Adriano Dal Pastro
3909646028
Punti 2-4 dell'improvement-sweep 2026-06-06 (fix di parita', nessun cambio di strategia): - TR01 (basket_trend_worker): fee per flip = POS * LEV * fee_rt/2 come il reference honest_improve2._tr_basket_daily:150 (mancava il fattore leva -> fee sotto-caricate 2x, bias ottimistico che gonfiava total_capital al ribilancio). - TR01: crossover EMA e booking del return valutati SOLO su barre 4h COMPLETE (la riga -1 e' la candela in corso finche' non e' trascorsa la sua durata) — lezione EXIT-16; evidenza live: flip SOL 0->1->0 in 59min nella stessa finestra 4h. - PairsWorker: entry ED exit valutati sul close di barra COMPLETA, come il backtest pairs_research (close settled). Stesso pattern bar_ms di strategy_worker.tick. - TSM01/ROT02: il silent return su panel inner-join troncato sotto il lookback ora emette WARN log + Telegram PANEL_SHORT (helper _warn_panel_short condiviso), gated su "era gia' operativo" (no falsi positivi al cold-start), una notifica per episodio. Test: 72/72 verdi (7 nuovi: forming-bar TR01/pairs + controllo positivo, fee con leva, PANEL_SHORT warn/dedup/cold-start). Replay parity: validate_worker_pairs ESATTO (ETH/BTC e BTC/LTC == backtest); validate_honest_workers invariato (TR01 -44% vs ref +42% IDENTICO pre/post fix: e' la divergenza di convenzione capitale-unico vs media-equity gia' documentata, da rivisitare a parte). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
import numpy as np
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import pandas as pd
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from src.live.tsmom_worker import TsmomWorker
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def _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("2023-01-01", periods=n, freq="1D", 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_tsmom_selects_full_consensus_uptrend(tmp_path):
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# tutti gli orizzonti positivi -> score=1>=thr; BTC su -> risk_on
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w = TsmomWorker(universe=["BTC", "AAA"], horizons=(63, 126, 252), thr=1.0,
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gross=0.30, data_dir=tmp_path)
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data = {"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)}
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w.tick(data)
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assert w.weights["BTC"] > 0 and w.weights["AAA"] > 0
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assert abs(sum(w.weights.values()) - 0.30) < 1e-9
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def test_tsmom_flat_when_risk_off(tmp_path):
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w = TsmomWorker(universe=["BTC", "AAA"], thr=1.0, gross=0.30, data_dir=tmp_path)
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data = {"BTC": _df(slope=-1.0), "AAA": _df(slope=2.0)}
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w.tick(data)
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assert sum(w.weights.values()) == 0.0
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def test_tsmom_persists_and_resumes(tmp_path):
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w = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
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w.tick({"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)})
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w2 = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
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assert w2.weights == w.weights
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def test_tsmom_warns_on_short_panel(tmp_path, monkeypatch):
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# worker GIA' operativo + panel sotto need=253 -> WARN invece di silent return
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calls = []
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monkeypatch.setattr("src.live.telegram_notifier.notify_event",
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lambda e, d=None: calls.append(e))
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w = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
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w.last_bar_ts = 123
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w.tick({"BTC": _df(n=100), "AAA": _df(n=100)})
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assert calls == ["PANEL_SHORT"] and w._panel_warned
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