In the ever-evolving landscape of financial markets, identifying key breakout patterns is crucial for informed decision-making. One of the most reliable strategies revolves around previous day high-low breakouts, where we analyze a stock's ability to breach its prior day's high or low. This method integrates historical market data, intraday movements, and past breakout patterns to create a data-driven trading approach.

Problem Framing

The core challenge of predicting high-low breakouts is modeled as a classification problem, allowing us to categorize potential market behavior based on key indicators.

Model Overview

We utilize a Multilayer Perceptron (MLP) model to solve this problem. The MLP, a type of artificial neural network, is well-suited for handling complex, nonlinear relationships in financial data. Each symbol is treated independently, with a separate model trained for each, ensuring tailored predictions that respect the unique behavior of individual stocks.

Performance Highlights

Below is a snapshot of the model's performance across various stocks and timeframes:

NIFTY

Prediction
	No Breakout	Breakout
High	0.8	0.88
Low	0.75	0.91

BANK NIFTY

Prediction
	No Breakout	Breakout
High	0.8	0.79
Low	0.73	0.93

Disclaimer

The models and strategies discussed in this blog are probabilistic in nature and do not indicate any strategy. Trading and investment decisions involve significant risk, and past performance is not indicative of future results. Market behavior can vary, and the models may adapt or change based on new data and ongoing research. Readers are advised to use their discretion and consult financial experts before making any trading decisions. The authors are not responsible for any financial losses incurred based on the use of these models or strategies.