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The strategy implemented in the code is a simple trend-following strategy that uses moving averages and the Relative Strength Index (RSI) indicator to generate buy and sell signals. Here is a brief description of what the strategy does:
It imports necessary libraries for data analysis and indicator calculations. The strategy is defined as a class named "test" and inherits from the IStrategy interface.
The strategy specifies its version and sets some configuration parameters, such as the trading timeframe, the use of a sell signal, stop loss value, etc.
The strategy defines a dictionary called "minimal_roi" that specifies the minimum return on investment (ROI) required for a successful trade.
The strategy initializes some variables and a custom_info dictionary to store additional information. The populate_indicators() function is responsible for calculating technical indicators, such as exponential moving averages (EMA) and RSI, and populates them in the input dataframe. The populate_buy_trend() function generates buy signals based on a "Golden Cross" condition, where the 5-day EMA crosses above the 10-day EMA. The populate_sell_trend() function generates sell signals based on the RSI crossing above 70, indicating an overbought condition. The custom_sell() function is a custom sell signal implementation that checks for a specific condition (RSI overbought) and returns a signal if the condition is met. The strategy uses these functions to generate buy and sell signals for backtesting trading strategies. Note: This description provides a high-level overview of the strategy's functionality. Further analysis and optimization may be required for actual trading purposes.
The strategy specifies its version and sets some configuration parameters, such as the trading timeframe, the use of a sell signal, stop loss value, etc.
The strategy defines a dictionary called "minimal_roi" that specifies the minimum return on investment (ROI) required for a successful trade.
The strategy initializes some variables and a custom_info dictionary to store additional information. The populate_indicators() function is responsible for calculating technical indicators, such as exponential moving averages (EMA) and RSI, and populates them in the input dataframe. The populate_buy_trend() function generates buy signals based on a "Golden Cross" condition, where the 5-day EMA crosses above the 10-day EMA. The populate_sell_trend() function generates sell signals based on the RSI crossing above 70, indicating an overbought condition. The custom_sell() function is a custom sell signal implementation that checks for a specific condition (RSI overbought) and returns a signal if the condition is met. The strategy uses these functions to generate buy and sell signals for backtesting trading strategies. Note: This description provides a high-level overview of the strategy's functionality. Further analysis and optimization may be required for actual trading purposes.
stoploss: -0.05 timeframe: 5m hash(sha256): 3b805f7179c136e02426b1e02f0819a0e5190e3be5b4559f673b109aef46ec69 indicators: buy buy_tag sell_custom rsi date ema5 ema10
No similar strategies found. (based on used indicators)
last change: 2023-07-05 14:55:00