The SOTCHDivergence strategy is designed to identify and signal potential trading opportunities based on divergence patterns observed in the Stochastic Oscillator (STOCH) and Relative Strength Index (RSI) indicators. In the populate_indicators method, the strategy calculates the RSI and STOCH indicators using the specified parameters. It then performs various calculations and transformations on the indicator values to identify divergence patterns.
This involves looking for pivot points and comparing the current oscillator values with previous minimum and maximum values.
The strategy also calculates additional values based on the divergence patterns observed.
In the populate_buy_trend method, the strategy defines the conditions for generating buy signals based on the identified divergence patterns. The conditions include criteria such as the presence of bullish or hidden bullish divergence, specific parameter ranges for the oscillator values, RSI values, Stochastic values, and the ADX indicator. The strategy also considers the volume of the trading asset. Overall, the SOTCHDivergence strategy aims to identify potential buying opportunities based on specific divergence patterns observed in the RSI and STOCH indicators, along with additional criteria such as the ADX indicator and volume.
This involves looking for pivot points and comparing the current oscillator values with previous minimum and maximum values.
The strategy also calculates additional values based on the divergence patterns observed.
In the populate_buy_trend method, the strategy defines the conditions for generating buy signals based on the identified divergence patterns. The conditions include criteria such as the presence of bullish or hidden bullish divergence, specific parameter ranges for the oscillator values, RSI values, Stochastic values, and the ADX indicator. The strategy also considers the volume of the trading asset. Overall, the SOTCHDivergence strategy aims to identify potential buying opportunities based on specific divergence patterns observed in the RSI and STOCH indicators, along with additional criteria such as the ADX indicator and volume.
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/RSIDivergence_238.py", line 412, in populate_sell_trend dataframe.to_csv('user_data/csvs/%s_%s.csv' % (self.__class__.__name__, metadata["pair"].replace("/", "_"))) File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/generic.py", line 3902, in to_csv return DataFrameRenderer(formatter).to_csv( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/formats/format.py", line 1152, in to_csv csv_formatter.save() File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/formats/csvs.py", line 247, in save with get_handle( ^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/common.py", line 739, in get_handle check_parent_directory(str(handle)) File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/common.py", line 604, in check_parent_directory raise OSError(rf"Cannot save file into a non-existent directory: '{parent}'") OSError: Cannot save file into a non-existent directory: 'user_data/csvs'
stoploss: -0.08 timeframe: 5m hash(sha256): 5d2dbdfc67856c8149e79a17b471bd0f57515d7ca619dd8ebc2f8cc38f307096 indicators: high_osc_buy hiddenBullCond EWO priceLH ewo_high oscLH oscHH close use_hidden_bull high_rsi_buy priceLL valuewhen_plFound_osc priceHL inrange_plFound_osc low_osc_buy low_adx_buy slowd volume use_bear valuewhen_plFound_low plFound oscLL max low_stoch_buy inrange_phFound_high inrange_phFound_osc high slowk valuewhen_phFound_high bearCond osc bullCond use_hidden_bear in_range oscHL valuewhen barrsince prevMin high_adx_buy low_rsi_buy min prevMax hiddenBearCond colFromIndex phFound inrange_plFound_l
Similar Strategies: (based on used indicators)
Strategy: RSIDivergence_2, Similarity Score: 85.11%
Strategy: RSIDivergence, Similarity Score: 78.72%
Strategy: RSIDivergence_758, Similarity Score: 78.72%
last change: 2024-04-28 04:56:46