Adriano Dal Pastro
14522262e6
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>
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
"""Filtro edge minimo (`min_tp_frac`): MR01/DIP01 NON devono emettere segnali il cui TP
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(la media) cade entro `min_tp_frac` dall'entry — sarebbero perdenti garantiti netto fee.
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Proprietà testate su dati reali BTC 1h:
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1. monotonia: alzando min_tp_frac il numero di segnali non aumenta;
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2. ogni segnale superstite ha gap TP > min_tp_frac;
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3. con min_tp_frac=0 il comportamento è invariato (default off = backtest validato intatto).
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"""
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import numpy as np
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import pytest
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from src.data.downloader import load_data
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from scripts.strategies.MR01_bollinger_fade import BollingerFade
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import importlib
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_dip_mod = importlib.import_module("scripts.strategies.DIP01_dip_buy")
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DipCls = next(v for k, v in vars(_dip_mod).items()
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if isinstance(v, type) and k.lower().startswith("dip"))
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@pytest.fixture(scope="module")
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def btc():
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df = load_data("BTC", "1h")
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return df, df.index # ts non usato dalle fade, basta un placeholder
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def _gaps(signals, df):
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c = df["close"].values
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return [abs(s.metadata["tp"] - c[s.idx]) / c[s.idx] for s in signals]
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def test_mr01_filter_monotone_and_gap(btc):
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df, ts = btc
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s = BollingerFade()
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base = dict(bb_window=50, k=2.5, sl_atr=2.0, max_bars=24)
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n0 = len(s.generate_signals(df, ts, **base, min_tp_frac=0.0))
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for f in (0.0010, 0.0015, 0.0020, 0.005):
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sig = s.generate_signals(df, ts, **base, min_tp_frac=f)
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assert len(sig) <= n0 # monotonia
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gaps = _gaps(sig, df)
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assert all(g > f for g in gaps) # nessun superstite sotto soglia
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def test_mr01_default_off_unchanged(btc):
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df, ts = btc
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s = BollingerFade()
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base = dict(bb_window=50, k=2.5, sl_atr=2.0, max_bars=24)
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a = s.generate_signals(df, ts, **base) # default (no kw)
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b = s.generate_signals(df, ts, **base, min_tp_frac=0.0)
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assert len(a) == len(b)
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def test_dip01_filter_gap(btc):
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df, ts = btc
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s = DipCls()
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base = dict(n=50, z_in=2.0, sl_atr=2.5, max_bars=24)
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n0 = len(s.generate_signals(df, ts, **base, min_tp_frac=0.0))
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sig = s.generate_signals(df, ts, **base, min_tp_frac=0.0020)
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assert len(sig) <= n0
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assert all(g > 0.0020 for g in _gaps(sig, df))
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def _load(mod_name):
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import importlib
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m = importlib.import_module(mod_name)
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return next(v() for k, v in vars(m).items()
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if isinstance(v, type) and getattr(v, "__module__", "") == m.__name__
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and hasattr(v, "generate_signals"))
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def test_mr02_mr07_filter_gap(btc):
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"""Anche MR02 (midpoint canale) e MR07 (ATR-scaled) onorano min_tp_frac."""
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df, ts = btc
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for mod, base in (
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("scripts.strategies.MR02_donchian_fade", dict(n=20, sl_atr=2.0, max_bars=24)),
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("scripts.strategies.MR07_return_reversal",
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dict(n=50, k=3.5, tp_atr=2.0, sl_atr=1.5, max_bars=24)),
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):
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s = _load(mod)
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n0 = len(s.generate_signals(df, ts, **base, min_tp_frac=0.0))
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sig = s.generate_signals(df, ts, **base, min_tp_frac=0.0015)
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assert len(sig) <= n0
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assert all(g > 0.0015 for g in _gaps(sig, df))
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