ALGORITHMIC TRADING: An Introduction in the Fascinating World of Financial Data Science, where Technology Meets Finance
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About the Course
The intersection of technology and finance has completely revolutionized global markets. Algorithmic trading replaces emotional decision-making and manual chart reading with disciplined, data-driven systems. By combining quantitative finance, statistics, and programming, traders can systematically identify market edges and execute orders at scale.
This course introduces learners to the core foundations of financial data science and automated trading systems. Participants will explore how quantitative models are built, backtested against historical data, and optimized while managing real-world market risks like slippage, overfitting, and volatility.
Course Objective
- To understand the architecture and core mechanics of automated, rules-based trading systems
- To master the fundamentals of backtesting, performance evaluation metrics, and the dangers of strategy overfitting
- To explore how data science, alternative data, and risk management principles are applied to build robust quantitative strategies
Course Outline
- Introduction to Algorithmic Trading- The evolution from manual floor execution to high-speed quantitative systems
- Core components of an automated trading pipeline: data, strategy, and execution
- Financial Data Science and Alternative Data- Sourcing, cleaning, and processing high-dimensional market price and volume data
- Leveraging alternative datasets like news sentiment and web metrics for alpha generation
- Strategy Development and Backtesting- Designing systematic trading logic such as mean reversion and momentum
- Testing strategies on historical data and avoiding the trap of curve-fitting/overfitting
- Execution, Latency, and Market Mechanics- Understanding order types, liquidity, and execution slippage
- The role of high-frequency trading (HFT) and infrastructure in modern markets
- Risk Management and Performance Metrics- Evaluating risk-adjusted returns using the Sharpe ratio and maximum drawdown
- Embedding automated stop-losses and position-sizing rules into code
Target Participants
This course is designed for:
- Finance professionals and analysts looking to transition into quantitative trading and data science
- Software engineers and developers interested in financial markets and trading automation
- Data scientists and students eager to apply statistical modeling to real-world financial data
- Retail traders wanting to replace emotional decision-making with disciplined, rules-based systems
Certificate Requirements
- Completion of recorded webinar + quiz
- Pass the 10-item multiple choice with 80% mark
- Comment your feedback on the main page of the course
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