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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"""S3-02: Lead-lag multi-asset squeeze.
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Quando BTC fa squeeze breakout, ETH/SOL spesso seguono.
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Usa il breakout di BTC per anticipare entrata su ETH (e viceversa).
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Testa anche correlazione inter-asset per conferma segnale.
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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
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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: r[i] = bb/kc
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return r
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def load_aligned(assets, tf):
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"""Carica e allinea dati multi-asset per timestamp."""
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dfs = {}
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for asset in assets:
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try:
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if asset == "SOL":
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df = pd.read_parquet(f"data/raw/sol_{tf}.parquet")
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df["datetime"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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else:
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df = load_data(asset, tf)
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dfs[asset] = df
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except Exception:
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pass
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if len(dfs) < 2:
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return None
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# Allinea per timestamp
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common_ts = set(dfs[list(dfs.keys())[0]]["timestamp"].values)
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for df in dfs.values():
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common_ts &= set(df["timestamp"].values)
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common_ts = sorted(common_ts)
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aligned = {}
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for asset, df in dfs.items():
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mask = df["timestamp"].isin(common_ts)
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aligned[asset] = df[mask].sort_values("timestamp").reset_index(drop=True)
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return aligned
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def detect_breakouts(close, high, low, volume, kcr, sq_thr=0.8, min_dur=5):
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"""Detect squeeze breakout events."""
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events = []
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in_sq = False
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sq_start = 0
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for i in range(1, len(close)):
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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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if i - sq_start < min_dur:
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continue
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first_ret = (close[i] - close[i-1]) / close[i-1] if close[i-1] > 0 else 0
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if abs(first_ret) < 0.001:
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continue
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events.append({
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"idx": i,
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"duration": i - sq_start,
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"direction": 1 if first_ret > 0 else -1,
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"first_ret": first_ret,
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})
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return events
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print("=" * 75)
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print(" S3-02: LEAD-LAG MULTI-ASSET SQUEEZE")
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print("=" * 75)
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for tf in ["1h", "15m"]:
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aligned = load_aligned(["BTC", "ETH", "SOL"], tf)
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if aligned is None:
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continue
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n = len(aligned["BTC"])
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ts = pd.to_datetime(aligned["BTC"]["timestamp"], unit="ms", utc=True)
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print(f"\n Timeframe: {tf}, Candles allineate: {n}")
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# Calcola squeeze per ogni asset
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asset_data = {}
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for asset in aligned:
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df = aligned[asset]
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c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
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kcr = keltner_ratio(c, h, l, 14)
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events = detect_breakouts(c, h, l, v, kcr)
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asset_data[asset] = {"close": c, "high": h, "low": l, "vol": v, "kcr": kcr, "events": events}
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print(f" {asset}: {len(events)} squeeze breakouts")
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# ================================================================
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# STRATEGIA A: Leader-follower
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# Quando BTC fa breakout, entra su ETH/SOL nella stessa direzione
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# ================================================================
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print(f"\n --- LEADER-FOLLOWER ({tf}) ---")
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for leader, follower in [("BTC", "ETH"), ("BTC", "SOL"), ("ETH", "BTC"), ("ETH", "SOL")]:
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if leader not in asset_data or follower not in asset_data:
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continue
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leader_events = asset_data[leader]["events"]
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fc = asset_data[follower]["close"]
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for hold in [3, 6]:
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for delay in [0, 1, 2]:
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yearly = {}
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for ev in leader_events:
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i = ev["idx"] + delay
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if i + hold >= n:
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continue
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# Anti-fakeout su follower
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entry = fc[i]
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exit_price = fc[min(i + hold, n - 1)]
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direction = ev["direction"]
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actual = (exit_price - entry) / entry * direction
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net = actual * LEVERAGE - FEE_RT * LEVERAGE
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year = ts.iloc[min(i, n-1)].year
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if year not in yearly:
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yearly[year] = {"w": 0, "t": 0, "pnls": []}
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yearly[year]["t"] += 1
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if actual > 0:
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yearly[year]["w"] += 1
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yearly[year]["pnls"].append(net * INITIAL)
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all_t = sum(d["t"] for d in yearly.values())
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all_w = sum(d["w"] for d in yearly.values())
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if all_t < 30:
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continue
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acc = all_w / all_t * 100
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pnl = sum(p for d in yearly.values() for p in d["pnls"])
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worst_y = min(yearly.items(), key=lambda x: x[1]["w"]/x[1]["t"] if x[1]["t"]>0 else 0)
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worst_acc = worst_y[1]["w"]/worst_y[1]["t"]*100 if worst_y[1]["t"]>0 else 0
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tag = "✅" if acc >= 76 else ""
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print(f" {leader}→{follower} d={delay} h={hold}: trades={all_t:5d} acc={acc:.1f}% pnl=€{pnl:+.0f} worst={worst_y[0]}({worst_acc:.0f}%) {tag}")
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# ================================================================
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# STRATEGIA B: Consensus multi-asset
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# Trade solo quando 2+ asset hanno squeeze breakout nello stesso momento
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# ================================================================
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print(f"\n --- CONSENSUS MULTI-ASSET ({tf}) ---")
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# Build event map: timestamp → list of (asset, direction)
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event_map = {}
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for asset, data in asset_data.items():
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for ev in data["events"]:
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idx = ev["idx"]
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if idx not in event_map:
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event_map[idx] = []
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event_map[idx].append((asset, ev["direction"]))
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for target in ["BTC", "ETH", "SOL"]:
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if target not in asset_data:
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continue
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tc = asset_data[target]["close"]
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for min_consensus in [2, 3]:
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for window_bars in [1, 3, 5]:
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yearly = {}
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daily_done = set()
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for idx in sorted(event_map.keys()):
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if idx + 6 >= n:
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continue
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day = ts.iloc[idx].strftime("%Y-%m-%d")
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if day in daily_done:
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continue
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# Count consensus within window
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nearby_events = []
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for j in range(max(0, idx - window_bars), idx + window_bars + 1):
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if j in event_map:
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nearby_events.extend(event_map[j])
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# Unique assets
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unique_assets = set(a for a, d in nearby_events)
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if len(unique_assets) < min_consensus:
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continue
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# Majority direction
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dirs = [d for a, d in nearby_events]
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majority = 1 if sum(dirs) > 0 else -1
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entry = tc[idx]
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exit_price = tc[min(idx + 3, n - 1)]
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actual = (exit_price - entry) / entry * majority
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net = actual * LEVERAGE - FEE_RT * LEVERAGE
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year = ts.iloc[idx].year
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if year not in yearly:
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yearly[year] = {"w": 0, "t": 0, "pnls": []}
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yearly[year]["t"] += 1
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if actual > 0:
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yearly[year]["w"] += 1
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yearly[year]["pnls"].append(net * INITIAL)
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daily_done.add(day)
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all_t = sum(d["t"] for d in yearly.values())
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all_w = sum(d["w"] for d in yearly.values())
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if all_t < 20:
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continue
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acc = all_w / all_t * 100
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pnl = sum(p for d in yearly.values() for p in d["pnls"])
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tag = "✅" if acc >= 76 else ""
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print(f" {target} consensus>={min_consensus} w={window_bars}: trades={all_t:4d} acc={acc:.1f}% pnl=€{pnl:+.0f} {tag}")
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# ================================================================
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# STRATEGIA C: Correlation-weighted squeeze
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# Peso il segnale squeeze in base alla correlazione rolling con BTC
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# ================================================================
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print(f"\n --- CORRELATION-WEIGHTED ({tf}) ---")
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for target in ["ETH", "SOL"]:
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if target not in asset_data:
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continue
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tc = asset_data[target]["close"]
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btc_c = asset_data["BTC"]["close"]
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# Rolling correlation
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corr_window = 48 # 48 bars
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rolling_corr = np.full(n, np.nan)
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ret_t = np.diff(np.log(np.where(tc == 0, 1e-10, tc)))
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ret_b = np.diff(np.log(np.where(btc_c == 0, 1e-10, btc_c)))
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for i in range(corr_window, len(ret_t)):
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c_val = np.corrcoef(ret_t[i-corr_window:i], ret_b[i-corr_window:i])[0, 1]
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rolling_corr[i + 1] = c_val if np.isfinite(c_val) else 0
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events = asset_data[target]["events"]
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for corr_thr in [0.5, 0.6, 0.7, 0.8]:
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yearly = {}
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for ev in events:
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i = ev["idx"]
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if i + 3 >= n or np.isnan(rolling_corr[i]):
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continue
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# Solo quando correlazione con BTC è alta
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if abs(rolling_corr[i]) < corr_thr:
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continue
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entry = tc[i - 1]
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exit_price = tc[min(i + 2, n - 1)]
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actual = (exit_price - entry) / entry * ev["direction"]
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net = actual * LEVERAGE - FEE_RT * 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] = {"w": 0, "t": 0, "pnls": []}
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yearly[year]["t"] += 1
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if actual > 0:
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yearly[year]["w"] += 1
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yearly[year]["pnls"].append(net * INITIAL)
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all_t = sum(d["t"] for d in yearly.values())
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all_w = sum(d["w"] for d in yearly.values())
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if all_t < 20:
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continue
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acc = all_w / all_t * 100
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pnl = sum(p for d in yearly.values() for p in d["pnls"])
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tag = "✅" if acc >= 76 else ""
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print(f" {target} corr>={corr_thr}: trades={all_t:4d} acc={acc:.1f}% pnl=€{pnl:+.0f} {tag}")
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