The "rad_testing" strategy is a trading strategy that uses technical indicators to generate buy and sell signals for a given financial instrument. Here is a breakdown of what the strategy does:
In the "populate_indicators" method:
It calculates the simple moving average (SMA) with a time period of 8 using the close prices. It calculates the price difference between the close price and the SMA.
It calculates the relative strength index (RSI) of the price difference with a time period of 5.
It calculates the average true range (ATR) with a time period of 8.
It calculates the exponential moving average (EMA) of the ATR with a time period of 5. It calculates a slower SMA with a time period of 13. It calculates the difference between the close price and the slower SMA. It calculates the RSI of the slower difference with a time period of 5 and then calculates its SMA with a time period of 8. It stores the selected columns of the dataframe for the specific pair in a custom_info dictionary. In the "populate_buy_trend" method: It identifies a buy signal when the following conditions are met: The RSI crosses below 23. The RSI of the previous time period is above 23. The RSI of two time periods ago is above 23. The SMA is greater than the close price. In the "populate_sell_trend" method: It identifies a sell signal when the following conditions are met: The RSI crosses below the slower RSI. The RSI of the previous time period is above 60. The strategy uses these buy and sell signals to populate the "buy" and "sell" columns in the dataframe, indicating the recommended actions to take for each data point. Please note that this description is based on the provided code snippet and may not capture the full functionality or intent of the strategy.
It calculates the relative strength index (RSI) of the price difference with a time period of 5.
It calculates the average true range (ATR) with a time period of 8.
It calculates the exponential moving average (EMA) of the ATR with a time period of 5. It calculates a slower SMA with a time period of 13. It calculates the difference between the close price and the slower SMA. It calculates the RSI of the slower difference with a time period of 5 and then calculates its SMA with a time period of 8. It stores the selected columns of the dataframe for the specific pair in a custom_info dictionary. In the "populate_buy_trend" method: It identifies a buy signal when the following conditions are met: The RSI crosses below 23. The RSI of the previous time period is above 23. The RSI of two time periods ago is above 23. The SMA is greater than the close price. In the "populate_sell_trend" method: It identifies a sell signal when the following conditions are met: The RSI crosses below the slower RSI. The RSI of the previous time period is above 60. The strategy uses these buy and sell signals to populate the "buy" and "sell" columns in the dataframe, indicating the recommended actions to take for each data point. Please note that this description is based on the provided code snippet and may not capture the full functionality or intent of the strategy.
eqtrade/freqtrade/strategy/strategy_wrapper.py", line 27, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/radtest.py", line 49, in custom_stoploss relative_sl = self.custom_info[pair].loc[current_time]['atr_ema'] ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1153, in __getitem__ return self._getitem_axis(maybe_callable, axis=axis) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1393, in _getitem_axis return self._get_label(key, axis=axis) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexing.py", line 1343, in _get_label return self.obj.xs(label, axis=axis) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/generic.py", line 4236, in xs loc = index.get_loc(key) ^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/datetimes.py", line 633, in get_loc raise KeyError(orig_key) from err KeyError: datetime.datetime(2024, 3, 31, 23, 48, tzinfo=datetime.timezone.utc)
stoploss: -0.5 timeframe: 30m hash(sha256): 174d5236f8216ece07e6fae0b04eaabee8d82fb1ae9ae27be11082bb81b5b463 indicators: atr slow_di price_dif slow_dif sma_slow rad close price_di date runmode slow_rad date atratr_ema atr_ema sma
Similar Strategies: (based on used indicators)
Strategy: rad_testing, Similarity Score: 93.75%
last change: 2024-04-28 05:22:34