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The BigZ04_TSL4 strategy is a trading strategy implemented as a class that inherits from the IStrategy class. It performs backtesting by applying various conditions to the input data and generating buy signals. The strategy consists of the following main components:
populate_indicators: This method is responsible for populating the indicators used in the strategy.
It takes a DataFrame and a metadata dictionary as inputs and returns a DataFrame with the indicators added.
It combines indicators from the informative 1-hour timeframe and the normal timeframe.
populate_buy_trend: This method determines the buy signals based on a set of conditions. It takes a DataFrame and a metadata dictionary as inputs and returns a DataFrame with additional columns indicating the buy signals. The conditions for generating buy signals are defined using a series of logical expressions. The important parts of the strategy include: Condition 1: It checks various conditions such as the current close price being above the 200-day exponential moving average (ema_200), the close price being above the lower Bollinger Band (bb_lowerband), and the RSI (Relative Strength Index) being below a certain threshold. Condition 2: It checks conditions related to histogram values, Bollinger Bands, and RSI, among others. It looks for specific patterns and relationships between different indicators. Condition 3: It checks conditions based on the close price, RSI, and volume mean slow. It ensures that the close price is above the 200-day exponential moving average of the 1-hour timeframe (ema_200_1h) and below the lower Bollinger Band. Condition 4: It checks conditions related to the 1-hour RSI, close price, and volume mean slow. Condition 5: It checks conditions related to exponential moving averages (ema_26 and ema_12), open price, and Bollinger Bands. It looks for a specific relationship between the moving averages and the open price, as well as the close price being below the lower Bollinger Band. The strategy uses various technical indicators such as exponential moving averages (ema), Bollinger Bands (bb), RSI, and volume-related measures to generate buy signals. By combining these conditions, the strategy aims to identify potential profitable trading opportunities.
It takes a DataFrame and a metadata dictionary as inputs and returns a DataFrame with the indicators added.
It combines indicators from the informative 1-hour timeframe and the normal timeframe.
populate_buy_trend: This method determines the buy signals based on a set of conditions. It takes a DataFrame and a metadata dictionary as inputs and returns a DataFrame with additional columns indicating the buy signals. The conditions for generating buy signals are defined using a series of logical expressions. The important parts of the strategy include: Condition 1: It checks various conditions such as the current close price being above the 200-day exponential moving average (ema_200), the close price being above the lower Bollinger Band (bb_lowerband), and the RSI (Relative Strength Index) being below a certain threshold. Condition 2: It checks conditions related to histogram values, Bollinger Bands, and RSI, among others. It looks for specific patterns and relationships between different indicators. Condition 3: It checks conditions based on the close price, RSI, and volume mean slow. It ensures that the close price is above the 200-day exponential moving average of the 1-hour timeframe (ema_200_1h) and below the lower Bollinger Band. Condition 4: It checks conditions related to the 1-hour RSI, close price, and volume mean slow. Condition 5: It checks conditions related to exponential moving averages (ema_26 and ema_12), open price, and Bollinger Bands. It looks for a specific relationship between the moving averages and the open price, as well as the close price being below the lower Bollinger Band. The strategy uses various technical indicators such as exponential moving averages (ema), Bollinger Bands (bb), RSI, and volume-related measures to generate buy signals. By combining these conditions, the strategy aims to identify potential profitable trading opportunities.
stoploss: -0.99 timeframe: 5m hash(sha256): d81e27f1dbc9325f7e946fca6ace3540775e644ca796ea279dff6fa53fc432e5 indicators: upper ema_200 ema_50 close sma_5 bb_lowerband ema_200_1h ma_sell_val volume open volume_mean_slow mid macd rsi_1h bb_lowerband_1h hist atr lower ema_12 signal bb_middleband rsi bb_upperband ema_26 low close_1h
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last change: 2022-07-02 19:54:08