I’ve been exploring the use of synthetic data as input data for the development of systematic trading strategies. Some of the benefits that made me interested in this are:
Mitigating Overfitting: Synthetic data helps in creating more robust trading strategies by mitigating the issue of overfitting that often arises when using limited historical data. This allows strategies to be more adaptable and effective in various market conditions.
Simulating Market Conditions: Synthetic data includes a range of market scenarios, including rare events and crises that may not be well-represented in historical data. This exposure helps in developing trading strategies that are robust to different market conditions and periods of uncertainty.
Software Testing: Synthetic financial data is used to test and validate trading algorithms and software systems without using real data. This limits the problems that arise when using historical data mulitple times.
In theory this would mean that synthetic data helps creating more robust trading strategies, and allows for a whole new area of innovation in terms of what kind of strategies you can create.
Does anyone have any practical experience with synthetic data?
Hi there, I don’t know what will be your methodology to generate data.
I share a bit what I found using AI to generate trading strategy.
What I see, trading system using statistical model tend to be more profit. It can survive more to market dynamic.
I’ve tried neural network, the result is not good after a long while. You need to train the model again. Sometime it get over fit the system becomes dull. If you are using in your real account, it wont be good thing.
I haven’t found good methodology to predict / forecast market. As long as I know all trading system that claim using AI are statistical based system.
I’ve been using GANs to generate data, and they are able to recreate characterstics as thicker tails and volatility clustering, and are statistically similar to real data. The idea is to generate an X amount of new data streams and use those as input data. So the trading strategies trained on synthetic data have seen X times more data than trading strategies trained on real data, including different market dynamics.
In the context of Forex trading and EA testing, synthetic data can simulate market conditions, price movements, and other relevant factors.
Using Synthetic Data in Forex EA Testing:
Backtesting: Backtesting involves applying trading strategies to historical data. Synthetic data can be used to create additional scenarios beyond the available historical data.
Scenario Testing: Synthetic data allows testing under various market conditions, including extreme scenarios that may not occur frequently in real data.
Stress Testing: Simulating extreme market events (e.g., flash crashes, sudden volatility spikes) using synthetic data helps assess EA performance.
Model Validation: Synthetic data helps validate EAs against known market patterns and behaviors.
Robustness Testing: EAs can be tested against synthetic data with different noise levels, gaps, or missing data.
Parameter Optimization: Synthetic data aids in fine-tuning EA parameters for optimal performance.