tradefloor MCP server

tradefloor

tradefloor is a market simulator you can run a strategy against. It has a Rust core and a Python API.

Give it a seed and a list of companies. It runs a market forward: prices, a limit order book, fills, and an economy that moves each day. Your orders match against the book's depth, so your trades move the price.

Real market data can't tell you what would have happened if you had traded differently, or what caused a move. tradefloor can, because it computed every price. You can fork a running market, change one thing in one branch (a rate rise, a liquidity crisis, a different agent), and measure where the two branches came apart. engine.truth() splits each move in the gap between a price and the model's fair value into eleven factors, and engine.explain(ticker, day) breaks down the move in the traded price, two records no historical dataset carries.

Documentation is at https://docs.tradefloor.dev.

Install

pip install tradefloor

There are wheels for Linux, macOS and Windows on CPython 3.11+, and no dependencies. The same engine is a Rust crate (cargo add tradefloor). Optional extras add the MCP server (tradefloor[mcp]), Arrow output (tradefloor[arrow]), the Gymnasium environment (tradefloor[rl]) and one extra per agent framework.

The API may change before 1.0. Model changes ship as new presets, so a market with no agent orders in it replays exactly on its named preset in later releases. tradefloor was called pretium until 0.5.0. Versions up to 0.4.3 still install under that name, and results recorded with them still replay.

A first run

import tradefloor as tf

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

spec = tf.StrategySpec.momentum(lookback_days=1.0, top_k=5)
scores = tf.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
scores["mine"].errors                # each step that raised or was refused
scores["mine"].sharpe                # annualised, from the daily closes
scores["mine"].time_in_market        # share of steps holding a position

That result comes from one random market, so it says as much about the seed as about the strategy. tf.rank runs many seeds and compares strategies with a paired sign test. Add tf.baselines.referenceagents() to the entrants and tf.versusbuyandhold(scores) reads each score against buy-and-hold on the same market.

Shortened. The full README is on GitHub.