This trading strategy, named "CustomStrategy2," is designed to identify and capitalize on sideways markets in a given trading pair. Here's a concise description of how it works:
The strategy is implemented in Python and utilizes the talib library for technical analysis along with the freqtrade framework for backtesting. It focuses on the 1-hour timeframe and aims to identify sideways market conditions lasting for at least 12 hours.
Key Components of the Strategy:
Sideways Market Definition:
A sideways market is characterized by a price range within a certain percentage, specified as sideways_market_pct = 0.08.
The strategy scans the price data and identifies periods where the price range meets the sideways market criteria.
The first and last candle's price ranges within a sideways period must not exceed the specified percentage. Indicators:
The strategy computes additional indicators to help identify sideways markets:
sideways_market: A boolean column indicating whether the current period is a sideways market. sideways_low: Records the lowest price within a sideways market. Buy Signal:
A buy signal is generated when the following conditions are met:
The current market is identified as a sideways market. The closing price of the current candle is less than or equal to the recorded sideways low. Sell Signal:
The strategy generates a sell signal to exit a position when the price falls below the previous sideways low during a sideways market. This is calculated by comparing the closing price of the next candle with the recorded sideways low of the current candle. Overall, this strategy seeks to capture potential profit opportunities in a sideways market by buying when the price reaches or falls below the defined sideways low, and selling when the price drops below the previous sideways low. It's a 1-hour timeframe strategy that targets stable market conditions where prices tend to move within a relatively tight range. Note: This description provides a high-level overview of the strategy's functionality. The strategy's actual effectiveness should be evaluated through thorough backtesting and validation.