The MyStrategyNew10 class represents a trading strategy implemented in a backtesting website. Here's a brief description of what the strategy does:
The populate_indicators method is used to calculate and populate indicators for the given DataFrame. It retrieves data from a data provider and applies a set of indicators with different periods to generate additional information for analysis.
The indicators used depend on the run mode, either backtest, live, or dry run.
The populate_buy_trend method is responsible for determining the conditions for entering a buy trade.
It iterates through a set of predefined conditions and checks if the indicators meet the specified criteria. If the conditions are met, the corresponding rows in the DataFrame are marked as a potential buying opportunity. The populate_sell_trend method is similar to populate_buy_trend but focuses on determining the conditions for selling or exiting a trade. It checks the indicators and conditions specific to selling and marks the appropriate rows in the DataFrame as a potential selling opportunity. Overall, the strategy uses indicators and predefined conditions to identify potential buying and selling opportunities in the financial market.
The indicators used depend on the run mode, either backtest, live, or dry run.
The populate_buy_trend method is responsible for determining the conditions for entering a buy trade.
It iterates through a set of predefined conditions and checks if the indicators meet the specified criteria. If the conditions are met, the corresponding rows in the DataFrame are marked as a potential buying opportunity. The populate_sell_trend method is similar to populate_buy_trend but focuses on determining the conditions for selling or exiting a trade. It checks the indicators and conditions specific to selling and marks the appropriate rows in the DataFrame as a potential selling opportunity. Overall, the strategy uses indicators and predefined conditions to identify potential buying and selling opportunities in the financial market.
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/MyStrategyNew10.py", line 262, in populate_indicators apply_indicator( File "/freqtrade/user_data/strategies/MyStrategyNew10.py", line 79, in apply_indicator result = getattr(ta, indicator)(dataframe, timeperiod=period) ^^^^^^^^^^^^^^^^^^^^^^ TypeError: attribute name must be string, not 'NoneType'
stoploss: -1 timeframe: 5m hash(sha256): e254339e0b473259b125d003c97a8d27eeffc28d9372dab7e5ca1cd2e40c3746 indicators: crossed_indicator volume main_indicator key
No similar strategies found. (based on used indicators)
last change: 2024-04-28 04:58:44