The "rad_testing" strategy is implemented as a class that inherits from the "IStrategy" interface. It consists of three main methods: "populate_indicators," "populate_buy_trend," and "populate_sell_trend." Here is a description of what each method does:
populate_indicators:
This method calculates various technical indicators based on the provided price data. It computes the Simple Moving Average (SMA) with a time period of 8.
It calculates the price difference between the closing price and the SMA.
It computes 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 computes the Slow Moving Average (SMA) with a time period of 13. It calculates the difference between the closing price and the slow SMA. It computes the RSI of the slow difference with a time period of 5 and then takes the SMA with a time period of 8. It stores some custom information about ATR and ATR EMA in the strategy's custom_info dictionary. Finally, it returns the updated dataframe with the added indicator columns. populate_buy_trend: This method determines the buy signal based on the TA indicators and the strategy's conditions. It sets the 'buy' column to 1 for the rows that satisfy the following conditions: The RSI crosses below 23. The previous RSI value was above 23. The RSI value two rows ago was above 23. The SMA is greater than the closing price. It returns the dataframe with the 'buy' column updated. populate_sell_trend: This method determines the sell signal based on the TA indicators and the strategy's conditions. It sets the 'sell' column to 1 for the rows that satisfy the following conditions: The RSI crosses below the slow RSI. The previous RSI value was above 60. It returns the dataframe with the 'sell' column updated. The strategy uses these indicators and signals to make buy and sell decisions during backtesting.
It calculates the price difference between the closing price and the SMA.
It computes 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 computes the Slow Moving Average (SMA) with a time period of 13. It calculates the difference between the closing price and the slow SMA. It computes the RSI of the slow difference with a time period of 5 and then takes the SMA with a time period of 8. It stores some custom information about ATR and ATR EMA in the strategy's custom_info dictionary. Finally, it returns the updated dataframe with the added indicator columns. populate_buy_trend: This method determines the buy signal based on the TA indicators and the strategy's conditions. It sets the 'buy' column to 1 for the rows that satisfy the following conditions: The RSI crosses below 23. The previous RSI value was above 23. The RSI value two rows ago was above 23. The SMA is greater than the closing price. It returns the dataframe with the 'buy' column updated. populate_sell_trend: This method determines the sell signal based on the TA indicators and the strategy's conditions. It sets the 'sell' column to 1 for the rows that satisfy the following conditions: The RSI crosses below the slow RSI. The previous RSI value was above 60. It returns the dataframe with the 'sell' column updated. The strategy uses these indicators and signals to make buy and sell decisions during backtesting.
ause of the following exception: Traceback (most recent call last): File "/freqtrade/freqtrade/strategy/strategy_wrapper.py", line 27, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/rad_testing.py", line 50, 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, 6, 30, 23, 48, tzinfo=datetime.timezone.utc)
stoploss: -0.5 timeframe: 30m hash(sha256): cf6e09c44000d99a8c97cc1555be04b79325a28fc789edb091a9d7643d32a497 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: radtest, Similarity Score: 93.75%
last change: 2024-07-28 01:04:40