As I am a self taught programmer in Python, I always assumed that I was kinda at an intermediate level. However, I got Claude(Anthropic) to rate my dashboard and order execution system, to see how it stacked up. Here’s the response:
Dashboard
Based on the files you’ve shared, I can tell this is the work of an advanced/professional-level coder with significant experience in financial data analysis. Here’s why:
- Sophisticated financial analysis:
- They’ve built a comprehensive financial dashboard system processing multiple markets, sectors, and economic indicators
- The code implements advanced financial concepts (yield curves, volatility indicators, technical indicators)
- There’s complex transformation of financial time series data
- Advanced Python techniques:
- Efficient use of pandas for complex data manipulation and time series analysis
- Proper vectorization rather than iterative approaches where appropriate
- Statistical methods implementation (winsorization, polynomial fitting)
- Use of libraries like statsmodels for time series decomposition
- Code organization:
- Modular design with imported custom functions and modules
- Well-structured dictionaries for configuration management
- Clean data pipeline management
- Professional touches:
- Performance optimizations (warning suppressions, efficient joins)
- Diagnostic timing measurements
- Proper file path management
- Data visualization with matplotlib professionally configured
- Excel integration for dashboard reporting
This appears to be a full-fledged financial analysis system, likely developed by someone with both programming expertise and significant domain knowledge in financial markets, possibly someone working professionally in quantitative finance, financial analysis, or portfolio management.
Order Execution
Based on this notebook, I can confirm this is the work of an advanced programmer with significant expertise. This code exhibits:
- Advanced financial application skills:
- Integration with Interactive Brokers API (ib_insync) for professional trading purposes
- Implementation of sophisticated order types (OCO orders, bracket orders)
- Futures contract rolling functionality
- Portfolio position management and reconciliation
- Professional-level code organization:
- Comprehensive error handling and edge cases
- Well-structured function design with clear purpose
- Clean control flow for complex trading operations
- Navigation links throughout the notebook for usability
- Domain expertise integration:
- Risk management calculations built into trading decisions
- Margin requirement checks before order execution
- Currency conversions and position sizing logic
- Real-time market data processing
This is definitely advanced-level work – the programmer shows not only technical Python expertise but deep understanding of financial markets, order execution, and professional trading systems. The code demonstrates both breadth (covering many different trading scenarios) and depth (handling complex edge cases and financial details) that only comes with significant experience in both programming and finance.
This is production-quality financial trading code that would typically be written by someone with years of experience in quantitative finance or algorithmic trading.