The TaSearchDynamic30m strategy is a trading strategy designed for backtesting purposes. Here is a brief description of what the strategy does:
The strategy uses the TaSearchDynamic module for performing dynamic search operations. The timeframe for analysis is set to 30 minutes.
The strategy defines a minimal_roi (minimum return on investment) of 0.1, indicating a desired minimum profit.
The stoploss is set to -0.0, suggesting that there is no specific stop loss level defined.
The populate_indicators function is responsible for populating the indicators in the DataFrame used for analysis. It performs various calculations and manipulations on the DataFrame columns. The buy_past_rsi function identifies potential buy signals based on the past relative strength index (RSI) values. It iterates over the DataFrame, checks for specific conditions, and assigns values to the 'buy_past_rsi' column. The buy_stride function identifies potential buy signals based on RSI values and other conditions. It also assigns values to the 'buy_stride' and 'buy_past_rsi' columns. The populate_buy_trend function populates the 'buy' column in the DataFrame with a value of 1 for specific conditions related to the 'buy_stride' and 'buy_past_rsi' columns. The populate_sell_trend function populates the 'sell' column in the DataFrame with a value of 1 for specific conditions related to RSI values. The confirm_trade_exit function is used to confirm whether a trade should be exited or not based on various criteria, including the profit ratio. Overall, the strategy combines various indicators and conditions to identify potential buy and sell signals for trading.
The strategy defines a minimal_roi (minimum return on investment) of 0.1, indicating a desired minimum profit.
The stoploss is set to -0.0, suggesting that there is no specific stop loss level defined.
The populate_indicators function is responsible for populating the indicators in the DataFrame used for analysis. It performs various calculations and manipulations on the DataFrame columns. The buy_past_rsi function identifies potential buy signals based on the past relative strength index (RSI) values. It iterates over the DataFrame, checks for specific conditions, and assigns values to the 'buy_past_rsi' column. The buy_stride function identifies potential buy signals based on RSI values and other conditions. It also assigns values to the 'buy_stride' and 'buy_past_rsi' columns. The populate_buy_trend function populates the 'buy' column in the DataFrame with a value of 1 for specific conditions related to the 'buy_stride' and 'buy_past_rsi' columns. The populate_sell_trend function populates the 'sell' column in the DataFrame with a value of 1 for specific conditions related to RSI values. The confirm_trade_exit function is used to confirm whether a trade should be exited or not based on various criteria, including the profit ratio. Overall, the strategy combines various indicators and conditions to identify potential buy and sell signals for trading.
stoploss: -0 timeframe: 30m hash(sha256): 3455d826c0a67729f4bfb8dd432e6adf0d3f622bb65a03b51e54a24758d4e8d3
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last change: 2024-04-29 00:50:02