The TaSearchDynamic5m strategy is designed for backtesting trading strategies using historical price data on a 5-minute timeframe. Here's a summary of what the strategy does:
Initialization:
The strategy initializes by setting a value for the parameter n (which is set to 72 in this case) and creates an instance of the TaSearchDynamic class. Indicator Population:
The populate_indicators function receives a DataFrame (df) containing historical price data and metadata.
It processes the DataFrame by performing the following steps: Renames the columns of the DataFrame to ['date', 'open', 'high', 'low', 'close', 'volume'].
Uses the TaSearchDynamic instance to find extremes in the price data.
Applies a "buy past RSI" calculation on the DataFrame. Applies a "buy stride" calculation on the DataFrame. Buy Trend: The populate_buy_trend function identifies potential buy signals based on specific conditions in the DataFrame (df) and metadata. The conditions include: The "buy stride" value falls within a certain range. The "buy past RSI" value is above a certain threshold. The "market" value is -1. Sell Trend: The populate_sell_trend function identifies potential sell signals based on specific conditions in the DataFrame (df) and metadata. The conditions include: The RSI (Relative Strength Index) value calculated over a 7-period window is above 75. The RSI value calculated over a 30-period window is above 60. Trade Exit Confirmation: The confirm_trade_exit function determines whether to exit a trade based on certain criteria. It receives parameters related to the trade, such as the pair being traded, trade details, current time, etc. The function calculates the profit ratio of the trade and checks if the exit reason is an "exit_signal" and if the profit is negative. If both conditions are met, the function returns False to prevent the trade exit; otherwise, it returns True. Overall, the TaSearchDynamic5m strategy uses various technical analysis indicators, such as RSI and market extremes, to generate buy and sell signals on a 5-minute timeframe.
It processes the DataFrame by performing the following steps: Renames the columns of the DataFrame to ['date', 'open', 'high', 'low', 'close', 'volume'].
Uses the TaSearchDynamic instance to find extremes in the price data.
Applies a "buy past RSI" calculation on the DataFrame. Applies a "buy stride" calculation on the DataFrame. Buy Trend: The populate_buy_trend function identifies potential buy signals based on specific conditions in the DataFrame (df) and metadata. The conditions include: The "buy stride" value falls within a certain range. The "buy past RSI" value is above a certain threshold. The "market" value is -1. Sell Trend: The populate_sell_trend function identifies potential sell signals based on specific conditions in the DataFrame (df) and metadata. The conditions include: The RSI (Relative Strength Index) value calculated over a 7-period window is above 75. The RSI value calculated over a 30-period window is above 60. Trade Exit Confirmation: The confirm_trade_exit function determines whether to exit a trade based on certain criteria. It receives parameters related to the trade, such as the pair being traded, trade details, current time, etc. The function calculates the profit ratio of the trade and checks if the exit reason is an "exit_signal" and if the profit is negative. If both conditions are met, the function returns False to prevent the trade exit; otherwise, it returns True. Overall, the TaSearchDynamic5m strategy uses various technical analysis indicators, such as RSI and market extremes, to generate buy and sell signals on a 5-minute timeframe.
stoploss: -0.02 timeframe: 5m hash(sha256): 46fc826e5438597cb0dca0a10ff87e4aa24f59d066c637dff7712a0d15428321
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last change: 2024-04-28 16:41:08