backtest2live

Check a backtest against what live execution would do. Evaluators run locally on your own files and public data, and each one returns a verdict with the numbers behind it.

Use it three ways: as a Python CLI (bt2live), as a library, or as a Claude Code plugin where the agent runs the CLI and interprets the verdict.

This is the public, MIT-licensed version of checks used in live crypto trading research at Martian Mobile.

Evaluators

Roadmap: ROADMAP.md and the milestones.

Install

pip install backtest2live

The core has no runtime dependencies (Python 3.9+). fill-gap needs numpy and pandas:

pip install 'backtest2live[data]'

Claude Code plugin. This repo is also a plugin marketplace. From inside Claude Code:

/plugin marketplace add martianmobile/backtest2live
/plugin install backtest2live@martianmobile

Then ask "are these variants converged?" or run /convergence results.csv --metric sharpe_oos. The skill installs the CLI if it is missing.

fill-gap: do your backtest's passive fills survive a queue?

A backtest that fills a resting order whenever price touches it counts fills a live order would never get: the order sits behind the queue already displayed at that price. fill-gap re-runs each resting order in your order log against top-of-book and trade prints, and grants a fill only when:

• the volume printed at exactly your price, after you joined, covers the queue ahead of you plus your own size, or
• a trade prints through your price, which means the level was cleared.

bt2live fill-gap examples/orders_btcusdt_2024-03-30.csv --venue binance-um --pair BTCUSDT

## Verdict: OVERSTATED

**Your backtest fills 95.2% of its resting orders; the queue proxy fills 66.2%.
30.4% of the counted fills (834 of 2,740) are not granted on this data.**

The report continues with fill rates by rule, median time to fill, misses by volatility tercile and by UTC hour, and P&L on missed fills if your log has a pnl column. --json gives the same result machine-readable. Exit code: 0 CONSISTENT, 1 OVERSTATED (more than --tolerance, default 10%, of your fills not granted), 3 error.

Your order log

CSV, one row per order. Column names are matched case-insensitively:

Orders marketable at arrival are taker orders: fill-gap skips them and cost-gap prices them. --latency-ms delays every order's arrival at the book.

Market data

Downloads are cached under ~/.cache/backtest2live (BT2LIVE_CACHE overrides); Binance files are checked against the published SHA-256. A downloaded day's L2 replay is cached next to it, so it is replayed once (about 10 s for a Bybit BTCUSDT day); files passed with --l2 are replayed on each run. fill-gap also needs trades: --venue fetches them; with --book/--l2 pass --trades trades.csv (ts, price, qty plus is_buyer_maker, true when a seller hit the bid, or the taker's side; OKX and Bybit trade exports work as is).

What the proxy cannot see

The touch rule is an upper bound: a resting order fills only if something trades at or through its price. The proxy is an estimate below it. Hidden size, your latency and your own impact make it too generous. Cancels ahead of you, and a queue position read from the size shown when the price reached you, make it too strict. How that nets out depends on venue, size and regime. Only your own live fills settle it; Martian Mobile runs that check against live fills.

cost-gap: what would your taker orders have paid?

A bar-based backtest fills every order at a bar price: the close, the open, or the mid. A real market order crosses the spread, eats whatever is displayed at the touch, and arrives some milliseconds after the book you saw. cost-gap re-prices each market order, and each limit that crosses the spread, against top-of-book at its arrival time and reports the cost against the price the backtest booked.

bt2live cost-gap examples/market_orders_btcusdt_2024-03-30.csv --venue binance-um --pair BTCUSDT

## Verdict: REPORTED

**Your backtest's 1,435 taker orders would have paid +0.01 bps on average
(+0.01 bps median) against the price it booked.**

That example is a toy momentum strategy sending 0.1 BTC market orders every minute and booking the mid. On BTCUSDT perpetuals the half-spread is under 0.01 bps and the touch absorbs 0.1 BTC, so the mid-fill assumption holds there, and the report says so. The check bites on thinner pairs, larger sizes and slower paths: the latency table (0 / 5 / 20 / 100 ms) shows the slope, and the by-volatility table shows where it concentrates. With a pnl column the verdict is OVERSTATED when the cost removes more than --tolerance (default 25%) of the backtest's P&L; without one it is REPORTED. Exit code: 0 CONSISTENT or REPORTED, 1 OVERSTATED, 3 error.

Your order log

Same columns as fill-gap, with three additions: type (market/limit), price for limits (a limit that crosses the touch is priced as a taker and capped at its limit; one that rests is counted and left to fill-gap), and fill_price, the price the backtest booked (without it, the mid at arrival is the baseline). A log with no price column is all market orders. A log with a price column but no type and no fill_price is read as a bar backtest's export: the price is the booked fill, every order is a market order, and the run says so.

What the book can and cannot see

Top-of-book shows the touch and its displayed size, nothing behind it. Size beyond the touch is priced one tick worse per displayed-size slice, the least impact consistent with the feed, so the cost is a floor, not a measurement. Only your own live fills settle it.

convergence: has your variant sweep converged?

Given one row per variant (parameters + metrics), it measures:

• Top-K dispersion — are the best variants clustered, or is one a lucky outlier?
• IS↔OOS rank stability — does the in-sample ordering survive out-of-sample? (Spearman)
• Parameter-space structure — do winners sit on a plateau, or are they isolated points (overfit)? Is the best variant pinned to the edge of the swept range?
• Sample sufficiency — are any top variants below a trade-count floor?

…then returns a verdict:

Usage

bt2live convergence examples/results_converged.csv --metric sharpe_oos
bt2live convergence examples/results_converged.csv --json    # machine-readable verdict
python -m backtest2live convergence results.csv              # same, without the script shim

Exit code encodes the verdict: 0 CONVERGED · 1 ITERATE · 2 KILL · 3 error (bad input or usage), so a CI job can gate on it.

Input format

A CSV with one row per variant:

variant_id, lookback, threshold, sharpe_is, sharpe_oos, n_trades
v001, 20, 0.5, 1.82, 1.61, 412
v002, 25, 0.5, 1.85, 1.58, 401
...

Column roles are inferred from names/types. IS/OOS pairs are detected by suffix (_is/_oos, _in/_out, _train/_test). For Parquet/SQLite, export to CSV first.

Flags

Example output

# Iteration Check | results_converged.csv | 2026-06-06

## Verdict: CONVERGED

**Top-5 cluster tightly (1.7% dispersion) on a parameter plateau, OOS-stable (ρ=0.99) — ship `v10`.**

## Convergence Analysis
| Metric             | Top-K Mean | Top-K Std | Dispersion | Threshold |
|--------------------|-----------|-----------|------------|-----------|
| sharpe_oos (rank)  | 1.58      | 0.03      | 1.7%       | < 5% ✓    |
| sharpe_is          | 1.82      | 0.02      | 1.3%       | < 5% ✓    |

IS→OOS rank stability (Spearman): 0.99 (threshold > 0.60 ✓)

## Parameter-Space Check
- lookback: top-K at [20, 25, 30] — contiguous
- threshold: top-K at [0.5, 0.6] — contiguous
[x] Plateau region — robust, safe to deploy

Limitations

• Not a backtester. It analyzes results you already have; it does not run strategies.
• Not a live monitor. Research-phase tooling — live trading needs different rails.
• Metric-agnostic, not metric-smart. It checks dispersion of whatever metric you point at; it doesn't know whether that metric is the right one.
• Convergence ≠ profit. Convergent variants can converge on losing strategies. The verdict reports convergence, not edge.

Why this exists

In real quant research, the failure mode isn't running too few backtests — it's calling one lucky variant a "winner" when its neighbors perform completely differently. Convergence-across-variants is the only honest signal that the parameter region has structure. This tool enforces the discipline — and gives the same treatment to the other ways a strategy can look better than it is.

Built by Martian Mobile. MIT licensed — see LICENSE.