semvec/pypi-packages huggingface dataset:
StreamTick 0.1.0
A dynamic Streamlit library for stock time-series forecasting using ARIMA and Monte Carlo simulations.
License: MIT
Requires Python: >=3.8

StreamTick: Stock Forecasting with ARIMA & Monte Carlo
StreamTick is a dynamic Python library for stock market analysis, combining powerful time-series forecasting with an interactive Streamlit user interface. It is designed for financial analysts and data scientists who need to quickly generate and visualize future stock price trends.

Key Features
Automated Forecasting: Utilizes the ARIMA (Autoregressive Integrated Moving Average) model to automatically find optimal parameters and generate accurate forecasts for stock prices.

Monte Carlo Simulation: Generates multiple possible future price paths to visualize volatility and forecast uncertainty, providing a more realistic view of potential outcomes than a single, deterministic forecast.

Dynamic and Interactive UI: The Streamlit-based user interface allows users to dynamically add and analyze stocks. Each stock gets its own dedicated section for input, analysis, and visualization.

Seamless Data Integration: Automatically fetches historical stock data from Yahoo Finance and performs all necessary preprocessing steps in the background.

Robust and Modular Code: The project is structured into distinct modules for data acquisition, preprocessing, ARIMA modeling, and UI, making the codebase clean and easy to extend.

Getting Started
Installation
To use StreamTick in your project, install it directly from PyPI once it is published:

Bash

pip install StreamTick
Usage
Once installed, you can use the tick_arima function to generate a forecast for a single stock and display it in a Streamlit dashboard.

Create a Python file (e.g., app.py) and add the following code:

Python

import streamlit as st
from streamtick import tick_arima

st.title("My Stock Forecasting Dashboard")

# Add a stock forecast to your dashboard
tick_arima('AAPL')

# You can add more stocks by calling the function again
tick_arima('MSFT')
Then, run your app from the terminal:

Bash

streamlit run app.py