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

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

Note: This skill uses Pandas (MLflow API requires it). The mlflow-python path is auto-skipped by the Polars preference hook.

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:

| 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

| 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:

| 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.

安装