AI-Powered Algorithmic Trading

Explore data management for ai-powered trading by contrasting fundamental and technical data, and learn to store open, high, low, close, and volume data in pandas data frames using Yahoo Finance.

Learn data cleaning techniques to remove duplicates, handle missing and inconsistent values, standardize formats, and validate data integrity using Python and pandas in a Jupyter notebook.

Discover data preprocessing for AI-powered trading, including cleaning, integrating, transforming, and reducing data to improve model reliability. Learn scaling, normalization, standardization, and discretization with minmax scaler and standard scaler.

Explore data visualization with Python and matplotlib to transform data into intuitive charts and plots, including 2D and 3D visuals, with hands-on Jupyter Notebook practice.

Explore the relative strength index (RSI), a momentum indicator that flags overbought and oversold conditions. Learn how threshold crossings at 70, 50, and 30 guide buy/sell decisions and divergences.

Learn how the directional movement index (DMI) uses ADX with plus DI and minus DI to gauge trend strength and generate bullish or bearish buy and sell signals.

Master Bollinger bands as a volatility indicator with upper and lower bands and a middle moving average. See how contraction and expansion signal sideways and trending markets, plus breakouts.

Learn to build trading strategies with moving averages, including simple and exponential moving averages, across long-term, positional, and intraday time frames, and generate buy-sell signals via crossovers.

Explore mean reversion strategies that trigger buy and sell signals when prices diverge from the mean, using moving averages, z-scores, Bollinger bands, and RSI across assets, pairs, sectors, and currencies.

Study volume based trading with VWAP and the 50-day moving average to generate buy and sell signals. Analyze patterns to confirm trends and implement signals in a notebook.

Learn to backtest trading strategies against historical data, evaluate performance with a moving average crossover using backtrader, QuantConnect, and Zipline, and analyze results with Cerebro and related libraries.

Assess strategy performance using returns, win rate, maximum drawdown, and average win loss, visualized with equity curves, drawdown plots, rolling matrices, via a moving average crossover in a Jupyter notebook.

Analyze risk adjusted returns with Sharpe, Sortino, Calmar ratios and annual volatility to measure returns per unit of risk, and visualize maximum drawdown, equity curves, drawdown charts, and cumulative returns.

Engage in feature engineering to transform raw financial data into meaningful features, address survivorship bias and non-stationarity, create lagged and rolling features, and apply PCA for dimensionality reduction.

Learn supervised learning with labeled data to map inputs to outputs for classification and regression; explore k nearest neighbor steps and predicting with open minus close and high minus low.

Explore unsupervised learning, clustering, and dimensionality reduction with k-means, including initialization, assignment, update, convergence, and applications such as market segmentation, pair trading, trend analysis, and anomaly detection in stock data.

Explore deep learning with neural networks, from perceptron to convolutional and recurrent models, learning activation functions, weights, and backpropagation for predicting stock returns using past data.

Demonstrates backpropagation in neural networks for AI trading using TensorFlow, with data preparation, feature engineering, normalization, and LSTM predictions to minimize mean squared error and generate trading signals.

Understand market, limit, stop, stop-limit, and trailing orders, with real-world examples showing how price control, fast execution, and risk management shape trading decisions.

Explore slippage and transaction costs in ai-powered algorithmic trading, and how liquidity, volatility, and order sizing affect profitability. Learn methods to minimize costs by optimizing timing, order type, and size.

Move to live trading with an event-driven approach, using real-time data and REST APIs to place orders, highlighting Zerodha Kite API, Alpaca API, and Interactive Broker API.

Connect to broker APIs to fetch real-time market data, place orders, and manage a live trading portfolio while learning key steps like API keys, authentication, and libraries.

Learn to set up Zerodha Kite Connect for live trading, authenticate with API key, request token, and access token, and place market orders via rest and WebSocket APIs in Python.

Learn to trade in real markets with the Alpaca REST API and commission-free trading, including account setup, KYC, API keys, and placing orders from a Jupyter notebook.