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The TaSearchLevelC30m strategy is designed for backtesting trading strategies. Here is a short description of what the strategy does:
It imports necessary libraries and modules for data analysis and trading indicators. The strategy implements the IStrategy interface, which is a requirement for all strategies in the backtesting website.
It sets the minimal return on investment (ROI) and stop loss values.
It enables short selling.
It defines parameters for trailing stop functionality. The populate_indicators function calculates technical indicators using the provided DataFrame of price data. It calculates the 7-day relative strength index (RSI). It initializes columns for level minimum and level maximum. It calls the do_long and do_short functions to identify buy signals and update the level maximum column. It returns the modified DataFrame. The do_long function identifies buy signals for long positions. It sets the lookback period to 200. It identifies local minima in the price series and stores them in the 'buy_min' column. It iterates over the DataFrame to check if each identified minimum is valid for a buy signal. It calculates the price difference between each valid buy minimum and the current close price. If the difference is below a certain threshold, it adds the current close price to the list of prices and updates the level maximum column. Finally, it checks if the difference between each price in the list and the current close price is below the threshold and updates the level maximum column accordingly. It returns the modified DataFrame. The do_short function identifies buy signals for short positions using a similar process as do_long. The populate_entry_trend function populates the entry trend columns based on the level maximum and level minimum columns. It sets the 'enter_short' column to 1 where the level maximum is greater than 0. It sets the 'enter_long' column to 1 where the level minimum is greater than 0. It returns the modified DataFrame. The populate_exit_trend function populates the exit trend columns based on the RSI values. It sets the 'exit_short' column to 1 where the RSI is below 10. It sets the 'exit_long' column to 1 where the RSI is above 90. It returns the modified DataFrame. The leverage function determines the leverage to be used for trading. It sets a fixed leverage value of 5.0. It returns the leverage value. The diff_percentage function calculates the percentage difference between two values. It takes two values, v2 and v1. It calculates the difference between the values and normalizes it based on the average of the values. It rounds the result to 4 decimal places. It returns the absolute value of the difference as a percentage. This strategy combines technical indicators such as RSI and local extrema to generate buy signals for long and short positions. It also defines exit conditions based on RSI values. The strategy uses a trailing stop and allows leverage for trading.
It sets the minimal return on investment (ROI) and stop loss values.
It enables short selling.
It defines parameters for trailing stop functionality. The populate_indicators function calculates technical indicators using the provided DataFrame of price data. It calculates the 7-day relative strength index (RSI). It initializes columns for level minimum and level maximum. It calls the do_long and do_short functions to identify buy signals and update the level maximum column. It returns the modified DataFrame. The do_long function identifies buy signals for long positions. It sets the lookback period to 200. It identifies local minima in the price series and stores them in the 'buy_min' column. It iterates over the DataFrame to check if each identified minimum is valid for a buy signal. It calculates the price difference between each valid buy minimum and the current close price. If the difference is below a certain threshold, it adds the current close price to the list of prices and updates the level maximum column. Finally, it checks if the difference between each price in the list and the current close price is below the threshold and updates the level maximum column accordingly. It returns the modified DataFrame. The do_short function identifies buy signals for short positions using a similar process as do_long. The populate_entry_trend function populates the entry trend columns based on the level maximum and level minimum columns. It sets the 'enter_short' column to 1 where the level maximum is greater than 0. It sets the 'enter_long' column to 1 where the level minimum is greater than 0. It returns the modified DataFrame. The populate_exit_trend function populates the exit trend columns based on the RSI values. It sets the 'exit_short' column to 1 where the RSI is below 10. It sets the 'exit_long' column to 1 where the RSI is above 90. It returns the modified DataFrame. The leverage function determines the leverage to be used for trading. It sets a fixed leverage value of 5.0. It returns the leverage value. The diff_percentage function calculates the percentage difference between two values. It takes two values, v2 and v1. It calculates the difference between the values and normalizes it based on the average of the values. It rounds the result to 4 decimal places. It returns the absolute value of the difference as a percentage. This strategy combines technical indicators such as RSI and local extrema to generate buy signals for long and short positions. It also defines exit conditions based on RSI values. The strategy uses a trailing stop and allows leverage for trading.
startup_candle_count : 50 rsi_7: 0.126%
Biased Indicators
buy_minBiased Entry Signals:
9
stoploss: -0.03 timeframe: 5m hash(sha256): b562893203d5148bce3d0feb8c853724fd15f46872ee18b76d004a4d9efeb67b
Was not able to fetch indicators from Strategyfile.
last change: 2023-07-04 19:44:59