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>

Old/tests/portfolio/test_basket_worker.py

@@ -0,0 +1,59 @@
+import numpy as np
+import pandas as pd
+from src.live.basket_trend_worker import BasketTrendWorker
+
+
+def _ramp_df(n=300, slope=1.0):
+    c = np.linspace(100, 100 + slope * n, n)
+    ts = (pd.date_range("2024-01-01", periods=n, freq="4h", tz="UTC").astype("int64") // 10**6)
+    return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
+
+
+def test_basket_goes_long_in_uptrend(tmp_path):
+    w = BasketTrendWorker(universe=["AAA", "BBB"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    data = {"AAA": _ramp_df(slope=1.0), "BBB": _ramp_df(slope=1.0)}
+    w.tick(data)
+    assert w.positions["AAA"] == 1.0 and w.positions["BBB"] == 1.0
+
+
+def test_basket_flat_in_downtrend(tmp_path):
+    w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    data = {"AAA": _ramp_df(slope=-1.0)}
+    w.tick(data)
+    assert w.positions["AAA"] == 0.0
+
+
+def test_basket_persists_and_resumes(tmp_path):
+    w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    w.tick({"AAA": _ramp_df(slope=1.0)})
+    w2 = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    assert w2.positions["AAA"] == 1.0          # stato ripreso da status.json
+
+
+def _ramp_df_now(n=300, slope=1.0, forming_close=None):
+    """Serie 4h che termina ADESSO: l'ultima riga e' la candela IN FORMAZIONE."""
+    import time
+    c = np.linspace(100, 100 + slope * n, n)
+    if forming_close is not None:
+        c = c.copy()
+        c[-1] = forming_close
+    bar = 4 * 3_600_000
+    now_ms = int(time.time() * 1000)
+    ts = now_ms - bar * np.arange(n - 1, -1, -1)
+    return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
+
+
+def test_basket_ignores_forming_bar(tmp_path):
+    # downtrend su barre COMPLETE (target flat); la candela in formazione e' un
+    # spike estremo che flipperebbe EMA20>EMA100 se contata -> va ignorata.
+    w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    w.tick({"AAA": _ramp_df_now(slope=-0.3, forming_close=1e6)})
+    assert w.positions["AAA"] == 0.0
+
+
+def test_basket_fee_includes_leverage(tmp_path):
+    # flip 0->1: fee = cap * POS * LEV * fee_rt/2 (reference honest_improve2:150)
+    w = BasketTrendWorker(universe=["AAA"], tf="4h", capital=1000.0, data_dir=tmp_path)
+    w.tick({"AAA": _ramp_df(slope=1.0)})
+    expected = 1000.0 - 1000.0 * w.position_size * w.leverage * (w.fee_rt / 2)
+    assert np.isclose(w.capital, expected)