MCP 服务器

mcp-strategy-research-db

An MCP server that provides access to a SQLite database for analyzing trading strategy backtest results and performance metrics. It enables AI assistants to identify robust strategies across different market regimes and compare them against benchmarks using risk-adjusted metrics.

README

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

Tool	Description
`get_database_status`	Database statistics: runs, strategies, symbols, date ranges
`list_search_runs`	List all backtest runs with filtering options
`get_run_details`	Get detailed information about a specific search run

Strategy Analysis

Tool	Description
`get_top_strategies`	Get top-ranked strategies with customizable sorting and filters
`get_strategy_details`	Get full details for a specific strategy ID
`compare_strategy_across_periods`	Analyze how a strategy performs across different market periods

Cross-Period Robustness

Tool	Description
`find_robust_strategies`	Find strategies that work consistently across ALL market periods
`get_period_summary`	Summary statistics for each market period tested

Benchmark Analysis

Tool	Description
`find_alpha_generators`	Find strategies that beat Buy & Hold benchmark
`get_risk_adjusted_rankings`	Rank strategies by Calmar ratio or other risk-adjusted metrics

Symbol Analysis

Tool	Description
`get_symbol_performance`	Performance breakdown by individual symbol
`find_best_symbols_for_strategy`	Find which symbols work best with a given strategy

Capital Deployment Analysis

Tool	Description
`get_capital_deployment_analysis`	Analyze portfolio capital utilization across periods using time-in-market data
`get_daily_position_counts`	Get exact daily position counts from trade-level data (requires Dec 2025+ runs)
`compare_timing_modes`	Compare Conservative (T+1) vs Aggressive (same-day) trade timing

Advanced

Tool	Description
`run_custom_query`	Execute custom SQL queries (read-only)
`get_schema`	Get database schema documentation
`list_strategy_ids`	List strategy IDs with optional pattern filtering

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.