2021: Algorithmic Trading with Machine Learning in Python

2021: Algorithmic Trading with Machine Learning in Python

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 10.5 Hours | 3.92 GB

Learn the cutting-edge in NLP with transformer models and how to apply them to the world of algorithmic trading

Hi there, we are James and Sajid. Both of us are working as data scientists for various banks here in London, and we have both gone a long way before arriving at our current position in the market.

Do you wish to become a data scientist and build yourself a strong portfolio? This course will allow you to develop your Python skills tutored by professionals. You will be able to add Natural Language Processing and Deep Learning to your CV and start getting paid for your skills.

In this course, you will learn how to apply the newest methods in machine learning and natural language processing to predictive analysis of the stock market and cryptocurrency.

Use the latest technologies available such as TensorFlow, PlotLy, HuggingFace’s Transformers, Flair, spaCy, and many of the essential classics like Pandas, RegEx, Numpy, and more!

We will cover:

– TensorFlow
– Sentiment Analysis
– Transformers (including Google AI’s BERT)
– APIs (including Twitter and Reddit)
– Trading for cryptocurrencies
– Named Entity Recognition (NER)

Take this course if you are learning Python and/or Machine Learning and looking to apply these skills to the stock market. We can’t promise to ‘fix’ on the stock market, but we can promise that you will learn many priceless skills that when applied correctly, can translate to a real benefit both in the job market, and the stock market.

The course is taught by two data scientists from the finance sector. Sajid of Trading 707, who works in Banking and Capital Markets. And James of Aurelio, who specializes in Natural Language Processing (NLP).

What you’ll learn

  • Algorithmic trading
  • Python
  • Machine learning
  • Programming
  • Finance
  • Trading
  • Keras
  • Natural Language Processing
  • Machine Learning
  • TensorFlow
  • Twitter API
  • Sentiment Analysis
Table of Contents

1 Introduction
2 Before we start
3 Coding is not difficult

Python 101
4 My first command
5 Python 101
6 Variable
7 Integers & Float
8 Strings
9 Strings
10 Numpy I
11 Numpy II
12 Numpy III
13 Pandas I
14 Pandas II

Data Visualisation Advanced for Algorithmic Trading
15 Introduction
16 Introduction to Data Visualisation Part I
17 Introduction to Data Visualisation Part II
18 Data visualisation Advanced for Algorithmic trading – Part I
19 Data visualisation Advanced for Algorithmic trading – Part II
20 Data visualisation Advanced for Algorithmic trading – Part III

Stay Connected to The Market
21 Pandas Data Reader
22 Quandl
23 How to get live market data (+- 1 second lag)
24 How to get Bitcoin, Ethereum, Ripple … data.
25 Mid-Term – Skills Test assignment

Introduction to TensorFlow
26 Environment Setup
27 Keras and TensorFlow
28 Sequential Model
29 Functional API
30 Training Parameters and Compile
31 Dataset Input Pipeline
32 Full Model Walkthrough
33 Get Training Data with Kaggle API

Introduction to Sentiment Analysis and Twitter API
34 What is Sentiment Analysis
35 Getting Twitter API Access
36 Using the Twitter API
37 Getting Files From This Course
38 Twitter Data Collection
39 TSLA Sentiment Analysis

Advanced Sentiment Analysis
40 Natural Language Processing and Transformers
41 NLP Refresher
42 Introduction to HuggingFace Transformers
43 Transformer Input Pipeline
44 Building a Prebuilt Model
45 Building a NN Model
46 Building a ConvNet Model
47 Building an LSTM Model
48 Final Model Walk-through

Hot and Trendy Shares Detector Prototype (Bonus)
49 Introduction
50 Prototype