The RSIDivTirail strategy is a trading strategy that uses the RSI (Relative Strength Index) indicator to identify potential divergences in price movement. Here's a breakdown of how the strategy works:
In the populate_indicators method, the RSI period (len) and RSI source (src) are defined. Lookback periods for pivot points are also set (lbR for right lookback and lbL for left lookback).
Additionally, ranges for the lookback period are defined (rangeUpper and rangeLower).
The strategy also provides options to plot different types of bullish and bearish conditions.
The RSI is calculated using the specified source (src) and period (len) for each data point in the input dataframe. Pivot points are identified by finding the minimum and maximum values of the RSI within the left lookback period (lbL). The previous minimum and maximum values are also calculated for comparison. Based on the pivot points, various conditions are evaluated to determine the presence of bullish or bearish signals. These conditions involve comparing the RSI values with the pivot points and other price-related values (e.g., low and high prices). If certain conditions are met, corresponding flags (bullCond, hiddenBullCond, bearCond, hiddenBearCond) are set. The strategy also calculates the EWO (Elliott Wave Oscillator) indicator using the specified fast and slow EWO parameters. In the populate_buy_trend method, the strategy checks for specific conditions (bullCond, hiddenBullCond) and positive volume to generate buy signals (buy flag). In the populate_sell_trend method, the strategy checks for specific conditions (bearCond, hiddenBearCond) and positive volume to generate sell signals (sell flag). Finally, the modified dataframe with the buy and sell signals is saved as a CSV file. This strategy aims to identify potential trading opportunities based on divergences in the RSI indicator and can be customized with various parameters and plot options.
Additionally, ranges for the lookback period are defined (rangeUpper and rangeLower).
The strategy also provides options to plot different types of bullish and bearish conditions.
The RSI is calculated using the specified source (src) and period (len) for each data point in the input dataframe. Pivot points are identified by finding the minimum and maximum values of the RSI within the left lookback period (lbL). The previous minimum and maximum values are also calculated for comparison. Based on the pivot points, various conditions are evaluated to determine the presence of bullish or bearish signals. These conditions involve comparing the RSI values with the pivot points and other price-related values (e.g., low and high prices). If certain conditions are met, corresponding flags (bullCond, hiddenBullCond, bearCond, hiddenBearCond) are set. The strategy also calculates the EWO (Elliott Wave Oscillator) indicator using the specified fast and slow EWO parameters. In the populate_buy_trend method, the strategy checks for specific conditions (bullCond, hiddenBullCond) and positive volume to generate buy signals (buy flag). In the populate_sell_trend method, the strategy checks for specific conditions (bearCond, hiddenBearCond) and positive volume to generate sell signals (sell flag). Finally, the modified dataframe with the buy and sell signals is saved as a CSV file. This strategy aims to identify potential trading opportunities based on divergences in the RSI indicator and can be customized with various parameters and plot options.
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.py", line 371, 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: -0.05 timeframe: 5m hash(sha256): 99148c68949e703e60de0e6e33dec1e55711a30f6c39bcb4df053061a59def48 indicators: rsi_buy hiddenBullCond use_bull EWO priceLH ewo_high oscLH oscHH close use_hidden_bull priceLL valuewhen_plFound_osc priceHL inrange_plFound_osc volume use_bear valuewhen_plFound_low plFound oscLL max inrange_phFound_high inrange_phFound_osc high valuewhen_phFound_high bearCond bullCond use_hidden_bear in_range oscHL valuewhen prevMin barrsince prevMax min hiddenBearCond colFromIndex phFound inrange_plFound_low low priceHH osc valuewhen_phFound_osc
Similar Strategies: (based on used indicators)
Strategy: RSIDivergence_758, Similarity Score: 97.67%
Strategy: RSIDivergence_238, Similarity Score: 88.37%
last change: 2024-04-28 05:03:33