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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"""S2-02: Funding Rate Strategy.
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Quando il funding rate è molto positivo → troppi long → short il perpetual.
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Quando molto negativo → troppi short → long il perpetual.
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Si cattura sia il mean reversion del prezzo che il funding rate stesso.
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Ingresso: quando funding > 0.03% o < -0.03% (8h rate).
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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 = 0.001
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INITIAL = 1000
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LEVERAGE = 3
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def simulate_funding_strategy(asset):
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"""Simula funding rate strategy usando il proxy: overnight returns.
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Crypto funding settlement ogni 8h → prezzo tende a correggersi dopo settlement.
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Proxy: se ultime 8h hanno avuto forte trend, aspettati reversal dopo settlement.
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"""
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print(f"\n{'#'*60}")
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print(f" {asset} — FUNDING RATE PROXY STRATEGY")
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print(f"{'#'*60}")
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df_1h = load_data(asset, "1h")
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close = df_1h["close"].values
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volume = df_1h["volume"].values
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n = len(close)
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split = int(n * 0.7)
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timestamps = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
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hours = timestamps.dt.hour.values
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# Funding settlement su Deribit: 00:00, 08:00, 16:00 UTC
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settlement_hours = {0, 8, 16}
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configs = [
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(0.01, 0.02, 8, 0.02, "mild_1pct"),
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(0.015, 0.025, 8, 0.015, "moderate_1.5pct"),
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(0.02, 0.03, 8, 0.015, "strong_2pct"),
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(0.01, 0.015, 4, 0.01, "fast_1pct_4h"),
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(0.02, 0.03, 12, 0.02, "slow_2pct_12h"),
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(0.025, 0.04, 6, 0.015, "extreme_2.5pct"),
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]
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for entry_thr, tp_mult_unused, hold_max, stop, name in configs:
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capital = float(INITIAL)
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correct = 0
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total = 0
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daily_trades = {}
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for i in range(max(split, 8), n - hold_max):
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hour = hours[i]
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if hour not in settlement_hours:
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continue
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day = timestamps[i].strftime("%Y-%m-%d")
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if daily_trades.get(day, 0) >= 1:
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continue
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# 8h return prima del settlement = proxy per funding pressure
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ret_8h = (close[i] - close[i - 8]) / close[i - 8]
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# Volume spike = conferma
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vol_avg = np.mean(volume[max(0, i - 48) : i])
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vol_recent = np.mean(volume[i - 8 : i])
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vol_spike = vol_recent / vol_avg if vol_avg > 0 else 1
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direction = None
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if ret_8h > entry_thr and vol_spike > 1.1:
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direction = "short" # troppi long, attendi reversal
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elif ret_8h < -entry_thr and vol_spike > 1.1:
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direction = "long" # troppi short, attendi rimbalzo
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if direction is None:
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continue
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entry_price = close[i]
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for j in range(i + 1, min(i + hold_max + 1, n)):
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price = close[j]
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if direction == "long":
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pnl_pct = (price - entry_price) / entry_price
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else:
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pnl_pct = (entry_price - price) / entry_price
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if pnl_pct <= -stop or pnl_pct >= stop * 2 or j == min(i + hold_max, n - 1):
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exit_price = price
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break
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else:
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exit_price = close[min(i + hold_max, n - 1)]
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if direction == "long":
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trade_ret = (exit_price - entry_price) / entry_price
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else:
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trade_ret = (entry_price - exit_price) / entry_price
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# Add funding rate income (approx 0.01% per 8h period if direction correct)
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funding_income = 0.0001 * (hold_max / 8) if trade_ret > 0 else 0
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net = (trade_ret + funding_income) * LEVERAGE - FEE * 2 * LEVERAGE
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capital += capital * 0.2 * net
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capital = max(capital, 0)
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total += 1
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if trade_ret > 0:
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correct += 1
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daily_trades[day] = daily_trades.get(day, 0) + 1
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if total < 10:
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continue
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acc = correct / total * 100
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ret = (capital - INITIAL) / INITIAL * 100
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test_days = (n - split) / 24
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test_years = test_days / 365.25
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ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
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dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
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days_active = len(daily_trades)
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tag = "✅" if acc >= 60 and ann >= 30 else ""
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print(f" {name:20s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active_days={days_active} {tag}")
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for asset in ["ETH", "BTC"]:
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simulate_funding_strategy(asset)
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