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The "quantumfirst" strategy is a trading strategy that utilizes various technical analysis (TA) indicators to generate buy and sell signals for a given financial instrument. Here is a brief description of what the strategy does:
populate_indicators(): This function adds several TA indicators to the input DataFrame. The indicators used in this strategy include MACD (Moving Average Convergence Divergence), MINUS_DI (Minus Directional Indicator), RSI (Relative Strength Index), Fisher RSI, STOCHF (Stochastic Fast), SAR (Stop and Reverse), and SMA (Simple Moving Average).
These indicators provide insights into market trends, momentum, and volatility.
populate_buy_trend(): Based on the TA indicators, this function populates the buy signal in the DataFrame.
The strategy considers multiple conditions for a buy signal, including the close price being greater than a specific threshold, volume being higher than the 200-period moving average multiplied by 4, the close price being below the simple moving average, the fast %D line being higher than the fast %K line, the RSI being above zero, the fast %D being positive, and the Fisher RSI normalized value being less than a specific threshold. populate_sell_trend(): Similar to the buy signal, this function populates the sell signal in the DataFrame. The strategy considers two conditions for a sell signal. The first condition is when the RSI crosses above 50, the MACD is negative, and the Minus Directional Indicator is positive. The second condition is when the SAR value is higher than the close price, and the Fisher RSI is greater than 0.3. By using these indicators and conditions, the strategy aims to identify potential buying opportunities based on positive market signals and selling opportunities based on negative market signals.
These indicators provide insights into market trends, momentum, and volatility.
populate_buy_trend(): Based on the TA indicators, this function populates the buy signal in the DataFrame.
The strategy considers multiple conditions for a buy signal, including the close price being greater than a specific threshold, volume being higher than the 200-period moving average multiplied by 4, the close price being below the simple moving average, the fast %D line being higher than the fast %K line, the RSI being above zero, the fast %D being positive, and the Fisher RSI normalized value being less than a specific threshold. populate_sell_trend(): Similar to the buy signal, this function populates the sell signal in the DataFrame. The strategy considers two conditions for a sell signal. The first condition is when the RSI crosses above 50, the MACD is negative, and the Minus Directional Indicator is positive. The second condition is when the SAR value is higher than the close price, and the Fisher RSI is greater than 0.3. By using these indicators and conditions, the strategy aims to identify potential buying opportunities based on positive market signals and selling opportunities based on negative market signals.
startup_candle_count : 50 minus_di: -0.333% rsi: 0.539% fisher_rsi: -9.500% fisher_rsi_norma: 3.224%
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 1363, in start data, timerange = self.load_bt_data() ^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 237, 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 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: -0.1 timeframe: 5m hash(sha256): 94dedeffe4cf45cb841ea6d4ef0162023f490af667240c67c7a08797a4438e16 indicators: macdsignal minus_di volume fisher_rsi fisher_rsi_norma close rsi macd fastd fastk sar sma
No similar strategies found. (based on used indicators)
last change: 2024-02-04 04:48:44