Portfolio Analytics Application

This repository provides a reference implementation of a professional-grade Python toolkit for financial portfolio analytics, optimisation and technical analysis. Key features include:

• Data ingestion – load holdings from an Excel file and retrieve historical market data via Bloomberg using the xbbg library.
• Analytics – calculate performance (Sharpe, alpha, beta), risk metrics (VaR, CVaR, volatility) and basic asset allocation breakdowns.
• Backtesting – run simple backtests using bt, vectorbt or backtrader.
• Technical analysis – compute RSI, MACD, Bollinger Bands and moving averages using TA-Lib and generate naïve trading signals.
• Optimisation – optimise portfolio weights with multiple objectives (maximise return, minimise risk, maximise Sharpe ratio) using cvxpy.
• Visualisation – produce performance charts, risk/return scatter plots, allocation pie charts and technical indicator overlays with matplotlib.
• Interactive dashboard – a Streamlit application (dashboard.py) allows uploading of Excel portfolios, selection of indicators and optimisation objectives, viewing of analytics and charts and exporting of results.

Getting started

pip install -r requirements.txt
streamlit run dashboard.py

The dashboard guides you through uploading a portfolio, fetching data, performing analytics and viewing optimisation results.