Lecture Notes: Creating a Crypto Trading Bot in Python

Introduction

• Objective: Building a customizable trading bot in Python for crypto trading
• Highlight: Bot leverages crypto's volatility for profitability
• Features:
  • Can manage multiple coins simultaneously
  • Free and open-source

Case Study: Performance Metrics

• Ethereum
  • Profit: 764% in a few years
  • Win Rate: 85%
  • Maximum Drawdown: 16%
  • Sharp Ratio: 1.3
• Bitcoin
  • Profit: 1,141%
  • Win Rate: 86%
  • Maximum Drawdown: 7%
  • Sharp Ratio: 1.4
• Disclaimer: Past performance is not an indicator of future results.

Technical Setup

• Code is in Python; suitable for those with basic Python knowledge
• Resources:
  • Comprehensive Python for Finance course available
  • GitHub repository with codes
  • Strategy also available in Pine Script

Strategy Overview

• Type: Mean Reversion Strategy, ideal for a 1-hour timeframe
• Key Components:
  • Short-term price average line and surrounding envelopes
  • Trigger orders based on price hitting these envelopes

Capital and Risk Management

• Position size based on capital divided by the number of envelopes
• Risk mitigation by spreading entries across several bands

Strategy Strengths and Weaknesses

• Strengths:
  • Effective in ranging markets
  • Good hedging tool
• Weaknesses:
  • Vulnerable to sudden, strong trends (pumps and dumps)

Handling Risks

• Tools:
  • Stop-loss management
  • Price jump percentage rule to handle spikes
• Market Considerations:
  • Avoid coins reacting violently to news

Backtesting with Python

• Utilize historical data from exchanges like Binance
• Parameters & rules testing
• Importance of robust parameters over optimization to avoid overfitting

Deployment

• Server Setup: AWS free instance for running the bot
• API Keys: Necessary for secure operations on exchanges
• Automation: Use of cron jobs for periodic execution

Practical Execution

• Script configuration for different coins
• Emphasis on balancing risk across multiple coins
• Analysis of backtest results and interpretation of metrics

Final Thoughts

• Encouragement to test and verify on historical data
• Note on the importance of careful financial management and risk assessment
• Contact and resource options for further support and community engagement

Conclusion

• Emphasis on learning and testing
• Reminder of the non-financial advice nature of the material

• For any further questions or comments, community Discord is available.
• Additional resources and bonuses available through registration links.