Python ile backtesting mantığını çok net anlattı, sadece bazı kütüphane sürümleri güncel değildi.
AI Trading Agents with Python: Backtesting Strategies
Build and backtest algorithmic trading agents using Python, modern data libraries, and AI-driven market signals.
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About this course
Algorithmic trading is no longer restricted to institutional quantitative analysts. With modern Python tools and AI frameworks, developers can build systems to analyze markets, generate signals, and execute strategies based on data.
This course guides you through the process of creating a functional, backtested AI trading agent from scratch. You will start with foundational financial terminology and Python data structures, then progress to designing intelligent algorithms that evaluate historical market data to make informed decisions.
What you'll learn:
- Understand fundamental market terminology, trading mechanics, and quantitative analysis basics.
- Process financial time-series data using modern dataframe libraries and Python type hints.
- Apply AI concepts, including basic sentiment analysis and predictive signals, to market data.
- Build custom algorithmic trading strategies based on technical indicators and AI outputs.
- Practice rigorous backtesting to evaluate strategy performance, risk, and historical drawdowns.
- Structure your trading agent using modern Python packaging and environment management.
The curriculum begins with essential financial concepts before moving into data manipulation and algorithmic logic. You will work through structured written exercises to build, test, and refine your own trading agent logic step-by-step.
Designed for beginner developers and programming enthusiasts with no prior finance background who want to explore quantitative trading.
Start building your automated trading strategies today.
What you'll get
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Certificate of completion
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Short & focused
2h 30m of practical content
Certificate of completion
Every course you complete on PickAClass issues a credential like this — original, with its own code, verifiable by URL, and detailed about what was actually demonstrated.
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AI Trading Agents with Python: Backtesting Strategies
Skills demonstrated
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Behavioral pattern analysis
Foundational
1.2 hrs
✓
Decision-architecture frameworks
Proficient
1.4 hrs
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A/B test design
Proficient
1.7 hrs
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AI Trading Agents with Python: Backtesting Strategies
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Performance detail
Coursework summary
Lessons completed
14 / 14
Practice questions
26 / 28
Assignments submitted
4 (avg 4.5 / 5)
Capstone project
Reviewed — 4.6 / 5
Total practice
6.2 hrs
Performance benchmark
Cohort rank
Top 12% of 1,625
Time to completion
11 days (median: 22)
Mastery score
91 / 100
Practice-question score
94%
Skill verification
Verified Skill Path
Reviews (8)
As aulas sobre backtesting com pandas me deram confiança pra testar minhas próprias estratégias antes de usar dinheiro de verdade.
This is exactly the hands-on backtesting course I was looking for. Building the agent piece by piece with real market data made the whole strategy-testing process finally click for me.
Useful backtesting methods, though examples feel dated.
Het backtesten van een handelsstrategie tegen historische data werd hier eindelijk concreet en begrijpelijk, echt een aanrader.
Coming from a finance background with shaky Python, I was nervous, but this laid out backtesting in a way that finally made sense. Building an agent that reads market signals and acts on a strategy, then testing it against historical data before risking anything, was exactly the workflow I'd been missing. The walkthrough on avoiding lookahead bias in the backtest saved me from a mistake I was clearly about to make. Every notebook ran and I could plug in my own ticker data easily. I've already started iterating on my own strategy with real confidence.
Der Kurs erklärt Backtesting mit Python wirklich verständlich, auch wenn manche Codebeispiele etwas veraltet wirken.
I went in mainly for the backtesting framework and came out understanding a lot more about how these trading agents actually make decisions. The instructor walks through building a strategy step by step with pandas and a couple of newer data libraries, which made the logic easy to follow even when the math got a little heavy. My only gripe is that the section on live paper-trading felt a bit thin compared to everything else, like it was added at the last minute. Still, the core backtesting workflow alone was worth going through twice, and I've already reused some of the code structure in my own project.
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Frequently asked
What do I need to take this course? +
Just a phone or computer with internet. No installs, no special hardware.
How do I pay? +
By card via Stripe. We don’t store card details — Stripe handles them securely.
Can I get a refund? +
Yes — full refund within 14 days, no questions asked.
How long will I have access? +
Forever. Once you purchase, the course is yours to revisit anytime.
Will I get a certificate? +
Yes. On completion you'll receive a certificate you can add to your LinkedIn profile.
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