"""altlib — SHARED HONEST EVALUATION LIBRARY for the alt-strategy fan-out (2026-06-20). Built for the "studia altre strategie alternative su Deribit" research wave: >=100 agents, each studying ONE distinct strategy hypothesis on the certified BTC/ETH (+ DVOL) universe. Every agent imports THIS module so that: * NO look-ahead is structurally possible: a target/weight decided at close[i] is held during bar i+1 (the evaluator shifts it for you — you never multiply by r[i] with a weight that used close[i] for the *same* bar). * Fees are realistic Deribit (0.10% RT taker = 0.0005/side) and a fee SWEEP is built in. * Metrics are comparable: FULL Sharpe/CAGR/maxDD, HOLD-OUT (2025-01-01+), per-year. * Only certified data exists (BTC/ETH from Deribit mainnet, DVOL from Deribit). load() raises on anything else — a physical guardrail. Two evaluation styles: 1. eval_weights(df, target) -> for CONTINUOUS-position strategies (trend, vol overlays, pairs, risk-parity). `target` is a per-bar position (fraction of equity, sign=dir), decided with data <= close[i]. VECTORIZED (numpy) -> fast even on 68k 1h bars. 2. eval_signals(df, entries) -> for DISCRETE entry/exit strategies (breakout w/ TP-SL, mean-reversion bounce). Wraps the project's trade-based harness. Use on 1h/1d only (the Python loop is O(n*max_bars); 5m has 840k bars -> too slow on 2 CPUs). Quick start (inside an agent script): import sys; sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt") import altlib as al rep = al.study_weights("MY-STRAT", lambda df: my_target(df), tfs=("1d","12h")) print(al.fmt(rep)); print(al.as_json(rep)) # human + machine """ from __future__ import annotations import inspect import json import sys from functools import lru_cache from pathlib import Path import numpy as np import pandas as pd # --- make `from src...` work no matter where the agent's script lives ------- _ROOT = Path(__file__).resolve().parents[3] if str(_ROOT) not in sys.path: sys.path.insert(0, str(_ROOT)) from src.backtest.harness import backtest_signals, load # noqa: E402 from src.strategies.trend_portfolio import resample_tf # noqa: E402 HOLDOUT = pd.Timestamp("2025-01-01", tz="UTC") FEE_SIDE = 0.0005 # 0.05%/side = 0.10% round-trip (Deribit taker) FEE_SWEEP = (0.0, 0.0005, 0.001, 0.0015) # per-side fee grid for robustness CERTIFIED = ("BTC", "ETH") DATA_DIR = _ROOT / "data" / "raw" # =========================================================================== # DATA (cached) — 1h base, resampled to >=4h; DVOL aligned causally. # =========================================================================== @lru_cache(maxsize=32) def get(asset: str, tf: str) -> pd.DataFrame: """Certified OHLCV with a tz-aware 'datetime' col and RangeIndex. tf in {5m,15m,1h} loaded directly; {4h,6h,8h,12h,1d,2d,3d,1w} resampled from 1h. Resample uses the leak-free per-single-TF path (trend_portfolio.resample_tf).""" asset = asset.upper() if asset not in CERTIFIED: raise ValueError(f"Asset non certificato: {asset}. Universo={CERTIFIED}.") tf = tf.lower() if tf in ("5m", "15m", "1h"): df = load(asset, tf) else: rule = {"4h": "4h", "6h": "6h", "8h": "8h", "12h": "12h", "1d": "1D", "2d": "2D", "3d": "3D", "1w": "1W"}.get(tf) if rule is None: raise ValueError(f"TF non gestito: {tf}") df = resample_tf(load(asset, "1h"), rule) df = df.reset_index(drop=True) if "datetime" not in df.columns: df["datetime"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True) return df @lru_cache(maxsize=8) def _dvol_raw(asset: str) -> pd.DataFrame: p = DATA_DIR / f"dvol_{asset.lower()}.parquet" if not p.exists(): raise FileNotFoundError(f"DVOL non trovato: {p}") d = pd.read_parquet(p).sort_values("timestamp").reset_index(drop=True) return d def dvol(df: pd.DataFrame, asset: str) -> np.ndarray: """Deribit DVOL (implied vol index) aligned CAUSALLY to df's bars. For each bar we take the most recent DVOL value timestamped at/before the bar's open (merge_asof backward) -> known by decision time. NaN before DVOL history (DVOL starts 2021-03). Returns float array len(df), in vol POINTS (e.g. 65.0).""" d = _dvol_raw(asset) left = pd.DataFrame({"timestamp": df["timestamp"].astype("int64").values}) merged = pd.merge_asof(left, d.rename(columns={"close": "dvol"}), on="timestamp", direction="backward") return merged["dvol"].values.astype(float) # =========================================================================== # INDICATORS (all causal: value at i uses data <= i) # =========================================================================== def simple_returns(c: np.ndarray) -> np.ndarray: r = np.zeros(len(c)); r[1:] = c[1:] / c[:-1] - 1.0 return r def log_returns(c: np.ndarray) -> np.ndarray: r = np.zeros(len(c)); r[1:] = np.log(c[1:] / c[:-1]) return r def ema(x: np.ndarray, span: int) -> np.ndarray: return pd.Series(x).ewm(span=span, adjust=False).mean().values def sma(x: np.ndarray, win: int) -> np.ndarray: return pd.Series(x).rolling(win, min_periods=win).mean().values def rolling_std(x: np.ndarray, win: int) -> np.ndarray: return pd.Series(x).rolling(win, min_periods=max(2, win // 2)).std().values def zscore(x: np.ndarray, win: int) -> np.ndarray: s = pd.Series(x) m = s.rolling(win, min_periods=win).mean() sd = s.rolling(win, min_periods=win).std() return ((s - m) / sd.replace(0, np.nan)).values def rsi(c: np.ndarray, win: int = 14) -> np.ndarray: d = np.diff(c, prepend=c[0]) up = pd.Series(np.where(d > 0, d, 0.0)).ewm(alpha=1 / win, adjust=False).mean() dn = pd.Series(np.where(d < 0, -d, 0.0)).ewm(alpha=1 / win, adjust=False).mean() rs = up / dn.replace(0, np.nan) return (100 - 100 / (1 + rs)).values def atr(df: pd.DataFrame, win: int = 14) -> np.ndarray: 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).ewm(alpha=1 / win, adjust=False).mean().values def realized_vol(r: np.ndarray, win: int, bars_per_year: float) -> np.ndarray: """Annualized realized vol from returns up to i inclusive (no leakage).""" return pd.Series(r).rolling(win, min_periods=max(2, win // 2)).std().values * np.sqrt(bars_per_year) def donchian(df: pd.DataFrame, win: int): """Upper/lower channel using bars STRICTLY before i (shifted) -> a close[i] that breaks the prior `win`-bar high is a real, tradeable breakout at close[i].""" hi = pd.Series(df["high"].values).rolling(win, min_periods=win).max().shift(1).values lo = pd.Series(df["low"].values).rolling(win, min_periods=win).min().shift(1).values return hi, lo def bbands(c: np.ndarray, win: int = 20, k: float = 2.0): m = pd.Series(c).rolling(win, min_periods=win).mean() sd = pd.Series(c).rolling(win, min_periods=win).std() return (m + k * sd).values, m.values, (m - k * sd).values def _call_target(fn, df: pd.DataFrame, asset: str): """Call a strategy fn as fn(df, asset) when it accepts 2 args, else fn(df). Lets DVOL/cross-asset strategies receive the asset cleanly (no inference hacks).""" try: n = len(inspect.signature(fn).parameters) except (ValueError, TypeError): n = 1 return fn(df, asset) if n >= 2 else fn(df) def bars_per_year(df: pd.DataFrame) -> float: dt = pd.to_datetime(df["datetime"]).diff().dt.total_seconds().median() return 86400 * 365.25 / dt if dt and dt > 0 else 365.25 def bars_per_day(df: pd.DataFrame) -> int: dt = pd.to_datetime(df["datetime"]).diff().dt.total_seconds().median() return max(1, round(86400 / dt)) def vol_target(target_dir: np.ndarray, df: pd.DataFrame, target_vol: float = 0.20, vol_win_days: int = 30, leverage_cap: float = 2.0) -> np.ndarray: """Scale a direction array in [-1,1] to a vol-targeted position (TP01-style). Causal: uses realized vol up to i. Returns position clipped to +/-leverage_cap.""" c = df["close"].values.astype(float) bpd = bars_per_day(df) bpy = bpd * 365.25 vol = realized_vol(simple_returns(c), max(2, vol_win_days * bpd), bpy) scal = np.where((vol > 0) & np.isfinite(vol), target_vol / vol, 0.0) tgt = np.clip(target_dir * scal, -leverage_cap, leverage_cap) tgt[~np.isfinite(tgt)] = 0.0 return tgt # =========================================================================== # METRICS # =========================================================================== def _metrics_from_net(net: np.ndarray, idx: pd.DatetimeIndex) -> dict: net = np.nan_to_num(net, nan=0.0) eq = np.cumprod(1.0 + np.clip(net, -0.99, None)) rr = net[np.isfinite(net)] bpy = 86400 * 365.25 / (pd.Series(idx).diff().dt.total_seconds().median() or 86400) sharpe = float(np.mean(rr) / np.std(rr) * np.sqrt(bpy)) if np.std(rr) > 0 else 0.0 pk = np.maximum.accumulate(eq) dd = float(np.max((pk - eq) / pk)) if len(eq) else 0.0 span_days = (idx[-1] - idx[0]).total_seconds() / 86400 if len(idx) > 1 else 1.0 years = max(span_days / 365.25, 1e-6) total = eq[-1] / eq[0] if len(eq) else 1.0 cagr = total ** (1 / years) - 1 if total > 0 else -1.0 return dict(sharpe=round(sharpe, 3), cagr=round(cagr, 4), maxdd=round(dd, 4), ret=round(total - 1, 4), n=int(len(rr))) def _yearly(net: np.ndarray, idx: pd.DatetimeIndex) -> dict: s = pd.Series(np.nan_to_num(net), index=idx) out = {} for y, g in s.groupby(s.index.year): eq = np.cumprod(1 + g.values); pk = np.maximum.accumulate(eq) out[int(y)] = dict(ret=round(float(eq[-1] - 1), 4), dd=round(float(np.max((pk - eq) / pk)), 4)) return out def eval_weights(df: pd.DataFrame, target: np.ndarray, fee_side: float = FEE_SIDE) -> dict: """Honest backtest of a CONTINUOUS position series. target[i] is decided with data <= close[i]; it is HELD during bar i+1. The shift is done HERE -> you cannot leak by construction. Fee charged on |Δposition| turnover. Returns {full, holdout, yearly, time_in_market, turnover_per_year, net, idx}.""" c = df["close"].values.astype(float) target = np.asarray(target, float) target = np.nan_to_num(target, nan=0.0) r = simple_returns(c) pos = np.zeros(len(target)); pos[1:] = target[:-1] # held during bar t = decided at t-1 gross = pos * r turn = np.abs(np.diff(pos, prepend=0.0)) net = gross - fee_side * turn net[0] = 0.0 idx = pd.DatetimeIndex(pd.to_datetime(df["datetime"], utc=True)) full = _metrics_from_net(net, idx) hmask = idx >= HOLDOUT hold = _metrics_from_net(net[hmask], idx[hmask]) if hmask.sum() > 3 else dict(sharpe=0.0, n=0) bpy_d = bars_per_day(df) * 365.25 return dict(full=full, holdout=hold, yearly=_yearly(net, idx), time_in_market=round(float(np.mean(pos != 0)), 3), turnover_per_year=round(float(turn.sum() / (len(turn) / bpy_d)), 1), net=net, idx=idx) def eval_signals(df: pd.DataFrame, entries: list, fee_rt: float = 2 * FEE_SIDE, leverage: float = 1.0, asset: str = "", tf: str = "") -> dict: """Honest backtest of DISCRETE entry/exit signals (TP/SL/max_bars). Wraps the project trade-based harness, adds a standardized hold-out split. Use on 1h/1d.""" m = backtest_signals(df, entries, fee_rt=fee_rt, leverage=leverage, asset=asset, tf=tf) idx = pd.DatetimeIndex(pd.to_datetime(m.eq_index, utc=True)) if m.eq_index is not None \ else pd.DatetimeIndex(pd.to_datetime(df["datetime"], utc=True)) eq = m.equity hmask = idx >= HOLDOUT hold = dict(sharpe=0.0, ret=0.0, n=0) if hmask.sum() > 3: he = eq[hmask] hr = np.diff(he) / he[:-1] bpy = m.bars_per_year or 365.0 hsharpe = float(np.mean(hr) / np.std(hr) * np.sqrt(bpy)) if len(hr) and np.std(hr) > 0 else 0.0 hold = dict(sharpe=round(hsharpe, 3), ret=round(float(he[-1] / he[0] - 1), 4), n=int(hmask.sum())) full = dict(sharpe=round(m.sharpe, 3), cagr=round(m.cagr, 4), maxdd=round(m.max_dd, 4), ret=round(m.net_return, 4), n=int(m.n_trades)) return dict(full=full, holdout=hold, n_trades=int(m.n_trades), win_rate=round(m.win_rate, 1), time_in_market=round(m.time_in_market, 3), yearly={int(y): round(v, 4) for y, v in m.yearly.items()}) # =========================================================================== # MARGINAL SCORING vs TP01 baseline — the lesson of the 2026-06-20 sweep. # # Absolute Sharpe is NOT enough to earn a portfolio slot: an overlay on TSMOM # (DVOL gate, low-vol filter, kill-switch, ...) inherits TP01's trend Sharpe and # scores ~1.0-1.3 while adding ZERO new return. The only question that matters for # a NEW sleeve is: does it IMPROVE the existing TP01 portfolio out-of-sample? # We answer it three ways: (a) blend uplift (Sharpe of TP01 + w*candidate minus # TP01 alone, full & hold-out), (b) correlation to TP01, (c) residual alpha after # removing the TP01 beta (the part of the candidate orthogonal to trend). # =========================================================================== def _sh(s) -> float: r = np.asarray(s.dropna().values, float) return float(np.mean(r) / np.std(r) * np.sqrt(365.25)) if len(r) > 2 and np.std(r) > 0 else 0.0 def _dd_ret(s) -> float: eq = np.cumprod(1.0 + np.asarray(s.dropna().values, float)) pk = np.maximum.accumulate(eq) return float(np.max((pk - eq) / pk)) if len(eq) else 0.0 def _to_daily(s: pd.Series) -> pd.Series: s = s.dropna().sort_index() if not isinstance(s.index, pd.DatetimeIndex): s.index = pd.to_datetime(s.index, utc=True) if s.index.tz is None: s.index = s.index.tz_localize("UTC") return ((1.0 + s).resample("1D").prod() - 1.0).dropna() @lru_cache(maxsize=2) def tp01_baseline_daily() -> pd.Series: """The reference a candidate must BEAT: TP01 CANONICAL, 50/50 BTC+ETH, net daily returns on common dates (full Sharpe ~1.30, hold-out ~0.31). Cached.""" from src.strategies.trend_portfolio import CANONICAL, TrendPortfolio tp = TrendPortfolio(**CANONICAL) series = {} for a in CERTIFIED: df = get(a, "1d") net, _ = tp.net_returns(df) series[a] = pd.Series(net, index=pd.DatetimeIndex(pd.to_datetime(df["datetime"], utc=True))) J = pd.concat(series, axis=1, join="inner").fillna(0.0) return _to_daily(0.5 * J[CERTIFIED[0]] + 0.5 * J[CERTIFIED[1]]) def candidate_daily(target_fn, tf: str = "1d", fee_side: float = FEE_SIDE) -> pd.Series: """Build a candidate's 50/50 BTC+ETH net DAILY return series (same convention as tp01_baseline_daily) from a continuous-position target_fn. Intraday TFs are compounded to daily so they align with the TP01 baseline grid.""" series = {} for a in CERTIFIED: df = get(a, tf) ev = eval_weights(df, _call_target(target_fn, df, a), fee_side=fee_side) series[a] = pd.Series(ev["net"], index=ev["idx"]) J = pd.concat(series, axis=1, join="inner").fillna(0.0) return _to_daily(0.5 * J[CERTIFIED[0]] + 0.5 * J[CERTIFIED[1]]) def marginal_vs_tp01(cand_daily: pd.Series, weights=(0.25, 0.5)) -> dict: """Does this candidate IMPROVE the TP01 portfolio? Returns correlation, blend uplift (full & hold-out, per weight), TP01-beta + residual alpha, and a verdict: ADDS -> meaningfully lifts the OOS blend and is not just leverage-of-trend REDUNDANT -> ~identical to TP01 (corr high, ~zero uplift): a re-skin, no slot DILUTES -> drags the blend down NEUTRAL -> changes little either way (a weak, optional satellite at best) Score a NEW sleeve on THIS, not on absolute Sharpe.""" B = tp01_baseline_daily() J = pd.concat({"B": B, "C": cand_daily}, axis=1, join="inner").dropna() if len(J) < 30: return dict(marginal_verdict="N/A", reason="insufficient overlap with TP01 baseline") if J["C"].std() == 0: return dict(marginal_verdict="NEUTRAL", reason="flat/zero candidate (no exposure)", corr_full=None, blends={"w25": dict(uplift_full=0.0, uplift_hold=0.0)}) JH = J[J.index >= HOLDOUT] has_h = len(JH) > 5 out = { "n_days": int(len(J)), "n_hold_days": int(len(JH)), "corr_full": round(float(J["B"].corr(J["C"])), 3), "corr_hold": round(float(JH["B"].corr(JH["C"])), 3) if has_h else None, "tp01_full_sharpe": round(_sh(J["B"]), 3), "tp01_hold_sharpe": round(_sh(JH["B"]), 3) if has_h else None, "cand_full_sharpe": round(_sh(J["C"]), 3), "cand_hold_sharpe": round(_sh(JH["C"]), 3) if has_h else None, } blends = {} for w in weights: bf, bh = (1 - w) * J["B"] + w * J["C"], (1 - w) * JH["B"] + w * JH["C"] blends[f"w{int(w * 100)}"] = dict( full=round(_sh(bf), 3), hold=round(_sh(bh), 3) if has_h else None, uplift_full=round(_sh(bf) - _sh(J["B"]), 3), uplift_hold=round(_sh(bh) - _sh(JH["B"]), 3) if has_h else None, dd=round(_dd_ret(bf), 4)) out["blends"] = blends b, c = J["B"].values, J["C"].values beta = float(np.cov(c, b)[0, 1] / np.var(b)) if np.var(b) > 0 else 0.0 resid = c - beta * b out["beta_to_tp01"] = round(beta, 3) out["resid_sharpe_full"] = round(float(np.mean(resid) / np.std(resid) * np.sqrt(365.25)) if np.std(resid) > 0 else 0.0, 3) out["alpha_ann"] = round(float(np.mean(resid) * 365.25), 4) # OOS robustness — the marginal point-estimate can be fooled by ONE lucky month # (e.g. KAMA: +0.06 uplift that flips to -0.03 when its best month is dropped). Require # the blend uplift to be positive in the earliest CLEAN hold-out year AND to survive a # drop-one-month jackknife. This is lesson #2 of the 2026-06-20 sweep, in code. out["clean_year_uplift"] = out["jackknife_min_uplift"] = None out["robust_oos"] = False if has_h: ww = 0.25 def _u(sub): return _sh((1 - ww) * sub["B"] + ww * sub["C"]) - _sh(sub["B"]) yrs = sorted(set(JH.index.year)) clean = JH[JH.index.year == yrs[0]] cu = _u(clean) if len(clean) > 20 else None months = sorted(set(zip(JH.index.year, JH.index.month))) jk = (min(_u(JH[~((JH.index.year == y) & (JH.index.month == mo))]) for y, mo in months) if len(months) > 1 else _u(JH)) out["clean_year_uplift"] = round(cu, 3) if cu is not None else None out["jackknife_min_uplift"] = round(jk, 3) if jk is not None else None out["robust_oos"] = bool(cu is not None and cu > 0.02 and jk is not None and jk > 0.0) # verdict (weight 0.25 = a satellite slot; hold-out is what the defensive stack cares about) up_h = blends["w25"]["uplift_hold"] up_f = blends["w25"]["uplift_full"] ch = out["corr_hold"] if out["corr_hold"] is not None else out["corr_full"] if out["corr_full"] > 0.9 and (up_h is None or abs(up_h) < 0.05): v = "REDUNDANT" elif up_h is not None and up_h >= 0.05 and up_f > -0.15 and ch < 0.85: v = "ADDS" elif up_f <= -0.10 and (up_h is None or up_h <= 0.0): v = "DILUTES" else: v = "NEUTRAL" out["marginal_verdict"] = v return out def study_marginal(name: str, target_fn, tf: str = "1d", fee_side: float = FEE_SIDE) -> dict: """Score a continuous candidate BOTH ways: absolute (study_weights) AND marginal vs TP01. The combined gate: a candidate earns a sleeve slot only if it is not FAIL on absolute robustness AND marginal_verdict == 'ADDS'.""" absolute = study_weights(name, target_fn, tfs=(tf,)) marg = marginal_vs_tp01(candidate_daily(target_fn, tf=tf, fee_side=fee_side)) abs_grade = absolute["verdict"]["grade"] earns_slot = (abs_grade != "FAIL" and marg.get("marginal_verdict") == "ADDS" and marg.get("robust_oos", False)) return dict(name=name, tf=tf, absolute=absolute, marginal=marg, abs_grade=abs_grade, marginal_verdict=marg.get("marginal_verdict"), earns_slot=earns_slot) def fmt_marginal(rep: dict) -> str: m = rep["marginal"] bl = m.get("blends", {}) lines = [f"=== {rep['name']} | abs={rep['abs_grade']} marginal={rep['marginal_verdict']} " f"EARNS_SLOT={rep['earns_slot']}"] lines.append(f" corr->TP01 full {m.get('corr_full')} hold {m.get('corr_hold')} " f"beta {m.get('beta_to_tp01')} resid Sharpe {m.get('resid_sharpe_full')} alpha/yr {m.get('alpha_ann')}") lines.append(f" OOS robustness: clean-year uplift {m.get('clean_year_uplift')} " f"drop-best-month {m.get('jackknife_min_uplift')} robust_oos={m.get('robust_oos')}") lines.append(f" standalone: TP01 full {m.get('tp01_full_sharpe')}/hold {m.get('tp01_hold_sharpe')} | " f"cand full {m.get('cand_full_sharpe')}/hold {m.get('cand_hold_sharpe')}") for w, d in bl.items(): uh = "n/a" if d["uplift_hold"] is None else f"{d['uplift_hold']:+.3f}" hold = "n/a" if d["hold"] is None else f"{d['hold']}" lines.append(f" blend {w}: full {d['full']} (uplift {d['uplift_full']:+.3f}) " f"hold {hold} (uplift {uh}) DD {d['dd'] * 100:.1f}%") return "\n".join(lines) # =========================================================================== # DRIVERS — run a hypothesis across both assets, several TFs, with a fee sweep. # =========================================================================== def _verdict(per_cell: list[dict]) -> dict: """A finding PASSES only if it is robust: positive FULL Sharpe AND positive HOLD-OUT on BOTH assets in its best TF, survives the fee sweep, and isn't a 1-cell fluke.""" if not per_cell: return dict(grade="FAIL", reason="no cells") ok = [c for c in per_cell if c.get("full_sharpe", -9) > 0] best = max(per_cell, key=lambda c: c.get("min_asset_holdout_sharpe", -9)) pass_ = (best.get("min_asset_full_sharpe", -9) >= 0.5 and best.get("min_asset_holdout_sharpe", -9) >= 0.2 and best.get("fee_survives", False)) weak = (best.get("min_asset_full_sharpe", -9) >= 0.3 and best.get("min_asset_holdout_sharpe", -9) >= 0.0) grade = "PASS" if pass_ else ("WEAK" if weak else "FAIL") return dict(grade=grade, best_tf=best.get("tf"), best_full_sharpe=best.get("min_asset_full_sharpe"), best_holdout_sharpe=best.get("min_asset_holdout_sharpe"), n_positive_cells=len(ok), n_cells=len(per_cell)) def study_weights(name: str, target_fn, tfs=("1d", "12h"), assets=CERTIFIED, fee_sweep=FEE_SWEEP) -> dict: """Run a CONTINUOUS-position hypothesis on each (asset,tf), report robustness. target_fn(df) -> per-bar position array (decided <= close[i]). Returns a dict ready to print/serialize, with a conservative PASS/WEAK/FAIL verdict.""" cells = [] for tf in tfs: per_asset = {} fee_ok_all = True for a in assets: df = get(a, tf) tgt = _call_target(target_fn, df, a) base = eval_weights(df, tgt, fee_side=FEE_SIDE) sweep = {f"{2*f*100:.2f}%RT": eval_weights(df, tgt, fee_side=f)["full"]["sharpe"] for f in fee_sweep} fee_ok = sweep.get("0.20%RT", -9) > 0 fee_ok_all = fee_ok_all and fee_ok per_asset[a] = dict(full=base["full"], holdout=base["holdout"], tim=base["time_in_market"], turnover=base["turnover_per_year"], fee_sweep=sweep, yearly=base["yearly"]) min_full = min(per_asset[a]["full"]["sharpe"] for a in assets) min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in assets) cells.append(dict(tf=tf, per_asset=per_asset, min_asset_full_sharpe=round(min_full, 3), min_asset_holdout_sharpe=round(min_hold, 3), full_sharpe=round(np.mean([per_asset[a]["full"]["sharpe"] for a in assets]), 3), fee_survives=fee_ok_all)) return dict(name=name, kind="weights", cells=cells, verdict=_verdict(cells)) def study_signals(name: str, entries_fn, tfs=("1d",), assets=CERTIFIED, fee_sweep=FEE_SWEEP, leverage: float = 1.0) -> dict: """Run a DISCRETE entry/exit hypothesis on each (asset,tf). entries_fn(df) -> list[dict|None] len(df). Use 1h/1d TFs only (Python loop).""" cells = [] for tf in tfs: per_asset = {} fee_ok_all = True for a in assets: df = get(a, tf) ent = _call_target(entries_fn, df, a) base = eval_signals(df, ent, fee_rt=2 * FEE_SIDE, leverage=leverage, asset=a, tf=tf) sweep = {f"{2*f*100:.2f}%RT": eval_signals(df, ent, fee_rt=2 * f, leverage=leverage)["full"]["sharpe"] for f in fee_sweep} fee_ok = sweep.get("0.20%RT", -9) > 0 fee_ok_all = fee_ok_all and fee_ok per_asset[a] = dict(full=base["full"], holdout=base["holdout"], n_trades=base["n_trades"], win_rate=base["win_rate"], fee_sweep=sweep, yearly=base["yearly"]) min_full = min(per_asset[a]["full"]["sharpe"] for a in assets) min_hold = min(per_asset[a]["holdout"].get("sharpe", 0.0) for a in assets) cells.append(dict(tf=tf, per_asset=per_asset, min_asset_full_sharpe=round(min_full, 3), min_asset_holdout_sharpe=round(min_hold, 3), full_sharpe=round(np.mean([per_asset[a]["full"]["sharpe"] for a in assets]), 3), fee_survives=fee_ok_all)) return dict(name=name, kind="signals", cells=cells, verdict=_verdict(cells)) # =========================================================================== # OUTPUT # =========================================================================== def _clean(o): if isinstance(o, dict): return {k: _clean(v) for k, v in o.items() if k not in ("net", "idx", "equity")} if isinstance(o, (list, tuple)): return [_clean(x) for x in o] if isinstance(o, (np.floating,)): return round(float(o), 4) if isinstance(o, (np.integer,)): return int(o) return o def as_json(rep: dict) -> str: return json.dumps(_clean(rep), default=str) def fmt(rep: dict) -> str: v = rep["verdict"] lines = [f"=== {rep['name']} [{rep['kind']}] -> {v['grade']} " f"(best {v.get('best_tf')}: full {v.get('best_full_sharpe')}, " f"hold {v.get('best_holdout_sharpe')})"] for c in rep["cells"]: lines.append(f" TF {c['tf']:>4s}: minFull={c['min_asset_full_sharpe']:+.2f} " f"minHold={c['min_asset_holdout_sharpe']:+.2f} feeOK={c['fee_survives']}") for a, pa in c["per_asset"].items(): yr = " ".join(f"{y}:{d['ret']*100 if isinstance(d, dict) else d*100:+.0f}%" for y, d in list(pa["yearly"].items())) lines.append(f" {a}: full Sh={pa['full']['sharpe']:+.2f} " f"DD={pa['full']['maxdd']*100:.0f}% ret={pa['full']['ret']*100:+.0f}% " f"hold Sh={pa['holdout'].get('sharpe', 0):+.2f} | {yr}") return "\n".join(lines) if __name__ == "__main__": # smoke test: buy&hold, TSMOM trend, donchian breakout print("--- SMOKE TEST altlib ---") bh = study_weights("BUYHOLD", lambda df: np.ones(len(df)), tfs=("1d",)) print(fmt(bh)) def tsmom(df): c = df["close"].values bpd = bars_per_day(df) d = np.zeros(len(c)) for h in (30 * bpd, 90 * bpd, 180 * bpd): s = np.full(len(c), np.nan); s[h:] = np.sign(c[h:] / c[:-h] - 1) d = d + np.nan_to_num(s) d = np.clip(np.sign(d), 0, None) return vol_target(d, df, 0.20, 30, 2.0) print(fmt(study_weights("TSMOM-LF", tsmom, tfs=("1d",)))) def donch(df): hi, lo = donchian(df, 20) c = df["close"].values pos = np.where(c > hi, 1.0, np.nan) pos = np.where(c < lo, 0.0, pos) return pd.Series(pos).ffill().fillna(0.0).values print(fmt(study_weights("DONCHIAN20-LF", donch, tfs=("1d",)))) print("\nJSON sample:", as_json(bh)[:300])