AI in Trading: Signal Creation

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Ali Abdoli

10:32:09

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1. Fundamental

1. Introduction.mp4

00:57

2. Trading Concepts.mp4

17:47

3. Trading Process.mp4

08:42

4. Machine Learning Basics.mp4

13:25

5. Machine Learning in Trading.mp4

02:53

2. Basic Python Packages

1. Introduction.mp4

00:26

2. Jupyter.mp4

00:55

3. Jupyter Tour.mp4

04:28

4. Pandas and Numpy.mp4

00:55

5. Numpy Basics.mp4

08:58

6. Pandas Basics.mp4

10:08

7. Plotting.mp4

00:35

8. Seaborn and MatPlotLib.mp4

11:48

3. Data Sources

1. Introduction.mp4

00:24

2. Types of Data.mp4

05:23

3. Methods to Access Data.mp4

02:27

4. Alpha Vantage.mp4

02:24

5. Alpha Vantage Access Data With Python.mp4

18:05

6. AlphaVantage Big Range.mp4

13:53

7. MetaTrader.mp4

02:20

8. Metatrader Access Data With Python.mp4

13:53

4. Feature and Target Design

1.1 av AAPL.csv

1. Introduction.mp4

00:29

2. Feature and Target Extraction.mp4

06:55

3. Data Usage Considerations.mp4

03:02

4. PandasTa.mp4

08:54

5. PandasTa Target Design.mp4

08:36

6. PandasTa Feature Feature Design.mp4

10:57

7. Visualizing Features and Targets.mp4

17:30

8. Visualizing Features and Targets Continuation.mp4

03:34

5. Probability Based Algorithms (Bayesian)

1.1 features targets.csv

1. Introduction.mp4

00:08

2. Bayesian Mindset.mp4

03:08

3. Bayesian Mathematics.mp4

04:51

4. Bayesian Application in Spam Detection.mp4

05:39

5. Bayesian For Parameter Estimation.mp4

05:04

6. Bayesian For Parameter Estimation Cont..mp4

03:28

7. Pymc.mp4

00:47

8. Pymc Basics.mp4

20:15

9. Parameter Estimation using Pymc.mp4

09:23

10. Classification vs Regression.mp4

11:10

11. Conver Returns to Classification.mp4

28:02

12. Bayesian Log Regression.mp4

04:02

13. Bayesian Log Regression for Price Direction in Python.mp4

22:09

14. Bayesian Log Regression for Price Direction in Python Cont..mp4

11:29

6. Decision Tree

1.1 features targets.csv

1. Introduction.mp4

00:16

2. Decistion Tree Basics.mp4

06:37

3. Decistion Tree Regression.mp4

05:30

4. Decistion Tree to Estimate Returns.mp4

10:37

5. Decistion Tree to Estimate Returns Cont..mp4

06:41

6. Decistion Tree to Estimate Returns Cont..mp4

12:58

7. Decistion Tree with All Features.mp4

03:53

8. Split Train and Test.mp4

05:07

9. Decision Tree for Classification.mp4

07:39

10. Decision Tree for Classification All Features.mp4

06:40

11. Cross Validation.mp4

05:53

12. Cross Validation with Python.mp4

08:16

13. Cross Validation with Python Cont..mp4

03:43

14. Tuning Decision Tree.mp4

10:09

15. Train Size Selection.mp4

13:27

16. Random Forest.mp4

05:13

17. Random Forest with Python.mp4

07:02

18. Random Forest Tuning.mp4

07:27

7. Gradient Boosting

1.1 features targets.csv

1. Introduction.mp4

00:14

2. Ensemble.mp4

10:54

3. DT Boosting.mp4

17:53

4. Boosting with Test data.mp4

08:47

5. Boosting Cross Validation.mp4

09:33

6. Boosting Cross Validation Cont..mp4

10:37

8. Deep Learning

1.1 features targets.csv

1. Introduction.mp4

00:16

2. Deep Learning.mp4

10:54

3. What is Gradient Descent.mp4

09:27

4. Feed Forward Neural Network.mp4

08:54

5. Feed Forward Neural Network Tensorflow.mp4

22:01

6. Feed Forward Neural Network Hyperparameter Tuning.mp4

12:08

7. Feed Forward Neural Network Hyperparameter Tuning Cont..mp4

02:54

9. Recurrent Neural Network

1.1 features targets.csv

1. Introduction.mp4

00:20

2. Recurrent Neural Network and LSTM.mp4

12:03

3. Data Preparation.mp4

09:56

4. LSTM with Tensorflow.mp4

12:36

5. LSTM Model Diagnosis.mp4

08:48

6. LSTM Model Diagnosis Cont..mp4

06:28

Description

Build AI Based Buy/Sell Signal/Indicator in Your Algorithmic Trading Bot, Boost Your Python Machine Learning Knowledge

What You'll Learn?

Create buy/sell signal using machine learning in your trading bot
Price prediction using Artificial Intelligence for algorithmic trading
Using ML Python packages (TensorFlow, PyMC, Scikit Learn, SciPy, ...)
Different types of Machine Learning models
Deep Learning and Artificial Neural Network
Probability based models (Bayesian) and Decision Tree based (Random Forest)
Different market data sources (OHLC), MetaTrader5, AlphaVantage
Using Pandas, Numpy, Jupyter-Notebook, Seaborn, Matplotlib, etc
Model Ensemble (Bagging and Boosting)
Feature extraction and target design for price prediction
Inuition behind every Machine Learning model without too much maths
Recurrent Nueral Network and LSTM

Who is this for?

Traditional Algorithmic Trader Who Wants enjoy Artificial Intelligence Benefits

A Python Programmer Who Would Like Use Machine Learning For Trading

What You Need to Know?

Basic Python Programming

Desktop Computer

Basic High School Math Skills

More details

Description
Welcome to one of the most comprehensive trading courses using Machine learning and AI to generate buy/sell signal

AI based trading bots are on the rise and their share of the market has been growing rapidly. Not only big trading quant financial institutions such as MLQ AI, Kavout, QuantAI, Precision Alpha, etc are using artificial intelligence for trading but also retail traders have been using this powerful tool to find the edge to the market. This makes having machine learning in your algorithmic trading bot a must.

The backbone of any trading setup is buy and sell signal generation, and this comes from having a reliable and correct price prediction. That is where machine learning and artificial intelligence can shine.

In this course, different asset classes' market data are downloaded, and different types of machine learning algorithms are applied to those types of data. Those algorithms are the ones widely used in the data science and trading. They include probability based, deep learning, artificial neural networks, decision trees, etc. Then, we use those algorithms to predict price and generate signals.

Hands on With Python
Every step in this course has coding sections with python. First, the intuition is explained then we develop some code to implement that idea using machine learning packages.

Exploring Data Sources (Market Data)
The very first step in any machine learning project is having access to data. Different market data providers have different ways to capture data.

Features and Targets
Before designing any machine learning model, it needs to be clear that what we expect our model to predict. In trading terminology, is it a trend, volatility, return that the buy/sell signal is focused

Also, giving raw data (OHLC) to the model, makes it difficult to predict any price movement. Designing the features that can contribute to signal generation is the must.

Machine Learning Models
Using different types of ML models that can create signals in different asset classes. There are countless number of ML algorithms, and they are still growing. Knowing and implementing big category of those algorithms enable us to explore and implement all other variations.

We only not implement those models in Python but also, we explore different ways of training them and tuning hyper parameters. We use well-known python packages that widely used in data science community.

Before implementing and using any package or algorithm, we first go through intuition and explain the idea behind that model. we use simple terms and avoid going through complicated Math formula and good enough to diagnose the model.
Who this course is for:
Traditional Algorithmic Trader Who Wants enjoy Artificial Intelligence Benefits
A Python Programmer Who Would Like Use Machine Learning For Trading

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A tech leader and retail trader for almost two decades.He has developed (hands-on) and supervised countless number of IT projects in many industries from E-commerce to Finance.Past few years, he has utilized the powerful AI and Machine Learning algorithms in trading and as a retail trader managed to develop a successful/profitable trading set up.

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