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The ClucHAnix_5m strategy is a backtesting strategy that uses various indicators to determine buy and sell signals in a trading market. Here's a breakdown of what the strategy does:
populate_indicators function:
Calculates Heikin-Ashi candlestick values (open, close, high, low). Calculates Bollinger Bands (mid, lower) using the typical price of Heikin-Ashi candles.
Computes the absolute difference between the mid Bollinger Band and the lower Bollinger Band (bbdelta).
Computes the absolute difference between consecutive Heikin-Ashi close prices (closedelta).
Computes the absolute difference between the close price and the low price (tail). Adds the Bollinger Bands, exponential moving averages (ema_fast, ema_slow), volume mean, ROCR, RSI, and Fisher Transform to the dataframe. Retrieves an informative pair dataframe based on the specified timeframe (1h) and merges it with the current dataframe. populate_buy_trend function: Determines buy signals based on specific conditions: ROCR value of the informative pair dataframe is greater than the specified threshold (rocr_1h). Several conditions related to Bollinger Bands, close price, and Heikin-Ashi candles are met. Sets the 'buy' column to 1 for rows that meet the buy signal criteria. populate_sell_trend function: Determines sell signals based on specific conditions: Fisher Transform value is greater than the specified threshold (sell_fisher). Conditions related to Heikin-Ashi high and close prices, exponential moving averages, Bollinger Bands, and volume are met. Sets the 'sell' column to 1 for rows that meet the sell signal criteria. The strategy uses these buy and sell signals to generate trading recommendations when backtesting trading strategies on the website.
Computes the absolute difference between the mid Bollinger Band and the lower Bollinger Band (bbdelta).
Computes the absolute difference between consecutive Heikin-Ashi close prices (closedelta).
Computes the absolute difference between the close price and the low price (tail). Adds the Bollinger Bands, exponential moving averages (ema_fast, ema_slow), volume mean, ROCR, RSI, and Fisher Transform to the dataframe. Retrieves an informative pair dataframe based on the specified timeframe (1h) and merges it with the current dataframe. populate_buy_trend function: Determines buy signals based on specific conditions: ROCR value of the informative pair dataframe is greater than the specified threshold (rocr_1h). Several conditions related to Bollinger Bands, close price, and Heikin-Ashi candles are met. Sets the 'buy' column to 1 for rows that meet the buy signal criteria. populate_sell_trend function: Determines sell signals based on specific conditions: Fisher Transform value is greater than the specified threshold (sell_fisher). Conditions related to Heikin-Ashi high and close prices, exponential moving averages, Bollinger Bands, and volume are met. Sets the 'sell' column to 1 for rows that meet the sell signal criteria. The strategy uses these buy and sell signals to generate trading recommendations when backtesting trading strategies on the website.
stoploss: -0.99 timeframe: 5m hash(sha256): 1d891b45265a8a5df63254fe980f016e2a5bae7573bc178b3c0c7b9c3c10921a indicators: rocr_1h ha_low close tail bb_lowerband bbdelta volume closedelta rocr open ema_fast volume_mean_slow ha_open fisher high mid ha_close ha_high lower bb_middleband rsi ema_slow low
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last change: 2022-07-02 19:54:08