Multi-Strategy Quant Systems in Python from Scratch – A First Course in Algorithmic Trading by HangukQuant

Curriculum Overview – 50 Lectures
Building a Full Algorithmic Trading System with Multiple Brokerages and Strategies

Introduction – Chapter 1
1: Acquisition of S&P 500 Tickers
2: Retrieval of the S&P Dataset
3: Completion of Missing Data and Additional Statistical Information
4: Data Pickling and Utility Functions
5: Creation of a Personal Code Library

Designing Adaptable and Reliable Quantitative Systems – Chapter 2
6: System Architecture and Version Control
7: Implementation of the Strategy API Interface
8: Development of an Indicator Calculator
9: Integration of the Strategy into Our Driver (CHECKPOINT 1)

Strategy Implementation for Alpha Signals – Chapter 3
10: LBMOM Strategy; Volatility Targeting and Voting Systems
11,12: Calculation of Profit and Loss, Strategy Volatility, and Debugging (CHECKPOINT 2)

Brokerage Implementation with Oanda – Chapter 4
13: Integration with Oanda; Writing Wrapper Classes for the Oanda REST API
14: Implementation of Wrapper Functions and Retrieval of Oanda OHLCV (CHECKPOINT 3)
15: Creation of Oanda Config Files
16: Implementation of the Oanda Database Pipeline (CHECKPOINT 4)

Working with FX and Non-USD Contracts – Chapter 5
17: Addition of FX Information to Data
18: Compilation of a Master Config File
19: Implementation of an FX Calculator (CHECKPOINT 5)

Incorporating Multiple Strategies – Chapter 6
20: Integration, Refactoring, and Debugging
21: Implementation of the LSMOM Strategy (CHECKPOINT 6)
22: Implementation of Diagnostic Tools
23: Debugging (CHECKPOINT 7)
24: Implementation of the SKPRM Strategy (CHECKPOINT 8)

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