PYTHON · RESEARCH · TRADING SYSTEMS
From a trading idea
to working Python.
Learn the language. Understand the backtest. Build an application around your strategy—one clear step at a time.
Start from zero. Follow the code. Understand the decisions.
THE LEARNING PATH
Five series.
One connected library.
Thirty short lessons connect Python, a ten-part backtester build, research, applications and operations. Follow release order, take the foundations-first path, or find one answer.
30 lessons · filming planned
THE RESEARCH WORKSPACE
A result you can
ask questions of.
The original animated bull-and-bear backtester brings strategy settings, trading assumptions, and results into one workflow. Look past the headline number: inspect entries, exits, costs, and the rules that produced them.
Open the backtesterFree practice preview: artificial prices, six strategy families, historical queries disabled.
Define the experiment
Strategy · symbols · dates · signal timing
Make assumptions explicit
Position size · fills · commission · slippage
Inspect what happened
Trade journal · equity · drawdown · limitations
KEEP BUILDING AFTER THE VIDEO
Useful files.
Clear next steps.
Complete backtester source, lesson examples and worksheets help you build at your own pace.
Your first program
Calculate a price change with a small example you can read line by line.
Download Python file WORKSHEET / STRATEGYTurn an idea into rules
Write down your entry, exit, sizing, data, and timing before you test.
Download worksheet WORKSHEET / EVIDENCEReview a backtest
Separate what the result measures from what it still cannot tell you.
Download worksheetThe complete backtester source
Python engine, original animated interface, media, tests and setup instructions. Credentials and private data are excluded.
Download complete source · 24 MB ↓Applied Quants retains ownership. A reuse license has not yet been selected.
Windows, Mac and Linux setup notes ↓ · 550-ticker case-study worksheet ↓ · Ten-part backtester build map ↓
HOW THE LESSONS WORK
Small examples.
Real engineering questions.
Short explanations, original visual humor and real checks make each lesson useful on its own. We’ll connect those ideas to the existing Applied Quants backtester and selected trading-system case studies, including Buy Bot.
The teaching starts with the presenter’s examples. Viewer-submitted results can later become case studies with permission and identifying details removed. AI can help with implementation; understanding and verification remain part of the job.
BUILD SOMETHING OF YOUR OWN
Have a strategy.
Need an application?
I can help turn defined requirements into Python research tools, backtesting workflows, and configurable applications.
- Strategy implementation and customization
- Data workflows and backtest reporting
- Application structure, testing, and troubleshooting
Working together through Upwork
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