Turn your trading ideas into Python projects

Learn to collect market data, backtest a strategy, and build a complete paper-trading bot. Six self-guided sections walk you through the full process, with lesson notes, code examples, and downloadable references.

Get the masterclass

$599 once. Learn at your own pace.

Prerequisites. No Python experience needed. Start with Python foundations, or skip ahead to market data and backtesting if you already code. You do not need to take the Challenge first.

500+
students
6
course sections
66
lessons

momentum_backtest.ipynb

In [12]: run_backtest(strategy, prices)

return

24.8%

drawdown

-9.6%

trades

148

Out [12]: costs applied, no lookahead flags

paper orders

timesymbolsidestatus
09:31:04SPYbuyfilled
09:31:08TLTsellfilled
09:32:11GLDholdchecked
Example output. Not a performance record.

What students say

★★★★★
“As a beginner in algorithmic trading, I was surprised how quickly it was possible to automatically backtest a trading strategy and to create a trading bot in Python. The bot already works and executes orders (on a demo account).”
DDanielPython for Traders student
★★★★★
“I recently joined the Python for Traders course, and I have to say it's one of the best structured and professionally presented courses I've come across in the trading space. The content is well-organized, making it much easier to apply Python to real trading scenarios.”
MMoritzPython for Traders student

The six-section roadmap

Start with Python, then work through trading concepts, market data, broker APIs, backtesting, and the final bot project. Open any course to read its lessons.

Start with the Welcome lesson, then work through these six sections.

Section 1

17 lessons

Python Foundations

Set up Python, notebooks, packages, and the core libraries used for market work.

A working research environment and your first market-data notebook.

Lessons

  1. Python 101
  2. Editors + Notebooks
  3. Package Managers
  4. Version Control
  5. AI Assistants
  6. Expressions + Variables
  7. Control Flow
  8. Data Structures
  9. Functions
  10. Classes
  11. List Comprehensions
  12. Pandas (Data Analysis)
  13. YFinance (Data Collection)
  14. Matplotlib (Data Visualization)
  15. Seaborn (Data Visualization)
  16. Sklearn (Machine Learning)
  17. Cheatsheet Download

Section 2

9 lessons

Quant Concepts

Learn where systematic returns can come from and what the main strategy families are testing.

A framework for turning a trading idea into a testable rule.

Lessons

  1. Excess Returns
  2. Risk Factors
  3. Mean Reversion
  4. Trend Following
  5. Growth + Value
  6. Sentiment Analysis
  7. Seasonality Effects
  8. Statistical Arbitrage
  9. Market Making

Section 3

15 lessons

Market Data

Design a database, build the data pipeline, and put daily updates on a schedule.

A reusable SQLite warehouse with daily prices for 5,700 stocks.

Lessons

  1. Data Providers
  2. Files and Databases
  3. Analyzing Market Data
  4. Database + Pipeline Project
  5. SQLite from Python
  6. DB Connection + Running SQL
  7. Designing Relational Tables
  8. Solution - Designing Relational Tables
  9. Creating the Full Schema
  10. Data Pipeline Design
  11. Extracting Data for 5700 Stocks
  12. Cleaning and Processing
  13. Loading into the Database
  14. Bringing It All Together
  15. Automating Collection with Cron

Section 4

9 lessons

Trading Automation

Read account data, positions, and quotes, then submit market and limit orders through an API.

A broker layer connected to an Alpaca paper-trading account.

Lessons

  1. Alpaca Markets
  2. Account and Position Data
  3. Real-Time Quotes and Trades
  4. Market and Limit Orders
  5. Architecture Overview
  6. Data Management
  7. Signal Generation
  8. Risk Management
  9. Trade Execution

Section 5

9 lessons

Backtesting

Measure returns and risk, include costs, catch common biases, and test on a hosted engine.

A backtest with performance reporting and bias checks.

Lessons

  1. Strategy Performance Metrics
  2. Avoiding Common Pitfalls
  3. Pandas + QuantStats
  4. QuantConnect Onramp
  5. Testing Simple Buy and Hold
  6. Our News-Based Strategy
  7. Adding Sentiment Analysis
  8. Constructing a Portfolio
  9. Putting Everything Together

Section 6

6 lessons

Capstone Project

Combine the data, signal, portfolio, risk, and execution pieces in one final project.

A complete momentum bot that runs through a paper-trading workflow.

Lessons

  1. Capstone Project
  2. Bot Configuration
  3. Stock Universe
  4. Data Manager
  5. Portfolio Management
  6. Putting Everything Together

6 sections + welcome / 66 lessons / self-paced

66 self-guided lessons, including 64 videos totaling about 13 hours, with code examples and 15 downloadable references.

Projects you’ll build

Build a market-data database, a backtest report, a broker connection, and a complete paper-trading bot.

PROJECT 01

Daily price warehouse

SQLite / scheduled

symboldateopenclose
AAPL2026-08-20230.41234.12
MSFT2026-08-20507.22509.78
NVDA2026-08-20181.06183.44

5,700

symbols

OHLCV

history

Daily

updates

PROJECT 02

Backtest report

A repeatable process for measuring performance and checking the mistakes that can make a result look better than it is.

  1. 01Trading costs appliedPASS
  2. 02Signals shifted before entryPASS
  3. 03Drawdown measured from equityPASS
  4. 04Lookahead bias checkedPASS

PROJECT 03

Broker API layer

GET/v2/account200
GET/v2/positions200
GET/v2/stocks/quotes200
POST/v2/orders201

PROJECT 04

Momentum trading bot

  1. 01Select the stock universe
  2. 02Update and clean prices
  3. 03Calculate the signal
  4. 04Size positions and check risk
  5. 05Send and reconcile paper orders

config.py

UNIVERSE = 500

POSITIONS = 20

MAX_WEIGHT = 0.05

PAPER = True

Tools used in the course

Use Python and pandas for research, yfinance for market data, QuantConnect for hosted backtests, and Alpaca for paper trading.

Python
pandas
yfinance
QuantConnect
Alpaca

What you get

01Python Foundations
02Quant Concepts
03Market Data
04Trading Automation
05Backtesting
06Capstone Project
+Lesson notes, code examples, and reference downloadsIncluded
+Self-guided access on your own scheduleIncluded

The masterclass is for you if

  • You have a trading idea and want to test it against historical data
  • You use spreadsheets and want a repeatable research process
  • You know Python but are new to market data, backtesting, or execution

It is not for you if

  • You want a guaranteed profitable strategy or trade signals to copy.
  • You do not want to write, run, and change Python code yourself.
  • You want advanced computer science theory instead of practical market projects.

Frequently asked questions

Do I need to take the Challenge first?
No. The Masterclass starts with Python foundations. The Challenge is an optional smaller project if you want to try the teaching approach first.
Are there live classes or coaching?
No. This is a self-guided course library. Study on your own schedule using the video lessons and included code. Coaching, live calls, and a managed community are not included.
How long does it take?
There are about 13 hours of video. Allow additional time to run the code and complete the projects. Work through the six sections on your own schedule.
Do I need a paid data subscription?
No. The course uses free data sources. It also explains when a paid source may be worth using.
Will this make me money?
No. The course teaches you to test a strategy and measure its risk. It does not provide trade signals or guarantee profits.
Which broker do you use?
Alpaca. You use a free paper-trading account.

Six sections, 66 lessons, one payment

Video lessons, code examples, and reference downloads. Python Foundations starts with installing Python.

$599

Get the masterclass

Educational content only. No profit claims, no trade signals, and every project runs on a paper account.

$599

6 sections, 66 lessons, self-guided

Get the masterclass