Apziva - ValueInvestor

Background:

We are a portfolio investment company and we make investments in the emerging markets around the world. Our company profits by investing in profitable companies, buying, holding and selling company stocks based on value investing principles.

Our goal is to establish a robust intelligent system to aid our value investing efforts using stock market data. We make investment decisions and based on intrinsic value of companies and do not trade on the basis of daily market volatility. Our profit realization strategy typically involves weekly, monthly and quarterly performance of stocks we buy or hold.

Data Description:

The data comes from the yahoo finance website (real stocks data)

Goal(s):

Predict stock price valuations on a daily, weekly and monthly basis. Recommend BUY, HOLD, SELL decisions. Maximize capital returns, minimize losses. Ideally a loss should never happen. Minimize HOLD period.

Success Metric(s):

• Evaluate on the basis of capital returns. Use Bollinger Bands to measure your systems effectiveness.

Project Structure

├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── app            <- streamlit app
│   │   └── main.py
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── LICENSE

Run

Run project using streamlit run src/app/main.py or python -m streamlit run src/app/main.py

---

Project based on the cookiecutter data science project template. #cookiecutterdatascience