The SqueezeMomentum strategy is a trading strategy that uses Bollinger Bands (BB) and Keltner Channels (KC) to identify periods of low volatility (squeeze) and potential momentum breakout. Here is a short description of what the strategy does:
In the populate_indicators method:
Bollinger Bands (BB) are calculated using the closing prices. Upper and lower bands are computed based on the standard deviation of the closing prices.
Keltner Channels (KC) are calculated using the moving average (MA) of the closing prices and the average true range (ATR).
The squeeze condition is determined by comparing the BB and KC bands.
A value, val, is calculated using linear regression on the difference between the source (closing prices) and the average of the highest high and lowest low over a period. Color codes for plotting are determined based on the value of val. In the populate_buy_trend method: The squeeze condition (is_sqzOn and is_sqzOff) is checked. Several conditions are evaluated to determine a buy signal, including the shift in val, ADX threshold, RSI overbought level, and volume. In the populate_sell_trend method: Conditions are evaluated to determine a sell signal based on the shift in val, comparison with the maximum value of val, and volume. The strategy calculates various indicators such as moving averages, standard deviations, average true range, linear regression, RSI, and ADX. It then uses these indicators to generate buy and sell signals based on the squeeze condition and other criteria. Please note that this description provides an overview of the strategy, but the actual implementation may involve additional details and considerations.
Keltner Channels (KC) are calculated using the moving average (MA) of the closing prices and the average true range (ATR).
The squeeze condition is determined by comparing the BB and KC bands.
A value, val, is calculated using linear regression on the difference between the source (closing prices) and the average of the highest high and lowest low over a period. Color codes for plotting are determined based on the value of val. In the populate_buy_trend method: The squeeze condition (is_sqzOn and is_sqzOff) is checked. Several conditions are evaluated to determine a buy signal, including the shift in val, ADX threshold, RSI overbought level, and volume. In the populate_sell_trend method: Conditions are evaluated to determine a sell signal based on the shift in val, comparison with the maximum value of val, and volume. The strategy calculates various indicators such as moving averages, standard deviations, average true range, linear regression, RSI, and ADX. It then uses these indicators to generate buy and sell signals based on the squeeze condition and other criteria. Please note that this description provides an overview of the strategy, but the actual implementation may involve additional details and considerations.
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 1392, in ft_advise_signals dataframe = self.advise_exit(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1443, in advise_exit df = self.populate_exit_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 244, in populate_exit_trend return self.populate_sell_trend(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/SqueezeMomentum.py", line 399, in populate_sell_trend dataframe.to_csv('user_data/csvs/%s_%s.csv' % (self.__class__.__name__, metadata["pair"].replace("/", "_"))) File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/generic.py", line 3902, in to_csv return DataFrameRenderer(formatter).to_csv( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/formats/format.py", line 1152, in to_csv csv_formatter.save() File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/formats/csvs.py", line 247, in save with get_handle( ^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/common.py", line 739, in get_handle check_parent_directory(str(handle)) File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/io/common.py", line 604, in check_parent_directory raise OSError(rf"Cannot save file into a non-existent directory: '{parent}'") OSError: Cannot save file into a non-existent directory: 'user_data/csvs'
stoploss: -0.99 timeframe: 5m hash(sha256): 6fcb840e9fe1bcd53d5cfed154131b5ed0dfc71f6ded389f6990c2731147769a indicators: ema_200 ema_50 avg_val ma_kc_length close BB_length val_stdev_val avg_hh_ll_val KC_length rangema_val volume stdev_bb_length val_min_val ADX_thresold avg_close_val rangema_kc_length high stdev_val ll_close_val KC_multifactor ma_bb_length hh_close_val ma_val adx RSI_overbought val_avg_val rsi use_true_range BB_multifactor low val_max_val val_val range
No similar strategies found. (based on used indicators)
last change: 2024-07-27 05:49:30