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Quant Crunch

Team members

Lucas Ng Yi Leang (ESD), Ng Jing Da (ESD), Evan Sidhi Peradijaya (ESD), Tan Yin Ling (ESD), Chew Wen Jie Jeremy (ISTD), Koh Ting Yew (ISTD), Leo Ding Hao (ISTD)


Kenny Choo, Wang Xingyin, Lynette Cheah

Writing Instructors:

Grace Kong

Teaching Assistant:

Cheong Rui Zhi Jeremy, Anirudh Gajendra Rathi

Quant Crunch Promo Video

Project Description

Quant Crunch is an aglorithmic platform that utilises a series of different Machine Learning and Deep Learning techniques to provide buy and sell recommendations to users. Users are able to keep track of the algorithm's performance through a logbook that records their open and closed positions. 

Project Motivation

Part of the energy that drives the project lies in the inherent passion the team has as retail traders. From the get go, the team was invested into making an algorithmic platform that seeks to simplify the onboarding process as well as deliver the assurance that users need when adopting a new technology.

Through a series of surveys and user testing evaluations, we found out that newbie traders were often bombarded with a huge amount of information. This led to Quant Crunch which is an algorithmic tool leverages upon the use of Deep Learning to provide trade recommendations that enables users to acquire an edge in their existing trading strategies.

User Requirements

User testing analysis was conducted by performing user observations studies to understand the general trading workflow, as well as to gather user requirements for the system. The process was conducted through Zoom where participants would share their webcam and screen with the interviewer so that their activities could be recorded. Any additional tools or systems that the participants used and their specific functionalities and purposes were also recorded and noted down. 

Upon completion of the study, the notes were consolidated and a simple affinity mapping was made to identify recurring themes and patterns. The plan was to use the qualitative data that the team obtained to generate insights which could be used to guide the design process for the user interface.

Needs of our Target Audience:

1) Ease of Use

2) Appropriate Risk-to-Return Performance


User Flow Diagram

User Flow Diagram

Model Considerations

The team tested various approaches and algorithms, and this is a top level overview to how we designed and evaluated our models.

The model can either forecast how the price might change or decide what the best action would be at a given time.

In terms of price prediction, the team tried out a neural network based model, which used a recurrent network architecture, as those have been shown to be useful when dealing with sequential data. We also looked into technical analysis indicators, which are conventional trading disciplines to help identify trends and patterns seen within price data. The use of TA helped to address one of the issues found when dealing with price forecasting models, which is the penchant to produce shifted outputs.

The team also looked at action-predictive models, including a simpler version of the price-predictive model that uses a binary classifier to decide which of buy or sell would be most appropriate at a given time, as well as a reinforcement learning model which uses trial and error to explore the trading environment and decide which action maximises its cumulative reward. 

Model Design

System Architecture

System Architecture

Features of Quant Crunch

Trading Journal
  1. Robust Recommender System

  2. Daily trade recommendations are neatly displayed on both the chart and in the form of a table with clear entry price, stop loss, target price and expected profit. Traders are able to look up critical information at a glance while comparing it against the S&P 500 benchmark.



In collaboration with SAH Global Marketing Pte Ltd

Trading Journal

A logbook that has separate sections for open and closed positions, this quickly allows users to differentiate the records while simplifying the process for executing trades or viewing their unrealised or realised profits. This feature personalised the page for the user, allowing them to see the progress of the trades they have participated in and can also double as a paper trading simulation.

Recommender System Performance


student Lucas Ng Yi Leang Engineering Systems and Design
student Ng Jing Da Engineering Systems and Design
student Evan Sidhi Peradijaya Engineering Systems and Design
student Tan Yin Ling Engineering Systems and Design
student Chew Wen Jie Jeremy Information Systems Technology and Design
student Koh Ting Yew Information Systems Technology and Design
student Leo Ding Hao Information Systems Technology and Design
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