https://quantfilab.github.io/pmarupanthorn/tags/python/PythonHugo Blox Builder (https://hugoblox.com)en-usSat, 07 Nov 2026 00:00:00 +0700https://quantfilab.github.io/pmarupanthorn/media/icon_hu68170e94a17a2a43d6dcb45cf0e8e589_3079_512x512_fill_lanczos_center_3.pnghttps://quantfilab.github.io/pmarupanthorn/tags/python/https://quantfilab.github.io/pmarupanthorn/event/quantbootcamp2026/Sat, 07 Nov 2026 00:00:00 +0700https://quantfilab.github.io/pmarupanthorn/event/quantbootcamp2026/<h2 id="quant-bootcamp-is-coming">Quant Bootcamp is coming</h2> <p>QuantCU and QuantCorner invite students, early-career professionals, and everyone interested in quantitative finance to build essential knowledge and complete practical projects using real data from <strong>efin.finance</strong>.</p> <p>The programme combines online learning, live sessions, coding practice, an onsite session, and hands-on quantitative projects. It is designed for a cohort of up to <strong>500 participants</strong>.</p> <h2 id="twelve-learning-modules">Twelve learning modules</h2> <ol> <li><strong>Introduction to Quant</strong> — Understand quantitative-finance roles and the work performed across the industry.</li> <li><strong>Markets, Instruments, and Financial Statements</strong> — Build a practical understanding of financial markets, instruments, and company information.</li> <li><strong>Python for Quant Work</strong> — Use Python to analyse data and develop programs for quantitative work.</li> <li><strong>Factor Models</strong> — Construct, evaluate, and select factors for investment models.</li> <li><strong>Statistics for Financial Data</strong> — Apply statistical methods to financial datasets.</li> <li><strong>Return and Risk</strong> — Measure investment performance and manage risk.</li> <li><strong>Time-Series Models</strong> — Analyse and forecast observations recorded sequentially through time.</li> <li><strong>Backtesting without Overfitting</strong> — Test strategies correctly while reducing research and model-selection errors.</li> <li><strong>Statistical Arbitrage</strong> — Study trading strategies built from statistical relationships.</li> <li><strong>E-Finance Toolkits</strong> — Use professional tools and financial data for analysis.</li> <li><strong>AI and LLMs in the Research Process</strong> — Apply artificial intelligence and large language models responsibly within quantitative research workflows.</li> <li><strong>Quant Careers and Industry Panel</strong> — Meet industry professionals during the onsite session and learn about career paths and real working experience.</li> </ol> <h2 id="hands-on-quantitative-projects">Hands-on quantitative projects</h2> <p>The programme concludes with practical projects that guide participants through the full research cycle: framing a question, preparing and analysing data, developing a quantitative idea, and testing an investment strategy independently.</p> <h2 id="registration-and-programme-information">Registration and programme information</h2> <ul> <li><strong>Registration period:</strong> 26 October 2026 at 20:00 through 6 November 2026</li> <li><strong>Programme begins:</strong> 7 November 2026</li> <li><strong>Onsite activity:</strong> 12 December 2026</li> <li><strong>Capacity:</strong> 500 participants</li> <li><strong>Student fee:</strong> THB 99</li> <li><strong>General-public fee:</strong> THB 499</li> <li><strong>Organizers:</strong> QuantCU and QuantCorner</li> <li><strong>Sponsor and data partner:</strong> efin.finance</li> </ul> <p>After all programme expenses are deducted, registration proceeds will support QuantCU activities, including member research and project development.</p> <p>Organizations interested in supporting the programme and helping young people develop quantitative-finance skills may contact <strong>QuantCU via Instagram: QuantCU</strong>.</p>