pretium

A market simulator you can run a strategy against. Rust core, Python API.

Give it a seed and a set of companies and it runs a market forward: prices, a limit order book, fills, and an economy that advances itself daily. Your orders match against real depth, so trading moves the price the way it would anywhere else.

It exists because historical data can't answer two questions. What would have happened if I'd traded differently, and what actually caused this price move.

Install

pip install pretium

The API may still move before 1.0. What will not move is a published result: anything that changes the simulated trajectory arrives as a new model preset rather than an edit to an existing one, so a run you cited last month reproduces this month.

In thirty seconds

import pretium as pt

universe = pt.Universe.random(40, seed=111)

spec = pt.StrategySpec.momentum(lookback_days=1.0, top_k=5)
scores = pt.evaluate({"mine": spec}, seed=7, universe=universe, days=10)

scores["mine"].return_pct            # what it made
scores["mine"].impact_bps            # what its own footprint cost
scores["mine"].strategy_fingerprint  # sha256, cite this

That's one market draw, which tells you as much about the seed as about the strategy. pt.rank runs many seeds and compares them with a paired sign test.

What makes it different

Determinism that's checked. The crate ships its own exp, log, sin and cos instead of calling the platform libm. Every release builds five targets, runs one fixed simulation inside each, and compares digests. A disagreement fails the release. A WebAssembly build produces the same digest as the native one.

Ground truth you can read. The simulator computed every price, so it can tell you why. One row per instrument per tick, with seven factor contributions that sum to the move and a residual around 1e-16. No historical dataset carries those labels. You can see that a stock fell, never that 60% of the fall was order-flow pressure.

Counterfactuals you can run. The same seed runs with and without your orders, so every fill is priced against the market where you never traded. That's the number TCA vendors approximate.

Results someone else can check. A StrategySpec is a declarative, hashable document rather than an arbitrary callable. A RunManifest carries the seed, universe, macro conditions, scenario, model and strategy together with the expected digest. reproduce() refuses on a mismatch and names the culprit, because a manifest that quietly reproduced a different market would be worse than no manifest.

Driving it from an agent

pip install "pretium[mcp]"
claude mcp add pretium -- pretium-mcp

Eleven read-only tools over the simulator, so a coding agent can ask whether a momentum strategy beats buy-and-hold here, and whether the difference is real. Strategies, universes and scenarios are composed as data. There's no path from a tool argument to code execution.

Every result carries computed caveats and full provenance. A model summarising a result has the tool output and nothing else, and will otherwise report return_pct: 88.7 as "the strategy made 88.7%". The MCP server has the tool list and the client configuration.

What it's bad at

Good results here don't predict real returns. The price process comes from a known model, so a strategy that fits that model's structure will look excellent and teach you nothing. A strategy that fails here is more informative, because it broke against a live order book under honest impact costs.

Realism is a stated envelope, not a score. pt.facts.measure() reports fourteen statistics against real-market bands. At 252 days the shipped pt-v3 preset holds twelve of the fourteen in band, and holds the original eleven on axes the calibration never saw: fresh seeds, and a fresh 60-name universe. The two it misses are both stated as gaps: volume shocks do not persist, and names in the same sector do not co-move more than names in different ones. Two further presets close those on their own terms and are selectable by name, never the default: pt-v7 gives the market industries that survive a crisis, and pt-v8 gives the market factor's variance a memory, so correlation that rose last month is still elevated this month and a crisis is a third more violent. Every earlier preset keeps reproducing bit for bit.

The tenth is the autocorrelation of volume changes, which misses by 13.7 seed-standard-deviations. It's excluded from the calibration objective deliberately, because an optimiser pointed at an unreachable target doesn't fail cleanly, it distorts every other parameter chasing it. In practice a volume forecast here is never wrong twice running, so an execution algorithm faces an easier scheduling problem than the one it was written for.

Six gaps are measured and named rather than assumed. The certified horizon is 252 days and the model doesn't hold beyond it. Volatility memory decays exponentially where real markets decay hyperbolically. Tails are too thin over multi-year windows. Scenario response is directionally right but not calibrated in magnitude. And certification was measured on a sector-balanced roster, which no real index is. The realism envelope states each gap and what it forbids. pt.envelope.check() refuses to certify a question that falls outside one.

Single venue, no latency, no strategic counterparties. Orders arrive instantly, there's one book per name, and you trade against a market maker and aggregate flow rather than agents that adapt to you.

Worked examples

Six notebooks and two scripts in examples/, numbered in reading order and executed as part of the test suite:

Or a whole study in one file. Sweep, evaluation, TCA and replay in about five seconds:

python examples/07-research-workflow.py

Documentation

Full docs: simoncoombes.github.io/pretium

Getting started and core concepts. Agents, evaluation and the RL environment. Scenarios and macro paths, strategy specs, model presets, checkpointing and forking, sharing a run, Arrow output and streaming sweeps, SEC EDGAR loading, transaction cost analysis, the MCP server, running in the browser, the realism envelope and metrics, and the conventions worth reading before you hit them.

Contributing

See CONTRIBUTING.md, and RELEASING.md for how a version reaches PyPI and crates.io. One rule shapes the rest: a change to the simulated trajectory is a breaking change, whatever its size. A market that runs differently from the same seed invalidates every published result that cited it.

Citing this

See CITATION.cff, or cite a specific result by its manifest. The seed, the universe fingerprint, the model fingerprint and the strategy fingerprint together identify exactly what ran.

Licence

MIT OR Apache-2.0, at your option. See LICENSE-MIT and LICENSE-APACHE.