Quantitative Finance with Python

Explore how stock markets enable trading of equity shares and price discovery, and how news events—like leadership changes or industry outlook—drive bullish moves and price movements.

Explore the major financial markets, including the foreign exchange, commodities, bonds, and cryptocurrency markets, with currency pairs, spot and futures, and primary versus secondary markets.

Explore three core market analyses—fundamental, technical, and quantitative—and learn how macroeconomic and company factors, price trends, and data models drive stock valuation and forecasting.

Explore the time value of money, including present value and future value, how compounding and interest grow investments, and how inflation and opportunity cost impact value.

Describe the capital asset pricing model (CAPM) and how it links systematic risk to expected return using beta, the risk-free rate, and the equity risk premium (market risk premium).

Explain modern portfolio theory as a method for risk-averse investors to build diversified portfolios that maximize return for risk, using variance, correlation, and ETFs or bonds to manage downside risk.

Explore the efficient market hypothesis, which says prices reflect information and beating the market is unlikely. Learn about weak, semi-strong, and strong forms and why passive index investing is favored.

Explore random walk theory, its claim that stock price changes are independent and identically distributed and unpredictable, and its implications for market efficiency and passive investing.

Measure how two securities move together with the correlation coefficient (-1 to 1), indicating positive, negative, or zero relationships for diversification and benchmarking (e.g., the S&P 500).

Learn to build a stock correlation matrix in Python using numpy, pandas, and pandas-datareader from Yahoo data, then visualize with a seaborn heatmap to inform portfolio diversification and hedging.

Explore arbitrage strategies that exploit price discrepancies across markets to earn risk-free profits, including stock, currency, and cross-asset opportunities via simple and triangular arbitrage.

Explore pair trading, a market-neutral strategy that longs the underperformer and shorts the outperformer to profit from convergence back to historical correlation between two highly correlated stocks.

Explore algorithmic trading with Python, where automated programs execute trades based on timing and price models. Moving average crossovers and VWAP-based rules drive precise, low-emotion execution.

Explore the Kelley criterion, a money-management formula using win probability and win‑loss ratio to determine optimal position sizes for diversification and cap exposure at 20–25% of capital.

Explore the Sharpe ratio, a risk-adjusted performance measure that compares portfolio returns to a benchmark using the risk-free rate and the standard deviation of returns.

Learn to retrieve stock ohlc data and key metrics with Yahoo Finance in Python, accessing the info dictionary and historical prices for Amazon and Tesla.

Learn how to plot Python candlestick charts, also known as Japanese charts, using mplfinance with open, high, low, close, and time data to visualize bullish and bearish days.

Learn to compute the simple moving average from stock closing prices in Python using pandas rolling, and visualize the 30-day SMA alongside price data.

Learn to compute the exponential moving average in Python by weighting recent data points more heavily, using pandas ewm with span, and compare it to the simple moving average.

Explore how derivatives derive value from underlying assets and trade on an exchange or over the counter. Learn how futures, forwards, swaps, options hedge risk, enable speculation, provide leverage.

Explore futures contracts as standardized financial derivatives that obligate buyers to purchase and sellers to deliver an asset at a predetermined future price and date, enabling hedging and speculation.

Learn how options give the right to buy or sell underlying assets, with call and put types, expiration, strike price, American vs European exercise, hedging, and Greeks.

Demonstrates the Black-Scholes model for pricing european options using five inputs: stock price, strike, time to expiry, risk-free rate, and volatility. Examines its assumptions and limitations in real markets.

Learn the core principles of technical analysis and price action using TradingView, analyzing line and candlestick charts with open, high, low, close, and volume data.

Discover how support and resistance define price points in technical analysis, driven by supply and demand, and apply trend lines and moving averages to identify potential pauses or reversals.

Explore key chart patterns used in technical analysis, including pennants, flags, wedges, triangles, cup-and-handle, head and shoulders, and double tops and bottoms, with breakout and volume signals.

Learn moving averages in quantitative finance with Python, including simple moving average and exponential moving average, to smooth price data and compare equal weighting with weighting recent prices.

Explore the relative strength index (RSI), a momentum oscillator that measures price changes to indicate overbought or oversold conditions, trend reversals, and its 14-period calculation using average gains and losses.

Learn Dow Theory in quantitative finance with Python, covering primary, secondary, and minor trends; index conformity, volume signals, and the three primary phases: accumulation, markup, and distribution.

Analyze Amazon stock with pandas, MATLAB and Python to build a moving average crossover strategy, assess volatility, and backtest signals from 2015 to 2020 with buy/sell plots and equity growth.

Predict the gold price with a random forest regression model using features like S&P 500, US oil, silver, and euro USD, after data cleaning and exploration, achieving an R^2 score.

Predict Tesla stock movements using machine learning, engineer date-derived features, and compare logistic regression, SVM, and a classifier with ROC AUC and overfitting considerations.

Learn to predict Apple's closing price with linear regression, using 3-day and 9-day moving averages as features, and evaluate with a train-test split.

Predict Microsoft stock closing prices with an lstm neural network, using a dataset of open, high, low, close, and volume, scaled for training.