PBO: Probability of Backtest Overfitting
One of the most challenging problems in machine learning is the assessment of a model's ability to generalize to unseen data. For applications in finance, this is made particularly acute by the sequential nature of the data and the failure of many standard cross-validation techniques to deal with long range dependence.
Any trading strategy built on overfitted models puts the firm's capital and the manager's reputation at risk. Thus, the field of financial machine learning is in great need of techniques to assess the generalization capability of learning algorithms.
In Bailey et al, they take the approach that a model's performance should be consistent in any subset of the training-validation pairs. By evaluating every combination of these pairs, one could quantify empirically the degree (probability) that a modelling process trained on historic data is overfitted (backtest procedure overfitting).
This is a R implementation of their algorithm, separately described in a recent book. The variable names closely follow the notation in Chapter 11 of said book.
Before I dive into the algorithm, let me explain what PBO is and what it is not. First, PBO is an assessment of the modeling process that traders use to find a good strategy. So, the basic requirement (the crucial assumption) is you are honest and provided all the data from the entire process that led you to the final model. I'll elaborate more on this below.
Thus, this algorithm probably should be named "Probability of Backtest Procedure Overfitting".
Secondly, PBO is not the right tool to evaluate a single model. Let's say you have this dream about a crazy complex moving average one night. Next morning you coded it up and tested it on the S&P, and voila you got a Sharpe ratio of 2.1! PBO can't help you with a probability that this strategy of yours is any good.
I'll start with a simple example and illustrate how the various functions work.
The typical case is that you think you've found a nice trading strategy using a very expressive deep learning model, e.g. LSTM recurrent neural network. You arrived at the final model after a grid search in the space of the LSTM hyperparameters, such as the number of layers, the number of memory cells, whether to use peep-hole connections, etc. You might have tested thousands of different model configurations before you become satisfied with the final one. This procedure, very typical in machine learning, is actually problematic for other reasons, but let's don't worry about them here.
At each attempt to find a better model, you run through all the data you have, beginning in 1989-09-07 and ending in 2018-09-07, and generate a return for every week in the 30 year period, totally 1,560 performance numbers. The grid search made 20 trials (let's keep this small), meaning that you really have 20 similar, but slightly different models.
Now, put all of these into a matrix, which would have 1,560 rows and 20 columns, and call this
M. For illustration, I use gaussian random variates to represent the return matrix
N = 20 TT = 1560 set.seed(99989) M = matrix(rnorm(N*TT, mean=0, sd=1), ncol=N, nrow=TT)
Pick an even number
S, which, as I'll explain below, should be more than 6 and less than 20.
S = 10
Then, you want to divide
M into 10 submatrices of equal size, which in this case would have 156 rows and 20 columns. The function
DivideMat() does that for you.
Ms = DivideMat(M, S) length(Ms)
Ms is a list of length 10, whose elements are 156 x 20 matrices. Next, you generate all combinations of 10 objects taking 5 at a time, resulting in a total of 252 combinations. This is done by calling
res <- TrainValSplit(Ms)
The function returns an object
res which is a list of two lists, each of length 252. The first is the combinatorially "perturbed" training sets, and the second has the data that's not in the first one, which is the validation set we want. If you find this confusing, pause and think about what this is doing.
With these two (very long) list objects, you're ready to calculate
lambda, which gives you a distribution from which you estimate the empirical probability of overfitting.
res1 <- CalcLambda(res$Train, res$Val, eval.method="ave") Lambda = res1$lambda
Lambda is a vector of length 252, ie. each value corresponds to one combination, which is the relative rank of the best in-sample strategy out-of-sample. Again, if this doesn't make sense, don't worry about it.
The last step is to call function
PBO, which returns a value in range of [0,1), and this is interpreted as a probability,
pbo = PBO(Lambda)  0.4126984
So, there is a 41% probability that the procedure you used has led to an overfitted model. High PBO is a warning of overfitting, but it alone may not be sufficient to reject the model. There are cautionary tales with regard to its proper usage, which I'll write about in a separate blog. Here is the histogram of Lambda.
The choice of
S is not critical but must be considered in the context of the computing capability you have access to. For example, on a Linux box with 40 Gb of RAM, I could push
S to around 20, which uses 33 Gb of memeory (sure R is not very memory efficient). Anything beyond 20 does not seem practical -- Bin(22,11) is 705,432, which requires at least 264 Gb.
This concludes the tutorial on PBO.
The above code can be found in the /demo subfolder. To run it,
To install directly from github, open a terminal, type R, then
You need the following packages. To install from a terminal, type
install.packages("gtools", "foreach", "parallel")
Just like any R package, type
?function at command line will bring up the function's help page. I invested a lot of effort in explaining what each function does.
As in many other combinatoric problems, the scale of computation and memory requirement grow exponentially with the number of partitions. It is recommended that
S be set to no more than 20.
A python implementation is forthcoming. Stay tuned.
Developed and tested on Linux (ubuntu 14.04), R 3.4.4.
Please report all bugs to firstname.lastname@example.org
 Bailey, D. H., Borwein, J., Lopez de Prado, M., & Zhu, Q. J. (2016). The probability of backtest overfitting. https://www.carma.newcastle.edu.au/jon/backtest2.pdf
 Lopez de Prado (2018), Advances in Financial Machine Learning, John Wiley & Sons, Inc.
 Hochreiter, S., Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
 Sculley, D., Snoek, J., Wiltschko, A., & Rahimi, A. (2018). Winner's Curse? On Pace, Progress, and Empirical Rigor. ICLR 2018.