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>
2026-06-19 15:16:03 +00:00
parent 8401a280b9
commit 14522262e6
383 changed files with 1971 additions and 779 deletions
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+"""Strategia 2: DTW pattern matching.
+Idea: per ogni finestra di N candele, cerca le K finestre più simili nel passato
+via DTW sui prezzi normalizzati. Se la maggioranza delle match passate è salita
+dopo, vai long. Se è scesa, vai short.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+from src.fractal.similarity import dtw_distance
+from src.fractal.patterns import normalize_pattern_window
+from src.backtest.engine import run_backtest
+
+print("=" * 60)
+print("  STRATEGIA 2: DTW PATTERN MATCHING — BTC 1H")
+print("=" * 60)
+
+df = load_data("BTC", "1h")
+close = df["close"].values
+
+WINDOW = 12
+LOOKAHEAD = 6
+K_NEIGHBORS = 20
+LOOKBACK = 2000
+THRESHOLD = 0.65
+
+split_idx = int(len(df) * 0.7)
+
+def normalize_window(arr: np.ndarray) -> np.ndarray:
+    mn, mx = arr.min(), arr.max()
+    if mx - mn == 0:
+        return np.zeros_like(arr)
+    return (arr - mn) / (mx - mn)
+
+def compute_returns(close_arr: np.ndarray, idx: int, ahead: int) -> float:
+    if idx + ahead >= len(close_arr):
+        return 0.0
+    return (close_arr[idx + ahead] - close_arr[idx]) / close_arr[idx]
+
+print(f"\nParametri: window={WINDOW}, lookahead={LOOKAHEAD}, K={K_NEIGHBORS}")
+print(f"Lookback: {LOOKBACK} candele, threshold: {THRESHOLD}")
+print(f"Train: 0→{split_idx}, Test: {split_idx}→{len(df)}")
+
+signals = pd.Series(0, index=df.index)
+accuracies = []
+
+step = 6
+test_range = range(split_idx, len(df) - LOOKAHEAD, step)
+total_steps = len(list(test_range))
+print(f"\nValutazione: {total_steps} punti (step={step})...")
+
+for count, i in enumerate(test_range):
+    if count % 500 == 0:
+        print(f"  Progresso: {count}/{total_steps} ({count/total_steps*100:.0f}%)")
+
+    current = normalize_window(close[i - WINDOW : i])
+
+    search_start = max(WINDOW, i - LOOKBACK)
+    search_end = i - LOOKAHEAD
+
+    if search_end - search_start < K_NEIGHBORS:
+        continue
+
+    distances = []
+    for j in range(search_start, search_end):
+        candidate = normalize_window(close[j - WINDOW : j])
+        if len(candidate) != len(current):
+            continue
+        d = dtw_distance(current, candidate)
+        future_ret = compute_returns(close, j, LOOKAHEAD)
+        distances.append((d, future_ret))
+
+    if len(distances) < K_NEIGHBORS:
+        continue
+
+    distances.sort(key=lambda x: x[0])
+    top_k = distances[:K_NEIGHBORS]
+    up_count = sum(1 for _, ret in top_k if ret > 0)
+    up_ratio = up_count / K_NEIGHBORS
+
+    if up_ratio >= THRESHOLD:
+        signals.iloc[i] = 1
+    elif up_ratio <= (1 - THRESHOLD):
+        signals.iloc[i] = -1
+
+    actual_ret = compute_returns(close, i, LOOKAHEAD)
+    predicted_up = up_ratio >= THRESHOLD
+    predicted_down = up_ratio <= (1 - THRESHOLD)
+    if predicted_up:
+        accuracies.append(1 if actual_ret > 0 else 0)
+    elif predicted_down:
+        accuracies.append(1 if actual_ret < 0 else 0)
+
+print(f"\nSegnali generati: {(signals != 0).sum()}")
+print(f"  Long: {(signals == 1).sum()}, Short: {(signals == -1).sum()}")
+
+if accuracies:
+    print(f"Accuratezza direzione: {np.mean(accuracies)*100:.1f}% su {len(accuracies)} segnali")
+
+test_df = df.iloc[split_idx:].reset_index(drop=True)
+test_signals = signals.iloc[split_idx:].reset_index(drop=True)
+
+result = run_backtest(test_df, test_signals, initial_capital=1000, fee_pct=0.001, max_hold_candles=LOOKAHEAD)
+
+print("\nRISULTATI TEST:")
+for k, v in result.summary().items():
+    print(f"  {k}: {v}")