The following AI (Artificial Intelligence) features are useful for trading and thus for the AI-TRADER development:

AI Freature: A deep-reinforcement-learning (DRL) AI agent/model learns how to trade funds, almost like humans do – DRL
Feature Description:
– DRL solves this optimization problem by maximizing the expected total reward from future actions over a time period.
Relevant Video(s) / Code(s) :
Ensemble AI Stock Trading with FinRL: Trade with Multiple AI Models
Paper 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).
AI Freature: Traditional/legacy trading may be replaced with algorithmic trading by levering the power of computers
Feature Description:
– 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.
Relevant Video(s) / Code(s) :
Data Mining Candlestick Patterns With a Genetic Algorithm
Code 1
AI Freature: Machine learning may be used to predict stock price movements – Random Forest, XGboost
Feature Description:
– Random forest or XGBoost may be applied to historical data in order to predict stock price movements.
Relevant Video(s) / Code(s) :
A machine learning approach to stock trading | Richard Craib and Lex Fridman
Predict The Stock Market With Machine Learning And Python / code
Python AI Quant Trading for Crypto – XGBoost and Mean Reversion to test the strategy/ code
AI Freature: Machine learning may be used for price chart pattern recognition – CNN
Feature Description:
– 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.
Relevant Video(s) / Code(s) :
Can Convolutional Neural Networks Predict Stock Prices? / code
ChatGPT Trading Strategy Made 19527% Profit ( FULL TUTORIAL )
How To Use AI Trading Patterns For Huge Profits
AI Freature: Trading financial instruments guided by financial markets sentiment analysis – ChatGPT
Feature Description:
– The text in newspaper headlines, press releases, tweets, Reddit communities chats, traders’ 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’ 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.
Relevant Video(s) / Code(s) :
Use ChatGPT API for Sentiment Analysis in Python / code
Analyzing Cryptocurrency Sentiment on Twitter with LangChain and ChatGPT | CryptoGPT / code

If you would like to learn more about this work, please contact us at 

Terms & Conditions | User Agreement