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The ActionZone strategy is a trading strategy that utilizes technical indicators to generate buy and sell signals for trading. Here's a breakdown of what the strategy does:
populate_indicators function:
This function calculates and adds several technical indicators to a given DataFrame of exchange data. The indicators used include the lowest price (lowest), fast exponential moving average (fastMA), and slow exponential moving average (slowMA).
The function returns the DataFrame with the added indicators.
populate_buy_trend function: This function populates the buy signal for the DataFrame based on the calculated indicators.
It identifies a buy signal when the following conditions are met: The fast moving average (fastMA) is greater than the slow moving average (slowMA), indicating a bullish trend. The closing price (close) is above the fast moving average (fastMA), indicating a price cross up. The volume is greater than 0, ensuring that there is trading activity. The function adds a 'buy' column to the DataFrame and sets the value to 1 for rows where the buy signal conditions are met. The updated DataFrame is returned. populate_sell_trend function: This function populates the sell signal for the DataFrame based on the calculated indicators. It identifies a sell signal when the following conditions are met: The fast moving average (fastMA) is less than the slow moving average (slowMA), indicating a bearish trend. The closing price (close) is below the fast moving average (fastMA), indicating a price cross down. The volume is greater than 0, ensuring that there is trading activity. The function adds a 'sell' column to the DataFrame and sets the value to 1 for rows where the sell signal conditions are met. The updated DataFrame is returned. Overall, the ActionZone strategy uses moving averages and price/volume conditions to determine when to generate buy and sell signals in the backtesting process.
The function returns the DataFrame with the added indicators.
populate_buy_trend function: This function populates the buy signal for the DataFrame based on the calculated indicators.
It identifies a buy signal when the following conditions are met: The fast moving average (fastMA) is greater than the slow moving average (slowMA), indicating a bullish trend. The closing price (close) is above the fast moving average (fastMA), indicating a price cross up. The volume is greater than 0, ensuring that there is trading activity. The function adds a 'buy' column to the DataFrame and sets the value to 1 for rows where the buy signal conditions are met. The updated DataFrame is returned. populate_sell_trend function: This function populates the sell signal for the DataFrame based on the calculated indicators. It identifies a sell signal when the following conditions are met: The fast moving average (fastMA) is less than the slow moving average (slowMA), indicating a bearish trend. The closing price (close) is below the fast moving average (fastMA), indicating a price cross down. The volume is greater than 0, ensuring that there is trading activity. The function adds a 'sell' column to the DataFrame and sets the value to 1 for rows where the sell signal conditions are met. The updated DataFrame is returned. Overall, the ActionZone strategy uses moving averages and price/volume conditions to determine when to generate buy and sell signals in the backtesting process.
startup_candle_count : 30 fastMA: -0.029% slowMA: -0.671%
stoploss: -1 timeframe: 1d hash(sha256): 7ababf275e8e258818e51a6bc80428111305580e1f732f129ea4d5cc09603710 indicators: volume slowMA lowest close fastMA
No similar strategies found. (based on used indicators)
last change: 2023-10-03 21:57:01