GuruSkippyasurmuni_strategy_4

The GuruSkippyasurmuni strategy is a backtesting strategy that makes trading decisions based on various indicators. Here's a breakdown of what the strategy does: populate_indicators function: This function is responsible for populating indicators for the given dataframe and metadata. It returns the updated dataframe.

should_long function: This function determines whether to enter a long position (buy) based on certain conditions.

It takes parameters like the current rate, time in force, sell reason, and current time.

If the sell reason is 'sell_signal' and the calculated profit ratio is less than 0.055, it returns False (do not enter long position). Otherwise, it returns True (enter long position). calculate_stake function: This function calculates the stake amount for a trade based on parameters like proposed stake, minimum stake, maximum stake, and other factors. It first calculates a custom stake amount based on the total stake amount in wallets, maximum open trades, and a position adjustment factor. If the custom stake is greater than or equal to the minimum stake, it returns the custom stake. If the custom stake is less than the minimum stake, it returns the minimum stake. If neither condition is met, it returns the proposed stake. calculate_stop_loss function: This function calculates the stop loss value for a trade based on the current rate, current profit, minimum stake, maximum stake, and other factors. It retrieves the analyzed dataframe for the trade's pair and timeframe. If the length of the dataframe is less than 1, it returns None. It gets the last candle from the dataframe. If the custom info for the trade's pair doesn't match the last candle's date, it updates the custom info. If the current profit is greater than -0.065, it returns None. If the last candle's 'buy' value is greater than 0, it calculates the stake amount based on the number of successful buys and a multiplier. If the stake amount is less than the minimum stake, it returns the minimum stake. Otherwise, it returns the stake amount. populate_entry_trend function: This function populates the entry trend for the dataframe based on specified buy indicators and conditions. It generates conditions based on buy indicators, crossed indicators, operators, and real numbers. It appends the generated conditions to a list. If there are any conditions, it updates the 'buy' column of the dataframe to 1 where all conditions are met. It returns the updated dataframe. populate_exit_trend function: This function populates the exit trend for the dataframe based on specified sell indicators and conditions. It generates conditions based on sell indicators, crossed indicators, operators, and real numbers. It appends the generated conditions to a list. If there are any conditions, it updates the 'sell' column of the dataframe to 1 where all conditions are met. It returns the updated dataframe. These functions collectively define the behavior of the GuruSkippyasurmuni backtesting strategy.