TRA

TRA

Autonomous trading agents with deterministic execution. The agents propose; the system decides.

TRA (trading research agents) is published as bitra on PyPI and runs as tra. Before 0.10.1 the package was called trading-research-agents, and the same package is still published under that name.

Language models are good at reading a market and saying what they think. They are bad at arithmetic, they don't know what a trade costs, and they will happily overrule a limit you set. This project uses them for the first part only. The system itself, fixed rules with no model involved, fetches the data and works out every figure. The agents read those figures and give an opinion. Then the system prices the trade, applies your limits, sizes it, and either writes it down or sends one order with the stop attached. Every decision and every refusal is kept on disk with its reason, so you can check afterwards what happened and why.

It's an early release, so expect settings to change between versions. What works today: linear perpetuals, spot pairs (stock perpetuals and tokenized stocks among them) and prediction market outcomes through one order path, a universe of perpetuals found and measured by the system and ordered by the room its own paths expect, the full autonomous cycle on live data, a structure lead that proposes, three readings of your own series computed by the system, up to three reviewers, stops and targets the lead names inside bounds you set, the gates that decide, sizing from your own rules, real orders on a venue's practice environment behind a mandate you sign and a signed risk gate, and two auditors that read the records. Under it, a market data plane records order flow, the order book, liquidations, derivatives and news minute by minute into a verified archive, and an arena replays any policy over that archive beside baselines built to be beaten, net of the desk's own costs.

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Contents

• What it solves
• How it is built
• Get started
• What you see, in order
• How one cycle decides
• Venues
• Models and providers
• Toward real orders
• Your money, your orders, your positions
• Watching a desk, and what is kept
• Command line
• What it guarantees, and what is not built yet
• Where it is going
• Documentation

What it solves

None of these is hypothetical. Why it is built this way lists the documented failures behind each one, with their sources.

How it is built

Three kinds of work, kept apart because they fail differently. The system is in control; Jev, TypeSafe's typed judge, is the fast System One model that perceives and verifies; the reasoning seats deliberate only inside what the system computed, and their answers are checked before anything counts.

flowchart LR

subgraph venues["venues, through ccxt"] direction TB streams["live streams trades, order book, liquidations"] rest["REST history funding, open interest, mark, index, long/short"] archives["daily trade archives"] end subgraph plane["market data plane: the system"] direction TB record["tra record one bar builder, hour parts, gap marks"] bars["tra data bars the same builder over the archive"] store[("verified archive Parquet, sha256 per file flow, book, derivatives, text")] end subgraph decide["decision: the system around the deciding team"] direction TB judge["Jev, the typed judge headlines into typed events reasons checked against the regime"] features["features 41 figures known at the bar's close"] readings["readings: macro, on-chain, sentiment your series against your lines, no model"] analyst["deciding team lead, blind confirmer, flow specialist propose, cite figures"] gates["gates cost, exits, paths, confluence, conviction"] end shelf[("sources/ dated series you declare")] subgraph act["risk and execution: the system"] direction TB risk["risk gate, signed caps, loss at the stop, switches, daily loss, drawdown"] order["one order stop and take profit attached"] end arena["tra arena policies replayed, settled net of costs, beside baselines and a random null"] streams --> record --> store archives --> bars --> store rest --> record rest --> bars store --> features store --> judge --> features analyst --> judge --> gates shelf --> readings --> gates features --> analyst --> gates --> risk --> order --> venues store --> arena features --> arena

The shipped cycle reads its own live snapshot; the archive feeds the arena today and the decision path as the work below lands. Where it is going, in the order it is being built:

Each step is measured in the arena before it is trusted, and the four guarantees at the end of this page hold through all of it.

Get started

A few minutes and one model key. Every setting has a working default, so a new desk runs before you change anything; the table under step 6 says what those defaults are.

1. Install. You need Python 3.11 or newer. uv is the quickest way:

uv tool install 'bitra[ccxt,openai,anthropic,google,data]'

That brings every model provider and every exchange in. Without uv, run pip install 'bitra[ccxt,openai,anthropic,google,data]' inside a virtual environment. To move to the newest release later:

uv tool upgrade bitra      # or: pip