{"id":162,"date":"2023-09-22T23:43:14","date_gmt":"2023-09-23T05:43:14","guid":{"rendered":"https:\/\/intelligenxe.com\/?page_id=162"},"modified":"2025-09-05T20:48:28","modified_gmt":"2025-09-06T02:48:28","slug":"trading-ai","status":"publish","type":"page","link":"https:\/\/intelligenxe.com\/?page_id=162","title":{"rendered":"Trading AI"},"content":{"rendered":"\n

The following AI (Artificial Intelligence) features are useful for trading and thus for the AI-TRADER development:<\/p>\n\n\n\n

T1<\/p>\n\n\n\n

AI Freature: <\/strong>A deep-reinforcement-learning (DRL) AI agent\/model learns how to trade funds, almost like humans do – DRL<\/td><\/tr>
Feature Description: <\/strong>
– DRL solves this optimization problem by maximizing the expected total reward from future actions over a time period.<\/td><\/tr>
Relevant Video(s) \/ Code(s) :<\/strong>
Ensemble AI Stock Trading with FinRL: Trade with Multiple AI Models<\/a>
code<\/a>
Paper<\/a> shows which performs best out of Stacked Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM) units, Convolutional Neural Network (CNN), and Multi-Layer Perceptron (MLP) (GRU-based agents used to model Q values show the best overall performance in the Univariate game to capture a wave-like price time series).<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

T2<\/p>\n\n\n\n

AI Freature: <\/strong>Having LLMs write all the necessary software to develop trading bots by just prompting trading strategies in regular\/human language – LLMs<\/td><\/tr>
Feature Description: <\/strong>
– Incredibly easy regular language prompts\/written-commands may be used to have an LLM code trading models that include ensembles\/mixtures of historical predictor, sentiment analyzer, news video replay analyzer and others.
– The prompt may state to only output code as a response and to execute the code (the trading strategy) as an output. 
– Occasionally the human-user\/trader may iteratively need to debug and install dependencies just by prompting the LLM.
– The resulting code may automatically call various relevant APIs and pull real time data to create the trading strategy.
– The workflow may be extended with AI agents monitoring market developments and updating and executing the code\/trading-strategies.<\/td><\/tr>
Relevant Video(s) \/ Code(s) :<\/strong>
I Built an AI Trading Bot with Llama 2!<\/a> \/ Code<\/a>
I Built an AI Sports Betting Bot with ChatGPT<\/a>
Can ChatGPT O1 Make Me Money?<\/a> \/ Code<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

T3<\/p>\n\n\n\n

AI Freature: <\/strong>Traditional\/legacy trading may be replaced with algorithmic trading by levering the power of computers<\/td><\/tr>
Feature Description: <\/strong>
– Algorithmic trading, also known as algo trading or automated trading, occurs when computer algorithms — not humans — execute trades based on predetermined rules.
– This type of trading attempts to leverage the speed and computational resources of computers relative to human traders.
– Pattern recognition in price candlesticks and other data may be used to trigger trades.<\/td><\/tr>
Relevant Video(s) \/ Code(s) :<\/strong>
Data Mining Candlestick Patterns With a Genetic Algorithm<\/a>
Code 1<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

T4<\/p>\n\n\n\n

AI Freature: <\/strong>Machine learning may be used to predict stock price movements – Random Forest, XGboost<\/td><\/tr>
Feature Description: <\/strong>
– Random forest or XGBoost may be applied to historical data in order to predict stock price movements. <\/td><\/tr>
Relevant Video(s) \/ Code(s) :<\/strong>
A machine learning approach to stock trading | Richard Craib and Lex Fridman<\/a>
Predict The Stock Market With Machine Learning And Python<\/a> \/ code<\/a>
Python AI Quant Trading for Crypto – XGBoost and Mean Reversion to test the strategy<\/a>\/ code<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

T5<\/p>\n\n\n\n

AI Freature: <\/strong>Machine learning may be used for price chart pattern recognition – CNN<\/td><\/tr>
Feature Description: <\/strong>
– Deep learning algorithms such as convolutional neural networks (CNN) may be used for the detection of patterns in price charts, even new\/unique patterns.
– Such technical analysis may be used to make trading decisions.<\/td><\/tr>
Relevant Video(s) \/ Code(s) :<\/strong>
Can Convolutional Neural Networks Predict Stock Prices?<\/a> \/ code<\/a>
ChatGPT Trading Strategy Made 19527% Profit ( FULL TUTORIAL )<\/a>
How To Use AI Trading Patterns For Huge Profits<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

T6<\/p>\n\n\n\n

AI Freature: <\/strong>Trading financial instruments guided by financial markets sentiment analysis – ChatGPT<\/td><\/tr>
Feature Description: <\/strong>
– The text in newspaper headlines, press releases, tweets, Reddit communities chats, traders\u2019 forums, etc. may be analyzed by LLMs to determine relevant market sentiment.
– In recent years this type of modeling has been greatly simplified\/abstracted by replacing embeddings models\u2019 pipelines with simply feeding the text to ChatGPT and asking this LLM to determine the sentiment.
– Sentiment analysis may be used to complement algorithmic trading.<\/td><\/tr>
Relevant Video(s) \/ Code(s) :<\/strong>
Use ChatGPT API for Sentiment Analysis in Python<\/a> \/ code<\/a>
Analyzing Cryptocurrency Sentiment on Twitter with LangChain and ChatGPT | CryptoGPT<\/a> \/ code<\/a><\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"

The following AI (Artificial Intelligence) features are useful for trading and thus for the AI-TRADER development: T1 T2 T3 T4 T5 T6<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"kt_blocks_editor_width":"","footnotes":""},"class_list":["post-162","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/intelligenxe.com\/index.php?rest_route=\/wp\/v2\/pages\/162","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/intelligenxe.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/intelligenxe.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/intelligenxe.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/intelligenxe.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=162"}],"version-history":[{"count":30,"href":"https:\/\/intelligenxe.com\/index.php?rest_route=\/wp\/v2\/pages\/162\/revisions"}],"predecessor-version":[{"id":907,"href":"https:\/\/intelligenxe.com\/index.php?rest_route=\/wp\/v2\/pages\/162\/revisions\/907"}],"wp:attachment":[{"href":"https:\/\/intelligenxe.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=162"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}