This course transforms classical technical analysis into a fully systematic, data-driven discipline. Learners master how to convert indicators into quantitative factors, build statistically testable trading signals, and validate strategies using robust backtesting methods grounded in real market constraints. With a strong focus on empirical testing, walk-forward validation, and risk-aware execution, the program equips traders to build reliable, reproducible, and bias-resistant trading systems. Designed for traders, quants, analysts, and developers, this course delivers the statistical, technical, and ethical foundations required to operate confidently in modern quantitative financial markets.
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Skills & learning objectives
There are 10 modules in this class
- Evolution from Classical to Quantitative Technical AnalysisUnlock this class to watch2m 15s
- Quantitative vs. Traditional Technical AnalysisUnlock this class to watch1m 34s
- Systematic vs. Discretionary TradingUnlock this class to watch1m 47s
- Data Sources: Market, Alternative, and High-Frequency DataUnlock this class to watch3m 16s
- Role of Statistical Thinking and Probability in QTAUnlock this class to watch3m 6s
- Tools for QTA: Python, R, QuantConnect, and Data Quality ControlUnlock this class to watch2m 48s
- Descriptive Statistics for Price and Volume DataUnlock this class to watch1m 52s
- Probability Distributions in Market ReturnsUnlock this class to watch1m 45s
- Correlation, Covariance, and Dependencies in Market DataUnlock this class to watch1m 45s
- Time Series Analysis: Stationarity, Trends, and SeasonalityUnlock this class to watch3m 59s
- Autocorrelation and Lag AnalysisUnlock this class to watch1m 42s
- Hypothesis Testing for Pattern and Strategy ReliabilityUnlock this class to watch1m 58s
- Translating Classical Indicators into Quantitative FactorsUnlock this class to watch2m 4s
- Trend-Following and Mean-Reversion Factor ModelsUnlock this class to watch1m 51s
- Volatility, Liquidity, and Volume-Based FactorsUnlock this class to watch1m 42s
- Composite Factor Construction and Weighting SchemesUnlock this class to watch1m 47s
- Volatility Modeling: GARCH, EWMA, and ATR-Based MetricsUnlock this class to watch1m 33s
- Invalidations and Exceptions by Asset Class and TimeframeUnlock this class to watch1m 53s
- Factor Validation: Robustness, Stability, and Statistical SignificanceUnlock this class to watch1m 28s
- Designing Entry and Exit Rules Using Quantitative SignalsUnlock this class to watch1m 28s
- Signal Filters: Multi-Factor and Multi-Timeframe IntegrationUnlock this class to watch1m 21s
- Ranking and Scoring Techniques for Trade SelectionUnlock this class to watch1m 18s
- Portfolio-Level Signal Aggregation and ConfluenceUnlock this class to watch1m 59s
- Handling Conflicting Signals: Decision HierarchiesUnlock this class to watch1m 28s
- Case Study: Factor-Based Strategies in Stocks, Forex, and CryptoUnlock this class to watch2m 55s
- Principles of Robust Backtesting and Walk-Forward TestingUnlock this class to watch1m 33s
- Performance Metrics: Sharpe, Sortino, Drawdown, and Hit RateUnlock this class to watch1m 12s
- Statistical Significance of Backtested ResultsUnlock this class to watch1m 20s
- Monte Carlo Simulations and Bootstrapping for RobustnessUnlock this class to watch1m 55s
- Avoiding Pitfalls: Look-Ahead Bias, Survivorship Bias, and Data SnoopingUnlock this class to watch1m 20s
- Out-of-Sample Validation and Strategy Stress TestingUnlock this class to watch1m 34s
- Position Sizing: Fixed, Volatility-Adjusted, and Kelly CriterionUnlock this class to watch1m 58s
- Quantitative Risk Metrics: VaR, CVaR, and Maximum DrawdownUnlock this class to watch1m 28s
- Portfolio Optimization: Mean-Variance and Black–Litterman ModelsUnlock this class to watch1m 42s
- Dynamic vs. Static Risk Management RulesUnlock this class to watch1m 27s
- Stress Testing and Scenario Analysis for Quantitative StrategiesUnlock this class to watch1m 24s
- Behavioral Risk and Cognitive Biases in QTAUnlock this class to watch1m 35s
- platforms for QTA: Python, QuantConnect, Backtrader, TradingViewUnlock this class to watch1m 36s
- Coding Systematic Strategies: Best Practices and Feature EngineeringUnlock this class to watch1m 41s
- Data Handling: Cleaning, Aggregation, and Real-Time ProcessingUnlock this class to watch1m 27s
- Automation: Execution, Alerts, and API IntegrationUnlock this class to watch2m 18s
- Monitoring Live Strategies and Real-Time Risk MetricsUnlock this class to watch1m 45s
- Case Study: Deploying a Quantitative Trading SystemUnlock this class to watch2m 7s
- Machine Learning in QTA: Supervised and Unsupervised ModelsUnlock this class to watch2m 48s
- Statistical Arbitrage and Pair Trading StrategiesUnlock this class to watch2m 2s
- High-Frequency vs. Low-Frequency Quantitative StrategieUnlock this class to watch2m 45s
- Alternative Data in QTA: Sentiment, News, and Social MediaUnlock this class to watch2m 2s
- Adaptive Models and Regime Switching TechniquesUnlock this class to watch1m 43s
- Case Study: Integrating QTA with Classical TA and Alternative DataUnlock this class to watch2m 20s
- Ethical Use of Quantitative and Automated Trading ToolsUnlock this class to watch1m
- Regulatory Considerations and Market Conduct in QTAUnlock this class to watch1m 8s
- Avoiding Overfitting, Curve Fitting, and Misleading ResultsUnlock this class to watch1m 4s
- Documentation and Audit Trails for Quantitative StrategiesUnlock this class to watch56s
- Continuous Learning: Journals, Competitions, and Research CommunitiesUnlock this class to watch45s
- Capstone Exercise: Build, Test, and Present a Quantitative StrategyUnlock this class to watch47s
- ClosingUnlock this class to watch53s
This class is part of the following certifications
ClassFAQs
The course aims to teach learners how to apply mathematical, statistical, and data-driven methods to enhance traditional technical analysis, build rule-based strategies, and validate trading models using quantitative rigor.
The course is designed for traders, analysts, and investors seeking to transition from discretionary technical analysis to systematic, evidence-based strategy design.
The module covers statistical thinking, randomness, non-stationarity, market behavior interpretation, and why quantitative validation is essential for improving technical analysis accuracy.
The course teaches backtesting methods, walk-forward validation, robustness checks, performance metrics like Sharpe and Sortino ratios, and techniques to avoid overfitting and data-snooping bias.
Learners will develop quantitative risk-management abilities such as volatility-based sizing, expectancy modeling, regime detection, noise reduction, and strategy adaptation across market conditions.
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