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The FrostAuraM115mStrategy is a trading strategy that makes purchase and sell decisions based on the Bollinger Bands (BB) and the Relative Strength Index (RSI). Here's a breakdown of what the strategy does:
The strategy uses the following indicators:
RSI (Relative Strength Index): Calculates the RSI value for the given data. Bollinger Bands: Calculates three sets of Bollinger Bands with different standard deviations (1, 2, and 3).
The strategy defines a minimal return on investment (ROI) and a stop loss value.
Minimal ROI: Defines the minimum desired return at different time intervals.
Stoploss: Sets the value at which a trade should be stopped to limit losses. The strategy specifies the timeframe for analyzing the data, which is set to 15 minutes. The strategy requires a minimum number of candles (30) before generating valid signals. The strategy populates indicators and generates buy and sell signals based on specific conditions: populate_indicators(): Calculates the RSI and Bollinger Bands for the given data. populate_buy_trend(): Generates a buy signal if the following conditions are met: The close price is below the second Bollinger Band. The close price is above a minimum coin price. populate_sell_trend(): Generates a sell signal if the following conditions are met: The RSI value is above 53. The close price is above the first Bollinger Band. The strategy provides optional configurations for order types and time in force. The strategy includes a plot configuration for visualizing indicators like TEMA, SAR, MACD, and RSI. Note: This description provides a general overview of the strategy's functionality. The specific implementation and performance details can be further explored in the code provided.
The strategy defines a minimal return on investment (ROI) and a stop loss value.
Minimal ROI: Defines the minimum desired return at different time intervals.
Stoploss: Sets the value at which a trade should be stopped to limit losses. The strategy specifies the timeframe for analyzing the data, which is set to 15 minutes. The strategy requires a minimum number of candles (30) before generating valid signals. The strategy populates indicators and generates buy and sell signals based on specific conditions: populate_indicators(): Calculates the RSI and Bollinger Bands for the given data. populate_buy_trend(): Generates a buy signal if the following conditions are met: The close price is below the second Bollinger Band. The close price is above a minimum coin price. populate_sell_trend(): Generates a sell signal if the following conditions are met: The RSI value is above 53. The close price is above the first Bollinger Band. The strategy provides optional configurations for order types and time in force. The strategy includes a plot configuration for visualizing indicators like TEMA, SAR, MACD, and RSI. Note: This description provides a general overview of the strategy's functionality. The specific implementation and performance details can be further explored in the code provided.
startup_candle_count : 30 rsi: -1.447%
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 1364, in start self.load_bt_data_detail() File "/freqtrade/freqtrade/optimize/backtesting.py", line 266, 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 319, 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 148, 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 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Not an Arrow file
stoploss: -0.343 timeframe: 15m hash(sha256): e9262b2563ac88544355a1c80281ab559e90ff854316a3dd7c4c2726632c1280 indicators: bb_upperband1 mid lower bb_lowerband1 upper bb_middleband2 bb_middleband3 bb_upperband3 close bb_middleband1 rsi bb_lowerband3 bb_lowerband2 bb_upperband2
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last change: 2024-03-01 00:35:51