The Ichi_trigger strategy is a backtesting strategy that involves the use of indicators to generate buy and sell signals. Here's a brief description of what the strategy does:
The populate_indicators function takes a DataFrame of price data and additional metadata as input. It checks the timeframe of the data and performs indicator calculations accordingly.
If the timeframe is the same as the informative timeframe, the indicators are calculated directly on the input DataFrame.
Otherwise, it retrieves the informative pair's DataFrame and calculates the indicators on it.
The resulting indicators are then merged with the original DataFrame, and unnecessary columns are removed. The populate_buy_trend function populates the "buy" column in the DataFrame based on a condition. If the value of the "bs_pic_sig" column is greater than the value of the "pic_l_s_trig" column, it sets the corresponding "buy" value to 1. The populate_sell_trend function populates the "sell" column in the DataFrame based on a condition. If the value of the "bs_pic_sig" column is less than the value of the "pic_l_s_trig" column, it sets the corresponding "sell" value to 1. In summary, the Ichi_trigger strategy calculates indicators, generates buy signals when certain conditions are met, and generates sell signals when other conditions are met. This information can be used for backtesting and evaluating the effectiveness of the strategy.
If the timeframe is the same as the informative timeframe, the indicators are calculated directly on the input DataFrame.
Otherwise, it retrieves the informative pair's DataFrame and calculates the indicators on it.
The resulting indicators are then merged with the original DataFrame, and unnecessary columns are removed. The populate_buy_trend function populates the "buy" column in the DataFrame based on a condition. If the value of the "bs_pic_sig" column is greater than the value of the "pic_l_s_trig" column, it sets the corresponding "buy" value to 1. The populate_sell_trend function populates the "sell" column in the DataFrame based on a condition. If the value of the "bs_pic_sig" column is less than the value of the "pic_l_s_trig" column, it sets the corresponding "sell" value to 1. In summary, the Ichi_trigger strategy calculates indicators, generates buy signals when certain conditions are met, and generates sell signals when other conditions are met. This information can be used for backtesting and evaluating the effectiveness of the strategy.
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_1_Ichi_trigger.py", line 184, in populate_indicators informative = self.do_indicators(informative.copy(), metadata) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/Discord_1_Ichi_trigger.py", line 123, in do_indicators dataframe['kumo_high'] = max(dataframe['leading_senkou_span_a'].shift(displacement-1), dataframe['leading_senkou_span_b'].shift(displacement-1)) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ftuser/.local/lib/python3.11/site-packages/pandas/core/generic.py", line 1519, in __nonzero__ raise ValueError( ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
stoploss: -0.99 timeframe: 5m hash(sha256): 6bf816a1fe2ac4c4d9e45809a693098fa26db9f71dd1f4b86bb925d573456c3c indicators: close senkou_a tenkan_sen cloud_red chikou_high leading_senkou_span_a leading_senkou_span_b kijun_sen high senkou_span_a kumo_high chikou_span senkou_b kumo_low bs_pic_sig senkou_span_b cloud_green low pic_l_s_trig future_green date open high low close volume
No similar strategies found. (based on used indicators)
last change: 2024-04-29 20:12:20