The BTCBigDrop strategy is a trading strategy implemented as a class called BTCBigDrop. It is used for backtesting trading strategies on a website. Here is a short description of what the strategy does:
populate_indicators function:
Adds various technical analysis (TA) indicators to the given DataFrame.
The indicators used include MFI (Money Flow Index), SMA (Simple Moving Average), MACD (Moving Average Convergence Divergence), Stochastic Fast, RSI (Relative Strength Index), Fisher RSI, Bollinger Bands, EMA (Exponential Moving Average), and SAR (Stop and Reverse).
The indicators are calculated and added as columns to the DataFrame.
populate_buy_trend function: Populates the buy signal for the given DataFrame based on certain conditions. The conditions include checking if MFI, Fisher RSI, and Bollinger Bands indicate a buying opportunity. It also checks for a specific drop in the price within a certain number of candles. If the conditions are met, the 'buy' column in the DataFrame is set to 1. populate_sell_trend function: Populates the sell signal for the given DataFrame. Currently, it sets the 'sell' column to 0 for all rows where the close price is greater than or equal to 0. Overall, the strategy aims to identify buying opportunities based on various technical indicators and a significant drop in the price, while the selling signal is not implemented in detail in the provided code.
The indicators used include MFI (Money Flow Index), SMA (Simple Moving Average), MACD (Moving Average Convergence Divergence), Stochastic Fast, RSI (Relative Strength Index), Fisher RSI, Bollinger Bands, EMA (Exponential Moving Average), and SAR (Stop and Reverse).
The indicators are calculated and added as columns to the DataFrame.
populate_buy_trend function: Populates the buy signal for the given DataFrame based on certain conditions. The conditions include checking if MFI, Fisher RSI, and Bollinger Bands indicate a buying opportunity. It also checks for a specific drop in the price within a certain number of candles. If the conditions are met, the 'buy' column in the DataFrame is set to 1. populate_sell_trend function: Populates the sell signal for the given DataFrame. Currently, it sets the 'sell' column to 0 for all rows where the close price is greater than or equal to 0. Overall, the strategy aims to identify buying opportunities based on various technical indicators and a significant drop in the price, while the selling signal is not implemented in detail in the provided code.
stoploss: 0 timeframe: 5m hash(sha256): 7f70b70d08270199b6218301d42c447851f9dac68033dadd2a39862b588f55b8 indicators: upper close open_5m bb_gain mfi bb_lowerband ema5 fastk sma fastd ema10 sar macdsignal mid fisher_rsi ema100 macd lower bb_middleband rsi bb_upperband ema50 close_5m
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last change: 2024-04-28 04:40:35