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The BigZ04 strategy is a trading strategy implemented as a class in Python. Here's a short description of what the strategy does:
The strategy consists of two main functions: populate_indicators and populate_entry_trend. The populate_indicators function calculates various technical indicators based on the given price data and metadata.
It merges the indicators from a higher timeframe (1 hour) with the current timeframe, using a forward-fill method.
Then, it calculates additional indicators for the current timeframe.
The populate_entry_trend function defines a set of conditions for initiating buy trades. The conditions are organized into different blocks, each corresponding to a specific buy condition. Each block contains a set of logical conditions that must be met for a buy trade to be initiated. These conditions involve comparisons between different price and indicator values, such as moving averages, Bollinger Bands, RSI (Relative Strength Index), volume, and open/close prices. The conditions also consider historical price and volume data by using shift operations. Overall, the strategy aims to identify favorable buying opportunities based on the specified conditions, which include technical indicators, price patterns, and volume characteristics. It utilizes a combination of moving averages, Bollinger Bands, RSI, and volume analysis to make buy decisions.
It merges the indicators from a higher timeframe (1 hour) with the current timeframe, using a forward-fill method.
Then, it calculates additional indicators for the current timeframe.
The populate_entry_trend function defines a set of conditions for initiating buy trades. The conditions are organized into different blocks, each corresponding to a specific buy condition. Each block contains a set of logical conditions that must be met for a buy trade to be initiated. These conditions involve comparisons between different price and indicator values, such as moving averages, Bollinger Bands, RSI (Relative Strength Index), volume, and open/close prices. The conditions also consider historical price and volume data by using shift operations. Overall, the strategy aims to identify favorable buying opportunities based on the specified conditions, which include technical indicators, price patterns, and volume characteristics. It utilizes a combination of moving averages, Bollinger Bands, RSI, and volume analysis to make buy decisions.
stoploss: -0.99 timeframe: 5m hash(sha256): 262b95ee511e2c246d19635028736778931e3cdceb723f40a1c00ede30f02070 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
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last change: 2023-04-26 07:49:04