research: fade TF sweep 1m/2m/5m/10m/30m — 15m confermato, 10m in watchlist, 1m/2m chiusi
Frontiera Sharpe monotona al scendere del tf ma margine fee si assottiglia: MR02_BTC muore a fee2x a 5m (-1.70); MR02 sotto i 15m e' fee-death nel regime corrente (1m -64%). 1m: flat share ETH 25.6% + niente storia full -> chiuso. Corr col 15m live: 5m 0.46 / 10m 0.53. Fix resample unit-safe (pandas 2.x ms). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Adriano Dal Pastro
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"""FADE TF SWEEP — i fade MR01/02/07 su 1m/2m/5m/10m/30m (oltre a 15m live e 1h).
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Ricerca 2026-06-12 (post-swap 15m). Diario: docs/diary/2026-06-12-fade-tf-sweep.md.
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Due banchi di prova:
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A. STORIA COMPLETA (parquet locale; 10m = resample dal 5m): engine canonico
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build_trades, daily equity su IDX comune, OOS da 2024-10, fee 0.10% e 2x.
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B. FINESTRA COMUNE RECENTE (2026-02-12 -> 06-12): 1m/2m (fetch Cerbero, non
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esiste storia locale 1m: il refresh la esclude per costo) vs 5m/15m sugli
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STESSI 120 giorni — confronto apples-to-apples sul regime corrente.
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Esiti:
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- La frontiera Sharpe e' MONOTONA al scendere del tf per MR01/MR07 (full
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history OOS: 5m > 10m > 15m > 30m > 1h)... ma il margine fee si assottiglia
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insieme: a fee 2x MR02_BTC muore a 5m (-1.70) e resta fragile a 10m (0.32).
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- MR02 (donchian, 3-6x i trade degli altri) sotto i 15m muore di fee nel
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regime corrente: 1m -64%, 2m -44%, 5m -22% sulla finestra recente.
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- 1m/2m: SCARTATI. MR01 a 1m brilla sulla finestra recente (ETH +60%, Sh 5.7)
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ma muore a fee 2x, il flat-share 1m e' alto (ETH 25.6%, BTC 13.3% -> rischio
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stale-print) e la validazione full-history e' impraticabile (storia 1m non
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mantenuta). Il regime recente e' CALMO: anche il 5m vi e' fiacco — i tf
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veloci pagano nella volatilita', non nella calma.
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- 10m: il miglior candidato OLTRE il 15m (quasi l'edge del 5m con piu' margine
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fee; corr daily col 15m live 0.53 media). Eventuale ADD da gateare in
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futuro, NON ora: il 15m e' appena andato live (v1.1.30), un cambio alla volta.
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- VERDETTO: tenere il 15m (ginocchio della frontiera margine-fee/rendimento);
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10m in watchlist; 1m/2m chiusi; 5m no-swap (fee-fragile su MR02_BTC).
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uv run python scripts/analysis/fade_tf_sweep.py
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(PROJECT_ROOT))
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from src.data.downloader import load_data
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from scripts.analysis.risk_management import strats_for, build_trades, INIT, POS
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from scripts.analysis.combine_portfolio import IDX, SPLIT, _norm, metrics
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EPOCH = pd.Timestamp(0, tz="UTC")
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WINDOW_START = "2026-02-12" # finestra comune del banco B
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RECENT_1M = {a: Path(f"/tmp/{a.lower()}_1m_recent.parquet") for a in ("BTC", "ETH")}
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def resample_ohlcv(df: pd.DataFrame, minutes: int) -> pd.DataFrame:
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"""Resample OHLCV unit-safe (pandas 2.x conserva datetime64[ms]: niente
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aritmetica diretta su .view int64 — il //10**6 doppio manda i ts nel 1970)."""
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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g = df.set_index(ts).resample(f"{minutes}min")
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out = pd.DataFrame({"open": g["open"].first(), "high": g["high"].max(),
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"low": g["low"].min(), "close": g["close"].last(),
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"volume": g["volume"].sum()}).dropna()
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out["timestamp"] = (out.index - EPOCH) // pd.Timedelta(milliseconds=1)
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return out.reset_index(drop=True)
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def daily_eq(df: pd.DataFrame, fn, params, fee_rt: float = 0.001) -> tuple[pd.Series, int]:
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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trades = build_trades(fn(df, **params), df, fee_rt=fee_rt, trend_max=3.0)
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n = len(df)
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eq = np.full(n, INIT)
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cap = INIT
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for i, j, ret in sorted(trades, key=lambda t: t[1]):
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cap = max(cap + cap * POS * ret, 10.0)
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eq[j:] = cap
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s = pd.Series(eq, index=ts).resample("1D").last().reindex(IDX).ffill().bfill()
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return _norm(s), len(trades)
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def full_history() -> None:
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print("=== A. STORIA COMPLETA (OOS da 2024-10, fee 0.10% RT; f2x = OOS Sharpe a fee 2x) ===")
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print(f"{'tf':<5} {'sleeve':<10} {'FULL%':>10} {'DD%':>6} {'Sh':>6} | {'OOS%':>8} {'oDD%':>6} {'oSh':>6} | {'f2x_oSh':>7} {'n':>6}")
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for tf in ("5m", "10m", "15m", "30m"):
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for asset in ("BTC", "ETH"):
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df = resample_ohlcv(load_data(asset, "5m"), 10) if tf == "10m" else load_data(asset, tf)
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for nm, (fn, params) in strats_for(asset).items():
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eq, n = daily_eq(df, fn, params)
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r = eq.pct_change().fillna(0.0)
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f, o = metrics(r), metrics(r, lo=SPLIT)
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eq2, _ = daily_eq(df, fn, params, fee_rt=0.002)
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o2 = metrics(eq2.pct_change().fillna(0.0), lo=SPLIT)
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print(f"{tf:<5} {nm + '_' + asset:<10} {f['ret']:>10.0f} {f['dd']:>6.1f} {f['sharpe']:>6.2f}"
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f" | {o['ret']:>8.0f} {o['dd']:>6.1f} {o['sharpe']:>6.2f} | {o2['sharpe']:>7.2f} {n:>6}")
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print()
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def trade_stats(df: pd.DataFrame, fn, params, fee_rt: float = 0.001) -> dict:
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trades = build_trades(fn(df, **params), df, fee_rt=fee_rt, trend_max=3.0)
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cap = peak = INIT
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dd = 0.0
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rets = []
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wins = 0
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for i, j, ret in sorted(trades, key=lambda t: t[1]):
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cap = max(cap + cap * POS * ret, 10.0)
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peak = max(peak, cap)
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dd = max(dd, (peak - cap) / peak)
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rets.append(ret * POS)
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wins += ret > 0
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n = len(trades)
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sh = float(np.mean(rets) / np.std(rets) * np.sqrt(n)) if n > 1 and np.std(rets) > 0 else 0.0
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return dict(ret=(cap / INIT - 1) * 100, dd=dd * 100, n=n,
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wr=wins / n * 100 if n else 0.0, sh=sh)
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def recent_window() -> None:
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if not all(p.exists() for p in RECENT_1M.values()):
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print("\n=== B. saltato: manca il parquet 1m recente (fetch Cerbero, vedi diario) ===")
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return
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start_ms = int(pd.Timestamp(WINDOW_START, tz="UTC").timestamp() * 1000)
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data: dict[tuple[str, str], pd.DataFrame] = {}
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for asset in ("BTC", "ETH"):
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m1 = pd.read_parquet(RECENT_1M[asset]).sort_values("timestamp").reset_index(drop=True)
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data[(asset, "1m")] = m1
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data[(asset, "2m")] = resample_ohlcv(m1, 2)
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for tf in ("5m", "15m"):
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df = load_data(asset, tf)
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data[(asset, tf)] = df[df["timestamp"] >= start_ms].reset_index(drop=True)
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print(f"\n=== B. FINESTRA COMUNE {WINDOW_START} -> oggi (regime corrente) ===")
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print("flat share (O=H=L=C):")
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for (asset, tf), df in sorted(data.items()):
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fl = ((df["open"] == df["high"]) & (df["high"] == df["low"]) & (df["low"] == df["close"])).mean() * 100
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print(f" {asset} {tf:>3}: {fl:5.1f}%")
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print(f"\n{'tf':<4} {'sleeve':<10} {'ret%':>8} {'DD%':>6} {'n':>5} {'WR%':>5} {'Sh_tr':>6} | {'fee2x_ret%':>10}")
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for tf in ("1m", "2m", "5m", "15m"):
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for asset in ("BTC", "ETH"):
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for nm, (fn, params) in strats_for(asset).items():
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r1 = trade_stats(data[(asset, tf)], fn, params)
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r2 = trade_stats(data[(asset, tf)], fn, params, fee_rt=0.002)
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print(f"{tf:<4} {nm + '_' + asset:<10} {r1['ret']:>8.1f} {r1['dd']:>6.1f} {r1['n']:>5}"
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f" {r1['wr']:>5.1f} {r1['sh']:>6.2f} | {r2['ret']:>10.1f}")
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print()
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def correlations() -> None:
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print("=== C. corr daily (storia completa) vs twin 15m LIVE ===")
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c5s, c10s = [], []
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for asset in ("BTC", "ETH"):
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d15, d5 = load_data(asset, "15m"), load_data(asset, "5m")
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d10 = resample_ohlcv(d5, 10)
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for nm, (fn, params) in strats_for(asset).items():
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e15 = daily_eq(d15, fn, params)[0].pct_change()
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c5 = daily_eq(d5, fn, params)[0].pct_change().corr(e15)
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c10 = daily_eq(d10, fn, params)[0].pct_change().corr(e15)
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c5s.append(c5)
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c10s.append(c10)
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print(f" {nm}_{asset:<4} 5m-15m {c5:.2f} 10m-15m {c10:.2f}")
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print(f" media: 5m-15m {np.mean(c5s):.2f} | 10m-15m {np.mean(c10s):.2f}")
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if __name__ == "__main__":
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full_history()
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recent_window()
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correlations()
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