mcp-strategy-research-db

A Model Context Protocol (MCP) server that provides Claude Code access to a strategy research SQLite database for analyzing trading strategy backtest results.

Features

• Analyze strategy performance across multiple market periods

• Find strategies that work across all market regimes (robust strategies)

• Compare strategies against Buy & Hold benchmarks

• Query risk-adjusted metrics (Calmar ratio, Sharpe ratio)

• Find alpha-generating strategies that beat benchmarks

• Analyze strategy consistency across different symbols

• Run custom SQL queries (read-only) for advanced analysis

Installation

# Clone the repository
git clone https://github.com/locupleto/mcp-strategy-research-db.git
cd mcp-strategy-research-db

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Configuration

Set the required environment variable:

export STRATEGY_DB_PATH=/path/to/strategy_research.db

The database is typically generated by the trading-lab project's strategy search scripts.

Register with Claude Code

MCP servers can be registered at two scopes:

Project Level (Recommended)

Registers the server for the current project only. The configuration is stored in ~/.claude.json under the project's path. This is the recommended approach for project-specific tools.

cd /Volumes/Work/development/projects/git/mcp-strategy-research-db

claude mcp add strategy-research-db \
    "$(pwd)/venv/bin/python3" \
    "$(pwd)/strategy_research_mcp_server.py"

This adds the server to ~/.claude.json under the project's mcpServers configuration:

{
  "projects": {
    "/Volumes/Work/development/projects/git/mcp-strategy-research-db": {
      "mcpServers": {
        "strategy-research-db": {
          "type": "stdio",
          "command": "/Volumes/Work/development/projects/git/mcp-strategy-research-db/venv/bin/python3",
          "args": [
            "/Volumes/Work/development/projects/git/mcp-strategy-research-db/strategy_research_mcp_server.py"
          ]
        }
      }
    }
  }
}

User Level (Global)

Registers the server globally, available in all projects. Use the -s user flag:

claude mcp add -s user strategy-research-db \
    "$(pwd)/venv/bin/python3" \
    "$(pwd)/strategy_research_mcp_server.py"

Verify Registration

# List all registered MCP servers
claude mcp list

# Debug mode for troubleshooting
claude --mcp-debug

Tools (17 total)

Database Overview

Strategy Analysis

Cross-Period Robustness

Benchmark Analysis

Symbol Analysis

Capital Deployment Analysis

Advanced

Key Metrics Explained

Performance Metrics

• Expectancy: Expected return per trade (%)

• Win Rate: Percentage of winning trades

• Profit Factor: Gross profit / Gross loss ratio

• CAGR: Compound Annual Growth Rate

Risk Metrics

• Max Drawdown: Largest peak-to-trough decline

• Calmar Ratio: CAGR / Max Drawdown (higher = better risk-adjusted returns)

• Sharpe Ratio: Risk-adjusted return relative to risk-free rate

Consistency Metrics

• Consistency Score: % of symbols where strategy is profitable

• Symbols Beating Benchmark: % of symbols that outperform Buy & Hold

Example Usage

# Get overview of the database
> get_database_status

# Find strategies that work in ALL market periods
> find_robust_strategies min_periods=6 min_consistency=0.7

# Get top strategies for a specific period
> get_top_strategies period_name="2008 Financial Crisis" sort_by="median_calmar_ratio" limit=10

# Compare a strategy across all periods
> compare_strategy_across_periods strategy_id="buy_adm_momentum_low__sell_adm_momentum_high"

# Find alpha generators
> find_alpha_generators min_alpha=5.0 min_beat_rate=0.6

Database Schema

The SQLite database contains four main tables:

search_runs

Metadata about each backtest run (study, period, date range, benchmark data)

aggregated_results

Strategy-level aggregated metrics using MEDIAN values across all symbols tested

symbol_results

Per-symbol backtest results for detailed analysis

trade_results (Dec 2025+)

Individual trade records with entry/exit dates for daily position count analysis. Enables exact portfolio-level capital deployment tracking over time.

Requirements

• Python 3.10+

• MCP SDK (mcp>=1.23.1)

• SQLite strategy research database (from trading-lab project)

Related Projects

• trading-lab: Strategy backtesting and signal research platform

• mcp-marketdata-db: Market data MCP server

License

MIT