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The "Obelisk_3EMA_StochRSI_ATR" strategy is designed for backtesting trading strategies. It utilizes several indicators to make buy and sell decisions based on specific conditions. Here is a brief description of what the strategy does:
The strategy first populates indicators by taking a DataFrame of price data and metadata as inputs.
It checks if the strategy is being run in backtest or hyperopt mode and verifies that the timeframe is either 5 minutes or 1 minute.
If the informative timeframe matches the current timeframe, it applies indicators to the given DataFrame.
Otherwise, it retrieves the informative DataFrame using the metadata and applies indicators to it. The informative pair's data is then merged with the original DataFrame, and unnecessary columns are removed. The "populate_buy_trend" function populates the "buy" column of the DataFrame. It sets the value to 1 when the "go_long" indicator crosses above 0. The "populate_sell_trend" function populates the "sell" column of the DataFrame. It sets the value to 0, indicating no selling signal. The "stoploss_from_open" function calculates the stop loss value based on the open price, current profit, and the stop percentage specified in the "open_df" DataFrame. If a take profit value is specified and the current rate exceeds it, the function returns 0.001 as a sell signal. Otherwise, it calculates the stop value based on the stop percentage and the current profit. The "plot_config" dictionary contains configuration settings for plotting various indicators and signals in the backtesting results. Overall, the strategy combines exponential moving averages (EMA), stochastic relative strength index (StochRSI), and average true range (ATR) indicators to generate buy and sell signals based on specific conditions.
It checks if the strategy is being run in backtest or hyperopt mode and verifies that the timeframe is either 5 minutes or 1 minute.
If the informative timeframe matches the current timeframe, it applies indicators to the given DataFrame.
Otherwise, it retrieves the informative DataFrame using the metadata and applies indicators to it. The informative pair's data is then merged with the original DataFrame, and unnecessary columns are removed. The "populate_buy_trend" function populates the "buy" column of the DataFrame. It sets the value to 1 when the "go_long" indicator crosses above 0. The "populate_sell_trend" function populates the "sell" column of the DataFrame. It sets the value to 0, indicating no selling signal. The "stoploss_from_open" function calculates the stop loss value based on the open price, current profit, and the stop percentage specified in the "open_df" DataFrame. If a take profit value is specified and the current rate exceeds it, the function returns 0.001 as a sell signal. Otherwise, it calculates the stop value based on the stop percentage and the current profit. The "plot_config" dictionary contains configuration settings for plotting various indicators and signals in the backtesting results. Overall, the strategy combines exponential moving averages (EMA), stochastic relative strength index (StochRSI), and average true range (ATR) indicators to generate buy and sell signals based on specific conditions.
stoploss: -0.99 timeframe: 5m hash(sha256): 40104f77d8fb196a379b6d4029b7fe260e04bd5617511af517b78fe9e7cc8bff indicators: ema14 atr srsi_k go_long sell close rsi srsi_d ema8 date runmode stop_loss ema50 stop_pct date stop_pct take_profit take_profit date open high low close volume
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last change: 2022-07-02 19:54:08