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The "prime" strategy is a trading strategy implemented as a class called "prime" that inherits from the "IStrategy" class. It performs various tasks to populate indicators, determine leverage, and generate entry and exit signals for backtesting. Here's a breakdown of what the strategy does:
populate_indicators: This method takes a DataFrame and a metadata dictionary as inputs and populates several technical indicators in the DataFrame, including pivot points, RSI (Relative Strength Index), MACD (Moving Average Convergence Divergence), and Stochastic Fast.

The calculated indicator values are added as new columns to the DataFrame.

proposed_leverage: This function takes various parameters related to the currently analyzed pair, current time, rate, proposed leverage, maximum leverage, and trade direction.

It customizes the leverage amount for each new trade and returns a float value between 1.0 and max_leverage. In this case, it always returns a leverage amount of 10.0. populate_entry_trend: This method populates the entry signal for the given DataFrame based on the calculated technical indicators. It checks certain conditions, such as candlestick patterns, moving average positions, and volume, to determine whether to enter a long or short trade. If the conditions are met, it sets the corresponding entry column value to 1. populate_exit_trend: This method populates the exit signal for the given DataFrame based on the calculated technical indicators. It checks the volume of the candle to determine whether to exit a trade. If the volume is zero, it sets the exit_long column value to 0, indicating that the long trade should be exited. If the volume is greater than zero, it sets the exit_short column value to 1, indicating that the short trade should be exited. Overall, the "prime" strategy calculates various technical indicators, customizes leverage, and generates entry and exit signals based on the indicators and volume conditions.

The calculated indicator values are added as new columns to the DataFrame.

proposed_leverage: This function takes various parameters related to the currently analyzed pair, current time, rate, proposed leverage, maximum leverage, and trade direction.

It customizes the leverage amount for each new trade and returns a float value between 1.0 and max_leverage. In this case, it always returns a leverage amount of 10.0. populate_entry_trend: This method populates the entry signal for the given DataFrame based on the calculated technical indicators. It checks certain conditions, such as candlestick patterns, moving average positions, and volume, to determine whether to enter a long or short trade. If the conditions are met, it sets the corresponding entry column value to 1. populate_exit_trend: This method populates the exit signal for the given DataFrame based on the calculated technical indicators. It checks the volume of the candle to determine whether to exit a trade. If the volume is zero, it sets the exit_long column value to 0, indicating that the long trade should be exited. If the volume is greater than zero, it sets the exit_short column value to 1, indicating that the short trade should be exited. Overall, the "prime" strategy calculates various technical indicators, customizes leverage, and generates entry and exit signals based on the indicators and volume conditions.

startup_candle_count :21rsi:-0.290%ema9:-0.003%

stoploss:-0.29timeframe:1hhash(sha256):450f3994e42732871a697f831fdd0fc94a9a11da052fc2c7e614389dd6b70647indicators:macdsignal volume fastk ema21 rsi close macd open pivot fastd macdhist ema9

No similar strategies found. (based on used indicators)last change: 2023-09-02 00:37:43