quant

Evolve, test and optimise trading strategies.

The engine behind the Quant Workbench extension, usable on its own from the command line. Everything it reports is JSON on stdout, with diagnostics on stderr, so it composes with whatever you already run.

Install

pip install sophostechne-quant

The distribution is sophostechne-quant; the import package and the command are both quant. It pulls in NautilusTrader and pandas, so the install is not small.

The idea

A backtest that reports only a return is a sales pitch. Three questions have to be answered together, and evaluate always answers all three — there is no flag to omit the awkward two:

• Did it make money? Net return, drawdown, profit factor, win rate, time in drawdown.
• Was the drawdown real? A Monte Carlo resample of the strategy's own trades, so the observed drawdown can be placed in the distribution of drawdowns it could have had.
• Was any of it better than chance? A noise floor of random strategies on the same bars. A strategy that does not beat its noise floor did not find an edge, whatever it returned.

Use

quant evaluate --strategy my.strategy --product BTC-USD --timeframe 6h --bars 2000

Searching emits NDJSON — one object per generation, then a done object carrying the survivors. Every survivor is scored twice: on the bars it was fitted to, and on a held-out tail the search never saw. The gap between the two is the entire result.

quant evolve --population 250 --generations 20 --survivors 5 --holdout 0.3

Walk-forward tests the method rather than any one strategy. The whole search re-runs in every training window and its winner is applied, untouched, to the window after.

quant walkforward --train-bars 1200 --test-bars 400

Costs are charged on every path: --fee (default 0.001) and --slippage (default 0.0005). --seed makes a run reproducible, and --regimes conditions the search on market regime.

Where the bars come from

Symbols route by shape. An exchange pair — BTC-USD, ETH-USD, anything ending in a quote currency — comes from Coinbase, which serves candles to anyone. A ticker like AAPL or BF-B comes from a published bars service:

quant evolve --product AAPL --timeframe 1d

That works with no configuration: QUANT_BARS_URL defaults to https://bars.sophostechne.com. Point it at your own service to use different data, or set it empty to refuse equities.

Equities need a service because every real-time equity feed is licensed per subscriber and cannot be redistributed; a service built from IEX HIST captures can be, so the engine needs no credentials of its own. With QUANT_BARS_URL set empty, a ticker fails with a message saying so rather than silently doing something else. See quant-daemon for running one.

A timeframe the service does not publish is built by resampling a finer one, which is exact. Buckets that no trade fell into stay absent rather than being carried forward — a gap is information, and filling it puts bars into a backtest that nothing supports.

Everything is cached under ~/.cache/quant, so a search that re-reads the same window across folds hits the network once.

Output

One JSON object per line, always with an ok field on terminal results. Failures are reported as JSON too, so a caller parsing the stream never has to strip a traceback out of it:

{"ok": false, "type": "error", "error": "FileNotFoundError: my.strategy"}

Development

uv sync
uv run pytest

License

MIT