Autonomous Trading Systems Inc (ATS) is a Miami-based company that provides high frequency trading (HFT) robots as a service (RaaS).
Formed in 2015, the team is made up of top executives from varying organizations (Sony, BTP Global) who identified a unique opportunity
in the electronic cryptocurrency space.
After several years analyzing trends within the various Bitcoin exchanges, ATS had developed a trading strategy that sought to capitalize
on certain recurring market inefficiencies. Critical to their strategy was discovering opportunities and then capitalizing on them. In the
cryptocurrency space as is true in other trading, the timeframe for recognizing an opportunity is often measured in milliseconds. Given the
limitations of a manual approach, ATS required the development of a sophisticated bot to automate the discovery, analysis, & execution for
these trades. Beyond speed & strategic viability, the system also needed to have Machine Learning/AI built in so that it could stay competitive
over time, evolving with the ever-changing market.
Given the nature of the request and the fact that this bot would be tasked with handling six figure portfolios on the low end, the margin for
error was substantially less than with other proof of concepts (POCs) that we’d worked on in the past. Traditionally we encourage the release
of products that incorporate a “Build, Measure, Learn” feedback loop, often prioritizing market feedback over a stakeholders internal perception
of completeness. With Bitbot however, market validation was derived entirely from the bot’s ability to make positive financial gains using the
algorithmic trading strategy that had been agreed upon. Following an extensive requirements gathering phase, the Codelitt team set out to build
the first iteration of the platform within 4 weeks. The goal: generate just one dollar of profit when the system went live.
Generate just one dollar of profit
when the system went live.
Robotic process automation (RPA) was the solution for this high frequency trading problem. RPA can be applied across many, many industries,
but it is commonly used by investment banks, hedge funds, and institutional investors to execute millions of orders and scan multiple
markets/exchanges in seconds. Removing the manual human element gives these institutions a huge advantage in the open market.
To build a fast and reliable trading bot, we leveraged technologies that provided a reliable testing environment, fast execution time, and
robust error handling which allowed us to introduce changes at the velocity required for this kind of project. Bitbot leverages a few Ruby/Java
microservices responsible for its trading system and Ruby on Rails on the backend. Even though the value of Bitbot is in the backend platform,
the client requested a simple dashboard to monitor the “status/health” and get a snapshot of its most recent activity. For this frontend
dashboard we chose to use React.js.
An intelligent bot is only as good as the information you provide it, so our development team built out a series of APIs that feed real-time
data to a secured web-based frontend with information pertaining to:
After the completion of the first phase of development, Bitbot executed a succession of micro-transactions yielding $5 in profit after being
live for only 2 minutes. This successful POC gave ATS the confidence they needed to confirm the viability of the initial trading strategy and
move forward with subsequent development iterations, shoring up the platform ahead of an invite to private investors.
Ruby on Rails
Bitbot executed a succession of micro-transactions, yielding $5 in profit after being live for only 2 minutes
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