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The "EMABreakout" strategy is a trading strategy that uses various technical analysis indicators to make buy and sell decisions. Here is a breakdown of what the strategy does:
populate_indicators: This function adds several technical indicators to the given DataFrame. The indicators used are:
RSI (Relative Strength Index)
MACD (Moving Average Convergence Divergence): It calculates MACD line, MACD signal line, and MACD histogram.
Bollinger Bands: It calculates the upper band, middle band, and lower band of Bollinger Bands, along with percentage and width calculations.
EMA (Exponential Moving Average): It calculates the Exponential Moving Average.
SMA (Simple Moving Average): It calculates the Simple Moving Average. SAR (Stop and Reverse): It calculates the SAR indicator. The function returns the DataFrame with all the added indicators. populate_buy_trend: This function populates the buy signal for the given DataFrame based on the indicators. The buy conditions considered are: Volume is greater than 0. If the MACD histogram is enabled, it checks if it is greater than or equal to 0. The closing price crossed above the Exponential Moving Average (EMA). If all the conditions are met, the 'buy' column in the DataFrame is set to 1. populate_sell_trend: This function populates the sell signal for the given DataFrame based on the indicators. The sell conditions considered are: If the sell hold option is enabled, it sets the 'sell' column to 0 for all rows. The closing price crossed below the Exponential Moving Average (EMA). If the sell hold option is disabled and the condition is met, the 'sell' column in the DataFrame is set to 1. Overall, the EMABreakout strategy combines various indicators like RSI, MACD, Bollinger Bands, EMA, SMA, and SAR to generate buy and sell signals based on specific conditions.
Bollinger Bands: It calculates the upper band, middle band, and lower band of Bollinger Bands, along with percentage and width calculations.
EMA (Exponential Moving Average): It calculates the Exponential Moving Average.
SMA (Simple Moving Average): It calculates the Simple Moving Average. SAR (Stop and Reverse): It calculates the SAR indicator. The function returns the DataFrame with all the added indicators. populate_buy_trend: This function populates the buy signal for the given DataFrame based on the indicators. The buy conditions considered are: Volume is greater than 0. If the MACD histogram is enabled, it checks if it is greater than or equal to 0. The closing price crossed above the Exponential Moving Average (EMA). If all the conditions are met, the 'buy' column in the DataFrame is set to 1. populate_sell_trend: This function populates the sell signal for the given DataFrame based on the indicators. The sell conditions considered are: If the sell hold option is enabled, it sets the 'sell' column to 0 for all rows. The closing price crossed below the Exponential Moving Average (EMA). If the sell hold option is disabled and the condition is met, the 'sell' column in the DataFrame is set to 1. Overall, the EMABreakout strategy combines various indicators like RSI, MACD, Bollinger Bands, EMA, SMA, and SAR to generate buy and sell signals based on specific conditions.
stoploss: -0.333 timeframe: 5m hash(sha256): 73e717aa307d2536b81890529eee86b829eb1694a836e161dee0bfd3a2a77085
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last change: 2022-12-04 20:28:10