Brainrot-Stocks 0.1.7

Get stock data with unwanted brainrot in your Python app.

Brainrot-Stocks

An exercise to create a Python package, build it, test it, distribute it, and use it. See instructions for details.

This package was created by generally following the Packaging Python Projects with the addition of some pipenv setup to manage virtual environments.

Project Description

Finance is boring and you hate crunching numbers all day? No problem, Brainrot-Stocks is a Python package designed for handling financial data while keeping you entertained with a constant stream of brainrot. It includes modules for managing stock quotes, performing various operations on financial data, and engaging learning experiences. Powered by AlphaVantage API.

Link to Package on PYPI

https://pypi.org/project/Brainrot-Stocks/#description

Team members

Reyhan Abdul Quayum	Rashed Alneyadi	Sia Chen	Yu Zhang	Chloe Han

Project Structure

• src/Financiers
  Contains the main modules:

  • quotes.py: Handles operations related to stock quotes.
  • stock.py: Manages stock data and related functionality.

• tests
  Contains test files for validating the code.

  • test_stock.py: Tests the stock.py module.

Usage

See the example usage Python program to see all the functions in action here.

Stock Class

 from Financiers.stock import Stock
 stock = Stock()

• Description: The Financiers module contains a class called Stock that contains functions which access data from AlphaVantage's API for your Python programs. To do this, import Stock and create an instance of it.

1. get_market_mood()

stock = Stock()
mood = stock.get_market_mood()

• Description: Provides a random statement about the current stock market’s vibe, referencing top gainers and losers.

2. get_earnings(symbol_string, annual=True, numDays=5)

earnings_df = stock.get_earnings("AAPL", annual=True, numDays=5)

• Arguments:
  • symbol_string (str): Ticker symbol of the stock (e.g., "AAPL").
  • annual (bool): If True, retrieves annual earnings; if False, retrieves quarterly earnings.
  • numDays (int): Number of recent entries to return.
• Returns: BrainrotDataFrame with a random brainrot quote and a DataFrame of earnings data.

3. get_price_data(symbol_string)

price_data = stock.get_price_data("AAPL")

• Arguments:
  • symbol_string (str): Ticker symbol of the stock.
• Returns: BrainrotDataFrame with the stock's historical price data.

4. forecast_prices(symbol_string, days=30)

forecast_df = stock.forecast_prices("AAPL", days=30)

• Arguments:
  • symbol_string (str): Ticker symbol of the stock.
  • days (int): Number of future days to forecast.
• Returns: BrainrotDataFrame with forecasted dates and predicted prices.

5. company_overview(symbol_string)

overview = stock.company_overview("AAPL")

• Arguments:
  • symbol_string (str): Ticker symbol of the company.
• Returns: BrainrotWrapper with a Company object containing information about the company.

6. plot_top_movers(symbols)

top_movers_df = stock.plot_top_movers(["AAPL", "GOOGL", "TSLA"])

• Arguments:
  • symbols (list): List of stock ticker symbols.
• Returns: BrainrotDataFrame with information on the top gainer and loser from the provided symbols. Generates plots for visual analysis.

7. project_future_estimates(symbols, days=30, pattern="neutral")

estimates_df = stock.project_future_estimates(["AAPL", "GOOGL"], days=30, pattern="bullish")

• Arguments:
  • symbols (list): List of stock ticker symbols.
  • days (int): Number of days to forecast.
  • pattern (str): Trend pattern ("bullish", "bearish", or "neutral").
• Returns: BrainrotDataFrame with forecasted prices for each symbol in the list.

8. calculate_atr(data, window=14)

atr = stock.calculate_atr(data, window=14)

• Arguments:
  • data (DataFrame): DataFrame containing columns 'high', 'low', and 'close'.
  • window (int): Period to calculate the Average True Range (default is 14).
• Returns: pd.Series with ATR values.

---

Company Class

1. forecast_by_date_range(date_range="last_month", days_to_forecast=30, model_type="linear", confidence_level=0.95, frequency="daily", return_format="dataframe")

forecast = company.forecast_by_date_range(date_range="last_year", days_to_forecast=30, model_type="random_walk")

• Arguments:
  • date_range (str): Specifies the historical range for data ("last_day", "last_month", "last_year").
  • days_to_forecast (int): Number of days to forecast.
  • model_type (str): Forecasting model ("linear", "random_walk").
  • confidence_level (float): Confidence level for forecast intervals.
  • frequency (str): Frequency of forecasted data ("daily", "weekly", "monthly").
  • return_format (str): Format of the return data ("dataframe", "json").
• Returns: BrainrotDataFrame or JSON with forecasted dates, prices, and confidence intervals (if applicable).

BrainrotDataFrame and BrainrotWrapper Classes

These classes are designed to wrap data in a custom way, displaying both the data and a randomly selected "brainrot" quote for humorous effect. Each can be instantiated directly or will be returned by functions within the Stock and Company classes.

How to install and use this package

Prerequisites: have latest versions of pip and python installed on your environment.

1. Navigate to your project directory. Create a virtual environment using pipenv, and install the latest version of the package with the following command:

pip install pipenv
pipenv install Brainrot-Stocks

And before moving onto the next step, activate the virtual environment:

pipenv shell

2. Import the package using the following command inside the file:

from Financiers.stock import Stock

Then, create an instance of Stock class:

stock = Stock()

And you're ready to go! Refer to this example file to see the example usage of package functions inside the code.

3. Run the program (replace <example_file> with your actual file name):

python <example_file>.py

Contribute to the project

Prerequisites: have latest versions of pip and python installed on your environment.

Environment Setup

1. Clone the repository:

git clone https://github.com/software-students-fall2024/3-python-package-financeeers.git
cd 3-python-package-financeeers

2. Install pipenv:

If pipenv has not been installed, install using the following command:

pip install pipenv

3. Create and Activate virtual environment:

pipenv install
pipenv shell

4. Add .env file: create .env file in the root directory of the project, and add the following content:

ALPHAVANTAGE_API_KEY = <api_key>

Replace <api_key> with the actual api key, which will be delivered to the admins via discord.

Make contribution

1. Modify & Run the file:

Make changes you want to make, and run the files. For example, if you want to run stock.py, run the following command:

python src/Financiers/stock.py

Test & Build

Make sure you are in the virtual environment by running the following command:

pipenv shell

1. Test

Run all unit tests with the following command:

pytest

2. Build

Build the package using the following command:

python -m build

Then, the artifacts will be produced at dist/.

If you want to exit the virtual environment, use this comand:

exit