GDA’s Next-Gen Strategy Testing Environment

Architecture and Design Principles

At the centre of this environment is a backtesting “core”. This essentially is a virtual exchange that has an order book, user interface, users, API connectivity, and other standard exchange features.

Design Principles

Virtual exchange powered backtesting core

Core Functionality

The primary purpose of this new testing environment is to simulate trades as accurately as possible to a real world environment. This will not only give us a robust and accurate understanding of the strategies we develop, it will also promote rapid strategy development. TradingView GUI will be integrated into the platform to give us the additional functionality that is offered by TradingView. The user interface will be able to display configured FBR, WCM, VHW and ELV technical charts, as well as configured volume and liquidity clusters. Buy and sell orders are also visible on the chart. These visualisations can be exported to the ACPR infrastructure as well.

FBR, WCM, VHW and ELV technical chart
Volume and liquidity clusters
Buy and sell order visualisation

Use Cases

This new platform, with its atomic level data, allows for the testing of advanced trading strategies that can’t be properly evaluated with existing strategy testing techniques. For example, high frequency strategies such as scalp trading, market making or arbitrage in any of its many forms, whether it be cross-exchange, intra-exchange, CEX-DEX arbitrage, or some other form of arbitrage, can be tested using this new environment. This environment isn’t limited to HFT strategies either. The best way to think of this new testing environment is as a virtual exchange with virtual capital — if you can implement your strategy on a real exchange, you can implement it here.

ACPRI Integration

We have already spoken about ACPR and ACPR infrastructure and you may be wondering what we are referring to. ACPR stands for automated chart pattern recognition. It is another development vertical of GDA’s R&D lab. As a machine learning project, it requires a sophisticated dataset of chart images. By incorporating it into the backtesting environment, development of this dataset can be accelerated. We will release more information on this project at a later date so stay tuned for that.



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GDA Fund

GDA Fund


GDA is developing the decentralized financial application development environment and rapid financial engineering protocol built on Ethereum.