Choosing the best stocks in the financial market can be a difficult task, especially when you rely solely on other people’s opinions and trending articles. However, the use of artificial intelligence (AI), and in particular of advanced language models such as GPT-4, is revolutionizing this field. In this article, we’ll explore how to use AI to intelligently skim stocks, facilitating informed, evidence-based investment decisions
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Table of Contents:
NexusTrade: A Revolutionary Platform
What is NexusTrade?
NexusTrade is an AI-powered investment research platform designed to simplify the development, testing, and deployment of automated and semi-automated trading strategies. Its flagship tool is an AI chat, called ‘Aurora’, which translates natural language into understandable configurations for
the platform.
Aurora: The Smart Trading Assistant
Aurora not only allows you to create and test trading strategies, but it is also capable of performing in-depth financial research. For example, it can analyze a company’s earnings, giving users a detailed understanding of business performance. However, for those who are new to investing or are looking for stocks in niche sectors, such an assistant may not be enough
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Intelligent Action Screening: A Necessary Innovation
The Power of Advanced Language Models
A practical example of the use of advanced language models is the search for the top five stocks in the semiconductor or artificial intelligence sector that have had the largest increase in income in the last three years. Such an operation, if done manually, would take a long time and would be prone to errors. Thanks to NexusTrade, this process becomes fast and efficient
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Implementing Smart Screening
To implement an intelligent action screening feature, several steps are required:
- Database Population: It is essential to create a well-structured database that contains all the relevant information on the actions. This includes fundamental data such as income, revenues, EBITDA, and other crucial financial metrics
- Data Generation: Using advanced language models and external sources such as Yahoo Finance, it is possible to generate and continuously update data relating to the industries to which the various shares belong.
- Use of the MongoDB Aggregation Framework: This framework allows you to perform complex queries on the database, filtering and ordering actions based on specific criteria such as income increase, sector of membership and other financial indicators.
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MongoDB Aggregation Framework
To perform intelligent screening, it’s essential to understand the MongoDB aggregation framework. This tool allows you to perform complex operations on data, such as merging documents from different collections, filtering results, sorting data, and much more. An example of an aggregation pipeline to find the top five stocks in the semiconductor or AI sector could be the following
:
const pipeline = [
{
$lookup: {
from: “Stock Industries”,
localField: “stockName”,
foreignField: “stockName”,
as: “IndustryData”
}
},
{$unwind: “$industryData”},
{
$match: {
$or: [{“IndustryData.is_AI”: true}, {“IndustryData.is_Semiconductor”: true}],
date: {$gte: threeYearsAgo}
}
},
{$sort: {stockName: 1, date: 1}},
{
$group: {
_id: “$stockName”,
FirstIncome: {$first: “$income”},
lastIncome: {$last: “$income”},
}
},
{
$project: {incomeIncrease: {$subtract: [“$lastIncome”, “$firstIncome”]}}
},
{$sort: {incomeIncrease: -1}},
{$limit: 5}
];
Calling and Learning Functions: A Few Examples
NexusTrade uses the configuration of generative AI functions to translate natural language into MongoDB aggregation queries. This allows users to perform complex searches simply by asking questions in natural language. For example, by asking “Find the top five stocks in the semiconductor industry with a PE ratio of less than 30,” the system can generate the appropriate query and return the desired results
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The integration of advanced language models into stock research and screening processes represents a significant step towards a more precise and accessible financial analysis. NexusTrade, with its AI-first approach, aims to provide users with powerful tools to make informed investment decisions, reducing dependence on subjective opinions and market trends. This innovation not only improves decision-making efficiency, but it also democratizes access to advanced trading strategies, ushering in a new era for retail algorithmic trading
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