Home

Welcome to the Algo-Trading-Risk-Return-Optimization wiki!

Title: Backtesting Algo-Trading Strategies, FinTech Analysis & Portfolio Optimization: NVDA, AMD, INTC, MSI vs S&P 500 Benchmark

https://github.com/alva922/Algo-Trading-Risk-Return-Optimization/blob/main/tech_gamechangers.png

Image template by @ondostudios via Canva.

The objective of this project is to perform robust backtesting and portfolio optimization of 4 tech game-changers (NVDA, AMD, INTC & MSI) vs the S&P 500 benchmark. Backtesting is a risk-free validation of (algo-)trading strategies, ensuring they are adaptable to various market conditions. The process of backtesting involves selecting relevant historical data, applying the technical analysis strategy, and then analyzing its potential profitability. Portfolio Optimization (PO) aims at resolving the risk-return trade-off by maximizing the return for every additional unit of risk taken in the portfolio. PO utilizes advanced financial models to generate optimal investment portfolios balancing desired returns with acceptable risk levels. PO takes into account a variety of factors: market trends, asset correlations, technical indicators, etc. What about the 4 top-rated tech stocks to be discussed in the sequel? Chip companies have been in focus this year due to the massive opportunity created by the generative artificial intelligence ambitions of tech giants following the success of OpenAI’s ChatGPT. Advanced Micro Devices (NASDAQ:AMD), Nvidia (NASDAQ:NVDA), and Intel (NASDAQ:INTC) are known to be the best chip stocks as per Wall Street experts. Motorola Solutions, Inc. (NYSE: MSI) is the leading global provider of mission critical communication services. MSI serves more than 100,000 public safety and commercial customers across more than 100 countries. MSI recently announced the launch of cutting-edge solutions with advanced capabilities. The present study entails a comprehensive fintech analysis of these 4 celebrated stocks in terms of their business performance and overall financial health over recent years. Using the proposed set of Python open-source tools and techniques to analyze historical stock data and financial statements can help investors make more informed business decisions to predict and improve ROI.