The provided code snippet represents a trading strategy implemented in Python using the Freqtrade framework. This strategy aims to make buy and sell decisions in the cryptocurrency markets based on a combination of technical indicators. Here's a breakdown of the strategy:
Strategy Description:
This strategy is designed to perform automated trading in the cryptocurrency markets.
It utilizes various technical indicators to make buy and sell decisions for different cryptocurrency pairs.
The goal is to achieve a minimal return on investment (ROI) while managing risk through a stop-loss mechanism.
Technical Indicators Used: ADX (Average Directional Index): Measures the strength of a trend. SMA (Simple Moving Average): Computes the average price over a specified time period. Awesome Oscillator (AO): Displays the market momentum by calculating the difference between a 5-period simple moving average and a 34-period simple moving average. MACD (Moving Average Convergence Divergence): Shows the relationship between two moving averages of a security's price. Bollinger Bands: Consists of a middle band being an SMA and upper and lower bands indicating price volatility. Buy Conditions: The strategy defines the conditions under which a buy order should be executed. These conditions include: ADX above 25 and the short-term SMA crossing above the long-term SMA. MACD above 0, with either MACD being above the MACD signal or AO being above 0 (while AO shifted down). Bollinger Bands percentage (bb_perc) below 0.1. Sell Conditions: The strategy defines the conditions for selling a position. These conditions include: ADX below 25 and the long-term SMA crossing above the short-term SMA. MACD below 0, with either MACD being below the MACD signal or AO being below 0 (while AO shifted up). The closing price is higher than the highest price over the last 60 periods. Risk Management: The strategy incorporates a stop-loss mechanism set at -0.07, which means that if a trade's value decreases by 7%, the position will be automatically sold to limit potential losses. Ticker Interval: The strategy is designed to work with cryptocurrency price data sampled at a 1-hour interval (ticker_interval = '1h'). Overall, this trading strategy aims to capture potential market trends using a combination of technical indicators. However, keep in mind that this is a simplified overview, and the actual performance of the strategy will depend on various factors including market conditions, asset selection, and parameter tuning. Always thoroughly test any trading strategy and consider risk management practices before deploying it in a live trading environment.
It utilizes various technical indicators to make buy and sell decisions for different cryptocurrency pairs.
The goal is to achieve a minimal return on investment (ROI) while managing risk through a stop-loss mechanism.
Technical Indicators Used: ADX (Average Directional Index): Measures the strength of a trend. SMA (Simple Moving Average): Computes the average price over a specified time period. Awesome Oscillator (AO): Displays the market momentum by calculating the difference between a 5-period simple moving average and a 34-period simple moving average. MACD (Moving Average Convergence Divergence): Shows the relationship between two moving averages of a security's price. Bollinger Bands: Consists of a middle band being an SMA and upper and lower bands indicating price volatility. Buy Conditions: The strategy defines the conditions under which a buy order should be executed. These conditions include: ADX above 25 and the short-term SMA crossing above the long-term SMA. MACD above 0, with either MACD being above the MACD signal or AO being above 0 (while AO shifted down). Bollinger Bands percentage (bb_perc) below 0.1. Sell Conditions: The strategy defines the conditions for selling a position. These conditions include: ADX below 25 and the long-term SMA crossing above the short-term SMA. MACD below 0, with either MACD being below the MACD signal or AO being below 0 (while AO shifted up). The closing price is higher than the highest price over the last 60 periods. Risk Management: The strategy incorporates a stop-loss mechanism set at -0.07, which means that if a trade's value decreases by 7%, the position will be automatically sold to limit potential losses. Ticker Interval: The strategy is designed to work with cryptocurrency price data sampled at a 1-hour interval (ticker_interval = '1h'). Overall, this trading strategy aims to capture potential market trends using a combination of technical indicators. However, keep in mind that this is a simplified overview, and the actual performance of the strategy will depend on various factors including market conditions, asset selection, and parameter tuning. Always thoroughly test any trading strategy and consider risk management practices before deploying it in a live trading environment.
Traceback (most recent call last): File "/freqtrade/freqtrade/main.py", line 42, in main return_code = args['func'](args) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/commands/optimize_commands.py", line 57, in start_backtesting backtesting = Backtesting(config) ^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 113, in __init__ self.strategylist.append(StrategyResolver.load_strategy(self.config)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/resolvers/strategy_resolver.py", line 48, in load_strategy strategy: IStrategy = StrategyResolver._load_strategy( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/resolvers/strategy_resolver.py", line 269, in _load_strategy strategy = StrategyResolver._load_object( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/resolvers/iresolver.py", line 156, in _load_object return module(**kwargs) ^^^^^^^^^^^^^^^^ TypeError: Can't instantiate abstract class IStrategy with abstract method populate_indicators
stoploss: -0.07 timeframe: 1h hash(sha256): 3bf804b9d3b89bd8b3ac8a3dda203877b5b2e6ae7ea796542eab494165bfb815 indicators: high macdsignal upper adx lower mid long bb_low close short ao macd bb_perc bb_upper macdhist bb_mid
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last change: 2024-04-28 16:36:20