Lecture 1: Introduction to Research — [
Lecture 2: Introduction to Python — [
Lecture 3: Introduction to NumPy — [
Lecture 4: Introduction to pandas — [
Lecture 5: Plotting Data — [
Lecture 6: Means — [
Lecture 7: Variance — [
Lecture 8: Statistical Moments — [
Lecture 9: Linear Correlation Analysis — [
Lecture 10: Instability of Estimates — [
Lecture 11: Random Variables — [
Lecture 12: Linear Regression — [
Lecture 13: Maximum Likelihood Estimation — [
Lecture 14: Regression Model Instability — [
Lecture 15: Multiple Linear Regression — [
Lecture 16: Violations of Regression Models — [
Lecture 17: Model Misspecification — [
Lecture 18: Residual Analysis — [
Lecture 19: The Dangers of Overfitting — [
Lecture 20: Hypothesis Testing — [
Lecture 21: Confidence Intervals — [
Lecture 22: p-Hacking and Multiple Comparisons Bias — [
Lecture 23: Spearman Rank Correlation — [
Lecture 24: Leverage — [
Lecture 25: Position Concentration Risk — [
Lecture 26: Estimating Covariance Matrices — [
Lecture 27: Introduction to Volume, Slippage, and Liquidity — [
Lecture 28: Market Impact Models — [
Lecture 29: Universe Selection — [
Lecture 30: The Capital Asset Pricing Model and Arbitrage Pricing Theory — [
Lecture 31: Beta Hedging — [
Lecture 32: Fundamental Factor Models — [
Lecture 33: Portfolio Analysis — [
Lecture 34: Factor Risk Exposure — [
Lecture 35: Risk-Constrained Portfolio Optimization — [
Lecture 36: Principal Component Analysis — [
Lecture 37: Long-Short Equity — [
Lecture 38: Example: Long-Short Equity Algorithm — [
Lecture 39: Factor Analysis with Alphalens — [
Lecture 40: Why You Should Hedge Beta and Sector Exposures (Part I) — [
Lecture 41: Why You Should Hedge Beta and Sector Exposures (Part II) — [
Lecture 42: VaR and CVaR — [
Lecture 43: Integration, Cointegration, and Stationarity — [
Lecture 44: Introduction to Pairs Trading — [
Lecture 45: Example: Basic Pairs Trading Algorithm — [
Lecture 46: Example: Pairs Trading Algorithm — [
Lecture 47: Autocorrelation and AR Models — [
Lecture 48: ARCH, GARCH, and GMM — [
Lecture 49: Kalman Filters — [
Lecture 50: Example: Kalman Filter Pairs Trade — [
Lecture 51: Introduction to Futures — [
Lecture 52: Futures Trading Considerations — [
Lecture 53: Mean Reversion on Futures — [
Lecture 54: Example: Pairs Trading on Futures — [
Lecture 55: Case Study: Traditional Value Factor — [
Lecture 56: Case Study: Comparing ETFs — [
Quantopian Lectures Saved
Thank you for this
Hi @ih2502mk and everyone!
How to download all these information at once ?
Thanks for this!!
Many thanks for saving this information
lets get this moneyyy
Thank you so much for this!!
Thank god you saved those resources
Thanks for saving and providing these great lectures!
Wow. I don't have the words to express how grateful and happy I am right now! Thank you!
Awesome! Thank you!
Agree with @muehlegger . Thanks for saving and posting these
Thanks
Thank you !!
thanks
Hi can anyone tell me how I can use this material? I dont have a programming background so I'm not sure how to start. Thanks!
how can i use those notebooks now that the quantopian site is down?
how can i use those notebooks now that the quantopian site is down?
Copy the raw file as text and then save its a ipynb file. :)
Heyymant
Thanks!
This is very much appreciated. Thank you
Just get the raw file and go from there.
Verify Github on Galxe. gid:GFdyFLCMfgCpmFU7d4fcZD
Thanks!
Thanks for the resource!
Round of applause !!!!
Thanks for the resources!
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Awesome @ih2502mk just started on learning and Quantopia went down. Thanks so much for this.