Market GPT for a trading edge?

GPT claims that it is capable of. Anyway, there are already AI-powered stock trading bots available in the market.


Here are some key advantages of using GPT:

• Data analysis and pattern recognition: GPT can process and analyze large amounts of financial data, helping traders identify complex patterns and relationships that may be difficult for humans to detect. This assists in making informed trading decisions.
  
  
• Market research and sentiment analysis: GPT can analyze news, social media, and other sources to provide insights into market sentiment. It helps traders assess market expectations and investor sentiment.
  
  
• Forecasting and prediction: GPT can be used to generate forecasts for price movements, market volatility, and other factors based on historical data analysis.
  
  
• Risk management: GPT can help identify risks, optimize portfolio allocation, and implement risk mitigation strategies.
  
  
• Automated trading systems: GPT can be integrated into algorithmic trading systems, where it analyzes real-time market data and executes trades based on predefined rules or strategies.

 

https://www.rt.com/news/577647-predictions-ai-human-brain/
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Despite the hype, artificial intelligence remains inferior to the human brain
With new alarming predictions being made every week, it is important to explore the limitations of AI. The human brain provides the best reference point
...
"

 

1. Define your trading goals and objectives:
   
   • Determine your preferred trading style, such as day trading, swing trading, or long-term investing.
   • Define your risk tolerance and desired return on investment. This will help shape your strategy and risk management approach.
2. Gather historical and real-time market data using reliable APIs:
   
   • Identify and choose reliable APIs for accessing real-time stock data, sentiment analysis, news updates, and community feeds.
   • Research and select APIs that provide the necessary data to support your trading strategy.
3. Choose Python as your programming language and set up a development environment:
   
   • Install Python on your machine and set up a Python environment.
   • Choose an Integrated Development Environment (IDE) like Visual Studio Code (VS Code) for prompt engineering, coding, and collaboration.
4. Install necessary libraries and use an IDE like VS Code for prompt engineering:
   
   • Install relevant libraries such as pandas, numpy, and scikit-learn to handle data manipulation, analysis, and modeling.
   • Use VS Code or any preferred IDE to create prompts, write code, and organize your project.
5. Preprocess and clean the data obtained from APIs:
   
   • Extract relevant information from the raw data obtained from APIs, such as stock prices, news headlines, or sentiment scores.
   • Handle missing values, outliers, and data inconsistencies using techniques like imputation or data interpolation.
6. Perform exploratory data analysis (EDA) to identify patterns and trends:
   
   • Visualize historical data using tools like matplotlib or seaborn to identify patterns, trends, or correlations.
   • Conduct statistical analysis and calculate key indicators, such as moving averages, volatility measures, or correlations.
7. Develop trading signals using technical indicators or statistical models:
   
   • Define technical indicators, such as moving average crossover, Bollinger Bands, or relative strength index, to generate trading signals.
   • Utilize statistical models like regression, classification, or time series forecasting to generate signals based on historical data.
8. Backtest your strategy using historical data and calculate performance metrics:
   
   • Use historical data to simulate trading based on your developed signals.
   • Implement a backtesting framework that takes into account transaction costs, slippage, and realistic trading conditions.
   • Calculate performance metrics such as return on investment (ROI), risk-adjusted return, maximum drawdown, or Sharpe ratio to evaluate strategy effectiveness.
9. Optimize your strategy parameters using techniques like grid search or genetic algorithms:
   
   • Fine-tune your strategy parameters to maximize performance using optimization techniques.
   • Implement grid search or genetic algorithms to systematically explore the parameter space and find the optimal combination.
10. Implement real-time data integration by integrating APIs for real-time stock data, sentiment analysis, news, and community feeds:
    
    • Integrate APIs that provide real-time data into your strategy.
    • Fetch real-time stock prices, sentiment analysis scores, news updates, and community feeds to update your trading signals and decision-making.
11. Set up a secure API key for accessing the paid version of ChatGPT:
    
    • Sign up for the paid version of the OpenAI API and obtain a secure API key.
    • Follow OpenAI's guidelines and best practices to ensure secure usage of the API key.
12. Install the OpenAI Python library (openai) for interacting with the ChatGPT API:
    
    • Use the pip package manager to install the OpenAI Python library.
    • Import the library into your Python project to interact with the ChatGPT API.
13. Incorporate ChatGPT into your strategy development
    

 

I have played around with these steps....work in progress...still learning.

 

Only your second point holds, all others are incorrect. Just because you see some hacks try it on YouTube means nothing. If it worked are you seriously suggesting that they rather post some video clips than running those models themselves?

So yes gpt is good for sentiment analysis because that was what it was designed to do.

 

Lol

 

Decoder only models are trained to predict the next target based on it's context in a much more efficient way w/ parallel multi-headed attention blocks, so for example on chunks of volatility series. Special consideration with how to successfully integrate positional-based encoders & how how to sample and batch your ticks. Similar to how BiRNNs are used (see Citadel), except with attention you can synchronously learn where the most important focus areas are in your source data essentially learning dynamics overtime.

 

Until the ML algos start accurately predicting your moves and turn your portfolio blood red.

 

I think it can be useful if it could do intelligent browsing. That would be awesome and all people should use it.

 

Hello kmiklas,

ML algos can not predict my moves, because I do not know even know my moves.

Every trade I take is an educated or best experienced guess. no setup, no thing, just guessing.

lol, who cares about algos, lets get rich man and lets hurry up and do it.

Stop worrying about algos and all that little stuff.