IB Analytics

Interactive Brokers portfolio analytics library with AI-powered investment analysis and development automation.

🎯 What This Does

For Investors (Mode 1 & 3):

• ⚡ 95% faster investment strategy generation (6-8 hours → 15-20 minutes)

• 📊 Parallel market analysis of all holdings simultaneously

• 🎯 Consolidated multi-account view with accurate portfolio metrics

• 📈 Professional options strategies with specific strikes, Greeks, and Max Pain

• 💰 Tax-optimized execution plans across multiple accounts

• 📉 Multi-timeframe technical analysis with entry/exit signals

For Developers (Mode 3):

• 🤖 90% faster issue resolution (80 minutes → 8 minutes via /resolve-gh-issue)

• ✅ Automated quality gates (black, ruff, mypy, pytest)

• 🔄 Complete TDD workflow (tests → code → PR automation)

• 📝 Auto-generated PR descriptions with comprehensive context

Time Savings Summary:

✨ Key Features

Investment Analysis:

• 📊 Multi-Account Support: Consolidated analysis across all IB accounts

• 📈 Market Analysis: Multi-timeframe technicals, options Greeks, IV metrics, Max Pain

• 💰 Tax Optimization: Cross-account tax harvesting and loss optimization

• 🎯 Options Strategies: Professional-grade recommendations with specific strikes

• 📉 Technical Analysis: Support/resistance, RSI, MACD, volume analysis

Portfolio Management:

• 🔄 Flex Query API Integration: Automated data fetching via IB Flex Query API v3

• 📈 Comprehensive Analysis: Performance, tax, cost, risk, and bond analytics

• 📊 Position History: SQLite time-series tracking for historical analysis

• 🎯 Type-Safe: Built with Pydantic v2 for robust data validation

• ⚡ Async Support: Parallel data fetching for multiple accounts

• 📄 Rich Reports: Console, HTML, and optional PDF reporting

Development Automation (Mode 3):

• 🤖 10 Specialized AI Agents: strategy-coordinator, market-analyst, data-analyzer, + 7 dev agents

• 📋 15 Slash Commands: Automated workflows for analysis and development

• ✅ Quality Gates: Automated black, ruff, mypy, pytest enforcement

• 🔄 GitHub Integration: Issue → Branch → Tests → Code → PR workflow

Installation

Quick Start

1. Configuration

Create a .env file with your IB Flex Query credentials:

Note: To analyze multiple accounts, configure them in your IB Flex Query settings. A single query can return data for multiple accounts.

2. Fetch Data

3. Run Analysis

4. Generate Reports

Docker Usage

Quick Start with Docker

Docker Compose

Create .env file:

Run:

Docker Security Features

• Non-root user: Runs as mcpuser (UID 1000)

• Read-only filesystem: Root filesystem is read-only

• Resource limits: CPU (2 cores) and memory (2GB) limits

• No new privileges: Prevents privilege escalation

• Data persistence: Data stored in mounted volume

Programmatic Usage

Project Structure

Available Analyzers

• PerformanceAnalyzer: Overall trading performance metrics

• TaxAnalyzer: Tax liability calculations (OID, capital gains)

• CostAnalyzer: Commission and cost efficiency analysis

• RiskAnalyzer: Interest rate and market risk scenarios

• BondAnalyzer: Bond-specific analytics (YTM, duration, etc.)

Investment Analysis Tools (MCP)

IB Analytics provides advanced investment analysis tools through the MCP server. These tools integrate Yahoo Finance data for comprehensive market analysis.

Stock & Fund Analysis

get_stock_info - Comprehensive Stock Information

Get detailed information about stocks, ETFs, and funds including fundamental metrics.

Features:

• Real-time price data (current, open, high, low, volume)

• Valuation metrics (P/E, P/B, P/S, PEG, EV/EBITDA, EV/Revenue)

• Profitability metrics (profit margin, ROE, ROA, operating margin, EPS)

• Financial health (total cash, debt, debt-to-equity, current/quick ratios, FCF)

• Growth metrics (revenue growth, earnings growth)

• Dividend information (yield, ex-date, payment date)

• Trading information (52-week high/low, market cap, volume)

Example Usage:

get_current_price - Quick Price Check

Get current price and key metrics for quick market checks.

Example Usage:

get_stock_data - Historical Price Data with Technical Indicators

Fetch OHLCV data with optional technical indicators (SMA, EMA, RSI, MACD, Bollinger Bands).

Example Usage:

Comparative Analysis

compare_with_benchmark - Performance vs Benchmark

Compare stock/fund performance against a benchmark (default: SPY).

Features:

• Return comparison (total return, annualized return)

• Volatility analysis (standard deviation comparison)

• Risk metrics (beta, alpha, correlation)

• Risk-adjusted returns (Sharpe ratio comparison)

• Interpretation and insights

Example Usage:

get_analyst_consensus - Analyst Ratings & Target Prices

Get analyst recommendations, target prices, and earnings estimates.

Features:

• Rating distribution (strong buy/buy/hold/sell/strong sell)

• Consensus rating derived from distribution

• Target prices (mean, median, high, low)

• Upside potential percentage

• Earnings estimates (date, EPS range, revenue estimates)

Example Usage:

Portfolio-Level Analysis

calculate_portfolio_metrics - Advanced Portfolio Metrics

Calculate comprehensive risk-adjusted performance metrics for your portfolio.

Features:

• Return metrics: Total return, annualized return, volatility

• Risk-adjusted ratios:

  • Sharpe Ratio: Risk-adjusted return (excess return / total risk)

  • Sortino Ratio: Downside risk-adjusted return

  • Calmar Ratio: Return / maximum drawdown

  • Treynor Ratio: Excess return / systematic risk (beta)

  • Information Ratio: Excess return / tracking error

• Risk metrics:

  • Maximum drawdown (largest peak-to-trough decline)

  • Downside deviation (volatility of negative returns)

  • Beta (systematic risk vs benchmark)

  • Tracking error (deviation from benchmark)

  • Alpha (excess return vs expected return)

Parameters:

• csv_path: Path to IB Flex Query CSV file

• benchmark: Benchmark symbol (default: "SPY")

• risk_free_rate: Annual risk-free rate (default: 0.05 = 5%)

• period: Time period for analysis (1mo, 3mo, 6mo, 1y, 2y, 5y)

Example Usage:

Limitations:

• ⚠️ Requires Yahoo Finance data for portfolio positions

• ⚠️ Will not work for bond-only portfolios (STRIPS bonds have no Yahoo Finance data)

• ✅ Works well for stock and ETF portfolios

analyze_portfolio_correlation - Diversification Analysis

Analyze correlation between portfolio positions to assess diversification.

Features:

• Correlation matrix between all positions

• High correlation pairs identification (|r| > 0.7)

• Portfolio beta vs benchmark (default: SPY)

• Diversification score (0-100)

• Position weights in portfolio

• Interpretation and recommendations

Parameters:

• csv_path: Path to IB Flex Query CSV file

• period: Time period for correlation analysis (1mo, 3mo, 6mo, 1y, 2y, 5y)

Example Usage:

Limitations:

• ⚠️ Requires Yahoo Finance data for portfolio positions

• ⚠️ Will not work for bond-only portfolios

• ✅ Best for analyzing stock/ETF portfolio diversification

Options Analysis

get_options_chain - Options Data

Get calls and puts data for a stock including strike prices, volume, open interest, and implied volatility.

calculate_put_call_ratio - Market Sentiment

Calculate put/call ratio based on open interest or volume to gauge market sentiment.

Example Usage:

News & Market Intelligence

get_stock_news - Latest News Articles

Get the latest news articles for any stock symbol from Yahoo Finance.

Features:

• Latest news articles (up to 50)

• Article metadata (title, publisher, publish time, summary)

• Direct links to full articles

• Thumbnail images for visual context

• Related ticker symbols

• Multiple content types (stories, videos, press releases)

Parameters:

• symbol: Stock ticker symbol (e.g., "AAPL", "TSLA", "QQQ")

• limit: Number of articles to return (default: 10, max: 50)

Example Usage:

Response Includes:

• Title: Article headline

• Publisher: News source (e.g., Yahoo Finance, Bloomberg, Reuters)

• Link: Direct URL to full article

• Publish Time: When the article was published (ISO 8601 format)

• Summary: Brief article summary or description

• Type: Content type (STORY, VIDEO, etc.)

• Thumbnail: Image URL for articles with visuals

• Related Tickers: Other stocks mentioned in the article

Use Cases:

• Investment Research: Stay updated on company news and developments

• Market Sentiment: Gauge market sentiment from news coverage

• Due Diligence: Research companies before making investment decisions

• Portfolio Monitoring: Track news for holdings in your portfolio

• Event-Driven Trading: Identify market-moving events quickly

Example Natural Language Queries:

Market Sentiment Analysis

analyze_market_sentiment - Multi-Source Sentiment Analysis

Get comprehensive market sentiment from multiple sources: news articles, options market, and technical indicators.

Features:

• News Sentiment: Sentiment analysis from recent news articles and headlines

• Options Market Sentiment: Derived from Put/Call ratios, IV Rank, and Max Pain

• Technical Sentiment: Based on RSI, MACD, trend analysis, and support/resistance

• Composite Sentiment: Weighted aggregation of all sources with configurable weights

• Confidence Scoring: Each sentiment source provides confidence level

• Key Themes: Identified sentiment drivers (e.g., "bullish_momentum", "oversold_rsi")

• Risk Factors: Highlighted concerns (e.g., "overbought_rsi", "near_resistance")

Parameters:

• symbol: Stock ticker symbol (e.g., "AAPL", "SPY", "QQQ")

• sources: Comma-separated sentiment sources (default: "composite")

  • "news": News article sentiment only

  • "options": Options market sentiment only

  • "technical": Technical indicator sentiment only

  • "composite": All sources combined (recommended)

Sentiment Score Range: -1.0 (very bearish) to +1.0 (very bullish)

Example Usage:

Composite Sentiment Weights (default):

• News Sentiment: 40%

• Options Market Sentiment: 30%

• Technical Sentiment: 30%

Response Includes:

• Overall Score: Composite sentiment score (-1.0 to +1.0)

• Confidence: Aggregated confidence level (0.0 to 1.0)

• Individual Sources: Breakdown by news, options, technical with individual scores

• Key Themes: Positive sentiment drivers identified across sources

• Risk Factors: Warning signs and bearish indicators

• Reasoning: Detailed explanation of sentiment calculation

• Recommendation: Suggested action (bullish/neutral/bearish stance)

Sentiment Interpretation Guide:

Confidence Interpretation:

• High (0.7-1.0): Multiple sources agree, strong signal reliability

• Medium (0.4-0.7): Some agreement, moderate signal reliability

• Low (0.0-0.4): Sources disagree, weak signal reliability

Key Themes Examples:

• bullish_momentum: Strong upward price movement

• oversold_rsi: RSI below 30, potential reversal

• bullish_options_flow: Low Put/Call ratio, bullish positioning

• positive_news_sentiment: Positive news coverage

• high_iv_environment: Elevated volatility, favorable for option sellers

Risk Factors Examples:

• overbought_rsi: RSI above 70, potential pullback

• near_resistance: Price approaching resistance level

• bearish_macd_cross: MACD bearish crossover

• high_put_call_ratio: Bearish options positioning

• negative_news_sentiment: Negative news coverage

Use Cases:

• Entry Timing: Identify optimal entry points with sentiment confirmation

• Exit Strategy: Detect sentiment shifts for position management

• Risk Assessment: Gauge market mood before major positions

• Contrarian Signals: Identify extreme sentiment for reversal trades

• Confirmation: Validate technical/fundamental analysis with sentiment

• Options Strategy: Choose premium-selling vs premium-buying based on IV environment

Example Natural Language Queries:

Integration with Symbol Analysis:

The sentiment analysis is automatically included when using /analyze-symbol command:

This provides:

• Multi-timeframe technical analysis

• Current price and fundamentals

• Options market analysis

• Comprehensive sentiment analysis (news + options + technical)

• Trading recommendation with sentiment-based conviction

Sentiment Components Breakdown:

1. News Sentiment (40% weight):

   • Analyzes recent news articles and headlines

   • Natural language processing for sentiment extraction

   • Publisher credibility weighting

   • Time decay for article recency

2. Options Market Sentiment (30% weight):

   • Put/Call Ratio: <0.7 bullish, 0.7-1.3 neutral, >1.3 bearish

   • IV Rank: Low IV (buy premium), High IV (sell premium)

   • Max Pain: Price gravitation toward max pain strike

3. Technical Sentiment (30% weight):

   • RSI: <30 oversold (bullish), >70 overbought (bearish)

   • MACD: Bullish/bearish crossovers and divergences

   • Trend Analysis: Short/medium/long term trend alignment

   • Support/Resistance: Price proximity to key levels

Performance Notes:

• Composite sentiment calculation: ~2-3 seconds

• Individual source queries: ~1-2 seconds each

• Cached results valid for 5 minutes (real-time market data)

• Concurrent analysis supported for multiple symbols

Code Examples:

See examples/sentiment_analysis_example.py for complete programmatic usage examples including:

• Composite sentiment analysis

• Individual source queries (technical, options, news)

• Natural language query examples

• Integration with /analyze-symbol command

• Sentiment interpretation guidelines

Usage Examples in Claude Desktop

Once you've set up the MCP server in Claude Desktop, you can use natural language:

Technical Notes

Data Sources:

• Portfolio data: Interactive Brokers Flex Query (CSV/XML)

• Market data: Yahoo Finance via yfinance library

• Real-time quotes: Yahoo Finance delayed quotes (15-20 minutes)

Performance Metrics Definitions:

• Sharpe Ratio: (Return - Risk-free rate) / Volatility. Higher is better. >1 is good, >2 is excellent.

• Sortino Ratio: Like Sharpe but only penalizes downside volatility. Better for asymmetric returns.

• Calmar Ratio: Annualized return / Maximum drawdown. Measures return per unit of downside risk.

• Treynor Ratio: (Return - Risk-free rate) / Beta. Measures return per unit of systematic risk.

• Information Ratio: (Portfolio return - Benchmark return) / Tracking error. Measures active management skill.

• Beta: Systematic risk relative to benchmark. 1.0 = same risk as benchmark, >1.0 = more volatile.

• Alpha: Excess return vs expected return (CAPM). Positive alpha = outperformance after risk adjustment.

• Maximum Drawdown: Largest peak-to-trough decline. Key risk metric for understanding worst-case scenarios.

Correlation Interpretation:

• |r| > 0.7: High correlation (limited diversification benefit)

• 0.3 < |r| < 0.7: Moderate correlation (some diversification)

• |r| < 0.3: Low correlation (good diversification)

• Negative correlation: Positions move in opposite directions (excellent diversification)

Usage Modes

IB Analytics supports three distinct usage modes for different user types and workflows:

1. Claude Desktop (Conversational Analysis) 💬

Target Users: Investors, portfolio managers, financial analysts

Use Case: Natural language portfolio analysis and investment research

Setup: Install MCP server in Claude Desktop configuration

What You Can Do:

• Portfolio Analysis: "What's my portfolio performance this quarter?"

• Investment Research: "Should I buy Apple stock right now?"

• Tax Planning: "Show me tax-loss harvesting opportunities"

• Market Analysis: "Analyze SPY with options strategies"

• Investment Strategy: "Create a comprehensive investment plan"

Key Benefits:

• ✅ Zero coding required - just ask questions in natural language

• ✅ Automated data fetching from Interactive Brokers

• ✅ Complete analysis from a single question

• ✅ Real-time market data integrated with your portfolio

Example Workflow:

Time Savings: 2-3 hours of manual analysis → 2 minutes

2. Claude Code + MCP (Custom Analysis) 🔧

Target Users: Data scientists, quantitative analysts, developers

Use Case: Flexible data analysis with composable MCP tools

Setup: Use Claude Code with MCP server enabled

What You Can Do:

• Custom Metrics: Calculate any combination of performance metrics

• Fine-grained Data: Access trades, positions, account data directly

• Period Comparisons: Compare performance across any date ranges

• Advanced Analysis: Build custom strategies and backtests

Key Benefits:

• ✅ Composable tools - mix and match for custom analysis

• ✅ Fine-grained control - filter by symbol, asset class, date range

• ✅ Programmatic access - integrate into your own workflows

• ✅ Strategy resources - tax context, rebalancing guides, risk analysis

Example Workflow:

Time Savings: 4-5 hours of data extraction and analysis → 15 minutes

3. Claude Code + Repository (Development Automation) 🚀

Target Users: Developers, power users, automation engineers

Use Case: Complete development workflow automation with specialized AI agents

Setup: Clone repository and use Claude Code in project directory

What You Can Do:

📊 Portfolio & Investment Analysis (Slash Commands)

• /investment-strategy - Master command: Complete investment strategy with portfolio + market analysis

  • Parallel market analysis: Analyzes all holdings simultaneously (80-90% time reduction)

  • Consolidated multi-account view: True portfolio-level analysis across ALL accounts

  • 2-year chart context for every position with entry/exit scenarios

  • Options strategies with specific strikes and premiums

  • Tax-optimized execution plans per account

  • Actionable priorities: Urgent (this week) → High (this month) → Medium (this quarter)

• /analyze-symbol SYMBOL - Comprehensive symbol analysis (stocks, ETFs, crypto, forex) with multi-timeframe technicals

  • Daily/weekly/monthly trend analysis with confluence scoring

  • Support/resistance levels with entry/exit signals

  • Options market analysis (IV Rank, Greeks, Max Pain) when applicable

  • News sentiment and catalysts

  • Buy/Sell/Hold rating with conviction level (1-10)

• /options-strategy SYMBOL - Detailed options analysis

  • IV environment assessment (buy vs sell premium)

  • Greeks analysis with risk assessment

  • 2-3 specific strategy recommendations with exact strikes

  • Max profit/loss, breakeven, probability of profit

  • Risk/reward comparison with best strategy selection

• /optimize-portfolio - Deep portfolio analysis and recommendations

• /tax-report --save - Tax planning with optimization strategies

• /compare-periods START END START END - Period-over-period performance comparison

Time Savings:

• Investment strategy: 6-8 hours manual research → 15-20 minutes (95% time reduction)

• Stock analysis: 1-2 hours research → 2-3 minutes (97% time reduction)

• Options strategy: 45-60 minutes → 3-5 minutes (93% time reduction)

🤖 AI-Powered Development (7 Specialized Sub-Agents)

Investment Analysis Agents:

• data-analyzer 📊: Portfolio analysis specialist

  • Consolidated multi-account analysis

  • Performance, tax, cost, risk, bond analytics

  • Time-series position tracking

• market-analyst 📈: Stock and options market specialist (NEW!)

  • Multi-timeframe technical analysis

  • Options Greeks, IV metrics, Max Pain calculations

  • Entry/exit timing with scenarios

  • Parallel execution: Analyzes 5-10 stocks simultaneously

• strategy-coordinator 🎯: Investment strategy orchestrator (NEW!)

  • Integrates portfolio + market analysis

  • Coordinates data-analyzer + market-analyst subagents

  • Parallel market analysis: 80-90% time reduction

  • Generates actionable, prioritized recommendations

Development Agents:

• test-runner 🧪: Testing and quality assurance

• code-implementer 💻: Feature implementation with TDD

• code-reviewer 📝: Code quality enforcement

• performance-optimizer ⚡: Profiling and optimization

• api-debugger 🔧: IB API troubleshooting

• issue-analyzer 🔍: GitHub issue analysis

Time Savings:

• Portfolio analysis: 3-4 hours → 5 minutes (95% reduction)

• GitHub issue resolution: 80 minutes → 8 minutes (90% reduction)

• Quality checks: 15 minutes → 2 minutes (87% reduction)

🔄 Complete Development Workflow Automation

GitHub Integration:

Quality Automation:

Expected Impact:

• Time Savings: 90% reduction in routine tasks

• Quality Improvement: Automated enforcement of best practices

• Consistency: Standardized workflows across team

• Documentation: Auto-generated PR descriptions and test reports

See .claude/README.md for complete documentation.

Feature Comparison

Recommendation:

• Start with Mode 1 if you're an investor looking for quick insights

• Use Mode 2 if you need custom analysis or programmatic access

• Adopt Mode 3 if you're developing features or need advanced automation

Position History & Time-Series Analysis

IB Analytics automatically stores daily position snapshots in SQLite for historical analysis and time-series tracking.

Features

• Automatic Sync: Positions are automatically saved to SQLite when fetching data via fetch_ib_data

• Historical Tracking: Query position history over any date range

• Portfolio Evolution: Compare portfolio snapshots between two dates

• Statistical Analysis: Calculate min/max/average metrics for positions over time

• Multi-Account Support: Store and query positions from multiple IB accounts

Storage Location

Position data is stored in data/processed/positions.db (SQLite database).

Usage

Automatic Sync (Recommended)

Position snapshots are automatically saved when fetching data:

Manual Sync

Sync existing XML files to SQLite:

Historical Queries (MCP Tools)

get_position_history - Time Series for a Symbol

Get position history for a specific symbol over a date range:

Returns: Daily snapshots including quantity, price, value, and P&L for each date.

get_portfolio_snapshot - Single-Day Portfolio

Get all positions for an account on a specific date:

Returns: All positions on that date with values, quantities, and P&L.

compare_portfolio_snapshots - Portfolio Changes

Compare portfolio composition between two dates:

Returns:

• Positions added/removed

• Value changes for continuing positions

• Total portfolio value change

• Percentage changes

get_position_statistics - Statistical Summary

Get min/max/average statistics for a position over time:

Returns: Min/max/average for price, value, and unrealized P&L.

get_available_snapshot_dates - Available Dates

List all dates with position snapshots for an account:

Use Cases

1. Performance Tracking: Monitor how positions perform over time

2. Rebalancing Analysis: Identify portfolio drift from target allocations

3. Tax Planning: Analyze holding periods and gain/loss trends

4. Portfolio Evolution: Visualize how your portfolio composition changes

5. Risk Management: Track concentration risk and diversification over time

6. Historical Analysis: Backtest strategies against actual position history

Database Schema

position_snapshots: Daily position data per account

• Composite key: (account_id, snapshot_date, symbol)

• Fields: quantity, price, value, cost basis, P&L, bond-specific fields

• Indexes: account+date, symbol+date, date, asset_class

snapshot_metadata: Snapshot-level statistics

• Tracks total positions, portfolio value, cash balance per snapshot

• Links to source XML files for audit trail

Data Integrity

• Deduplication: Automatic deduplication via UNIQUE constraints

• Decimal Precision: TEXT storage preserves financial calculation precision

• Transaction Safety: ACID compliance for data consistency

• Indexed Queries: Fast queries via strategic indexes

• Audit Trail: Source XML file tracking for each snapshot

MCP Server Integration

IB Analytics provides a Model Context Protocol (MCP) server for integration with Claude Desktop and other MCP clients.

Quick Start

Features

• 22 Tools:

  • IB Portfolio: Fetch data, performance/cost/bond/tax/risk analysis, portfolio summary

  • Position History: Position history, portfolio snapshots, snapshot comparison, statistics, available dates

  • Stock Analysis: Stock info, current price, historical data, options chain, put/call ratio, news, market sentiment

  • Investment Analysis: Benchmark comparison, portfolio metrics, correlation analysis, analyst consensus

• 6 Resources: Access portfolio data, account info, trades, and positions via URI patterns

• 5 Prompts: Pre-configured analysis templates for common workflows

Claude Desktop Setup

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

See .claude/CLAUDE.md for detailed MCP documentation and usage patterns.

Development

Requirements

• Python 3.9+

• Interactive Brokers account with Flex Query access

Dependencies

• requests (2.32.5+): HTTP client for API calls

• pandas (2.2.3+): Data analysis and manipulation

• pydantic (2.10.0+): Data validation and settings management

• httpx (0.27.0+): Async HTTP client for parallel requests

• rich (13.7.0+): Beautiful console output

• typer (0.12.0+): CLI framework

• yfinance (0.2.40+): Yahoo Finance data integration

• fastmcp (2.0.0+): Model Context Protocol server framework

Security

MCP Server Security

The IB Analytics MCP server implements security best practices:

• Error Masking: Internal error details are masked from clients (configurable with IB_DEBUG=1)

• Input Validation: All inputs are validated before processing

• File Path Protection: Path traversal attacks are prevented

• File Size Limits: Maximum file size: 10MB

• Timeout Protection: All operations have timeout limits

• Retry Logic: Automatic retry for transient errors (max 3 attempts)

Debug Mode

Enable debug mode for detailed error information (development only):

Warning: Never enable debug mode in production as it exposes internal error details.

Credentials Security

• Never commit .env files to version control

• Store credentials in environment variables or secure secret management systems

• Use separate credentials for development and production

• Regularly rotate API tokens

Troubleshooting

Common Issues

1. "Configuration error: Failed to load credentials"

Problem: QUERY_ID or TOKEN not found in environment

Solution:

2. "Validation error for 'start_date': Invalid date format"

Problem: Date format is incorrect

Solution: Use YYYY-MM-DD format

3. "IB API error: Statement not yet ready"

Problem: IB Flex Query is still processing

Solution: The server automatically retries (up to 3 times). If it still fails, wait a few minutes and try again.

4. "Yahoo Finance API error: No data found for SYMBOL"

Problem: Invalid stock symbol or no data available

Solution:

• Verify the ticker symbol is correct

• Check if the symbol is traded on supported exchanges

• Try a different period or interval

5. "Timeout: IB API call timed out after 60 seconds"

Problem: API call took too long

Solution:

• Check your internet connection

• Reduce the date range

• Try again during off-peak hours

Testing

Run tests to verify your installation:

MCP Server Testing

Test the MCP server connection:

Performance Optimization

If experiencing slow performance:

1. Reduce date ranges: Fetch smaller time periods

2. Use file caching: Reuse previously fetched data

3. Limit technical indicators: Only request needed indicators

4. Parallel processing: Use async operations where possible

License

MIT

Author

Kenichiro Nishioka

Support

For issues and questions, please check the IB Flex Query documentation.