Backtesting Trading Strategies
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- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Backtesting & Live Trading
- Describe the steps involved in backtesting, such as data collection and hypothesis formation, to ensure robust results. Evaluate the performance of the backtest
- Explain the fetching and pre-processing of data, including validating data quality, performing sanity checks, and working with missing data.
- Define trading rules and generate trading signals of the strategy to backtest
- Apply trade-level analytics such as win ratio, average p&l for winning trade, profit factor and average trade duration
- Perform performance analysis of the backtest results using drawdown, sharpe ratio, cagr and equity curve
- Explain the process of improving the backtest by implementing transaction costs and slippage
- Describe the common pitfalls of backtesting trading strategies, such as data snooping. Identify and avoid biases based on industry practices
- Paper trade and live trade your strategy

Why Choose This Course
- Realistic Backtesting (slippage, etc.)
- Trade-level Analytics
- No-Setup Python Environment
- Ready-to-use Strategy Template
- Practice + Theory
- One-Click Trading Platform Integration
- Expert Support & Community Access
- Multiple Case Studies and Strategies
- Python libraries used in the course
Features | This Course | Most Intro-level Courses |
---|---|---|
Realistic Backtesting (slippage, etc.) | Included for accuracy | Partially covered or may skip key assumptions |
Trade-level Analytics | Detailed metrics & insights | Basic or limited coverage |
No-Setup Python Environment | Start coding instantly | May require local setup |
Ready-to-use Strategy Template | Provided & customizable | Rarely included |
Practice + Theory | Focused on real application | Often theory-heavy |
One-Click Trading Platform Integration | Enabled with Blueshift | Often not included |
Expert Support & Community Access | Mentor help & peer support | Basic or limited community support |
Multiple Case Studies and Strategies | Includes several trading strategy examples with complete walkthroughs and evaluation. | Cover fewer real-world strategy implementations. |
Python libraries used in the course | Freely available | Free/Paid both |
Skills Covered for Backtesting
learning track 1
This course is a part of the Learning Track: Algorithmic Trading for Beginners
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Course Features
- Community
Faculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Capstone Project
Capstone Project Using Real Market Data
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
Learn with Jupyter Notebooks
This course uses Jupyter Notebooks to make learning Python and trading concepts interactive and beginner-friendly.
- No setup: Start instantly with a pre-configured browser environment
- All-in-one: Learn with explanations, code, and output in one place
- Interactive: Run code alongside explanations to reinforce concepts
- Practice Ready: Learn concepts by experimenting with code
Prerequisites
Basic knowledge of Python and Python libraries such as Pandas. The knowledge of financial markets such as placing orders to buy and sell assets will be helpful.
Backtesting Trading Strategies Course
- IntroductionBacktesting helps to evaluate a trading strategy from different perspectives. The interactive methods used will help you not only grasp the concepts but also answer all questions related to backtesting. This section helps you understand the course structure, and the various teaching tools used in the course: videos, quizzes, coding exercises and also the capstone project.
Backtesting
- Financial Data
- Data Pre-Processing
Trading Rules
- Trade Level Analytics
- Performance Metrics
- Risk Management
Transaction Costs and Slippage
Paper Trading
- Live Trading on Blueshift
- Live Trading Template
Common Pitfalls in Backtesting
- FAQs
- Capstone Project
- Run Codes Locally on Your Machine
- Course Summary
Why quantra®?
- More in Less Time
Gain more in less time
- Expert Faculty
Get taught by practitioners
- Self-paced
Learn at your own pace
- Data & Strategy Models
Get data & strategy models to practice on your own
Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
- Are there any webinars, live or classroom sessions available in the course?
- Is there any support available after I purchase the course?
- What are the system requirements to do this course?
- What is the admission criteria?
- Is there a refund available?
- Is the course downloadable?
- Can the python strategies provided in the course be immediately used for trading?
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
- If I plug in the Quantra code to my trading system, am I sure to make money?
- What does "lifetime access" mean?