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Moving Average Trading Bot

Algorithmic Trading • Automated Systems • Crossover Strategy
15.67%
CAGR
1.89
Sharpe Ratio
-15.23%
Max Drawdown
Live & Operating Profitably

Objective

To develop an automated trading bot based on the moving average crossover strategy that can operate autonomously in live markets with minimal human intervention.

Methodology

The bot continuously monitors the price of a given asset and executes trades when the short-term moving average crosses the long-term moving average.

  • Real-time price monitoring with moving average calculations
  • Automated trade execution on crossover signals
  • Backtrader library for backtesting and live trading
  • REST API integration for market data and order management
  • Docker containerization for consistent deployment

Results

The trading bot has been deployed and is operating profitably in live markets.

Demonstrated effectiveness of moving average strategy in various market conditions

24/7 autonomous operation with consistent performance

Robust fault-tolerance and error handling mechanisms

Challenges

Key challenges overcome during development and deployment:

  • Implementing a reliable and fault-tolerant system for 24/7 operation
  • Ensuring system stability without manual intervention
  • Handling network issues and API connection failures gracefully
  • Optimizing parameters for different market regimes
  • Implementing proper logging and monitoring for production environment

Technologies Used

PythonPandasBacktraderREST APIDocker
View on GitHubBack to Portfolio

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Data Engineer focused on building robust data pipelines, scalable architectures, and automated workflows. Enabling teams to make smarter, data-driven decisions through reliable systems and practical engineering skills.

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