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The provided code appears to define a trading strategy within a class called "abbas". Here's a short description of what the strategy does:
The strategy involves populating various technical indicators for a given financial dataframe. These indicators include moving averages (MA), Hull Moving Average (HMA), Exponential Moving Averages (EMA), Simple Moving Average (SMA), the Elder's Force Index (EWO), Relative Strength Index (RSI), and Bollinger Bands.
The populate_indicators function calculates and adds the indicators to the dataframe, including multiple EMAs, HMA, SMA, EWO, RSI, and Bollinger Bands.
The populate_buy_trend function determines the buy signals based on specific conditions.
It checks for conditions such as low RSI, close price below a certain moving average multiplied by an offset, high EWO value, and volume above zero. It assigns a "buy" signal and a corresponding tag to the dataframe rows that meet these conditions. The populate_sell_trend function is currently empty, indicating that it doesn't define any sell signals or conditions. Lastly, there's a separate code snippet outside the class definition that calculates the percentage difference between two exponential moving averages (EMA) based on a given dataframe. Overall, this strategy seems to use a combination of technical indicators to identify potential buy signals in the market. The specific conditions and parameters for generating buy signals can be adjusted based on the strategy's requirements.
The populate_indicators function calculates and adds the indicators to the dataframe, including multiple EMAs, HMA, SMA, EWO, RSI, and Bollinger Bands.
The populate_buy_trend function determines the buy signals based on specific conditions.
It checks for conditions such as low RSI, close price below a certain moving average multiplied by an offset, high EWO value, and volume above zero. It assigns a "buy" signal and a corresponding tag to the dataframe rows that meet these conditions. The populate_sell_trend function is currently empty, indicating that it doesn't define any sell signals or conditions. Lastly, there's a separate code snippet outside the class definition that calculates the percentage difference between two exponential moving averages (EMA) based on a given dataframe. Overall, this strategy seems to use a combination of technical indicators to identify potential buy signals in the market. The specific conditions and parameters for generating buy signals can be adjusted based on the strategy's requirements.
startup_candle_count : 200 hma_50: -0.001% ema_100: 0.014% rsi_slow: 0.002% hma_50_1h: -0.002% ema_100_1h: -0.041% ema_50_1h: 0.001% rsi_slow_1h: -0.001%
stoploss: -0.04 timeframe: 5m hash(sha256): 2d466bc82723d20a84aa352a8aeb0907eed038cc3f362adac68f0ea4c04454e1 indicators: enter_tag high rsi_slow close rsi low ewo rsi_fast
No similar strategies found. (based on used indicators)
last change: 2024-05-02 10:53:40