Exploring the Potential of Synthetic Data in Trading Strategies

[AlgoSjoerd]

I recently completed a research project focused on creating synthetic data using Generative Adversarial Networks (GANs). This innovative approach has shown significant promise in enhancing trading strategy development by addressing overfitting challenges and expanding datasets.

Why Synthetic Data Works: Synthetic data mimics real-world data patterns and can be used in the same way as your regular OHLC data. By using GANs, we can generate high-quality, diverse datasets that improve the robustness and accuracy of trading models.

Why It’s Useful: You can generate an infinate amount of additional data, of each market and every specific timeframe. In addition, it enhances model performance by reducing overfitting, synthetic data helps create more reliable and profitable trading strategies.

I’ve seen impressive results, including a significant increase in trading profits and Sharpe ratio during my research.

I’m now looking to connect with others interested in exploring the benefits of synthetic data further, so please feel free to respond.

 

[FranziskaSchulz]

what exactly is synthetic data? I am not sure if I know it @

 

[AlgoSjoerd]

Synthetic data is basically fake data, but has the same characteristics as real data. So training your models on synthetic data would give similar results to training them on real data. If you ever experienced data shortage, using synthetic data can be a solution. A synthetic data stream looks just like a real data stream.

 

[morehastelessspeed]

Synthetic data is basically fake data, but has the same characteristics as real data. So training your models on synthetic data would give similar results to training them on real data. If you ever experienced data shortage, using synthetic data can be a solution. A synthetic data stream looks just like a real data stream.

Hi I am intrigued by this approach and would like to learn more about the basic premises. Any references to learn more about this approach?
I am guessing one advantage might be to avoid backtest results skewed by highly improbable, extreme outliers, which can appear with excessive backtesting on real history.

 

[AlgoSjoerd]

Hi I am intrigued by this approach and would like to learn more about the basic premises. Any references to learn more about this approach?
I am guessing one advantage might be to avoid backtest results skewed by highly improbable, extreme outliers, which can appear with excessive backtesting on real history.

Yes you are correct, excessive backtesting on real data will overfit the results. Another benefit where I use synthetic data for is on the front end of creating strategies. Imagine having 10x the amount of training data, including a wide range of different market scenarios. The result will be much more robust strategies.

With regards to references, Johnathan Kinlay wrote a few articles about this that helped me understand the importance of synthetic data. In addition, I recently posted an article on Medium.