Quant PlaygroundQuant finance learning lab

Lessons → problems → Python → historical tools

Learn quant finance by working through it.

Understand a concept, solve a guided problem, code the calculation, and test the idea with historical data. Quant Playground is free to begin and built for learning—not predictions or financial advice.

See the learning loop

No brokerage connection. No real-money trading. Your first lesson takes about five minutes.

Inside the product

One concept, carried through four kinds of work

Real curriculum content · illustrative historical prices

Lesson · Simple Return

01 · Module 3 · 5 min

Measure a gain or loss relative to the value at the start of the interval.

r = V₁ / V₀ − 1

Return stays decimal until display.

Practice · Calculate it

02 · Beginner · 4 min

A share price moves from $50 to $54. What is the simple return?

  • 4%
  • 8%
  • 10%
  • 108%

Code · Build the function

03 · Browser Python · Pyodide

def simple_return(
    start_price,
    end_price,
):
    if start_price <= 0:
        raise ValueError("invalid start")
    ratio = end_price / start_price
    return ratio - 1

Apply · Inspect the interval

04 · Stock Lookup · historical sample

Starting close$50.00
Ending close$54.00
Simple return+8.0%

Sample values explain the workflow; they are not a live quote or an investment result.

What is included in Quant Playground

102
bite-sized lessons
20
learning modules
45
guided problems
40
Python exercises
5
application tools

The learning loop

The same idea, seen from four angles.

Quant ideas stick when a formula connects to reasoning, code, and a real question. The product is organized around that connection—not a collection of disconnected dashboards.

Browse the 20-module curriculum
  1. 01

    Learn

    Build the idea from a short lesson, a formula, a worked example, and reviewed sources.

    Example · Simple return: r = V₁ / V₀ − 1

  2. 02

    Practice

    Calculate one case by hand and get an explanation after you commit to an answer.

    Example · $50 → $54 is an 8% return

  3. 03

    Code

    Turn the same calculation into a small Python function that runs in your browser.

    Example · simple_return(start, end)

  4. 04

    Apply

    Use a focused tool to inspect historical data, allocations, or a defined backtest.

    Example · Compare two recorded closes in Stock Lookup

Three outcomes, one product

Read less like a catalog. Work more like a student.

Every area has a job: establish the concept, build the skill, or investigate a bounded historical question.

01

Learn the concepts

Follow a deliberate curriculum from market data and returns through risk, portfolios, backtesting, Python, and complete projects.

  • 20 ordered modules
  • 102 bite-sized lessons
  • Worked examples, checkpoints, and further reading
Explore the curriculum

02

Build technical skill

Move beyond reading. Solve guided questions, then write and run financial Python without leaving the browser.

  • 45 guided problems
  • 40 Python exercises
  • Progressive hints and server-checked results
See the practice path

03

Test ideas responsibly

Bring a concrete question to Portfolio Builder, Backtester, Stock Lookup, Paper Trading, or the Code Editor.

  • 5 application tools
  • Historical and simulated states labeled clearly
  • Assumptions, costs, and limitations kept visible
Open the tool guide

What you will study

A complete beginner-to-project sequence.

The roadmap is finite and visible. You can start at the beginning, see what comes next, and revisit any lesson as your code and tools become more advanced.

  1. 01

    Getting Started with Quant Finance

    7 lessons · Beginner

  2. 02

    Financial Markets and Data

    8 lessons · Beginner

  3. 03

    Returns and Growth

    8 lessons · Beginner

  4. 04

    Risk Basics

    8 lessons · Beginner

  5. 05

    Portfolio Foundations

    8 lessons · Intermediate

  6. 06

    Backtesting Foundations

    10 lessons · Intermediate

  7. 07

    Python for Quant Finance

    10 lessons · Beginner

  8. 08

    Building Quant Projects

    9 lessons · Intermediate

  9. 09

    Probability 1 · Probability Language

    3 lessons · Beginner

  10. 10

    Probability 2 · Counting and Combinatorics

    2 lessons · Beginner

  11. 11

    Probability 3 · Conditional Probability

    3 lessons · Beginner

  12. 12

    Probability 4 · Bayes' Theorem

    2 lessons · Intermediate

  13. 13

    Probability 5 · Random Variables and Expected Value

    3 lessons · Beginner

  14. 14

    Probability 6 · Important Discrete Distributions

    3 lessons · Intermediate

  15. 15

    Probability 7 · Continuous Variables and Distributions

    3 lessons · Intermediate

  16. 16

    Probability 8 · Joint Probability

    3 lessons · Intermediate

  17. 17

    Probability 9 · Sampling and Limit Results

    3 lessons · Intermediate

  18. 18

    Probability 10 · Simulation and Monte Carlo

    3 lessons · Intermediate

  19. 19

    Probability 11 · Probability in Quantitative Research

    3 lessons · Intermediate

  20. 20

    Probability 12 · Final Review and Capstone

    3 lessons · Intermediate

The boundaries are part of the lesson.

Quant work is only useful when the data source, assumptions, and limits stay attached to the result. The product says what it can—and cannot—show.

  • Educational only—not financial advice
  • No real-money trading or brokerage connection
  • Historical data may be delayed, partial, or unavailable
  • Backtests describe assumptions and do not predict future performance
  • Lessons include sources and further reading
  • Python exercises run locally in the browser through Pyodide
  • Account progress is stored through Supabase with row-level security

Free to begin

Start with one lesson. Keep the chain going.

Begin with the quant workflow, then follow each idea into a problem, a Python exercise, and a responsible historical tool.