The "Mean1h" strategy is designed for backtesting trading strategies on 1-hour candlestick data. Here's a brief description of what the strategy does:
Indicator Calculation:
Calculates the 'typical' price using the qtpylib library. Calculates the simple moving average (SMA) of the 'typical' price over a window of 10 periods.
Calculates the daily mean price based on the rolling minimum and maximum values of the 'typical' price over a period of 12 hours.
Calculates various exponential moving averages (EMAs) including EMA5, EMA20, EMA50, and EMA100.
Calculates the average directional index (ADX) and the money flow index (MFI). Buy Signal: Generates a buy signal when the following conditions are met: The EMA50 is greater than the EMA100. The EMA20 is greater than the EMA50. The 'daily_mean' crosses below the 'typical_sma'. Sell Signal: Generates a sell signal when the 'daily_mean' crosses above the 'typical_sma'. The strategy also includes settings for stop loss, trailing stop, and ROI (return on investment). The minimal ROI is set to 0.1, and the stop loss is set to -0.075. Please note that this description provides a high-level overview of the strategy's key components and may not include all the details and intricacies of the code.
Calculates the daily mean price based on the rolling minimum and maximum values of the 'typical' price over a period of 12 hours.
Calculates various exponential moving averages (EMAs) including EMA5, EMA20, EMA50, and EMA100.
Calculates the average directional index (ADX) and the money flow index (MFI). Buy Signal: Generates a buy signal when the following conditions are met: The EMA50 is greater than the EMA100. The EMA20 is greater than the EMA50. The 'daily_mean' crosses below the 'typical_sma'. Sell Signal: Generates a sell signal when the 'daily_mean' crosses above the 'typical_sma'. The strategy also includes settings for stop loss, trailing stop, and ROI (return on investment). The minimal ROI is set to 0.1, and the stop loss is set to -0.075. Please note that this description provides a high-level overview of the strategy's key components and may not include all the details and intricacies of the code.
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 1322, in backtest_one_strategy preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/data/converter/converter.py", line 156, in trim_dataframes trimed_df = trim_dataframe(df, timerange, startup_candles=startup_candles) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/data/converter/converter.py", line 135, in trim_dataframe df = df.iloc[startup_candles:, :] ~~~~~~~^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1147, in __getitem__ return self._getitem_tuple(key) ^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1656, in _getitem_tuple return self._getitem_tuple_same_dim(tup) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 994, in _getitem_tuple_same_dim retval = getattr(retval, self.name)._getitem_axis(key, axis=i) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1691, in _getitem_axis return self._get_slice_axis(key, axis=axis) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1726, in _get_slice_axis labels._validate_positional_slice(slice_obj) File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 4196, in _validate_positional_slice self._validate_indexer("positional", key.start, "iloc") File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 6637, in _validate_indexer self._raise_invalid_indexer(form, key) File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 4306, in _raise_invalid_indexer raise TypeError(msg) TypeError: cannot do positional indexing on RangeIndex with these indexers [116.0] of type float
stoploss: -0.075 timeframe: 1h hash(sha256): e9020274f5588cabab62acdedc87a503ba951f69e5bdf94ca936a193187c0717 indicators: adx typical_sma daily_mean typical ema20 ema100 mfi ema5 ema50 ma300
No similar strategies found. (based on used indicators)
last change: 2024-04-28 23:38:02