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
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+"""Strategia 3: Fourier decomposition e proiezione.
+Ispirata al paper Pythagoras Trading Prediction.
+Idea: scomponi il prezzo in componenti sinusoidali via FFT,
+ricostruisci con le N componenti più forti, proietta nel futuro.
+"""
+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.backtest.engine import run_backtest
+
+print("=" * 60)
+print("  STRATEGIA 3: FOURIER PROJECTION — BTC 1H")
+print("=" * 60)
+
+df = load_data("BTC", "1h")
+close = df["close"].values
+n_total = len(close)
+
+WINDOW = 588  # dal paper: 588 candele per l'indicatore H-C
+N_COMPONENTS = 25  # dal paper: 25 linee verticali
+LOOKAHEAD = 6
+STEP = 6
+
+split_idx = int(n_total * 0.7)
+
+def fourier_project(series: np.ndarray, n_components: int, ahead: int) -> np.ndarray:
+    """Ricostruisci serie con top-N componenti Fourier e proietta avanti."""
+    n = len(series)
+    detrended = series - np.linspace(series[0], series[-1], n)
+    fft_vals = np.fft.fft(detrended)
+    freqs = np.fft.fftfreq(n)
+
+    magnitudes = np.abs(fft_vals)
+    magnitudes[0] = 0
+    top_indices = np.argsort(magnitudes)[-n_components * 2:]
+
+    fft_filtered = np.zeros_like(fft_vals)
+    fft_filtered[top_indices] = fft_vals[top_indices]
+
+    t_extended = np.arange(n + ahead)
+    reconstruction = np.zeros(n + ahead)
+    for idx in top_indices:
+        amp = np.abs(fft_vals[idx]) / n
+        phase = np.angle(fft_vals[idx])
+        freq = freqs[idx]
+        reconstruction += amp * np.cos(2 * np.pi * freq * t_extended / 1 + phase)
+
+    trend_slope = (series[-1] - series[0]) / n
+    trend_extended = series[0] + trend_slope * t_extended
+    reconstruction += trend_extended
+
+    return reconstruction
+
+
+print(f"\nParametri: window={WINDOW}, components={N_COMPONENTS}, lookahead={LOOKAHEAD}")
+print(f"Train: 0→{split_idx}, Test: {split_idx}→{n_total}")
+
+signals = pd.Series(0, index=df.index)
+accuracies = []
+
+test_range = range(max(split_idx, WINDOW), n_total - LOOKAHEAD, STEP)
+total_steps = len(list(test_range))
+print(f"Valutazione: {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}%)")
+
+    window_data = close[i - WINDOW : i]
+    projected = fourier_project(window_data, N_COMPONENTS, LOOKAHEAD)
+
+    current_price = close[i - 1]
+    projected_price = projected[-1]
+    change_pct = (projected_price - current_price) / current_price
+
+    if change_pct > 0.005:
+        signals.iloc[i] = 1
+    elif change_pct < -0.005:
+        signals.iloc[i] = -1
+
+    actual_ret = (close[i + LOOKAHEAD - 1] - current_price) / current_price
+    if signals.iloc[i] == 1:
+        accuracies.append(1 if actual_ret > 0 else 0)
+    elif signals.iloc[i] == -1:
+        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}")
+
+# Varianti con parametri diversi
+print("\n\n--- VARIANTI PARAMETRI ---")
+for n_comp in [5, 10, 15, 25, 50]:
+    for window in [144, 288, 588]:
+        sigs = pd.Series(0, index=df.index)
+        accs = []
+        test_r = range(max(split_idx, window), n_total - LOOKAHEAD, STEP)
+        for i in test_r:
+            w = close[i - window : i]
+            proj = fourier_project(w, n_comp, LOOKAHEAD)
+            cp = close[i - 1]
+            pp = proj[-1]
+            ch = (pp - cp) / cp
+            if ch > 0.005:
+                sigs.iloc[i] = 1
+            elif ch < -0.005:
+                sigs.iloc[i] = -1
+            ar = (close[i + LOOKAHEAD - 1] - cp) / cp
+            if sigs.iloc[i] == 1:
+                accs.append(1 if ar > 0 else 0)
+            elif sigs.iloc[i] == -1:
+                accs.append(1 if ar < 0 else 0)
+
+        if not accs:
+            continue
+        t_sigs = sigs.iloc[split_idx:].reset_index(drop=True)
+        res = run_backtest(test_df, t_sigs, initial_capital=1000, fee_pct=0.001, max_hold_candles=LOOKAHEAD)
+        acc = np.mean(accs) * 100
+        print(f"  W={window:3d} N={n_comp:2d} → acc={acc:.1f}% trades={res.total_trades} ret={res.total_return*100:+.1f}% sharpe={res.sharpe_ratio:.2f}")