/sigma_hackathon

Primary LanguagePython

Portfolio Analysis with Streamlit

This project aims to analyze portfolio data using Streamlit, a popular Python library for building interactive web applications. It provides insights into the portfolio value, optimal buy indices, and transition distribution based on the given dataset.

Problem Statement

Investors often seek tools to analyze their portfolio performance and make informed decisions. This project addresses this need by providing a user-friendly interface to analyze portfolio data and understand market trends.

Code Flow for analysis.py

  1. Data Preparation: The dataset is read and processed to extract relevant information such as closing prices.

  2. Calculations: Returns are calculated based on the closing prices, and the state of the market (Bull, Flat, or Bear) is determined.

  3. Portfolio Analysis: Portfolio value is calculated based on predefined rules (e.g., buying during Bull markets and selling during Bear markets). Optimal buy indices are identified.

  4. Transition Distribution: The distribution of state transitions (e.g., from Bull to Flat, Flat to Bear) is computed to understand market behavior.

  5. Streamlit Integration: The results are displayed using Streamlit, providing an interactive and user-friendly interface for portfolio analysis.

Interface Preview

image

image

Code Flow for plot_analysis.py

  1. Data: The dataset contains historical time series data.

  2. Auto ARIMA Model: The auto_arima function from the pmdarima library is used to automatically select the optimal ARIMA parameters for the time series forecasting.

  3. Training and Testing: The dataset is split into training and testing sets. The last 50 data points are used for testing, and the remaining data is used for training.

  4. Forecasting: The Auto ARIMA model is trained on the training data, and then used to forecast future values for the test period.

  5. Visualization: The forecasted values are plotted along with the actual test data to visualize the performance of the model.

Interface Preview

Figure_1

Use of Streamlit

Streamlit is a powerful tool for building interactive web applications with minimal effort. In this project, Streamlit is used to create a user interface for portfolio analysis, allowing users to:

  • View portfolio value over time
  • Identify optimal buy indices
  • Explore transition distribution of market states