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The Apollo11 strategy is a trading strategy implemented as a class that inherits from the IStrategy interface. It contains several methods that are used in the backtesting process. The populate_indicators method calculates and populates various technical indicators in the input dataframe.
These indicators include exponential moving averages (EMAs), Bollinger Bands, average true range (ATR), and other moving averages.
The calculated values are stored in additional columns in the dataframe.
The populate_buy_trend method determines the conditions for initiating a buy signal based on the calculated indicators. It checks for specific conditions such as the relationship between different EMAs, crossed thresholds, and volume requirements. If the conditions are met, the corresponding rows in the dataframe are marked with a "buy" signal and a descriptive tag. The populate_sell_trend method is responsible for populating the sell signals in the dataframe. In this strategy, it simply sets the "sell" column to 0 for all rows, indicating no sell signals. The calculate_fee method calculates the trading fee based on the current profit. It uses a series of if-else statements to determine the fee percentage based on the profit amount. The fee percentage is returned as a floating-point value. Overall, the Apollo11 strategy uses various technical indicators to generate buy signals based on specific conditions. It does not provide explicit sell signals and calculates a trading fee based on the current profit.
These indicators include exponential moving averages (EMAs), Bollinger Bands, average true range (ATR), and other moving averages.
The calculated values are stored in additional columns in the dataframe.
The populate_buy_trend method determines the conditions for initiating a buy signal based on the calculated indicators. It checks for specific conditions such as the relationship between different EMAs, crossed thresholds, and volume requirements. If the conditions are met, the corresponding rows in the dataframe are marked with a "buy" signal and a descriptive tag. The populate_sell_trend method is responsible for populating the sell signals in the dataframe. In this strategy, it simply sets the "sell" column to 0 for all rows, indicating no sell signals. The calculate_fee method calculates the trading fee based on the current profit. It uses a series of if-else statements to determine the fee percentage based on the profit amount. The fee percentage is returned as a floating-point value. Overall, the Apollo11 strategy uses various technical indicators to generate buy signals based on specific conditions. It does not provide explicit sell signals and calculates a trading fee based on the current profit.
startup_candle_count : 480 s1_ema_xxl: -0.009% s2_ema_xxl_off: -0.005% s2_ema_xxl: -0.005%
stoploss: -0.16 timeframe: 5m hash(sha256): 33f26cf34657545af995b9c657ab79a47cb52834cdc2f44c9db7abd6f8b63957 indicators: s1_ema_xxl close s2_bb_lower_band s3_ma_slow s2_fib_lower_band s1_ema_md s2_bb_lower_offset volume s3_bb_lowerband s2_ema_offset_input s3_slow_ma s1_ema_xl s2_bb_std_dev_value high s2_ema s3_ema_long s2_fib_atr_len slowMA s1_ema_xs s2_ema_input s2_ema_xxl_off s3_ema_short s2_ema_xxl s1_ema_sm s2_fib_sma_len hist vwmacd s3_fast_ma signal s3_ma_fast fastMA s2_bb_sma_length low s2_bb_std_dev_length
No similar strategies found. (based on used indicators)
last change: 2023-06-24 10:57:23