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The "TaSearchLevelB30m" strategy is a trading strategy implemented as part of a backtesting website. It aims to identify potential entry and exit points for trades based on technical analysis indicators. Here is a breakdown of how the strategy works:
The strategy uses the following indicators: RSI (Relative Strength Index), level_min, and level_max.
These indicators are calculated using the "ta" module from the "talib" library.
The strategy first populates the indicators by calling the "populate_indicators" function.
This function takes a DataFrame of price data and calculates the RSI values for a time period of 7. It also initializes the level_min and level_max columns with zeros. The "do_long" function is called to identify potential entry points for long trades. It uses a parameter "n" set to 200 to determine the minimum points for a local minimum. It calculates the buy_min values by finding the local minima of the closing prices using the "signal.argrelextrema" function. It then iterates over the DataFrame and checks for conditions where the buy_min value is greater than zero. It calculates the percentage difference between the current close price and the previous local minimum and checks if it is below 0.5. If it is, the level_min column is set to 1. Similarly, the "do_short" function is called to identify potential entry points for short trades. It follows a similar process as the "do_long" function but calculates the buy_max values based on local maxima of the closing prices. The "populate_entry_trend" function is called to populate additional columns in the DataFrame indicating the entry trends. It sets the "enter_short" column to 1 for rows where the level_max is greater than 0, indicating a potential short entry. It sets the "enter_long" column to 1 for rows where the level_min is greater than 0, indicating a potential long entry. The "populate_exit_trend" function is called to populate additional columns in the DataFrame indicating the exit trends. It sets the "exit_short" column to 1 for rows where the RSI_7 value is less than 10, indicating a potential short exit. It sets the "exit_long" column to 1 for rows where the RSI_7 value is greater than 90, indicating a potential long exit. The strategy also includes a "leverage" function that determines the leverage to be used for a trade. In this case, it returns a fixed leverage of 5.0 for all trades. The "diff_percentage" function calculates the percentage difference between two values using a formula. It takes two values, v2 and v1, and calculates the percentage difference as ((v2 - v1) / ((v2 + v1) / 2)) * 100. The result is rounded to four decimal places and returned as the absolute value. Overall, the strategy uses RSI values, local minima and maxima of closing prices, and specific threshold values to identify potential entry and exit points for long and short trades. It also includes functionality to set leverage for trades and calculate percentage differences between values.
These indicators are calculated using the "ta" module from the "talib" library.
The strategy first populates the indicators by calling the "populate_indicators" function.
This function takes a DataFrame of price data and calculates the RSI values for a time period of 7. It also initializes the level_min and level_max columns with zeros. The "do_long" function is called to identify potential entry points for long trades. It uses a parameter "n" set to 200 to determine the minimum points for a local minimum. It calculates the buy_min values by finding the local minima of the closing prices using the "signal.argrelextrema" function. It then iterates over the DataFrame and checks for conditions where the buy_min value is greater than zero. It calculates the percentage difference between the current close price and the previous local minimum and checks if it is below 0.5. If it is, the level_min column is set to 1. Similarly, the "do_short" function is called to identify potential entry points for short trades. It follows a similar process as the "do_long" function but calculates the buy_max values based on local maxima of the closing prices. The "populate_entry_trend" function is called to populate additional columns in the DataFrame indicating the entry trends. It sets the "enter_short" column to 1 for rows where the level_max is greater than 0, indicating a potential short entry. It sets the "enter_long" column to 1 for rows where the level_min is greater than 0, indicating a potential long entry. The "populate_exit_trend" function is called to populate additional columns in the DataFrame indicating the exit trends. It sets the "exit_short" column to 1 for rows where the RSI_7 value is less than 10, indicating a potential short exit. It sets the "exit_long" column to 1 for rows where the RSI_7 value is greater than 90, indicating a potential long exit. The strategy also includes a "leverage" function that determines the leverage to be used for a trade. In this case, it returns a fixed leverage of 5.0 for all trades. The "diff_percentage" function calculates the percentage difference between two values using a formula. It takes two values, v2 and v1, and calculates the percentage difference as ((v2 - v1) / ((v2 + v1) / 2)) * 100. The result is rounded to four decimal places and returned as the absolute value. Overall, the strategy uses RSI values, local minima and maxima of closing prices, and specific threshold values to identify potential entry and exit points for long and short trades. It also includes functionality to set leverage for trades and calculate percentage differences between values.
startup_candle_count : 50 rsi_7: 0.126%
Biased Indicators
level_max, buy_maxBiased Entry Signals:
16Biased Exit Signals:
1
stoploss: -0.05 timeframe: 5m hash(sha256): 9bd922ec55ae1da4aa81b758d21dc33b4ea9f5e9bdc3457c736ab06b08827cde
Was not able to fetch indicators from Strategyfile.
last change: 2023-11-01 16:34:52