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

Requirements

Basic Python Programming

Desktop Computer

Basic High School Math Skills

Description

Welcome to one of the most comprehensive trading courses using Machine learning and AI to generate buy/sell signalAI 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 PythonEvery 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 TargetsBefore 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 focusedAlso, 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 ModelsUsing 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

AI_in_Trading_Signal_Creation.part1.rar - 995.0 MB

AI_in_Trading_Signal_Creation.part2.rar - 995.0 MB

AI_in_Trading_Signal_Creation.part3.rar - 995.0 MB

AI_in_Trading_Signal_Creation.part4.rar - 511.1 MB