:>6.1f}%"
|
|
||||||
print(line_y)
|
|
||||||
print(line_a)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
run()
|
|
||||||
Reference in New Issue
Block a user