fidetolabs/qanat

An MCP server that turns filings and prices into valuation indicators, and tests each one inside its own industry.

Qanat does three things.

1. Takes the financials out of a filing and adjusts them, each number dated by the day it was knowable rather than the quarter it describes.
2. Builds valuation indicators from them. Relative ones, like PER and EV/EBITDA, which need a price as well as a filing. Absolute ones, like the floor the balance sheet supports and how far the price sits below it.
3. Tests whether an indicator holds inside the industry the company belongs to. It ranks that industry's companies on the indicator, holds the top of the ranking, prices what that would have earned after fees, and counts the attempt.

Your agent drives all three over MCP, because which indicators value a factory are not the ones that value a software company, and nobody knows the right set in advance. Point it at any source that carries a timestamp. Connect it to your agent and ask.

Python 3.10 or newer.

uv tool install qanat-fdtl                 # or: pip install qanat-fdtl
qanat init my-research --demo && cd my-research

--demo wires one indicator and one sort over it, runs the pipeline, and prices what each basket held, in about ten seconds. The data is synthetic, so it needs no key and no network. Leave --demo off and you get the same project, unrun.

Qanat makes no network calls of its own. The only ones are what your sources do.

claude mcp add qanat -- qanat mcp --scope research

Any MCP client works:

{ "mcpServers": { "qanat": { "command": "qanat", "args": ["mcp"], "cwd": "/path/to/my-research" } } }

Then ask for things:

• "What is in this project, and what feeds the momentum indicator?" It traces the table back to its sources and answers from the rows.
• "Add a trailing-return indicator and test it." Before running anything it comes back with what your data covers and asks you to pick the window, the rebalance and the costs.
• "Which of my indicators held up?" It lists every step that writes a basket, with what each one earned.
• "Why did it lose money in March?" It opens that period and shows what was held and what each name returned.

--scope picks which tools your agent gets. Each one contains the one before it.

Every tool definition is sent on every request, so a narrow scope costs fewer tokens and leaves the agent a shorter list to choose from. Pick the smallest one that does the job.

The split also decides what a caller can break. set_bar sits in full, so an agent connected at research can record what it tried and cannot lower the line those attempts are held to.

Full table in docs/agents.md.

qanat mcp --http --port 8421 --scope research --token "$QANAT_MCP_TOKEN"   # server only
qanat serve --port 8421 --scope research --token "$QANAT_MCP_TOKEN"        # and the scheduler

Both speak MCP's Streamable HTTP transport on /mcp. The scope is fixed by the flag that started the server, so nothing in a request can widen it.

One process serves one project, because the store takes one writer. It binds to 127.0.0.1 unless you say otherwise, and it refuses any other address without a token.

Everything is in one qanat.yaml.

project: equity
store: ./data/qanat.duckdb

universes:                         # which symbols a basket may hold
  - id: sp100
    symbols: ./universes/sp100.csv

stages:                            # order here is order in the pipeline
  - { id: raw,        kind: raw }
  - { id: normalized, kind: normalized }
  - { id: features,   kind: features }
  - { id: weights,    kind: weights }
  - { id: pnl,        kind: pnl }

sources:
  - id: prices
    to: [raw.daily_prices]
    connector: rest
    options:
      url: https://api.example.com/v1/bars
      headers: { Authorization: "Bearer ${PRICE_API_KEY}" }

steps:
  - id: normalize
    from: [raw.daily_prices]
    to:   [normalized.prices]
    script: steps/normalize.sql

  - id: momentum                   # an indicator: one number per symbol per date
    from: [normalized.prices]
    to:   [features.momentum]
    script: steps/momentum.py
    options: { reads: normalized.prices, lookback: 20 }

  - id: sort_on_momentum           # the step that writes weights ends the pipeline
    from: [features.momentum]
    to:   [weights.momentum]
    script: steps/sort_on_momentum.py
    universe: sp100
    options: { reads: features.momentum, column: momentum, top_n: 4 }
    rebalance: 20d

backtest:
  prices: normalized.prices
  fee_bps: 5
  slippage_bps: 10

A step is a .sql file, or a .py file with a run(ctx):

def run(ctx):
    bars = ctx.read("normalized.prices")      # only tables the step declared in `from`
    return df

ctx.read() refuses any table the step did not list, so a missing dependency is an error instead of a wrong number.

Data moves forward through the stages and never back: raw holds it as it arrived, normalized types and deduplicates it, features holds what you measure, weights holds one table per step, and pnl holds what each one earned. qanat check refuses a project that breaks the rules: a normalized stage after something computed, a second weights stage, a step writing into pnl. They are written out in docs/contract.md.

qanat backtest --from 2015-01-01 --to 2024-12-31 --step sort_on_momentum
qanat report 14

A replay walks the period one date at a time. Every step reads only the rows that existed on that date, so a step cannot see the future. It prices what the basket held, takes off fees and slippage, and the headline number is what is left. --step picks which weights table to price, and is needed once a project writes more than one.

A basket is one cut of a ranking. --indicator features.momentum:momentum scores the column itself as well: the rank correlation with the return that followed, date by date, and the top group's return against the bottom group's. A column can order the universe well and still lose money in a basket of four names, so the two numbers answer different questions and the replay reports both.

qanat backtest --from … --to … --step sort_on_momentum --indicator features.momentum:momentum

Three kinds of period come out of it: in-sample, out-of-sample, and live. Live is the only one that cannot be arrived at by looking, because it is priced after a live_from date that was stamped before the rows existed.

More in docs/backtest.md.

A Sharpe of 2.2 on the first attempt is interesting. The same figure picked out of fifty is what noise looks like, and nothing inside a backtest tells those apart. So Qanat keeps the count.

qanat backtest --from … --to …
# then, whether it worked or not:
#   record_trial: what you were testing, and what you decided
#   list_trials:  how many distinct questions that adds up to

Set a bar and the count feeds into it:

backtest:
  bar:
    rule: count        # none · floor · count
    significance: 0.05
    gate: true

count divides the significance by the number of attempts. floor is a fixed t-statistic whatever you tried. With gate: true, calling a result live fails until it clears. Loosening the bar is recorded with who did it, because the thing proposing indicators is also the thing that can move the line.

qanat init --demo writes two steps: an indicator, and a sort over it. The sort takes a column, which end of it is the good end, and how many names to hold, then ranks the universe on that column, keeps the top few, and weights them equally. One sort covers every indicator, because testing one always means the same thing: build the basket on each rebalance date and price it. The engine is what is given away here. The blend I use is not in it.

Four connectors: rest for an HTTP endpoint, sql for anything SQLAlchemy reaches, csv for a path or a URL, and synthetic for a fake market that runs before you have any key. The store is a DuckDB file, or Postgres on localhost.

Five examples. examples/filings-sample is the shape this is built for, and it is invented: a quarterly filings panel where a row has two dates, the quarter it describes and the day it was published. Its README is about which of the two is the clock, and what a replay reads when nobody says. examples/fx-bundled is the other kind: real data in the repo, 27 years of ECB rates, 128 KB, no key needed.

Beta. It does what this page says on my own work, and few other people have run it. If something breaks, open an issue or say so on Discord.

MCP is the only interface. What you install is the engine, the CLI that operates it, and the MCP server. There is no screen and no second HTTP API: your agent client is the screen. qanat serve runs the scheduler and serves this same MCP over HTTP from the process holding the store, which is also how the unattended pass reaches the project.

Two of the three things on this page are only partly here. Nothing in the package reads a filing: there are four generic connectors and no EDGAR parser, so step 1 is yours to write or to point at a feed you already have. Ranking inside an industry needs a point-in-time classification and one indicator set per industry, and neither exists, so today a rank is against whatever universe you declare. What is built is the engine under all of it: the point-in-time replay, the pricing after fees, and the trial count.

Also not implemented: backfills, incremental windows, and live trading. Qanat produces a basket, on history and going forward. It does not place an order, and no agent it serves ever will.

Two limits. The window is not sealed: the point-in-time views stop a step seeing the future, but nothing stops the agent reading past a date while it decides what to try. And there is no search goal, because a loop that proposes indicators and keeps the winners is an expensive way to fool yourself until the bar gates rather than reports.

Qanat runs one kind of pipeline, the kind that ends in a basket. Airflow, Dagster and Prefect handle arbitrary DAGs and distributed execution.

Issues and pull requests welcome. See CONTRIBUTING.md.

MIT. See LICENSE.

Copyright (c) 2026 fidetolabs