Financial Engineering Portfolio Optimization Project

Project Overview

This project focuses on constructing an optimized financial portfolio using various technical strategies to achieve a Sharpe ratio greater than any single instrument within the portfolio. It leverages historical price data of various securities from 12/31/1999 to 12/31/2018.

Key Features

• Analysis of a diverse set of financial instruments.
• Application of technical strategies like Moving Average Crossover (MA), Bollinger Bands (BB), and Benchmarking (BMK).
• Optimization techniques to maximize the portfolio's Sharpe ratio.

Data

The project utilizes close price data for the following securities:

• Equities:
  • Apple (AAPL) - Technology
  • Exelon Corp (EXC) - Utility
  • General Electric (GE) - Diversified High-Tech Industrial
  • Intel (INTC) - Technology
  • Pfizer (PFE) - Pharmaceutical
  • S&P 500 ETF (SPY) - Broad Market ETF
• Fixed Income:
  • Fidelity Investment Grade Bond Index (FBNDX)
  • Vanguard Total Bond Market Index (VBTIX)
• Commodities:
  • S&P GSCI Broad Commodity Index (SPGSCI)
  • Platinum (XPT)
• Currencies:
  • Canadian Dollar (CAD)
  • British Pound (GBP)

Strategies Employed

• MA Crossover Strategy (flat and short)
• Bollinger Band Strategy
• Benchmark Strategy Each strategy was applied to enhance the portfolio's performance.

Constraints

The project adheres to the following constraints to ensure a robust and unbiased approach:

• MA Crossover Strategy:
  • Uniform (fastWindow, slowWindow) parameters across all equity instruments.
  • Separate or identical (fastWindow, slowWindow) parameters for fixed income, commodities, and currencies.
• Bollinger Band Strategy:
  • Uniform parameters across all instruments.
  • Minimum lookbackWindow of 20 to ensure stability of the standard deviation estimate.

Analysis

• Calculation of correlation matrices.
• Mean-Variance Optimization for optimal asset allocation.
• Monte Carlo simulation for exploring various portfolio compositions.

Portfolio Construction

A portfolio of eight chosen strategies was created, meeting specific constraints and optimization parameters to achieve a high Sharpe ratio.

Installation and Usage

• Ensure you have Jupyter Notebook or an equivalent environment to run .ipynb files.
• Clone the repository and navigate to the notebook file.