Deep Learning and Machine Learning for Stock predictions

Description: This is for learning, studying, researching, and analyzing stock in deep learning (DL) and machine learning (ML). Predicting Stock with Machine Learning method or Deep Learning method with different types of algorithm. Experimenting in stock data to see how it works and why it works or why it does not work that way. Using different types of stock strategies in machine learning or deep learning. Using Technical Analysis or Fundamental Analysis in machine learning or deep learning to predict the future stock price. In addition, to predict stock in long terms or short terms.

Machine learning is a subset of artificial intelligence involved with the creating of algorithms that can change itself without human intervention to produce an output by feeding itself through structured data. On the other hand, deep learning is a subset of machine learning where algorithms created, but the function are like machine learning and many of the different type of algorithms give a different interpretation of the data. The network of algorithms called artificial neural networks and is similar to neural connections that exist in the human brain.

Three main types of data: Categorical, Discrete, and Continuous variables

1. Categorical variable(Qualitative): Label data or distinct groups.
   Example: location, gender, material type, payment, highest level of education
2. Discrete variable (Class Data): Numerica variables but the data is countable number of values between any two values.
   Example: customer complaints or number of flaws or defects, Children per Household, age (number of years)
3. Continuous variable (Quantitative): Numeric variables that have an infinite number of values between any two values. Example: length of a part or the date and time a payment is received, running distance, age (infinitly accurate and use an infinite number of decimal places)

Data Use

1. For 'Quantitative data' is used with all three centre measures (mean, median and mode) and all spread measures.
2. For 'Class data' is used with median and mode.
3. For 'Qualitative data' is for only with mode.

Two types of problems:

1. Classification (predict label)
2. Regression (predict values)

Python Reviews

Step 1 through step 8 is a reviews in python.
After step 8, everything you need to know that is relate to data analysis, data engineering, data science, machine learning, and deep learning.

List of Machine Learning Algorithms for Stock Trading

Most Common Regression Algorithms

1. Simple Linear Regression Model
2. Logistic Regression
3. Lasso Regression
4. Support Vector Machines
5. Polynomial Regression
6. Stepwise Regression
7. Ridge Regression
8. Multivariate Regression Algorithm
9. Multiple Regression Algorithm
10. K Means Clustering Algorithm
11. Naïve Bayes Classifier Algorithm
12. Random Forests
13. Decision Trees
14. Nearest Neighbours
15. Lasso Regression
16. ElasticNet Regression
17. Reinforcement Learning
18. Artificial Intelligence
19. MultiModal Network
20. Biologic Intelligence

Different Types of Machine Learning Algorithms and Models

Algorithms is a process and set of instructions to solve a class of problems. In addition, algorithms perform a computation such as calculations, data processing, automated reasoning, and other tasks. A machine learning algorithms is a method that provides the systems to have the ability to automatically learn and improve from experience without being formulated.

Prerequistes

Python 3.5+
Jupyter Notebook Python 3

🔲 Add more of algorithms and different types of algorithms

Authors

* Tin Hang

Disclaimer

🔻 Do not use this code for investing or trading in the stock market. However, if you are interest in the stock market, you should read 📚 books that relate to stock market, investment, or finance. On the other hand, if you into quant or machine learning, read books about 📘 machine trading, algorithmic trading, and quantitative trading. You should read 📗 about Machine Learning and Deep Learning to understand the concept, theory, and the mathematics. On the other hand, you should read academic paper and do research online about machine learning and deep learning on 💻