MCP server

Can your own AI coding assistant use VBT directly? The VBT MCP server connects any client that supports the Model Context Protocol, such as Claude Code, Codex, or Cursor, to your installation, so the assistant can search the documentation, look up the API, read source code, and run Python backtests. Keep working in your editor while the assistant checks VBT examples and tests changes against your installed package.

vbt mcp config claude-code  
claude mcp add --transport stdio vectorbtpro -- /path/to/python -m vectorbtpro.mcp_server

Asked to add a 5% stop loss and a 10% take profit to a moving average crossover, an assistant connected to the server makes calls like these. The tools are Python functions in vbt.mcp, so the same calls run without a server:

import json
from vectorbtpro import mcp

found = mcp.search(  
    "stop loss and take profit in from_signals",
    asset_names=["docs"],
    n=3,
)
for result in json.loads(found):
    print(result["name"])

Stop signals
Discretionary Signal Backtesting
High-level VBT

print(mcp.resolve_refnames(["vbt.PF.from_signals"]))  

OK vbt.PF.from_signals vectorbtpro.portfolio.base.Portfolio.from_signals

print(mcp.run_code("""
data = vbt.YFData.pull("BTC-USD", start="2020-01-01", end="2025-01-01")
fast = data.close.vbt.rolling_mean(20)
slow = data.close.vbt.rolling_mean(50)
pf = vbt.PF.from_signals(
    data,
    fast.vbt.crossed_above(slow),
    fast.vbt.crossed_below(slow),
    sl_stop=0.05,
    tp_stop=0.10,
    fees=0.001,
)
print(pf.stats(["total_return", "max_dd", "total_trades", "win_rate"]))
"""))  

Total Return [%]   -18.880116
Max Drawdown [%]    38.686652
Total Trades               19
Win Rate [%]        26.315789
dtype: object

The assistant found the tutorial on stop signals, confirmed the method, and ran a real backtest in your environment. You get the code and calculated statistics together, ready to inspect the trades, adjust the settings, and run the next comparison.

From a question to a tested backtest

The connection is useful throughout a research project. You can ask your assistant to:

• Find the VBT method and examples for a strategy idea before writing the code.
• Read the relevant API and source to investigate an error in your backtest.
• Compare fees, slippage, or stop settings and show the resulting statistics.
• Use walk-forward validation to test chosen parameters on later data.

For example, after the backtest above, ask it to compare the same strategy with and without stops while keeping fees and signals fixed. The persistent kernel keeps data, fast, slow, and pf available for follow-up calls, so the assistant can reuse the loaded data and inspect the trades.

You can also give it a member page URL and ask it to read the page. The server retrieves the text from the release's knowledge assets, using your configured GitHub access. It does not need to control your browser or use its sign-in session. Long pages can be read section by section, and community answers can be opened as whole threads.

Agent skills for research tasks

VBT ships with step-by-step instructions for common tasks, including backtesting, indicators, parameter optimization, and cross-validation. The assistant can discover them through list_skills and read the relevant instructions with get_skill. This gives it a procedure to follow alongside the API reference and code examples.

For a parameter search, for example, the cross-validation skill covers train and test periods, indicator warmup, and what happens to open positions when parameters change. The members-only skills pages show the procedures available to your assistant.

Server

The server runs on your machine with your VBT installation, so API and source lookups use the version you have installed. Clients usually start it themselves over stdio. To run it in another environment, such as a container or WSL, start it over HTTP with vbt mcp serve --transport streamable-http --port 8000 and register its /mcp address in the client. Over HTTP, the server binds to the local machine by default. run_code, which executes code, is available only over stdio.

Over stdio, run_code uses a persistent Jupyter kernel in your VBT environment. Your client's approval controls let you decide which code the assistant runs. The setup guide below covers installation and client configuration.

get_environment reports the Python interpreter and the Python and VBT versions, so an assistant can check its setup first. The first search after starting the server loads the knowledge assets, which takes the most time, and later searches reuse them. When embeddings for some documents are missing, search ranks them by keywords instead of generating the embeddings first. Tools support token budgets and pagination, so the assistant can request a focused result and read more when needed.

Tools

Every tool also runs from the terminal, as in vbt mcp search "rolling Sharpe ratio", and from Python through vbt.mcp. The server exposes these functions as MCP tools, including the tools that read documentation. You can also use them from the AI research assistant inside Python.