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Prerequisites:

  • Basic skills with at least one programming language are desirable.

 

Training Program Description:

• Complete real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio.

• In this program, you’ll analyze real data and build financial models for trading. Whether you want to pursue a new job in finance, launch yourself on the path to a quant trading career, or master the latest AI applications in quantitative finance, this program offers you the opportunity to master valuable data and AI skills.

What will you learn ?
• market mechanics and how to generate signals with stocks. Your first project is to develop a momentum trading strategy.
• get to know the workflow that a quant follows for signal generation, and also learn to apply advanced quantitative methods in trading.
• portfolio optimization, and financial securities formed by stocks such as market indices, vanilla ETFs, and Smart Beta ETFs.
• alpha factors and risk factors, and construct a portfolio with advanced portfolio optimization techniques.
• fundamentals of text processing and use them to analyze corporate filings and generate sentiment-based trading signals.
• advanced techniques to select and combine the factors that you’ve generated from both alternative data and market data.
• refine trading signals by running a rigorous backtest. You will know how to keep track of your P&L while your algorithm buys and sells.

Projects

    • This program is comprised of many career-oriented projects. Each project you build will be an opportunity to demonstrate what you’ve learned in the lessons. Your completed projects will become part of a career portfolio that will demonstrate to potential employers that you have skills in data analysis and feature engineering, machine learning algorithms, and training and evaluating models.
    • One of our main goals at ETI is to help you create a job-ready portfolio of completed projects. Building a project is one of the best ways to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers or colleagues. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects
    • Building a project is one of the best ways both to test the skills you’ve acquired and to demonstrate your newfound abilities to future employers. Throughout this program, you’ll have the opportunity to prove your skills by building the following projects:

 

Project 1: Trading with Momentum
• Project 2: Breakout Strategy
• Project 3: Smart Beta and Portfolio Optimization
• Project 4: Multi-factor Model
• Project 5: Sentiment Analysis using NLP
• Project 6: Deep Neural Network with News Data
• Project 7: Backtesting
• Project 8: Combine Signals for Enhanced Alpha


Capstone project
s in many fields

  • Business
  • Trading

 

Program Duration: 150 hours

Program Language: English / Arabic

Location: EPSILON TRAINING INSTITUTE | Head Office

 

Participants will be granted a completion certificate from Epsilon Training Institute, USA if they attend a minimum of 80 percent of the direct contact hours of the Program and after fulfilling program requirements (passing both Final Exam and Project to obtain the Certificate)

 

COURSE CONTENTS

I- Python 3 Topics

  • Introduction
    • syntax
    • data types and operations
    • I/O
    • Operators and bitwise
    • Lists
    • Tuples
    • If statements
    • For – while loops
  • Object-Oriented Programming (OOP)
    • Special Functions
    • Strings
    • Classes
    • Inheritance
    • Regular expressions
    • Working with files
    • Python generators
    • Python Decorators
    • Exceptions
    • Regular expressions
    • Multithreading and multiprocessing Sockets and APIs
  • Introduction to Gui
    • Gui grid
    • Gui events
    • Gui styles
  • Intro to data science
    • Database with SQLite
    • Numpy and matrix operations
    • Pandas
    • Matplotlib
    • Building your own server
    • Data visualization
    • Git command line and GUI based
    • Web Scraping for data collecting

 

II- Machine Learning Topics

  • Linear algebra
  • Calculus
  • Statistics
  • Introduction to ML and Business cases
    • The difference between ML, Big data, Data analysis and Deep Learning
    • Linear Algebra and Statistics for ML
    • Data preprocessing
  • Data preprocessing
    • Importing libraries
    • Data acquisition
    • Data cleaning
    • Handling missing data
    • Categorical data
    • Data splitting
    • Feature scaling
  • Regression problem
    • Linear Regression
    • Multi-linear regression
    • Polynomial regression
    • K-nearest neighbour regression
    • Decision tree regression
    • Regression Evaluation Metrics
  • Classification problem
    • Logistic Regression
    • Naive Bayes
    • K-nearest neighbour classifier
    • Support vector machine (SVM)
    • Decision tree classifier
    • Ensemble learning
    • Classification Evaluation Metrics
  • Clustering Problems
    • Dimensionality reduction
    • K-means
    • DBSCAN
    • hierarchical clustering
    • Association Rules
  • Reinforcement learning
    • Upper confidence bond
    • Thompson sampling
  • Model Selection and evaluation
    • Loss functions
    • Gradient descent
    • Bias-variance tradeoff
    •  Cross-validation
    • Hyperparameter tuning
  • Result communication and report

 

III- Data Structures & Algorithms Topics

  • Introduction
    • How to Solve Problems
    • Big O Notation
  • Data Structures
    • Collection data structures (lists, arrays, linked lists, queues, stack)
    • Recursion
    • Trees
    • Maps and Hashing
  • Basic Algorithms
    • Binary Search
    • Sorting Algorithms
    • Divide & Conquer Algorithms
    • Maps and Hashing
    • Practice Problems: Randomized Binary Search, K-smallest elements using Heaps, Build Red-Black Tree, bubble sort, merge sort, quick sort, sorting strings, Linear-time median finding
  • Advanced Algorithms
    • Greedy Algorithms
    • Graph Algorithms
    • Dynamic Programming
    • Linear Programming
    • Practice Problems: Graph Traversals, Diijkstra’s Algorithm, Shortest Hops, A* Search, Longest Palindromic subsequence, web crawler

IV- Quantitative Analyst Topics

  • Basic Quantitative Trading
    • Introduction
    • Stock Prices
    • Market Mechanics
    • Data Processing
    • Stock Returns
    • Momentum Trading
  • Advanced Quantitative Trading
    • Quant Workflow
    • Outliers and Filtering Signals
    • Regression
    • Time Series Modeling
    • Volatility
    • Pairs Trading and Mean Reversion
  • ETFs, Indices, Stocks
    • Stocks, Indices and Funds
    • ETFs
    • Portfolio Risk and Return
    • Portfolio Optimization
  • Multi-factor Model
    • Factors Models of Returns
    • Risk Factor Models
    • Alpha Factors
    • Advanced Portfolio Optimization with Risk and Alpha Factors Models
  • Sentiment Analysis with Natural Language Processing
    • Intro to Natural Language Processing
    • Text Processing
    • Feature Extraction
    • Financial Statements
    • Basic NLP Analysis
  • Advanced Natural Language Processing with Deep Learning
    • Introduction to Neural Networks
    • Training Neural Networks
    • Deep Learning with PyTorch
    • Recurrent Neural Networks
    • Embeddings & Word2Vec
    • Sentiment Prediction RNN
  • Combining Multiple Signals
    • Overview
    • Decision Trees
    • Model Testing and Evaluation
    • Random Forests
    • Feature Engineering
    • Overlapping Labels
    • Feature Importance
  • Simulating Trades with Historical Data
    •  Overview
    • Intro to Backtesting
    • Optimization with Transaction Costs
    • Attribution

 

 

 

 

Download Quantitative Analyst Brochure PDF

Quantitative_Analyst

 

 

 

 

Course Curriculum

Python 3 Topics
Introduction to Python 3
Object-Oriented Programming (OOP)
Introduction to Gui
Intro to data science
Machine Learning Topics
Linear algebra
Calculus
Statistics
Introduction to ML and Business cases
Data preprocessing
Regression problem
Classification problem
Clustering Problems
Reinforcement learning
Model Selection and evaluation
Result communication and report
Data Structures & Algorithms Topics
Data Structures
Basic Algorithms
Advanced Algorithms
Quantitative Analyst Topics
Basic Quantitative Trading
Advanced Quantitative Trading
ETFs, Indices, Stocks
Multi-factor Model
Sentiment Analysis with Natural Language Processing
Advanced Natural Language Processing with Deep Learning
Combining Multiple Signals
Simulating Trades with Historical Data

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