The Miku_PP_v3 strategy is an implementation of the IStrategy interface for backtesting trading strategies. It consists of three main components: populating indicators, populating the buy trend, and populating the sell trend. The populate_indicators method takes a DataFrame and metadata as input and returns a modified DataFrame.
This method utilizes the slow_tf_indicators function to populate the indicators on a slower time frame.
The modified DataFrame with the populated indicators is then returned.
The populate_buy_trend method takes a DataFrame and metadata as input and returns a modified DataFrame. It sets the 'buy' column of the DataFrame to 1 for rows where the 'ichimoku_ok' column value is greater than 0. This indicates a buy signal based on the Ichimoku indicator. The populate_sell_trend method takes a DataFrame and metadata as input and returns a modified DataFrame. It sets the 'sell' column of the DataFrame to 1 for rows where the 'trending_over' column value is greater than 0. This indicates a sell signal based on a trending condition. Overall, the Miku_PP_v3 strategy populates indicators on a slower time frame, generates buy signals based on the Ichimoku indicator, and sell signals based on a trending condition.
This method utilizes the slow_tf_indicators function to populate the indicators on a slower time frame.
The modified DataFrame with the populated indicators is then returned.
The populate_buy_trend method takes a DataFrame and metadata as input and returns a modified DataFrame. It sets the 'buy' column of the DataFrame to 1 for rows where the 'ichimoku_ok' column value is greater than 0. This indicates a buy signal based on the Ichimoku indicator. The populate_sell_trend method takes a DataFrame and metadata as input and returns a modified DataFrame. It sets the 'sell' column of the DataFrame to 1 for rows where the 'trending_over' column value is greater than 0. This indicates a sell signal based on a trending condition. Overall, the Miku_PP_v3 strategy populates indicators on a slower time frame, generates buy signals based on the Ichimoku indicator, and sell signals based on a trending condition.
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: 'kijun_sen_355_5m' 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/Discord_Miku_PP_v3.py", line 208, in populate_indicators dataframe = self.slow_tf_indicators(dataframe, metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/Discord_Miku_PP_v3.py", line 150, in slow_tf_indicators (dataframe['kijun_sen_355_5m'] >= dataframe['tenkan_sen_355_5m']) & ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^ 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: 'kijun_sen_355_5m'
stoploss: -0.1 timeframe: 1m hash(sha256): 32d0fa58a171ebd074d6870544e39ce161e7e336c5b4bacb00c3862e1dc6bd5c indicators: senkou_a_9 1dr1 senkou_b_20 trending_over 1d ema20 senkou_b_88 ichimoku_ok s1 senkou_a_20 kijun_sen_20 tenkan_sen_9 close 1dpivot pivot pivots_ok tenkan_sen tenkan_sen_444 senkou_a_conversion_line_period tenkan_sen_355 rS1 kijun_sen_355_5m 1drS1 kijun_sen high kijun_sen_355 senkou_a_100 senkou_span_a 1ds1 tenkan_sen_20 senkou_b_444 ema20_5m pivot_1d r1_1d senkou_span_b kijun_sen_9 r1 tenkan_sen_88 kijun_sen_conversion_line_period rS1_1d tenkan_sen_355_5m senkou_b_conversion_line_period low senko
Similar Strategies: (based on used indicators)
Strategy: Miku_PP_v3, Similarity Score: 97.78%
Strategy: Miku_PP_v4, Similarity Score: 88.89%
last change: 2024-04-29 01:41:03