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FinzData market data

by io.github.FinzData
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Run backtest

run_backtest
Idempotent

Runs a deterministic backtest from a JSON spec (universe, start, end, signal, rebalance, top_n, costs_bps, side='long'|'long_short', blind=true to hide tickers). universe: a list of symbols, or 'top500'|'top1000'|'top3000'|'all' for the liquid US market (each month the N most-traded names priced >= min_price, default $5, by 63-day dollar volume; 'all' = liquid_top names, default 1000, max 3000; delisted names included while they traded; membership is set on each month's last trading day and used from the next). rebalance: D|W|M|Q (or daily/weekly/monthly/quarterly). signal.type: momentum (lookback, skip days), ma_cross, factor, or expr with signal.expr a formula over close prices, e.g. 'rank(ret(252)) - rank(vol(60))' (functions: ret, logret, sma, ema, vol, rsi, high, low, dist_high, lag, rank, zscore, demean, abs, log, sign, clip, min, max, where). Point-in-time fundamentals for US stocks (from 2011) work as expr variables and as signal.type factor fields (set ascending false to buy the highest): earnings_yield, book_to_market, sales_yield, ebitda_ev, dividend_yield, roe, leverage, log_mcap, e.g. 'rank(book_to_market) + rank(ret(252) - ret(21))'; such results carry a 'fundamentals' coverage and survivorship report. Every result includes 'overfitting': trials counted per API key and the deflated Sharpe ratio. Prices are fetched point-in-time with a 400-trading-day lookback buffer and clipped to as_of, so results have no look-ahead. Identical specs on the same snapshot are cached and free. Returns stats, benchmark stats, equity curve, warnings, snapshot id and a disclaimer.

Input Schema

NameRequiredDescriptionDefault
specYesBacktest spec.
as_ofNoPoint-in-time cutoff (YYYY-MM-DD). Nothing dated after this is returned, preventing look-ahead bias. Defaults to today (UTC).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes well beyond the annotations by disclosing determinism, point-in-time price handling, caching behavior, no-look-ahead guarantees, overfitting diagnostics, and the exact categories of returned results. These details materially help an agent understand the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core action and then organized into useful details, but it is a very dense single paragraph. Most content earns its place because the nested spec is otherwise undocumented, though the density reduces readability.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex backtesting tool with a nested spec object, no output schema, and partial annotation coverage, the description is complete enough to call correctly. It covers required spec semantics, key behavioral guarantees, and the broad shape of returned results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Although the schema only labels spec as 'Backtest spec.', the description richly documents the nested spec fields: universe semantics, rebalance values, signal types, expression functions, fundamentals variables, side, blind, top_n, and costs_bps. This is substantial added meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: it runs a deterministic backtest from a JSON spec. It distinguishes the core action from siblings like backtest_export or compare_runs by implication, but it does not explicitly name alternatives or sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives extensive guidance on how to construct the spec, including universe choices, rebalance frequencies, signal types, and expression functions. However, it does not explicitly say when to use this tool versus siblings such as compare_runs, backtest_export, or survivorship_audit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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