Not Enough Data to display!
Average Overall
| Not Enough Data! / Avg statistics not populated yet. |
|---|
The MultiMA_TSL strategy is a trading strategy that uses multiple moving averages (MAs) along with other indicators to determine buy and sell signals. Here's a breakdown of what the strategy does:
In the populate_indicators function:
Calculates the EWO (Elder's Force Index) using the provided parameters. Calculates the RSI (Relative Strength Index) with a time period of 14.
Returns the updated dataframe with the added indicators.
In the populate_buy_trend function: Sets up empty conditions list.
Calculates the buy offset values based on EMA (Exponential Moving Average) and TRIMA (Triangular Moving Average) using the provided parameters. Compares the closing price to the buy offset values and checks conditions involving EWO and RSI. Appends the buy offset conditions to the conditions list. Adds a 'buy_tag' column to the dataframe. Combines the buy offset conditions using logical operators and stores the result in the 'buy' column. Returns the updated dataframe. In the populate_sell_trend function: Calculates the sell offset value based on EMA using the provided parameters. Sets the 'sell' column to zero for all rows. Returns the updated dataframe. The last part of the code snippet outside the class definition appears to be a separate function that calculates the difference between two EMAs and normalizes it by dividing it by the closing price and multiplying by 100. It returns the result. Overall, the strategy uses indicators like EWO, RSI, and moving averages to determine buy and sell signals based on specific conditions and offsets from the current price.
Returns the updated dataframe with the added indicators.
In the populate_buy_trend function: Sets up empty conditions list.
Calculates the buy offset values based on EMA (Exponential Moving Average) and TRIMA (Triangular Moving Average) using the provided parameters. Compares the closing price to the buy offset values and checks conditions involving EWO and RSI. Appends the buy offset conditions to the conditions list. Adds a 'buy_tag' column to the dataframe. Combines the buy offset conditions using logical operators and stores the result in the 'buy' column. Returns the updated dataframe. In the populate_sell_trend function: Calculates the sell offset value based on EMA using the provided parameters. Sets the 'sell' column to zero for all rows. Returns the updated dataframe. The last part of the code snippet outside the class definition appears to be a separate function that calculates the difference between two EMAs and normalizes it by dividing it by the closing price and multiplying by 100. It returns the result. Overall, the strategy uses indicators like EWO, RSI, and moving averages to determine buy and sell signals based on specific conditions and offsets from the current price.
stoploss: -0.15 timeframe: 5m hash(sha256): 9779e72087fc467f9168da3266b1149899626a4dcc2a01927ebb889413b90be6 indicators: volume close rsi date ema_offset_buy trima_offset_buy ema_offset_sell ewo buy_tag
No similar strategies found. (based on used indicators)
last change: 2024-05-02 17:52:07