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

T1

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
code
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).

T2

AI Freature: Having LLMs write all the necessary software to develop trading bots by just prompting trading strategies in regular/human language – LLMs
Feature Description:
– 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.
Relevant Video(s) / Code(s) :
I Built an AI Trading Bot with Llama 2! / Code
I Built an AI Sports Betting Bot with ChatGPT
Can ChatGPT O1 Make Me Money? / Code

T3

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

T4

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

T5

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

T6

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

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