Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Stock Data Analysis Excel Vs Python [Algo Trading Webinar]

Learn to analyse and backtest financial market data using Excel and Python. Understand the difference between both tools and their respective pros and cons. Know which one is best suited for you.
Complete Webinar Recording
Key Takeaways
- Steps involved in backtesting a strategy
- Demo of backtesting using Excel
- Why Excel and what does it enable us to do?
- Where does Excel fall short?
- How to overcome it?
- Demo of backtesting on multiple stocks
About the Speaker
Jay Parmar (Associate, Content & Research at QuantInsti)
Jay Parmar works as an Associate, Content & Research at QI and comes with several years of experience in the BFSI industry. He is actively engaged in content development for quant finance courses and mentors EPAT participants across the globe. He is passionate about algo trading and programming and enjoys developing automated trading systems. He holds a Bachelors’ in Computer Science and the EPAT Certificate. His research interests are in applying machine learning models to various facets of trading.
About EPAT
This event was held on:
Tuesday, February 23, 2021
8:30 AM ET | 7:00 PM IST | 9:30 PM SGT
Most useful links:
Join EPAT – Executive Programme in Algorithmic Trading:
Visit us at:
Link to our Blog:
Like us on Facebook @
Follow us on Twitter @
Follow us on LinkedIn @
Follow us on Instagram @
E-mail us @

  • Be the first to comment

  • Be the first to like this

Stock Data Analysis Excel Vs Python [Algo Trading Webinar]

  1. 1. Jay Parmar Stock Data Analysis: Excel vs Python
  2. 2. Stock Analysis Different Kinds of Analysis ● Analysing the distribution of a stock returns ● Deriving a stock beta ● Determining correlations between two or more stocks ● Calculating rate of change ● Backtesting a strategy ● and so on...
  3. 3. Conventional Steps Backtesting a Strategy ● Fetch stock data ● Compute buy and hold returns ● Create rules ● Generate trading signals ● Compute strategy returns
  4. 4. Why Excel? Reasons ● Used across the industry ● Has comparatively smoother learning curve ● Handy tool to test your hypotheses
  5. 5. Excel Demo
  6. 6. Where Does Excel Fall Short? Common Constraints ● Cannot handle large and unstructured data ● Gets slower as the size of the data increases ● Difficult to backtest a strategy on multiple stocks ● Difficult to optimize a strategy with more than two variables ● Difficult to backtest strategies involving advanced techniques like machine learning or neural networks
  7. 7. How to Overcome Shortfalls? Enter the world of programming ● Python to the rescue Why? ● It is faster compared to Excel ● Handles large datasets easily ● Can be used for backtesting as well as live-trading ● Various packages available to handle unstructured data ● Can use the same code for multiple stocks ● Supports advanced statistical tasks such as time-series analysis, deep neural networks and so on
  8. 8. Python Demo
  9. 9. Q&A
  10. 10. Click here for the WEBINAR RECORDING