The strategy described in the code is a trading strategy that uses various indicators and conditions to determine entry and exit points for trades. Here is a breakdown of what the strategy does:
The populate_indicators function is responsible for calculating and adding several indicators to the input dataframe. These indicators include moving averages (sma_15), commodity trend index (cti), relative strength index (rsi), Heikin Ashi candlestick values (ha_open, ha_close, ha_high, ha_low), Bollinger Bands (lower, mid), and others.
The strategy retrieves informative data from a different timeframe (inf_tf) using self.dp.get_pair_dataframe and merges it with the current dataframe using the merge_informative_pair function.
Additional indicators such as exponential moving averages (ema_8, ema_16, ema_12, ema_26), average directional index (adx), and stochastic fast (fastd, fastk) are calculated and added to the dataframe.
Bollinger Bands are calculated again using a different method (bollinger2) and their upper, middle, and lower bands are added to the dataframe. The width of the Bollinger Bands is also calculated. Rolling mean volumes (volume_mean_12, volume_mean_24, volume_mean_4) are calculated and added to the dataframe. The populate_entry_trend function defines the conditions for entering a trade. It checks various criteria such as the EWO (Elliott Wave Oscillator) indicator, Bollinger Bands, exponential moving averages, and closes prices to determine if a buy signal should be generated. The populate_exit_trend function is responsible for defining the conditions for exiting a trade. In the provided code, it sets the exit_long and exit_tag values to indicate that the long position should be closed. The current_profit function is used to determine the appropriate exit action based on the current profit of a trade. It checks the profit value, the "fastk" indicator, and the time elapsed since the trade was opened to decide whether to sell or continue holding the position. Overall, this strategy uses a combination of technical indicators and conditions to generate entry and exit signals for trading.
The strategy retrieves informative data from a different timeframe (inf_tf) using self.dp.get_pair_dataframe and merges it with the current dataframe using the merge_informative_pair function.
Additional indicators such as exponential moving averages (ema_8, ema_16, ema_12, ema_26), average directional index (adx), and stochastic fast (fastd, fastk) are calculated and added to the dataframe.
Bollinger Bands are calculated again using a different method (bollinger2) and their upper, middle, and lower bands are added to the dataframe. The width of the Bollinger Bands is also calculated. Rolling mean volumes (volume_mean_12, volume_mean_24, volume_mean_4) are calculated and added to the dataframe. The populate_entry_trend function defines the conditions for entering a trade. It checks various criteria such as the EWO (Elliott Wave Oscillator) indicator, Bollinger Bands, exponential moving averages, and closes prices to determine if a buy signal should be generated. The populate_exit_trend function is responsible for defining the conditions for exiting a trade. In the provided code, it sets the exit_long and exit_tag values to indicate that the long position should be closed. The current_profit function is used to determine the appropriate exit action based on the current profit of a trade. It checks the profit value, the "fastk" indicator, and the time elapsed since the trade was opened to decide whether to sell or continue holding the position. Overall, this strategy uses a combination of technical indicators and conditions to generate entry and exit signals for trading.
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 1390, in start data, timerange = self.load_bt_data() ^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 241, in load_bt_data 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: -0.25 timeframe: 5m hash(sha256): 9e94c151ea91716df5994b0e0dc97886231fb8ce1780abf677fa8b3c1d35ebe0 indicators: rocr_1h upper ha_low ema_13 close tail exit_long exit_tag bb_lowerband volume_mean_12 bbdelta fastk rsi_fast bb_middleband2 volume closedelta rocr open fastd ha_open sma_15 bb_upperband2 volume_mean_24 high volume_mean_4 mid ema_8 ema_16 bb_width ha_close cti ha_high adx lower rsi_slow ema_12 bb_middleband rsi ema_slow ema_26 low bb_lowerband2
No similar strategies found. (based on used indicators)
last change: 2024-11-01 18:39:58