You will be redirected to the original Strategy in 15 seconds.
The BigZ04 strategy is a trading strategy implemented in a class called BigZ04 that inherits from the IStrategy class. It involves the following components:
populate_indicators: This method is responsible for calculating and populating the indicators used in the strategy. It takes a DataFrame and metadata as input and returns the modified DataFrame.
populate_buy_trend: This method determines the conditions for initiating a buy trade.
It creates a list of conditions based on various indicators and their values.
If these conditions are met, a buy trade is initiated. The method takes a DataFrame and metadata as input and returns the modified DataFrame. Here are some important conditions used in the populate_buy_trend method: Condition 1: It checks if the close price is greater than the 200-day exponential moving average (ema_200), the 200-day exponential moving average on the 1-hour timeframe (ema_200_1h), and satisfies additional criteria related to Bollinger Bands, relative strength index (RSI), volume, and candlestick patterns. Condition 2: It checks if the close price is greater than the 200-day exponential moving average (ema_200), and satisfies additional criteria related to histogram, Bollinger Bands, RSI, volume, and candlestick patterns. Condition 3: It checks if the close price is greater than the 200-day exponential moving average (ema_200), satisfies certain RSI conditions, and meets criteria related to volume. Condition 4: It checks if the close price is greater than the 200-day exponential moving average (ema_200), satisfies certain Bollinger Bands conditions, and meets criteria related to volume. Condition 5: It checks if the close price is greater than the 200-day exponential moving average (ema_200), the 200-day exponential moving average on the 1-hour timeframe (ema_200_1h), and satisfies conditions related to moving average convergence divergence (MACD), Bollinger Bands, and volume. Condition 6: It checks if the 1-hour RSI is below a certain value, and satisfies conditions related to exponential moving averages, volume, and RSI. These conditions help determine the buy signals for the trading strategy. The strategy likely has additional methods, such as populate_sell_trend, to determine the conditions for initiating sell trades.
populate_buy_trend: This method determines the conditions for initiating a buy trade.
It creates a list of conditions based on various indicators and their values.
If these conditions are met, a buy trade is initiated. The method takes a DataFrame and metadata as input and returns the modified DataFrame. Here are some important conditions used in the populate_buy_trend method: Condition 1: It checks if the close price is greater than the 200-day exponential moving average (ema_200), the 200-day exponential moving average on the 1-hour timeframe (ema_200_1h), and satisfies additional criteria related to Bollinger Bands, relative strength index (RSI), volume, and candlestick patterns. Condition 2: It checks if the close price is greater than the 200-day exponential moving average (ema_200), and satisfies additional criteria related to histogram, Bollinger Bands, RSI, volume, and candlestick patterns. Condition 3: It checks if the close price is greater than the 200-day exponential moving average (ema_200), satisfies certain RSI conditions, and meets criteria related to volume. Condition 4: It checks if the close price is greater than the 200-day exponential moving average (ema_200), satisfies certain Bollinger Bands conditions, and meets criteria related to volume. Condition 5: It checks if the close price is greater than the 200-day exponential moving average (ema_200), the 200-day exponential moving average on the 1-hour timeframe (ema_200_1h), and satisfies conditions related to moving average convergence divergence (MACD), Bollinger Bands, and volume. Condition 6: It checks if the 1-hour RSI is below a certain value, and satisfies conditions related to exponential moving averages, volume, and RSI. These conditions help determine the buy signals for the trading strategy. The strategy likely has additional methods, such as populate_sell_trend, to determine the conditions for initiating sell trades.
stoploss: -0.99 timeframe: 5m hash(sha256): 03ec618496acb3c9cbfd8e2e748f7f6b68709d9c5a623e91b1cab970d7b67c7d indicators: upper ema_200 ema_50 close sma_5 bb_lowerband ema_200_1h volume open volume_mean_slow mid macd rsi_1h bb_lowerband_1h hist lower ema_12 signal bb_middleband rsi bb_upperband ema_26 low close_1h
Similar Strategies: (based on used indicators)
Strategy: BigZ04, Similarity Score: 96%
Strategy: BigZ04HO, Similarity Score: 96%
Strategy: BigZ04HO2, Similarity Score: 96%
Strategy: BigZ04_2, Similarity Score: 96%
Strategy: BigZ04_TSL1, Similarity Score: 96%
Strategy: BigZ04_TSL2, Similarity Score: 96%
Strategy: BigZ04_TSL3, Similarity Score: 96%
Strategy: BigZ04_TSL3_2, Similarity Score: 96%
Strategy: BigZ04_TSL4, Similarity Score: 96%
Strategy: BigZ04_TSL4_2, Similarity Score: 96%
Strategy: BigZ04_TSL4_24, Similarity Score: 96%
Strategy: BigZ04_TSL4_69, Similarity Score: 96%
Strategy: BigZ04_TSL4_761, Similarity Score: 96%
Strategy: BigZ06, Similarity Score: 96%
Strategy: Discord_BigZ04_TSL3, Similarity Score: 96%
Strategy: Discord_BigZ04_TSL3a, Similarity Score: 96%
Strategy: BcmbigzV1, Similarity Score: 92%
Strategy: BigZ07, Similarity Score: 92%
Strategy: BigZ07HX, Similarity Score: 92%
Strategy: BigZ07H_fix, Similarity Score: 92%
Strategy: BigZ03, Similarity Score: 88%
Strategy: BigZ03HO, Similarity Score: 88%
last change: 2022-07-02 19:54:08