The Cuncontrade strategy is a backtesting strategy implemented in Python using the Freqtrade library. It compares a stake currency with USDT (Tether) and aims to achieve a positive return on investment (ROI) by making buy and sell decisions based on various technical indicators. Here are the important parts of the strategy:
Minimal ROI: The strategy sets predefined minimal ROI targets for different time periods, such as 60 minutes, 30 minutes, 20 minutes, and 0 minutes (immediate).
These targets indicate the minimum expected profit before considering a sell signal.
Stoploss: The strategy sets an optimal stoploss value to limit potential losses.
In this case, it is set to -0.10, which means that if the price drops by 10%, the position will be sold to minimize further losses. Ticker Interval: The strategy uses a ticker interval of 5 minutes. This means that it analyzes price data at 5-minute intervals to make trading decisions. Indicators: The strategy populates various technical indicators using the populate_indicators method. These indicators include Average Directional Index (ADX), Simple Moving Averages (SMA), Awesome Oscillator (AO), Moving Average Convergence Divergence (MACD), and Bollinger Bands (BB). Buy Trend: The populate_buy_trend method determines the conditions for entering a buy position. It checks for specific criteria, such as the ADX value being above 25, the short SMA crossing above the long SMA, positive MACD values, positive AO values, and low Bollinger Bands percentage. Sell Trend: The populate_sell_trend method determines the conditions for selling a position. It checks for conditions like the ADX value being below 25, the long SMA crossing above the short SMA, negative MACD values, negative AO values, and the price exceeding the rolling maximum of the previous 60 periods. Overall, the strategy aims to capitalize on potential trends and momentum in the market by using a combination of technical indicators. However, it's worth noting that the strategy mentioned in the code is described as not performing well, serving as an example rather than a recommended trading strategy.
These targets indicate the minimum expected profit before considering a sell signal.
Stoploss: The strategy sets an optimal stoploss value to limit potential losses.
In this case, it is set to -0.10, which means that if the price drops by 10%, the position will be sold to minimize further losses. Ticker Interval: The strategy uses a ticker interval of 5 minutes. This means that it analyzes price data at 5-minute intervals to make trading decisions. Indicators: The strategy populates various technical indicators using the populate_indicators method. These indicators include Average Directional Index (ADX), Simple Moving Averages (SMA), Awesome Oscillator (AO), Moving Average Convergence Divergence (MACD), and Bollinger Bands (BB). Buy Trend: The populate_buy_trend method determines the conditions for entering a buy position. It checks for specific criteria, such as the ADX value being above 25, the short SMA crossing above the long SMA, positive MACD values, positive AO values, and low Bollinger Bands percentage. Sell Trend: The populate_sell_trend method determines the conditions for selling a position. It checks for conditions like the ADX value being below 25, the long SMA crossing above the short SMA, negative MACD values, negative AO values, and the price exceeding the rolling maximum of the previous 60 periods. Overall, the strategy aims to capitalize on potential trends and momentum in the market by using a combination of technical indicators. However, it's worth noting that the strategy mentioned in the code is described as not performing well, serving as an example rather than a recommended trading strategy.
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 58, in start_backtesting backtesting.start() File "/freqtrade/freqtrade/optimize/backtesting.py", line 1401, in start min_date, max_date = self.backtest_one_strategy(strat, data, timerange) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 1335, in backtest_one_strategy results = self.backtest( ^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 1213, in backtest data: Dict = self._get_ohlcv_as_lists(processed) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 381, in _get_ohlcv_as_lists df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair}) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1392, in ft_advise_signals dataframe = self.advise_exit(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1443, in advise_exit df = self.populate_exit_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 244, in populate_exit_trend return self.populate_sell_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/Cuncontrading.py", line 89, in populate_sell_trend ((dataframe['macd'] < dataframe['macdsignal']) | TypeError: 'Series' object is not callable
stoploss: -0.1 timeframe: 5m hash(sha256): 8c0098fefcb2e8747b48b9319ff7a7b9200625bd030f844c3e34843fe8b51d96 indicators: high macdsignal upper adx lower mid long bb_low close short ao macd bb_perc bb_upper macdhist bb_mid
Similar Strategies: (based on used indicators)
Strategy: IStrategy, Similarity Score: 94.12%
last change: 2024-07-28 02:24:30