Marcos M. Lopez de Prado

SOFTWARE

Several of my papers discuss implementations of various algorithms in Python language. This facilitates the replication of my results, and allows the reader to run independent experiments. Enthought's Canopy product contains all the necessary libraries. Although the code is generally included in the appendix, it is easier to download the source code from the table below. In order to do that, please follow these steps:

1) Point your mouse on a link.
2) Right-click, select "Save as" and press "Save".
3) Rename the saved file, by removing the extension ".txt"

Note: All code in this website is provided “as is”, and contributed to the academic community for non-business purposes only, under a GNU-GPL license. Users explicitly renounce to any claim against the authors. The authors retain the commercial rights of any for-profit application of this software, which must be pre-authorized in written by the authors.

AUTHOR	VERSION	LINK	PAPER	DESCRIPTION
Berkeley Lab	2014	Backtest Overfitting Simulator	Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance	This online tools overfits a seasonal investment strategy within the parameter ranges specified by the user. Special thanks to Stephanie Ger, who set up this web application and prepared this research poster.
Lopez de Prado, Marcos	20151227	HRP.py.txt HRP_MC.py.txt	Building Diversified Portfolios that Outperform Out-Of-Sample	HRP.py performs a hierarchical portfolio construction. Changing the np.random.seed in generateData() will produce different random correlation matrices on which the method can be tested. HRP_MC.py runs a Monte Carlo experiment to show that HRP solutions deliver lower variance than CLA's out-of-sample, even though minimum-variance is CLA's optimization goal.
Lopez de Prado, Marcos	20140731	DSR.py.txt	The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting and Non-Normality	DSR.py computes the expected value of the maximum of a series of IID Normal random variables, applying our closed-form solution. The accuracy of this solution is verified numerically.
Lopez de Prado, Marcos	20140407	KCA_1.py.txt KCA_2.py.txt KCA_3.py.txt KCA_4.py.txt	Kinetic Component Analysis	KCA_1.py implements the Kinetic Component Analysis (KCA) algorithm. KCA_2.py carries out the analogous calculation using a Fast Fourier Transform (FFT). KCA_3.py performs a comparison of KCA vs. FFT. KCA_4.py compares KCA with the Locally Weighted Scatterplot Smoothing (LOWESS) method.
Lopez de Prado, Marcos	20140131	OTR.py.txt	Determining Optimal Trading Rules without Backtesting	OTR.py contains the code needed to determine the Optimal Trading Rule (OTR) on an instrument with a price that follows a discrete Ornstein-Uhlenbeck process.
Lopez de Prado, Marcos	20140119	SFD_1.py.txt	Stochastic Flow Diagrams	SFD_1.py contains the code needed to build Stochastic Flow Diagrams.
Lopez de Prado, Marcos	20140119	SFD_2.py.txt	The Topology of Macro Financial Flows: An Application of Stochastic Flow Diagrams	SFD_2.py implements Fisher's variance stabilizing transformation on the Correlation coefficient.
Lopez de Prado, Marcos	20130813	CSCV_1.py.txt CSCV_2.py.txt	The Probability of Backtest Overfitting	CSCV_1.py implements the PBO estimation via Monte Carlo. CSCV_2.py implements the PBO estimation by Extreme Value Theory.
Lopez de Prado, Marcos	20130813	CSCV_3.py.txt	Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance	CSCV_3.py simulates the performance of a seasonal trading strategy under various overfitting scenarios, which can be used to corroborate the Probability of Backtest Overfitting (PBO) usefulness in real investment applications.
Lopez de Prado, Marcos	20130108	DD1.py.txt DD2.py.txt DD3.py.txt DD4.py.txt Data1.csv.txt Data2.csv.txt	Drawdown-based Stop-Outs and the "Triple Penance" rule	DD1.py contains the Monte Carlo which confirms the accuracy of our close-formula derivation of the quantile function. DD2.py computes the maximum of the Drawdown (DD) function, under the more general assumption of first-order serially-correlated outcomes. DD3.py the maximum of the Time under Water (TuW) function, under the more general assumption of first-order serially-correlated outcomes. DD4.py reproduces our empirical analysis on hedge fund indices (Data1.csv and Data2.csv).
Lopez de Prado, Marcos	20130108	CLA.py.txt CLA_Main.py.txt CLA_Data.csv.txt	An Open-Source implementation of the Critical-Line Algorithm for Portfolio Optimization	A Python class containing the Critical-Line Algorithm for quadratic optimization subject to inequality constraints. CLA.py is the class. CLA_Main.py is an example of how to use the CLA class. CLA_Data.csv is sample data. A seminar on this code can be watched here, and the presentation downloaded here. Enthough has built a GUI on our CLA class, which can be downloaded here.
Lopez de Prado, Marcos	20120516	ERC.py.txt	Balanced Baskets: A New Approach to Trading and Hedging Risks	ERC basket optimization algorithm.
Lopez de Prado, Marcos	20120516	MMSC.py.txt	Balanced Baskets: A New Approach to Trading and Hedging Risks	MMSC basket optimization algorithm.
Lopez de Prado, Marcos	20120516	Clustering.py.txt	Balanced Baskets: A New Approach to Trading and Hedging Risks	Covariance clustering algorithm.
Lopez de Prado, Marcos	20120502	PSR.py.txt	The Sharpe Ratio Efficient Frontier	Implementation of PSR statistics.
Lopez de Prado, Marcos	20120502	PSR_Optimal_Constrained.py.txt	The Sharpe Ratio Efficient Frontier	Implementation of PSR portfolio optimization.
Lopez de Prado, Marcos	20120316	OEH.py.txt	Optimal Execution Horizon	Determination of the optimal execution horizon.
Lopez de Prado, Marcos	20120217	EF3M.py.txt	Markowitz meets Darwin: Portfolio Oversight and Evolutionary Divergence	Implementation of the EF3M algorithm.