Hey there, traders! If you've spent time analyzing charts, keeping an eye on Fibonacci levels, and deciphering candlestick patterns, you're already ahead of the game in the world of technical analysis. But have you ever wondered how you could take your trading to the next level using Artificial Intelligence (AI) and Machine Learning (ML) models?
Don’t let the jargon scare you off—these tools are becoming increasingly user-friendly, with platforms like Coincharted offering intuitive, no-code interfaces that make it as easy as clicking a button.Have a look at this
Running Your First Model—How to “Train” Without Coding
First things first, what does it mean to “train” a model? Simply put, training a model involves feeding it historical data—like past Bitcoin prices, market sentiment, or trading volumes—and letting the algorithm learn from this data. This helps the model make future price predictions.
On a no-code platform, you usually start by selecting the cryptocurrency you’re interested in. Then, choose the data parameters like date ranges, and the algorithm aka Analytic Method you want to use. Once everything is set, hit the “Start Modelling” button and let the magic happen. It’s that simple!
How Models are Evaluated? For any model that you build, you must select a range of data to train the model on, for example, you can train the model on data from the previous 100 days. However, the algorithm you choose doesn’t get trained on all the data you select. What happens is that the most recent data -let’s say data from the last 40 days- are isolated, then the algorithm is trained only on data from the oldest 60 days, then tested on the most recent 40 days.
Here is an example to explain it more: 1. You select historical daily prices from the last 100 days to be your “Data Source”. 2. The data is split into 60 days for training and 40 days for testing/evaluation. 3. The algorithm is trained on daily prices from the the 60 days and a model is created. 4. The Model is evaluated by comparing the price predictions of the model for the 40 days with the real market prices in the same 40 days. 5. You see the model evaluation results and you decide whether the model is accurate enough or not.
Model Evaluation Results—What Do These Numbers Mean?
After the model is created and evaluated, you will see model evaluation results which include metrics like “Average Error” or “Direction Accuracy”. Now, you don’t have to get into the details of what these terms mean mathematically. In plain English, here’s how to interpret them:
- Prediction Error per point (%): This is the difference between price predictions using the model that you just created and the real market prices during the Evaluation period. This difference is plotted so you can see in which point the Error is high or low.
The term “point” or “data point” refers to “days” when you build a model using historical daily data and to “minutes” when you build a model using historical minutely data.
- ِAverage Prediction Error: This is simply the average error between price prediction error and real prices during the evaluation period. This value of does not consider the direction of the error, so you should read it as a +/- (plus or minus) value. For example an average error of 2% means that the error is plus or minus 2%
- Direction Accuracy: This value measures how many times (in percentage) the model managed to predict the price direction ( increase or decrease) correctly. its range is from 0 to 100%
These metrics give you a quick snapshot of how well your model is likely to perform. You should therefore prefer the models that has a low prediction error and high direction accuracy.
By running your first model and understanding its results, you’re already miles ahead of traders who limit themselves to traditional analysis methods. Take the leap, embrace AI, and unlock a whole new world of trading possibilities.
Ready to take the plunge?
