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Klaymatrix Data Labs
Klaymatrix Data Labs

Case Study - Trading Strategy Backtesting

17

Client: US-based Individual Trader / Investment Firm
Project Type: Trading Strategy Backtesting
Tools Used: Python, API Integration (Market Data), Pandas, NumPy, Matplotlib
Services: Strategy Simulation, Performance Analysis, Portfolio Modeling
Sector: Financial Markets, Trading

About the Client

A US-based trader or investment enthusiast looking to validate a custom trading strategy using real-world market data. The client aimed to understand historical performance before actual deployment.

The Challenge

  • Validate the viability of a custom trading strategy using real-time and historical market data

  • Incorporate multiple data streams via APIs

  • Measure key metrics like drawdown, Sharpe ratio, and overall returns

  • Simulate dynamic portfolio behavior with buy/sell signals

Our Solution

  • Developed a Python-based backtesting engine tailored to client’s strategy

  • Integrated market data via APIs to pull relevant time-series data

  • Programmed logic to simulate buy/sell decisions

  • Calculated performance metrics: Max Drawdown, Sharpe Ratio, Total Return

  • Modeled dynamic portfolio allocation to reflect real trading behavior

  • Used VBA to automate calculations and backend logic

  • Developed automated reports across separate sheets for daily and weekly review

  • Enabled collation of data from multiple Excel files

Implementation Highlights

  • Data fetched using robust API handling with fallback mechanisms

  • Extensive use of Python and its libraries

  • Visualized performance and signal points using Matplotlib

  • Modular code allowing client to adjust parameters or test variations

Demonstrated Expertise

  •  Advanced use of Python for financial analysis and strategy validation

  • Practical risk analysis using industry-standard metrics (Sharpe ratio, drawdown)

  • API-driven data handling and dynamic simulation of portfolio behavior

  • Delivered reusable and customizable script enabling client-led experimentation

Ready to Solve Similar Challenges?

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