introduction-to-quantitative-finance-in-python

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introduction-to-quantitative-finance-in-python

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streamlit appliations and colab notebooks with introductory material to qf in Python

introduction-to-quantitative-finance-in-python 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Introduction to Quant Finance in Python

Streamlit applications and Google Colaboratory notebooks with introductory material to qf in Python.

[autoML.py](https://github.com/hofmannj0n/auto-machine-learning/blob/main/myapp.py) file contains streamlit app with automated machine learning

[portfolio.py](https://github.com/hofmannj0n/auto-machine-learning/blob/main/myapp.py) file contains streamlit app with auto portfolio optimizer & EDA

[tutorials](https://github.com/hofmannj0n/Introduction-to-Quantitative-Finance-in-Python/tree/main/tutorials) folder contains tutorials for various python packages dealing with financial data and time series modeling

Inside the tutorials folder you will find notebooks detailing comprehensive looks into common trading algorithims, ARIMA modeling, and some of the more common finance libraries.

[Video tutorial located here](https://www.youtube.com/watch?v=ofzrvCPRRjw&t=5073s)

# Prerequisites

make sure you have a code editor setup with python >= 3.10 and a zsh / bash based shell, VS code is my editor of choice:

[VS code](https://code.visualstudio.com/)

[download Python](https://www.python.org/downloads/)

# Cloning the repository (MacOS)

1. clone repository inside code-editor of choice
   
2. setup enviroment in terminal: (make sure to select enviroment as workspace interpreter)
   `python3.10 -m venv automl`

3. install dependencies
   `pip install -r requirements.txt`

4. run applications:
   `streamlit run autoML.py`
   `streamlit run portfolio.py`

5. explore the jupyter notebooks located in tutorials to further your understanding of qf

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