The newstrat class is a trading strategy that uses technical indicators to generate buy and sell signals. Here's a breakdown of what the strategy does:
populate_indicators method:
This method populates the dataframe with technical indicators. It iterates over a range of count_max and gap_max values.
For each combination of count and gap, it checks if the product of count and gap is greater than 1 and not already present in the dataframe's keys.
If the condition is satisfied, it calculates the Triple Exponential Moving Average (TEMA) with a time period of count*gap and adds it to the dataframe.
It also prints the trading pair's metadata. Finally, it returns the updated dataframe. populate_buy_trend method: This method populates the dataframe with buy signals based on moving averages. It initializes an empty list called conditions. It iterates over a range of buy_ma_count.value (a variable that determines the number of moving averages to consider) and calculates the corresponding keys. For each moving average, it checks if the current key and the previous key are present in the dataframe and if the previous key is greater than 1. If the conditions are met, it adds a condition to the conditions list where the current key's value is less than the previous key's value. If there are any conditions in the list, it sets the "buy" column of the dataframe to 1 where all the conditions are true. Finally, it returns the updated dataframe. populate_sell_trend method: This method populates the dataframe with sell signals based on moving averages. It follows a similar process to the populate_buy_trend method, but the condition for adding a condition to the conditions list is when the current key's value is greater than the previous key's value. If there are any conditions in the list, it sets the "sell" column of the dataframe to 1 where any of the conditions are true. Finally, it returns the updated dataframe. Overall, the newstrat strategy calculates the TEMA indicator and generates buy and sell signals based on moving averages. The strategy can be used for backtesting trading decisions on historical data.
For each combination of count and gap, it checks if the product of count and gap is greater than 1 and not already present in the dataframe's keys.
If the condition is satisfied, it calculates the Triple Exponential Moving Average (TEMA) with a time period of count*gap and adds it to the dataframe.
It also prints the trading pair's metadata. Finally, it returns the updated dataframe. populate_buy_trend method: This method populates the dataframe with buy signals based on moving averages. It initializes an empty list called conditions. It iterates over a range of buy_ma_count.value (a variable that determines the number of moving averages to consider) and calculates the corresponding keys. For each moving average, it checks if the current key and the previous key are present in the dataframe and if the previous key is greater than 1. If the conditions are met, it adds a condition to the conditions list where the current key's value is less than the previous key's value. If there are any conditions in the list, it sets the "buy" column of the dataframe to 1 where all the conditions are true. Finally, it returns the updated dataframe. populate_sell_trend method: This method populates the dataframe with sell signals based on moving averages. It follows a similar process to the populate_buy_trend method, but the condition for adding a condition to the conditions list is when the current key's value is greater than the previous key's value. If there are any conditions in the list, it sets the "sell" column of the dataframe to 1 where any of the conditions are true. Finally, it returns the updated dataframe. Overall, the newstrat strategy calculates the TEMA indicator and generates buy and sell signals based on moving averages. The strategy can be used for backtesting trading decisions on historical data.
stoploss: -0.1 timeframe: 5m hash(sha256): 85a786c19ae4c6f7f5c4cc09c124cb2ff9aa1e9c52097b3fb888b044f79ced1f indicators: count*gap key
No similar strategies found. (based on used indicators)
last change: 2024-04-29 00:33:24