Advanced Algorithmic Trading and Portfolio Management | EveryEng

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Self-paced Advanced

Advanced Algorithmic Trading and Portfolio Management

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FREE

1425 min

Anytime

English

1335 views

Team EveryEngMechanical Engineering

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Why enroll

Participants join this course to understand how algorithmic trading systems analyze market data and automatically execute trades. It helps learners develop skills in quantitative analysis, financial modeling, and data-driven investment strategies. The course also teaches portfolio management techniques to balance risk and maximize returns. Many participants enroll to explore career opportunities in fintech, quantitative finance, and investment management.

Your instructor

Team EveryEng

Engineer

Mechanical Engineering

22+ yrs experience·India

Is this course for you?

You should take this if

You work in Aerospace or Automotive
You're a Data Science & Analysis professional
You have 3+ years of hands-on experience in this field
You prefer self-paced learning you can revisit

You should skip if

You're new to this field with no prior experience
You need a different specialisation outside Data Science & Analysis
You need live interaction with an instructor

Course details

Advanced Algorithmic Trading and Portfolio Management is a comprehensive course designed to help learners understand how technology and data are used to make smart financial investment decisions. The course introduces the fundamentals of algorithmic trading, where computer programs automatically execute trades based on predefined rules and market conditions. Participants will learn how financial markets work and how algorithms can analyze large amounts of market data quickly and efficiently. The course also covers portfolio management strategies used to balance risk and return while investing in different assets. Learners will explore concepts such as quantitative analysis, trading strategies, backtesting, and risk management. Practical examples help students understand how trading algorithms are designed and evaluated. The course also explains how diversification and asset allocation help in building strong investment portfolios. By the end of the course, participants will gain insights into modern trading technologies and data-driven investment methods. This course is ideal for students, finance enthusiasts, and professionals who want to explore the intersection of finance, mathematics, and technology. It provides a strong foundation for those interested in careers in fintech, quantitative finance, and investment management.

Course suitable for

Key topics covered

Develop advanced algorithmic trading strategies using Python and popular libraries (Pandas, NumPy, scikit-learn)
Implement machine learning and AI techniques for trading decision-making
Optimize portfolio performance using risk management and diversification methods
Analyze market data and identify profitable trading opportunities
Backtest and evaluate trading strategies

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

28 lectures23 hr 45 min

Introduction to R Programming and Installation
60 min
Data Handling and Data Cleaning With R
48 min
Visulaizing the Weekly Gasoline Price Using Barplot
37 min
Basic of Return Risk Framework
43 min
Investment Performance and Return Distribution
50 min
Portfolio Construction With Two Securities: Expected Returns
40 min
Portfolio Construction Recap I
46 min
Efficient Frontier Scenarios: Multi Security Case: II
48 min
Assumptions With CAPM
30 min
Single Index Models and Correlation Structure
55 min
Arbitrage Pricing Theory
65 min
Portfolio Mangement Strategies
37 min
Portfolio Performance Evaluation
30 min
Portfolio Performance Evaluation: Timing
31 min
Mean Variance Framework Recap
75 min
Feasible Portfolio
72 min
Dow Theory
46 min
Introduction to Moving Averages
40 min
Introduction to Momentum Oscillators
65 min
Trading Indicators
58 min
Bollinger Bands Analysis
55 min
Introduction to Panel Methods
46 min
Case Study: Prediction of Broad Marketwide Returns
61 min
Short Selling in The fullest sense - Minimum Variance Frontier
61 min
Fallings of CAPM
52 min
Non Marketable Assets
57 min
Volatility Models
61 min
Value at Risk
56 min

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Questions and Answers

Q: You're live with a simple VWAP execution algo and the market suddenly widens. You're searching "VWAP algo slippage increases when bid ask spread widens". One control variable changes: average spread doubles over 30 seconds. What happens downstream and what's the least unsafe response?

A: The number that matters is participation rate versus instantaneous spread. Doubling spread feeds straight into market impact if you keep the same aggression. VWAP doesn't cancel spread cost; it averages price, not microstructure. A controlled reduction in participation rate limits further damage while keeping the order alive.

Q: A mean-reversion strategy shows flat PnL intraday but sharp losses overnight. You're googling "intraday mean reversion loses money overnight carry". What failure mode best explains all symptoms?

A: The boundary is holding period. Intraday edges are often on the order of a few basis points; overnight gaps are an order of magnitude larger. Flat intraday PnL rules out fees as the driver, and a regime shift wouldn't wait for the close.

Q: Your PM asks for a sanity check and you're searching "back of envelope Sharpe ratio estimation from hit rate and payoff". A strategy wins 55% of trades, average win is 1.2 units, average loss is 1 unit, trades are independent. What's the defensible order-of-magnitude Sharpe per trade?

A: Start with expectancy: 0.55×1.2 − 0.45×1.0 ≈ 0.21. Variance is dominated by outcomes around ±1, so σ is near 1. Expectancy divided by σ puts you near a tenth, not anywhere close to unity.

Q: Two market data feeds disagree by 2 ticks for 200 ms during a spike. You're typing "market data feed discrepancy which price to trust trading system". What happens if you naively trust the faster feed, and what's the correct response?

A: The hard threshold is the reconciliation window. Hundreds of milliseconds is long enough for bad prints or crossed markets. Acting on the first feed without validation exposes you to phantom liquidity.