How do you use AI in technical analysis?

Simple! I developed a tool called AutoUFOs, where UFOs stands for UnFilled Orders. This AI patented application finds the likely location for UnFilled Orders to buy and sell in all markets and all timeframes. Then as a trader, analysist or hedger it is extremely easy to use such information to set entries and exits for all trading styles and methods. This post is limited to 750 characters and I could discuss this forever 😊 You may want to check @tradewithufos for more info and video examples on how I use it when doing my analysis and executions... Also, UFOs run in different platforms such as NinjaTrader or TradingView, but talking about data, TradingView provides data feeds for almost every market worldwide. Data is key when running AI...

In my experience, to employ AI in technical analysis of stocks, follow these steps: 1) Collect and preprocess historical stock price data. 2) Select a machine learning algorithm. 3) Split data into training and testing sets. 4) Train the model using the training set. 5) Evaluate the model's performance on the testing set. 6) Develop trading strategies based on the model's predictions. 7) Backtest strategies on historical data. 8) Deploy the model for real-time analysis. One tip: Combine AI analysis with other approaches like fundamental analysis and human judgment for more robust investment decisions.

In my experience, technical analysis is a very useful tool and AI can be a great 'nudge' to look for certain levels or trends. However, technicals are just one tool in the kit- the human judgement element is also vital in making good decisions. So in summary, use AI as an assistant, don't let it drive (in my opinion!)

AI Revolutionizes Technical Analysis Using AI for technical analysis revolutionizes trading and investing. AI's capabilities, like data processing, pattern recognition, and prediction, simplify complex tasks. It automates data handling and tackles challenging datasets, enhancing decision-making and uncovering hidden opportunities

Is technical analysis effective for you (and your goals)? Only then you should think about automating it (maybe with the help of AI) ...

One of the significant benefits of using AI in technical analysis is its ability to eliminate emotions and biases from the decision-making process. Unlike humans, AI models operate purely based on data-driven insights, devoid of fear, greed, or other emotional influences. Emotions can lead to irrational behavior and biased judgments, causing investors to make suboptimal decisions. AI, on the other hand, remains consistent and unaffected by emotional fluctuations. AI can result in a more disciplined and systematic approach to technical analysis, increasing the likelihood of making rational investment choices. This impartiality can lead to more accurate predictions and better risk management.

I believe that AI has enormous potential to be used in technical analysis to try to predict the future movement of a stock based on certain patterns that have a high probability of repeating. In this way, the algorithm could analyze hundreds of thousands of charts similar to the one being studied and determine the future movement based on the movement that has been most observed in the analyzed charts.

From my experience, the only plausible way to construct AI is inside the environment for AI like PyTorch and TensorFlow; commercial software is a terrible choice. With AI, you are limitless; you can try to reproduce existing things from TA, like oscillators or price patterns, by simply training a supervised machine learning model where labels are results from classical TA approaches. After training, it will find you potential signals. But why reproduce? Better use AI to do its job, e.g., I have trained convolutional auto-encoders as a clustering method. I could find any price patterns there quickly and analyze their performances (guess what - any typical patterns as double tops or bottoms didn't work better than random).

The ultimate challenge of using AI in technical analysis is that it is a model that is the best description of the past and being used to predict the future. Even if it were a perfect representation of the past, the future depends on other inputs not yet known. And we know it is not even a perfect representation for various reasons detailed elsewhere. That doesn't imply that there is no value in it, but it is important to appreciate the limitations and to communicate those to the non-experts.

A weakness of using AI in technical analysis is its susceptibility to overfitting. Overfitting occurs when an AI model becomes overly tuned to the training data and performs poorly on new, unseen data. This can happen if the model is excessively complex or if there isn't enough diverse data to capture the full range of market conditions. Additionally, AI models may struggle to adapt to sudden market shifts or unforeseen events, as they rely heavily on historical patterns. It's important to regularly validate and update the models to avoid relying on outdated or biased information. Human oversight is crucial to interpret results, consider market context, and make informed decisions.

Using AI in technical analysis requires adherence to best practices. Ensure quality data, relevant feature selection, and suitable model choice. Divide data into training and testing sets for accurate evaluation. Regularly update the model as market conditions change, and implement proper risk management. Aim for interpretability in models to build trust and manage biases. Backtest the model on historical data to assess effectiveness. Diversify strategies and use AI as one tool, not the sole basis for trading decisions. Comply with financial regulations. Remember, AI insights don't guarantee success; combining human expertise with AI can lead to more informed decisions.