From Research Paper to Python Code | Quant Trading Strategy Analysis with ChatGPT

In this in-depth walkthrough, Mohak Pachisia, Senior Quant at QuantInsti, demonstrates how ChatGPT can assist in reading, decoding, and implementing academic quantitative finance research using Python. The featured study explores a striking finding in the markets: the majority of U.S. equity returns come from overnight (non-trading hour) returns rather than intraday (trading hour) returns.

This video is tailored for early-stage quants, finance students, and trading enthusiasts who find it intimidating to work through long research papers or implement ideas from scratch. It shows how to turn academic literature into actionable Python code, step by step.

Key highlights include:

-Breaking down a 48-page finance paper using ChatGPT
-Creating a research implementation flowchart
-Automating data extraction, return calculations, and plotting
-Analyzing night versus day returns for SPY and GLD
-Visualizing return patterns and volatility
-Drawing insights for trading, portfolio management, and risk

If you are looking to integrate Python, ChatGPT, and real-world quant research, this session is a practical guide to build your confidence and skillset.

To further your journey into quantitative trading, explore the following learning resources from QuantInsti.

Learn More:
EPAT – Executive Programme in Algorithmic Trading
A globally recognized programme that covers algorithmic trading, Python, machine learning, statistics, trading systems, and risk management, with hands-on training from industry experts.

Quantra – Interactive, Self-Paced Courses
Get hands-on experience through short, self-paced courses covering algorithmic trading, Python programming, options strategies, technical indicators, and more.

Chapters
00:00 – Introduction and Overview
01:10 – Role of ChatGPT in Quantitative Research
03:00 – Selecting the Research Paper on Night vs Day Returns
05:00 – Understanding Trading vs Non-Trading Hour Returns
07:10 – Using ChatGPT to Summarize Complex Papers
10:00 – Requesting and Interpreting a Research Flow Diagram
12:30 – Downloading and Inspecting Historical Price Data
14:00 – Calculating Night and Day Returns with Python
16:00 – Plotting Cumulative Returns for Multiple Instruments
18:00 – Generating Summary Statistics and CAGR
19:30 – Creating Visualizations Using Seaborn
21:00 – Adding Rolling Volatility Bands and Interpretations
23:30 – Addressing Visualization Challenges with Volatility Bands
25:00 – Discussing Real-World Applications of the Findings
27:00 – Final Takeaways, Modular Coding, and What’s Next

What You Will Learn
-How to structure a quant research workflow using Python
-The practical use of ChatGPT in financial data analysis
-Step-by-step return decomposition: trading hours vs non-trading hours
-Visualization techniques for return patterns
-How to draw meaningful trading and risk insights from research
-A beginner-friendly approach to replicating academic studies

About the Speaker
Mohak Pachisia is a Senior Quant at QuantInsti, specializing in research and product development in quantitative and algorithmic trading. He has designed and delivered learning programmes for aspiring traders, analysts, and finance professionals across India and abroad.

Mohak is a certified EPAT alumnus, has cleared all levels of the Chartered Market Technician (CMT) certification, two levels of the CFA program, and is currently pursuing the Certificate in Quantitative Finance (CQF).

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