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
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Save ih2502mk/50d8f7feb614c8676383431b056f4291 to your computer and use it in GitHub Desktop.
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!
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 )
Thank you!
Thank you !!
Hey, does anyone know how to get the forum notebooks - the test algorithm (in ref. to the ending of the beta hedging video)?
Thank you so much.
Thanks a lot! Quite helpful!!
Thank you, very useful.
Hi, I'm new to quant and want to work in the quant field want to know does this roadmap will help me in 2025 quant journey!!
Yes, it still will, I even have a 2017 course and I am sure I can still get the foundations to guide me through
Copy the raw file as text and then save its a ipynb file. :)