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This project presents a meticulously designed cryptocurrency backtesting framework built on Python's Backtrader library and Ta-Lib technical indicators. It empowers traders to evaluate diverse trading strategies across multiple digital assets and timeframes. By simulating real-market conditions with historical data, it helps identify the most profitable trading approaches.
Technical Breakdown
1. Candlestick Data Acquisition (getdata)
- Utilizes the Python Binance library to fetch historical candlestick data via Binance API
- Supports multiple time intervals (1min, 1hr, 1day, etc.)
- Processes and stores data as CSV files for streamlined analysis
2. Performance Calculation (getresults)
get_result.pyscript evaluates all strategy-parameter combinationsRecords key metrics:
- Final portfolio value
- Win rate
- Net profit
- System Quality Number (SQN)
- Exports comprehensive CSV reports for comparative analysis
3. Backtesting Engine (backtest)
Implements Backtrader-based strategy execution with:
- Simple Moving Average (SMA) crossover
- Relative Strength Index (RSI) signals
Decision logic examples:
- Buy when price crosses above SMA or RSI < 30
- Sell when price crosses below SMA or RSI > 70
Practical Applications
Ideal for both individual traders and institutions seeking to:
✔ Validate strategies before live deployment
✔ Assess risk profiles across different approaches
✔ Optimize parameters for technical indicators
✔ Compare performance across cryptocurrencies (BTC, ETH, etc.)
Key Features
🔹 Multi-Asset Coverage: Supports major cryptocurrencies and customizable timeframes
🔹 Flexible Configuration: Adjustable commission rates, initial capital, and trade sizing
🔹 Visual Analytics: Detailed CSV outputs with graphical representations
🔹 Continuous Improvement: Planned auto-updates for strategy refinement
👉 Explore advanced trading tools
FAQ
Q: What programming skills are needed to use this framework?
A: Basic Python knowledge suffices. Familiarity with pandas and Backtrader is beneficial but not required.
Q: Can I test strategies on non-crypto assets?
A: While optimized for cryptocurrencies, the framework can be adapted for traditional markets with data source adjustments.
Q: How does this compare to commercial backtesting platforms?
A: It offers similar functionality without licensing costs, with full transparency via open-source code.
Q: What's the minimum historical data needed?
A: At least 3 months of daily data is recommended for statistically significant results.
Q: Are there pre-built strategy templates?
A: Yes, including SMA, RSI, and MACD implementations. Users can easily modify these or create custom strategies.
Q: How computationally intensive are the backtests?