chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita
Reset del progetto su fondamenta verificate dopo la scoperta che l'intera libreria "validata OOS" era artefatto di feed contaminato (print fantasma del feed Cerbero TESTNET + storico Binance/USDT). - Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE 50-82% barre flat; XRP/BNB non certificabili). - Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST con segnale residuo, da ri-validare in isolamento. - Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio, runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/ portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/ (preservati, non cancellati). Diario consolidato in un unico documento. - Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal + src/backtest/engine + load_data; tool dati certificati (rebuild_history, certify_feed, audit_feed, multi_source_check). - Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Adriano Dal Pastro
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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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