The SagesGym3 strategy is a trading strategy that utilizes various technical indicators to make buy and sell decisions. Here is a breakdown of its key components:
populate_indicators: This function calculates several technical indicators based on the provided DataFrame of raw data. The indicators include Relative Strength Index (RSI), Awesome Oscillator (AO), Moving Average Convergence Divergence (MACD), Aroon Up and Down, and current price.
The calculated indicators are added as columns to the DataFrame and returned.
populate_buy_trend: Using the populated indicators DataFrame, this function determines the buy signal for each data point.
It utilizes a machine learning model (self.rl_model_predict) to predict the action (buy or not) based on the indicators. The buy signal is represented as a binary column (buy) in the DataFrame. populate_sell_trend: Similar to populate_buy_trend, this function determines the sell signal based on the indicators. It also uses the machine learning model to predict the action. The sell signal is represented as a binary column (sell) in the DataFrame. populate_sell_trend (continued): Additionally, this function prints the count of sell signals and logs the completion of populating the sell signal. populate_action_output: This function prepares the output DataFrame (action_output) to store the action predictions for each data point. It initializes the DataFrame and iterates through the indicators to predict the action. The predicted action is stored in the action_output DataFrame. manage_stake: This function calculates the stake amount based on the current state of the strategy. It takes inputs such as the current trading pair, current time, current rate, proposed stake, minimum stake, maximum stake, and entry tag. It retrieves the analyzed DataFrame for the trading pair and the last candle's data. It uses the percent_of_balance_dict to determine the stake amount based on the percent of balance. The calculated stake amount is returned. zscore: This function calculates the z-score of a given data series. It takes the data series, calculates the rolling mean and standard deviation, and computes the z-score based on the formula (data - mean) / standard deviation. The z-scored values are returned. These functions work together to populate the necessary indicators, generate buy and sell signals based on the indicators, manage stake amounts, and calculate z-scores for the data.
The calculated indicators are added as columns to the DataFrame and returned.
populate_buy_trend: Using the populated indicators DataFrame, this function determines the buy signal for each data point.
It utilizes a machine learning model (self.rl_model_predict) to predict the action (buy or not) based on the indicators. The buy signal is represented as a binary column (buy) in the DataFrame. populate_sell_trend: Similar to populate_buy_trend, this function determines the sell signal based on the indicators. It also uses the machine learning model to predict the action. The sell signal is represented as a binary column (sell) in the DataFrame. populate_sell_trend (continued): Additionally, this function prints the count of sell signals and logs the completion of populating the sell signal. populate_action_output: This function prepares the output DataFrame (action_output) to store the action predictions for each data point. It initializes the DataFrame and iterates through the indicators to predict the action. The predicted action is stored in the action_output DataFrame. manage_stake: This function calculates the stake amount based on the current state of the strategy. It takes inputs such as the current trading pair, current time, current rate, proposed stake, minimum stake, maximum stake, and entry tag. It retrieves the analyzed DataFrame for the trading pair and the last candle's data. It uses the percent_of_balance_dict to determine the stake amount based on the percent of balance. The calculated stake amount is returned. zscore: This function calculates the z-score of a given data series. It takes the data series, calculates the rolling mean and standard deviation, and computes the z-score based on the formula (data - mean) / standard deviation. The z-scored values are returned. These functions work together to populate the necessary indicators, generate buy and sell signals based on the indicators, manage stake amounts, and calculate z-scores for the data.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.99 timeframe: 15m hash(sha256): c5a27f5e136a59584cda2b4d121facb5b015c625d94f432bb5e92545b7eb4641 indicators: high macdsignal aroonup sell close top current_price rsi ao macd low aroondown buy macdhist
Similar Strategies: (based on used indicators)
Strategy: my_gym4, Similarity Score: 93.33%
last change: 2024-07-28 20:49:35