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Merge pull request #1 from mayur7garg/marimo
Added interactive notebook
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‎.devcontainer/devcontainer.json

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"customizations": {
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"vscode": {
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"extensions": [
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"ms-python.python"
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"ms-python.python",
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"marimo-team.vscode-marimo"
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]
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}
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}

‎README.md

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<summary>Click to expand</summary>
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- [Data](#data)
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- [Project Requirements](#project-requirements)
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- [Project Prerequisites](#project-prerequisites)
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- [Usage](#usage)
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- [Notes and Caveats](#notes-and-caveats)
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- [About this project](#about-this-project)
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</details>
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## Data
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### Sources
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This project uses historical data acquired from publicly available sources listed below:
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1. [NSE India](https://www.nseindia.com/)
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2. [Screener.in](https://www.screener.in/)
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1. [NSE India](https://www.nseindia.com/) - Daily stock prices
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2. [Screener.in](https://www.screener.in/) - Stock PE
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### Update schedule
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Stock pricing data is updated 3 times a week for a set of symbols via a [cron workflow](.github/workflows/main.yml) running on GitHub actions.
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Updating PE data, however, is a manual process. Hence, PE information is not likely to be correct for recent dates and should not be relied upon.
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## Project Requirements
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## Project Prerequisites
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### Docker
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Development of this project was done inside a developmental Docker container using the base image of `Python v3.11.6`. The same environment can be set up using the `docker-compose.yml` available [here](./docker-compose.yml).
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Development of this project was done inside a Docker container using the base image of `Python v3.11.6`. The same environment can be set up using the `docker-compose.yml` available [here](./docker-compose.yml).
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If using VS Code or GitHub Codespaces, a developmental container can be instantiated using the [devcontainer.json](.devcontainer/devcontainer.json) file
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### Requirements
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All top level packages used in this project are listed in the [requirements](./requirements.txt) file. This project does not use any niche functionality from any of the listed packages and hence should work with their most recent releases.
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## Usage
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### Static reports
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This project generates static HTML reports which are hosted using GitHub Pages which can be viewed at [FinancialModelling](https://mayur7garg.github.io/FinancialModelling/).
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To regenerate the reports and the associated plots, run the following command:
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```sh
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cd 'Stock Forecasting' && python main.py
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```
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Once generated, the reports can be viewed by opening [index.html](index.html) in any web browser.
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### Interactive notebook
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An interactive [marimo](https://marimo.io/) notebook has been included which can be run using the following command:
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```sh
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cd 'Stock Forecasting' && marimo run stock_interactive.py
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```
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## Notes and Caveats
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- This project only focuses on day level stock price data and does not factor in intraday price changes.
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- This project is only meant to be educational and analytical purposes and should not be interpreted as a financial advice.
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- This project only focuses on day level stock price data and does not factor in intraday price changes or company fundamentals.
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- Data used in this project is not updated in realtime and may be out of date.
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- Currently, the analysis doesn't factor in stock splits, bonus shares, etc. as structured data for the same is difficult to obtain.
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- This project is only meant to be educational and should not be interpreted as a financial advice.
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## About this project
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Feel free to leave a star if you liked this project.
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```bibtex
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@misc{MayurGarg_FinancialModelling,
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title={Financial Modelling},

‎Stock Forecasting/data_process.py

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) -> None:
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self.symbol = symbol
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self.image_out_path = image_out_path
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consolidated_data_path = stock_data_dir.joinpath(symbol, "consolidated.parquet")
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self.consolidated_data_path = stock_data_dir.joinpath(symbol, "consolidated.parquet")
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if reload_data or (not (consolidated_data_path.exists() and consolidated_data_path.is_file())):
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if reload_data or (not (self.consolidated_data_path.exists() and self.consolidated_data_path.is_file())):
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self.raw_data = self.consolidate_data(
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stock_data_dir,
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eps_data_dir
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)
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self.raw_data.to_parquet(consolidated_data_path, index = False)
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self.raw_data.to_parquet(self.consolidated_data_path, index = False)
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else:
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self.raw_data = pd.read_parquet(consolidated_data_path)
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self.raw_data = pd.read_parquet(self.consolidated_data_path)
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self.last_close = self.raw_data['Close'].iloc[-1]
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‎Stock Forecasting/layouts/stock_interactive.grid.json

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}

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