I fixed this as discussed, but:
- I didn't use `last()` because it's only available for `DatetimeIndex`
- I didn't use `.iloc[-1]` because there are a lot of stern warnings in the docs do don't use it and I don't want to confuse less technically aligned user by now using it in an "official" sample
- I did use `.iat[-1]` because it's supposed to be fasted by working on the Series of the requested column directly
## Hello dear community,
I like to share my newest hyperopt with you. I though about how cool it would be to know what's the perfect timeperiod for CCI indicator. In a strategy you do something like this: `dataframe['cci'] = ta.CCI(timeperiod=14)`
You would do this by hand for each timeperiod which is very annoying. Therefore, I created this hyperopt to looking for the perfect timeperiod for the CCI indicator. Please review this pull request very critical and share your minds.
Since the last two months (from 1st Jan 2021 until now) this strategy in BTC/USDT 30m chart had worked **very very** well. After two months I now optimize this strategy again.
I provided both, strategy and hyperopt file, in the attachements.
## Summary
The goal of this hyper-optimization is to find the perfect timeframe of the CCI indicator (from 10 to 100) within a range from -400 to +400. The MACD indicator here is just a favorite of myself, replace with your favorit indicator if you like.
I like to share my newest hyperopt with you. I though about how cool it would be to know what's the perfect timeperiod for CCI indicator. In a strategy you do something like this: dataframe['cci'] = ta.CCI(timeperiod=14)
You would do this by hand for each timeperiod which is very annoying. Therefore, I created this hyperopt to looking for the perfect timeperiod for the CCI indicator. Please review this pull request very critical and share your minds.
Since the last two months (from 1st Jan 2021 until now) this strategy in BTC/USDT 30m chart had worked very very well. After two months I now optimize this strategy again.
Matthias