Trading Journal Analytics MCP
MCP server that processes trading journal data to compute performance metrics, risk statistics, and trade pattern insights. It enables programmatic querying of historical trades for backtesting and optimization. Traders, quantitative analysts, and portfolio managers use it to extract actionable data from journal entries.
Overview
The trading-journal-analytics-mcp server provides Model Context Protocol (MCP) endpoints for analyzing trading journals. It ingests trade logs, positions, and notes to deliver quantitative insights, supporting data-driven decisions in trading strategies.
Key Capabilities
Capabilities focus on trading journal processing, though specific tools are not enumerated. Typical functions include:
- Parsing journal entries to extract trade details like entry/exit prices, volumes, and timestamps.
- Calculating metrics such as win rate, Sharpe ratio, maximum drawdown, and profit factor.
- Identifying patterns like recurring losses or high-performing assets via statistical analysis.
Use Cases
- Performance Review: A day trader uploads CSV journal files to compute monthly P&L and risk-adjusted returns, adjusting strategies based on sharpe_ratio outputs.
- Backtesting Validation: Quantitative developers query historical trades to validate algo performance against journal records.
- Portfolio Optimization: Fund managers analyze multi-asset journals for correlation insights and rebalancing signals.
- Compliance Reporting: Generate audit-ready summaries of trade sequences and PnL attribution.
Who This Is For
Day traders tracking personal performance, quantitative analysts building models from real trade data, portfolio managers reviewing team journals, and developers integrating trading analytics into custom platforms.