[Major Update!] : New update regarding to reduce Yfinance API limit Error

[SrigadaAkshayKumar]

This is the major update in this project all the interested contributors who are good at backend development are welcome please go through with the discussion for more details

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[ASR1015]

Hi @SrigadaAkshayKumar ,

I'm a contributor from GSSoC'25 and would love to work on this backend enhancement to help reduce the YFinance API limit issue. I’ve gone through the discussion and am confident I can contribute effectively.

Please let me know if I can take this up. Looking forward to collaborating!

[SrigadaAkshayKumar]

@ASR1015 I appreciate your interest in being part of the project. Are you comfortable with backend dev? Please let us know which part you'd like to work on and if there’s any area where you need help, so we can divide the project into modules accordingly, are you familiar with Air flow. come to discussions for more conversations

[ASR1015]

Thank you @SrigadaAkshayKumar for the response!

Yes, I’m comfortable working with backend development and I’d be happy to contribute wherever needed. I’ve worked with APIs, database optimization, and task scheduling before, but I’m not very familiar with Airflow, I’m open to learning it though!

I'd love to understand how the tasks can be divided and which part I could begin with. I’ll join the Discussions tab as suggested to continue the conversation and get better clarity.

Looking forward to contributing!

[SrigadaAkshayKumar]

proposed backend directory structure

Here is the proposed backend structure currently we have combined all api's in app.py but now we are using modular approach so we divide the services as
│ │ └── stock_service.py # Read/process CSV data sends graph and table data to frontend also contains news logic
│ │ └── stock_predict.py # contains prediction graph and prediction logic

so that we can easily complete the tasks one by one

backend/
│
├── app/                            # Main Flask application package
│   ├── routes/                     # Flask route handlers
│   │   └── stock_routes.py     # API: GET /stock/<symbol>, etc.
│   ├── services/                   # Data logic layer
│   │   └── stock_service.py        # Read/process CSV data sends graph and table data to frontend also contains news logic
│   │   └── stock_predict.py       # contains prediction graph and prediction logic
│   └── config.py                   # App configuration (CSV paths, env)
│
├── data/                           # Daily updated CSVs by Airflow DAGs
│   └── stock_TCS.csv
│   └── stock_ITC.csv
│   └── ...
│
├── airflow_dags/                   # Apache Airflow DAGs
│   └── fetch_stock_data_dag.py     # Daily fetch and append logic using yfinance
│
├── Dockerfile                      # Dockerfile to containerize Flask app
├── requirements.txt                # Python dependencies (Flask, pandas, etc.)
├── .env                            # Environment variables (used in config.py)
├── app.py                          # Entry point for Flask (runs the app)

any suggestions or improvements?

[ASR1015]

Hi @SrigadaAkshayKumar,

The proposed backend directory structure looks great and modular. I’m comfortable with backend development and familiar with Flask, pandas, and CSV handling. I can take up implementing stock_service.py and stock_predict.py along with setting up the routes and config as per the structure.

Shall I start working on this and raise a PR with the initial setup?

[SrigadaAkshayKumar]

Hi @SrigadaAkshayKumar,

The proposed backend directory structure looks great and modular. I’m comfortable with backend development and familiar with Flask, pandas, and CSV handling. I can take up implementing stock_service.py and stock_predict.py along with setting up the routes and config as per the structure.

Shall I start working on this and raise a PR with the initial setup?

done we already have the stock functions and prediction function in app.py just modulate them and connect then in app.py as per the new directory structure. take a sample datasets in data folder TCS.csv, ICT.csv(for testing) to test the function in output make sure the output should be same as now with graph, table and news feature. we just modulating the functions