You will be redirected to the original Strategy in 15 seconds.
The "Low_BB" strategy is designed for backtesting trading strategies on a website. Here is a brief description of what the strategy does:
The strategy uses Bollinger Bands and MACD indicators to make buy and sell decisions. It sets the minimal ROI (Return on Investment) values for different time periods, specifying the expected minimum returns for the strategy.
It sets the stop loss value, which determines the maximum acceptable loss before selling.
The strategy is optimized for a ticker interval of 1 minute.
The "populate_indicators" function calculates various indicators such as Bollinger Bands and MACD for the given dataframe. The "populate_buy_trend" function populates the "buy" signal based on the condition that the closing price is less than or equal to 0.98 times the lower Bollinger Band. The "populate_sell_trend" function populates the "sell" signal for all rows in the dataframe. Overall, the strategy aims to buy when the price crosses below 0.98 times the lower Bollinger Band and sell when the trailing stop loss is hit.
It sets the stop loss value, which determines the maximum acceptable loss before selling.
The strategy is optimized for a ticker interval of 1 minute.
The "populate_indicators" function calculates various indicators such as Bollinger Bands and MACD for the given dataframe. The "populate_buy_trend" function populates the "buy" signal based on the condition that the closing price is less than or equal to 0.98 times the lower Bollinger Band. The "populate_sell_trend" function populates the "sell" signal for all rows in the dataframe. Overall, the strategy aims to buy when the price crosses below 0.98 times the lower Bollinger Band and sell when the trailing stop loss is hit.
stoploss: -0.015 timeframe: 1m hash(sha256): 53dc01ddfdb9034fa186f89e54ecb914a6d89af0cbe9e8ada6b9507981a5b530 indicators: upper mid lower bb_middleband close bb_upperband bb_lowerband ema50
No similar strategies found. (based on used indicators)
last change: 2022-07-02 19:54:08