Final-API-Contest-Pi42

Crypto Trading Strategies: BTC & ETH Volume-Based Algorithms

Overview

This repository contains two robust algorithmic trading strategies designed for high-volume trading in crypto markets. These strategies aim to optimize trading performance by leveraging backtested data, effective stop-loss mechanisms, and profit-taking buffers.

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Strategy Details

Strategy 1: BTC/USDT

• Volume Generated: ₹25,00,000
• Loss Incurred: ₹790
• Observations:
  • Designed to maximize trading activity while maintaining controlled losses.
  • While the strategy performed well under normal conditions, stop-losses were frequently hit during high-volatility periods, leading to higher-than-expected losses.

Strategy 2: ETH/USDT

• Volume Generated: ₹17,00,000
• Loss Incurred: ₹155
• Highlights:
  • Implements a tighter stop-loss mechanism with a reduced risk threshold (maximum ₹200 loss).
  • Backtested with 0.03 ETH quantity to ensure scalable performance.
  • Focuses on minimizing drawdowns while maintaining significant trading volume.
  • Tested it with different quantities of eth and it works fine.

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Drawbacks

Strategy 1:

• High Volatility Sensitivity: During high-volatility events (e.g., Mondays and NY market opens), stop-losses were triggered more frequently, leading to avoidable losses.
• Limited Risk Control: The stop-loss buffer was not optimized for extreme market conditions, reducing profitability.

Strategy 2:

• Conservative Approach: The tighter stop-loss and price buffer resulted in fewer opportunities for profit.
• Capital Threshold Dependence: Strategy halts when capital falls below the minimum threshold, which might leave profits on the table during recovery phases.

Future Enhancements

To improve the performance and robustness of both strategies, the following steps can be taken:

1. Dynamic Stop-Loss Adjustments:

   • Implement an adaptive stop-loss mechanism based on real-time volatility metrics like ATR (Average True Range).
   • Use trailing stop-losses to lock in profits during strong price movements.

2. Market Condition Segmentation:

   • Categorize market conditions (e.g., low, medium, high volatility) and switch between strategies dynamically.
   • Develop specialized parameters for different scenarios to mitigate risks.

3. Fee Optimization:

   • Factor in exchange fees explicitly and minimize trades to reduce costs.

4. Diversification:

   • Backtest the strategies on multiple trading pairs to reduce dependence on a single market.

5. Machine Learning Integration:

   • Use reinforcement learning to optimize trading behavior.