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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"""S2-12: Strategie SOLO su perpetual — dati 100% reali.
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Niente opzioni, niente IV stimata. Solo prezzo OHLCV.
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Mix di approcci diversi da quelli già testati su main.
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1. Intraday range breakout con filtro volatilità
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2. Daily open range breakout (prima ora di trading)
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3. RSI divergence (prezzo fa nuovo min/max, RSI no)
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4. Close-to-close momentum filtrato da volatilità regime
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5. Multi-timeframe confirmation (15m signal + 1h trend)
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Test per-anno, onesto, con fee reali perpetual (0.05% taker × 2 = 0.1% roundtrip).
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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_RT = 0.002 # 0.1% taker roundtrip
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INITIAL = 1000
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LEVERAGE = 3
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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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if al == 0:
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result[i + 1] = 100
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else:
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result[i + 1] = 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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def run_all_perpetual(asset):
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print(f"\n{'#'*70}")
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print(f" {asset} — STRATEGIE PERPETUAL (dati reali)")
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print(f" Fee: {FEE_RT*100}% roundtrip, Leva: {LEVERAGE}x")
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print(f"{'#'*70}")
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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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h1h = df_1h["high"].values
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l1h = df_1h["low"].values
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v1h = df_1h["volume"].values
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n1h = len(c1h)
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ts1h = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
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rsi_14 = rsi(c1h, 14)
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ema_20 = ema(c1h, 20)
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ema_50 = ema(c1h, 50)
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results = {}
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# ======================================================
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# STRAT 1: Daily Open Range Breakout
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# Prima ora (08-09 UTC) definisce il range. Breakout = entrata.
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# ======================================================
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for hold, stop_m in [(6, 1.0), (12, 1.5), (4, 0.8)]:
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name = f"ORB_h{hold}_s{stop_m}"
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capital = float(INITIAL)
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yearly = {}
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for i in range(50, n1h - hold):
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if ts1h.iloc[i].hour != 9: # fine della prima ora
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continue
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day = ts1h.iloc[i].strftime("%Y-%m-%d")
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if day in yearly and len(yearly[day]) >= 1:
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continue
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range_high = h1h[i - 1]
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range_low = l1h[i - 1]
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range_size = range_high - range_low
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if range_size <= 0:
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continue
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# ATR per stop
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atr_14 = np.mean(h1h[max(0,i-14):i] - l1h[max(0,i-14):i])
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if atr_14 <= 0:
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continue
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# Breakout detection: la candela attuale rompe il range
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if c1h[i] > range_high:
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direction = "long"
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elif c1h[i] < range_low:
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direction = "short"
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else:
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continue
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entry = c1h[i]
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stop_dist = atr_14 * stop_m
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exit_price = c1h[min(i + hold, n1h - 1)]
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for j in range(i + 1, min(i + hold + 1, n1h)):
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if direction == "long":
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if l1h[j] <= entry - stop_dist:
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exit_price = entry - stop_dist
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break
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if h1h[j] >= entry + stop_dist * 2:
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exit_price = entry + stop_dist * 2
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break
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else:
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if h1h[j] >= entry + stop_dist:
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exit_price = entry + stop_dist
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break
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if l1h[j] <= entry - stop_dist * 2:
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exit_price = entry - stop_dist * 2
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break
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exit_price = c1h[j]
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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_RT * LEVERAGE
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capital += capital * 0.15 * net
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capital = max(capital, 10)
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year = ts1h.iloc[i].year
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if year not in yearly:
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yearly[year] = []
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yearly[year].append(net > 0)
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if day not in yearly:
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yearly[day] = []
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if sum(len(v) for v in yearly.values() if isinstance(v, list) and all(isinstance(x, bool) for x in v)) > 30:
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all_wins = [w for v in yearly.values() if isinstance(v, list) for w in v if isinstance(w, bool)]
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acc = sum(all_wins) / len(all_wins) * 100
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ret = (capital - INITIAL) / INITIAL * 100
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results[name] = {"acc": acc, "ret": ret, "trades": len(all_wins), "capital": capital}
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# ======================================================
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# STRAT 2: RSI Divergence
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# Prezzo fa nuovo low, RSI no = bullish divergence → long
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# ======================================================
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for lookback, rsi_thr_low, rsi_thr_high, hold in [(20, 30, 70, 6), (14, 25, 75, 8), (10, 35, 65, 4)]:
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name = f"RSIdiv_lb{lookback}_h{hold}"
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capital = float(INITIAL)
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trades_list = []
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for i in range(max(50, lookback + 1), n1h - hold):
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day = ts1h.iloc[i].strftime("%Y-%m-%d")
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# Bullish divergence: price new low, RSI higher low
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price_new_low = c1h[i] < np.min(c1h[i - lookback : i])
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rsi_higher = rsi_14[i] > np.min(rsi_14[i - lookback : i]) and rsi_14[i] < rsi_thr_low
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# Bearish divergence: price new high, RSI lower high
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price_new_high = c1h[i] > np.max(c1h[i - lookback : i])
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rsi_lower = rsi_14[i] < np.max(rsi_14[i - lookback : i]) and rsi_14[i] > rsi_thr_high
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direction = None
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if price_new_low and rsi_higher:
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direction = "long"
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elif price_new_high and rsi_lower:
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direction = "short"
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if direction is None:
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continue
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entry = c1h[i]
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exit_price = c1h[min(i + hold, n1h - 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_RT * LEVERAGE
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capital += capital * 0.12 * net
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capital = max(capital, 10)
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trades_list.append({"year": ts1h.iloc[i].year, "win": trade_ret > 0})
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if len(trades_list) > 30:
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acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
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ret = (capital - INITIAL) / INITIAL * 100
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results[name] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
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# ======================================================
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# STRAT 3: Momentum regime — trend following solo in low-vol regime
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# ======================================================
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for fast, slow, vol_w, vol_thr, hold in [
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(8, 21, 48, 0.8, 12),
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(5, 13, 24, 0.8, 6),
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(13, 34, 72, 0.7, 24),
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(8, 21, 48, 0.9, 8),
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]:
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name = f"MomReg_f{fast}s{slow}_h{hold}"
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ema_f = ema(c1h, fast)
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ema_s = ema(c1h, slow)
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rv_short = np.full(n1h, np.nan)
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rv_long = np.full(n1h, np.nan)
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lr = np.diff(np.log(np.where(c1h == 0, 1e-10, c1h)))
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for idx in range(vol_w, len(lr)):
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rv_short[idx + 1] = np.std(lr[idx - min(12, vol_w) : idx])
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rv_long[idx + 1] = np.std(lr[idx - vol_w : idx])
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capital = float(INITIAL)
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trades_list = []
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daily_done = set()
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for i in range(max(60, slow + 1), n1h - hold):
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if np.isnan(ema_f[i]) or np.isnan(ema_s[i]) or np.isnan(rv_short[i]) or np.isnan(rv_long[i]):
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continue
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if rv_long[i] <= 0:
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continue
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day = ts1h.iloc[i].strftime("%Y-%m-%d")
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if day in daily_done:
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continue
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# Only trade in low-vol regime
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vol_ratio = rv_short[i] / rv_long[i]
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if vol_ratio > vol_thr:
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continue
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# EMA crossover signal
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cross_up = ema_f[i] > ema_s[i] and ema_f[i - 1] <= ema_s[i - 1]
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cross_down = ema_f[i] < ema_s[i] and ema_f[i - 1] >= ema_s[i - 1]
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if not (cross_up or cross_down):
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continue
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direction = "long" if cross_up else "short"
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entry = c1h[i]
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exit_price = c1h[min(i + hold, n1h - 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_RT * LEVERAGE
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capital += capital * 0.15 * net
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capital = max(capital, 10)
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trades_list.append({"year": ts1h.iloc[i].year, "win": trade_ret > 0})
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daily_done.add(day)
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if len(trades_list) > 30:
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acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
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ret = (capital - INITIAL) / INITIAL * 100
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results[name] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
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# ======================================================
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# STRAT 4: Multi-TF confirmation (15m entry, 1h trend)
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# ======================================================
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c15 = df_15m["close"].values
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h15 = df_15m["high"].values
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l15 = df_15m["low"].values
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ts15 = df_15m["timestamp"].values
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n15 = len(c15)
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ema_1h_50 = ema(c1h, 50)
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rsi_15m = rsi(c15, 14)
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capital = float(INITIAL)
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trades_list = []
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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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# 15m signal: RSI extreme
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if rsi_15m[i] > 35 and rsi_15m[i] < 65:
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continue
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# Find matching 1h candle
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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 >= n1h or np.isnan(ema_1h_50[h_idx]):
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continue
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# 1h trend confirmation
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trend_up = c1h[h_idx] > ema_1h_50[h_idx]
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trend_down = c1h[h_idx] < ema_1h_50[h_idx]
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direction = None
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if rsi_15m[i] < 30 and trend_up:
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direction = "long" # oversold in uptrend
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elif rsi_15m[i] > 70 and trend_down:
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direction = "short" # overbought in downtrend
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if direction is None:
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continue
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entry = c15[i]
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hold_bars = 12 # 12 × 15m = 3h
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exit_price = c15[min(i + hold_bars, 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_RT * LEVERAGE
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capital += capital * 0.12 * net
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capital = max(capital, 10)
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trades_list.append({"year": ts_dt.year, "win": trade_ret > 0})
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daily_done.add(day)
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if len(trades_list) > 30:
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acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
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ret = (capital - INITIAL) / INITIAL * 100
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results["MultiTF_15m1h"] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
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# === PRINT RESULTS ===
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print(f"\n {'Strategy':<25s} {'Trades':>7s} {'Acc':>6s} {'Return':>10s} {'Capital':>10s}")
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print(f" {'-'*60}")
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for name, r in sorted(results.items(), key=lambda x: x[1]["acc"], reverse=True):
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tag = "✅" if r["acc"] >= 60 and r["ret"] > 30 else ""
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print(f" {name:<25s} {r['trades']:>7d} {r['acc']:>5.1f}% {r['ret']:>+9.1f}% €{r['capital']:>9,.0f} {tag}")
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for asset in ["ETH", "BTC"]:
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run_all_perpetual(asset)
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