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 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). |
| 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 |
| 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 |
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