Let’s say that you are backtesting your high-frequency trading (HFT) strategy.
The best that you can do is to take your historical market data and play the unreliable game of "what would have been". However, you cannot be sure how the market would have responded, so you essentially estimate a performance "discount" to adjust and lower your expectations. Moreover, you cannot go beyond what has already happened, so you are always limited to previously observed market scenarios. This is far from ideal. Instead, consider the following solution.
There is a deep learning foundation model that you can essentially "play against", as though you were playing against the market (hence the name "GAMR"). This generative model is designed not only to provide an appropriate market response, but also to produce any market conditions that you prescribe. You start with some historical episode (your historical market data), and then you go from there. Based on a simple and intuitive user interface, you come up with some more or less detailed market scenario, and the generative model shows you precisely what that would look like. More robustness, more resiliency, and more realism.
This is what we are able to provide.
Here’s the tl;dr.
Half a year into our start-up journey, we are (almost) done building our company and therefore finally able to shift more toward building our product. For the time being, we will pursue both product development and brand recognition, meaning that we will build things on the one hand, and we will write about the things being built on the other hand.
In terms of product, we will actively work on our “Generative Adversarial Market Response” (GAMR) market simulation framework, our trading system, and our messaging engine. The development of both trading system and messaging engine will take some time, so we are putting emphasis on GAMR to achieve MVP (minimum viable product) status in Q3 2024.
In terms of company, we will need to incorporate only one additional subsidiary company before our foundational structure is complete, installing the “Obliquid Financial Intellectual Property GmbH” (OFIP) to hold most of our software. The foundational structure is designed as a safeguard for our intellectual property, and it will grow with every additional company division that we will start in the future (one division, one subsidiary).
In terms of content, we rely on our medium publication to build sufficient momentum and open doors downstream. Having amassed large amounts of useful information, we are finally starting to share that information in what we consider to be an early marketing (and sales) initiative. Ultimately, the intention behind our content strategy is to grow our reach, so that enough people will know about our product when we launch.
Yes, you heard that right.
We are building a capital markets infrastructure provider (CMIP) for institutional market participants, and we will be operating entirely in the cloud. In anticipation of increasingly more capital markets infrastructure moving off-premise, our intent is to design technology at the frontier of cloud-native, ultra-low-latency (ULL) trading solutions, and to promote its wide-spread adoption in the trading industry. Our approach is from a perspective of technological feasibility, and we work overtime to make things a lot faster, not only cheaper.
We specialize in latency-critical systems and market microstructure research, and we put particular emphasis on deep learning models to support our trading operations. In the medium and long term, we will provide trading solutions for institutional market participants, allowing them to streamline their trading operations through means of the (public) cloud. In the meantime, we will build research and simulation solutions, working toward a disruption in foundation models for capital markets that is similar to what GPT-x has brought to other industries.
But why the name, you may ask.
Think of obliquid as oblique + liquid, a combination between the subtlety in (individual) trading strategy and the fluidity in (aggregated) trading outcome, together forming the complexity in capital markets. This relates to our mission insofar as we are building solutions that help navigate this complexity, and that make capital markets more accessible. Ultimately, the obliquid brand will represent all of our business areas which include, amongst others, our research services (OFRS), trading services (OFTS), and advisory services (OFAS).