The SampleStrategy is a trading strategy implemented as a class in Python. It inherits from the IStrategy class. The strategy consists of several methods, each serving a specific purpose.
The populate_indicators method takes a DataFrame and a metadata dictionary as input and returns a modified DataFrame.
It calculates and adds various indicators to the DataFrame, including informative indicators from a 1-hour timeframe and normal timeframe indicators.
The populate_buy_trend method populates the buy signals in the DataFrame based on specific conditions. It uses multiple protection filters and logical conditions to determine the buy triggers. The protection filters include checks on exponential moving averages (EMA), close prices relative to EMAs, rising moving averages, safe dips, and safe pumps. The logical conditions involve checks on relative strength index (RSI), money flow index (MFI), open and close prices, Bollinger Bands, and volume. The resulting buy triggers are stored in the DataFrame. The strategy supports multiple buy conditions, each with its own set of protection filters and logical conditions. The conditions are stored in a list, and if enabled, their respective buy triggers are added to the DataFrame. Overall, the SampleStrategy class provides methods to calculate indicators and generate buy signals based on various conditions and filters, allowing for the backtesting of different trading strategies.
The populate_indicators method takes a DataFrame and a metadata dictionary as input and returns a modified DataFrame.
It calculates and adds various indicators to the DataFrame, including informative indicators from a 1-hour timeframe and normal timeframe indicators.
The populate_buy_trend method populates the buy signals in the DataFrame based on specific conditions. It uses multiple protection filters and logical conditions to determine the buy triggers. The protection filters include checks on exponential moving averages (EMA), close prices relative to EMAs, rising moving averages, safe dips, and safe pumps. The logical conditions involve checks on relative strength index (RSI), money flow index (MFI), open and close prices, Bollinger Bands, and volume. The resulting buy triggers are stored in the DataFrame. The strategy supports multiple buy conditions, each with its own set of protection filters and logical conditions. The conditions are stored in a list, and if enabled, their respective buy triggers are added to the DataFrame. Overall, the SampleStrategy class provides methods to calculate indicators and generate buy signals based on various conditions and filters, allowing for the backtesting of different trading strategies.
startup_candle_count : 400 ema_100_1h: 0.001% ema_200_1h: 0.078% ewo: 0.086%
stoploss: -0.99 timeframe: 5m hash(sha256): 3f7eea8127f280e610af3dd6b83f44260905ab0eba8ee4ec602e552890ccd462 indicators: sell_pump_36_3_1h sma_200_1h upper safe_pump_24_30 close ema_15 sell_pump_36_2 sma_5 sell_pump_24_1_1h buy_22_trigger ema_200_1h ewo buy_17_trigger safe_pump_24_100 volume sma_200_dec_20 buy_09_trigger safe_pump_24_120 safe_dips_70 sell_pump_36_1_1h ema_20 buy_21_trigger safe_pump_48_30 safe_pump_48_100_1h oc_pct_change_48 sell_pump_48_1_1h high tpct_change_2 safe_dips_30 sell_pump_48_3 safe_pump_48_10 safe_dips_80 rsi_1h bb20_2_mid ema_12 tpct_change_12 safe_pump_48_50 sell_pump_48_2 safe_pump_
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last change: 2024-05-02 11:44:43