LoadRLModel

The LoadRLModel strategy is designed to populate indicators and generate buy and sell signals based on technical analysis (TA) indicators. Here is a brief description of what the strategy does: populate_indicators: This method populates various TA indicators on the given dataframe, including ADX, PLUS_DM, PLUS_DI, MINUS_DM, MINUS_DI, AROON, AROONOSC, awesome_oscillator, ULTOSC, CCI, RSI, fisher_rsi, slowd, slowk, fastd, fastk, fastd_rsi, MACD, macdsignal, macdhist, MFI, and ROC. populate_buy_trend: Using the populated indicators, this method generates the buy signal for the dataframe.

It makes use of a RL (Reinforcement Learning) model to predict the action to take.

The predicted buy actions are marked as 1 in the 'buy' column of the dataframe.

populate_sell_trend: Similar to populate_buy_trend, this method generates the sell signal for the dataframe using the RL model. The predicted sell actions are marked as 1 in the 'sell' column of the dataframe. data: This section of code prepares the indicator data for input to the RL model. The indicators from the dataframe are collected and reshaped into a numpy array. nan_list: It checks for any NaN values in the indicator data. action: The RL model predicts the action to take based on the indicator data. Finally, the action and nan_list are returned as the output of the strategy. Please note that this description is based on the provided code snippet and may not capture the full functionality or context of the strategy.