The NostalgiaForInfinityNext strategy is a backtesting strategy that involves multiple steps and indicators to make buying decisions in a trading system. Here is a breakdown of the important parts of the strategy:
Indicators:
The strategy begins by populating various indicators based on the provided dataframe and metadata. This includes indicators for BTC (Bitcoin) on different timeframes (5 minutes and 1 hour) as well as an informative timeframe.
BTC Base and Info Timeframes:
If the strategy has BTC as the base timeframe, additional indicators are calculated for the BTC/USDT pair on the base timeframe.
Similarly, if it has BTC on the informative timeframe, indicators are calculated for the BTC/USDT pair on the informative timeframe.
These indicators are merged into the main dataframe. Informative Timeframe:
If an informative timeframe is specified, informative indicators are calculated based on the main dataframe and metadata. These indicators are merged into the main dataframe. Resampled Timeframe:
If a resampled timeframe is specified, the main dataframe is resampled to the desired timeframe. Indicators are calculated for the resampled dataframe, and the resampled dataframe is merged back into the main dataframe. Normal Timeframe Indicators:
Finally, indicators are calculated for the normal (5-minute) timeframe of the main dataframe. Buy Trend Conditions:
The strategy determines buy conditions based on a combination of indicators and buy protection parameters. Buy protection parameters include conditions related to EMAs (Exponential Moving Averages), SMAs (Simple Moving Averages), safe dips, safe pumps, and BTC not being in a downtrend. The strategy has multiple buy conditions (up to 4) that can be enabled or disabled individually. Each buy condition consists of a set of logical conditions that must be met for a buy signal to be generated. Overall, the strategy involves calculating various indicators, merging dataframes, and applying buy conditions based on a combination of indicators and buy protection parameters.