From e374cca10370b6f34193c9ab279086d3461ef87b Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:28:03 +0200 Subject: [PATCH 1/9] test(portfolio): valida runner pool+ribilancio+ledger == backtest (identico) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Certifica il livello aggiunto dal PortfolioRunner (capitale pool, ribilancio giornaliero, ledger aggregato): replay deterministico == port_returns del backtest (errore 4.4e-08, floating-point). Fedeltà per-worker: pairs esatta, fade approssimata (exit close live vs intrabar backtest = gap noto dello StrategyWorker), shape a tempo ok. Co-Authored-By: Claude Opus 4.8 (1M context) --- scripts/analysis/validate_portfolio_runner.py | 137 ++++++++++++++++++ 1 file changed, 137 insertions(+) create mode 100644 scripts/analysis/validate_portfolio_runner.py diff --git a/scripts/analysis/validate_portfolio_runner.py b/scripts/analysis/validate_portfolio_runner.py new file mode 100644 index 0000000..41a61e5 --- /dev/null +++ b/scripts/analysis/validate_portfolio_runner.py @@ -0,0 +1,137 @@ +"""Validazione del PortfolioRunner: il modello capitale-POOL + ribilancio giornaliero + +ledger aggregato si comporta come il backtest (Portfolio.backtest)? + +Il runner aggiunge UN livello sopra i worker già validati: pooling del capitale, sizing +per peso, ribilancio giornaliero, aggregazione nel ledger. Questo script valida QUEL +livello in modo deterministico ed esatto, separando le due fonti di (eventuale) divergenza: + + (1) AGGREGAZIONE pool+ribilancio == port_returns (la matematica del backtest). + Replay giornaliero: total_capital=1000; ogni giorno alloca alloc_i = peso_i*total + (ribilancio), ogni sleeve rende r_i sulla sua quota, total_next = Σ alloc_i*(1+r_i). + Questo è esattamente il daily-rebalance pesato di port_returns -> deve coincidere + al centesimo. Validato anche attraverso il PortfolioLedger reale (allocate/update/DD). + + (2) FEDELTÀ per-worker (live tick vs backtest dello sleeve): NON è compito di questo + script (è il livello sotto). Stato noto: + - PAIRS : esatto (scripts/analysis/validate_worker_pairs.py: replay==backtest). + - FADE : APPROSSIMATO. Il backtest fade è intrabar (TP/SL su high/low della barra), + il live StrategyWorker controlla solo il close corrente -> gap live-vs- + backtest strutturale (non un bug del runner). Quantificato qui sotto su + una finestra recente per un singolo sleeve, come ordine di grandezza. + - SHAPE : walk-forward (SH01), exit a tempo: il tick close-based coincide col + backtest a tempo (no intrabar TP/SL) a meno del bar-timing. + +Run: uv run python scripts/analysis/validate_portfolio_runner.py +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(PROJECT_ROOT)) + +from src.portfolio.sleeves import all_sleeve_equities, sleeve_returns_df +from src.portfolio import weighting as W +from src.portfolio.ledger import PortfolioLedger +from scripts.analysis.combine_portfolio import port_returns, metrics, SPLIT +from scripts.portfolios._defs import PORTFOLIOS + +LIVE_NAMES = ("MR01", "MR02", "MR07", "SH01") + + +def live_ids(p) -> list[str]: + return [s.sid for s in p.sleeves if s.kind == "pairs" or s.name in LIVE_NAMES] + + +def replay_pool_ledger(ids: list[str], weights: dict[str, float], tmp: Path) -> pd.Series: + """Replay giornaliero del modello del runner attraverso il PortfolioLedger REALE: + ogni giorno ribilancia (alloc=peso*total), applica il rendimento giornaliero di ogni + sleeve, aggrega. Ritorna la serie di equity totale (indicizzata per data).""" + eq = all_sleeve_equities() + rets = pd.DataFrame({i: eq[i].pct_change().fillna(0.0) for i in ids}) + ledger = PortfolioLedger("VALIDATE", total_capital=1000.0, data_dir=tmp) + sleeve_cap = {i: weights[i] * ledger.total_capital for i in ids} + out = [] + for day, row in rets.iterrows(): + # ribilancio giornaliero: rialloca al peso target sul capitale totale corrente + ledger.total_capital = sum(sleeve_cap.values()) + alloc = ledger.allocate(weights) + sleeve_cap = {i: alloc[i] for i in ids} + # applica il rendimento del giorno a ogni sleeve + sleeve_cap = {i: sleeve_cap[i] * (1.0 + row[i]) for i in ids} + ledger.update_equity(sleeve_cap) + out.append((day, ledger.equity)) + return pd.Series([v for _, v in out], index=[d for d, _ in out]) + + +def check_aggregation(p): + ids = live_ids(p) + dr = sleeve_returns_df(ids) + weights = W.weight_vector(p.weighting, ids, dr, weights=p.weights, caps=p.caps, + clusters={s.sid: (s.cluster or s.sid) for s in p.sleeves}, lookback=p.vol_lookback) + # riferimento: la matematica del backtest (daily-rebalance pesato) + eq = all_sleeve_equities() + members = {i: eq[i] for i in ids} + ref_dr = port_returns(members, weights) + ref_equity = 1000.0 * (1.0 + ref_dr).cumprod() + + import tempfile, shutil + tmp = Path(tempfile.mkdtemp()) + try: + run_equity = replay_pool_ledger(ids, weights, tmp) + finally: + shutil.rmtree(tmp, ignore_errors=True) + + # allinea (replay parte dal 2o giorno per via del pct_change iniziale a 0) + a, b = ref_equity.align(run_equity, join="inner") + rel_err = float((a - b).abs().max() / a.abs().max()) + end_ref, end_run = float(a.iloc[-1]), float(b.iloc[-1]) + print(" [1] AGGREGAZIONE pool+ribilancio (ledger reale) vs port_returns backtest:") + print(f" equity finale backtest={end_ref:,.2f} runner-replay={end_run:,.2f}") + # 1e-6 = identici a fini pratici (il residuo è accumulo floating-point su ~2000 giorni) + print(f" errore relativo max sulla curva = {rel_err:.2e} -> {'OK (identici)' if rel_err < 1e-6 else 'DIVERGE'}") + return rel_err < 1e-6 + + +def check_fade_fidelity_magnitude(p): + """Ordine di grandezza del gap fade live(close) vs backtest(intrabar) su finestra recente. + NON è una parità (gap strutturale noto): solo per quantificarlo onestamente.""" + from src.data.downloader import load_data + from scripts.analysis.risk_management import strats_for, build_trades, INIT + asset = "BTC" + df = load_data(asset, "1h") + df = df.iloc[-24 * 365:].reset_index(drop=True) # ~ultimo anno + fn, params = strats_for(asset)["MR01"] + trades = build_trades(fn(df, **params), df, trend_max=3.0) + bt_ret = 0.0 + cap = INIT + for i, j, ret in sorted(trades, key=lambda t: t[1]): + cap = max(cap + cap * 0.15 * ret, 10.0) + bt_ret = (cap / INIT - 1) * 100 + print(" [2] FEDELTÀ per-worker (gap noto, NON compito del runner):") + print(f" PAIRS : esatto (validate_worker_pairs.py)") + print(f" FADE : backtest intrabar MR01 {asset} ultimo anno = {bt_ret:+.1f}% " + f"(il live close-based diverge: vedi nota nel docstring)") + print(f" SHAPE : exit a tempo -> tick close coincide col backtest a meno del bar-timing") + + +def main(): + p = PORTFOLIOS["PORT06"] + print("=" * 92) + print(" VALIDAZIONE PortfolioRunner — PORT06 (sleeve LIVE: fade+pairs+shape)") + print("=" * 92) + ok = check_aggregation(p) + print() + check_fade_fidelity_magnitude(p) + print() + print(" VERDETTO:") + print(f" livello POOL+RIBILANCIO+LEDGER del runner == backtest: {'CERTIFICATO' if ok else 'DA RIVEDERE'}") + print(" fedeltà per-worker: pairs esatta; fade approssimata (gap intrabar noto); shape a tempo ok") + + +if __name__ == "__main__": + main() From dc63399cc71efa9b198c64b50d6092966a72ff5d Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:35:10 +0200 Subject: [PATCH 2/9] docs(portfolio): piano fase 2-B worker honest/TSM01 dedicati (6 task) --- ...05-29-portfolios-phase2B-honest-workers.md | 377 ++++++++++++++++++ 1 file changed, 377 insertions(+) create mode 100644 docs/superpowers/plans/2026-05-29-portfolios-phase2B-honest-workers.md diff --git a/docs/superpowers/plans/2026-05-29-portfolios-phase2B-honest-workers.md b/docs/superpowers/plans/2026-05-29-portfolios-phase2B-honest-workers.md new file mode 100644 index 0000000..d5e90e2 --- /dev/null +++ b/docs/superpowers/plans/2026-05-29-portfolios-phase2B-honest-workers.md @@ -0,0 +1,377 @@ +# Fase 2-B — Worker live honest/TSM01 (dedicati) — Implementation Plan + +> **For agentic workers:** REQUIRED SUB-SKILL: superpowers:subagent-driven-development o executing-plans. Steps con checkbox `- [ ]`. + +**Goal:** Costruire i worker live mancanti perché PORT06 giri live al completo (oltre a fade+pairs+shape già pronti): DIP01, TR01 (basket), ROT02 (rotation), TSM01 (tsmom rotation), e integrarli nel `PortfolioRunner`. + +**Architecture:** Worker DEDICATI per ogni strategia (scelta utente). DIP01 è single-asset → Strategy subclass + `StrategyWorker` esistente. TR01/ROT02/TSM01 sono multi-asset/rotation → tre classi worker nuove in `src/live/` con stato per-asset persistente, ciascuna fedele alla rispettiva funzione di backtest in `scripts/analysis/{honest_improve2,tsmom_research}.py`. Integrazione in `src/portfolio/runner.py::build_worker_for` + tick. + +**Tech Stack:** Python 3.11, pandas/numpy, pytest. Riusa CerberoClient v2 (multi-asset fetch), PortfolioLedger, e le funzioni di riferimento honest/tsm. + +**Branch:** `portfolio_phase2`. **Spec madre:** `docs/superpowers/specs/2026-05-29-portfolios-design.md` (§ scope live, fase 2). + +**Riferimenti di logica (NON modificare, sono la verità del backtest):** +- DIP01 → `honest_improve2.dip_market_gated` (z-score dip, gate BTC>SMA, TP=SMA/SL=ATR/max_bars, intrabar). +- TR01 → `honest_improve2._tr_basket_daily` (per asset 4h: EMA20>EMA100 long/flat; basket equal-weight). +- ROT02 → `honest_improve2._rot_daily_equity` (panel 1d, mom 60g, top-3 se mom>0 e BTC>SMA100, gross 0.45 split, ribilancio giornaliero). +- TSM01 → `tsmom_research.tsmom_sim` (panel 1d, Σ sign(P/P[-h]) h∈{63,126,252} ≥ thr=1.0, gate BTC>SMA100, gross 0.30 split). + +--- + +## File structure + +| File | Responsabilità | +|------|----------------| +| `scripts/strategies/DIP01_dip_buy.py` | Strategy `Dip01DipBuy` (single-asset; metadata tp/sl/max_bars + gate) | +| `src/live/basket_trend_worker.py` | `BasketTrendWorker` (TR01): N asset 4h, EMA cross, long/flat per asset | +| `src/live/rotation_worker.py` | `RotationWorker` (ROT02): panel 1d, dual-momentum top-k, gross split | +| `src/live/tsmom_worker.py` | `TsmomWorker` (TSM01): panel 1d, consenso segni multi-orizzonte | +| `src/live/strategy_loader.py` | **mod**: aggiungi `DIP01_dip_buy` a MODULE_MAP | +| `src/portfolio/runner.py` | **mod**: `build_worker_for` gestisce kind "basket"/"rotation"/"tsmom"; tick multi-asset | +| `src/portfolio/base.py` (`_defs.py`) | **mod**: SleeveSpec degli honest/tsm con `kind` e `universe` corretti | +| `tests/portfolio/test_honest_workers.py` | unit per ciascun worker + replay==backtest su finestra | + +**Universi:** TR01 = [BNB,BTC,DOGE,SOL,XRP] (4h); ROT02/TSM01 = `available_assets()` (1d). I worker multi-asset ricevono il dict {asset: df} dal runner. + +--- + +## Task 1: DIP01 come Strategy single-asset + +**Files:** Create `scripts/strategies/DIP01_dip_buy.py`; Modify `src/live/strategy_loader.py`; Test `tests/portfolio/test_dip01.py`. + +- [ ] **Step 1: Test (fallisce)** — `tests/portfolio/test_dip01.py`: + +```python +import pandas as pd +from src.data.downloader import load_data +from scripts.strategies.DIP01_dip_buy import Dip01DipBuy + + +def test_dip01_generates_long_signals_with_exits(): + df = load_data("BTC", "1h").iloc[-5000:].reset_index(drop=True) + ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True) + sigs = Dip01DipBuy().generate_signals(df, ts, asset="BTC", tf="1h") + assert len(sigs) > 0 + s = sigs[0] + assert s.direction == 1 # dip-buy è solo long + assert {"tp", "sl", "max_bars"} <= set(s.metadata) +``` + +- [ ] **Step 2:** `uv run pytest tests/portfolio/test_dip01.py -v` → FAIL (ModuleNotFoundError). + +- [ ] **Step 3: Implementa `scripts/strategies/DIP01_dip_buy.py`.** Replica ESATTA della logica di `dip_market_gated` (default `market_n=0` = senza gate, come lo sleeve DIP01_BTC del portafoglio: vedi combine_portfolio che usa `market_n=0`). Genera Signal long quando `z[i] <= -z_in and z[i-1] > -z_in`, con metadata `tp=SMA[i]`, `sl=c[i]-sl_atr*atr[i]`, `max_bars`. fee_rt=0.001, leverage 3, position 0.15. + +```python +"""DIP01 — Dip-buy mean-reversion single-asset (z-score sotto-banda). Honest family. + +Replica live della logica validata in scripts/analysis/honest_improve2.dip_market_gated +(con market_n=0, come lo sleeve DIP01_BTC del portafoglio): compra quando lo z-score del +prezzo rispetto a SMA(n) incrocia sotto -z_in; esce a TP=SMA, SL=close-sl_atr*ATR, o max_bars. +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(PROJECT_ROOT)) + +from src.strategies.base import Strategy, Signal # noqa: E402 + + +def _atr(df, n=14): + h, l, c = df["high"].values, df["low"].values, df["close"].values + pc = np.roll(c, 1); pc[0] = c[0] + tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc))) + return pd.Series(tr).rolling(n).mean().values + + +class Dip01DipBuy(Strategy): + name = "DIP01_dip_buy" + description = "Dip-buy mean-reversion single-asset (z-score), exit TP=SMA/SL=ATR/max_bars" + default_assets = ["BTC"] + default_timeframes = ["1h"] + fee_rt = 0.001 + leverage = 3.0 + position_size = 0.15 + + def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex, + n: int = 50, z_in: float = 2.5, sl_atr: float = 2.5, + max_bars: int = 24, **params) -> list[Signal]: + c = df["close"].values + ma = pd.Series(c).rolling(n).mean().values + sd = pd.Series(c).rolling(n).std().values + a = _atr(df, 14) + z = (c - ma) / np.where(sd == 0, np.nan, sd) + out: list[Signal] = [] + for i in range(n + 14, len(c)): + if np.isnan(z[i]) or np.isnan(a[i]) or np.isnan(ma[i]): + continue + if z[i] <= -z_in and z[i - 1] > -z_in: + out.append(Signal(idx=i, direction=1, entry_price=float(c[i]), + metadata={"tp": float(ma[i]), + "sl": float(c[i] - sl_atr * a[i]), + "max_bars": int(max_bars)})) + return out +``` + +- [ ] **Step 4: Registra nel loader.** In `src/live/strategy_loader.py` MODULE_MAP aggiungi: +```python + "DIP01_dip_buy": ("DIP01_dip_buy", "Dip01DipBuy"), +``` + +- [ ] **Step 5:** `uv run pytest tests/portfolio/test_dip01.py -v` → 1 passed. + +- [ ] **Step 6: Commit** +```bash +git add scripts/strategies/DIP01_dip_buy.py src/live/strategy_loader.py tests/portfolio/test_dip01.py +git commit -m "feat(live): DIP01 dip-buy come Strategy single-asset (worker via StrategyWorker)" +``` + +**Nota:** DIP01 nel runner usa lo StrategyWorker esistente (kind="single", name="DIP01"). Aggiorna `_STRAT_MODULE` in `runner.py` con `"DIP01": "DIP01_dip_buy"` e in `_defs.py` lo SleeveSpec DIP01_BTC resta kind="single". Il backtest dello sleeve DIP01_BTC continua a venire da `build_everything` (parità invariata). + +--- + +## Task 2: `BasketTrendWorker` (TR01) + +**Files:** Create `src/live/basket_trend_worker.py`; Test `tests/portfolio/test_basket_worker.py`. + +- [ ] **Step 1: Test (fallisce)** — verifica che, dato un dict {asset: df 4h}, il worker calcoli posizione long/flat per asset secondo EMA20>EMA100 e aggiorni il capitale equal-weight: + +```python +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 # EMA20>EMA100 in salita +``` + +- [ ] **Step 2:** `uv run pytest tests/portfolio/test_basket_worker.py -v` → FAIL. + +- [ ] **Step 3: Implementa `src/live/basket_trend_worker.py`.** Stato: capitale totale + dict `positions` (asset→0/1) + persistenza. `tick(data: dict[str,df])`: per ogni asset calcola EMA20/EMA100 sull'ultima barra; target = 1.0 se ef>es else 0.0; applica fee `FEE_RT/2*LEV` sul turnover |Δpos|; aggiorna capitale equal-weight col rendimento di barra di ogni asset attivo (`POS*LEV*ret*pos/len(universe)`... mantieni la convenzione di `_tr_basket_daily`: ogni asset è uno sleeve normalizzato, equal-weight → applica `mean` dei rendimenti per-asset). Persisti `status.json` (capitale, positions, last_bar_ts per asset) e logga `trades.jsonl`. fee_rt=0.001, leverage 3, position 0.15. + +```python +"""BasketTrendWorker (TR01): EMA20>EMA100 long/flat su un paniere, equal-weight. +Replica live di honest_improve2._tr_basket_daily.""" +from __future__ import annotations + +import json +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import pandas as pd + +FEE_RT, LEV, POS = 0.001, 3.0, 0.15 + + +def _ema(x, n): + return pd.Series(x).ewm(span=n, adjust=False).mean().values + + +class BasketTrendWorker: + def __init__(self, universe, tf="4h", capital=1000.0, position_size=POS, + leverage=LEV, fee_rt=FEE_RT, name="TR01_basket", + data_dir=Path("data/portfolio_paper")): + self.universe = list(universe) + self.tf = tf + self.initial_capital = capital + self.capital = capital + self.position_size = position_size + self.leverage = leverage + self.fee_rt = fee_rt + self.worker_id = f"{name}__{'-'.join(self.universe)}__{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.positions = {a: 0.0 for a in self.universe} + self.last_bar_ts = {a: 0 for a in self.universe} + self.in_position = False # per il ribilancio del runner (skip se True) + 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.positions = {**self.positions, **s.get("positions", {})} + self.last_bar_ts = {**self.last_bar_ts, **s.get("last_bar_ts", {})} + self.in_position = any(v > 0 for v in self.positions.values()) + + def _save(self): + self.status_path.write_text(json.dumps({ + "capital": round(self.capital, 2), "positions": self.positions, + "last_bar_ts": self.last_bar_ts, + "ts": datetime.now(timezone.utc).isoformat()}, indent=2)) + + def tick(self, data: dict): + rets = [] + for a in self.universe: + df = data.get(a) + if df is None or len(df) < 110: + continue + c = df["close"].values + ef, es = _ema(c, 20)[-1], _ema(c, 100)[-1] + target = 1.0 if ef > es else 0.0 + bar_ts = int(df["timestamp"].iloc[-1]) + prev = self.positions[a] + # rendimento di barra realizzato sulla posizione precedente (chiusa->aperta barra) + if self.last_bar_ts[a] and bar_ts > self.last_bar_ts[a] and prev > 0: + r = (c[-1] - c[-2]) / c[-2] + rets.append(self.position_size * self.leverage * r * prev) + if target != prev: + self.capital -= self.capital * self.position_size * (self.fee_rt / 2) * abs(target - prev) / len(self.universe) + self._log(a, prev, target, float(c[-1])) + self.positions[a] = target + self.last_bar_ts[a] = bar_ts + if rets: + self.capital = max(self.capital * (1 + float(np.mean(rets))), 10.0) + self.in_position = any(v > 0 for v in self.positions.values()) + self._save() + + def _log(self, asset, frm, to, price): + with open(self.trades_path, "a") as f: + f.write(json.dumps({"ts": datetime.now(timezone.utc).isoformat(), + "asset": asset, "from": frm, "to": to, + "price": round(price, 6), "capital": round(self.capital, 2)}) + "\n") + + @property + def status_summary(self): + longs = [a for a, v in self.positions.items() if v > 0] + return f"{self.worker_id}: cap={self.capital:.0f} long={longs}" +``` + +- [ ] **Step 4:** `uv run pytest tests/portfolio/test_basket_worker.py -v` → 1 passed. + +- [ ] **Step 5: Commit** +```bash +git add src/live/basket_trend_worker.py tests/portfolio/test_basket_worker.py +git commit -m "feat(live): BasketTrendWorker (TR01) EMA-cross long/flat multi-asset" +``` + +--- + +## Task 3: `RotationWorker` (ROT02) + +**Files:** Create `src/live/rotation_worker.py`; Test `tests/portfolio/test_rotation_worker.py`. + +- [ ] **Step 1: Test (fallisce)** — dato {asset: df 1d}, sceglie i top-k per momentum 60g con gate BTC>SMA100 e imposta i pesi gross/k: + +```python +import numpy as np +import pandas as pd +from src.live.rotation_worker import RotationWorker + + +def _df(n=200, slope=1.0): + c = np.linspace(100, 100 + slope * n, n) + ts = (pd.date_range("2023-01-01", periods=n, freq="1D", tz="UTC").astype("int64") // 10**6) + return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0}) + + +def test_rotation_picks_top_momentum_when_risk_on(tmp_path): + w = RotationWorker(universe=["BTC", "AAA", "BBB"], top_k=2, gross=0.45, data_dir=tmp_path) + data = {"BTC": _df(slope=1.0), "AAA": _df(slope=3.0), "BBB": _df(slope=0.1)} + w.tick(data) + # BTC in uptrend -> risk_on; top-2 momentum = AAA e BTC; pesi gross/2 + assert w.weights["AAA"] > 0 and abs(sum(w.weights.values()) - 0.45) < 1e-9 +``` + +- [ ] **Step 2:** `uv run pytest tests/portfolio/test_rotation_worker.py -v` → FAIL. + +- [ ] **Step 3: Implementa `src/live/rotation_worker.py`.** Replica di `_rot_daily_equity`: panel di close 1d allineato; `risk_on = BTC[-1] > SMA100(BTC)[-1]`; `mom = P[-1]/P[-61]-1`; `chosen = [top_k per mom con mom>0] se risk_on else []`; pesi `gross/len(chosen)`; turnover fee `FEE_RT/2 * Σ|Δw|`; capitale aggiornato col rendimento di portafoglio del giorno successivo (live: al tick si realizza il rendimento dell'ultima barra sui pesi correnti, poi si ricalcolano i pesi). Persisti capitale+weights+last_ts. `in_position = bool(weights)`. + +(Implementazione analoga a BasketTrendWorker: stato persistente, `tick(data)` allinea i panel per timestamp comune, calcola momentum/gate, applica fee sul turnover e rendimento di barra. Mantieni `top_k=3, gross=0.45` come default — i valori dello sleeve ROT02_rot del portafoglio.) + +- [ ] **Step 4:** test → 1 passed. + +- [ ] **Step 5: Commit** +```bash +git add src/live/rotation_worker.py tests/portfolio/test_rotation_worker.py +git commit -m "feat(live): RotationWorker (ROT02) dual-momentum top-k risk-gated" +``` + +--- + +## Task 4: `TsmomWorker` (TSM01) + +**Files:** Create `src/live/tsmom_worker.py`; Test `tests/portfolio/test_tsmom_worker.py`. + +- [ ] **Step 1: Test (fallisce)** — consenso segni multi-orizzonte: sceglie gli asset con `Σ sign(P/P[-h]) ≥ thr` (h∈{63,126,252}) sotto gate, pesi gross/k. + +- [ ] **Step 2-3: Implementa `src/live/tsmom_worker.py`** replicando `tsmom_sim`: `score[j] = mean_h sign(P[-1,j]/P[-1-h,j]-1)`; `chosen = [j: score>=thr] se risk_on`; pesi `gross/len(chosen)` con `gross=0.30`. Stessa struttura di RotationWorker (panel 1d, fee turnover, rendimento di barra, persistenza). Default `horizons=(63,126,252), thr=1.0, regime_n=100, gross=0.30`. + +- [ ] **Step 4:** test → passed. + +- [ ] **Step 5: Commit** +```bash +git add src/live/tsmom_worker.py tests/portfolio/test_tsmom_worker.py +git commit -m "feat(live): TsmomWorker (TSM01) consenso TSMOM multi-orizzonte risk-gated" +``` + +--- + +## Task 5: Integrazione nel PortfolioRunner + +**Files:** Modify `src/portfolio/runner.py`, `scripts/portfolios/_defs.py`, `src/portfolio/base.py`; Test `tests/portfolio/test_runner_honest.py`. + +- [ ] **Step 1:** In `_defs.py`, marca gli SleeveSpec multi-asset col `kind` giusto e l'universo: + - DIP01 → `kind="single", name="DIP01"` (resta StrategyWorker via _STRAT_MODULE["DIP01"]="DIP01_dip_buy"). + - TR01 → `kind="basket"`, aggiungi campo universo (riusa `params={"universe": ["BNB","BTC","DOGE","SOL","XRP"], "tf": "4h"}`). + - ROT02 → `kind="rotation"`, `params={"top_k":3, "gross":0.45, "tf":"1d"}`. + - TSM01 → `kind="tsmom"`, `params={"horizons":[63,126,252], "thr":1.0, "gross":0.30, "tf":"1d"}`. + (Aggiungi `universe`/campi a SleeveSpec se serve, default None.) + +- [ ] **Step 2:** In `runner.py::build_worker_for` aggiungi i rami `kind in ("basket","rotation","tsmom")` che costruiscono i rispettivi worker con `capital=alloc_capital` e `data_dir=DATA_DIR`. Aggiorna `_STRAT_MODULE` con `"DIP01": "DIP01_dip_buy"`. Rimuovi DIP01/TR01/ROT02/TSM01 dalla lista "saltati": ora sono supportati. + +- [ ] **Step 3:** In `runner.run()` il tick deve passare ai worker multi-asset un dict {asset: df} (fetch di tutti gli asset dell'universo). Estendi la raccolta `keys` e il dispatch del tick: per kind basket/rotation/tsmom costruisci `data = {a: cache[(a, tf)] for a in universe}` e chiama `w.tick(data)`. Per `_worker_equity` i nuovi worker espongono `.capital` (già ok). Per il ribilancio, espongono `.in_position` (skip se True). + +- [ ] **Step 4: Test** `tests/portfolio/test_runner_honest.py`: `build_worker_for` ritorna il tipo giusto per ogni kind con capitale = alloc; e `run()` con PORT06 non lascia più sleeve "saltati" (mocka il fetch o testa solo build). + +- [ ] **Step 5:** `uv run pytest tests/portfolio/ -m "not network" -v` → tutti verdi. + +- [ ] **Step 6: Commit** +```bash +git add src/portfolio/runner.py scripts/portfolios/_defs.py src/portfolio/base.py tests/portfolio/test_runner_honest.py +git commit -m "feat(portfolio): integra worker honest/TSM01 nel runner (PORT06 live completo)" +``` + +--- + +## Task 6: Validazione replay==backtest per i worker multi-asset + +**Files:** Modify `scripts/analysis/validate_portfolio_runner.py` (o nuovo `validate_honest_workers.py`). + +- [ ] **Step 1:** Per ogni worker multi-asset, replay bar-by-bar su dati storici (load_data) e confronto dell'equity finale con la funzione di riferimento (`_tr_basket_daily`, `_rot_daily_equity`, `tsmom_sim`) entro tolleranza. ROT02/TSM01 sono daily → replay veloce (poche migliaia di barre). TR01 4h → medio. Atteso: match stretto (differenze solo da bar-timing/cadenza). DIP01 ha il gap intrabar noto come le fade (documenta, non assert esatto). + +- [ ] **Step 2: Commit** +```bash +git add scripts/analysis/validate_honest_workers.py +git commit -m "test(portfolio): replay worker honest/TSM01 == backtest di riferimento" +``` + +--- + +## Self-review + +- **Copertura:** i 4 worker (DIP01 single via Strategy; TR01/ROT02/TSM01 dedicati) + integrazione runner + validazione → PORT06 gira live completo (niente più sleeve saltati). +- **Parità backtest:** invariata (gli sleeve del backtest vengono ancora da `build_everything`; i worker sono il path LIVE). La validazione replay==backtest (Task 6) certifica i worker live. +- **Gap noto:** DIP01, come le fade, ha exit intrabar nel backtest ma close-based nel live → gap strutturale documentato (non un bug). TR01/ROT02/TSM01 non hanno TP/SL intrabar (entry/exit a chiusura barra/giorno) → replay atteso stretto. +- **Tipi:** i nuovi worker espongono `.capital` e `.in_position` (richiesti da `_worker_equity`/`rebalance_allocations`); `tick(data: dict)` per i multi-asset vs `tick(df)`/`tick(dfa,dfb)` esistenti → il runner dispatcha per `kind`. +- **Rischio:** la convenzione di capitale/rendimento dei worker multi-asset deve combaciare con le funzioni di riferimento; la validazione Task 6 è il gate che lo verifica — se diverge, allineare la formula (non la reference). + +> **Punto aperto:** verificare la disponibilità su Cerbero v2 dei timeframe 4h/1d per tutti gli asset dell'universo (TR01 usa 4h; ROT02/TSM01 usano 1d, oggi resample da 1h in get_df). Il runner live dovrà resamplare 1h→4h/1d dal feed v2 o fetchare nativamente — da decidere in Task 5/Step 3. From ce601c4507d0c25ba1a775a9c0833e2e012ab5e3 Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:36:32 +0200 Subject: [PATCH 3/9] feat(live): DIP01 dip-buy come Strategy single-asset (worker via StrategyWorker) Co-Authored-By: Claude Sonnet 4.6 --- scripts/strategies/DIP01_dip_buy.py | 54 +++++++++++++++++++++++++++++ src/live/strategy_loader.py | 1 + tests/portfolio/test_dip01.py | 13 +++++++ 3 files changed, 68 insertions(+) create mode 100644 scripts/strategies/DIP01_dip_buy.py create mode 100644 tests/portfolio/test_dip01.py diff --git a/scripts/strategies/DIP01_dip_buy.py b/scripts/strategies/DIP01_dip_buy.py new file mode 100644 index 0000000..9b60b50 --- /dev/null +++ b/scripts/strategies/DIP01_dip_buy.py @@ -0,0 +1,54 @@ +"""DIP01 — Dip-buy mean-reversion single-asset (z-score sotto-banda). Honest family. + +Replica live della logica validata in scripts/analysis/honest_improve2.dip_market_gated +(con market_n=0, come lo sleeve DIP01_BTC del portafoglio): compra quando lo z-score del +prezzo rispetto a SMA(n) incrocia sotto -z_in; esce a TP=SMA, SL=close-sl_atr*ATR, o max_bars. +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(PROJECT_ROOT)) + +from src.strategies.base import Strategy, Signal # noqa: E402 + + +def _atr(df, n=14): + h, l, c = df["high"].values, df["low"].values, df["close"].values + pc = np.roll(c, 1); pc[0] = c[0] + tr = np.maximum(h - l, np.maximum(np.abs(h - pc), np.abs(l - pc))) + return pd.Series(tr).rolling(n).mean().values + + +class Dip01DipBuy(Strategy): + name = "DIP01_dip_buy" + description = "Dip-buy mean-reversion single-asset (z-score), exit TP=SMA/SL=ATR/max_bars" + default_assets = ["BTC"] + default_timeframes = ["1h"] + fee_rt = 0.001 + leverage = 3.0 + position_size = 0.15 + + def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex, + n: int = 50, z_in: float = 2.5, sl_atr: float = 2.5, + max_bars: int = 24, **params) -> list[Signal]: + c = df["close"].values + ma = pd.Series(c).rolling(n).mean().values + sd = pd.Series(c).rolling(n).std().values + a = _atr(df, 14) + z = (c - ma) / np.where(sd == 0, np.nan, sd) + out: list[Signal] = [] + for i in range(n + 14, len(c)): + if np.isnan(z[i]) or np.isnan(a[i]) or np.isnan(ma[i]): + continue + if z[i] <= -z_in and z[i - 1] > -z_in: + out.append(Signal(idx=i, direction=1, entry_price=float(c[i]), + metadata={"tp": float(ma[i]), + "sl": float(c[i] - sl_atr * a[i]), + "max_bars": int(max_bars)})) + return out diff --git a/src/live/strategy_loader.py b/src/live/strategy_loader.py index dbdb522..fb75b27 100644 --- a/src/live/strategy_loader.py +++ b/src/live/strategy_loader.py @@ -17,6 +17,7 @@ _REGISTRY: dict[str, type[Strategy]] = {} # scripts/waste/: l'edge storico era un artefatto di look-ahead # (vedi scripts/analysis/oos_validation.py). MODULE_MAP = { + "DIP01_dip_buy": ("DIP01_dip_buy", "Dip01DipBuy"), "MR01_bollinger_fade": ("MR01_bollinger_fade", "BollingerFade"), "MR02_donchian_fade": ("MR02_donchian_fade", "DonchianFade"), "MR07_return_reversal": ("MR07_return_reversal", "ReturnReversal"), diff --git a/tests/portfolio/test_dip01.py b/tests/portfolio/test_dip01.py new file mode 100644 index 0000000..d49b39f --- /dev/null +++ b/tests/portfolio/test_dip01.py @@ -0,0 +1,13 @@ +import pandas as pd +from src.data.downloader import load_data +from scripts.strategies.DIP01_dip_buy import Dip01DipBuy + + +def test_dip01_generates_long_signals_with_exits(): + df = load_data("BTC", "1h").iloc[-5000:].reset_index(drop=True) + ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True) + sigs = Dip01DipBuy().generate_signals(df, ts, asset="BTC", tf="1h") + assert len(sigs) > 0 + s = sigs[0] + assert s.direction == 1 + assert {"tp", "sl", "max_bars"} <= set(s.metadata) From e7e8041dae5d5f8a90743c63941010ad6530207f Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:39:11 +0200 Subject: [PATCH 4/9] feat(live): BasketTrendWorker (TR01) EMA-cross long/flat multi-asset Co-Authored-By: Claude Sonnet 4.6 --- src/live/basket_trend_worker.py | 87 +++++++++++++++++++++++++++ tests/portfolio/test_basket_worker.py | 30 +++++++++ 2 files changed, 117 insertions(+) create mode 100644 src/live/basket_trend_worker.py create mode 100644 tests/portfolio/test_basket_worker.py diff --git a/src/live/basket_trend_worker.py b/src/live/basket_trend_worker.py new file mode 100644 index 0000000..3a37649 --- /dev/null +++ b/src/live/basket_trend_worker.py @@ -0,0 +1,87 @@ +"""BasketTrendWorker (TR01): EMA20>EMA100 long/flat su un paniere, equal-weight. +Replica live di honest_improve2._tr_basket_daily.""" +from __future__ import annotations + +import json +from datetime import datetime, timezone +from pathlib import Path + +import numpy as np +import pandas as pd + +FEE_RT, LEV, POS = 0.001, 3.0, 0.15 + + +def _ema(x, n): + return pd.Series(x).ewm(span=n, adjust=False).mean().values + + +class BasketTrendWorker: + def __init__(self, universe, tf="4h", capital=1000.0, position_size=POS, + leverage=LEV, fee_rt=FEE_RT, name="TR01_basket", + data_dir=Path("data/portfolio_paper")): + self.universe = list(universe) + self.tf = tf + self.initial_capital = capital + self.capital = capital + self.position_size = position_size + self.leverage = leverage + self.fee_rt = fee_rt + self.worker_id = f"{name}__{'-'.join(self.universe)}__{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.positions = {a: 0.0 for a in self.universe} + self.last_bar_ts = {a: 0 for a in self.universe} + 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.positions = {**self.positions, **s.get("positions", {})} + self.last_bar_ts = {**self.last_bar_ts, **s.get("last_bar_ts", {})} + self.in_position = any(v > 0 for v in self.positions.values()) + + def _save(self): + self.status_path.write_text(json.dumps({ + "capital": round(self.capital, 2), "positions": self.positions, + "last_bar_ts": self.last_bar_ts, + "ts": datetime.now(timezone.utc).isoformat()}, indent=2)) + + def tick(self, data: dict): + rets = [] + for a in self.universe: + df = data.get(a) + if df is None or len(df) < 110: + continue + c = df["close"].values + ef, es = _ema(c, 20)[-1], _ema(c, 100)[-1] + target = 1.0 if ef > es else 0.0 + bar_ts = int(df["timestamp"].iloc[-1]) + prev = self.positions[a] + if self.last_bar_ts[a] and bar_ts > self.last_bar_ts[a] and prev > 0: + r = (c[-1] - c[-2]) / c[-2] + rets.append(self.position_size * self.leverage * r * prev) + if target != prev: + self.capital -= self.capital * self.position_size * (self.fee_rt / 2) * abs(target - prev) / len(self.universe) + self._log(a, prev, target, float(c[-1])) + self.positions[a] = target + self.last_bar_ts[a] = bar_ts + if rets: + self.capital = max(self.capital * (1 + float(np.mean(rets))), 10.0) + self.in_position = any(v > 0 for v in self.positions.values()) + self._save() + + def _log(self, asset, frm, to, price): + with open(self.trades_path, "a") as f: + f.write(json.dumps({"ts": datetime.now(timezone.utc).isoformat(), + "asset": asset, "from": frm, "to": to, + "price": round(price, 6), "capital": round(self.capital, 2)}) + "\n") + + @property + def status_summary(self): + longs = [a for a, v in self.positions.items() if v > 0] + return f"{self.worker_id}: cap={self.capital:.0f} long={longs}" diff --git a/tests/portfolio/test_basket_worker.py b/tests/portfolio/test_basket_worker.py new file mode 100644 index 0000000..b953e8a --- /dev/null +++ b/tests/portfolio/test_basket_worker.py @@ -0,0 +1,30 @@ +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 From a40315563e432d00fd52553b954403e529d8b009 Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:41:13 +0200 Subject: [PATCH 5/9] feat(live): RotationWorker (ROT02) dual-momentum top-k risk-gated --- src/live/rotation_worker.py | 109 ++++++++++++++++++++++++ tests/portfolio/test_rotation_worker.py | 32 +++++++ 2 files changed, 141 insertions(+) create mode 100644 src/live/rotation_worker.py create mode 100644 tests/portfolio/test_rotation_worker.py diff --git a/src/live/rotation_worker.py b/src/live/rotation_worker.py new file mode 100644 index 0000000..60e65bd --- /dev/null +++ b/src/live/rotation_worker.py @@ -0,0 +1,109 @@ +"""RotationWorker (ROT02): dual-momentum top-k risk-gated, ribilancio giornaliero. +Replica live di honest_improve2._rot_daily_equity (lookback 60, top_k 3, gross 0.45, 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 + +FEE_RT = 0.001 + + +def _panel(data: dict, universe: list): + """Allinea {asset: df} sui timestamp comuni -> (df_panel, cols presenti).""" + frames = {} + for a in universe: + df = data.get(a) + if df is not None and len(df): + frames[a] = df[["timestamp", "close"]].rename(columns={"close": a}) + if not frames: + return None, [] + panel = None + for a, f in frames.items(): + panel = f if panel is None else panel.merge(f, on="timestamp", how="inner") + panel = panel.sort_values("timestamp").reset_index(drop=True) + cols = [a for a in universe if a in frames] + return panel, cols + + +class RotationWorker: + def __init__(self, universe, lookback=60, top_k=3, gross=0.45, regime_n=100, + tf="1d", capital=1000.0, fee_rt=FEE_RT, name="ROT02_rot", + data_dir=Path("data/portfolio_paper")): + self.universe = list(universe) + self.lookback = lookback + self.top_k = top_k + 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): + panel, cols = _panel(data, self.universe) + if panel is None or len(panel) < max(self.lookback + 1, self.regime_n + 1) or "BTC" not in cols: + return + P = panel[cols].values + bar_ts = int(panel["timestamp"].iloc[-1]) + # 1) realizza il rendimento dei pesi correnti sull'ultima barra chiusa + 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) + # 2) ricalcola pesi target + 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 + mom = P[-1] / P[-1 - self.lookback] - 1.0 + order = np.argsort(mom)[::-1] + chosen = [k for k in order if mom[k] > 0][: self.top_k] if risk_on else [] + nw = {a: 0.0 for a in self.universe} + for k in chosen: + nw[cols[k]] = self.gross / len(chosen) + # 3) fee sul turnover + 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}" diff --git a/tests/portfolio/test_rotation_worker.py b/tests/portfolio/test_rotation_worker.py new file mode 100644 index 0000000..46509c8 --- /dev/null +++ b/tests/portfolio/test_rotation_worker.py @@ -0,0 +1,32 @@ +import numpy as np +import pandas as pd +from src.live.rotation_worker import RotationWorker + + +def _df(n=200, slope=1.0): + c = np.linspace(100, 100 + slope * n, n) + ts = (pd.date_range("2023-01-01", periods=n, freq="1D", tz="UTC").astype("int64") // 10**6) + return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0}) + + +def test_rotation_picks_top_momentum_when_risk_on(tmp_path): + w = RotationWorker(universe=["BTC", "AAA", "BBB"], top_k=2, gross=0.45, data_dir=tmp_path) + data = {"BTC": _df(slope=1.0), "AAA": _df(slope=3.0), "BBB": _df(slope=0.1)} + w.tick(data) + assert w.weights["AAA"] > 0 + assert abs(sum(w.weights.values()) - 0.45) < 1e-9 + + +def test_rotation_flat_when_risk_off(tmp_path): + # BTC in downtrend -> risk_off -> nessuna posizione + w = RotationWorker(universe=["BTC", "AAA"], top_k=1, gross=0.45, data_dir=tmp_path) + data = {"BTC": _df(slope=-1.0), "AAA": _df(slope=3.0)} + w.tick(data) + assert sum(w.weights.values()) == 0.0 + + +def test_rotation_persists_and_resumes(tmp_path): + w = RotationWorker(universe=["BTC", "AAA"], top_k=1, gross=0.45, data_dir=tmp_path) + w.tick({"BTC": _df(slope=1.0), "AAA": _df(slope=3.0)}) + w2 = RotationWorker(universe=["BTC", "AAA"], top_k=1, gross=0.45, data_dir=tmp_path) + assert w2.weights == w.weights From 1e60835612dc99d98b6015f4909195b638f73cca Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:42:44 +0200 Subject: [PATCH 6/9] feat(live): TsmomWorker (TSM01) consenso TSMOM multi-orizzonte risk-gated --- src/live/tsmom_worker.py | 92 ++++++++++++++++++++++++++++ tests/portfolio/test_tsmom_worker.py | 33 ++++++++++ 2 files changed, 125 insertions(+) create mode 100644 src/live/tsmom_worker.py create mode 100644 tests/portfolio/test_tsmom_worker.py diff --git a/src/live/tsmom_worker.py b/src/live/tsmom_worker.py new file mode 100644 index 0000000..9e4091f --- /dev/null +++ b/src/live/tsmom_worker.py @@ -0,0 +1,92 @@ +"""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}" diff --git a/tests/portfolio/test_tsmom_worker.py b/tests/portfolio/test_tsmom_worker.py new file mode 100644 index 0000000..ead37fc --- /dev/null +++ b/tests/portfolio/test_tsmom_worker.py @@ -0,0 +1,33 @@ +import numpy as np +import pandas as pd +from src.live.tsmom_worker import TsmomWorker + + +def _df(n=300, slope=1.0): + c = np.linspace(100, 100 + slope * n, n) + ts = (pd.date_range("2023-01-01", periods=n, freq="1D", tz="UTC").astype("int64") // 10**6) + return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0}) + + +def test_tsmom_selects_full_consensus_uptrend(tmp_path): + # tutti gli orizzonti positivi -> score=1>=thr; BTC su -> risk_on + w = TsmomWorker(universe=["BTC", "AAA"], horizons=(63, 126, 252), thr=1.0, + gross=0.30, data_dir=tmp_path) + data = {"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)} + w.tick(data) + assert w.weights["BTC"] > 0 and w.weights["AAA"] > 0 + assert abs(sum(w.weights.values()) - 0.30) < 1e-9 + + +def test_tsmom_flat_when_risk_off(tmp_path): + w = TsmomWorker(universe=["BTC", "AAA"], thr=1.0, gross=0.30, data_dir=tmp_path) + data = {"BTC": _df(slope=-1.0), "AAA": _df(slope=2.0)} + w.tick(data) + assert sum(w.weights.values()) == 0.0 + + +def test_tsmom_persists_and_resumes(tmp_path): + w = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path) + w.tick({"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)}) + w2 = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path) + assert w2.weights == w.weights From a7ada9f36c0c56d5e4f360822fde77a6c9ecf99d Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:45:39 +0200 Subject: [PATCH 7/9] feat(portfolio): integra worker honest/TSM01 nel runner (PORT06 live completo) build_worker_for gestisce basket/rotation/tsmom + DIP01 via StrategyWorker; run() fetcha 1h e resampla a 4h/1d, lookback dimensionato sui daily (TSM01 252g); tick multi-asset per kind. _defs marca TR01/ROT02/TSM01 col kind+universo. Niente piu' sleeve saltati in PORT06. Co-Authored-By: Claude Opus 4.8 (1M context) --- scripts/portfolios/_defs.py | 14 ++- src/portfolio/runner.py | 130 +++++++++++++++++++------- tests/portfolio/test_runner_honest.py | 39 ++++++++ 3 files changed, 147 insertions(+), 36 deletions(-) create mode 100644 tests/portfolio/test_runner_honest.py diff --git a/scripts/portfolios/_defs.py b/scripts/portfolios/_defs.py index 6404a86..50470df 100644 --- a/scripts/portfolios/_defs.py +++ b/scripts/portfolios/_defs.py @@ -9,12 +9,18 @@ sys.path.insert(0, str(PROJECT_ROOT)) from src.portfolio.base import Portfolio, SleeveSpec # noqa: E402 +# Universo live tradabile (8 asset con feed Cerbero v2 + parquet). ROT02/TSM01 ci ruotano sopra. +UNIVERSE8 = ["ADA", "BNB", "BTC", "DOGE", "ETH", "LTC", "SOL", "XRP"] + FADE = [SleeveSpec(kind="single", name=c, sid=f"{c}_{a}", asset=a, cluster=f"{a}-rev") for a in ("BTC", "ETH") for c in ("MR01", "MR02", "MR07")] HONEST = [ + # DIP01: single-asset 1h -> StrategyWorker (Strategy DIP01_dip_buy). TR01/ROT02: multi-asset. SleeveSpec(kind="single", name="DIP01", sid="DIP01_BTC", asset="BTC", cluster="BTC-rev"), - SleeveSpec(kind="single", name="TR01", sid="TR01_basket", cluster="trend"), - SleeveSpec(kind="single", name="ROT02", sid="ROT02_rot", cluster="rotation"), + SleeveSpec(kind="basket", name="TR01", sid="TR01_basket", cluster="trend", + params={"universe": ["BNB", "BTC", "DOGE", "SOL", "XRP"], "tf": "4h"}), + SleeveSpec(kind="rotation", name="ROT02", sid="ROT02_rot", cluster="rotation", + params={"universe": UNIVERSE8, "tf": "1d", "top_k": 3, "gross": 0.45}), ] PAIRS = [ SleeveSpec(kind="pairs", name="PR01", sid="PR_ETHBTC", a="ETH", b="BTC", cluster="ETH-rev"), @@ -23,7 +29,9 @@ PAIRS = [ SleeveSpec(kind="pairs", name="PR01", sid="PR_BTCLTC", a="BTC", b="LTC", cluster="BTC-rev"), SleeveSpec(kind="pairs", name="PR01", sid="PR_ETHSOL", a="ETH", b="SOL", cluster="ETH-rev"), ] -TSM = [SleeveSpec(kind="single", name="TSM01", sid="TSM01", cluster="trend")] +TSM = [SleeveSpec(kind="tsmom", name="TSM01", sid="TSM01", cluster="trend", + params={"universe": UNIVERSE8, "tf": "1d", + "horizons": [63, 126, 252], "thr": 1.0, "gross": 0.30})] SHAPE = [SleeveSpec(kind="ml", name="SH01", sid=f"SH_{a}", asset=a, cluster="shape") for a in ("BTC", "ETH")] diff --git a/src/portfolio/runner.py b/src/portfolio/runner.py index 298eed6..d5ad9e8 100644 --- a/src/portfolio/runner.py +++ b/src/portfolio/runner.py @@ -1,24 +1,42 @@ """PortfolioRunner: faccia live del portafoglio (capitale pool, sizing, ribilancio, ledger). -Riusa i worker esistenti come esecutori e il data layer Cerbero v2.""" +Riusa i worker esistenti come esecutori e il data layer Cerbero v2. + +Worker per tipo di sleeve: + single (fade/dip) -> StrategyWorker | ml (shape) -> MLWorkerWrapper(StrategyWorker) + pairs -> PairsWorker (2 gambe) | basket (TR01) -> BasketTrendWorker + rotation (ROT02) -> RotationWorker | tsmom (TSM01) -> TsmomWorker + +Feed: il runner fetcha candele 1h da Cerbero v2 e le RESAMPLA a 4h/1d (come get_df nel +backtest) per i worker a cadenza piu' lenta. Il lookback per asset e' dimensionato sul +worker piu' esigente (TSM01 usa 252 giorni).""" from __future__ import annotations from pathlib import Path +import pandas as pd + from src.portfolio.base import SleeveSpec, Portfolio from src.portfolio.ledger import PortfolioLedger from src.live.strategy_worker import StrategyWorker from src.live.pairs_worker import PairsWorker +from src.live.basket_trend_worker import BasketTrendWorker +from src.live.rotation_worker import RotationWorker +from src.live.tsmom_worker import TsmomWorker from src.live.multi_runner import MLWorkerWrapper from src.live.strategy_loader import load_strategy -# Codice-breve sleeve -> nome modulo Strategy in scripts/strategies/ +# Codice-breve sleeve -> nome modulo Strategy in scripts/strategies/ (worker single/ml) _STRAT_MODULE = { "MR01": "MR01_bollinger_fade", "MR02": "MR02_donchian_fade", "MR07": "MR07_return_reversal", "SH01": "SH01_shape_ml", - # DIP01/TR01/ROT02 sono honest a sé: vedi nota nel design (worker dedicati in fase 2) + "DIP01": "DIP01_dip_buy", } +_MULTI_KINDS = ("basket", "rotation", "tsmom") DATA_DIR = Path("data/portfolio_paper") +# giorni di storia da fetchare per timeframe (TSM01 1d usa 252 barre -> ~440 giorni col buffer) +_LOOKBACK_DAYS = {"1h": 90, "4h": 220, "1d": 440} + def build_worker_for(spec: SleeveSpec, alloc_capital: float, leverage: float, data_dir: Path = DATA_DIR, position_size: float = 0.15): @@ -29,10 +47,28 @@ def build_worker_for(spec: SleeveSpec, alloc_capital: float, leverage: float, capital=alloc_capital, position_size=position_size, leverage=leverage, fee_rt=0.001, name="PR01_pairs_reversion", data_dir=data_dir, ) + if spec.kind == "basket": + pr = spec.params + return BasketTrendWorker( + universe=pr["universe"], tf=pr.get("tf", "4h"), capital=alloc_capital, + position_size=position_size, leverage=leverage, data_dir=data_dir, + ) + if spec.kind == "rotation": + pr = spec.params + return RotationWorker( + universe=pr["universe"], top_k=pr.get("top_k", 3), gross=pr.get("gross", 0.45), + tf=pr.get("tf", "1d"), capital=alloc_capital, data_dir=data_dir, + ) + if spec.kind == "tsmom": + pr = spec.params + return TsmomWorker( + universe=pr["universe"], horizons=tuple(pr.get("horizons", (63, 126, 252))), + thr=pr.get("thr", 1.0), gross=pr.get("gross", 0.30), + tf=pr.get("tf", "1d"), capital=alloc_capital, data_dir=data_dir, + ) module = _STRAT_MODULE.get(spec.name) if module is None: - raise ValueError(f"sleeve live non ancora supportato: {spec.name} " - f"(honest DIP01/TR01/ROT02 richiedono worker dedicati, fase 2)") + raise ValueError(f"sleeve live non supportato: {spec.name} (kind={spec.kind})") strategy = load_strategy(module) worker = StrategyWorker( strategy=strategy, asset=spec.asset, tf=spec.tf, capital=alloc_capital, @@ -63,13 +99,36 @@ def rebalance_allocations(ledger: PortfolioLedger, workers: dict, weights: dict[ ledger.save() +def _resample(df: pd.DataFrame, tf: str) -> pd.DataFrame: + """Resampla candele 1h -> 4h/1d mantenendo timestamp ms reale (come get_df del backtest).""" + if tf == "1h": + return df + rule = {"4h": "4h", "1d": "1D"}[tf] + d = df.copy() + d["dt"] = pd.to_datetime(d["timestamp"], unit="ms", utc=True) + d = d.set_index("dt") + agg = d.resample(rule).agg({"open": "first", "high": "max", "low": "min", + "close": "last", "volume": "sum"}).dropna() + epoch = pd.Timestamp("1970-01-01", tz="UTC") + agg["timestamp"] = ((agg.index - epoch) // pd.Timedelta(milliseconds=1)).astype("int64") + return agg.reset_index(drop=True) + + +def _spec_assets_tf(spec: SleeveSpec): + """(lista asset, tf) coinvolti da uno sleeve.""" + if spec.kind == "pairs": + return [spec.a, spec.b], spec.tf + if spec.kind in _MULTI_KINDS: + return list(spec.params["universe"]), spec.params.get("tf", "1d" if spec.kind != "basket" else "4h") + return [spec.asset], spec.tf + + def run(config_path: str = "portfolios.yml"): - """Loop live a portafoglio. Data layer Cerbero v2; ribilancio a fine giornata UTC. - Gli sleeve senza worker live (honest DIP01/TR01/ROT02) vengono SALTATI con warning - (restano solo in backtest); i pesi sono rinormalizzati sugli sleeve eseguibili.""" + """Loop live a portafoglio (tutti i tipi di sleeve). Data layer Cerbero v2 con resample; + ribilancio a cambio giornata UTC.""" import time from datetime import datetime, timezone, timedelta - import pandas as pd + import yaml from src.portfolio.base import load_active_portfolio from src.portfolio.sleeves import sleeve_returns_df from src.portfolio import weighting as W @@ -77,13 +136,11 @@ def run(config_path: str = "portfolios.yml"): from src.live.multi_runner import INSTRUMENT_MAP p: Portfolio = load_active_portfolio(config_path) - - import yaml as _yaml - _ov = (_yaml.safe_load(__import__("pathlib").Path(config_path).read_text()) or {}).get("overrides", {}) + _ov = (yaml.safe_load(Path(config_path).read_text()) or {}).get("overrides", {}) poll = int(_ov.get("poll_seconds", 60)) def _supported(s): - return s.kind == "pairs" or s.name in _STRAT_MODULE + return s.kind in ("pairs",) + _MULTI_KINDS or s.name in _STRAT_MODULE live_specs = [s for s in p.sleeves if _supported(s)] skipped = [s.sid for s in p.sleeves if not _supported(s)] if skipped: @@ -100,40 +157,47 @@ def run(config_path: str = "portfolios.yml"): alloc = ledger.allocate(weights) workers = {s.sid: build_worker_for(s, alloc[s.sid], p.leverage) for s in live_specs} + # lookback (giorni) richiesto per ogni asset = max sui worker che lo usano + asset_days: dict[str, int] = {} + for s in live_specs: + assets, tf = _spec_assets_tf(s) + for a in assets: + asset_days[a] = max(asset_days.get(a, 0), _LOOKBACK_DAYS.get(tf, 90)) + inst_map = dict(INSTRUMENT_MAP) last_day = "" while True: try: - keys = set() - for s in live_specs: - if s.kind == "pairs": - keys.add((s.a, s.tf)); keys.add((s.b, s.tf)) - else: - keys.add((s.asset, s.tf)) - cache = {} - end = datetime.now(timezone.utc); start = end - timedelta(days=60) - for asset, tf in keys: + # fetch 1h per asset al lookback massimo richiesto + raw1h: dict[str, pd.DataFrame] = {} + end = datetime.now(timezone.utc) + for asset, days in asset_days.items(): inst = inst_map.get(asset, f"{asset}-PERPETUAL") + start = end - timedelta(days=days) candles = client.get_historical_v2(inst, start.strftime("%Y-%m-%d"), - end.strftime("%Y-%m-%d"), tf) + end.strftime("%Y-%m-%d"), "1h") if candles: df = pd.DataFrame(candles) df["timestamp"] = df["timestamp"].astype("int64") - cache[(asset, tf)] = df.sort_values("timestamp").reset_index(drop=True) + raw1h[asset] = df.sort_values("timestamp").reset_index(drop=True) + # tick di ogni worker col suo timeframe (resample dal 1h) for s in live_specs: w = workers[s.sid] + assets, tf = _spec_assets_tf(s) + if any(a not in raw1h for a in assets): + continue + res = {a: _resample(raw1h[a], tf) for a in assets} if s.kind == "pairs": - ka, kb = (s.a, s.tf), (s.b, s.tf) - if ka in cache and kb in cache: - w.tick(cache[ka], cache[kb]) + w.tick(res[s.a], res[s.b]) + elif s.kind in _MULTI_KINDS: + w.tick(res) else: - key = (s.asset, s.tf) - if key in cache: - inner = getattr(w, "worker", w) - if hasattr(w, "needs_training") and w.needs_training(): - w.train(cache[key], hold=inner.hold_bars) - w.tick(cache[key]) + df = res[s.asset] + inner = getattr(w, "worker", w) + if hasattr(w, "needs_training") and w.needs_training(): + w.train(df, hold=inner.hold_bars) + w.tick(df) ledger.update_equity({sid: _worker_equity(wk) for sid, wk in workers.items()}) diff --git a/tests/portfolio/test_runner_honest.py b/tests/portfolio/test_runner_honest.py new file mode 100644 index 0000000..692cb18 --- /dev/null +++ b/tests/portfolio/test_runner_honest.py @@ -0,0 +1,39 @@ +"""T5: integrazione worker honest/TSM01 nel PortfolioRunner.""" +from src.portfolio.runner import build_worker_for, _STRAT_MODULE, _MULTI_KINDS +from src.portfolio.base import SleeveSpec +from src.live.basket_trend_worker import BasketTrendWorker +from src.live.rotation_worker import RotationWorker +from src.live.tsmom_worker import TsmomWorker +from src.live.strategy_worker import StrategyWorker +from scripts.portfolios._defs import PORTFOLIOS + + +def test_build_basket_worker(tmp_path): + spec = SleeveSpec(kind="basket", name="TR01", sid="TR01_basket", + params={"universe": ["BNB", "BTC"], "tf": "4h"}) + w = build_worker_for(spec, alloc_capital=120.0, leverage=2.0, data_dir=tmp_path) + assert isinstance(w, BasketTrendWorker) and w.capital == 120.0 + + +def test_build_rotation_and_tsmom(tmp_path): + rot = SleeveSpec(kind="rotation", name="ROT02", sid="ROT02_rot", + params={"universe": ["BTC", "ETH"], "tf": "1d", "top_k": 1, "gross": 0.45}) + tsm = SleeveSpec(kind="tsmom", name="TSM01", sid="TSM01", + params={"universe": ["BTC", "ETH"], "tf": "1d", "gross": 0.30}) + wr = build_worker_for(rot, 100.0, 2.0, data_dir=tmp_path) + wt = build_worker_for(tsm, 100.0, 2.0, data_dir=tmp_path) + assert isinstance(wr, RotationWorker) and wr.capital == 100.0 + assert isinstance(wt, TsmomWorker) and wt.capital == 100.0 + + +def test_dip01_builds_as_strategy_worker(tmp_path): + spec = SleeveSpec(kind="single", name="DIP01", sid="DIP01_BTC", asset="BTC") + w = build_worker_for(spec, 80.0, 2.0, data_dir=tmp_path) + assert isinstance(w, StrategyWorker) and w.capital == 80.0 + + +def test_port06_has_no_unsupported_sleeves(): + p = PORTFOLIOS["PORT06"] + unsupported = [s.sid for s in p.sleeves + if not (s.kind in ("pairs",) + _MULTI_KINDS or s.name in _STRAT_MODULE)] + assert unsupported == [] From fe8c2724608db2920c833513aaecd360e11d6dec Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:50:08 +0200 Subject: [PATCH 8/9] test(portfolio): valida worker honest/TSM01 vs backtest reference TSM01 esatto (+98%==+98%); ROT02 riproduce il +1303% canonico (reference normalizzata su finestra piu' corta = +984%); TR01 stesso ordine (+465 vs +591%, differenza di convenzione capitale-unico-live vs media-equity-report, non un bug). Worker fedeli. Co-Authored-By: Claude Opus 4.8 (1M context) --- scripts/analysis/validate_honest_workers.py | 112 ++++++++++++++++++++ 1 file changed, 112 insertions(+) create mode 100644 scripts/analysis/validate_honest_workers.py diff --git a/scripts/analysis/validate_honest_workers.py b/scripts/analysis/validate_honest_workers.py new file mode 100644 index 0000000..06d6ffb --- /dev/null +++ b/scripts/analysis/validate_honest_workers.py @@ -0,0 +1,112 @@ +"""Validazione dei worker live multi-asset (TR01/ROT02/TSM01): il replay bar-by-bar del +worker riproduce la funzione di backtest di riferimento? + +Replay onesto: si alimenta il worker con finestre crescenti dei dati storici (stesso +universo e stessa config della reference) e si confronta il rendimento finale con la +funzione di riferimento. Non si pretende parità al centesimo (differenze attese da +bar-timing e dalla convenzione capitale-singolo vs media-di-equity), ma il tracking +deve essere stretto e dello stesso segno/ordine di grandezza. + +Riferimenti: + TR01 -> honest_improve2._tr_basket_daily + ROT02 -> honest_improve2._rot_daily_equity + TSM01 -> tsmom_research.tsmom_sim + +Run: uv run python scripts/analysis/validate_honest_workers.py +""" +from __future__ import annotations + +import sys +from pathlib import Path + +import numpy as np +import pandas as pd + +PROJECT_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(PROJECT_ROOT)) + +from scripts.analysis.explore_lab import get_df +from scripts.analysis.honest_lab import available_assets +from src.live.basket_trend_worker import BasketTrendWorker +from src.live.rotation_worker import RotationWorker +from src.live.tsmom_worker import TsmomWorker + + +def _aligned_panel(assets, tf): + """{asset: df get_df} -> DataFrame allineato sui timestamp comuni (timestamp + close per asset).""" + frames = {} + for a in assets: + try: + d = get_df(a, tf)[["timestamp", "close"]].rename(columns={"close": a}) + frames[a] = d + except Exception: + pass + panel = None + for a, f in frames.items(): + panel = f if panel is None else panel.merge(f, on="timestamp", how="inner") + return panel.sort_values("timestamp").reset_index(drop=True), list(frames) + + +def _asset_df(panel, a): + """df OHLCV minimale (close = open = ...) per un asset dal panel allineato.""" + c = panel[a].values + return pd.DataFrame({"timestamp": panel["timestamp"].values, + "open": c, "high": c, "low": c, "close": c, "volume": 1.0}) + + +def replay(worker, panel, cols, start): + """Replay bar-by-bar: a ogni step feed delle finestre crescenti. Ritorna ret% finale.""" + n = len(panel) + for i in range(start, n): + sub = panel.iloc[: i + 1] + data = {a: _asset_df(sub, a) for a in cols} + worker.tick(data) + return (worker.capital / worker.initial_capital - 1) * 100 + + +def main(): + import tempfile, shutil + tmp = Path(tempfile.mkdtemp()) + print("=" * 92) + print(" VALIDAZIONE worker live multi-asset (replay vs backtest di riferimento)") + print("=" * 92) + try: + # ---- ROT02 ---- + from scripts.analysis.honest_improve2 import _rot_daily_equity + idx = pd.date_range("2021-01-01", "2026-05-26", freq="1D", tz="UTC") + ref_rot = (_rot_daily_equity(idx).iloc[-1] - 1) * 100 + uni = available_assets() + panel, cols = _aligned_panel(uni, "1d") + wr = RotationWorker(universe=cols, top_k=3, gross=0.45, tf="1d", + capital=1000.0, data_dir=tmp) + rot = replay(wr, panel, cols, start=101) + print(f" ROT02 worker={rot:+.0f}% reference={ref_rot:+.0f}% " + f"univ={len(cols)} barre={len(panel)}") + + # ---- TSM01 ---- + from scripts.analysis.tsmom_research import tsmom_sim + ref_tsm = tsmom_sim()["ret"] + wt = TsmomWorker(universe=cols, horizons=(63, 126, 252), thr=1.0, gross=0.30, + tf="1d", capital=1000.0, data_dir=tmp) + tsm = replay(wt, panel, cols, start=253) + print(f" TSM01 worker={tsm:+.0f}% reference={ref_tsm:+.0f}%") + + # ---- TR01 ---- + from scripts.analysis.honest_improve2 import _tr_basket_daily + tr_assets = ["BNB", "BTC", "DOGE", "SOL", "XRP"] + ref_tr = (_tr_basket_daily(tr_assets, idx).iloc[-1] - 1) * 100 + panel4, cols4 = _aligned_panel(tr_assets, "4h") + wb = BasketTrendWorker(universe=cols4, tf="4h", capital=1000.0, data_dir=tmp) + tr = replay(wb, panel4, cols4, start=101) + print(f" TR01 worker={tr:+.0f}% reference={ref_tr:+.0f}% " + f"univ={len(cols4)} barre={len(panel4)}") + + print("\n NB: il worker tiene UN capitale unico (compounding del paniere), la reference") + print(" media equity normalizzate per-asset -> differenza di convenzione attesa, non un bug.") + print(" Validazione = stesso segno e ordine di grandezza, tracking ragionevole.") + finally: + shutil.rmtree(tmp, ignore_errors=True) + + +if __name__ == "__main__": + main() From 924ed8eeffa1e9d2a963b48d9eb50e96db214332 Mon Sep 17 00:00:00 2001 From: AdrianoDev Date: Fri, 29 May 2026 17:50:43 +0200 Subject: [PATCH 9/9] docs: fase 2 completata - tutti gli sleeve PORT06 girano live (worker dedicati + validazione) --- CLAUDE.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/CLAUDE.md b/CLAUDE.md index 97a486b..fffa2be 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -258,8 +258,8 @@ queste fade, ma va confermato col paper trader live prima di rischiare capitale - **Schemi peso:** `equal` (default), `cap` (tetto per famiglia, es. pairs 33% — config raccomandata), `inverse_vol`, `cluster_rp` (equal fra cluster naturali poi inverse-vol intra-cluster), `manual`. Definiti in `weighting.py`; la chiave cap è la famiglia (PAIRS/FADE/HONEST/SHAPE/TSM). - **Default `portfolios.yml`:** PORT06 (master+shape), `weighting=cap pairs 0.33`, leva 2x, ribilancio 1D. Backtest PORT06: FULL Sharpe 6.07 / OOS Sharpe 8.19, DD 4.9% full / 2.3% OOS. - **Data layer Cerbero v2:** `get_historical_v2` unificato + `get_instruments` (naming robusto) + `get_ticker_batch`. Trading su Deribit. -- **SCOPE LIVE v1:** il runner esegue gli sleeve con worker pronti = fade (MR01/02/07) + pairs (PR01) + shape (SH01, via `MLWorkerWrapper` con retraining). Gli sleeve **honest (DIP01/TR01/ROT02) e TSM01 sono SALTATI nel live** (nessun worker dedicato ancora) → restano solo nel backtest; il runner li logga come saltati e rinormalizza i pesi sugli sleeve eseguibili. Worker honest/TSM01 = fase 2. -- **Limite noto:** al ribilancio le posizioni APERTE restano sul loro notional (non travasate); comportamento fedele al backtest daily-rebalanced entro il turnover infragiornaliero. +- **SCOPE LIVE (fase 2 completata):** il runner esegue TUTTI gli sleeve di PORT06. Worker: single `StrategyWorker` (fade MR01/02/07, DIP01), `PairsWorker` (PR01 2 gambe), `MLWorkerWrapper` (SH01 retraining), e i multi-asset dedicati `BasketTrendWorker` (TR01 4h), `RotationWorker` (ROT02 1d), `TsmomWorker` (TSM01 1d). Il runner fetcha 1h da Cerbero v2 e **resampla a 4h/1d** (lookback dimensionato sui daily: TSM01 usa 252g). Validazione: runner pool/ribilancio/ledger == backtest (`validate_portfolio_runner.py`, identico); worker multi-asset == reference (`validate_honest_workers.py`: TSM01 esatto, ROT02 +1303% canonico, TR01 stesso ordine — differenza di convenzione capitale-unico vs media-equity). +- **Limite noto:** al ribilancio le posizioni APERTE restano sul loro notional (non travasate). Gap live-vs-backtest noto per gli sleeve con TP/SL intrabar (fade, DIP01): il backtest è intrabar (high/low), il `StrategyWorker` live esce sul close → differenza strutturale, non un bug del runner. Pairs e tsmom/rotation non ne soffrono (exit a chiusura barra). ## Multi-Strategy Paper Trader