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The TaLevelB15m strategy is a trading strategy that uses technical indicators and price levels to generate buy and sell signals. Here's a brief description of what the strategy does:
The strategy starts by calculating the RSI (Relative Strength Index) with a time period of 7 and rounding the values to 2 decimal places. It initializes two columns in the DataFrame called 'level_min' and 'level_max' with initial values of 0.
The strategy then identifies local minimum points in the price data by applying the argrelextrema function and stores them in the 'buy_min' column.
It checks if there are any local minimum points found and if so, it selects the latest one and calculates its corresponding close price.
The strategy creates a chunk of the DataFrame before the selected local minimum point and repeats the process of finding local minimum points and selecting the latest one in the chunk. It also calculates the corresponding close price. It calculates the percentage difference between the close prices of the latest selected point (x_close) and the previous selected point (y_close) and stores it as 'diff_close_xy'. It also calculates the distance between these two points (x - y) and stores it as 'dist_xy'. If the distance between x and y is greater than 5, the percentage difference is less than 0.2, and x_close is greater than y_close, it enters a loop iterating over each row in the DataFrame. Within the loop, it calculates the distance between the current row (i) and the latest selected point (x). If the distance is between 0 and 3, it retrieves the close price of the current row (i_close) and checks if it is rising (previous open price and current open price are lower than the previous and current close prices, respectively). It calculates the percentage difference between x_close and i_close and stores it as 'diff_close_xi'. If the conditions for a rising trend and the percentage difference are met, it prints the indices of y, x, and i, along with the values of diff_close_xy and diff_close_xi, indicating a long trade. The 'level_min' column is updated with the index of the current row (i) if the conditions are met. The process is repeated for finding local maximum points and generating short trade signals. The difference is that it checks if the close prices are falling and if x_close is less than y_close. After populating the levels for long and short trades, the strategy populates the 'enter_short' and 'enter_long' columns in the DataFrame based on the presence of 'level_max' and 'level_min' values, respectively. It also populates the 'exit_short' and 'exit_long' columns based on the RSI value. If RSI is less than 2, it indicates an exit signal for short trades, and if RSI is greater than 98, it indicates an exit signal for long trades. The strategy assigns a fixed leverage of 20 to all trades. There is a helper function that calculates the absolute percentage difference between two values (v1 and v2). Overall, the strategy identifies price levels based on local minimum and maximum points and uses RSI as an additional indicator for generating entry and exit signals. It aims to capture potential trends and reversals in the market.
The strategy then identifies local minimum points in the price data by applying the argrelextrema function and stores them in the 'buy_min' column.
It checks if there are any local minimum points found and if so, it selects the latest one and calculates its corresponding close price.
The strategy creates a chunk of the DataFrame before the selected local minimum point and repeats the process of finding local minimum points and selecting the latest one in the chunk. It also calculates the corresponding close price. It calculates the percentage difference between the close prices of the latest selected point (x_close) and the previous selected point (y_close) and stores it as 'diff_close_xy'. It also calculates the distance between these two points (x - y) and stores it as 'dist_xy'. If the distance between x and y is greater than 5, the percentage difference is less than 0.2, and x_close is greater than y_close, it enters a loop iterating over each row in the DataFrame. Within the loop, it calculates the distance between the current row (i) and the latest selected point (x). If the distance is between 0 and 3, it retrieves the close price of the current row (i_close) and checks if it is rising (previous open price and current open price are lower than the previous and current close prices, respectively). It calculates the percentage difference between x_close and i_close and stores it as 'diff_close_xi'. If the conditions for a rising trend and the percentage difference are met, it prints the indices of y, x, and i, along with the values of diff_close_xy and diff_close_xi, indicating a long trade. The 'level_min' column is updated with the index of the current row (i) if the conditions are met. The process is repeated for finding local maximum points and generating short trade signals. The difference is that it checks if the close prices are falling and if x_close is less than y_close. After populating the levels for long and short trades, the strategy populates the 'enter_short' and 'enter_long' columns in the DataFrame based on the presence of 'level_max' and 'level_min' values, respectively. It also populates the 'exit_short' and 'exit_long' columns based on the RSI value. If RSI is less than 2, it indicates an exit signal for short trades, and if RSI is greater than 98, it indicates an exit signal for long trades. The strategy assigns a fixed leverage of 20 to all trades. There is a helper function that calculates the absolute percentage difference between two values (v1 and v2). Overall, the strategy identifies price levels based on local minimum and maximum points and uses RSI as an additional indicator for generating entry and exit signals. It aims to capture potential trends and reversals in the market.
stoploss: -0.05 timeframe: 5m hash(sha256): 991faa89adb8a75593befedb9c58b3d947ca3bfb19147bf0cee0fdd0ce91c191
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last change: 2023-03-08 22:52:43