What the System Is

The Quant Scientist Algorithmic Trading System is:

A structured algorithmic trading course + toolkit that teaches users how to:

• Set up an algorithmic trading environment in Python and link it to broker APIs such as Interactive Brokers for live/paper trading.

• Develop trading strategies using quantitative models (e.g., momentum, volatility targeting).

• Backtest strategies effectively using historical data and frameworks like Zipline Reloaded.

• Manage risk using performance metrics, stop-losses, and position sizing.

• Automate execution to remove emotion and react to market conditions.

It’s pitched both as a learning program and a system for systematic trading that can help users make data-driven and automated trading decisions.

Who Created It

Jason Strimpel

• Quantitative finance and algorithmic trading professional with over 20+ years of experience.

• Held senior quant and risk roles (e.g., derivatives market risk, engineering, data science) at institutions including BP Trading, JP Morgan Chase, and Rio Tinto.

• Focuses on building Python-based trading systems and hedge-fund-style algorithms.

Matt Dancho

• Experienced data scientist, developer, and investor, with expertise in analytics and time-series data.

• Combines data science with quantitative trading; his personal trading and portfolio growth figures are often highlighted in program materials (e.g., multi-million USD portfolios).

• Co-founded Quant Science with Jason to build this course and system.

Together, they teach participants how to apply professional quant techniques to individual algorithmic trading.

Core Elements of the System

Trading Strategy Development

• Offers a 5-step framework including specific strategies like momentum and volatility targeting.

• Emphasizes automation so trades execute when conditions are met, without manual input.

Backtesting & Evaluation

• Teaches event-based backtesting and use of frameworks such as Zipline Reloaded.

• Helps users understand performance metrics (returns, Sharpe ratios, drawdowns).

Risk Management

• Covers methods such as stop-loss settings, position sizing, and diversification.

• Encourages systematic risk oversight rather than discretionary trading.

Community & Support

• Access to community channels where users share strategies, results, and receive guidance.

Target Audience

The system is marketed primarily to:

• Python programmers who want to apply coding skills to markets.

• Beginner to intermediate traders seeking automated/quantitative approaches.

• Those interested in data-driven investing instead of manual chart picking.

Performance & Claims

Some materials promoting the system include performance claims, such as:

• Annualized returns exceeding traditional benchmarks.

• Win rates above typical industry averages.

Be aware these claims appear on promotional sites and may not be independently verified; due diligence is always recommended before investing real capital.

Summary

In short, The Quant Scientist Algorithmic Trading System is:
✔ A Python-based algorithmic trading education program
✔ A step-by-step guide to building automated trading systems
✔ Co-created by Jason Strimpel (quant finance professional) and Matt Dancho (data scientist & coder)