ALpaca a Free API for Stock and Crypto Trading Stock Market Analysis

A module that queries the Stock Market exchange API and extracts relevant “features” or “data sets”, for example it might periodically extract vectors of length 10 which consist of the last 10 stock price points. Another “feature” could be something like “the last 5 daily averages”. More exotic features might be things like “whether or not there has been an election in the last week”, or “the entropy of the market in the last hour”. (feature extractor module)

A module that uses the feature set to make predictions whether to buy or sell stock market and how many (trading strategy module).

STOCK M PREDICTOR ABSTRACT
Project description
The stock market’s workings are fraught with doubt and anticipation. They are also
influenced by a wide range of events, like securing a large new contract, accounting
errors or scandals or the introducing of a new product to the market. As a result, stock
market forecasting is an essential part of the financial and commercial worlds.
Predictions may be made using either a technical or a fundamental examination.
Machine learning algorithms are used to analyze historical stock price data to perform
technical analysis. Extracting technical indexes from data sets is the first step in this
process. Then, the results of the algorithms used will be assessed, and stock values
will be evaluated. In order to produce accurate stock price forecasts in the future, the
newly taught application may be put to work. With the use of artificial neural networks,
the system will try to identify the best moments when a user can buy or sell and build
its market strategies by analyzing stock market data.
Aiming results
Simulators may then show us how our designs will perform on either historical or realworld data. In addition, a database manager would keep track of all the trends and
compare them later. Training and selecting the best-trained networks would be the two
most important features. Starting with random weights, the network is then trained by
adjusting its weights according to errors. Using the simulator, they may then employ a
previously trained network in a real-world setting to see whether the system is capable
of accurately forecasting stock market patterns. It sends the information to a wallet
where users see the money they’ve earned or lost. Finally, the application saves the
price values, neural network, and weights so that users may compare them in the
future and pick the best neural network to make stock judgments in the future.