About FLOX MCP Server
FLOX MCP Server is an MCP server for the FLOX trading framework, aimed at quant researchers and strategy developers using Claude or Cursor. It exposes about 30 tools covering backtests, strategy scaffolding, lookahead bias validation, indicators, order placement and PnL queries.
Key features
- 01About 30 MCP tools
- 02Backtest execution
- 03Strategy scaffolding
- 04Lookahead bias validation
- 05Technical indicator computation
- 06Order placement and PnL queries
Use cases
- 01Running strategy backtests from Claude or Cursor
- 02Scaffolding and validating new trading strategies
- 03Querying orders and profit and loss
MCP support
This project is an MCP server that exposes FLOX framework tools for backtesting, strategies, indicators and trading to MCP clients such as Claude and Cursor.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMElrvx
lrvx is a framework for building trading systems.
Strategies, backtests, paper trading, and live execution sit behind one toolkit. AI agents discover the surface and drive it end-to-end through an MCP control plane. One strategy class runs backtest, paper, and live. Bindings for Python, Node.js, Codon, embedded JavaScript, and a stable C API.
Documentation is available at docs.lrvx.dev
Language bindings
| Language | Install | Docs |
|---|---|---|
| Python | pip install lrvx | reference |
| Node.js | npm install @lrvx/lrvx | reference |
| Codon | build from source | reference |
| JavaScript (embedded) | bundled with C++ build | reference |
| C API | liblrvx_capi.so | reference |
All bindings expose the same strategy API, indicators, order books, backtesting, and data I/O. The C API is the integration point for adding support for any other language.
AI companion
lrvx-mcp is a Model Context Protocol server that gives AI coding agents (Cursor, Claude Code, Cline) grounded access to the lrvx surface — symbol lookups across bindings, scaffolders, indicator and backtest tools, full-text doc search.
pip install lrvx-mcp
See the package README for setup and the full tool list.
Connectors
Native exchange connectors (Bybit, Bitget, Hyperliquid, Polymarket) live under connectors/ and build with -DLRVX_BUILD_CONNECTORS=ON. The flag defaults to OFF so a backtest-only or research build doesn't pay the dependency cost. See the connectors README for adapter notes and the optional Polymarket Rust toolchain step.
Venue
Beyond trading on a market, lrvx can be one: the optional venue/ module adds a matching engine and the machinery around it, so many participants can trade against a single order book. See the venue docs.
Build options
The CMake options that gate every optional artefact (bindings, demo, tools, tests, benchmarks, connectors) are catalogued in docs/build/feature-flags.md. Defaults are OFF so a bare cmake -B build produces only the core C++ static library.
Commercial Services
For commercial support, enterprise connectors, and custom development, visit lrvx.dev/services.
Contributing
Contributions are welcome via pull requests.
Please follow the existing structure and naming conventions.
Tests, benchmarks, and documentation should be included where appropriate.
Code style is enforced via clang-format. See contributing guide.
License
lrvx is licensed under the MIT License. See LICENSE for details.
Disclaimer
lrvx is provided "as is" without warranty of any kind, express or implied. The authors are not liable for any damages or financial harm arising from its use, including trading losses or system failures. This software is intended for educational and research purposes. Production use is at your own risk. See DISCLAIMER.md for full legal notice.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.