The BinHModWhiteHOV0 strategy is a trading strategy that uses various indicators to generate buy and sell signals. Here is a brief explanation of what the strategy does:
In the populate_indicators method, the strategy calculates and adds several indicators to the input dataframe, including:
'bottom' and 'top': the minimum and maximum values of the 'open' and 'close' prices. Bollinger Bands: calculates the lower, middle, and upper bands using a 40-day window and 2 standard deviations.
'bbwidth': the width of the Bollinger Bands as a percentage of the middle band.
'space': the rolling mean of 'bbwidth' over a 60-day period.
'bbdelta': the absolute difference between the middle band and the lower band of the Bollinger Bands. 'bottomdelta': the absolute difference between the current 'bottom' value and the previous 'bottom' value. 'topdelta': the absolute difference between the current 'top' value and the previous 'top' value. 'tail': the absolute difference between the current 'bottom' value and the 'low' price. In the populate_buy_trend method, the strategy generates buy signals based on the following conditions: If the shorter-term exponential moving average ('ema_trend') is greater than the longer-term exponential moving average ('ema_slow'). If the longer-term exponential moving average ('ema_slow') is increasing over a specified number of periods ('ema_rise'). If the EWO (Elliott Wave Oscillator) indicator is above a certain threshold ('ewo_high'). If additional conditions related to Bollinger Bands, 'bottom', 'bbdelta', 'tail', 'bottomdelta', 'topdelta', and 'volume' are met. In the populate_sell_trend method, the strategy generates sell signals based on the condition that the closing price is above a certain offset ('high_offset') multiplied by the middle band value, and the 'volume' is positive. Overall, the strategy combines multiple technical indicators and conditions to identify potential buying and selling opportunities in the market.
'bbwidth': the width of the Bollinger Bands as a percentage of the middle band.
'space': the rolling mean of 'bbwidth' over a 60-day period.
'bbdelta': the absolute difference between the middle band and the lower band of the Bollinger Bands. 'bottomdelta': the absolute difference between the current 'bottom' value and the previous 'bottom' value. 'topdelta': the absolute difference between the current 'top' value and the previous 'top' value. 'tail': the absolute difference between the current 'bottom' value and the 'low' price. In the populate_buy_trend method, the strategy generates buy signals based on the following conditions: If the shorter-term exponential moving average ('ema_trend') is greater than the longer-term exponential moving average ('ema_slow'). If the longer-term exponential moving average ('ema_slow') is increasing over a specified number of periods ('ema_rise'). If the EWO (Elliott Wave Oscillator) indicator is above a certain threshold ('ewo_high'). If additional conditions related to Bollinger Bands, 'bottom', 'bbdelta', 'tail', 'bottomdelta', 'topdelta', and 'volume' are met. In the populate_sell_trend method, the strategy generates sell signals based on the condition that the closing price is above a certain offset ('high_offset') multiplied by the middle band value, and the 'volume' is positive. Overall, the strategy combines multiple technical indicators and conditions to identify potential buying and selling opportunities in the market.
startup_candle_count : 500 Recursive Analysis found no issues while using 500 startup_candle_count
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 1391, in start self.load_bt_data_detail() File "/freqtrade/freqtrade/optimize/backtesting.py", line 270, in load_bt_data_detail self.detail_data = history.load_data( ^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/data/history/history_utils.py", line 99, in load_data hist = load_pair_history(pair=pair, timeframe=timeframe, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/data/history/history_utils.py", line 57, in load_pair_history return data_handler.ohlcv_load(pair=pair, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/data/history/idatahandler.py", line 320, in ohlcv_load pairdf = self._ohlcv_load( ^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/data/history/featherdatahandler.py", line 62, in _ohlcv_load pairdata = read_feather(filename) ^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/feather_format.py", line 129, in read_feather return feather.read_feather( ^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pyarrow/feather.py", line 226, in read_feather return (read_table( ^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pyarrow/feather.py", line 252, in read_table reader = _feather.FeatherReader( ^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow/_feather.pyx", line 79, in pyarrow._feather.FeatherReader.__cinit__ File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Not an Arrow file
stoploss: -0.3 timeframe: 5m hash(sha256): 9ec3fc495e9d3db61ee69d4ce07746fe0780173419d3a6b3dfe4dfa526ae88dd indicators: upper ewo_high close tail bbwidth bbdelta volume high_offset open ema_trend ema_rise mid bb_bottom_delta_dif bb_width open close ewo_step ewo_base bottomdelta bb_delta_dif bb_top_delta_dif lower bb_tail_dif top ema_slow low bottom space topdelta
Similar Strategies: (based on used indicators)
Strategy: BHMWHOV0, Similarity Score: 96.67%
Strategy: BHMWHOV10, Similarity Score: 96.67%
last change: 2024-11-01 18:41:54