B
BijectiveLabs/trading-research-agents
Autonomous trading agents with deterministic execution. The agents propose; the system decides.
SECURITY.md ✓
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 最终跳转到 github.com
安装与配置
取自该仓库的安装命令
pip install 'bitra[ccxt,openai,anthropic,google,data]
解析自我们于 2026-10-26 抓取的该仓库 README;我们未执行过。
这条是仓库自己写的安装方式,不是 MCP 客户端配置,所以不生成 JSON。
配置项的键名以你的客户端为准:客户端与 CLI
- ★ 数取值
- 41 ★ · BijectiveLabs/trading-research-agents · 取自该仓库的 GitHub 页面 · (2026-11-13 20:00:50 核对)
- 来源明细
- 收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
- 收录与活跃
- 收录于 2026-10-23 · 最近见到 2026-11-14 · 共见到 26 次 · 被 23 个来源列出
- 评分构成
- 综合分 2,300 · 41 ★ · 23 个来源 · mcp
- 安装
pip install 'bitra[ccxt,openai,anthropic,google,data]
常见问题(答案都来自我们的记录)
怎么安装?
该仓库给出的安装命令是
pip install 'bitra[ccxt,openai,anthropic,google,data]。我们的记录里它是仓库自己提供的;不保证在你的客户端上直接可用。它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库
BijectiveLabs/trading-research-agents;我们收录于 2026-10-23;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。TRA
以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →
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, a screener that picks where to look, the full autonomous cycle on live data, four analysts and up to three validators, stops and
targets the analyst 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, a risk manager that can
only make the desk more careful, and two auditors that read the records.
## Contents
What it solves
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
| The usual problem with trading agents | What happens here |
| The model invents or miscalculates a number | Models never compute. Every figure comes from the system, and a figure a model cites is checked against its brief, value included. |
| Nobody knows what a trade cost until it's done | The round trip (fees, spread, impact, funding, slippage) is priced before an order exists, and a trade whose claimed edge doesn't clear it is refused. |
| The agent sizes its own position | Size comes from your conviction ladder and your risk limit. No model can make a trade bigger. |
| A model "decides" to ignore your settings | Your declared thresholds decide. A model's opinion is recorded beside them, never instead of them. |
| Something went wrong and there is no trace | Each cycle keeps its snapshot, briefs, answers, the intent and what the venue said. Nothing is rewritten. |
| A crash leaves a position with no stop | The stop and take profit go to the venue with the entry, or right after the fill where the venue takes them no other way, so they survive this process dying. |
| Text from a feed tells the model what to do | The model that proposes a direction reads numbers code computed; outside text reaches only roles that decide nothing, and no model can send an order or reach a key. |
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, a screener that picks where to look, the full autonomous cycle on live data, four analysts and up to three validators, stops and
targets the analyst 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, a risk manager that can
only make the desk more careful, and two auditors that read the records.
## Contents
What it solves
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
| The usual problem with trading agents | What happens here |
| The model invents or miscalculates a number | Models never compute. Every figure comes from the system, and a figure a model cites is checked against its brief, value included. |
| Nobody knows what a trade cost until it's done | The round trip (fees, spread, impact, funding, slippage) is priced before an order exists, and a trade whose claimed edge doesn't clear it is refused. |
| The agent sizes its own position | Size comes from your conviction ladder and your risk limit. No model can make a trade bigger. |
| A model "decides" to ignore your settings | Your declared thresholds decide. A model's opinion is recorded beside them, never instead of them. |
| Something went wrong and there is no trace | Each cycle keeps its snapshot, briefs, answers, the intent and what the venue said. Nothing is rewritten. |
| A crash leaves a position with no stop | The stop and take profit go to the venue with the entry, or right after the fill where the venue takes them no other way, so they survive this process dying. |
| Text from a feed tells the model what to do | The model that proposes a direction reads numbers code computed; outside text reaches only roles that decide nothing, and no model can send an order or reach a key. |
收录徽章(可选)
想把收录标记放进 README 就复制这行,可选,不影响收录。
[](https://anclum.com/zh/s/bijectivelabs-trading-research-agents/)
- 来源
- awesome · https://github.com/BijectiveLabs/trading-research-agents
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