Quantitative Finance Analytics Dashboard

A multi-tab interactive Shiny dashboard for quantitative financial analysis. Pulls live data from Yahoo Finance and combines portfolio optimization, risk analytics, Monte Carlo simulation, technical analysis, and time-series forecasting in a single dark-themed interface.

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Features

Market Analysis

• Candlestick chart with volume overlay
• Toggleable SMA (20 / 50 / 200-day), Bollinger Bands
• RSI (14) and MACD (12, 26, 9) sub-panels
• Full statistics table per asset

Portfolio Optimization (Markowitz)

• Efficient frontier computed via quadratic programming
• Maximum Sharpe Ratio and Minimum Variance portfolios highlighted
• Optimal weight allocation pie chart
• Cumulative performance vs equal-weight and SPY benchmark

Risk Analysis

• Historical VaR (95% / 99%) and CVaR (Expected Shortfall)
• Underwater / drawdown chart
• Rolling 60-day VaR
• 13-metric performance table: Sharpe, Sortino, Calmar, skewness, kurtosis, best/worst day, and more

Monte Carlo Simulation (GBM)

• Geometric Brownian Motion with up to 10,000 paths
• Fan chart with configurable confidence bands and IQR
• Final portfolio value distribution with scenario table

Forecasting

• Auto ARIMA, ETS, and Theta models
• 80% and 95% confidence intervals
• STL decomposition trend overlay
• ACF / PACF correlogram

Overview

• Per-asset KPI cards (total return, volatility, Sharpe)
• Normalized multi-asset performance chart
• Return correlation heatmap
• Return distribution histograms and rolling volatility

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Setup

1. Install dependencies

source("install_packages.R")

Packages used: shiny, shinydashboard, shinyWidgets, tidyverse, quantmod, PerformanceAnalytics, plotly, DT, quadprog, forecast, xts, zoo, TTR, moments, scales, lubridate.

2. Launch

shiny::runApp(".", launch.browser = TRUE)

Or open quantr.Rproj in RStudio and run source("run_app.R").

Note: An internet connection is required — data is fetched live from Yahoo Finance via quantmod::getSymbols().

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Usage

1. Select assets from the sidebar picker (supports stocks, ETFs, crypto)
2. Set the date range and risk-free rate
3. Click Fetch Market Data
4. Navigate between tabs to explore the analysis
5. On the Monte Carlo tab, configure simulations and click Run Simulation
6. On the Forecast tab, choose a model and click Fit & Forecast

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Project Structure

QuantR/
├── global.R              # Package loading, constants, shared theme helpers
├── ui.R                  # Shiny UI (shinydashboard, 6 tabs)
├── server.R              # Reactive server logic (~500 lines)
├── run_app.R             # One-liner launcher
├── install_packages.R    # Dependency installer
├── R/
│   ├── portfolio.R       # Markowitz efficient frontier (quadprog)
│   ├── risk.R            # VaR, CVaR, rolling risk, full metrics table
│   ├── montecarlo.R      # GBM simulation, path bands, summary table
│   └── technical.R       # OHLCV helpers, SMA/BB/RSI/MACD, candlestick builder
└── www/
    └── custom.css        # Dark GitHub-style theme (~250 lines)

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Tech Stack

Layer	Libraries
UI framework	shiny, shinydashboard, shinyWidgets
Data	quantmod, xts, zoo
Optimization	quadprog
Risk / performance	PerformanceAnalytics, moments
Charting	plotly
Forecasting	forecast (ARIMA, ETS, Theta)
Technical indicators	TTR
Data wrangling	tidyverse