This repository is part of a curated collection of academic, engineering, and internship projects and is maintained in a finalized and stable state. The project is preserved as a complete and authoritative record, with its scope and contents intentionally fixed to ensure long-term academic and professional reference.
As a finalized internship project, only the version listed below is authoritative:
| Version | Supported |
|---|---|
| 1.0.0 | Yes |
In accordance with established academic and professional standards for security disclosure, security-related observations associated with this internship project are documented through formal scholarly channels.
To document a security concern, communication is facilitated with the project curators:
- Curator: Amey Thakur
- Collaborator: Mega Satish
- Method: Reports are submitted via the repository’s GitHub Issues interface to formally record security-related findings.
Submissions include:
- A precise and technically accurate description of the identified issue.
- Demonstrable steps or technical evidence sufficient to contextualize the finding.
- An explanation of the issue’s relevance within the defined scope of the project.
This project consists of an implementation of a Reinforcement Learning model (Q-Learning) to optimize stock trading strategies, developed as part of a Data Science internship at Technocolabs Software.
- Scope Limitation: This policy applies exclusively to the documentation, code, and datasets contained within this repository and does not extend to the execution environment (Python/Streamlit runtime) or third-party libraries (Pandas, NumPy, etc.).
This repository is preserved as a fixed academic, engineering, and internship project. Security-related submissions are recorded for documentation and contextual reference and do not imply active monitoring, response obligations, or subsequent modification of the repository.
This document defines the security posture of a finalized internship project.