First blog post and portfolio post

_portfolio/bl-portfolio.md

@@ -1,7 +1,21 @@
 ---
-title: "Portfolio item number 1"
-excerpt: "Short description of portfolio item number 1<br/><img src='/images/500x300.png'>"
+title: "Portfolio construction using Black-Litterman Model"
+excerpt: "Construct a portfolio using MPT and BL model to outperform the market return. Using various techniques in portfolio selection, weight allocation and incorporate views in portfolio optimisation process. 1<br/><img src='/images/blogs/bl-construct.png'>"
 collection: portfolio
 ---
 
-This is an item in your portfolio. It can be have images or nice text. If you name the file .md, it will be parsed as markdown. If you name the file .html, it will be parsed as HTML. 
+This project explores portfolio optimization and factor investing strategies using quantitative methods. The project focuses on constructing an optimal portfolio by selecting assets based on risk factors, utilizing statistical techniques for covariance matrix estimation, and implementing portfolio optimization models.
+
+Key Findings
+
+1. **Covariance Matrix Estimation:**
+   - Implemented the shrinkage estimator proposed by Ledoit-Wolf (2003) to address the instability of the sample covariance matrix.
+
+2. **Portfolio Optimization Models:**
+   - Explored Mean-Variance Optimization, Max Sharpe Ratio Optimization, and the Black-Litterman model to derive optimal portfolio weight allocations.
+
+3. **Factor Exposure Analysis:**
+   - Investigated factor exposures of securities to target factors, providing insights into the diversification and risk management strategies.
+
+4. **Future Enhancements:**
+   - Recommendations for future improvements include incorporating international markets, refining optimization methods, and exploring advanced topics like individual investor uncertainty.

_posts/2024-08-29-ohlc-resampling.md

@@ -1,11 +1,11 @@
 ---
 title: 'Enhancing Algorithmic Trading Backtesting with Custom OHLC Resampling'
-date: 2012-08-14
+date: 2024-08-29
 permalink: /posts/2012/08/blog-post-1/
 tags:
-  - cool posts
-  - category1
-  - category2
+  - time series
+  - python
+  - backtesting
 ---
 
 In algorithmic trading, the accuracy of backtesting can make or break a strategy. The ability to simulate how your trading algorithm would have performed historically is crucial for refining and optimizing your approach. A key aspect of this process is how time series data, particularly OHLC (Open, High, Low, Close) data, is handled. While pandas provides a built-in method for resampling, it often falls short when dealing with the unique demands of financial data. In this post, we’ll explore a custom OHLC resampling function that offers greater precision, allowing for more reliable backtesting results.