sharpebench (Python)
Is my Sharpe real, or an artifact of luck and multiple testing?
Python distribution of SharpeBench's honest-backtest statistics, a pyo3
binding over the same deterministic Rust kernel the SharpeBench CLI and the
@general-liquidity/sharpebench npm package use. Bring your own return series;
everything takes plain numeric sequences (lists, tuples, numpy arrays,
df["ret"].to_numpy()) and returns plain floats, lists and dicts.
import numpy as np
from sharpebench import is_my_sharpe_real, bootstrap_dsr_ci
returns = df["strategy_ret"].to_numpy() # per-period, NOT annualized
# n_trials is the honest one: how many variants did you try before keeping this?
v = is_my_sharpe_real(returns, n_trials=200)
print(v["sharpe"], v["deflated_sharpe"], v["verdict"], v["explanation"])
ci = bootstrap_dsr_ci(returns, n_trials=200)
print(ci["lower"], ci["point"], ci["upper"])
Surface
| Function | Answers |
|---|---|
sharpe_ratio(returns) |
observed per-period Sharpe |
moments(returns, target=0.0) |
mean / std / skew / kurtosis / downside deviation / Sortino |
probabilistic_sharpe_ratio(returns, sr_benchmark=0.0) |
P(true Sharpe > benchmark) (PSR) |
deflated_sharpe_ratio(returns, n_trials, trials_sr_std=0.5) |
PSR deflated for the size of the search (DSR) |
expected_max_sharpe(trials_sr_std, n_trials) |
the Sharpe the best of n_trials shows with zero skill |
min_track_record_length(returns, ...) |
periods needed before the Sharpe is believable |
bootstrap_dsr_ci(returns, n_trials, ...) |
{point, se, lower, upper} on the DSR itself |
bootstrap_pvalue(excess, ...) |
stationary-bootstrap p-value for one series |
is_my_sharpe_real(returns, n_trials=1, ...) |
LITE verdict dict: pass | borderline | fail + explanation |
is_my_sharpe_real_full(field, ...) |
FULL verdict over a whole candidate field (LITE + snooping family + PBO + HLZ) |
reality_check_pvalue(field, ...) |
White's Reality Check over the field |
spa_pvalue / spa_consistent_pvalue(field, ...) |
Hansen's SPA (liberal / consistent) |
step_down_significant(field, ..., alpha=0.05) |
Romano-Wolf step-down, per candidate, FWER-controlled |
probability_of_backtest_overfitting(perf_matrix, s=16) |
CSCV PBO |
benjamini_hochberg(p_values, q=0.05) / fdr_verdict(...) |
BH-FDR rejections and the operator summary |
hlz_gate(t_stat, t_threshold=None) |
the Harvey-Liu-Zhu |t| >= 3.0 factor bar |
selection_robustness(candidates, n_trials, ...) |
best vs median DSR: is the headline a lucky pick? |
runs_for_power(effect, alpha, power) |
how many runs to detect an effect |
pass_k(passed_per_run, mode="all", n=None) |
pass^k reliability: won on every run, not on average |
Matrix orientation
Two conventions, deliberately unchanged from the papers they come from:
- the data-snooping family (
reality_check_pvalue,spa_*,step_down_significant,is_my_sharpe_real_full) takes a field: N rows (strategies) x T cols (time); probability_of_backtest_overfittingtakes the transpose: T rows (time) x N cols (strategies).
Determinism
No I/O, no clock, no ambient randomness. The bootstraps take an explicit seed
(defaulted to a fixed constant, so a result is reproducible unless you ask for
otherwise). The same input yields byte-identical output on any platform.
Relationship to sharpearena
sharpearena is the environment: a leak-free, point-in-time arena where a
trading agent produces a track, scored end-to-end by score_run. sharpebench
is the judge for a track you already have: your own backtest, live P&L, or a
field of candidate strategies. They share one Rust statistics kernel, so the
verdict is identical either way; this package simply does not, and will not,
duplicate arena run-scoring.
Building from source
python -m pip install maturin
python -m maturin develop --manifest-path crates/sharpebench-py/Cargo.toml
python -m pytest crates/sharpebench-py/tests
MIT OR Apache-2.0.
1 maintainer
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