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Top 10s of 2016: The Most Cloned Backtests and Notebooks

We thought the community would like to see some of the most-cloned notebooks and backtests from 2016. First, the backtests:

1.Reply to: A Simple Momentum Rotation System for Stocks by Karen Rubin
2.Reply to: Long Short Pipeline Multi-Factor by James Christopher
3.Post-Earnings Drift Trading Strategy with Estimize (PEAD) by Seong Lee
4.Reply to: Robin Hood Extreme Vetting by Charles Witt
5.Pipeline Trading Universe - Best Practice by Nathan Wolfe
6.Reply to: The Gold King And His Knights by Charles Witt

These popular algorithms were all part of the Quantpedia Trading Strategy Series:

7.Quantpedia Trading Strategy Series: Are Earnings Predictable? by Seong Lee
8.Quantpedia Trading Strategy Series: Reversals during Earnings Announcements by Nathan Wolfe
9.Quantpedia Trading Strategy Series: Reversals in the PEAD by Matthew Lee
10.3 Earnings Example Strategies based on the Quantpedia Trading Strategy Series by Seong Lee

... and the 10 most cloned notebooks:

1.Machine Learning on Quantopian by Thomas Wiecki
2.New Feature: Comprehensive Backtest Analysis (pyfolio) by Thomas Wiecki
3.Reply to: QuantCon 2016: Using the Kalman Filter in Algorithmic Trading by Aidan O'Mahony
4.Alphalens - a new tool for analyzing alpha factors by James Christopher
5.QuantCon 2016: Dual Momentum Strategy by Lotanna Ezenwa

The following belong to the Quantopian Lecture Series

6.Quantopian Lecture Series: Linear Regression by Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie
7.Updated Pairs Trading Lecture: Now With Less Wind Resistance by Delaney Granizo-Mackenzie and Maxwell Margenot
8.Quantopian Lecture Series: Fundamental Factor Models by Evgenia "Jenny" Nitishinskaya, Delaney Granizo-Mackenzie, and Maxwell Margenot
9.Quantopian Lecture Series: Long/Short Traditional Value Case Study by Gil Wassermann
10.Quantopian Lecture Series: VaR and CVaR (Expected Shortfall) by Jonathan Larkin and Delaney Granizo-Mackenzie

If you enjoy this list, you might enjoy a similar post we made in June 2016.

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