The Cluc4 strategy is a backtesting strategy that uses various indicators to make buy and sell decisions in the cryptocurrency market. Here's a breakdown of what the strategy does:
Bollinger Bands Calculation:
The strategy calculates Bollinger Bands, which are volatility bands placed above and below a moving average. It uses the bollinger_bands function to calculate the rolling mean and lower band based on the closing prices of the stock.
The window size is set to 40 and the number of standard deviations is set to 2.
Indicator Population: The strategy populates several indicators in the populate_indicators function.
These indicators include: lower: The lower Bollinger Band value calculated in the previous step. bbdelta: The absolute difference between the mid Bollinger Band and the lower Bollinger Band. closedelta: The absolute difference between the current closing price and the previous closing price. tail: The absolute difference between the current closing price and the lowest price within the same candle. bb_lowerband: The lower Bollinger Band value calculated using the qtpylib library. bb_middleband: The middle Bollinger Band value calculated using the qtpylib library. ema_slow: The exponential moving average with a time period of 50. volume_mean_slow: The rolling mean of the volume over a window of 30. rocr: The rate of change ratio calculated using the ta (TALib) library. It also fetches informative data from the 1-hour timeframe and merges it with the current dataframe. Buy Trend: The populate_buy_trend function determines the conditions for entering a buy trade. The conditions include: The rate of change ratio (rocr_1h) from the informative data is greater than 0.65. Two sets of conditions: Conditions for buying based on the lower Bollinger Band, its delta, closing price delta, tail, and price movement. Conditions for buying based on the closing price being below the exponential moving average (ema_slow), below a certain percentage of the lower Bollinger Band, and with low volume. If any of the conditions are met, a value of 1 is assigned to the 'buy' column of the dataframe. Sell Trend: The populate_sell_trend function determines the conditions for exiting a buy trade and initiating a sell trade. The condition is that the closing price crosses above the middle Bollinger Band (bb_middleband) and there is a significant increase in volume. If the condition is met, a value of 1 is assigned to the 'sell' column of the dataframe. The strategy sets a minimal return on investment (ROI) and a stop loss value. It also specifies the timeframe of 1 minute for trading. Additionally, it uses informative pairs from the whitelist and defines some settings related to the use of sell signals. Overall, the strategy combines Bollinger Bands, moving averages, rate of change, and volume analysis to make buy and sell decisions in the cryptocurrency market during backtesting.
The window size is set to 40 and the number of standard deviations is set to 2.
Indicator Population: The strategy populates several indicators in the populate_indicators function.
These indicators include: lower: The lower Bollinger Band value calculated in the previous step. bbdelta: The absolute difference between the mid Bollinger Band and the lower Bollinger Band. closedelta: The absolute difference between the current closing price and the previous closing price. tail: The absolute difference between the current closing price and the lowest price within the same candle. bb_lowerband: The lower Bollinger Band value calculated using the qtpylib library. bb_middleband: The middle Bollinger Band value calculated using the qtpylib library. ema_slow: The exponential moving average with a time period of 50. volume_mean_slow: The rolling mean of the volume over a window of 30. rocr: The rate of change ratio calculated using the ta (TALib) library. It also fetches informative data from the 1-hour timeframe and merges it with the current dataframe. Buy Trend: The populate_buy_trend function determines the conditions for entering a buy trade. The conditions include: The rate of change ratio (rocr_1h) from the informative data is greater than 0.65. Two sets of conditions: Conditions for buying based on the lower Bollinger Band, its delta, closing price delta, tail, and price movement. Conditions for buying based on the closing price being below the exponential moving average (ema_slow), below a certain percentage of the lower Bollinger Band, and with low volume. If any of the conditions are met, a value of 1 is assigned to the 'buy' column of the dataframe. Sell Trend: The populate_sell_trend function determines the conditions for exiting a buy trade and initiating a sell trade. The condition is that the closing price crosses above the middle Bollinger Band (bb_middleband) and there is a significant increase in volume. If the condition is met, a value of 1 is assigned to the 'sell' column of the dataframe. The strategy sets a minimal return on investment (ROI) and a stop loss value. It also specifies the timeframe of 1 minute for trading. Additionally, it uses informative pairs from the whitelist and defines some settings related to the use of sell signals. Overall, the strategy combines Bollinger Bands, moving averages, rate of change, and volume analysis to make buy and sell decisions in the cryptocurrency market during backtesting.
stoploss: -0.01 timeframe: 1m hash(sha256): b1c9929b686df7537b4d5763d0971a5d9d31088f62a7c785aa7886775077d6f5 indicators: rocr_1h closedataframebb_middleband mid lower volume close bb_middleband closedelta tail rocr ema_slow bb_lowerband low volume_mean_slow bbdelta
Similar Strategies: (based on used indicators)
Strategy: Cluc4werk, Similarity Score: 93.75%
last change: 2024-05-02 20:02:41