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PythagorasGoal/scripts/research/alt/altlib.py
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Adriano Dal Pastro 5ac4e16af8 research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer 
Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi
distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3
scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge
-> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack
TP01+XS01+VRP01 resta imbattuto.

- altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights,
  fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01.
- MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline
  TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year +
  drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay
  su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA
  (ADDS ma muore al jackknife).
- runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow.
- Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor.
- test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde.

Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-20 19:50:39 +00:00

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"""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])
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