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The EMABBRSI strategy is a trading strategy implemented in Python using the Freqtrade framework. It aims to generate buy and sell signals for cryptocurrency trading based on various technical indicators. Here's a breakdown of what the strategy does:
It defines the minimal return on investment (ROI) and stop loss parameters for the strategy.
The strategy operates on 1-hour candlestick data.
It uses several technical indicators to make trading decisions, including Exponential Moving Averages (EMA) with different time periods (7, 25, 50, and 200), Relative Strength Index (RSI), and Bollinger Bands.
The populate_indicators function calculates the values of these indicators and adds them to the input DataFrame. The populate_buy_trend function determines the buy signals based on the indicator values. It considers conditions such as RSI being above 33, the price crossing above the lower Bollinger Band with 3 standard deviations, the price crossing above the 200-day EMA, and the 50-day EMA crossing above the 200-day EMA. The populate_sell_trend function identifies sell signals based on conditions like the price crossing above the lower Bollinger Band, the RSI being above 91, and the 50-day EMA crossing below the 200-day EMA. The resulting DataFrame includes a "buy" column populated with 1 for buy signals and a "sell" column populated with 1 for sell signals. This strategy aims to capture potential buying opportunities when certain technical conditions are met and sell when specific conditions indicate potential price declines. It combines moving averages, RSI, and Bollinger Bands to make trading decisions.
The strategy operates on 1-hour candlestick data.
It uses several technical indicators to make trading decisions, including Exponential Moving Averages (EMA) with different time periods (7, 25, 50, and 200), Relative Strength Index (RSI), and Bollinger Bands.
The populate_indicators function calculates the values of these indicators and adds them to the input DataFrame. The populate_buy_trend function determines the buy signals based on the indicator values. It considers conditions such as RSI being above 33, the price crossing above the lower Bollinger Band with 3 standard deviations, the price crossing above the 200-day EMA, and the 50-day EMA crossing above the 200-day EMA. The populate_sell_trend function identifies sell signals based on conditions like the price crossing above the lower Bollinger Band, the RSI being above 91, and the 50-day EMA crossing below the 200-day EMA. The resulting DataFrame includes a "buy" column populated with 1 for buy signals and a "sell" column populated with 1 for sell signals. This strategy aims to capture potential buying opportunities when certain technical conditions are met and sell when specific conditions indicate potential price declines. It combines moving averages, RSI, and Bollinger Bands to make trading decisions.
startup_candle_count : 50 ema25: -0.023% rsi: 0.533%
stoploss: -0.2 timeframe: 1h hash(sha256): ab6bed3c354ef0177f319f7bc1610e10b12613c3aa07b80c028cc2cf1ea6431e indicators: upper mid lower bb_middleband3 bb_upperband3 bb_middleband rsi close bb_upperband bb_lowerband low ema50 bb_lowerband3 ema7 ema25 ema200
No similar strategies found. (based on used indicators)
last change: 2023-06-24 12:29:39