prior-lang 0.9.0

PRIOR — a tiny declarative language for trading strategies. Your hypothesis, written down.

PRIOR

Your hypothesis, written down.

PRIOR is a tiny declarative language for expressing trading strategies as testable hypotheses. A complete strategy fits in a few lines that read like the idea in your head:

when $NVDA at [lower_bollinger std=1]
  buy [5% portfolio]

sell when $NVDA at [middle_bollinger]
  or [stop 1.5%]
  or [after 5 bars]

The name is Bayesian: a prior is your belief before you see the data. A .prior file is exactly that — your trading thesis, committed to writing, before the backtest runs.

Why a language this small

PRIOR is deliberately not a programming language. No variables, no loops, no user functions, no arithmetic. The vocabulary is a set of bracket tags, and each tag is a semantic macro that bundles what a competent quant means by the phrase:

[lower_bollinger] means the 20-period, 2-standard-deviation Bollinger band, touched or crossed this bar, with NaN warmup handled and the entry firing once on the touch rather than every bar price sits there. That is ~15 lines of careful pandas, invisible.

Because the language has no way to reference a future bar, you cannot write a lookahead bug in PRIOR. The most common way retail backtests lie is unrepresentable.

How it runs

strategy.prior  →  JSON strategy object  →  generated Python  →  backtest / paper / live

PRIOR compiles to an open JSON interchange format, then to plain Python you can read, audit, and run. prior explain shows every layer, plus an English readback of what your strategy does. Nothing is magic.

The reference runner is AutoQuant, where PRIOR strategies scan live markets, backtest against full market history, and deploy to paper or live trading. The format is open; nothing prevents other runners.

The toolchain

prior validate strategy.prior          errors (with line numbers and suggestions) or ok
prior fmt strategy.prior               canonical formatting (--write rewrites in place)
prior compile strategy.prior           emit runnable Python (--json for the interchange format)
prior explain strategy.prior           every layer: English readback, JSON, generated Python
prior backtest strategy.prior --data bars.csv    metrics over your own OHLCV data
                                                 (CSV, Parquet, JSON, or JSONL; add a ticker
                                                 column to run a whole universe from one file)
prior backtest ... --trades            the per-trade log: entry/exit, bars held, return,
                                                 and WHICH exit fired (stop? target? time?)
prior backtest ... --capital 25000     apply the sizing tags and report dollars
prior backtest ... --fee-bps 5 --slippage-bps 5    trading costs per side
prior backtest ... --contract-fee 0.65 options commission per contract per fill
prior backtest ... --json              metrics as JSON for scripting
prior backtest ... --from 2024-01-01 --to 2025-12-31    backtest a date window
prior backtest ... --equity out.csv     export the daily equity curve for charting
prior trace strategy.prior --data bars.csv --date 2026-03-14
                                                 why did/didn't it fire: every condition's
                                                 verdict on any bar

Strategies are accepted as .prior text or as the interchange .json — every verb takes either, and prior fmt strategy.json converts JSON back into readable PRIOR text.

Try it immediately with real sample data (free, no account, no API keys):

prior sample                 list what's available
prior sample crypto          5 years of daily bars for the [crypto_majors] pairs
prior sample stocks          5 years of daily bars for 20 US large caps
prior sample forex           5 years of daily closes for 7 majors
prior sample crypto --timeframe 1h    2 years of hourly bars (multi-timeframe ready)

Every category also comes in 15m, 5m, and 1m flavors (--timeframe 15m and so on);
window sizes shrink with bar size because that is what the free sources allow.

prior backtest examples/eth_oversold_recovery.prior --data prior-samples/crypto_1d.csv.gz

There is deliberately no options sample: real chain data cannot be redistributed under any free license. Options strategies — the wheel, cash-secured puts, covered calls, and multi-leg structures (put/call spreads, iron condors, straddles, strangles) — backtest locally on chains YOU bring (prior backtest wheel.prior --data f.csv --chains chains.csv — one row per contract per day: date, expiry, strike, right, delta, mid), or in AutoQuant where licensed chain data is built in. A bundled synthetic universe also ships in examples/data/ for fully offline use.

Install: pip install prior-lang (add [backtest] for the backtester's pandas dependency).

Deploy to live trading

The CLI validates, formats, explains, and backtests. To run a strategy live on paper or real money, deploy it through AutoQuant, which executes locally on your own machine and broker keys, so your strategy and keys never touch anyone's servers:

prior deploy strategy.prior

Every account includes a 14-day trial with live paper trading. See autoquant.ai/prior/deploy.

Status

Pre-1.0; syntax may change. Working today: the spec, the parser, the canonical formatter, the reference code generator, the English readback, a local reference backtester (bring your own CSV/Parquet bars), free sample data via prior sample, and a deploy handoff to AutoQuant for live trading.

Editor support

The VS Code extension gives you syntax highlighting, tag completions with parameter docs, hovers that show what every tag expands to, live compiler diagnostics with quick fixes, and prior fmt as the document formatter.

Install it from the Marketplace — search "PRIOR" in the Extensions panel, or:

code --install-extension autoquant.prior-lang

Highlighting, completions, and hovers work immediately. Diagnostics and formatting shell out to the CLI so the editor reports exactly what the compiler will say — pip install prior-lang, or point the prior.command setting at any environment that has it.

Documentation

• Guides and tutorials: autoquant.ai/prior
• Language specification: spec/SPEC.md — the source of truth for implementers
• Tag reference: spec/TAGS.md — every tag, its defaults, and exactly what it expands to

Repository layout

spec/SPEC.md         language specification (grammar, semantics, error contract)
spec/TAGS.md         every tag: params, defaults, exact semantics, readback strings
examples/*.prior     complete strategies, from one-liners to pairs trades — the executable spec
python/prior_lang/   the reference implementation (zero-dependency parser + CLI)
editors/vscode/      VS Code extension: highlighting, completions, hovers, live diagnostics

License

MIT.

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PRIOR is built and stewarded by AutoQuant, the local-first desktop platform for researching, backtesting, and deploying trading strategies.