QuantPortfolio

TradeSense.io( Quantitative Finance App)

A powerful and user-friendly quantitative finance app designed to provide comprehensive market insights and portfolio management tools. This GitHub repository contains the source code and project files for the app. It allows users to track real-time market data, analyze investment opportunities, and make informed trading decisions.

Desktop Version of the prototype:

Features:

• Real-time market data updates and financial news aggregation
• Portfolio management tools for tracking investments and performance
• Advanced charting and technical analysis indicators for market evaluation
• Watchlist creation and personalized alerts for monitoring specific stocks
• Historical data analysis and performance tracking
• Integrated trading platform connectivity for executing trades
• User-friendly interface with intuitive navigation and responsive design

Breaking into the Quant world:

Building a quantitative portfolio to showcase my skills and projects related to finance and investing requires-
       1. Planning,
       2. Execution, and
       3. Documentation.

Strategies:

1. Algorithmic Trading Strategy:

Develop and backtest an algorithmic trading strategy using historical market data. Implement quantitative models, technical indicators, or machine learning algorithms to identify trading signals and automate the execution of trades.

2. Portfolio Optimization:

Construct an optimal portfolio by applying modern portfolio theory and asset allocation techniques. Utilize historical returns, risk measures, and correlations to determine the optimal allocation of assets that maximizes return and minimizes risk.

3. Risk Modeling:

Build a risk model to assess and manage the risk of a portfolio or investment. Explore techniques such as value-at-risk (VaR), stress testing, or Monte Carlo simulations to quantify and analyze the potential downside risk.

4. Option Pricing Model:

Develop a quantitative model to price options using methods like Black-Scholes-Merton or binomial trees. Implement the model to calculate option prices and compare them with market prices to identify potential mispricings.

5. Market Microstructure Analysis:

Investigate the dynamics of order flow, bid-ask spreads, or market liquidity using high-frequency trading data. Analyze market microstructure patterns and their impact on trading strategies or market efficiency.

6. Event Study Analysis:

Conduct an event study to assess the impact of specific events (such as earnings releases, mergers, or economic announcements) on stock prices. Apply statistical techniques to measure abnormal returns and evaluate the significance of the event.

7. Sentiment Analysis:

Use natural language processing (NLP) techniques to analyze news articles, social media data, or financial reports to gauge market sentiment. Assess the sentiment's impact on asset prices or build sentiment-based trading strategies.

8. Machine Learning Applications:

Apply machine learning algorithms(linear regression, RF, Gradient boosting, SVM, RNN, LSTM, DNN, Reinforcement Learning) to solve finance-related problems, such as credit risk assessment, fraud detection, or investment recommendation systems. Use supervised or unsupervised learning techniques to make predictions or generate insights.

JPMorgan Chase Quantitative Research Virtual Experience Program

I finished the Virtual Quantitative Research Virtual program.