StockSharp Odysseus MCP server

Odysseus

<!-- mcp-name: io.github.StockSharp/odysseus -->

An MCP server that turns any AI agent into a quantitative researcher — and holds it to the discipline that makes a result mean something.

The C# namespaces are rooted at StockSharp.Odysseus. The MCP server reports StockSharp.Odysseus as its name and StockSharp Odysseus as its display title; the client configuration below uses odysseus.

For MCP Registry publication and Claude Desktop extensions, see MCP distribution.

You run it locally beside your own agent. It downloads real market history into one StockSharp storage every project shares, measures what is actually in it, translates a formal hypothesis into a real StockSharp Strategy class, compiles it, backtests it with costs, searches its parameters, and reports what every run came to.

It measures. It does not judge. There is no score in it, no threshold, no accept-or-reject. Whether a drawdown is bearable or a return is worth having depends on what you are researching, and that is your call, not a server's.

Why an MCP server

Because the agent is the researcher, and it already exists.

A backtesting product with a chat window has to ship a model, a conversation, an interface, and an opinion about what a good strategy is. This ships none of them. Your agent brings the reasoning; this brings the parts an agent is bad at on its own — real data that cannot quietly change under a result, a translator that turns a hypothesis into the same code every time, an emulator that charges for the spread, and a slice of history it is not allowed to look at until the end.

That last one is the point. An agent asked to find a profitable strategy will find one. The only thing that separates a finding from a search is data the search could not reach.

What it does

create_project
  → import_history          real bars, into the shared storage
  → analyze_market          what the instrument does, before you propose anything for it
  → propose_spec            the hypothesis, as a formal document
  → build_candidate         translated to C#, compiled, hashed
  → evaluate_candidate      six fixed runs, reported in full
  → measure_on_closed_data  once, against data nobody was allowed to see
  → complete_strategy       you decide; the server keeps the code and every number behind it

Forty-eight tools in all: projects, connectors, data, instrument lookup, hypotheses, candidates and a check of whether one gives the same answer twice, backtests, evaluations, a seeded genetic search and a walk-forward over it, the closed-data measurement, paper trading against a broker's paper account, the record of what you completed, and — off unless an operator turns it on — installing StockSharp products on the machine the server runs on.

analyze_market measures the project's dataset — how much the instrument moves, whether its moves continue or come back, and what followed the events a strategy would trade. Read it before proposing anything: a breakout hypothesis on an instrument whose breakouts led nowhere costs a generation of candidates to disprove, and these numbers say so beforehand.

The data discipline

Bars live in one StockSharp market-data storage that every project shares — a folder of its own, ODYSSEUSMARKETDATA (./market-data by default). A project does not copy them: its dataset is a symbol list, a candle length and a range, and runs read that range out of the shared storage. A run is recorded by what it read — instrument, candle length, from and to — so importing the same range again finds every run already made over it.

Shortened. The full README is on GitHub.