The STRATEGY_CORAL_AND_WAVETREND strategy is designed to backtest trading strategies using several technical analysis (TA) indicators. It consists of three main parts: indicator population, buy signal population, and sell signal population. In the populate_indicators method, the strategy adds three exponential moving averages (EMAs) to the given DataFrame: EMA10, EMA100, and EMA1000.
These indicators are calculated based on the closing price of the asset.
After populating the indicators, the strategy calculates additional values using a set of formulas.
These calculations involve a series of iterative calculations based on previous values. The resulting values are stored in columns named 'i1' to 'i6'. Using the calculated values, the strategy calculates a trend indicator called 'coral_trend'. The calculation involves weighted combinations of the 'i' values and specific coefficients. In the populate_buy_trend method, the strategy determines the buy signal based on the crossing of EMA10 above EMA100. When this condition is met, the 'buy' column in the DataFrame is set to 1. In the populate_sell_trend method, the strategy determines the sell signal based on the crossing of EMA100 above EMA10. When this condition is met, the 'sell' column in the DataFrame is set to 1. Overall, this strategy uses EMAs, iterative calculations, and trend indicators to generate buy and sell signals for backtesting trading strategies.
These indicators are calculated based on the closing price of the asset.
After populating the indicators, the strategy calculates additional values using a set of formulas.
These calculations involve a series of iterative calculations based on previous values. The resulting values are stored in columns named 'i1' to 'i6'. Using the calculated values, the strategy calculates a trend indicator called 'coral_trend'. The calculation involves weighted combinations of the 'i' values and specific coefficients. In the populate_buy_trend method, the strategy determines the buy signal based on the crossing of EMA10 above EMA100. When this condition is met, the 'buy' column in the DataFrame is set to 1. In the populate_sell_trend method, the strategy determines the sell signal based on the crossing of EMA100 above EMA10. When this condition is met, the 'sell' column in the DataFrame is set to 1. Overall, this strategy uses EMAs, iterative calculations, and trend indicators to generate buy and sell signals for backtesting trading strategies.
Traceback (most recent call last): File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 3791, in get_loc return self._engine.get_loc(casted_key) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "index.pyx", line 152, in pandas._libs.index.IndexEngine.get_loc File "index.pyx", line 181, in pandas._libs.index.IndexEngine.get_loc File "pandas/_libs/hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas/_libs/hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 'i1' The above exception was the direct cause of the following exception: 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 1318, in backtest_one_strategy preprocessed = self.strategy.advise_all_indicators(data) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1378, in advise_all_indicators return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1378, inreturn {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/strategy/interface.py", line 1410, in advise_indicators return self.populate_indicators(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/STRATEGY_CORAL_AND_WAVETREND.py", line 149, in populate_indicators dataframe['i1'] = c1*dataframe["close"] + c2*DataFrame.fillna(dataframe['i1'].shift(1)) ~~~~~~~~~^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/frame.py", line 3893, in __getitem__ indexer = self.columns.get_loc(key) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/indexes/base.py", line 3798, in get_loc raise KeyError(key) from err KeyError: 'i1'
stoploss: -0.1 timeframe: 5m hash(sha256): 8635548c9747650280301d366bb4de3e892035081e114bc057c6bd3371184156 indicators: i6 ema100 close i5 coral_trend ema1000 i1 i3 ema10 i4 i2
No similar strategies found. (based on used indicators)
last change: 2024-04-28 09:33:20