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The "MADisplaceV3" strategy is designed to backtest trading strategies. It has three main functions: "populate_indicators," "populate_buy_trend," and "populate_sell_trend."
The "populate_indicators" function is responsible for calculating and populating the necessary indicators for the strategy. It takes a DataFrame and metadata as inputs and returns the updated DataFrame with indicators.
If the strategy is running in "hyperopt" mode, it retrieves an informative DataFrame and merges it with the input DataFrame.
Then it calculates the main indicators and adds them to the DataFrame.
The "populate_buy_trend" function determines the buy signals for the strategy. It takes a DataFrame and metadata as inputs and returns the updated DataFrame with buy signals. In "hyperopt" mode, if the 'uptrend' indicator is not present in the DataFrame, it retrieves informative indicators, merges them with the DataFrame, and calculates the main indicators. Then it applies specific conditions to identify buy signals based on various factors such as RSI, uptrend, close price, moving averages, and volume. The "populate_sell_trend" function determines the sell signals for the strategy. It takes a DataFrame and metadata as inputs and returns the updated DataFrame with sell signals. Similar to the "populate_buy_trend" function, it retrieves informative indicators, merges them with the DataFrame, calculates the main indicators, and then applies conditions to identify sell signals based on factors such as uptrend, close price, moving averages, and volume. The code also includes additional lines to merge informative pairs, rename columns, and handle data skipping. Overall, the strategy uses a combination of indicators and specific conditions to generate buy and sell signals based on the given input DataFrame.
If the strategy is running in "hyperopt" mode, it retrieves an informative DataFrame and merges it with the input DataFrame.
Then it calculates the main indicators and adds them to the DataFrame.
The "populate_buy_trend" function determines the buy signals for the strategy. It takes a DataFrame and metadata as inputs and returns the updated DataFrame with buy signals. In "hyperopt" mode, if the 'uptrend' indicator is not present in the DataFrame, it retrieves informative indicators, merges them with the DataFrame, and calculates the main indicators. Then it applies specific conditions to identify buy signals based on various factors such as RSI, uptrend, close price, moving averages, and volume. The "populate_sell_trend" function determines the sell signals for the strategy. It takes a DataFrame and metadata as inputs and returns the updated DataFrame with sell signals. Similar to the "populate_buy_trend" function, it retrieves informative indicators, merges them with the DataFrame, calculates the main indicators, and then applies conditions to identify sell signals based on factors such as uptrend, close price, moving averages, and volume. The code also includes additional lines to merge informative pairs, rename columns, and handle data skipping. Overall, the strategy uses a combination of indicators and specific conditions to generate buy and sell signals based on the given input DataFrame.
startup_candle_count : 200 rsi_slow: 0.002%
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: File is too small to be a well-formed file
stoploss: -0.2 timeframe: 5m hash(sha256): 421a02c82cad7cd971de08772dcf372f27c60e43081f05c8a50949eb1401d907 indicators: close rsi_fast_threshold informative_fast_length ma_middle_1_offset rsi_fast ma_lower volume ma_middle_1 rsi_slow_descending ma_upper ema_fast rsi_slow_length rsi_fast_length uptrend ma_upper_offset ma_lower_offset rsi_slow_confirmation rsi_slow ma_upper_length ma_lower_length ema_slow runmode informative_slow_length ma_middle_1_length date open high low close volume
No similar strategies found. (based on used indicators)
last change: 2024-03-01 00:36:19