📌 I’m currently working on
Building a strong foundation for quantitative finance and data science, with hands-on projects around financial data analysis, time-series modeling, and ML pipelines using Python.

🤝 I’m looking to collaborate on

Stock market prediction & forecasting projects

Time-series analysis (ARIMA, SARIMA, LSTM, Prophet)

Quant trading strategies (alpha signals, backtesting, factor models)

Portfolio optimization & risk analysis

ML/DL models applied to financial data

Market sentiment analysis using news & social media data

🤲 I’m looking for help with

Improving model robustness and avoiding overfitting in financial data

Understanding real-world quant workflows used in hedge funds / prop firms

Advanced backtesting frameworks and performance metrics

Translating academic finance concepts into production-ready code

🌱 I’m currently learning
Statistics using Python for financial analysis, including:

Probability distributions & statistical inference

Hypothesis testing and confidence intervals

Regression analysis (OLS, multivariate, regularization)

Time-series concepts (stationarity, autocorrelation, volatility modeling)

Risk metrics like VaR, CVaR, Sharpe & Sortino ratios

Libraries: NumPy, Pandas, SciPy, Statsmodels, scikit-learn

💬 Ask me about

Quantitative finance & data science career paths

Python for financial analysis and ML

Stock markets, trading strategies, and risk management

Turning math & statistics into real-world financial models

Learning resources for quants and data scientists

⚡ Fun fact
I enjoy breaking down complex financial models into simple logic — if I can’t explain it clearly, I assume I don’t understand it well enough 😄