name	mlflow-python
description	Log experiment metrics, parameters, and artifacts using MLflow Python API. Query and analyze runs with DataFrame operations. Use when user mentions "log backtest", "MLflow metrics", "experiment tracking", "log parameters", "search runs", "MLflow query", or needs to record strategy performance.
allowed-tools	Read, Bash, Grep, Glob

MLflow Python Skill

Unified read/write MLflow operations via Python API with QuantStats integration for comprehensive trading metrics.

ADR: 2025-12-12-mlflow-python-skill

When to Use This Skill

CAN Do:

Log backtest metrics (Sharpe, max_drawdown, total_return, etc.)
Log experiment parameters (strategy config, timeframes)
Create and manage experiments
Query runs with SQL-like filtering
Calculate 70+ trading metrics via QuantStats
Retrieve metric history (time-series data)

CANNOT Do:

Direct database access to MLflow backend
Artifact storage management (S3/GCS configuration)
MLflow server administration

Prerequisites

Authentication Setup

MLflow uses separate environment variables for credentials (NOT embedded in URI):

# Option 1: mise + .env.local (recommended)
# Create .env.local in skill directory with:
MLFLOW_TRACKING_URI=http://mlflow.eonlabs.com:5000
MLFLOW_TRACKING_USERNAME=eonlabs
MLFLOW_TRACKING_PASSWORD=<password>

# Option 2: Direct environment variables
export MLFLOW_TRACKING_URI="http://mlflow.eonlabs.com:5000"
export MLFLOW_TRACKING_USERNAME="eonlabs"
export MLFLOW_TRACKING_PASSWORD="<password>"

Verify Connection

/usr/bin/env bash << 'SKILL_SCRIPT_EOF'
cd ${CLAUDE_PLUGIN_ROOT}/skills/mlflow-python
uv run scripts/query_experiments.py experiments
SKILL_SCRIPT_EOF

Quick Start Workflows

A. Log Backtest Results (Primary Use Case)

/usr/bin/env bash << 'SKILL_SCRIPT_EOF_2'
cd ${CLAUDE_PLUGIN_ROOT}/skills/mlflow-python
uv run scripts/log_backtest.py \
  --experiment "crypto-backtests" \
  --run-name "btc_momentum_v2" \
  --returns path/to/returns.csv \
  --params '{"strategy": "momentum", "timeframe": "1h"}'
SKILL_SCRIPT_EOF_2

B. Search Experiments

uv run scripts/query_experiments.py experiments

C. Query Runs with Filter

uv run scripts/query_experiments.py runs \
  --experiment "crypto-backtests" \
  --filter "metrics.sharpe_ratio > 1.5" \
  --order-by "metrics.sharpe_ratio DESC"

D. Create New Experiment

uv run scripts/create_experiment.py \
  --name "crypto-backtests-2025" \
  --description "Q1 2025 cryptocurrency trading strategy backtests"

E. Get Metric History

uv run scripts/get_metric_history.py \
  --run-id abc123 \
  --metrics sharpe_ratio,cumulative_return

QuantStats Metrics Available

The log_backtest.py script calculates 70+ metrics via QuantStats, including:

Category	Metrics
Ratios	sharpe, sortino, calmar, omega, treynor
Returns	cagr, total_return, avg_return, best, worst
Drawdown	max_drawdown, avg_drawdown, drawdown_days
Trade	win_rate, profit_factor, payoff_ratio, consecutive_wins/losses
Risk	volatility, var, cvar, ulcer_index, serenity_index
Advanced	kelly_criterion, recovery_factor, risk_of_ruin, information_ratio

See quantstats-metrics.md for full list.

Bundled Scripts

Script	Purpose
`log_backtest.py`	Log backtest returns with QuantStats metrics
`query_experiments.py`	Search experiments and runs (replaces CLI)
`create_experiment.py`	Create new experiment with metadata
`get_metric_history.py`	Retrieve metric time-series data

Configuration

The skill uses mise [env] pattern for configuration. See .mise.toml for defaults.

Create .env.local (gitignored) for credentials:

MLFLOW_TRACKING_URI=http://mlflow.eonlabs.com:5000
MLFLOW_TRACKING_USERNAME=eonlabs
MLFLOW_TRACKING_PASSWORD=<password>

Reference Documentation

Authentication Patterns - Idiomatic MLflow auth
QuantStats Metrics - Full list of 70+ metrics
Query Patterns - DataFrame operations
Migration from CLI - CLI to Python API mapping

Migration from mlflow-query

This skill replaces the CLI-based mlflow-query skill. Key differences:

Feature	mlflow-query (old)	mlflow-python (new)
Log metrics	Not supported	`mlflow.log_metrics()`
Log params	Not supported	`mlflow.log_params()`
Query runs	CLI text parsing	DataFrame output
Metric history	Workaround only	Native support
Auth pattern	Embedded in URI	Separate env vars

See migration-from-cli.md for detailed mapping.

mlflow-python

Install Skill

SKILL.md