Back to Community
Exercises for Lectures Are Now Available

Quantopian is building a first-class educational platform to teach the world about quant finance. A place where anyone, anywhere can dive in and learn financial concepts, statistical applications, and programming best practices.

Today, you already have over 50 lectures, accompanied by:
- 50 interactive research notebooks
- 28 video walkthroughs
- 6 backtest example algorithms

And now, we've added exercises to the mix!

With this release, you can take the concepts you learned and practice them. Try out sample exercises; work your way through problems of varying difficulty, and then see how you did with the answer key.

The first exercise is available now on the Introduction to Pairs Trading. We are planning on rolling more out soon.

Happy learning,
Alisa

Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

10 responses

Awesome!! Will give this a try today.

Hi @Alisa,

Am I able to 'send' (or share) the notebook with my answers to [email protected]? I'm struggling a bit with the extra credit one - I believe I'm close, but struggling to plot, and it would be great if someone could give me a hint as to what I'm doing wrong (something to do with trying to plot a numpy array I believe). Should I just share the notebook in a new post in the forum?

This is exactly what i missed in your lectures! I'm watching the lecture, playing with some parts of it and then... do not know what to do next. Did i understand everything correctly and remember the most important things? I do not know. Hopefully the exercises will be available for every lecture very soon because that is crucial thing in the learning process. Thanks and keep going!

@Joakim, sorry I missed this earlier. Absolutely, send over your questions (or request for an answer key) to [email protected]. We'll help you out.

@Artem, glad you like it!

@all, We've now released 2 more exercises and a case study.
Hypothesis testing: https://www.quantopian.com/lectures/hypothesis-testing
Confidence Intervals: https://www.quantopian.com/lectures/confidence-intervals
Analyze returns distributions of multiple ETFs: https://www.quantopian.com/lectures/case-study-comparing-etfs#exercises

To check your answers against the answer key, send us an email to [email protected]. Enjoy!

The answer key is now available side-by-side with the exercises (try any link above). You can reference it if you need a hint, or better yet, see how you fared once you've completed the problem set.

@all, We just released 5 more sets of exercises and answer keys:

Introduction to pandas: https://www.quantopian.com/lectures/introduction-to-pandas
Plotting Data: https://www.quantopian.com/lectures/plotting-data
Means: https://www.quantopian.com/lectures/means
Linear Regression: https://www.quantopian.com/lectures/linear-regression#notebook
Position Concentration Risk: https://www.quantopian.com/lectures/position-concentration-risk#notebook

For any questions, send us an email to [email protected]. Happy learning!

Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

@all, This week we released 6 more sets of exercises and answer keys:

Variance: https://www.quantopian.com/lectures/variance
Statistical Moments: https://www.quantopian.com/lectures/statistical-moments
Linear Correlation Analysis: https://www.quantopian.com/lectures
Instability of Estimates: https://www.quantopian.com/lectures/instability-of-estimates
Random Variables: https://www.quantopian.com/lectures/random-variables#notebook
Linear Regression: https://www.quantopian.com/lectures/linear-regression#notebook

As always, feel free to reach out with any questions to [email protected]. Enjoy!

@all, We released 7 more sets of exercises and answer keys:

Maximum Likelihood Estimation: https://www.quantopian.com/lectures/maximum-likelihood-estimation#notebook
Hypothesis Testing: https://www.quantopian.com/lectures/hypothesis-testing
Confidence Intervals: https://www.quantopian.com/lectures/confidence-intervals#notebook
Universe Selection: https://www.quantopian.com/lectures/universe-selection
Integration, Cointegration, and Stationarity: https://www.quantopian.com/lectures/integration-cointegration-and-stationarity#notebook
Introduction to Futures: https://www.quantopian.com/lectures/introduction-to-futures#notebook
Mean Reversion on Futures: https://www.quantopian.com/lectures/mean-reversion-on-futures#notebook

Reach out with any questions to [email protected]. Enjoy!

I found a problem in the exercise of random variables.

cutoff = 0.01  
_, p_value, skewness, kurtosis = stattools.jarque_bera(returns)  
print "The JB test p-value is: ", p_value  
print "We accept the hypothesis that the data are normally distributed ", p_value < cutoff  
print "The skewness of the returns is: ", skewness  
print "The kurtosis of the returns is: ", kurtosis  

The JB test p-value is: 0.923015693884
We accept the hypothesis that the data are normally distributed False
The skewness of the returns is: -0.102081900914
The kurtosis of the returns is: 3.07608657316

https://en.wikipedia.org/wiki/P-value
https://en.wikipedia.org/wiki/Misunderstandings_of_p-values
https://stats.stackexchange.com/questions/130368/why-do-i-get-this-p-value-doing-the-jarque-bera-test-in-r

the p-value is the probability of obtaining the observed sample results (or a more extreme result) when the null hypothesis is actually true.

JB test's null hypothesis is that your sample is from normal distribution.

We should say we cannot reject null hypothesis, i.e., we cannot say it is NOT normal distribution. However, this does not imply that the distribution that the data were supposedly a random sample from is normal.

Is that a typo?
accept->reject

All these double negatives is making my head spin. Would be good to get the above (from @Wei Xue) confirmed.