The TaSearchLevelH15m strategy is designed for backtesting trading strategies. It performs technical analysis on a given DataFrame and generates various indicators to assist in decision-making. Here's a summary of what the strategy does:
Populates indicators: The strategy calculates several indicators, including RSI (Relative Strength Index), minimum and maximum levels, and buy signals for both short and long positions.
Finding minimum levels: The strategy searches for local minimum price levels within a specified order range.
It stores the lowest price value found and records the corresponding time.
Analyzing minimum levels: For each identified minimum level, the strategy performs additional checks. It calculates the percentage difference between the current price and the identified minimum level. If the difference falls within a specified range (0 to 0.3), it logs the information. Identifying long entry signals: If certain conditions are met, such as a positive difference within the specified range, the strategy marks a long entry signal and stores the relevant price and other data. Finding maximum levels: Similarly, the strategy searches for local maximum price levels within the given order range. It records the highest price value and the corresponding time. Analyzing maximum levels: For each identified maximum level, the strategy checks if the percentage difference between the current price and the maximum level falls within the specified range (0 to 0.3). If so, it logs the information. Identifying short entry signals: If certain conditions are met, including a positive difference within the specified range, the strategy marks a short entry signal and stores relevant data. Populating entry and exit trends: Based on the buy signals generated for short and long positions, the strategy populates entry trends accordingly. Defining exit conditions: The strategy sets exit conditions based on the RSI indicator. If the RSI value is below 10, it marks an exit signal for short positions, and if it is above 90, it marks an exit signal for long positions. Proposed leverage: The strategy includes a method that calculates the proposed leverage based on input parameters. Overall, the strategy combines technical analysis indicators, such as RSI and local extrema, to generate entry and exit signals for both short and long positions in a backtesting environment.
Finding minimum levels: The strategy searches for local minimum price levels within a specified order range.
It stores the lowest price value found and records the corresponding time.
Analyzing minimum levels: For each identified minimum level, the strategy performs additional checks. It calculates the percentage difference between the current price and the identified minimum level. If the difference falls within a specified range (0 to 0.3), it logs the information. Identifying long entry signals: If certain conditions are met, such as a positive difference within the specified range, the strategy marks a long entry signal and stores the relevant price and other data. Finding maximum levels: Similarly, the strategy searches for local maximum price levels within the given order range. It records the highest price value and the corresponding time. Analyzing maximum levels: For each identified maximum level, the strategy checks if the percentage difference between the current price and the maximum level falls within the specified range (0 to 0.3). If so, it logs the information. Identifying short entry signals: If certain conditions are met, including a positive difference within the specified range, the strategy marks a short entry signal and stores relevant data. Populating entry and exit trends: Based on the buy signals generated for short and long positions, the strategy populates entry trends accordingly. Defining exit conditions: The strategy sets exit conditions based on the RSI indicator. If the RSI value is below 10, it marks an exit signal for short positions, and if it is above 90, it marks an exit signal for long positions. Proposed leverage: The strategy includes a method that calculates the proposed leverage based on input parameters. Overall, the strategy combines technical analysis indicators, such as RSI and local extrema, to generate entry and exit signals for both short and long positions in a backtesting environment.
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 57, in start_backtesting backtesting = Backtesting(config) ^^^^^^^^^^^^^^^^^^^ File "/freqtrade/freqtrade/optimize/backtesting.py", line 114, in __init__ validate_config_consistency(self.config) File "/freqtrade/freqtrade/configuration/config_validation.py", line 95, in validate_config_consistency validate_config_schema(conf, preliminary=preliminary) File "/freqtrade/freqtrade/configuration/config_validation.py", line 65, in validate_config_schema raise ValidationError( jsonschema.exceptions.ValidationError: 'stoploss' is a required property
stoploss: 0 timeframe: 5m hash(sha256): ef7c567fbae4afabf838ca02e87901e9a4c282dd53e4027909d775b666b19640 indicators: high buy_short2 min_level buy_long max_x close i_low i_open buy_short i_close buy_long2 i_high low max_local open rsi_7 max_level min_local
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last change: 2024-04-28 23:29:04