refactor: riorganizzazione script — Strategy ABC, folder strategies/waste/analysis
- src/strategies/base.py: Strategy ABC con Signal, BacktestResult, YearlyStats - src/strategies/indicators.py: keltner_ratio, detect_squeezes, ema, atr, rv, corr - scripts/strategies/: SQ01-SQ04 (squeeze puro/filtri), ML01 (squeeze+GBM) - scripts/waste/: W01-W22 script scartati + REF originali - scripts/analysis/: compare, best_yearly, final_report, paper_status - CLAUDE.md aggiornato con nuova struttura e tabella strategie Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""S2-04: Momentum microstructure su 5m.
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Approccio: cattura micro-trend intraday.
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- Identifica breakout da consolidamento su 5m
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- Conferma con volume e acceleration
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- Hold breve (15-30 min), stop stretto
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- Target: molti piccoli guadagni, alta frequenza
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"""
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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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FEE = 0.001
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INITIAL = 1000
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LEVERAGE = 3
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def ema(arr: np.ndarray, period: int) -> np.ndarray:
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result = np.full(len(arr), np.nan)
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k = 2 / (period + 1)
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result[period - 1] = np.mean(arr[:period])
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for i in range(period, len(arr)):
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result[i] = arr[i] * k + result[i - 1] * (1 - k)
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return result
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def atr(high: np.ndarray, low: np.ndarray, close: np.ndarray, period: int = 14) -> np.ndarray:
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tr = np.maximum(high - low, np.maximum(np.abs(high - np.roll(close, 1)), np.abs(low - np.roll(close, 1))))
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tr[0] = high[0] - low[0]
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return ema(tr, period)
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def run_momentum(asset):
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print(f"\n{'#'*60}")
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print(f" {asset} 5m — MOMENTUM MICROSTRUCTURE")
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print(f"{'#'*60}")
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df = load_data(asset, "5m")
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close = df["close"].values
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high = df["high"].values
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low = df["low"].values
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volume = df["volume"].values
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n = len(close)
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split = int(n * 0.7)
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timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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ema_fast = ema(close, 8)
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ema_slow = ema(close, 21)
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ema_trend = ema(close, 55)
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atr_vals = atr(high, low, close, 14)
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configs = [
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# (consolidation_bars, breakout_atr_mult, hold_bars, stop_atr, tp_atr, min_vol_mult, name)
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(12, 1.5, 3, 1.0, 2.0, 1.3, "tight_12bar"),
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(12, 1.5, 6, 1.5, 2.5, 1.2, "medium_12bar"),
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(24, 2.0, 6, 1.5, 3.0, 1.5, "wide_24bar"),
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(6, 1.2, 3, 1.0, 1.5, 1.1, "fast_6bar"),
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(12, 1.5, 3, 0.8, 2.0, 1.3, "tight_stop"),
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(18, 1.8, 4, 1.2, 2.5, 1.4, "balanced_18bar"),
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]
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for consol_bars, brk_mult, hold_bars, stop_m, tp_m, vol_mult, name in configs:
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capital = float(INITIAL)
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correct = 0
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total = 0
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daily_trades = {}
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for i in range(max(split, 60), n - hold_bars):
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if np.isnan(ema_fast[i]) or np.isnan(ema_slow[i]) or np.isnan(atr_vals[i]) or atr_vals[i] == 0:
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continue
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day = timestamps.iloc[i].strftime("%Y-%m-%d")
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if daily_trades.get(day, 0) >= 5:
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continue
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# Consolidation: range delle ultime N barre < 1.5 ATR
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consol_range = np.max(high[i - consol_bars : i]) - np.min(low[i - consol_bars : i])
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if consol_range > 1.5 * atr_vals[i]:
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continue
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# Breakout: current bar breaks consolidation range
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consol_high = np.max(high[i - consol_bars : i])
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consol_low = np.min(low[i - consol_bars : i])
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breakout_up = close[i] > consol_high + atr_vals[i] * (brk_mult - 1)
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breakout_down = close[i] < consol_low - atr_vals[i] * (brk_mult - 1)
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if not (breakout_up or breakout_down):
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continue
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# Volume confirmation
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vol_avg = np.mean(volume[max(0, i - 24) : i])
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if vol_avg > 0 and volume[i] < vol_avg * vol_mult:
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continue
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# Trend filter: only trade in direction of trend
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if breakout_up and close[i] < ema_trend[i]:
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continue
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if breakout_down and close[i] > ema_trend[i]:
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continue
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direction = "long" if breakout_up else "short"
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entry = close[i]
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stop_price = entry - atr_vals[i] * stop_m if direction == "long" else entry + atr_vals[i] * stop_m
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tp_price = entry + atr_vals[i] * tp_m if direction == "long" else entry - atr_vals[i] * tp_m
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exit_price = close[min(i + hold_bars, n - 1)]
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for j in range(i + 1, min(i + hold_bars + 1, n)):
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if direction == "long":
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if low[j] <= stop_price:
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exit_price = stop_price
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break
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if high[j] >= tp_price:
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exit_price = tp_price
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break
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else:
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if high[j] >= stop_price:
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exit_price = stop_price
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break
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if low[j] <= tp_price:
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exit_price = tp_price
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break
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exit_price = close[j]
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if direction == "long":
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trade_ret = (exit_price - entry) / entry
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else:
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trade_ret = (entry - exit_price) / entry
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net = trade_ret * LEVERAGE - FEE * 2 * LEVERAGE
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capital += capital * 0.1 * net
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capital = max(capital, 0)
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total += 1
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if trade_ret > 0:
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correct += 1
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daily_trades[day] = daily_trades.get(day, 0) + 1
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if total < 30:
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continue
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acc = correct / total * 100
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ret = (capital - INITIAL) / INITIAL * 100
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test_days = (n - split) / (24 * 12)
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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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dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
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days_active = len(daily_trades)
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tag = "✅" if acc >= 55 and ann >= 30 else ""
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print(f" {name:20s}: trades={total:5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} t/day={total/days_active:.1f} {tag}")
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for asset in ["ETH", "BTC"]:
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run_momentum(asset)
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