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Noon Barbari Backtesting

by xyz.noonbarbari

check_overfitting

Read-only

Compute the Deflated Sharpe Ratio (Bailey & Lopez de Prado 2014) for YOUR OWN backtest: given its annualised Sharpe, length, and how many strategy variants you tried before selecting it, returns the probability the result is real skill rather than selection luck, the luck bar it must clear, and a plain verdict. Works on any backtest, not just ours.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
skewNoSkewness of the return series (default 0).
sharpeYesAnnualised Sharpe ratio of the selected backtest.
kurtosisNoNon-excess kurtosis of returns (Gaussian = 3, the default).
n_trialsYesHow many strategy/parameter variants were tried before picking this one.
timeframeNoBar timeframe of the returns (default 1d).
length_daysYesLength of the backtest in calendar days.

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already mark readOnlyHint=true; description adds what it returns (probability, luck bar, verdict) and the statistical method used. No contradictions.

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

Conciseness5/5

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

Two sentences, front-loaded with the metric name and purpose, then scope. Every word earns its place.

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

Completeness4/5

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

Explains input, output, and scope. With 6 parameters fully described in schema, the description adds sufficient context. Missing details like probability range and verdict format are minor given the tool's statistical nature.

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

Parameters3/5

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

Schema coverage is 100% with descriptions for each parameter. The description mainly reinforces that information (annualised Sharpe, length, trials) without adding new parameter-level detail, so baseline 3 is appropriate.

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

Purpose5/5

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

Description clearly states it computes the Deflated Sharpe Ratio for a backtest, returning probability of skill vs luck and a verdict. Distinguishes from siblings by emphasizing it works on any backtest and quantifying selection bias.

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

Usage Guidelines4/5

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

Explicitly says 'for YOUR OWN backtest' and 'works on any backtest', implying when to use it. Does not explicitly state when not to use or list alternatives, but the context makes it clear.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: statistical checks, strategy comparison, historical returns, live signals, DCA, overfitting index, strategy listing, dataset query, backtesting, Q&A search, and glossary. No overlap in functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., check_overfitting, run_backtest, search_glossary). No mixing of conventions.

Tool Count5/5

11 tools is well-scoped for a crypto backtesting server, covering the full workflow from strategy selection, backtesting, overfitting analysis, to educational queries without excess or deficiency.

Completeness4/5

The tool set covers core backtesting, overfitting diagnostics, data retrieval, and knowledge base searches. Minor gaps like strategy modification or saving results are present, but the main lifecycle is complete.

Resources