test(strategy3): lead-lag multi-asset — leader-follower fallito, corr-weighted 76.8%
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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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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