Aug 2023 – Jul 2024 · 11 mos · Amsterdam, North Holland, Netherlands · On-site

• Delta1 Trader in the Amsterdam office.

Jul 2022 – Aug 2023 · 1 yr 1 mo

• Created and leading the 1st Management Committee with a team of 7 people and directly managing 50+ members.

May 2021 – Jul 2022 · 1 yr 2 mos

• Published an Open-source package on PyPi by the name of ‘quant-risk’ to help club members initialise their environment easily. Implemented statistical functions such as calculating relevant statistics and ratios, mean-variance portfolio optimisation, risk parity portfolios and covariance shrinkage methods.

May 2022 – Aug 2022 · 3 mos · Singapore

• Performed sentiment analysis on corporate transcripts and analyzing their relationship with the price series of the company.
• Identified keywords in transcripts utilizing TopicRank/SOTA models, thus expediting the analysis pipeline for analysts.

Jan 2022 – Apr 2022 · 3 mos · Singapore

• Made a volatility-based trading strategy on 6-month IVs of 6 US dollar denominated FX pairs that places relative bets on volatility by entering a long or short straddle position based on expected value of future volatility among other factors.
• Incorporated theta-decay as well as relevant entry and exit signals; Achieved a Sharpe of over 1.57 over a period of 10Y.

May 2021 – Aug 2021 · 3 mos · Singapore

• Implemented an Inflation nowcasting model with the help of a Bayesian Structural Time Series model with a regression component comprising of Google Trends data.
• ▪ Utilised Google Trends data to Industry-specific queries together using Gaussian Mixture models. Used BERT and Universal Sentence Encoder to create a dashboard to show which CPI components were peaking in Search Volume.
• ▪ Utilised an Expanding Vector Error Correction model for forecasting the 5Y5Y Breakeven Inflation rate and implemented a trading strategy with the same.
• ▪ Created page on inflation forecast and feature importance using Random Forest Regressor using Angular/Typescript.
• ▪ Experimented with Mahalanobis distance to measure market regime similarity with past periods

Aug 2021 – May 2022 · 9 mos

• Implemented a stochastic chaotic simulated annealing (SCSA) algorithm for finding the global optimum in optimisation problems and demonstrated its efficiency over the classic technique of stochastic simulated annealing.

Aug 2020 – Jun 2021 · 10 mos · Singapore

• ▪ Forecasted alternative asset prices, such as exchange rates and cryptocurrencies using machine learning and time series analysis under the guidance of Asst. Professor Pun Chi Seng.
• ▪ Calculated Granger-Causality between two cryptocurrency pairs and attempted to forecast the noise using an LSTM model

Oct 2020 – Mar 2021 · 5 mos

Aug 2020 – Dec 2020 · 4 mos · Singapore

• Developed 2 Natural Language Processing model pipelines using Google Cloud Platform under the supervision of the Chief Data Scientist.
• Made use of State-of-the-Art pre-trained models such as BERT (Bidirectional Encoder Representations from Transformers) and ELMo (Embeddings from Language Models) from TensorFlow.hub to extract embeddings.
• Utilised Approximate Nearest Neighbours for Semantic Similarity to assist the firm in creating a Recommender System for companies similar to the queried company description.
• Employed VADER, a non-parametric algorithm, for Sentiment Analysis to build a Twitter Sentiment model to predict fluctuations in stock prices based on social media sentiment.

Feb 2020 – May 2020 · 3 mos · Gurgaon, Haryana, India

• Worked with the Managing Director to price different products and ranges of the company to help them reinvent their pricing for their products.
• Found breakeven by calculating the overheads and the raw material cost required for any given product.
• Helped them transition from an offline store to an online furniture store.
• Found prices of similar products in the market and priced products accordingly. Had to find a balance between competitors’ prices and our own for maximising profit.