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The FrostAuraM11hStrategy is a trading strategy that aims to make purchase decisions based on Bollinger Bands (BB) and Relative Strength Index (RSI). It uses various indicators to identify potential buying and selling opportunities. Here's a breakdown of the important components and steps involved in the strategy:
Importing necessary libraries and modules:
numpy and pandas for data manipulation and analysis.
DataFrame from pandas for creating and working with data frames.
IStrategy from freqtrade.strategy.interface for defining the strategy interface.
talib.abstract as ta for calculating technical indicators. qtpylib.indicators from freqtrade.vendor for additional indicators. Defining the strategy class: The strategy class inherits from IStrategy to implement the required methods and properties. It also includes a docstring describing the strategy and its optimizations. Setting strategy parameters and configurations: INTERFACE_VERSION: The version of the strategy interface used. minimal_roi: The minimal return on investment (ROI) designed for the strategy. stoploss: The optimal stop loss value for the strategy. trailing_stop: A boolean indicating whether trailing stop loss is enabled. timeframe: The optimal ticker interval for the strategy. process_only_new_candles: A boolean indicating whether to populate indicators only for new candles. use_sell_signal: A boolean indicating whether to use a sell signal. sell_profit_only: A boolean indicating whether to sell for profit only. ignore_roi_if_buy_signal: A boolean indicating whether to ignore ROI if a buy signal is present. startup_candle_count: The number of candles required before producing valid signals. order_types: Optional mapping of order types. order_time_in_force: Optional order time in force. plot_config: Configuration for the plots displayed in the backtesting results. Implementing indicator population: The populate_indicators() method is overridden to calculate and populate the required indicators. The indicators used are RSI and Bollinger Bands with different standard deviations. Implementing buy signal generation: The populate_buy_trend() method is overridden to determine when to generate a buy signal. The conditions for a buy signal include RSI, close price, and Bollinger Bands. Implementing sell signal generation: The populate_sell_trend() method is overridden to determine when to generate a sell signal. The conditions for a sell signal include RSI and close price relative to the upper Bollinger Band. Overall, the FrostAuraM11hStrategy uses RSI and Bollinger Bands to identify potential buy and sell signals based on specified conditions. It aims to optimize returns and includes various parameters and configurations that can be adjusted.
DataFrame from pandas for creating and working with data frames.
IStrategy from freqtrade.strategy.interface for defining the strategy interface.
talib.abstract as ta for calculating technical indicators. qtpylib.indicators from freqtrade.vendor for additional indicators. Defining the strategy class: The strategy class inherits from IStrategy to implement the required methods and properties. It also includes a docstring describing the strategy and its optimizations. Setting strategy parameters and configurations: INTERFACE_VERSION: The version of the strategy interface used. minimal_roi: The minimal return on investment (ROI) designed for the strategy. stoploss: The optimal stop loss value for the strategy. trailing_stop: A boolean indicating whether trailing stop loss is enabled. timeframe: The optimal ticker interval for the strategy. process_only_new_candles: A boolean indicating whether to populate indicators only for new candles. use_sell_signal: A boolean indicating whether to use a sell signal. sell_profit_only: A boolean indicating whether to sell for profit only. ignore_roi_if_buy_signal: A boolean indicating whether to ignore ROI if a buy signal is present. startup_candle_count: The number of candles required before producing valid signals. order_types: Optional mapping of order types. order_time_in_force: Optional order time in force. plot_config: Configuration for the plots displayed in the backtesting results. Implementing indicator population: The populate_indicators() method is overridden to calculate and populate the required indicators. The indicators used are RSI and Bollinger Bands with different standard deviations. Implementing buy signal generation: The populate_buy_trend() method is overridden to determine when to generate a buy signal. The conditions for a buy signal include RSI, close price, and Bollinger Bands. Implementing sell signal generation: The populate_sell_trend() method is overridden to determine when to generate a sell signal. The conditions for a sell signal include RSI and close price relative to the upper Bollinger Band. Overall, the FrostAuraM11hStrategy uses RSI and Bollinger Bands to identify potential buy and sell signals based on specified conditions. It aims to optimize returns and includes various parameters and configurations that can be adjusted.
startup_candle_count : 30 rsi: 2.932%
stoploss: -0.406 timeframe: 1h hash(sha256): 7efa54048d03a869bb55f4abfa499e2dc3b9c4a5eac62d1a752acd319fe9c8ea indicators: bb_upperband1 mid lower bb_lowerband1 upper bb_middleband2 bb_middleband3 bb_upperband3 close bb_middleband1 rsi bb_lowerband3 bb_lowerband2 bb_upperband2
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last change: 2023-06-28 09:23:41