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The strato strategy is a default strategy provided by the freqtrade bot for backtesting trading strategies. It is not intended to be modified and serves as an internal reference. It uses various technical analysis (TA) indicators to generate buy and sell signals based on Bollinger Bands.
Here's a breakdown of the important components: minimal_roi: This defines the minimal return on investment (ROI) that the strategy aims to achieve.
It specifies different time periods and their corresponding ROI values.
stoploss: This sets the optimal stop-loss value for the strategy, which is the percentage at which a trade should be exited to limit losses. ticker_interval: This indicates the desired interval for the ticker data used in the strategy. In this case, it's set to '3m', representing 3-minute intervals. order_types: This mapping defines the order types for different trading actions. For example, it specifies 'limit' orders for buying and 'market' orders for selling and stop-loss. startup_candle_count: This specifies the number of candles (time periods) required before the strategy can produce valid signals. In this case, it's set to 20. populate_indicators: This function adds TA indicators to the dataframe. It calculates Bollinger Bands using the qtpylib library and stores the lower band in the 'bblow' column and the upper band in the 'bbhigh' column. populate_buy_trend: This function generates buy signals based on the TA indicators. It checks if the average of the close and low prices is below the lower Bollinger Band and sets the 'buy' column to 1 for those periods. populate_sell_trend: This function generates sell signals based on the TA indicators. It checks if the high price is above the upper Bollinger Band and sets the 'sell' column to 1 for those periods. Please note that this is just a brief summary of the strategy. The actual implementation and functionality of the strategy can be more complex and may involve additional factors and considerations.
Here's a breakdown of the important components: minimal_roi: This defines the minimal return on investment (ROI) that the strategy aims to achieve.
It specifies different time periods and their corresponding ROI values.
stoploss: This sets the optimal stop-loss value for the strategy, which is the percentage at which a trade should be exited to limit losses. ticker_interval: This indicates the desired interval for the ticker data used in the strategy. In this case, it's set to '3m', representing 3-minute intervals. order_types: This mapping defines the order types for different trading actions. For example, it specifies 'limit' orders for buying and 'market' orders for selling and stop-loss. startup_candle_count: This specifies the number of candles (time periods) required before the strategy can produce valid signals. In this case, it's set to 20. populate_indicators: This function adds TA indicators to the dataframe. It calculates Bollinger Bands using the qtpylib library and stores the lower band in the 'bblow' column and the upper band in the 'bbhigh' column. populate_buy_trend: This function generates buy signals based on the TA indicators. It checks if the average of the close and low prices is below the lower Bollinger Band and sets the 'buy' column to 1 for those periods. populate_sell_trend: This function generates sell signals based on the TA indicators. It checks if the high price is above the upper Bollinger Band and sets the 'sell' column to 1 for those periods. Please note that this is just a brief summary of the strategy. The actual implementation and functionality of the strategy can be more complex and may involve additional factors and considerations.
stoploss: -0.002 timeframe: 3m hash(sha256): 7376058ffb6e3208565771d09977d3d16d1ce839eced8e7715fcc56301807d01 indicators: high upper lower close low bbhigh bblow
No similar strategies found. (based on used indicators)
last change: 2023-07-06 07:32:40