The NotAnotherSMAOffsetStrategyModHOv3 trading strategy is a complex algorithm designed to identify potential buying and selling opportunities in financial markets. Here's a breakdown of its key components and functionalities:
Indicators:
Exponential Moving Averages (EMAs) are calculated for various time periods and used as trend indicators for potential buying and selling points. Hull Moving Average (HMA) is calculated using a weighted moving average formula to smooth out price data.
Simple Moving Average (SMA) is calculated to track the average price over a specified period.
Elder's Force Index (EFI) is calculated to gauge the force behind price movements.
Relative Strength Index (RSI) is calculated to assess the speed and change of price movements. Average Directional Index (ADX) is calculated to determine the strength of a trend. Other Indicators: Stochastic Fast %D and %K values are calculated to identify overbought and oversold conditions. Commodity Trading Index (CTI) is computed to assess price trends based on the relationship between close prices and a moving average. Money Flow Index (MFI) is calculated to evaluate the buying and selling pressure in the market. Triple Exponential Moving Average (TEMA) is used to provide a smoother moving average representation. The "hlc3" indicator calculates an average of high, low, and close prices. Signal Generation: Various conditions are evaluated to determine potential buy and sell signals. Buy signals are generated based on combinations of RSI, EMA, CTI, MFI, and price movement conditions. Sell signals are generated based on combinations of RSI, EMA, HMA, and price movement conditions. Additional Features: The strategy considers the rolling sums of percentage price changes to identify pumping behaviors. Different variations of conditions are used to generate buy and sell signals based on indicators' values and price movement. Exit Criteria: The strategy uses exit criteria such as RSI and EMA values to determine when to sell a position. Overall, this strategy uses a combination of various technical indicators and conditions to identify potential trading opportunities. It aims to capture trends and momentum in the market and provides rules for entering and exiting positions based on these indicators. It's important to note that this is a highly detailed strategy, and the effectiveness of such strategies heavily depends on the specific market conditions and the accuracy of the chosen indicators.
Simple Moving Average (SMA) is calculated to track the average price over a specified period.
Elder's Force Index (EFI) is calculated to gauge the force behind price movements.
Relative Strength Index (RSI) is calculated to assess the speed and change of price movements. Average Directional Index (ADX) is calculated to determine the strength of a trend. Other Indicators: Stochastic Fast %D and %K values are calculated to identify overbought and oversold conditions. Commodity Trading Index (CTI) is computed to assess price trends based on the relationship between close prices and a moving average. Money Flow Index (MFI) is calculated to evaluate the buying and selling pressure in the market. Triple Exponential Moving Average (TEMA) is used to provide a smoother moving average representation. The "hlc3" indicator calculates an average of high, low, and close prices. Signal Generation: Various conditions are evaluated to determine potential buy and sell signals. Buy signals are generated based on combinations of RSI, EMA, CTI, MFI, and price movement conditions. Sell signals are generated based on combinations of RSI, EMA, HMA, and price movement conditions. Additional Features: The strategy considers the rolling sums of percentage price changes to identify pumping behaviors. Different variations of conditions are used to generate buy and sell signals based on indicators' values and price movement. Exit Criteria: The strategy uses exit criteria such as RSI and EMA values to determine when to sell a position. Overall, this strategy uses a combination of various technical indicators and conditions to identify potential trading opportunities. It aims to capture trends and momentum in the market and provides rules for entering and exiting positions based on these indicators. It's important to note that this is a highly detailed strategy, and the effectiveness of such strategies heavily depends on the specific market conditions and the accuracy of the chosen indicators.
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 1391, in ft_advise_signals dataframe = self.advise_entry(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1425, in advise_entry df = self.populate_entry_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 225, in populate_entry_trend return self.populate_buy_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/NotAnotherSMAOffsetStrategyModHOv3.py", line 374, in populate_buy_trend (dataframe['cti'] > 0.1) | File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/ops/common.py", line 76, in new_method return method(self, other) ^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/arraylike.py", line 78, in __or__ return self._logical_method(other, operator.or_) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/series.py", line 5814, in _logical_method res_values = ops.logical_op(lvalues, rvalues, op) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/ops/array_ops.py", line 456, in logical_op res_values = na_logical_op(lvalues, rvalues, op) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/ops/array_ops.py", line 364, in na_logical_op result = op(x, y) ^^^^^^^^ ValueError: operands could not be broadcast together with shapes (9129,) (2,)
stoploss: -0.32 timeframe: 5m hash(sha256): 2ce380924b9faeaa15967164624b6331a7c43576148178ff532d6929183ef08d indicators: rsi_buy EWO pct_change_int_short ewo_high ispumping ewo_low high_offset_2 close cti_mean pct_change_short plus_dibad sell_signal mfi isshortpumping pct_change fastk rsi_fast ma_sell_val ma_buy_val volume hlc3 low_offset retries high_offset date open plus_di fastd open_ok base_nb_candles_buy pct_change_int islongpumping lambo2cti high fast_d ewobad ___retries block_trade_exit base_nb_candles_sell hma_50 TEMA buy buy_tag cti sma_9 ema_100 recentispumping di_up adx max_slippage rsi_slow minus_di rs
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last change: 2024-08-03 04:56:45