Algorithmic Trading & Market Forecasting: AI Strategies for Financial Intelligence
artificial-intelligence-ai.
Course Modules:
Module 1: Introduction to Algorithmic Trading
What is algorithmic trading?
Types of trading strategies (momentum, mean-reversion, arbitrage)
Role of AI and machine learning in market prediction
Module 2: Financial Time Series Analysis
Time series components: trend, seasonality, noise
Stationarity and transformations (log returns, differencing)
Autocorrelation and feature extraction from price data
Module 3: Technical Indicators and Signal Generation
Moving averages, RSI, MACD, Bollinger Bands
Rule-based strategy development
Signal labeling for supervised learning
Module 4: Machine Learning & Deep Learning for Forecasting
Regression and classification models for financial data
Random Forest, XGBoost, and LSTM models for sequential prediction
Feature selection, tuning, and ensemble modeling
Module 5: Strategy Evaluation and Backtesting
Risk-adjusted performance metrics (Sharpe ratio, drawdown, CAGR)
Train/test split for time series, cross-validation techniques
Backtesting frameworks (Backtrader, Zipline)
Module 6: Capstone Project – Build and Test a Trading Strategy
Select a financial asset (stocks, crypto, forex, indices)
Create a trading strategy using AI/ML models
Submit backtest results, code, and performance analysis report
Tools & Technologies Used:
Python
Pandas, NumPy, Matplotlib, Seaborn
Scikit-learn, XGBoost, TensorFlow/Keras (for LSTM)
Backtrader / Zipline (for backtesting and simulation)
Target Audience:
Traders and investors transitioning to AI
Financial data scientists and quants
Developers creating fintech or trading automation tools
Students and professionals in finance, economics, and AI
Global Learning Benefits:
Build and validate your own AI-driven trading strategies
Forecast price movements using statistical and deep learning models
Automate trading with real-time data pipelines
Apply AI to generate alpha and manage financial risk
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