How to Build a Simple AI Trading System Using Symbolic Regression

Introduction

In the world of financial trading, developing AI-based trading systems has become a popular approach for attempting to predict market movements. A powerful tool for this purpose is symbolic regression, a method that can derive mathematical models from data, offering highly interpretable trading strategies.

In this blog post, we will explore how you can build a simple AI trading system using TuringBot, a cross-platform data science software designed for symbolic regression.

Why Symbolic Regression for AI Trading?

Unlike traditional regression methods, symbolic regression doesn't start with a predefined model structure. Instead, it searches through mathematical expressions to find the best-fitting formula for your dataset. This is particularly useful in financial markets, where relationships between variables are complex and not always linear.

With TuringBot, you can automatically generate equations based on historical data such as stock prices, trading volumes, and technical indicators. These models can then be used to attempt to predict future market behavior.

Step-by-Step Guide to Building an AI Trading System with TuringBot

1. Prepare Your Data

First, gather historical data that you want to model. For instance, you might have daily stock prices, trading volumes, and other indicators like moving averages. Ensure your data is in CSV format, and that it contains purely numerical data.

Here’s an example of what your data might look like:

Date, Open, High, Low, Close, Volume, RSI, Moving_Avg
2023-01-01, 150, 155, 148, 152, 1000000, 60, 150.5
2023-01-02, 152, 160, 151, 158, 1200000, 65, 152.3
2023-01-03, 158, 165, 157, 160, 1100000, 70, 154.1

Note that the date field is not numerical, so it will be parsed as an arbitrary number. Make sure to not use it as an input variable! Just uncheck "Date" in the UI before starting the search.

2. Load Data into TuringBot

Open TuringBot and load your data. The software will populate input fields allowing you to select which columns to use as inputs and output. In this case, you might use Volume, RSI (Relative Strength Index), and Moving_Avg as input variables, and Close as the output.

3. Run Symbolic Regression

Next, start the symbolic regression search. TuringBot will try a number of equations for predicting the target variable in terms of the input variables, and will show you the best ones of each size. The generated equation might look like this:

Close_Price = (Volume * 0.003) + (RSI * 1.2) - (Moving_Avg * 0.5)

This model indicates how the close price is influenced by trading volume, RSI, and moving average. Note that input variables that are not predictive can easily be detected: they will not appear in the equations.

4. Evaluate and Fine-Tune the Model

TuringBot provides built-in tools to evaluate the accuracy of the generated equations. It offers metrics like R² to measure how well the equation fits the data and an Observed vs Predicted plot.

Of particular interest is the cross-validation feature, which allows you to easily rule out models that do not generalize past the training dataset. It is highly recommended to use cross-validation while developing quantitative trading models.

To further improve the model, you can try to remove unnecessary variables and include new ones, such as technical indicators like Bollinger Bands or MACD (Moving Average Convergence Divergence).

5. Deploy the Model for Real-Time Trading

Once you have an accurate model, export the equation from TuringBot and integrate it into your trading system. You can write a Python script to automatically fetch the latest market data, apply the model, and generate buy/sell signals based on its predictions.

Here's an example of Python script to implement the model:

import pandas as pd

# Load the latest market data
data = pd.read_csv('market_data.csv')

# Define the symbolic regression model
def predict_close_price(volume, rsi, moving_avg):
    return (volume * 0.003) + (rsi * 1.2) - (moving_avg * 0.5)

# Apply the model to predict today's close price
data['Predicted_Close'] = predict_close_price(data['Volume'], data['RSI'], data['Moving_Avg'])

# Make trading decision
if data['Predicted_Close'].iloc[-1] > data['Close'].iloc[-1]:
    print("Buy Signal")
else:
    print("Sell Signal")

Conclusion

Building an AI trading system using symbolic regression allows you to create transparent and interpretable trading models. TuringBot simplifies this process, enabling you to easily derive mathematical equations that capture the hidden patterns in financial data. By integrating these models into your trading strategy, you can make informed decisions and enhance profitability.

With symbolic regression's ability to generate accurate and understandable models, it’s a valuable tool for anyone looking to dive into AI-based trading.