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>
60 lines
2.5 KiB
Python
60 lines
2.5 KiB
Python
import numpy as np
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import pandas as pd
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from src.live.basket_trend_worker import BasketTrendWorker
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def _ramp_df(n=300, slope=1.0):
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c = np.linspace(100, 100 + slope * n, n)
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ts = (pd.date_range("2024-01-01", periods=n, freq="4h", tz="UTC").astype("int64") // 10**6)
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return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
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def test_basket_goes_long_in_uptrend(tmp_path):
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w = BasketTrendWorker(universe=["AAA", "BBB"], tf="4h", capital=1000.0, data_dir=tmp_path)
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data = {"AAA": _ramp_df(slope=1.0), "BBB": _ramp_df(slope=1.0)}
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w.tick(data)
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assert w.positions["AAA"] == 1.0 and w.positions["BBB"] == 1.0
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def test_basket_flat_in_downtrend(tmp_path):
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w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
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data = {"AAA": _ramp_df(slope=-1.0)}
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w.tick(data)
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assert w.positions["AAA"] == 0.0
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def test_basket_persists_and_resumes(tmp_path):
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w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
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w.tick({"AAA": _ramp_df(slope=1.0)})
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w2 = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
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assert w2.positions["AAA"] == 1.0 # stato ripreso da status.json
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def _ramp_df_now(n=300, slope=1.0, forming_close=None):
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"""Serie 4h che termina ADESSO: l'ultima riga e' la candela IN FORMAZIONE."""
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import time
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c = np.linspace(100, 100 + slope * n, n)
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if forming_close is not None:
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c = c.copy()
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c[-1] = forming_close
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bar = 4 * 3_600_000
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now_ms = int(time.time() * 1000)
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ts = now_ms - bar * np.arange(n - 1, -1, -1)
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return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
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def test_basket_ignores_forming_bar(tmp_path):
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# downtrend su barre COMPLETE (target flat); la candela in formazione e' un
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# spike estremo che flipperebbe EMA20>EMA100 se contata -> va ignorata.
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w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
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w.tick({"AAA": _ramp_df_now(slope=-0.3, forming_close=1e6)})
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assert w.positions["AAA"] == 0.0
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def test_basket_fee_includes_leverage(tmp_path):
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# flip 0->1: fee = cap * POS * LEV * fee_rt/2 (reference honest_improve2:150)
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w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
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w.tick({"AAA": _ramp_df(slope=1.0)})
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expected = 1000.0 - 1000.0 * w.position_size * w.leverage * (w.fee_rt / 2)
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assert np.isclose(w.capital, expected)
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