feat(live): TsmomWorker (TSM01) consenso TSMOM multi-orizzonte risk-gated

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2026-05-29 17:42:44 +02:00
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"""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, 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._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,
"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:
return
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}"