NUSwap – Simulated Trading Exchange

Overview

We have developed a cryptocurrency simulated trading platform to provide a controlled, risk-free environment for students to experiment with and test their investment strategies using real-time market data. By bridging theoretical knowledge with practical application, the platform fosters experiential learning while also serving as a valuable resource for research.

Key Features

Experiential Learning

Enables students to apply investment strategies in a realistic but risk-free setting.

Algorithmic Trading

Test automated strategies with real-time market data.

Data Collection

Generate datasets for future research on trading behaviors and performance.

Key Milestones

Global Experiment
Participants from NUS, Morgan State University (US), and FGV (Brazil) participated in a trading contest using the platform.
Recognition
Findings presented at UBRI Connect 2024 in ETH Zurich.
Collaboration
Professors from NUS, MSU (U.S.), and FGV (Brazil) are incorporating the platform into their courses.
Global Experiment
Participants from NUS, Morgan State University (US), and FGV (Brazil) participated in a trading contest using the platform.
Recognition
Findings presented at UBRI Connect 2024 in ETH Zurich.
Collaboration
Professors from NUS, MSU (U.S.), and FGV (Brazil) are incorporating the platform into their courses.
Global Experiment
Participants from NUS, Morgan State University (US), and FGV (Brazil) participated in a trading contest using the platform.
Recognition
Findings presented at UBRI Connect 2024 in ETH Zurich.
Collaboration
Professors from NUS, MSU (U.S.), and FGV (Brazil) are incorporating the platform into their courses.
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Testimonial

As a PhD student specializing in information systems and working part-time at the National FinTech Center, my research focuses on blockchain technology and cryptocurrency adoption. During SIMTRADE, I traded based on news and market sentiment, monitoring crypto news and social media trends to adjust my trades. I increased positions in assets with positive news and reduced exposure to those affected by negative sentiment. This experience highlighted the critical role of staying attuned to market sentiment and real-time events, emphasizing how market psychology and adaptability are essential for trading success.
Kazem AlkharsMorgan State University
As an undergrad studying mathematics, quantitative finance, computer science, and statistics, I focus on academics and passively invest in ETFs like QQQ, as actively trading feels too risky. My experience in algorithmic trading competitions like Optiver’s Ready Trader Go and IMC Prosperity 2024 prepared me for SIMTRADE, where I wrote an algorithm to trade automatically, saving time and effort compared to manual trading. I learned that trading cryptocurrencies is much harder than social media portrays, with many participants ending up with a negative P&L. This experience gave me a deeper understanding of trading algorithms and confirmed my preference for algorithmic trading, as it’s less tiring and more efficient."
Lee Isaac National University of Singapore
As a PhD student specializing in information systems and working part-time at the National FinTech Center, my research focuses on blockchain technology and cryptocurrency adoption. During SIMTRADE, I traded based on news and market sentiment, monitoring crypto news and social media trends to adjust my trades. I increased positions in assets with positive news and reduced exposure to those affected by negative sentiment. This experience highlighted the critical role of staying attuned to market sentiment and real-time events, emphasizing how market psychology and adaptability are essential for trading success.
Kazem AlkharsMorgan State University
As an undergrad studying mathematics, quantitative finance, computer science, and statistics, I focus on academics and passively invest in ETFs like QQQ, as actively trading feels too risky. My experience in algorithmic trading competitions like Optiver’s Ready Trader Go and IMC Prosperity 2024 prepared me for SIMTRADE, where I wrote an algorithm to trade automatically, saving time and effort compared to manual trading. I learned that trading cryptocurrencies is much harder than social media portrays, with many participants ending up with a negative P&L. This experience gave me a deeper understanding of trading algorithms and confirmed my preference for algorithmic trading, as it’s less tiring and more efficient."
Lee Isaac National University of Singapore
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Application Demo

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We invite educators, researchers, and institutions to join us in expanding this initiative. If you’re interested in using the platform, please email us at fintech[at]comp.nus.edu.sg. Together, let’s shape the future of education and research in trading and behavioural finance.