Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [
Lecture 6: Means — [📝Lecture Notebooks] [
Lecture 7: Variance — [📝Lecture Notebooks] [
Lecture 8: Statistical Moments — [📝Lecture Notebooks] [
Lecture 9: Linear Correlation Analysis — [📝Lecture Notebooks] [
Lecture 10: Instability of Estimates — [📝Lecture Notebooks] [
Lecture 11: Random Variables — [📝Lecture Notebooks]
Lecture 12: Linear Regression — [📝Lecture Notebooks] [
Lecture 13: Maximum Likelihood Estimation — [📝Lecture Notebooks]
Lecture 14: Regression Model Instability — [📝Lecture Notebooks] [
Lecture 15: Multiple Linear Regression — [📝Lecture Notebooks]
Lecture 16: Violations of Regression Models — [📝Lecture Notebooks] [
Lecture 17: Model Misspecification — [📝Lecture Notebooks] [
Lecture 18: Residual Analysis — [📝Lecture Notebooks]
Lecture 19: The Dangers of Overfitting — [📝Lecture Notebooks] [
Lecture 20: Hypothesis Testing — [📝Lecture Notebooks]
Lecture 21: Confidence Intervals — [📝Lecture Notebooks]
Lecture 22: p-Hacking and Multiple Comparisons Bias — [📝Lecture Notebooks] [
Lecture 23: Spearman Rank Correlation — [📝Lecture Notebooks] [
Lecture 24: Leverage — [📝Lecture Notebooks]
Lecture 25: Position Concentration Risk — [📝Lecture Notebooks] [
Lecture 26: Estimating Covariance Matrices — [📝Lecture Notebooks]
Lecture 27: Introduction to Volume, Slippage, and Liquidity — [📝Lecture Notebooks]
Lecture 28: Market Impact Models — [📝Lecture Notebooks]
Lecture 29: Universe Selection — [📝Lecture Notebooks] [
Lecture 30: The Capital Asset Pricing Model and Arbitrage Pricing Theory — [📝Lecture Notebooks]
Lecture 31: Beta Hedging — [📝Lecture Notebooks] [
Lecture 32: Fundamental Factor Models — [📝Lecture Notebooks] [
Lecture 33: Portfolio Analysis — [📝Lecture Notebooks]
Lecture 34: Factor Risk Exposure — [📝Lecture Notebooks] [
Lecture 35: Risk-Constrained Portfolio Optimization — [📝Lecture Notebooks]
Lecture 36: Principal Component Analysis — [📝Lecture Notebooks]
Lecture 37: Long-Short Equity — [📝Lecture Notebooks]
Lecture 38: Example: Long-Short Equity Algorithm — [📝Lecture Notebooks]
Lecture 39: Factor Analysis with Alphalens — [📝Lecture Notebooks] [
Lecture 40: Why You Should Hedge Beta and Sector Exposures (Part I) — [📝Lecture Notebooks]
Lecture 41: Why You Should Hedge Beta and Sector Exposures (Part II) — [📝Lecture Notebooks]
Lecture 42: VaR and CVaR — [📝Lecture Notebooks]
Lecture 43: Integration, Cointegration, and Stationarity — [📝Lecture Notebooks] [Video]
Lecture 44: Introduction to Pairs Trading — [📝Lecture Notebooks] [
Lecture 45: Example: Basic Pairs Trading Algorithm — [📝Lecture Notebooks]
Lecture 46: Example: Pairs Trading Algorithm — [📝Lecture Notebooks]
Lecture 47: Autocorrelation and AR Models — [📝Lecture Notebooks] [
Lecture 48: ARCH, GARCH, and GMM — [📝Lecture Notebooks]
Lecture 49: Kalman Filters — [📝Lecture Notebooks] [
Lecture 50: Example: Kalman Filter Pairs Trade — [📝Lecture Notebooks]
Lecture 51: Introduction to Futures — [📝Lecture Notebooks]
Lecture 52: Futures Trading Considerations — [📝Lecture Notebooks]
Lecture 53: Mean Reversion on Futures — [📝Lecture Notebooks]
Lecture 54: Example: Pairs Trading on Futures — [📝Lecture Notebooks]
Lecture 55: Case Study: Traditional Value Factor — [📝Lecture Notebooks]
Lecture 56: Case Study: Comparing ETFs — [📝Lecture Notebooks]
Last active
November 15, 2024 20:32
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Save ih2502mk/50d8f7feb614c8676383431b056f4291 to your computer and use it in GitHub Desktop.
Quantopian Lectures Saved
Thanks for the resource!
Round of applause !!!!
Thanks for the resources!
Huge thanks!
thank you for this!
thanks
Thank You :)
Thank you ~~~!
Thank You !
Thanks @ih2502mk
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. :)
Thanks
Im new to quant, can someone tell me how prepared will this resource make me if my goal is to grab an internship at any tier 2 quant company
Wow this series is pretty great.
I paired it with https://quantessential.io/ for my quant prep.
Hopefully this lands me a top tier job
Promised land, thank you very much for your efforts for this,god bless you
Thanks man for the resources!!!
thanks )
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