Stock Analytica

Description

• NSE minute-level data for stocks.
• Data comprises opening price, highest price, closing price, traded volume for a stock at every minute in the period from 2018 to 2021.
• Prioritise Building the MLOps pipeline, don’t spend much time in modelling efficiency

Instructions

for the first iteration of the model (v0), use data in StockAnalyticaData/v0 data folder comprising of these stocks:

• AARTIIND
• ABCAPITAL

For the second iteration of the model (v1), add StockAnalyticaData/v1 data to StockAnalyticaData/v0 - the data from v1 data folder comprising of these stocks:

• ABFRL

• ADANIENT

• ADANIGAS

• refer to data_processing_reference.ipynb

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Submission Expectations

1. Private Git Repository URL ( 🔗 Tutorial)

• Repository name format:
  <ROLLNUMBER_IN_CAPITAL_CASE>_IITMBS_MLOPS_OPPE1
• Grant collaborator access to:
  • IITMBSMLOps or
  • da5014_1@study.iitm.ac.in
• Commit all required files directly to the Git repo - (No separate ZIP file submission)
• Commit history will be scrutinized
  • Commit and push to remote after every successful integration with clear comments/commit messages
• ✅ Repository must contain:
  • Code/scripts used to complete the objective (*.py, *.ipynb, *.sh, etc.)
  • Output files (if any) showing successful completion
  • README.md explaining the purpose of each file
  • Standard dataset splits used for training
  • Any binary artifacts (pickle files, trained models, etc.)
• ❌ Should not contain:
  • Video Screencast

2. Video Screencast

✅ Must cover the following:

• Explanation of the problem statement
• Approach to reach the objective
• Demonstration of cloud compute setup configuration
• Explanation of input files/data
• Demonstration of sequence of actions performed
  • Examples of Actions:
  • Creating a Virtual Machine
  • SSH into VM
  • Running scripts
• Detailed explanation of scripts/code and objectives
• Errors encountered & how you resolved them
• Working demonstration in GCP environment
• Explanation of output files/data

3. AI Tool Usage Document - 🔗Reference Document

Should contain:

• AI tools used
• Prompts used
• Link(s) to shared chat(s)
• This can also be included in the Git repo as: AI_USAGE_DOC.md

Examination Instructions

• Usage of AI tools is permitted
• Do not discuss anything with your peers.
• Plagiarism if found, will be dealt with severe consequences according to IIT-Madras Student Conduct
• Please submit within allotted time even if the pipeline is incomplete - partial marks are awarded appropriately
• Students are instructed to start the screencast recording only after the completion of whole pipeline or when they are done with their attempts in case of partial completion
• Students are expected to take permission from the proctor about the screencast recording - helps proctor not to flag the instance as malpractice
• Students are expected to inform proctor after successful google form submission
• Students are expected to leave the meet only after completion of scheduled examination time slot - in case of early submissions
• Proctors do note the time of initiating video screencast creation, form submission for operational co-ordination based on student interaction
• In case of no acknowledgement from proctor, please message in Google Meet Chat ( Starting the Screencast Recording or Submission Successful)