Creating an optimized financial portfolio is one of the most critical strategies for success in financial markets. Whether you trade stocks, cryptocurrencies, or other assets, selecting the right combination of assets can determine the difference between high returns and unexpected losses. In this article, I explore a project where I designed an intelligent portfolio optimization system using Havan, a powerful program tailored for financial computations. This system not only follows classical financial principles but also integrates web scraping to identify the best-performing assets (such as cryptocurrencies) for investment.
Why Is Financial Portfolio Optimization Important?
The primary goal of portfolio optimization is to maximize returns while minimizing risk. This is achieved by diversifying investments across assets with low correlations. For example, if Bitcoin’s price drops, gold prices might rise. Combining these assets in a portfolio helps mitigate significant losses.
Key factors considered in this project include:
- Price volatility
- Asset correlations
- Historical returns
- Real-time financial news (via web scraping)
The Role of Havan in Building a Smart Portfolio
Havan is an efficient program designed for scientific and financial computations. In this project, I used Havan to develop a function that executes three core steps:
1. Data Collection via Web Scraping
The custom-built function automatically scrapes the following data from trusted financial websites (e.g., CoinMarketCap for cryptocurrencies):
- Real-time asset prices
- Trading volumes
- Relevant news (e.g., forks, regulatory updates)
- 30-day volatility trends
This data helps the system identify high-potential assets.
2. Calculating Optimal Asset Weights
The optimization function is inspired by the Markowitz Modern Portfolio Theory (MPT). The formula is:
![\[\text{Maximize: } Expected\ Return - \lambda \times Risk\]](https://amirhooshang.com/blog/wp-content/ql-cache/quicklatex.com-714d43e87a123702c73f0299c6707acc_l3.png "Rendered by QuickLaTeX.com")
- λ (Lambda): Represents the user’s risk tolerance.
- Risk: Measured using standard deviation.
3. Generating the Final Portfolio & Profit Forecast
After calculations, the system suggests an asset allocation (e.g., 40% Bitcoin, 30% Ethereum, 20% Company X stock, 10% gold) and predicts potential returns based on different market scenarios.
Benefits of Combining Web Scraping with Portfolio Optimization
- Real-Time News Responsiveness:
- If web scraping detects a cryptocurrency listing on a major exchange, the system automatically increases its weight in the portfolio.
- Eliminating High-Risk Assets:
- Assets exceeding a volatility threshold are removed from the list.
Challenges & Solutions
- Incomplete Data:
Some websites use anti-scraping techniques. To solve this, I implemented random user-agent rotation and delays between requests. - Heavy Computations:
The Markowitz algorithm can be slow for large portfolios (20+ assets). By optimizing the code in Havan, I reduced execution time by 3x.
How to Use This System
- Input your preferred assets (or use the system’s suggestions).
- Set your risk tolerance (λ).
- The system generates an optimized portfolio and updates it every 24 hours.
Conclusion
Portfolio optimization is no longer optional—it’s essential for every trader. This project demonstrates how combining financial theories with modern tools like web scraping can deliver exceptional results. If you aim to maximize profits with minimal risk, building a smart portfolio is the first step.
