BSc Economics & Accounting student at the University of Bristol with a strong interest in financial markets, crypto, trading, portfolio risk and data-driven market research.

My current work focuses on practical Python projects that test market ideas, backtest trading logic, analyse crypto portfolio risk and explore how crypto behaves across wider macro and cross-asset market regimes.

I am developing a public market research portfolio using Python, pandas, yfinance and matplotlib.

The aim is to connect market intuition with structured analysis by:

• Forming clear market hypotheses
• Testing ideas using historical data
• Converting research questions into rules-based analysis
• Evaluating returns alongside drawdowns and risk
• Analysing portfolio behaviour across different market conditions
• Explaining results in a clear and commercially relevant way

An event-study analysis testing whether BTC, ETH and SOL tend to mean-revert after large daily sell-offs.

The project analyses forward returns after different sell-off thresholds and compares behaviour across assets and market trend conditions.

Analysis Includes: Sell-off threshold testing, forward return analysis, trend-filtered comparison and cross-asset crypto market behaviour.

A rules-based crypto mean-reversion backtest that turns the sell-off hypothesis into a testable trading strategy.

The project includes transaction costs, fixed holding periods, drawdown analysis, buy-and-hold benchmarking and time-in-market comparison.

Analysis Includes: Strategy backtesting, transaction-cost adjustment, drawdown analysis, holding-period comparison and buy-and-hold benchmarking.

A crypto portfolio risk dashboard analysing a multi-asset portfolio of BTC, ETH, SOL, BNB, XRP, ADA and LINK.

The dashboard measures portfolio returns, volatility, drawdowns, correlations, stress scenarios and asset-level risk contribution.

Analysis Includes: Portfolio performance analysis, volatility and drawdown measurement, correlation analysis, stress testing and risk-contribution breakdown.

A macro and cross-asset dashboard analysing how BTC, ETH and SOL behave across wider market regimes.

The project compares crypto returns against Nasdaq, S&P 500, VIX, the US Dollar Index, US 10-Year Treasury Yield and gold.

Analysis Includes: Cross-asset correlation analysis, rolling market relationships, risk-on/risk-off regime comparison and higher-beta crypto behaviour across macro conditions.

The projects are designed as a connected research sequence:

Crypto Sell-Off Research
        ↓
Rules-Based Backtesting
        ↓
Portfolio Risk Analysis
        ↓
Macro And Cross-Asset Regime Analysis

Together, they show a progression from analysing individual crypto market behaviour to understanding strategy performance, portfolio risk and wider macro conditions.

The overall objective is to move beyond isolated market observations and build a structured research portfolio that connects market hypotheses, Python analysis, risk management and commercial interpretation.

• Python
• pandas
• NumPy
• yfinance
• matplotlib
• Google Colab
• GitHub

• Financial markets
• Crypto and digital assets
• Trading strategy research
• Portfolio risk
• Macro regimes
• Market structure
• Cross-asset analysis
• Data-driven investment research

These projects are for educational and research purposes only. They do not constitute financial advice, investment recommendations or live trading systems. Historical relationships may not persist in the future.