GDAC DeFi: Connecting a Centralized Exchange to On-Chain Markets
GDAC DeFi brought decentralized-finance functionality into the experience of a centralized exchange. I integrated external market data, contributed to smart-contract development, and analyzed behavior after launch to test our product assumptions. The work connected technical implementation and product judgment because CeFi operations and on-chain execution had to function as one service.
Project context
Centralized exchanges and DeFi process transactions and distribute responsibility differently. Exchange users expect consistent pricing and order status within an account, while on-chain execution depends on signatures, network confirmation, and smart-contract state. Connecting the two required more than hiding their differences. We had to define which price the user saw, the conditions of execution, and what the system could promise when a transaction failed.
Reliable market information was equally important. If real-time order books and historical chart data differed in timing, or an external source was delayed, what a user saw could diverge from actual execution conditions.
My role
- Software engineer at Peertec/GDAC Exchange
- Backend and external market-data integration
- Smart-contract development and testing
- Post-launch analysis and business-opportunity research
Period: November 2023–June 2024
What I worked on
I implemented backend integrations for real-time order books and historical price data from external exchanges. Data formats and update cycles had to be normalized, with clear handling for missing or delayed information. Consistency and timing mattered because the output directly informed a user’s decision to trade.
On the on-chain side, I contributed to contract development and testing and supported security review. We examined not only successful execution but also invalid inputs, permissions, and unexpected external conditions. Even when execution sits behind a CeFi interface, the irreversibility and public nature of an on-chain transaction remain.
After launch, I used market and usage data to evaluate whether behavior matched our initial hypotheses. With Python, Jupyter, and Pandas, I organized the data and translated the findings into reports that could guide product improvements and potential business opportunities. The focus was not that a feature had shipped, but when users chose it and where they stopped.
What I learned
The project taught me that a product connecting two financial architectures cannot be designed in the language of only one. It required an understanding of on-chain execution and of the stability and explanations exchange users expected. A mismatch among market data, backend state, and contract results could quickly erode trust.
It also reinforced that post-launch analysis should test the next decision rather than decorate the previous one. We needed to ask whether technically possible functionality created real user value. GDAC DeFi moved my work beyond implementation toward evaluating technical execution and product value through the same evidence.
Technology and link
- Backend: Node.js, NestJS
- Data and storage: S3
- On-chain: smart contracts
- Analysis: Python, Jupyter, Pandas
- GDAC Exchange: gdac.com
Technologies: Node.js, NestJS, Python, smart contracts, DeFi, CeFi integration