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Quant Science

Algorithmic trading and quantitative finance education platform founded 2023. Offers Python-based courses for algorithmic trading, investing, and stock portfolio analysis. 8-week course program using Interactive Brokers API. Founders claim double S&P 500 returns with same risk (2022-2025). Free newsletter 'Sunday Quant Scientist' on GitHub. No external funding raised.

Reputation & Trust

1 reviews
350Users

Overview

Pricing Model
one-time

Products & Services

Quant Scientist Algorithmic Trading System

education

Cohort-based course teaching algorithmic trading with Python including proprietary Hedge Fund in a Box app, 3 core strategies, and Discord access

Free 5-day mini-course available
  • Hedge Fund in a Box Python app
  • 3 Core Trading Strategies
  • Live expert training
  • Discord community
  • Backtesting frameworks
Learn more

Free 5-Day Algo Trading Course

education

Free email mini-course with 150+ lines of Python code and Endless Trading Strategy Generator

Free tier available Full 5-day course
5-day trial
  • 150+ lines of Python code
  • Endless Trading Strategy Generator
  • 5 daily lessons
Learn more

QS Newsletter

education

Free weekly newsletter on algorithmic trading topics

Free tier available Full newsletter
  • Weekly algo trading insights
  • Python code examples
Learn more

Company Information

Legal Name
Quant Science, LLC
Headquarters
Murrysville, Pennsylvania, United States
Team Size
1-10
Primary Timezone
America/New_York

Founding

Founder
Jason Strimpel & Matt Dancho
Background
Jason: 20+ yr quant (JP Morgan, AWS). Matt: 18+ yr data scientist (Business Science founder)

Competitive Analysis

Proposition: Python algo trading education with proprietary app, co-founded by JP Morgan quant veteran

Key Advantages
  • Hedge Fund in a Box app
  • Cohort-based live training
  • Active Discord

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All information is for educational use only and is not investment advice. There is a substantial risk of loss in trading commodity futures, stocks, options and foreign exchange products. Past performance is not indicative of future results.
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