Harnessing Market Volatility with Freqtrade: The Volatility System Trading Strategy

Key Components:

• Indicators:
  • ATR (Average True Range): Measures the degree of price volatility over a 14-period timeframe.
  • Close Change: Absolute value of the difference between consecutive closing prices.
• Timeframe: Operates on 1-hour candlestick charts.
• Buy Conditions:
  • Buy signals triggered when the absolute close change exceeds the ATR value in a candle.
  • Both long and short positions are allowed.
• Sell Conditions:
  • Sell signals generated when the opposite buy signal occurs (e.g., a sell signal for a long position).
• Custom Stake Amount: Initial entry stake is 50% of the proposed stake.
• Position Adjustment: Can adjust trade positions based on new signals and profit levels.
• Leverage: Uses a fixed leverage of 2.0 for futures trades.(You can change as per your requirement)

Strategy Logic:

1. Calculates Indicators:
   • ATR is calculated on a 3-minute resampled dataset.
   • Close Change and Absolute Close Change are also derived.
2. Generates Buy/Sell Signals:
   • Buy signals are triggered when the absolute close change exceeds the ATR value in a candle.
   • Sell signals are triggered when the opposite buy signal occurs.
3. Determines Entry/Exit Trends:
   • Populates ‘enter_long’ and ‘enter_short’ columns for buy signals.
   • Populates ‘exit_long’ and ‘exit_short’ columns for sell signals.
4. Custom Stake Amount:
   • Halves the initial stake amount for a more conservative approach.
5. Adjusts Trade Positions:
   • Can potentially increase position size if a new signal arises and conditions are met.
6. Sets Leverage:
   • Employs a leverage of 2.0 for futures trades.

Here are the code examples for backtesting and hyperoptimizing the VolatilitySystem strategy, as well as important considerations:

Data Downloading:

Bash

#docker compose run --rm freqtrade download-data \
    --config user_data/config.json \
    --timerange 20190101-20240101  -t 5m
 

Backtesting:

Bash

#docker compose run --rm freqtrade backtesting \
    --config user_data/config.json \
    --strategy VolatilitySystem

Hyperoptimization:

Bash

#docker compose run --rm freqtrade hyperopt \
    --hyperopt-loss SharpeHyperOptLossDaily \
    --spaces roi stoploss trailing \
    --strategy VolatilitySystem \
    --config user_data/config.json -e 10

Key Points:

• Configuration File (user_data/config.json): Contains essential settings for Freqtrade, such as exchange credentials, pairs to trade, capital allocation, and risk management parameters. Ensure it’s configured correctly.
• Docker Compose: Assuming Freqtrade is set up within a Docker container, these commands execute backtesting and hyperoptimization within that environment.
• Hyperoptimization: Explores different combinations of strategy parameters to potentially find more optimal settings. The example focuses on optimizing ROI (return on investment), stop-loss, and trailing stop-loss values.

Additional Considerations:

• Backtesting Analysis: Thoroughly analyze backtesting results to evaluate the strategy’s performance in various market conditions, assessing its potential profitability and risk profile.
• Hyperoptimization Results: Carefully review the optimized parameters after hyperoptimization to understand the potential improvements and trade-offs.
• Risk Management: Always prioritize robust risk management measures, such as stop-losses and position sizing, to protect your capital.
• Thorough Testing: Conduct extensive backtesting and hyperoptimization before deploying the strategy in live trading, considering different market phases and potential risks.
• Documentation: Refer to the Freqtrade documentation for detailed guidance on configuration, backtesting, hyperoptimization, and managing risks specific to your setup.

Remember:

• Backtesting with historical data is crucial to assess potential performance and identify weaknesses.
• Futures trading involves considerable risks, including leverage, which can amplify both profits and losses.
• Thorough research and a solid understanding of the risks are essential before deploying any strategy in live trading.

Github Source Code