Stock-Trading Test Bed

 

If you buy or sell stocks, it’s handy to test your strategy before you put real money at risk. Researchers devised a fresh approach to simulating market behavior. 

What's new: Andrea Coletta and colleagues at Sapienza University of Rome used a Conditional Generative Adversarial Network (cGAN) to model a market’s responses to an automated trader’s actions.

Key insight: Previous approaches tested a simulated trader in a virtual market populated by other simulated traders. However, real-world markets tend to be too complex to be modeled by interactions among individual agents. Instead of simulating market participants, a cGAN can model aggregated sales and purchases in each slice of time.

Conditional GAN basics: Given a random input, a typical GAN learns to produce realistic output through competition between a discriminator that judges whether output is synthetic or real and a generator that aims to fool the discriminator. A cGAN works the same way but adds an input — in this case, details about individual buy and sell orders and the overall market — that conditions both the generator’s output and the discriminator’s judgment. 

How it works: The authors built a simulated stock exchange based on the Agent-Based Interactive Discrete Event Simulation (ABIDES) framework to match buy and sell orders. They trained a cGAN to generate such orders based on two days of market data for Apple and Tesla stocks. Then they added orders by an independent trader.

• The authors simulated the stock market as a whole by connecting these components in a feedback loop. The first time through the loop, the exchange received historical buy and sell orders; subsequent times, it received orders from the agent and/or the cGAN.
• The exchange paired offers with purchases. Then it sent details for each order (price, volume, buy or sell, and time since the previous trade) and the market as a whole (best price, highest volume, average price, and time when the details were calculated) to the cGAN.
• Given the details provided by the exchange, the cGAN generated a new order along with a wait time. After waiting, it passed the order to the exchange, which triggered the cGAN to generate another order.
• For a half-hour within a period of several hours, an independent agent sent its own purchases to the exchange. In each of 30 minutes, it observed the trading volume and issued a buy order based on its observation. The authors set the volume: 1, 10, or 25 percent of the observed volume.

GANs are usually associated with image generation. This paper adds to a growing body of research showing that they can successfully generate data outside of perceptual domains.