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The ObeliskIM_v1_1 strategy is a trading strategy that uses a combination of technical indicators to generate buy and sell signals. Here's a breakdown of what the strategy does:
In the populate_indicators method:
Exponential Moving Averages (EMAs) with different time periods (3, 5, and 10) are calculated and added to the dataframe. The Relative Strength Index (RSI) with a time period of 14 is calculated and added to the dataframe.
Ichimoku Cloud indicators are calculated using the ftt.ichimoku function, with specific parameters for conversion line period, base line periods, lagging span, and displacement.
Different components of the Ichimoku Cloud (tenkan_sen, kijun_sen, senkou_span_a, senkou_span_b, cloud_green, and cloud_red) are extracted and added to the dataframe.
Additional derived indicators (cloud_green_strong and cloud_red_strong) are calculated based on specific conditions involving the Ichimoku Cloud components and the tenkan_sen and kijun_sen lines. In the populate_buy_trend method: A list of conditions is created to determine the buy signals. The conditions include checking for a strong green cloud, a bullish cross between tenkan_sen and kijun_sen, a positive EMA trend (ema35_ok), a price close higher than the previous close, a price close higher than the previous recent high, and no recent spikes. If all the conditions are met simultaneously, a "buy" signal (1) is assigned to the corresponding row in the dataframe. In the populate_sell_trend method: A list of conditions is created to determine the sell signals. The conditions include checking for a bearish cross below tenkan_sen and kijun_sen or a bearish cross below the close price and kijun_sen. If any of the conditions are met, a "sell" signal (1) is assigned to the corresponding row in the dataframe. The strategy calculates various technical indicators and combines them with specific conditions to generate buy and sell signals for trading.
Ichimoku Cloud indicators are calculated using the ftt.ichimoku function, with specific parameters for conversion line period, base line periods, lagging span, and displacement.
Different components of the Ichimoku Cloud (tenkan_sen, kijun_sen, senkou_span_a, senkou_span_b, cloud_green, and cloud_red) are extracted and added to the dataframe.
Additional derived indicators (cloud_green_strong and cloud_red_strong) are calculated based on specific conditions involving the Ichimoku Cloud components and the tenkan_sen and kijun_sen lines. In the populate_buy_trend method: A list of conditions is created to determine the buy signals. The conditions include checking for a strong green cloud, a bullish cross between tenkan_sen and kijun_sen, a positive EMA trend (ema35_ok), a price close higher than the previous close, a price close higher than the previous recent high, and no recent spikes. If all the conditions are met simultaneously, a "buy" signal (1) is assigned to the corresponding row in the dataframe. In the populate_sell_trend method: A list of conditions is created to determine the sell signals. The conditions include checking for a bearish cross below tenkan_sen and kijun_sen or a bearish cross below the close price and kijun_sen. If any of the conditions are met, a "sell" signal (1) is assigned to the corresponding row in the dataframe. The strategy calculates various technical indicators and combines them with specific conditions to generate buy and sell signals for trading.
startup_candle_count : 50 rsi: 1.737% cloud_green: -100.000% cloud_green_strong: -100.000%
stoploss: -0.04 timeframe: 5m hash(sha256): 34cb590f22d00414310899f8b59c9317c3c178592fe7829ea4b85026145c62e5 indicators: recent_high close senkou_a cloud_red_strong ema35_ok ema5 tenkan_sen cloud_red tk_cross_up ema10 kijun_sen spike high senkou_span_a senkou_b senkou_span_b ema3 cloud_green_strong rsi cloud_green
No similar strategies found. (based on used indicators)
last change: 2023-06-27 13:09:47