Multi-Strategy Quant Systems - Algorithmic Trading In Python
Last updated 2/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 4.12 GB | Duration: 6h 58m
Program in Python an End-to-End trading system from data management to order execution and risk management.
What you'll learn
Implement Multi-Strategy Quantitative Portfolios with Python
Build Modular Trading System such that components can be altered to trading preferences
Implement code for data pipelines, alpha research, alpha strats, portfolio allocation and order submission
Integrate with multiple brokerages with a single code base
Handle multiple alpha signals to create unified signals, and learn industry standard risk management techniques
Requirements
Basic Financial/Quantitative Literacy is expected.
Programming methodology (not necessarily in Python) is expected for coding in Python.
Ability to self-learn and comprehend documentation and code is expected.
Knowledge of tech stacks are NOT expected, basic software will be used.
Description
Multi-Strategy Quant Systems in Python from Scratch - A First Course in Algorithmic Trading by HangukQuant.As this is not a Python tutorial, we get right down to business and adopt a no-nonsense coding approach. It is advised that you slow down the pace of the course to your own needs. Even though only 7 hours of lectures have been distilled, the material within is fairly heavy. It is a walkthrough without the introductory explanations - since there are no explanations within the lecture walkthroughs, all students are expected to be hands-on and actively participate by asking questions; which we will compile in the Questions Section. Code is downloadable in Checkpoint Lectures.Watch a (sped-up) live recording of a quant implementing a shoestring trading system for non-HFT in Python, from data pipelines to order management. Build robust and flexible modular systems + take home a professional setup to adapt and improve.HangukQuant is the author of the 2nd highest rated quant trading blogs on Substack - Mathematics, Finance and Their Babies, with many years of experience in both quant research teams in both the industry and in academia.Implement a Multi Strategy Quantitative System in Python from Scratch, while learning how to build robust frameworks for implementation of alpha signals and capital allocation. Learn industry standards in risk management, such as volatility targeting schemes and technical diversification. Code along an algorithmic trading system with modular approaches to integrate with multiple brokerages and switch between them in a matter of seconds, all with the same code.Build diagnostic tools to analyse your trading system.Integrate with multiple brokerages in a single code base.Learn industry standards in risk management, such as volatility targeting schemes and technical diversification.Take back with you the final product - Your Very Own, Robust Quant Systems to hone and develop!Build quantitative strategies implementing factor premia and be given guidance for serious students of the market.This course is not a beginner course; financial literacy, and programming methodology is expected. Students are expected to be able to understand code without being prompted, or at least learn how to comprehend medium-sized code systems of thousands of lines of code. The course was first recorded and then sped up and voiced over chosen sections to meet Udemy course requirements. You may also choose to code along without the audio as the textual explanations should be primarily useful. As the course is an Advanced-Intermediate level, do note that you may face some difficulties along the way. It is expected that you would need to rewatch some of the lectures, and please do ask away in the Questions Section.Those without programming experience are encouraged to first undergo courses in Python / Programming Methodology / Data Science for Finance courses.Note that this course is not part of the Udemy Deals program, and no Udemy promotions are available. We believe that the material within is highly valuable and the cost price is cost de minimis in your quant journey.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Obtaining S&P Tickers
Lecture 3 Obtaining the S&P Dataset
Lecture 4 Filling Missing Data and Supplementing Statistics
Lecture 5 Utility Functions and Pickling Data
Lecture 6 Developing Your Own Private Code Library
Section 2: Designing Flexible and Robust Quantitative Systems
Lecture 7 System Architecture and Version Tracking
Lecture 8 Implementing the Strategy API
Lecture 9 A Calculator For Indicators
Lecture 10 CHECKPOINT 1; Connecting The Strategy To Our Driver
Section 3: Strategy Implementation; Getting Alpha Signals
Lecture 11 LBMOM Strategy, Vol Targeting and Voting Systems
Lecture 12 CHECKPOINT 2; Calculating PnL, Strat Vol and Debugging
Section 4: Brokerage Implementation; Oanda
Lecture 13 Integrating with Oanda. Writing Wrapper Classes for the Oanda REST API
Lecture 14 CHECKPOINT 3; Implementing the Wrapper Functions and Getting Oanda OHLCV
Lecture 15 Writing Oanda Config Files
Lecture 16 CHECKPOINT 4; Implementing the Oanda Database Pipeline
Section 5: Working with FX and Non-USD Contracts
Lecture 17 Adding FX Information to the Data
Lecture 18 Writing a Master Config File
Lecture 19 CHECKPOINT 5; Implementing an FX Calculator
Section 6: Adding Multi Strategies
Lecture 20 Integration, Refactoring and Debugging
Lecture 21 CHECKPOINT 6; Implementing the LSMOM Strategy
Lecture 22 Implementing Diagnostic Tools
Lecture 23 CHECKPOINT 7; Debugging
Lecture 24 CHECKPOINT 8; Implementing the SKPRM Strategy
Section 7: Portfolio Allocations with Strategy Signals
Lecture 25 Combining Alpha Signals For Portfolio Analysis
Lecture 26 CHECKPOINT 9; Controlling Nominal Exposure in a Vol Target Framework
Section 8: Integrating Order Logic with a Brokerage - Oanda
Lecture 27 Writing a Service Class API for Oanda
Lecture 28 Implementing the Service Class API and Creating an Order Config
Lecture 29 Getting Live Optimal Allocations and Integration with the Brokerage
Lecture 30 CHECKPOINT 10; Implementing Internal Order Logic and Utilities
Lecture 31 Completing Order Logic and Considerations for Other Brokerages
Section 9: Dual Brokerages - Integrating with Darwinex Platform
Lecture 32 Setting Up With Darwinex - MQL and Python Scripts
Lecture 33 Obtaining Data Through a TCP Socket Connection with an Open MT4 Terminal
Lecture 34 CHECKPOINT 11; Writing Client-Server Scripts In Python and MQL
Lecture 35 Scraping the Darwinex Website for Contracts
Lecture 36 CHECKPOINT 12; Writing the Darwinex Wrapper API and Configs
Lecture 37 Implementing the Darwinex Service Client
Lecture 38 Implementing the Darwinex Trade Client
Section 10: Multiple Brokerages and Contract Switching
Lecture 39 CHECKPOINT 13; Integrating Another Brokerage (Darwinex) with a Master Switch
Lecture 40 Getting The Darwinex Dataset
Lecture 41 CHECKPOINT 14; Integrating the Darwinex Data Pipeline
Lecture 42 Integrating Darwinex Strategy Configs
Section 11: Trade Order Submission and Obtaining Portfolio Allocations
Lecture 43 Market Order FOK - Oanda
Lecture 44 Data Cleaning and Rerunning Tests
Lecture 45 CHECKPOINT 15; End-to-End Testing of Order Logic and Concluding Oanda
Lecture 46 Writing the Trader Order Logic in Darwinex Trade Client
Lecture 47 Min Heap Data Structure for Open Trades
Lecture 48 Implementing the Darwinex Trade Order Logic - Smallest In First Out
Lecture 49 End to End Testing of Darwinex Trade Order Logic
Lecture 50 CHECKPOINT FINALE; Conclusion
This course is for traders and programmers looking to implement multi-strategy quantitative portfolios for research or trading purposes in non-HFT spaces.,For algorithmic traders who wish to have a more professional setup to integrate their strategies into more robust frameworks for their return harvesting.
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