Finance Toolkit
by JerBouma
500+ transparent financial methods calculated from raw statements, not third-party endpoints.
Computer or cloud★ 5,496MITUpdated yesterday
Install
TRA
Overview
Description from the maker (original English)
Read-only gates of autonomous trading agents with deterministic execution: cost, data, validation.
PopNx MCP’s three pillars
These three are never merged into one score: opinions, measurements and popularity are different things.
- User voiceiNo reviews yetReviews open together with accounts in a later step
- Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
- UsageiNot measured yet★ –
What you can do with it
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
- Connection: Runs on your computer
- Transport: stdio
- Category: Accounting, finance & tax
Install
Pick the AI app you use, then copy the settings.
Before you install
- Basic checks: 1 of 1 passed
- Runs on your computer with your user’s permissions (files, network, apps).
- The registry lists no key to fill in.
You need
- PyPIPython’s uv installed (for the uvx command)
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"bitra": {
"command": "uvx",
"args": [
"bitra",
"mcp",
"serve"
]
}
}
}Run this in your terminal
claude mcp add bitra -- uvx bitra mcp serveOr copy the settings yourself
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"bitra": {
"command": "uvx",
"args": [
"bitra",
"mcp",
"serve"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Or copy the settings yourself
Paste into .vscode/mcp.json in your project
{
"servers": {
"bitra": {
"type": "stdio",
"command": "uvx",
"args": [
"bitra",
"mcp",
"serve"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
What it can do
Runs on your computer; the tool list appears in your AI app after you install it.
Basic checksi
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
- ✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account BijectiveLabs, which owns the repo
- … Not checked yet
Still maintained
We have not read this repo from GitHub yet
- … Not checked yet
Has a licence
We have not read this repo from GitHub yet
- – Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
- – Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
- … Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints
Compared with others in Accounting, finance & taxi
- More stars than 0% of the other 1722 servers in this category
- Published to the registry more recently than 85% of the other 1722 servers in this category
- Basic checks equal to or better than 100% of the other 1722 servers in this category
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
Common questions
Is TRA safe?
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
What keys or codes does it need?
The registry does not list a required key, though some servers ask you to sign in on first use.
Does it run on my computer or in the cloud?
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
Is it still maintained?
The GitHub repo last changed –, and the latest version (0.12.2) reached the registry 6 days ago.
Which apps can use it?
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
What can I use instead?
In the same category: Finance Toolkit, SolidInvoice, Korea Tax Calculator
Alternatives
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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
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Install
README from the maker
Open the README
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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.
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
| 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 the system computed; outside text reaches no model at all, and no model can send an order or reach a key. |
| A strategy looks good on a lucky sample | The arena replays it over the recorded market beside buy-and-hold, momentum, its own fade and a seeded random null, net of costs, and says "sample too small" until the sample is not. |
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.
| Does | Never does | Today | |
|---|---|---|---|
| The system | records the market, computes every figure, reads your own series against your lines (the macro, on-chain and sentiment readings), prices the round trip, applies your limits, sizes, sends one order with its exits, settles every replayed idea | guess, or carry a stale value forward as current | everything around the model call |
| A reasoning model | reads figures the system computed and proposes a direction, a setup and its levels, citing the figures it used | compute a number, size a trade, touch an order or a key, loosen a limit | the structure lead, with a blind confirmer and a flow specialist when you declare the team; reviewers when you declare them |
| Jev, the typed judge | turns dated text into typed events with probabilities, screens text for instructions to a machine, checks each structure reason against the regime the system measured, and from 0.13 checks every claim against its brief | decide a direction from numbers, size, or reach an order | Jev through the TypeSafe adapter, behind a vendor-neutral port ([judge]): the archived headlines into text features, and the deciding team's reasons checked before a trade |
flowchart LRsubgraph 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:
| Release | Adds |
|---|---|
| 0.11, in 0.12.0 | the market data plane (tra data bars, tra record), the feature library, the arena; the macro, on-chain and sentiment analysts become readings that ask no model |
| 0.12, in 0.12.0 | the lead's levels read against the instrument's own paths, a forecast and a calibrator fitted on recorded outcomes, risk as its own signed gate; Jev for dated text and the team's reasons |
| 0.13 | the reasoning model woken only when the market gives it something to weigh, its claims checked by the judge; the v1 cycle retired once the arena shows the new one is better |
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: pipOnly the beginning is shown; read the rest in the repo