llms.txt
+22 −17
https://docs.bijectivelabs.dev/llms.txt
− # trading-research-agents− > An open-source Python package and command line (tra): autonomous trading agents with deterministic execution. Language model agents read the market and propose a trade; fixed, tested rules compute every figure, price the round trip, size the position and decide whether an order goes out. It runs in a sandbox by default, trades linear perpetuals today on any exchange ccxt supports, with more market types on the way, and works with Anthropic, OpenAI, Google Gemini, Groq, DeepSeek, OpenRouter, Ol− Current release: 0.7.1.− Install: uv tool install 'trading-research-agents[ccxt,openai,anthropic,google,data]'− Source: https://github.com/BijectiveLabs/trading-research-agents− Package: https://pypi.org/project/trading-research-agents/− - [Introduction](https://docs.bijectivelabs.dev/opensource/index.md): What trading-research-agents is: autonomous trading agents with deterministic execution, where language models propose and fixed, tested rules price, size and send. What it does, what it never lets a model do, and where to start.− - [Quick start](https://docs.bijectivelabs.dev/opensource/quick-start.md): Install trading-research-agents, make a desk and run a first cycle.− - [The cycle](https://docs.bijectivelabs.dev/opensource/cycle.md): One cycle of trading-research-agents, from market data to an order intent or a refusal with a reason, and how to run it on a schedule with tra run.− - [Agents](https://docs.bijectivelabs.dev/opensource/agents.md): The agent roster in trading-research-agents: what each role reads, the closed schema it answers in, and why no agent can loosen a limit, size a trade or send an order.− - [Venues](https://docs.bijectivelabs.dev/opensource/venues.md): Which exchanges work with trading-research-agents today through ccxt, how to add one, and the plugin contract for archived data, live feeds and orders.− - [Working with language models](https://docs.bijectivelabs.dev/opensource/llm.md): How trading-research-agents works with language models without an orchestration framework: schemas, retries, caching, budgets and provider failover.− - [The MCP server](https://docs.bijectivelabs.dev/opensource/mcp.md): The MCP server: read-only tools that let any agent host call the research gates of trading-research-agents over stdio.− - [Architecture](https://docs.bijectivelabs.dev/opensource/architecture.md): Package layout, dependency direction and the file formats that form the contract of trading-research-agents.− - [Boundaries](https://docs.bijectivelabs.dev/opensource/boundaries.md): What trading-research-agents guarantees and the tests that hold it, and what is not built yet and how a contribution could add it.− - [Liability](https://docs.bijectivelabs.dev/opensource/liability.md): Where responsibility sits when you run trading-research-agents: the sandbox default, the keys and mandate you supply, and the MIT License terms.− - [Changelog](https://github.com/BijectiveLabs/trading-research-agents/blob/master/CHANGELOG.md)+ # TRA+ > An open-source Python package and command line (tra): autonomous trading agents with deterministic execution. Language model agents read the market and propose a trade; fixed, tested rules compute every figure, price the round trip, size the position and decide whether an order goes out. It runs in a sandbox by default, trades linear perpetuals, spot pairs, stock perpetuals, tokenized stocks and prediction market outcomes on the exchanges ccxt supports, and works with Anthropic, OpenAI, Google+ Current release: 0.10.1.+ Install: uv tool install 'bitra[ccxt,openai,anthropic,google,data]'+ Source: https://github.com/BijectiveLabs/bitra+ Package: https://pypi.org/project/bitra/+ - [Introduction](https://docs.bijectivelabs.dev/opensource/index.md): What TRA is: autonomous trading agents with deterministic execution, where language models propose and fixed, tested rules price, size and send. What it does, what it never lets a model do, and where to start.+ - [Quick start](https://docs.bijectivelabs.dev/opensource/quick-start.md): Install TRA, make a desk and run a first cycle.+ - [Why it is built this way](https://docs.bijectivelabs.dev/opensource/why.md): The documented failures this software is built against: language models trading real money, wrong numbers in financial answers, instructions hidden in data, agents with authority over funds, rules a model steps past or makes up, and automation without pre-trade controls. Each with its source and what the software does about it.+ - [The cycle](https://docs.bijectivelabs.dev/opensource/cycle.md): One cycle of TRA, from market data to an order intent or a refusal with a reason, and how to run it on a schedule with tra run.+ - [Agents](https://docs.bijectivelabs.dev/opensource/agents.md): The agent roster in TRA: what each role reads, the closed schema it answers in, and why no agent can loosen a limit, size a trade or send an order.+ - [Your own data](https://docs.bijectivelabs.dev/opensource/your-data.md): Bringing your own data to TRA: trade history, dated series and live feeds from a vendor, in CSV, JSON or Parquet, normalised and checked before any agent reads it.+ - [Sizing from the record](https://docs.bijectivelabs.dev/opensource/sizing.md): How a desk's own finished trades lower its risk per trade: the Kelly fraction from the record, a seeded Monte Carlo of the drawdown ahead, and the excursion of every trade, all computed by code and repeatable to the digit.+ - [Venues](https://docs.bijectivelabs.dev/opensource/venues.md): Which exchanges work with TRA today through ccxt, how to add one, and the plugin contract for archived data, live feeds and orders.+ - [Exchange accounts and keys](https://docs.bijectivelabs.dev/opensource/exchange-keys.md): How to open an exchange's practice environment and make an API key for TRA, venue by venue, for demo, testnet and live trading, with the settings each exchange needs.+ - [For exchanges](https://docs.bijectivelabs.dev/opensource/for-exchanges.md): For exchanges: how TRA reaches a venue, what is verified before a venue is listed as run end to end, how orders identify themselves, and how to reach Bijective about integration.+ - [Working with language models](https://docs.bijectivelabs.dev/opensource/llm.md): How TRA works with language models without an orchestration framework: schemas, retries, caching, budgets and provider failover.+ - [The MCP server](https://docs.bijectivelabs.dev/opensource/mcp.md): The MCP server: read-only tools that let any agent host call the research gates of TRA over stdio.+ - [Architecture](https://docs.bijectivelabs.dev/opensource/architecture.md): Package layout, dependency direction and the file formats that form the contract of TRA.+ - [Boundaries](https://docs.bijectivelabs.dev/opensource/boundaries.md): What TRA guarantees and the tests that hold it, and what is not built yet and how a contribution could add it.+ - [Liability](https://docs.bijectivelabs.dev/opensource/liability.md): Where responsibility sits when you run TRA: the sandbox default, the keys and mandate you supply, and the MIT License terms.+ - [Changelog](https://github.com/BijectiveLabs/bitra/blob/master/CHANGELOG.md)