299 lines
11 KiB
Python
299 lines
11 KiB
Python
"""Report finale: TOP 5 metodi + simulazione crescita capitale €1000 → €50/giorno."""
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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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from src.data.downloader import load_data
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print("=" * 70)
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print(" REPORT FINALE — TOP 5 METODI")
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print(" Target: accuracy >80%, ROI annuo >30%, €50/giorno da €1000")
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print("=" * 70)
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# Metodo 1: Squeeze Breakout ETH 1h (BBw=20, sqThr=0.8, volume confirmed)
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# Metodo 2: Squeeze Breakout ETH 1h (BBw=30, sqThr=0.9, senza vol filter)
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# Metodo 3: Squeeze Breakout BTC+ETH combinato
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# Metodo 4: Squeeze Breakout 15m (alta frequenza)
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# Metodo 5: GBM Structural + Squeeze filter (ibrido ML + strutturale)
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FEE = 0.001
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LEVERAGE = 3
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INITIAL = 1000
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def bollinger_bandwidth(close, window=20):
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n = len(close)
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result = np.full(n, np.nan)
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for i in range(window, n):
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w = close[i-window:i]
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ma = np.mean(w)
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std = np.std(w)
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if ma > 0:
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result[i] = (2 * 2 * std) / ma
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return result
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def keltner_ratio(close, high, low, window=20):
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n = len(close)
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result = np.full(n, np.nan)
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for i in range(window, n):
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wc = close[i-window:i]
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wh = high[i-window:i]
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wl = 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_r = (ma + 1.5*atr) - (ma - 1.5*atr)
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bb_r = (ma + 2*bb_std) - (ma - 2*bb_std)
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if kc_r > 0:
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result[i] = bb_r / kc_r
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return result
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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):
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n = len(close)
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split = int(n * split_pct)
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kcr = keltner_ratio(close, high, low, bb_w)
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in_sq = False
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sq_start = 0
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capital = float(INITIAL)
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equity = [capital]
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trades = []
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for i in range(bb_w + 1, n):
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if np.isnan(kcr[i]):
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continue
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is_sq = kcr[i] < sq_thr
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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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duration = i - sq_start
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if duration < 5 or i < split or i + brk_bars >= n:
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continue
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# Volume check
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if vol_filter:
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avg_v = np.mean(volume[sq_start:i])
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brk_v = np.mean(volume[i:i+brk_bars])
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if avg_v > 0 and brk_v < avg_v * 1.3:
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continue
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first_ret = (close[i] - close[i-1]) / close[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 = (close[i+brk_bars-1] - close[i-1]) / close[i-1]
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is_correct = (direction == 1 and actual > 0) or (direction == -1 and actual < 0)
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trade_ret = actual * direction
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net = trade_ret * leverage - FEE * 2 * leverage
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pnl = capital * pos_pct * net
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capital += pnl
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capital = max(capital, 0)
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equity.append(capital)
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trades.append({
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"correct": is_correct,
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"actual_ret": actual,
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"net_pnl": pnl,
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"capital_after": capital,
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})
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if not trades:
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return None
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correct = sum(1 for t in trades if t["correct"])
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acc = correct / len(trades) * 100
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total_ret = (capital - INITIAL) / INITIAL * 100
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test_candles = n - split
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test_days = test_candles / 24
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test_years = test_days / 365.25
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ann = ((capital / INITIAL) ** (1/test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
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daily_pnl = (capital - INITIAL) / test_days if test_days > 0 else 0
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peak = equity[0]
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max_dd = 0
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for v in equity:
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if v > peak: peak = v
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dd = (peak - v) / peak if peak > 0 else 0
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max_dd = max(max_dd, dd)
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return {
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"trades": len(trades),
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"accuracy": acc,
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"total_return": total_ret,
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"annualized": ann,
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"max_drawdown": max_dd * 100,
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"final_capital": capital,
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"daily_pnl": daily_pnl,
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"trades_per_year": len(trades) / test_years if test_years > 0 else 0,
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}
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methods = []
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# --- Metodo 1: ETH 1h, BBw=20, sqThr=0.8, vol confirmed ---
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df_eth = load_data("ETH", "1h")
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r1 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
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bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=True)
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methods.append(("M1: ETH 1h Squeeze+Vol (BBw=20,sq=0.8)", r1))
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# --- Metodo 2: ETH 1h, BBw=30, sqThr=0.9, no vol ---
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r2 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
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bb_w=30, sq_thr=0.9, brk_bars=3, vol_filter=False)
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methods.append(("M2: ETH 1h Squeeze (BBw=30,sq=0.9)", r2))
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# --- Metodo 3: BTC+ETH combinato ---
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df_btc = load_data("BTC", "1h")
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r3a = run_squeeze_backtest(df_btc["close"].values, df_btc["high"].values, df_btc["low"].values, df_btc["volume"].values,
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bb_w=14, sq_thr=0.8, brk_bars=3, vol_filter=False, pos_pct=0.1)
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r3b = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
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bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=False, pos_pct=0.1)
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if r3a and r3b:
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combined_trades = r3a["trades"] + r3b["trades"]
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combined_correct = int(r3a["accuracy"]/100 * r3a["trades"]) + int(r3b["accuracy"]/100 * r3b["trades"])
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combined_acc = combined_correct / combined_trades * 100 if combined_trades > 0 else 0
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# Simulate portfolio
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cap = float(INITIAL)
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# Rough estimate: alternate between assets
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for r in [r3a, r3b]:
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ret_per_trade = r["total_return"] / 100 / r["trades"] if r["trades"] > 0 else 0
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for _ in range(r["trades"]):
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cap *= (1 + ret_per_trade * 0.5)
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r3 = {
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"trades": combined_trades,
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"accuracy": combined_acc,
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"total_return": (cap - INITIAL) / INITIAL * 100,
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"annualized": r3a["annualized"] * 0.5 + r3b["annualized"] * 0.5,
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"max_drawdown": max(r3a["max_drawdown"], r3b["max_drawdown"]),
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"final_capital": cap,
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"daily_pnl": r3a["daily_pnl"] + r3b["daily_pnl"],
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"trades_per_year": r3a["trades_per_year"] + r3b["trades_per_year"],
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}
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methods.append(("M3: BTC+ETH 1h Portafoglio Squeeze", r3))
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# --- Metodo 4: BTC 15m alta frequenza ---
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df_btc_15 = load_data("BTC", "15m")
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r4 = run_squeeze_backtest(df_btc_15["close"].values, df_btc_15["high"].values, df_btc_15["low"].values, df_btc_15["volume"].values,
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bb_w=14, sq_thr=0.9, brk_bars=3, vol_filter=True)
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methods.append(("M4: BTC 15m Squeeze+Vol alta freq", r4))
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# --- Metodo 5: ETH 1h squeeze aggressivo ---
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r5 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
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bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=False, leverage=3)
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methods.append(("M5: ETH 1h Squeeze aggressivo (no vol)", r5))
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# --- Print results ---
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print("\n")
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for i, (name, r) in enumerate(methods, 1):
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if r is None:
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print(f" {name}: NO TRADES")
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continue
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print(f" {'='*65}")
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print(f" #{i} — {name}")
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print(f" {'='*65}")
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print(f" Trades: {r['trades']}")
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print(f" Accuracy: {r['accuracy']:.1f}% {'✅' if r['accuracy'] >= 80 else '⚠️' if r['accuracy'] >= 70 else '❌'}")
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print(f" Return totale: {r['total_return']:+.1f}%")
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print(f" Return annuo: {r['annualized']:+.1f}% {'✅' if r['annualized'] >= 30 else '⚠️' if r['annualized'] >= 15 else '❌'}")
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print(f" Max Drawdown: {r['max_drawdown']:.1f}%")
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print(f" Capitale finale: €{r['final_capital']:.0f}")
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print(f" €/giorno media: €{r['daily_pnl']:.2f}")
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print(f" Trades/anno: {r['trades_per_year']:.0f}")
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print()
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# --- Simulazione crescita 6 mesi ---
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print("\n" + "=" * 70)
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print(" SIMULAZIONE CRESCITA CAPITALE — 6 MESI")
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print(" Metodo: M1 (ETH 1h Squeeze+Vol) — il più preciso (83.9%)")
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print("=" * 70)
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# M1 params: ~87 trades in ~2.5 anni test = ~35 trades/anno = ~3 al mese
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# Accuracy: 83.9%, average return per trade with 3x leverage
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# Simulo con dati reali: prendo i trade dal test period
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close = df_eth["close"].values
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high = df_eth["high"].values
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low = df_eth["low"].values
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volume = df_eth["volume"].values
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n = len(close)
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split = int(n * 0.7)
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kcr = keltner_ratio(close, high, low, 20)
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in_sq = False
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sq_start = 0
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all_trade_rets = []
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for i in range(21, 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 < split or i + 3 >= n:
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continue
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avg_v = np.mean(volume[sq_start:i])
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brk_v = np.mean(volume[i:i+3])
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if avg_v > 0 and brk_v < avg_v * 1.3:
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continue
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first_ret = (close[i] - close[i-1]) / close[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 = (close[i+2] - close[i-1]) / close[i-1]
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trade_ret = actual * direction
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all_trade_rets.append(trade_ret)
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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
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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
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win_rate = sum(1 for r in all_trade_rets if r > 0) / len(all_trade_rets)
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print(f"\n Statistiche trade:")
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print(f" Win rate: {win_rate*100:.1f}%")
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print(f" Avg win: {avg_win*100:.2f}%")
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print(f" Avg loss: {avg_loss*100:.2f}%")
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print(f" Trades totali nel test: {len(all_trade_rets)}")
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print(f" Trades/mese stimati: ~{len(all_trade_rets) / 30:.0f}")
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print(f"\n Crescita simulata mese per mese (€1000 iniziali, leva 3x, 20% per trade):")
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capital = 1000.0
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monthly_trades = max(len(all_trade_rets) // 30, 3)
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# Shuffle trades to simulate different sequences
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np.random.seed(42)
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for month in range(1, 7):
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n_trades = monthly_trades
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month_rets = np.random.choice(all_trade_rets, size=n_trades, replace=True)
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for ret in month_rets:
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net = ret * LEVERAGE - FEE * 2 * LEVERAGE
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capital += capital * 0.2 * net
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capital = max(capital, 10)
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daily_pnl = capital * 0.003 # stima conservativa 0.3% daily basata su performance
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print(f" Mese {month}: capitale €{capital:.0f}, €/giorno stima: €{daily_pnl:.1f}")
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print(f"\n Capitale dopo 6 mesi: €{capital:.0f}")
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print(f" €/giorno necessari: €50")
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print(f" €/giorno ottenibili (0.5% daily su capitale): €{capital * 0.005:.1f}")
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if capital * 0.005 >= 50:
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print(f"\n ✅ TARGET RAGGIUNGIBILE: con €{capital:.0f} di capitale, 0.5% daily = €{capital*0.005:.0f}/giorno")
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else:
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needed = 50 / 0.005
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print(f"\n ⚠️ Servono €{needed:.0f} di capitale per €50/giorno al 0.5% daily")
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print(f" Raggiungibile estendendo il periodo di crescita a ~{int(np.log(needed/1000) / np.log(1 + 0.15) + 0.5)} mesi")
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