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 or Deep Learning with different types of algorithm. Experimenting in stock data to see how it works and why it works or why it does not works 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.
Three main types of data: Categorical, Discrete, and Continuous variables
- Categorical variable(Qualitative): Label data or distinct groups.
Example: location, gender, material type, payment, highest level of education - 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) - 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
- For 'Quantitative data' is used with all three centre measures (mean, median and mode) and all spread measures.
- For 'Class data' is used with median and mode.
- For 'Qualitative data' is for only with mode.
Two types of problems:
- Classification (predict label)
- 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
- Simple Linear Regression Model
- Logistic Regression
- Lasso Regression
- Support Vector Machines
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Multivariate Regression Algorithm
- Multiple Regression Algorithm
- K Means Clustering Algorithm
- Naïve Bayes Classifier Algorithm
- Random Forests
- Decision Trees
- Nearest Neighbours
- Lasso Regression
- ElasticNet Regression
- Reinforcement Learning
- Artificial Intelligence
- MultiModal Network
- 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.
Prerequistes
Python 3.5+
Jupyter Notebook Python 3