# Using AI Algorithm and Machine Learning to Trade in the Stock Market

Machine learning empowers traders to accelerate and automate one of the most complex, time-

Machine learning empowers traders to accelerate and automate one of the most complex, time-consuming, and challenging aspects of algorithmic trading, providing competitive advantage beyond rules-based trading. Stock market prediction and analysis are the most challenging task to complete. There are numerous causes for this, including market volatility and various other dependent and independent variables that influence the value of a specific stock in the market. These variables make it extremely difficult for any stock market expert to anticipate the rise and fall of the market with great precision. The most fundamental strategy in the stock market is buying low and selling high. So you wanted to create a model that predicts the lows and highs of stocks as accurately as possible. Here are the steps to the strategy

Use the ML model to predict if buying the stock is favorable on a specific day.

If favorable(green dots), buy the stock.

Once the stock rises, a certain percentage sell the stock for a gain.

If the stock dips a certain percentage, sell the stock for a loss.

Some other details

The algorithm will only hold one stock at a time

The selling percentages are two hyperparameters of the model that we can choose to maximize gains.

**The Algorithm**
**Logistic regression** *(LR) *is a statistical model that, in its basic form, uses a logistic function to model a binary dependent variable, although many more complex extensions exist. In regression analysis, logistic regression (or logit regression) estimates the parameters of a logistic model (a form of binary regression).
**Simple Linear Regression** is a regression model that estimates the relationship between one independent variable and one dependent variable using a straight line. Both variables should be quantitative.
**k-Nearest Neighbors** algorithm falls under the Supervised Learning category and is used for classification (most commonly) and regression. It is also a versatile algorithm for imputing missing values and resampling datasets.
**Machine Learning
**
However, with the introduction of Machine Learning and its robust algorithms, the most recent market research and Stock Market Prediction advancements have begun to include such approaches in analyzing stock market data.
In summary, Machine Learning Algorithms are widely utilized by many organizations in Stock market prediction.
**Multilayer Perceptron** *(MLP)*s are suitable for classification prediction problems where inputs are assigned a class or label. They are also ideal for regression prediction problems where a real-valued quantity is predicted given a set of information.
**Types of Neural Network**
Neural Networks can be classified into multiple types based on their Layers and depth activation filters, Structure, Neurons used, Neuron density, data flow, and so on. The types of Neural Networks are as follows:

Perceptron

Multi-Layer Perceptron or Multi-Layer Neural Network

Feed Forward Neural Networks

Convolutional Neural Networks

Radial Basis Function Neural Networks

Recurrent Neural Networks

Sequence to Sequence Model

Modular Neural Network

**Python**
Python trading has also gained traction in the quant finance community as it makes it easy to build intricate statistical models with ease due to the availability of sufficient scientific libraries like:

Pandas

NumPy

PyAlgoTrade

Pybacktest and more.

Technology has become an asset in finance: financial institutions are now evolving to technology companies rather than only staying occupied with just the financial aspect: besides the fact that technology brings about innovation the speeds and can help to gain a competitive advantage, the rate and frequency of financial transactions, together with the large data volumes, makes that financial institutions’ attention for technology has increased over the years and that technology has indeed become the leading enabler in finance.