sharpebench (Python)
Is my Sharpe real, or an artifact of luck and multiple testing?
Python distribution of SharpeBench's luck-robust quantitative evaluation
statistics, a pyo3 binding over the same deterministic Rust kernel used by the
SharpeBench CLI and the @general-liquidity/sharpebench npm package. 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 |
budget_curve(...) |
DSR by search budget, marginal DSR, and overfit-onset diagnostics |
rank_board(submissions, config_json="") / score_one(...) |
Full composite scoring over the CLI-compatible JSON contract |
rank_returns(field, config_json="") |
Build and rank a board from agent IDs and per-run return arrays |
default_score_config() |
Serialize the default scoring configuration |
never_catastrophic_config() |
Serialize the preset that asks only whether every run avoids catastrophe |
relative_to_benchmark_config(id) |
Serialize the benchmark-relative pass preset |
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 Python suite matches the public example board to the committed
Rust golden on Ubuntu CI. Rust CI separately checks two committed golden fields
on Linux, macOS, and Windows; neither check covers every possible input or host.
Relationship to sharpearena
sharpearena is the environment: a point-in-time market API where a
trading agent produces a track, scored end-to-end by its score_run helper. It
does not provide process containment. 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
Metadata
1 maintainer
Release files for sharpebench 0.18.3
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Built distributions (wheels)
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| sharpebench-0.18.3-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.13 | CPython 3.13 | Linux glibc 2.17+ x86-64 | Details |
| sharpebench-0.18.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl | CPython 3.12 | CPython 3.12 | Linux glibc 2.17+ x86-64 | Details |
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Total release size: 2.5 MB
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