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PythagorasGoal/Old/src/live/tsmom_worker.py
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Adriano Dal Pastro 14522262e6 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>
2026-06-19 15:20:59 +00:00

98 lines
4.3 KiB
Python

"""TsmomWorker (TSM01): consenso TSMOM multi-orizzonte risk-gated, ribilancio giornaliero.
Replica live di tsmom_research.tsmom_sim (horizons 63/126/252, thr 1.0, gross 0.30, SMA100 gate)."""
from __future__ import annotations
import json
from datetime import datetime, timezone
from pathlib import Path
import numpy as np
import pandas as pd
from src.live.rotation_worker import _panel, _warn_panel_short, FEE_RT
class TsmomWorker:
def __init__(self, universe, horizons=(63, 126, 252), thr=1.0, gross=0.30,
regime_n=100, tf="1d", capital=1000.0, fee_rt=FEE_RT,
name="TSM01", data_dir=Path("data/portfolio_paper")):
self.universe = list(universe)
self.horizons = tuple(horizons)
self.thr = thr
self.gross = gross
self.regime_n = regime_n
self.tf = tf
self.initial_capital = capital
self.capital = capital
self.fee_rt = fee_rt
self.worker_id = f"{name}__{tf}"
self.work_dir = Path(data_dir) / self.worker_id
self.work_dir.mkdir(parents=True, exist_ok=True)
self.status_path = self.work_dir / "status.json"
self.trades_path = self.work_dir / "trades.jsonl"
self.weights = {a: 0.0 for a in self.universe}
self.last_bar_ts = 0
self.in_position = False
self._panel_warned = False # dedup WARN panel corto (per episodio, non persistito)
self._load()
def _load(self):
if self.status_path.exists():
s = json.loads(self.status_path.read_text())
self.capital = s.get("capital", self.capital)
self.weights = {**{a: 0.0 for a in self.universe}, **s.get("weights", {})}
self.last_bar_ts = s.get("last_bar_ts", 0)
self.in_position = any(v > 0 for v in self.weights.values())
def _save(self):
self.status_path.write_text(json.dumps({
"capital": round(self.capital, 2), "weights": self.weights,
"in_position": self.in_position, # per hourly_report (osservabilita')
"last_bar_ts": self.last_bar_ts,
"ts": datetime.now(timezone.utc).isoformat()}, indent=2))
def tick(self, data: dict):
need = max(max(self.horizons) + 1, self.regime_n + 1)
panel, cols = _panel(data, self.universe)
if panel is None or len(panel) < need or "BTC" not in cols:
self._panel_warned = _warn_panel_short(
self.worker_id, panel, cols, need, self.last_bar_ts, self._panel_warned)
return
self._panel_warned = False
P = panel[cols].values
bar_ts = int(panel["timestamp"].iloc[-1])
if self.last_bar_ts and bar_ts > self.last_bar_ts:
day_ret = P[-1] / P[-2] - 1.0
port_r = sum(self.weights.get(cols[k], 0.0) * day_ret[k] for k in range(len(cols)))
self.capital = max(self.capital * (1.0 + float(port_r)), 10.0)
btc = P[:, cols.index("BTC")]
bma = pd.Series(btc).rolling(self.regime_n).mean().values
risk_on = btc[-1] > bma[-1] if not np.isnan(bma[-1]) else False
score = np.zeros(len(cols))
for h in self.horizons:
score += np.sign(P[-1] / P[-1 - h] - 1.0)
score /= len(self.horizons)
chosen = [k for k in range(len(cols)) if score[k] >= self.thr] if risk_on else []
nw = {a: 0.0 for a in self.universe}
for k in chosen:
nw[cols[k]] = self.gross / len(chosen)
turnover = sum(abs(nw[a] - self.weights.get(a, 0.0)) for a in self.universe)
self.capital -= self.capital * turnover * (self.fee_rt / 2)
if turnover > 0:
self._log(nw, float(self.capital))
self.weights = nw
self.last_bar_ts = bar_ts
self.in_position = any(v > 0 for v in nw.values())
self._save()
def _log(self, weights, cap):
with open(self.trades_path, "a") as f:
f.write(json.dumps({"ts": datetime.now(timezone.utc).isoformat(),
"weights": {a: round(w, 4) for a, w in weights.items() if w > 0},
"capital": round(cap, 2)}) + "\n")
@property
def status_summary(self):
held = {a: round(w, 3) for a, w in self.weights.items() if w > 0}
return f"{self.worker_id}: cap={self.capital:.0f} held={held}"