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The provided code represents a trading strategy called "CombinedBinHAndClucV3" implemented in Python. Here's a brief description of what the strategy does:
The strategy uses Bollinger Bands, moving averages, and other indicators to make buying and selling decisions based on certain conditions in the price data. It defines a function called "bollinger_bands" that calculates the Bollinger Bands for a given stock price.
It takes the stock price, window size, and the number of standard deviations as input and returns the rolling mean and lower band.
The strategy sets various parameters such as the minimal return on investment (ROI), stop loss percentage, timeframe, and sell signal configuration.
It implements a custom stop loss logic that adjusts the stop loss based on the time and current profit of a trade. The "populate_indicators" function calculates additional indicators and appends them to the input dataframe. These indicators include Bollinger Bands, exponential moving averages (EMA), and volume-related metrics. The "populate_buy_trend" function determines the conditions for initiating a buy signal based on the calculated indicators and assigns a value of 1 to the "buy" column in the dataframe when the conditions are met. The "populate_sell_trend" function determines the conditions for initiating a sell signal and assigns a value of 1 to the "sell" column in the dataframe when the conditions are met. Overall, the strategy combines multiple indicators and conditions to generate buy and sell signals for trading. It utilizes Bollinger Bands, moving averages, and volume-related metrics to identify potential entry and exit points in the market.
It takes the stock price, window size, and the number of standard deviations as input and returns the rolling mean and lower band.
The strategy sets various parameters such as the minimal return on investment (ROI), stop loss percentage, timeframe, and sell signal configuration.
It implements a custom stop loss logic that adjusts the stop loss based on the time and current profit of a trade. The "populate_indicators" function calculates additional indicators and appends them to the input dataframe. These indicators include Bollinger Bands, exponential moving averages (EMA), and volume-related metrics. The "populate_buy_trend" function determines the conditions for initiating a buy signal based on the calculated indicators and assigns a value of 1 to the "buy" column in the dataframe when the conditions are met. The "populate_sell_trend" function determines the conditions for initiating a sell signal and assigns a value of 1 to the "sell" column in the dataframe when the conditions are met. Overall, the strategy combines multiple indicators and conditions to generate buy and sell signals for trading. It utilizes Bollinger Bands, moving averages, and volume-related metrics to identify potential entry and exit points in the market.
startup_candle_count : 30 lower: -100.000% bbdelta: -100.000% bb_lowerband: 0.287% bb_middleband: 0.151% bb_upperband: 0.016%
stoploss: -0.99 timeframe: 5m hash(sha256): 6b69674564467f3c887e6de1d56afad3de16cc30074cf91cbc6489be39581276 indicators: high upper mid lower volume ema_200 close bb_middleband closedelta tail bb_upperband bb_lowerband ema_slow low volume_mean_slow bbdelta
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last change: 2023-08-03 16:06:07