This study investigates the application of machine learning (ML) models in stock market forecasting, with a focus on their integration using PineScript, a domain-specific language for algorithmic trading. Leveraging diverse datasets, including historical stock prices and market sentiment data, we developed and tested various ML models such as neural networks, decision trees, and linear regression.

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[1] Gautam Narla, "Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/8cbk-bc40. Accessed: Feb. 17, 2025.
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doi = {10.21227/8cbk-bc40},
url = {http://dx.doi.org/10.21227/8cbk-bc40},
author = {Gautam Narla },
publisher = {IEEE Dataport},
title = {Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach},
year = {2024} }
TY - DATA
T1 - Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach
AU - Gautam Narla
PY - 2024
PB - IEEE Dataport
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Gautam Narla. (2024). Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach. IEEE Dataport. http://dx.doi.org/10.21227/8cbk-bc40
Gautam Narla, 2024. Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach. Available at: http://dx.doi.org/10.21227/8cbk-bc40.
Gautam Narla. (2024). "Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach." Web.
1. Gautam Narla. Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/8cbk-bc40
Gautam Narla. "Enhancing Stock Market Forecasting with Machine Learning A PineScript-Driven Approach." doi: 10.21227/8cbk-bc40