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The "brain" strategy is a trading strategy implemented in Python for backtesting purposes. It is designed to evaluate and optimize trading decisions based on a set of technical indicators. Here is a short description of what the strategy does:
The strategy utilizes a configurable number of "nodes" to make trading decisions.

It uses the Freqtrade library and requires the installation of the "ta" library.

The strategy consists of two main functions: "populate_buy_trend" and "populate_sell_trend." In the "populate_indicators" function, various technical indicators are calculated and added to the input dataframe.

The "populate_buy_trend" function evaluates the buy conditions based on the configured nodes. Each node has inputs, enabled status, reversal factor, and weight. The inputs are multiplied by the enabled, reversed, and weighted factors to determine the overall buy decision. The "populate_sell_trend" function follows a similar logic to evaluate the sell conditions based on the configured nodes. The strategy uses a combination of technical indicators to generate buy and sell signals. The buy and sell conditions are based on the calculated values from the technical indicators and the configured parameters (inputs, enabled status, reversal factor, and weight). The strategy defines a minimal ROI (Return on Investment) table and a stop-loss level. The timeframe for analysis is set to 1 hour. Please note that this is a brief description of the strategy, and the actual implementation may contain additional details and considerations.

It uses the Freqtrade library and requires the installation of the "ta" library.

The strategy consists of two main functions: "populate_buy_trend" and "populate_sell_trend." In the "populate_indicators" function, various technical indicators are calculated and added to the input dataframe.

The "populate_buy_trend" function evaluates the buy conditions based on the configured nodes. Each node has inputs, enabled status, reversal factor, and weight. The inputs are multiplied by the enabled, reversed, and weighted factors to determine the overall buy decision. The "populate_sell_trend" function follows a similar logic to evaluate the sell conditions based on the configured nodes. The strategy uses a combination of technical indicators to generate buy and sell signals. The buy and sell conditions are based on the calculated values from the technical indicators and the configured parameters (inputs, enabled status, reversal factor, and weight). The strategy defines a minimal ROI (Return on Investment) table and a stop-loss level. The timeframe for analysis is set to 1 hour. Please note that this is a brief description of the strategy, and the actual implementation may contain additional details and considerations.

Biased Indicators

trend_kst, trend_kst_sig, trend_kst_diff

stoploss:-0.256timeframe:1hhash(sha256):560a1e6aa00a103cd412a91f0d28c51dbcff4ff48a9301917e8219d89016a203

Was not able to fetch indicators from Strategyfile.last change: 2023-06-29 11:42:28