The TAD (Technical Analysis Dashboard) strategy is a trading strategy implemented as a class that extends the IStrategy class. It involves the following key components:
populate_indicators function: This function populates the indicators required for the strategy. It calculates the Relative Strength Index (RSI) with a time period of 14 and adds it to the dataframe.
Additionally, it determines the "dontbuy" condition based on the day and hour of the candle, which is also added to the dataframe.
populate_buy_trend function: This function determines the buy signals based on specific conditions.
It uses the RSI values and the "dontbuy" condition to generate different buy triggers (e.g., buy_01_trigger, buy_02_trigger) using logical operations. If any of the buy triggers are activated at the 998th candle, a corresponding buy tag is assigned. The function checks for multiple buy triggers and assigns a value of 1 to the "buy" column in the dataframe if any of the conditions are met. populate_sell_trend function: This function determines the sell signals. Currently, it sets the "sell" column to 1 if a specific condition is met, such as having a non-zero volume. Other utility functions and variable assignments: The code includes additional utility functions and variable assignments. For example, it extracts necessary values from ticker data, calculates the time difference, and handles the validation of the modified dataframe. It's important to note that the provided code snippet is incomplete and contains commented-out sections, which may affect the overall functionality of the strategy.
Additionally, it determines the "dontbuy" condition based on the day and hour of the candle, which is also added to the dataframe.
populate_buy_trend function: This function determines the buy signals based on specific conditions.
It uses the RSI values and the "dontbuy" condition to generate different buy triggers (e.g., buy_01_trigger, buy_02_trigger) using logical operations. If any of the buy triggers are activated at the 998th candle, a corresponding buy tag is assigned. The function checks for multiple buy triggers and assigns a value of 1 to the "buy" column in the dataframe if any of the conditions are met. populate_sell_trend function: This function determines the sell signals. Currently, it sets the "sell" column to 1 if a specific condition is met, such as having a non-zero volume. Other utility functions and variable assignments: The code includes additional utility functions and variable assignments. For example, it extracts necessary values from ticker data, calculates the time difference, and handles the validation of the modified dataframe. It's important to note that the provided code snippet is incomplete and contains commented-out sections, which may affect the overall functionality of the strategy.
stoploss: -0.99 timeframe: 5m hash(sha256): 6d1f61fb4365b521fa0efa354e15e5892c54d1d7b804eb3ff03cd72bdc00f66b indicators: roi_t1 roi_p6 roi_t8 roi_p3_min close open highlow buy_01_trigger 980993rsi roi_p4_min roi_p5_min roi_t3 roi_p8_max cohl roi_t3_min roi_t8_min roi_t6_min roi_p1_min roi_t3_max roi_t7 roi_t8_max roi_p5_max roi_p6_max roi_t5_min volume roi_p1 roi_t4 roi_p2 roi_t4_min roi_p3 roi_t5 roi_p7 date roi_t1_min buy_06_trigger roi_p3_max roi_p2_max buy_03_trigger buy_07_trigger roi_p7_max buy_05_trigger roi_p1_max roi_t2_max roi_p6_min roi_p4 roi_t7_min roi_p2_min buy_02_trigger roi_t6_max roi_t1_max buy r
Similar Strategies: (based on used indicators)
Strategy: Discord_1_TAD, Similarity Score: 94.34%
Strategy: Discord_2_TAD, Similarity Score: 77.36%
last change: 2024-05-05 23:42:16