The MeanV1 strategy is a backtesting trading strategy implemented in Python. It uses technical indicators to determine buy and sell signals for trading. Here is a brief description of how the strategy works:
The strategy is designed to work with ticker intervals of 30 minutes.
The startup candle count is calculated based on the interval and is used for initializing the strategy.
The strategy includes both buy and sell signals and uses a trailing stop for risk management.
It sets a minimal return on investment (ROI) for different time periods. These ROI values define the profit target for selling the assets. It also includes a stop-loss value, which represents the maximum acceptable loss before selling the assets. The strategy calculates various indicators such as the typical price, simple moving average, and daily mean. The buy signal is generated when the daily mean crosses below the typical simple moving average. The sell signal is generated when the daily mean crosses above the typical simple moving average. The strategy provides a list of informative pairs, although this particular implementation returns an empty list. Please note that this description provides a general overview of the strategy, and there may be additional details and considerations involved in its implementation and performance.
The startup candle count is calculated based on the interval and is used for initializing the strategy.
The strategy includes both buy and sell signals and uses a trailing stop for risk management.
It sets a minimal return on investment (ROI) for different time periods. These ROI values define the profit target for selling the assets. It also includes a stop-loss value, which represents the maximum acceptable loss before selling the assets. The strategy calculates various indicators such as the typical price, simple moving average, and daily mean. The buy signal is generated when the daily mean crosses below the typical simple moving average. The sell signal is generated when the daily mean crosses above the typical simple moving average. The strategy provides a list of informative pairs, although this particular implementation returns an empty list. Please note that this description provides a general overview of the strategy, and there may be additional details and considerations involved in its implementation and performance.
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: -0.085 timeframe: 30m hash(sha256): 3b334f11ded3a57de08fa8f87ad3107bffa2d732dd3c863ae0f3eadc5278d020 indicators: typical_sma typical daily_mean
No similar strategies found. (based on used indicators)
last change: 2025-01-13 22:33:35