QuantInsti Articles

Treasury analyst Fatema Alkhabbaz used EPAT to learn Python from scratch and build systematic strategies, betting that the future of treasury is quantitative.

Treasury analyst Fatema Alkhabbaz used EPAT to learn Python from scratch and build systematic strategies, betting that the future of treasury is quantitative.

Six sequenced prompts that take a momentum research paper from summary to adaptation, data, backtest, and a critical review of the results in Claude.

How chartered accountant Manikandan K traded discretionary decisions for systematic research, discovered volatility modelling through EPAT, and now chases alpha as an independent quant.

After his quant firm in France shut down, Mayank Hedaoo used EPAT's faculty and placement support to land a bond trading role in global commodity markets.

Discover how Ishwar C. moved from deep theoretical finance knowledge to practical trading system design through EPAT, and how he now hires and mentors the next generation of quants.

Recap of QuantInsti's live AMA with Nitesh Khandelwal on algo trading careers, AI and LLMs, Python, and the EPAT journey from learner to practitioner.

Explore the top HFT, prop trading and quant firms in 2026 across India, US, UK, Singapore and UAE, plus hiring trends, pay and skills needed to break in.

An EPAT project walk-forward tests time-series momentum, MA crossover, and breakout strategies across seven FX pairs from 2003 to 2025, finding limited but real tradeability.

An EPAT project walk-forward tests time-series momentum, MA crossover, and breakout strategies across seven FX pairs from 2003 to 2025, finding limited but real tradeability.

An EPAT project compares Logistic Regression, Random Forest, and XGBoost on the S&P 500 and Brent Crude, testing machine learning trading signals against buy and hold from 2022 to 2025.

An EPAT project compares Logistic Regression, Random Forest, and XGBoost on the S&P 500 and Brent Crude, testing machine learning trading signals against buy and hold from 2022 to 2025.

FINRA is removing the PDT rule from June 4, 2026. Learn how the $25,000 minimum, day trading limits, and margin rules are changing for US retail traders.

Learn how to build an AI-powered forex backtest using DeepSeek LLM regime labels vs KMeans. Includes Python code, walk-forward optimization, and OOS results.

Machine Learning basics, algorithms, concepts and techniques are the talk of the day! Machine Learning has dramatically altered every field. Dive into the basics of machine learning, and learn all about it.

Discover how Renan, a fixed-income trader with a neuroscience background, used EPAT to build systematic algorithms, apply mean reversion strategies, and work toward launching his own trading shop.

Discover how Brian moved from emotional manual trading to disciplined, systematic strategy building through EPAT, Python, and data-driven thinking.

In this article, we will understand how support vector machines work and its application in trading. We will also go through the maths behind the SVM and the process of using it in a non-linear model.

Learn about the basics of unsupervised learning algorithms and their use cases in Finance/Investment/Trading with examples in Python.

Build a Dify-based multi-agent workflow that turns a plain-English trading idea into a risk-managed Python backtesting script. Understand the agent roles, how the handoffs work, and how to adapt the example from the AI-in-Trading-Workflow GitHub repository.