Quantitative Finance with Python
Master financial market analysis with Python! Learn data science, machine learning & technical analysis techniques. Predict trends & make smarter investment decisions.
Course Content
Quantitative Finance with Python is an all-levels Udemy course taught by Raj Chhabria. It runs 4h 4m, is taught in English, and includes a certificate of completion. Learners rate it 3.7 out of 5 from 299 reviews, with 38,828 students enrolled. It is a paid course, first published November 2022.
Looking to launch a career in quantitative finance? Whether you're a seasoned finance professional or a data science expert seeking to transition, this course provides the essential knowledge to become a full-fledged quantitative analyst (quant).
The role of a quant in investment banks, hedge funds, and other financial companies is highly sought after. If you possess strong quantitative skills, this course bridges the gap between your existing expertise and the specialized knowledge required for success.
This comprehensive course is your gateway to mastering quantitative finance and financial engineering, building upon your technical foundation. Upon completion, you'll be equipped to excel in this dynamic field.
Here's a detailed look at the topics covered:
- Market Analysis: Deep dive into stock markets, commodity markets, and forex trading.
- Emerging Assets: Understand the intricacies of cryptocurrency markets.
- Technical Analysis: Master technical analysis techniques, including interpreting candlestick charts and utilizing OHLC data for stocks.
- Financial Derivatives: Explore financial derivatives like futures and options, and understand time value of money.
- Portfolio Management: Learn Modern Portfolio Theory and the Efficient Market Hypothesis.
- Predictive Modeling: Develop stock price prediction models using machine learning, including LSTM neural networks (deep learning) and gold price prediction techniques.
- Trading Strategies: Learn to develop and backtest trading strategies in Python.
- Technical Indicators: Utilize technical indicators such as moving averages and RSI.
- Algorithmic Trading: Explore algorithmic trading methodologies.
- Advanced Strategies: Discover advanced trading methodologies like arbitrage and pair trading.
- Market Theory: Understand Random Walk Theory and the Capital Asset Pricing Model (CAPM).
- Performance Metrics: Calculate and interpret the Sharpe Ratio.
- Python for Finance: Gain proficiency in using Python for finance applications.
- Correlation Analysis: Analyze the correlation between different stocks and asset classes.
- Risk Management: Implement optimal position sizing using the Kelly Criterion, and master diversification and risk management strategies.
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