Method and System Using a Neural Network for Prediction of Stocks and/or Other Market Instruments Price Volatility, Movements and Future Pricing

Abstract

A method for providing stock predictive information by a cloud-based computing system implementing a random forest algorithm via a machine learning model by receiving a set of stock data from multiple sources of stock data wherein the set of stock data at least comprises stock prices at the open and close of a market, changes in stock prices during the open and close of a market, and real-time stock data; defining a range in time contained in a window defined of an initial selected month, a day or real-time period and an end of the selected month, day and real-time period; applying the random forest model to the set of stock data by creating multiple decision trees to predict a stock price in a quantified period, amount or percentage change in a stock price; and presenting the predicted stock price in a graphic user interface to an user.

1 . A system comprising:
 one or more processors; and   a memory storing computer-executable instructions, which when executed by the one or more processors, cause the system to perform operations comprising:   training a neural network using a set of stock valuation data from a quote database or a research database;   generating an output of the neural network that comprises a stock price for a stock of interest;   determining using a support vector model based on the output of the stock price for the stock of interest by the neural network using one of a volatility algorithm or a frequency algorithm for analysis of the stock price to the stock valuation data with comparisons to a set of historical valuation metrics associated with the stock valuation data to determine whether a volatility spike or momentum in the stock price;   generating one or more bands in a graphical user interface to determine whether the stock price will experience a breakout above or below the one or more bands resulting from the volatility spike or momentum in the stock price; and   providing one or more alerts in a graphic user interface about a future breakout trend in conjunction with the stock price to signal a buy, hold, or sell alert to a user to assist the user in making a selection of an action comprising one of a sell, hold, or buy of the stock.   
     
     
         2 . The system of  claim 1 , wherein the neural network further comprises a prediction model that implements a supervised and unsupervised learning process using the stock valuation data to determine the stock price. 
     
     
         3 . The system of  claim 2 , wherein at least a supervised model uses stock valuation data that is within a range selected in the graphical user interface by the user. 
     
     
         4 . The system of  claim 3 , wherein the neural network further comprises a random forest model that further comprises a number of decision trees for implementation by the one or more processors of a prediction model wherein each decision tree receives stock valuation data for processing to predict a change in the stock price in a selected period. 
     
     
         5 . The system of  claim 1 , further comprising:
 implementing, by the one or more processors, a security check to enable the neural network access to the graphical user interface to display alerts to the user.   
     
     
         6 . The system of  claim 1 , further comprising:
 selecting, by the one or more processors, one or more of a set of rules for intraday stock movements of the set of valuation data to determine the volatility spike.   
     
     
         7 . The system of  claim 1 , further comprising:
 applying, by the one or more processors a natural language processing model for receiving as input the set of valuation data for triggering the random forest application wherein the random forest application is connected to an independent natural language processing module for receiving voice commands and sending voice results to the user.   
     
     
         8 . The system of  claim 1 , further comprising:
 processing, by the one or more processors, the neural network by receiving one or more target stocks of interest and a corresponding set of target stock-related information based on stock indicators comprising at least one of a simple moving average, a relative strength index, a moving average convergence divergence signal, an exponential weighted moving average, and a Bollinger band.   
     
     
         9 . The system of  claim 1 , further comprising:
 training, by the one or more processors, the neural network using a random forest application with inputs for a preset number of hidden layers in the neural network.   
     
     
         10 . A non-transitory processor-readable medium storing processor-executable instructions for natural language processing, the instructions being executable by a processor to perform operations comprising:
 receiving, at an input, a natural language input of a question;   receiving a set of stock data in response to the natural language input of the question from multiple sources of stock data comprising brokerage accounts, content sources, and stock quote providers;   training a neural network using a set of stock valuation data from a trade and quote database or a research database;   generating an output of the neural network that comprises a stock price;   determining using support vector model based on the output of the stock price for the stock of interest by the neural network using one of a volatility algorithm or a frequency algorithm for analysis of the stock price to the stock valuation data with comparisons to a set of historical valuation metrics associated with the stock valuation data to determine whether a volatility spike or momentum in the stock price;   generating one or more bands in a graphical user interface to determine whether the stock price will experience a breakout above or below the one or more bands resulting from the volatility spike or momentum in the stock price; and   providing one or more alerts in a graphic user interface about a future breakout trend in conjunction with the stock price to signal a buy, hold, or sell alert to a user to assist the user in making a selection of an action comprising one of a sell, hold, or buy of the stock.   
     
     
         11 . The instructions being executable by the processor to perform operations according to  claim 10 , further comprising:
 training a prediction model based on supervised and unsupervised learning of the set of stock valuation data wherein at least a supervised model uses stock valuation data results that are within a range selected by a user.   
     
     
         12 . The instructions being executable by the processor to perform operations according to  claim 11 , further comprising: using at least a supervised model with input of the stock valuation data that is selected within a range in the graphical user interface by the user. 
     
     
         13 . The instructions being executable by the processor to perform operations according to  claim 11 , further comprising: using at least a supervised model with input of the stock valuation data that is selected within a range in the graphical user interface by the user. 
     
     
         14 . The instructions being executable by the processor to perform operations according to  claim 10 , wherein the neural network further comprises a random forest model that further comprises a number of decision trees for implementation of a prediction model wherein each decision tree receives stock valuation data for processing to predict a change in the stock price in a selected period. 
     
     
         15 . The instructions being executable by the processor to perform operations according to  claim 10 , further comprising: implementing a security check to enable the neural network access to the graphical user interface to display alerts to the user. 
     
     
         16 . The instructions being executable by the processor to perform operations according to  claim 10 , further comprising: selecting one or more of a set of rules for intraday stock movements of the set of valuation data to determine the volatility spike. 
     
     
         17 . The instructions being executable by the processor to perform operations according to  claim 10 , further comprising: applying a natural language processing model for receiving as input the set of valuation data for triggering the random forest application wherein the random forest application is connected to an independent natural language processing module for receiving voice commands and sending voice results to the user. 
     
     
         18 . The instructions being executable by the processor to perform operations according to  claim 10 , further comprising:
 processing the neural network by receiving one or more target stocks of interest and a corresponding set of target stock related information based on stock indicators comprising at least one of a simple moving average, a relative strength index, a moving average convergence divergence signal, an exponential weighted moving average, and a Bollinger band.   
     
     
         19 . The instructions being executable by the processor to perform operations according to  claim 10 , further comprising: training the neural network using a random forest application with inputs for a preset number of hidden layers in the neural network. 
     
     
         20 . A method comprising:
 training a neural network using a set of stock valuation data from a quote database or a research database;   generating an output of the neural network that comprises a stock price for a stock of interest;   determining using a support vector model based on the output of the stock price for the stock of interest by the neural network using one of a volatility algorithm or a frequency algorithm for analysis of the stock price to the stock valuation data with comparisons to a set of historical valuation metrics associated with the stock valuation data to determine whether a volatility spike or momentum in the stock price;   generating one or more bands in a graphical user interface to determine whether the stock price will experience a breakout above or below the one or more bands resulting from the volatility spike or momentum in the stock price; and   providing one or more alerts in a graphic user interface about a future breakout trend in conjunction with the stock price to signal a buy, hold, or sell alert to a user to assist the user in making a selection of an action comprising one of a sell, hold, or buy of the stock.