The TaSearchLevelJ15m strategy is designed for backtesting trading strategies. It utilizes technical indicators and price levels to generate entry and exit signals. Here is a brief description of what the strategy does:
The strategy first populates various indicators such as RSI (Relative Strength Index) and sets up additional columns in the DataFrame for storing calculated values.
It identifies local minimum price levels by comparing the current price with the preceding and following prices.
These local minimum levels are stored in the 'min_local' column.
It iterates over the DataFrame and checks if there are any local minimum levels. If found, it calculates the percentage difference between each local minimum price and the current price. If the difference falls within a certain range (0 to 0.5), it logs the information and marks the 'min_level' column as 1. Further calculations are performed based on the 'min_level' values to identify potential buying opportunities. If the difference between the current price and a previous price falls within the specified range and certain conditions are met, the 'buy_long' or 'buy_long2' column is marked as 1. Similarly, the strategy identifies local maximum price levels and performs calculations to identify potential selling opportunities. The strategy also populates entry and exit trend columns based on certain conditions and indicators. The final part of the strategy defines a function to calculate the percentage difference between two values and applies the Heikin-Ashi candlestick formula to modify the open, close, high, and low prices in the DataFrame. Overall, the strategy aims to identify potential buying and selling opportunities based on local price levels, technical indicators, and predefined conditions.
It identifies local minimum price levels by comparing the current price with the preceding and following prices.
These local minimum levels are stored in the 'min_local' column.
It iterates over the DataFrame and checks if there are any local minimum levels. If found, it calculates the percentage difference between each local minimum price and the current price. If the difference falls within a certain range (0 to 0.5), it logs the information and marks the 'min_level' column as 1. Further calculations are performed based on the 'min_level' values to identify potential buying opportunities. If the difference between the current price and a previous price falls within the specified range and certain conditions are met, the 'buy_long' or 'buy_long2' column is marked as 1. Similarly, the strategy identifies local maximum price levels and performs calculations to identify potential selling opportunities. The strategy also populates entry and exit trend columns based on certain conditions and indicators. The final part of the strategy defines a function to calculate the percentage difference between two values and applies the Heikin-Ashi candlestick formula to modify the open, close, high, and low prices in the DataFrame. Overall, the strategy aims to identify potential buying and selling opportunities based on local price levels, technical indicators, and predefined conditions.
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): 3051b1e2fea1321bd5da27a12f925b39950d0c4e8d3349802c54931ebb3d5278
Was not able to fetch indicators from Strategyfile.
last change: 2024-04-29 02:02:19