The NostalgiaForInfinityNextGen strategy is a backtesting strategy for trading. It performs several tasks to populate indicators and determine buy signals. Here's a breakdown of what the strategy does:
Populating Indicators:
The strategy fetches Bitcoin (BTC) price data at different timeframes (5 minutes, 1 hour, and daily) and merges it with the main dataframe.
It calculates various indicators for each timeframe, such as moving averages (EMA and SMA), volume, and price levels.
The strategy removes unnecessary columns from the dataframe.
Informative Timeframe:
The strategy checks if an informative timeframe (1 day or 1 hour) is specified. If so, it calculates informative indicators for that timeframe and merges them with the main dataframe. It removes unnecessary columns from the dataframe. Resampled Timeframe:
The strategy checks if a resampled timeframe is specified. If so, it resamples the dataframe to the specified timeframe and calculates resampled indicators. The resampled data is merged with the main dataframe, and the column names are adjusted. Unnecessary columns related to the resampled timeframe are removed. Normal Timeframe Indicators:
The strategy calculates additional indicators for the normal timeframe (5 minutes) on the main dataframe. These indicators can include exponential moving averages (EMA), simple moving averages (SMA), and percentage changes. Populating Buy Trend:
The strategy defines conditions for determining buy signals. It iterates through a set of buy protection parameters and evaluates various conditions based on these parameters. Conditions can include checks on moving averages, close price levels, rising trends, and safe dips/pumps. If a condition is met, the corresponding item in the buy protection list is set to True. A 'buy_tag' column is added to the dataframe to mark buy signals. The strategy performs these steps to populate indicators and generate buy signals for backtesting trading strategies.