Quant Systems Lab

The Quant Systems Lab is a long-term initiative by myself to unify control theory, finance, and risk management into one coherent teaching ecosystem. Its purpose is to help professionals from both domains understand how modern markets can be modelled, stabilised, and optimised using system-thinking principles. The Lab will evolve as part of the Quant with Vahab platform — starting with simulation-driven online courses and expanding into advanced research modules, publications, and real-world trading systems. Each track builds a bridge between disciplines:

Teaching Roadmap Overview

(Quant Systems Lab — Draft v2, October 2025)

The Teaching Roadmap outlines the structure and evolution of the Quant Systems Lab learning ecosystem. It presents a clear progression from conceptual understanding to applied implementation, bridging the gap between control theory and financial practice. Each track targets a specific audience but contributes to a unified framework — empowering learners to model, simulate, and control risk across financial systems. Together, they form the foundation of a quantitative education platform built on engineering precision and financial insight.

Track Audience Title Tagline
1 Engineers to Finance Finance for Control Engineers Learn to see markets as dynamic systems and design stability in wealth.
2 Finance to Engineering Control Engineering for Finance Professionals Master feedback, stability, and robustness to manage financial risk.
3 Advanced Integration Control Engineering for Trading & Risk Management Apply model predictive control to real portfolios and live trading.

TRACK 1

  • Title: Finance for Engineers: From Control Systems to Financial Thinking
  • Goal: help engineers translate system intuition into financial reasoning.

This track helps engineers translate their intuition about systems, stability, and feedback into the language of finance. It introduces financial markets as dynamic processes, portfolios as controllable systems, and risk as measurable noise. Through practical coding, simulation, and optimisation, learners discover how familiar engineering principles — such as feedback control and model predictive control — can guide disciplined investment decisions. By the end, engineers will see finance not as speculation, but as applied system design under uncertainty.

Course 1A

  • Title: Finance for Control Engineers
  • Tagline: Think of finance as a feedback system — where portfolios evolve like dynamic processes.

This course introduces engineers to the structure and behaviour of financial systems through the lens of control theory. It replaces abstract financial jargon with familiar system concepts — states, inputs, feedback, and stability. Learners explore how portfolios evolve dynamically, how risk behaves like noise, and how optimisation shapes decisions under uncertainty. By the end, participants will see finance as a controllable system — measurable, predictable, and open to design.

Module Title Highlight
1 Systems Meet Markets Discover how control loops mirror market dynamics.
2 Speaking the Language of Finance Translate returns, risk, and volatility into engineering signals.
3 Portfolios as Dynamic Systems Model wealth as a state equation with controllable weights.
4 Managing Risk through Feedback Use variance and drawdown as real-time error signals.
5 Optimisation as Decision-Making Turn portfolio rebalancing into a model-predictive-control problem.
6 Capstone Project Build and stress-test your own controlled portfolio simulator.

Course 1B

  • Title: Python for Financial Engineering
  • Tagline: Prototype, simulate, and analyse portfolios with Python — fast and clean.

This course provides the computational foundation for modelling and analysing financial systems. It equips engineers with practical tools to simulate markets, visualise uncertainty, and optimise portfolios using Python. Each lecture focuses on a single, reproducible concept — from basic data handling to convex optimisation and dashboard creation. By completing the course, learners will gain hands-on fluency in translating financial ideas into working code.

Module Title Highlight
1 Tools of the Trade Get fluent with Jupyter, NumPy, pandas, and matplotlib.
2 Working with Market Data Pull, clean, and simulate realistic price and return data.
3 Visualising Uncertainty Plot risk, volatility, and rolling metrics interactively.
4 Portfolio Simulation Vectorise wealth evolution and test leverage scenarios.
5 Optimisation in Python Use cvxpy to find efficient portfolios under constraints.
6 Mini Project Deliver a two-asset dashboard with live analytics.

TRACK 2

  • Title: Engineering for Finance: From Finance Practitioners to Control Systems Thinking
  • Goal: teach finance professionals how engineers stabilise complex systems.

This track reverses the direction of learning. It empowers finance professionals to adopt the mindset and tools of control engineers. Participants learn how feedback, observability, and robustness explain market stability and risk management — transforming complex financial intuition into structured, quantitative reasoning. Coding modules translate these concepts into live simulations, giving risk managers and quants a clear view of how to stabilise portfolios through adaptive, data-driven feedback control. The result is a finance-to-engineering translation layer for modern risk thinking.

Course 2A

  • Title: Control Concepts for Finance Professionals
  • Tagline: Learn how feedback, stability, and robustness explain market behaviour.

This course introduces the essential language of control engineering to finance professionals. It reframes markets, risk systems, and crises through the lens of dynamics, feedback, and stability. Learners gain intuitive understanding of how hidden states, delays, and adaptation shape financial behaviour — without relying on heavy mathematics. By the end, participants will be able to interpret market phenomena as control problems and think with the precision of an engineer.

Module Title Highlight
1 Why Finance Needs Control Thinking See the 2008 crisis through the lens of unstable feedback.
2 Dynamics & States Understand lag, memory, and state evolution in risk systems.
3 Feedback and Stability Explore positive and negative feedback in market cycles.
4 Observability & Estimation Reveal hidden states with Kalman-filter intuition.
5 Robustness & Adaptive Control Handle uncertainty and regime shifts with adaptive logic.
6 Control Mindset in Risk Management Translate control loops into real-world risk policies.

Course 2B

  • Title: Python for Control-Based Risk Management
  • Tagline: Bring engineering rigour into risk control with code that reacts and adapts.

This course translates control-theoretic principles into practical, code-driven risk management. Participants learn to model wealth dynamics, estimate volatility, and design feedback mechanisms that adapt to changing market conditions. Through hands-on Python exercises, they explore how risk controllers can monitor, limit, and stabilise portfolio performance in real time. The goal is to develop data-driven intuition for control under financial uncertainty.

Module Title Highlight
1 Python Refresher for Risk Analysts Revisit core Python tools for financial data.
2 Modelling Wealth as a System Simulate dynamic wealth updates via state equations.
3 Kalman Filter for Volatility Tracking Estimate hidden market noise step-by-step.
4 Feedback Control for Drawdown Limits Design gain-based risk brakes that self-correct.
5 Robust Control Simulation Test portfolio stability under severe shocks.
6 Mini Project Build a live VaR-tracking risk controller dashboard.

TRACK 3

  • Title: Control Engineering for Trading & Risk Management
  • Goal: merge both domains — design and deploy algorithmic trading systems as feedback controllers.

Track 3 merges both worlds into a single applied domain. Here, learners design algorithmic trading systems as closed-loop controllers — capable of forecasting, acting, and adapting in real time. The courses progress from theory (model predictive control for portfolios) to practice (robust and adaptive trading strategies) and culminate in a professional capstone project: a complete trading bot with monitoring and reporting. This final stage transforms conceptual understanding into implementation, producing quant-engineers who can design, deploy, and govern live financial systems.

Course 3A

  • Title: Control Engineering for Trading & Risk
  • Tagline: Turn trading decisions into a closed-loop optimisation problem.

This course introduces the foundations of control engineering applied directly to trading and portfolio management. It bridges predictive modelling and real-time execution, showing how trading can be framed as a closed-loop optimisation process. Learners design controllers that forecast, decide, and act under uncertainty, using model predictive control to balance profit and stability. By the end, participants will be able to structure trading strategies as controlled dynamic systems that respond intelligently to market feedback.

Module Title Highlight
1 Markets as Control Systems Relate trend-following and mean-reversion to feedback polarity.
2 From Signals to States Transform indicators into measurable system states.
3 Forecast → Decision → Execution Loop Close the loop between prediction and action.
4 Model Predictive Control for Portfolios Optimise future returns under constraints using cvxpy.
5 Risk Constraints in Real Time Embed VaR and drawdown directly into your controller.
6 Capstone: MPC Crypto Strategy Backtest a live MPC algorithm on exchange data.

Course 3B

  • Title: Robust & Adaptive Control in Algorithmic Trading
  • Tagline: Design trading systems that survive noise, shocks, and regime change.

This course extends the control-engineering framework into the unpredictable world of real markets. It focuses on designing algorithms that remain stable and profitable under changing conditions, structural breaks, and noisy data. Learners explore how robustness safeguards performance and how adaptive control enables continuous learning in live environments. By combining stochastic modelling with real-time updates, the course equips participants to build resilient trading systems that evolve with the market.

Module Title Highlight
1 Why Strategies Fail Diagnose fragility, overfitting, and changing regimes.
2 Robust Control → Risk Hedging Protect performance under worst-case uncertainty.
3 Adaptive Gain & Reinforcement Let controllers learn by updating parameters online.
4 Online Learning & Kalman Updates Track markets in real time through recursive estimation.
5 Stochastic MPC Add probabilistic safety margins to trading control.
6 Capstone: Adaptive Crypto Portfolio Blend stochastic MPC with online volatility learning.

Course 3C

  • Title: Professional Capstone: Build & Deploy a Quant Trading System
  • Tagline: Move from theory to production — launch your own live trading bot.

The Professional Capstone marks the transition from controlled simulations to live execution. Learners consolidate everything from the previous courses to architect, test, and deploy a fully functioning trading system. The course mirrors the workflow of a real quant engineer — from system design and data handling to model integration, automation, and investor reporting. It is the final step in transforming analytical knowledge into operational capability.

Module Title Highlight
1 Architecture of a Trading Bot Map data, strategy, and execution layers clearly.
2 Data Pipelines & APIs Stream live market data into databases in real time.
3 Strategy Integration Plug in your MPC and adaptive algorithms cleanly.
4 Backtesting & Forward Testing Evaluate Sharpe, drawdown, and controller stability.
5 Deployment & Monitoring Automate with Docker, cron, and Telegram alerts.
6 Reporting & Presentation Create investor-ready performance dashboards.

Summary

The Quant Systems Lab represents the complete synthesis of engineering precision and financial reasoning. Each track is designed as a self-contained learning path but connects seamlessly to the next, forming a coherent progression from intuition to implementation. Track 1 builds foundational literacy for engineers entering finance, Track 2 equips finance professionals with control-theoretic thinking, and Track 3 unifies both into deployable, real-world trading systems. Together, they create a modular ecosystem for lifelong learners who aim to think like quants and design like engineers.

Track Courses Core Highlight
1 Finance for Control Engineers · Python for Financial Engineering Bridges engineers into finance using system intuition.
2 Control Concepts for Finance Professionals · Python for Control-Based Risk Management Teaches finance pros to reason like control engineers.
3 Control Engineering for Trading & Risk · Robust & Adaptive Control in Trading · Professional Capstone Delivers applied, deployable quant-control systems.