Browse Strategies

Strategy based on Fractal Energy principles by Doc Severson.

Strategy based on Dow Theory principles for trend detection.

BB_RPB_TSL @author jilv220 Simple bollinger brand strategy inspired by this blog ( https://hacks-for-life.blogspot.com/2020/12/freqtrade-notes.html ) RPB, which stands for Real Pull Back, taken from ( https://github.com/GeorgeMurAlkh/freqtrade-stuff/blob/main/user_data/strategies/TheRealPullbackV2.py ) The trailing custom stoploss taken from BigZ04_TSL from Perkmeister ( modded by ilya ) I modified it to better suit my taste and added Hyperopt for this strategy.

This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/

This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/

This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/

This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/

This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/

This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/

author@: Gert Wohlgemuth

Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.freqai.start(dataframe, metadata)

Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.freqai.start(dataframe, metadata)

Example strategy showing how the user connects their own IFreqaiModel to the strategy. Namely, the user uses: self.freqai.start(dataframe, metadata)

The following freqaimodel is released to sponsors of the non-profit FreqAI open-source project. If you find the FreqAI project useful, please consider supporting it by becoming a sponsor. We use sponsor money to help stimulate new features and to pay for running these public experiments, with a an objective of helping the community make smarter choices in their ML journey.