Lorentzian Python-Advanced ML with Lorentzian Distance

Example

Modifying the Lorentzian kernel function to adjust the influence of distant neighbors in the K-NN algorithm.

Scenario

A developer wants to experiment with the sensitivity of the trading indicator to price movements over different time spans. By adjusting the Lorentzian kernel function, they can make the algorithm more responsive to recent price changes or, conversely, more influenced by longer-term trends.

Example

Tweaking the data preprocessing or feature selection methods in MLExtensions.py to improve prediction accuracy.

Scenario

A data scientist aims to enhance the indicator's performance by incorporating additional market indicators (like volume or volatility) into the feature set. They modify the preprocessing steps to include these indicators, potentially improving the model's predictive power and trading strategy's profitability.

Example

Changing the classification threshold or the decision strategy in Classifier.py to better align with risk management strategies.

Scenario

A trader seeks to minimize false positives in trade signals to adhere to a conservative risk management strategy. By customizing the classification threshold, they can adjust the indicator to only signal trades when there's a higher probability of success, thereby potentially reducing the frequency of trades but increasing their quality.