Programming for Quants
Python and C++ are the languages of quant finance. You'll cover NumPy, Pandas, low-latency C++, and the algorithm patterns (binary search, DP, graphs) that show up in coding rounds.
Lessons
1. Intro
2. NumPy Fundamentals
Fast numerical computing
3. Pandas for Time Series
Financial data manipulation
4. SciPy for Optimization
Mathematical tools
5. Scikit-learn Basics
Machine learning for finance
6. Quiz: Python Libraries
Test your understanding
7. Vectorization Over Loops
Writing fast code
8. Common Interview Patterns
What you'll be asked
9. Memory Management
The foundation of C++ performance
10. Move Semantics
Avoiding unnecessary copies
11. Quiz: C++ Performance
Test your understanding
12. Templates and Compile-Time
Generic and fast code
13. STL Containers
Know their performance characteristics
14. Lock-Free Programming
Advanced concurrency
15. Cache and Latency
Hardware-aware programming
16. Quiz: Data Structures
Test your understanding
17. Arrays and Hashing
The most common patterns
18. Binary Search
Finding boundaries efficiently
19. Dynamic Programming
Breaking problems into subproblems
20. Trees and Graphs
Traversals and shortest paths
21. Quiz: Algorithm Patterns
Test your understanding
22. Numerical Problems
Math-heavy coding
23. Coding Interview Tips
How to perform well
24. Python Standard Library
25. Implementing Data Structures
26. Quiz: Testing
Test your understanding
27. Matrix Operations
28. Time Series Analysis
29. Backtesting Basics
30. SQL for Quants
31. Quiz: Backtesting
Test your understanding