The TAD (Technical Analysis Divergence) strategy is a trading strategy implemented as a class in Python. Here's a short description of what the strategy does:
The populate_indicators function takes a dataframe and calculates technical indicators such as the Relative Strength Index (RSI) and a condition called "dontbuy." It returns the updated dataframe. The strategy collects ticker data using a WebSocket API and stores it in the tickerData variable.
The populate_buy_trend function populates the buy signals in the dataframe based on various conditions.
It extracts values from the ticker data and calculates different buy triggers using the RSI indicator and other conditions.
If any of the buy triggers are met, it sets the corresponding buy tag and assigns a value of 1 to the "buy" column in the dataframe. The populate_sell_trend function populates the sell signals in the dataframe based on certain conditions related to volume. If the conditions are met, it sets the "sell" column to 1. The strategy includes a check to ensure that the dataframe returned from the strategy functions is valid and has all the required elements. Overall, the TAD strategy uses technical indicators like RSI and specific conditions to determine buy and sell signals in a trading backtesting scenario.
The populate_buy_trend function populates the buy signals in the dataframe based on various conditions.
It extracts values from the ticker data and calculates different buy triggers using the RSI indicator and other conditions.
If any of the buy triggers are met, it sets the corresponding buy tag and assigns a value of 1 to the "buy" column in the dataframe. The populate_sell_trend function populates the sell signals in the dataframe based on certain conditions related to volume. If the conditions are met, it sets the "sell" column to 1. The strategy includes a check to ensure that the dataframe returned from the strategy functions is valid and has all the required elements. Overall, the TAD strategy uses technical indicators like RSI and specific conditions to determine buy and sell signals in a trading backtesting scenario.
stoploss: -0.99 timeframe: 5m hash(sha256): f79b69db70d585fda6750ff8d73ae9ca71613f1f8d1b4e0e97c43a391905854c indicators: roi_t1 roi_p6 roi_t8 roi_p3_min close open highlow buy_01_trigger 980998rsi 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 982998rsi 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
Similar Strategies: (based on used indicators)
Strategy: Discord_TAD, Similarity Score: 94.23%
Strategy: Discord_2_TAD, Similarity Score: 75%
last change: 2024-04-28 04:53:45