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The Obelisk_Ichimoku_Slow_v1_1 strategy is an implementation of the IStrategy class for backtesting trading strategies. Here is a breakdown of what the strategy does:
populate_indicators function:
This function takes a DataFrame and metadata as input parameters. If the timeframe of the strategy is equal to the informative timeframe, it applies indicators to the DataFrame using the do_indicators method.
Otherwise, if there is no informative DataFrame available, it returns the original DataFrame.
If there is an informative DataFrame, it applies indicators to the informative DataFrame using the do_indicators method.
Then, it merges the informative DataFrame into the original DataFrame using the merge_informative_pair function, aligning the timeframes. It renames columns by removing the informative timeframe suffix from column names, except for certain skip columns. Finally, it returns the modified DataFrame. populate_buy_trend function: This function takes a DataFrame and metadata as input parameters. It checks for a condition where the 'go_long' column in the DataFrame is greater than 0. For rows that satisfy the condition, it sets the 'buy' column to 1. It returns the modified DataFrame. populate_sell_trend function: This function takes a DataFrame and metadata as input parameters. It checks for a condition where the 'go_long' column in the DataFrame is equal to 0. For rows that satisfy the condition, it sets the 'sell' column to 1. It returns the modified DataFrame. Overall, the strategy involves populating indicators, determining buy signals based on the 'go_long' column, and determining sell signals based on the absence of a buy signal.
Otherwise, if there is no informative DataFrame available, it returns the original DataFrame.
If there is an informative DataFrame, it applies indicators to the informative DataFrame using the do_indicators method.
Then, it merges the informative DataFrame into the original DataFrame using the merge_informative_pair function, aligning the timeframes. It renames columns by removing the informative timeframe suffix from column names, except for certain skip columns. Finally, it returns the modified DataFrame. populate_buy_trend function: This function takes a DataFrame and metadata as input parameters. It checks for a condition where the 'go_long' column in the DataFrame is greater than 0. For rows that satisfy the condition, it sets the 'buy' column to 1. It returns the modified DataFrame. populate_sell_trend function: This function takes a DataFrame and metadata as input parameters. It checks for a condition where the 'go_long' column in the DataFrame is equal to 0. For rows that satisfy the condition, it sets the 'sell' column to 1. It returns the modified DataFrame. Overall, the strategy involves populating indicators, determining buy signals based on the 'go_long' column, and determining sell signals based on the absence of a buy signal.
stoploss: -0.99 timeframe: 1h hash(sha256): fe9adba884b79b44070a1da327143aee499fb236909e8ab09dcc389801a6fcd0 indicators: close senkou_a tenkan_sen cloud_red smaHigh chikou_high leading_senkou_span_a ATR buy_criteria ssl_up sslDown leading_senkou_span_b open hlv smaLow go_long high sslUp kijun_sen senkou_span_a chikou_span senkou_b sell_criteria ssl_down senkou_span_b ssl_high cloud_green low future_green date open high low close volume
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last change: 2022-07-02 19:54:08