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The "BuyAllSellAllStrategy" is a trading strategy implemented in Python using the Freqtrade library. It is designed to backtest trading strategies on historical data. Here's a breakdown of what the strategy does:
The strategy inherits from the "IStrategy" interface provided by Freqtrade.
It defines various parameters and attributes, such as "stoploss" (set to -0.25), "timeframe" (set to '5m'), and flags for using sell signals, selling for profit only, and ignoring ROI if there is a buy signal.
The "populate_indicators" method is responsible for calculating any required indicators based on the input dataframe and metadata.
In this case, it returns the original dataframe as is, without any additional indicators. The "populate_buy_trend" method generates a "buy" column in the dataframe, filled with randomly generated values of 0 or 1. This represents the decision to buy or not for each data point. The "populate_sell_trend" method adds a "sell" column to the dataframe, initially filled with zeros. The "custom_sell" method is a custom sell signal implementation. It takes various parameters such as the trading pair, current trade information, current time, current rate, and current profit. It then retrieves the analyzed dataframe for the given pair and timeframe and checks the last candle of the dataframe. If the last candle exists (not None), it returns True to indicate a sell signal; otherwise, it returns None. Overall, this strategy randomly generates buy signals and waits for the last candle of the analyzed dataframe before triggering a sell signal. The stop loss is set to -0.25, and the timeframe is set to 5 minutes.
It defines various parameters and attributes, such as "stoploss" (set to -0.25), "timeframe" (set to '5m'), and flags for using sell signals, selling for profit only, and ignoring ROI if there is a buy signal.
The "populate_indicators" method is responsible for calculating any required indicators based on the input dataframe and metadata.
In this case, it returns the original dataframe as is, without any additional indicators. The "populate_buy_trend" method generates a "buy" column in the dataframe, filled with randomly generated values of 0 or 1. This represents the decision to buy or not for each data point. The "populate_sell_trend" method adds a "sell" column to the dataframe, initially filled with zeros. The "custom_sell" method is a custom sell signal implementation. It takes various parameters such as the trading pair, current trade information, current time, current rate, and current profit. It then retrieves the analyzed dataframe for the given pair and timeframe and checks the last candle of the dataframe. If the last candle exists (not None), it returns True to indicate a sell signal; otherwise, it returns None. Overall, this strategy randomly generates buy signals and waits for the last candle of the analyzed dataframe before triggering a sell signal. The stop loss is set to -0.25, and the timeframe is set to 5 minutes.
stoploss: -0.25 timeframe: 5m hash(sha256): 26ef4042d8ca097aa8e2925716eb308e43e1515313b058b7953fdc8873e2320f
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-02 19:54:08