honest-backtest 0.1.0

A backtester for binary prediction markets that grades against the price you could actually transact at — realizable fills, fees, and an adverse-selection diagnostic — not the optimistic paper fill.

honest-backtest

Your prediction-market backtest is lying to you. This one tells the truth.

Most backtests paper-fill at the displayed ask and report a win rate. On an efficient venue the transactable price already contains the signal — so WR ≈ ask by construction, and the "edge" you see is an artifact of filling at prices you could never actually get. Deploy on that and you lose money the backtest swore you'd make.

honest-backtest grades every decision against the price you could actually transact at:

• edge_real = mean(won − fill_px) — the only honest headline. Win rate alone is meaningless; if you pay the ask and the ask equals your win rate, your edge is zero.
• Realizable fills, not the paper ask — two lenses:
  • book-persistence: did the ask still rest at your limit ~1s later, or did it pull above it (the classic "asks vanish on the winners")?
  • tape-corroboration: did a real trade print at your price? Proves the ask was genuine, not a thin phantom ladder.
• Real fees — PM taker fee is fee_rate·p·(1−p) per share; makers pay 0. No hand-wavy min(p,1−p).
• Adverse-selection diagnostic — a Fokker-Planck fair-value model flags trades where the book baited you (cheap ask) but the physics disagree: the fills you get are the losers; the winners reject you.
• ghost_gap = paper edge − honest edge = the adverse-selection drag, in one number.

This is the harness that retrodicted six consecutive live strategy deaths on Polymarket up/down markets — every one looked great on paper and lost real money.

Install

pip install git+https://github.com/JohanAlvarado/honest-backtest.git   # numpy is the only dependency

(PyPI release pending — install from source meanwhile.)

Quick start

from honest_backtest import Signal, Decision, evaluate

class BuyTheCheapNo(Signal):
    name, family, mode = "cheap_no", "demo", "taker"
    def decide(self, ctx, i):
        # read ONLY indices <= i (no lookahead). Return a Decision or None.
        if ctx.book_ok(i) and 0 < ctx.na[i] < 0.40 and ctx.yes_mid(i) >= 0.56:
            return Decision(i=i, ts_ms=int(ctx.ts[i]), token_yes=False,
                            action="taker", target_px=float(ctx.na[i]), size=20.0)
        return None

row = evaluate(BuyTheCheapNo(), ctxs)   # ctxs: iterable of SlotCtx
print(row["headline_edge_real"], row["ghost_gap"])

Bring your own SlotCtx stream (build them from your data with SlotCtx.from_rows), or use the bundled SQLite adapter for the open Polymarket up/down dataset:

from honest_backtest.adapters.sqlite_pm import load_corpus
ctxs = load_corpus("open_dataset.sqlite", coins=("btc",), durations=("5m",))

The calibration anchor

The shipped example no_overpriced is a calibration gate, not a strategy to trade. Live it ran 325 fills at WR 0.382 vs avg ask 0.386 → edge_real = −0.004. A naive paper backtest of the same rule showed +0.07. The honest harness must reproduce ~0/negative here:

python -m honest_backtest.examples.no_overpriced open_dataset.sqlite

If it prints a strongly positive edge_real, the fill model has drifted optimistic — that's a bug in fills.py, not an edge.

Why edge_real, not WR

WR > 50% with ask > WR is not an edge — the market priced higher than you win. Only WR > ask is a real edge.

On an efficient venue, corr(signal, outcome) is usually positive (momentum, spot-vs-strike do predict) — but that's not the same as beating the transactable price. The gap between the two is adverse selection, and it's exactly what ghost_gap measures.

API

function	what it does
evaluate(signal, ctxs)	run a signal over a corpus → one leaderboard row
run_signal(signal, ctxs)	→ raw per-decision grade dicts
grade_taker(ctx, decision)	book-persistence + tape-corroboration fill grade
grade_maker(ctx, decision)	queue-aware resting-bid fill grade
leaderboard_row(name, family, mode, recs)	aggregate grades → edge_real, CIs, $ROI, breakdowns
physics_features(ctx, i, buy_yes) / adverse_score(feat, buy_yes)	adverse-selection diagnostic
won_buy, fee_per_share, fp_prob_yes, bootstrap_ci	building blocks

Signal, SlotCtx, SlotMeta, Decision are the data model. A Signal implements decide(ctx, i) reading only indices <= i.

Dataset

The companion open dataset (6 weeks of real Polymarket BTC/ETH/SOL/XRP up/down microstructure — book snapshots, trade tape, and gamma-oracle resolutions) ships in the schema this library's SQLite adapter reads. See the dataset card for provenance and caveats.

License

Apache-2.0.