Remember the handcrafting method, which I described in this series of posts?
Motivating portfolio construction
Methodology
Implementing
Testing
Adjusting portfolio weights for Sharpe Ratios
All very nice, all very theoretically grounded, except for one thing: the 'candidate matrices'. Remember, what we do is group our portfolio into subportfolios of 2 or 3 assets, and find some initial risk weights (following which there is some funny business involved IDMs and SR adjustments).
For two assets the risk weights are trivial (50% each), and then for three assets we have the problem of coming up with some portfolio weights that account for different correlations, and yet are robust to the uncertainty in correlation estimates.
Note: I account for uncertainty in Sharpe Ratios. I ignore uncertainty in volatility estimates, as that is relatively small and has minimal effect on portfolio weights.
I do this by coming up with 'candidate' matrices. Basically a series of correlation matricies that cover the possible combinations of three assets. Each of these then has a matching set of portfolio weights. We find the closest correlation matrix and voila, we have the right weights! (possibly with some extrapolation if no matrix is perfect).
Now this has always been a slightly unsatisfactory part of the method, which reflects it's roots as a heuristic method used by humans requiring minimal computation. As humans, it is nice to glance at correlation matrices and say 'ah this looks like a classic #3 matrix. I have some nice robust weights for this in a draw somewhere'. But as I've made the transition towards fully systematizing and automating this method, we can do better.
To be precise, we can do what we need with the SR adjustments. In the original post, with the method taken from my first book, this was a somewhat vague method that took no precise account for how much uncertainty there is in SR estimates. In my subsequent post on the subject, I did exactly that: computed the expected distribution of parameter estimates, and took the average portfolio weight over various points
So in this post, I'm going to take a similar approach. To whit, I will replace the 'candidate matching' stage in handcrating with the following:
- Get central estimates for correlations in a given asset portfolio
- Get the amount of time for which those correlations are estimated over
- Calculate the parameter distribution for the correlations
- Optimise the portfolio weights over different points on the correlation distribution
- Take an average of those portfolio weights
I'd recommend reading this post even if you're not a fan of the handcrafting method. It will give you some interesting insight into the uncertainty of correlation estimates, their effect on portfolio weights, and (bonus feature!) the optimal amount of time to estimate correlations over.
Setup
Let's start by setting ourselves up with some boiler plate optimisation code that will give us some weights for a three asset portfolio, based purely on the correlations (I'll assume SR=0.5 and some arbitrary standard deviation for all three assets).
from scipy.optimize import minimize
import numpy as np
def optimise_for_corr_matrix(corr_matrix):
mean_list = [.05]*3
std = [.1]*3
return optimise_inner(mean_list, corr_matrix, std)
def optimise_inner(mean_list, corr_matrix, std):
stdev_list = [std]*len(mean_list)
sigma = sigma_from_corr_and_std(stdev_list, corr_matrix)
return optimise_with_sigma(sigma, mean_list)
def optimise_with_sigma(sigma, mean_list):
mus = np.array(mean_list, ndmin=2).transpose()
number_assets = sigma.shape[1]
start_weights = [1.0 / number_assets] * number_assets
# Constraints - positive weights, adding to 1.0
bounds = [(0.0, 1.0)] * number_assets
cdict = [{'type': 'eq', 'fun': addem}]
ans = minimize(
neg_SR,
start_weights, (sigma, mus),
method='SLSQP',
bounds=bounds,
constraints=cdict,
tol=0.00001)
weights = ans['x']
return weights
def neg_SR(weights, sigma, mus):
# Returns minus the Sharpe Ratio (as we're minimising)
weights = np.matrix(weights)
estreturn = (weights * mus)[0, 0]
std_dev = (variance(weights, sigma)**.5)
return -estreturn / std_dev
def variance(weights, sigma):
# returns the variance (NOT standard deviation) given weights and sigma
return (weights * sigma * weights.transpose())[0, 0]
def sigma_from_corr_and_std(stdev_list, corrmatrix):
stdev = np.array(stdev_list, ndmin=2).transpose()
sigma = stdev * corrmatrix * stdev
return sigma
def addem(weights):
# Used for constraints
return 1.0 - sum(weights)
labelledCorrelations = namedtuple("labelledCorrelationList", 'ab ac bc')
def three_asset_corr_matrix(labelled_correlations):
"""
:return: np.array 2 dimensions, size
"""
ab = labelled_correlations.ab
ac = labelled_correlations.ac
bc = labelled_correlations.bc
m = [[1.0, ab, ac], [ab, 1.0, bc], [ac, bc, 1.0]]
m = np.array(m)
return m
Let's check a few stylised examples:
>>> optimise_for_corr_matrix(three_asset_corr_matrix(labelledCorrelations(ab=0,ac=0,bc=0)))
array([0.33333333, 0.33333333, 0.33333333])
>>> optimise_for_corr_matrix(three_asset_corr_matrix(labelledCorrelations(ab=0.9,ac=0,bc=0)))
array([0.25627057, 0.25627056, 0.48745887])
>>> optimise_for_corr_matrix(three_asset_corr_matrix(labelledCorrelations(ab=0.9,ac=0.9,bc=0)))
array([0. , 0.49999999, 0.50000001])
>>> optimise_for_corr_matrix(three_asset_corr_matrix(labelledCorrelations(ab=0.9,ac=0.9,bc=0.9)))
array([0.33333333, 0.33333333, 0.33333333])
That all looks pretty sensible.
Correlation uncertainty
Now, what is the correlation uncertainty given some sample size and the correlation value? I could paste in some LaTex, but instead let me point you towards the wiki page, and show you some code that implements it:
def get_correlation_distribution_point(corr_value, data_points, conf_interval):
fisher_corr = fisher_transform(corr_value)
point_in_fisher_units = \
get_fisher_confidence_point(fisher_corr, data_points, conf_interval)
point_in_natural_units = inverse_fisher(point_in_fisher_units)
return point_in_natural_units
def fisher_transform(corr_value):
return 0.5*np.log((1+corr_value) / (1-corr_value)) # also arctanh
def get_fisher_confidence_point(fisher_corr, data_points, conf_interval):
if conf_interval<0.5:
confidence_in_fisher_units = fisher_confidence(data_points, conf_interval)
point_in_fisher_units = fisher_corr - confidence_in_fisher_units
elif conf_interval>0.5:
confidence_in_fisher_units = fisher_confidence(data_points, 1-conf_interval)
point_in_fisher_units = fisher_corr + confidence_in_fisher_units
else:
point_in_fisher_units = fisher_corr
return point_in_fisher_units
def fisher_confidence(data_points, conf_interval):
data_point_root =fisher_stdev(data_points)
conf_point = get_confidence_point(conf_interval)
return data_point_root * conf_point
def fisher_stdev(data_points):
data_point_root = 1/((data_points-3)**.5)
return data_point_root
def get_confidence_point(conf_interval):
conf_point = norm.ppf(1-(conf_interval/2))
return conf_point
def inverse_fisher(fisher_corr_value):
return (np.exp(2*fisher_corr_value) - 1) / (np.exp(2*fisher_corr_value) + 1)
Let's have a play with this. First of all, the 0.5 point should give us the central estimate for correlation:
>>> get_correlation_distribution_point(0.9, 100, 0.5)
0.9
>>> get_correlation_distribution_point(0.0, 100, 0.5)
0.0
We can back out lower and upper estimates:
>>> get_correlation_distribution_point(0.0, 100, 0.05)
-0.1964181176820594
>>> get_correlation_distribution_point(0.0, 100, 0.95)
0.19641811768205936
These get wider if we use less data:
>>> get_correlation_distribution_point(0.0, 10, 0.05)
-0.6296263003883293
>>> get_correlation_distribution_point(0.0, 10, 0.95)
0.6296263003883295
The distribution gets narrower and slightly assymetric at extremes:
>>> get_correlation_distribution_point(0.99, 100, 0.05)
0.9851477380139932
>>> get_correlation_distribution_point(0.99, 100, 0.95)
0.9932723911926727
And so on.
Three asset portfolio weights under conditions of portfolio uncertainty
For a three asset portfolio we can now back out the optimised portfolio weights given the relevant points on the correlation distributions:
def optimise_for_corr_matrix_with_uncertainty(corr_matrix, conf_intervals, data_points):
labelled_correlations = extract_asset_pairwise_correlations_from_matrix(corr_matrix)
labelled_correlation_points = calculate_correlation_points_from_tuples(labelled_correlations, conf_intervals, data_points)
corr_matrix = three_asset_corr_matrix(labelled_correlation_points)
weights = optimise_for_corr_matrix(corr_matrix)
return weights
def extract_asset_pairwise_correlations_from_matrix(corr_matrix):
ab = corr_matrix[0][1]
ac = corr_matrix[0][2]
bc = corr_matrix[1][2]
return labelledCorrelations(ab=ab, ac=ac, bc=bc)
def calculate_correlation_points_from_tuples(labelled_correlations, conf_intervals, data_points):
correlation_point_list = [get_correlation_distribution_point(corr_value, data_points, confidence_interval)
for corr_value, confidence_interval in
zip(labelled_correlations, conf_intervals)]
labelled_correlation_points = labelledCorrelations(*correlation_point_list)
return labelled_correlation_points
Let's try this out:
>>> corr_matrix = three_asset_corr_matrix(labelledCorrelations(0,0,0))
>>> conf_intervals = labelledCorrelations(.5,.5,.5)
>>> optimise_for_corr_matrix_with_uncertainty(corr_matrix, conf_intervals, 100)
array([0.33333333, 0.33333333, 0.33333333])
# Makes sense, taking the median point off the distribution just recovers equal weights
>>> conf_intervals = labelledCorrelations(.5,.5,.95)
>>> optimise_for_corr_matrix_with_uncertainty(corr_matrix, conf_intervals, 100)
array([0.37431944, 0.31284028, 0.31284028])
# with a higher confidence point for correlation BC we put more in asset A
>>> conf_intervals = labelledCorrelations(.5,.5,.05)
>>> optimise_for_corr_matrix_with_uncertainty(corr_matrix, conf_intervals, 100)
array([0.286266, 0.356867, 0.356867])
# with a lower correlation for BC we put less in asset A
>>> optimise_for_corr_matrix_with_uncertainty(corr_matrix, conf_intervals, 10)
array([0.15588428, 0.42205786, 0.42205786])
# With less data the confidence intervals get wider
Seems to be working. Now I need to use the same technique as in the previous post on SR adjustments, which is get the portfolio weights over a number of points on the distribution, and then take an average.
def optimised_weights_given_correlation_uncertainty(corr_matrix, data_points, p_step=0.1):
dist_points = np.arange(p_step, stop=(1-p_step)+0.000001, step=p_step)
list_of_weights = []
for conf1 in dist_points:
for conf2 in dist_points:
for conf3 in dist_points:
conf_intervals = labelledCorrelations(conf1, conf2, conf3)
weights = optimise_for_corr_matrix_with_uncertainty(corr_matrix, conf_intervals, data_points)
list_of_weights.append(weights)
array_of_weights = np.array(list_of_weights)
average_weights = np.nanmean(array_of_weights, axis=0)
return average_weights
Slightly niggly thing here is that there are three inputs, so I need to iterate over 3 sets of distributions. Hence I'm using a 'p-step' of 0.1 (meaning we have to do 9^3 optimisations); using smaller values doesn't change the results (whilst being exponentially slower!), larger values are a bit too coarse.
Recalculating the candidate matrices
Now I'm going to recalculate the candidate matrices used in the original handcrafting. This involves doing this kind of thing:
>>> corr_matrix = three_asset_corr_matrix(labelledCorrelations(0.,0.,0.))
# get raw optimised weights, no uncertainty
>>> optimise_for_corr_matrix(corr_matrix)
array([0.33333333, 0.33333333, 0.33333333])
# get optimised weights with uncertainty, using data_periods = 5
>>> optimised_weights_given_correlation_uncertainty(corr_matrix, 5)
array([0.33333332, 0.33333346, 0.33333323])
Here are the results. First the raw weights with no uncertainty (first 3 columns are pairwise correlations, next 3 are weights):
Corr AB Corr AC Corr BC Raw A Raw B Raw C
0 0.5 0 0.286 0.428 0.286
0 0.9 0 0.256 0.487 0.256
0.5 0 0.5 0.5 0 0.5
0 0.5 0.9 0.5 0.5 0
0.9 0 0.9 0.5 0 0.5
0.5 0.9 0.5 0.263 0.473 0.263
0.9 0.5 0.9 0.5 0 0.5
0 0 0 0.33 0.33 0.33
Now for the original handcrafted weights:
Corr AB Corr AC Corr BC H/C A H/C B H/C C
0 0.5 0 0.3 0.4 0.3
0 0.9 0 0.27 0.46 0.27
0.5 0 0.5 0.37 0.26 0.27
0 0.5 0.9 0.45 0.45 0.1
0.9 0 0.9 0.39 0.22 0.39
0.5 0.9 0.5 0.29 0.42 0.29
0.9 0.5 0.9 0.42 0.16 0.42
0 0 0 0.33 0.33 0.33
Now let's do the new stuff, with 5 data points (anything less than 4 will break, for obvious reasons):
Corr AB Corr AC Corr BC 5dp A 5dp B 5dp C
0 0.5 0 0.298 0.404 0.298
0 0.9 0 0.264 0.472 0.264
0.5 0 0.5 0.379 0.241 0.379
0 0.5 0.9 0.462 0.349 0.189
0.9 0 0.9 0.44 0.12 0.44
0.5 0.9 0.5 0.279 0.441 0.279
0.9 0.5 0.9 0.411 0.178 0.411
0 0 0 0.33 0.33 0.33
Some observations:
- For 'nice' matrices (like equal correlations or the first row: 0,.5,0) the results of both the original handcrafting and the new method are very close to the raw optimisation
- In most cases the original handcrafting and the new method give very similar results. The main exceptions are the fourth row and the third row.
Now let's see how things change with more data, say data_points=20:
Corr AB Corr AC Corr BC 20dp A 20dp B 20dp C
0 0.5 0 0.278 0.444 0.278
0 0.9 0 0.252 0.496 0.252
0.5 0 0.5 0.437 0.125 0.437
0 0.5 0.9 0.497 0.443 0.06
0.9 0 0.9 0.5 0 0.5
0.5 0.9 0.5 0.256 0.489 0.256
0.9 0.5 0.9 0.4955 0.009 0.4955
0 0 0 0.33 0.33 0.33
For the 'nicer' correlation matrices, not very much happens as we have more data. However for the more extreme versions, like rows 3 and 4, we get closer to the raw optimised weights (since we can be more confident that the central correlation estimates are correct). In fact for row 5 we end up with the raw weights, and the penultimate row isn't far off. And finally data_points=100:
Corr AB Corr AC Corr BC 100dp A 100dp B 100dp C
0 0.5 0 0.283 0.434 0.283
0 0.9 0 0.252 0.496 0.252
0.5 0 0.5 0.471 0.059 0.471
0 0.5 0.9 0.5 0.5 0
0.9 0 0.9 0.5 0 0.5
0.5 0.9 0.5 0.256 0.489 0.256
0.9 0.5 0.9 0.5 0 0.5
0 0 0 0.33 0.33 0.33
We're pretty much at the same point as the raw weights here, although row 3 hasn't quite got there.
How many data points should we use?
Now, I don't know about you, but I'm quite happy with the weights produced by the datapoints=5, and less so with the others. This presents us with a problem, since I use weekly data to estimate correlations, which means we'd usually have anything from 52 to potentially 2000+ data points. It doesn't seem logical that having a years data would make you certain enough about correlations to completely drop an asset from your portfolio.
OK, let's take a detour for a moment. What are we trying to do here? We're not really interested in the uncertainty of past correlations. We're interested in the uncertainty of future correlations, given the data we have about the past. The sampling distribution of the past is just a proxy for this. In particular, it assumes that the latent correlation structure is static (as if!), and that joint Gaussian normal is a good model for financial data (if only!).
Now for Sharpe Ratios, where the sampling distribution is massive, and we're appalling at predicting the future, this is a pretty good approximation. Not so for correlations. Consider the following:
This is an estimate of the correlation between S&P 500 and US 10 year bond futures, estimated using all historic data on an expanding window basis. The red and the blue lines show the upper and lower 95% confidence intervals for the correlation at the start of a given year. The green line shows the actual correlation.
Now if the confidence intervals were any good, we'd expect the green line to be outside of the red and blues about 1 in 20 years. The actual figure is 15 out of 18 years. That's not great. What's not helping is there is a secular downtrend in the correlation, but even without that things are poor. Even without that, the true 95% range for correlations is probably around 0.5 rather than the 0.14 distance between the red and the green lines at the end.
Here's a similar picture for the highly correlated US 5 year and 10 year bond futures:
Although the confidence intervals are tighter (since the correlation is closer to 1) the record is just as bad: 14 misses in 18 years. No secular trend here, but the typical pattern of highly correlated assets: correlations break down in a crisis (1999, 2008). Again the true 95% range is probably 0.04 rather than the 0.01 show here.
What all this means is that using the parameter distribution error as a proxy for future uncertainy of correlations will probably underestimate the likely uncertainty by roughly a factor of 4.
There are a number of solutions to this. Most are hacky, for example, imposing some kind of minimum weight. Or dividing the time_periods by some arbitrary number. We could do something more sophisticated, like actually trying to estimate (on a rolling basis!) the true estimation error (outturn versus forecast distribution).
I'm going to do something that's fairly simple, and only a little bit hacky. I'm literally going to multiply the uncertainty by 4:
def fisher_confidence(data_points, conf_interval):
data_point_root =fisher_stdev(data_points)*4
conf_point = get_confidence_point(conf_interval)
return data_point_root * conf_point
Note the precise value will depend on the level of correlation, since we should strictly use the fisher equivalent of 4, but this seems to work well enough.
Let's see if this works. Here's the original 95% confidence intervals for correlations of -0.25 and 0.96 (roughly what we have in the plots above), with 1000 data points (about 20 years of weekly data):
>>> get_correlation_distribution_point(-0.25, 1000, 0.025)
-0.31528119007914723
>>> get_correlation_distribution_point(-0.25, 1000, 0.975)
-0.18236395028476593
>>> get_correlation_distribution_point(.96, 1000, 0.025)
0.9540384541438645
>>> get_correlation_distribution_point(.96, 1000, 0.975)
0.9652020604250287
Now with the new fudge factor applied:
>>> get_correlation_distribution_point(-0.25, 1000, 0.025)
-0.4925007507489921
>>> get_correlation_distribution_point(-0.25, 1000, 0.975)
>>> 0.028523195562247212
>>> get_correlation_distribution_point(.96, 1000, 0.025)
0.9304815655421306
>>> get_correlation_distribution_point(.96, 1000, 0.975)
0.9771329891421301
These are much closer to the likely forecasting errors we'd see in reality. Let's look at the candidate correlations using the revised method, with 100 data points (so for comparision with the final table above), or about 2 years of weekly data:
Corr AB Corr AC Corr BC 100dp A 100dp B 100dp C
0 0.5 0 0.285 0.43 0.285
0 0.9 0 0.253 0.494 0.253
0.5 0 0.5 0.404 0.192 0.404
0 0.5 0.9 0.495 0.386 0.118
0.9 0 0.9 0.498 0.003 0.498
0.5 0.9 0.5 0.261 0.4777 0.261
0.9 0.5 0.9 0.451 0.097 0.451
0 0 0 0.333 0.333 0.333
In most cases we've got weights that are pretty robust looking, and much closer to the original handcrafted versions than the weights we had before. Now with 1000 data points (about 20 years):
Corr AB Corr AC Corr BC 1000dp A 1000dp B 1000dp C
0 0.5 0 0.282 0.437 0.282
0 0.9 0 0.253 0.494 0.253
0.5 0 0.5 0.464 0.071 0.464
0 0.5 0.9 0.5 0.5 0
0.9 0 0.9 0.5 0 0.5
0.5 0.9 0.5 0.256 0.489 0.256
0.9 0.5 0.9 0.5 0 0.5
0 0 0 0.333 0.333 0.333
OK, so there are more zeros here than before. It looks like with 20 years there is enough evidence to produce relatively extreme weights for certain kinds of portfolio.
Much as it pains me, I think a further hack is required here. I'm just uncomfortable with allocating 0 to anything (although arguably if the entire portfolio is large enough, the odd zero won't matter too much). Even if the weight is small, there is the opportunity for the IDM or SR scaling to increase it.
Let's apply a minimum weight of 9%, which in a three asset portfolio is pretty underweight (so for example in the 40 or so futures contracts I trade, if all assets are in groups of 3 - which they're not - the minimum instrument weight would be 0.7%).
def apply_min_weight(average_weights):
weights_with_min = [min_weight(weight) for weight in average_weights]
adj_weights = weights_sum_to_total(weights_with_min)
return adj_weights
def min_weight(weight):
if weight<0.1:
return 0.1
else:
return weight
def weights_sum_to_total(weights_with_min):
sum_weights = np.sum(weights_with_min)
adj_weights = weights_with_min / sum_weights
return adj_weights
Ignoring the horrible use of constants rather than variables, why is the 0.1 there not 0.09? Well if you try using this with weights of [0.5,0.5,0] you will get:
>>> apply_min_weight(average_weights)
array([0.45454545, 0.09090909, 0.45454545])
... because of the effect of making the weights add up to 1.
It isn't pretty is it? These fixed numbers, 4 and 0.1, smack of being slightly arbitrary, and the 0.1 is just a personal preference with no scientific grounding whatsoever. Still you have the code. Feel free to do this differently. I won't judge you. Much.
As a final note this new technique has a huge advantage over the old candidate matching method. We can use it for any correlation matrix. That means we don't have to do the candidate matching and extrapolation. We just feed in the correlation matrix directly and out will pop the appropriate weights.
Note: in theory you could use this technique for any size of correlation matrix, but the time involved would increase exponentially.
In particular, it will generate much more sensible weights than raw optimisation when we have negative correlations (something that I completely ignored when generating the candiate matrices).
Should we use all of our data to estimate correlations?
There's a common tradeoff in a lot of the work I do: should we use more data and get a robust estimate, or less data and be more responsive? For example, should we use shorter or longer moving average crossovers? (Answer: probably both).
In the space of estimating the parameters of a Gaussian normal distribution: mean, standard deviation and correlation, the answers are different. For means (or Sharpe Ratios), I am a fan of using as much data as possible (although there is some evidence that faster trend following no longer works as well as it used to, especially for equity indices). For volatility, as a rule of thumb something like a 30 day estimate of volatility works much better than using more data.
Sharpe Ratios are hard to predict so we use a lot of data. Vol is relatively easy to predict so we can use less data and update our estimates frequently. Logically correlations, which fall somewhere between those two stools in terms of predictability, could be candidates for more updating. But, in my current handcrafting code, I use all the available data to estimate correlation matrices.
Under the new method the length of data used will have a big effect. A shorter window will result in less extreme weights. Hey, we might even be able to avoid using the horrible hack of minimum weight constraints. However we shouldn't arbitrarily reduce the correlation lookback purely to get a more robust outcome, unless the evidence justifies it.
So let's get some understanding of the optimal length of data history to use for predicting correlations. I'll consider three portfolios. Each contains two highly correlated assets, and another that probably isn't so correlated:
- Underlying instrument returns for different instruments
- Trading subsystem returns for different instruments
- Returns of trading rules
from systems.provided.futures_chapter15.basesystem import futures_system
system = futures_system()
## underlying returns
del(system.config.instrument_weights)
instrument_list = system.get_instrument_list()
perc_returns = [system.rawdata.get_percentage_returns(instrument_code) for instrument_code in instrument_list]
perc_returns_df = pd.concat(perc_returns, axis=1)
perc_returns_df.columns = instrument_list
## subsystem returns
subsys_returns = [system.accounts.pandl_for_subsystem(instrument_code) for instrument_code in instrument_list]
subsys_returns_df = pd.concat(subsys_returns, axis=1)
subsys_returns_df.columns = instrument_list
## trading rule returns
def get_rule_returns(instrument_code):
rules = list(system.rules.trading_rules().keys())
rule_returns = [system.accounts.pandl_for_instrument_forecast(instrument_code, rule) for rule in rules]
rule_returns_df = pd.concat(rule_returns, axis=1)
rule_returns_df.columns = rules
return rule_returns_df
use_returns = perc_returns_df # change as required
use_returns = use_returns[pd.datetime(1998,1,1):] # common timestamp for fair comparison
use_returns = use_returns.resample("5B").sum() # good enough approx for weekly returns
Before we dive into the code, we need to answer a question: Over what period are we trying to predict correlations? It makes sense to forecast volatility over the next 30 days, since that is roughly our average holding period and we adjust our positions according to vol on a day to day basis. In a long only portfolio quarterly rebalancing is common, so for the underlying instrument perc_returns we'll try and predict the next 3 months (13 weeks) correlations.
We could do monthly rebalancing, but predicting 5 weeks of weekly correlation estimates is probably a mugs game, and most long only portfolios don't have turnover that is as high as all that.
But correlations are used to define instrument and trading rule weights which we keep for a while - in backtesting they are refitted annually. So let's use try and predict next years correlations for trading rule and subsystem returns.
Here's the functions we need:
def get_forecast_and_future_corr(Nweeks_back, Nweeks_forward):
forecast = get_historic_correlations(Nweeks_back)
future = get_future_correlations(Nweeks_forward)
pd_result = merge_forecast_and_future(forecast, future, Nweeks_forward)
return pd_result
def merge_forecast_and_future(forecast, future, Nweeks_forward):
assets = forecast.columns # should be the same won't check
pd_result = []
for asset in assets:
result_for_asset = pd.concat([forecast[asset], future[asset]], axis=1)
# remove tail with nothing
result_for_asset = result_for_asset[:-Nweeks_forward]
# remove overlapping periods which bias R^2
selector = range(0, len(result_for_asset.index), Nweeks_forward)
result_for_asset = result_for_asset.iloc[selector]
result_for_asset.columns = ['forecast', 'turnout']
pd_result.append(result_for_asset)
pd_result = pd.concat(pd_result, axis=0)
return pd_result
def get_future_correlations(Nweeks_forward):
corr = get_rolling_correlations(use_returns, Nweeks_forward)
corr = corr.ffill()
future_corr = corr.shift(-Nweeks_forward)
return future_corr
def get_historic_correlations(Nweeks_back):
corr = get_rolling_correlations(use_returns, Nweeks_back)
corr = corr.ffill()
return corr
def get_rolling_correlations(use_returns, Nperiods):
roll_df = use_returns.rolling(Nperiods, min_periods=4).corr()
perm_names = get_asset_perm_names(use_returns)
roll_list = [get_rolling_corr_for_perm_pair(perm_pair, roll_df) for perm_pair in perm_names]
roll_list_df = pd.concat(roll_list, axis=1)
roll_list_df.columns = ["%s/%s" % (asset1, asset2) for (asset1, asset2) in perm_names]
return roll_list_df
def get_asset_perm_names(use_returns):
asset_names = use_returns.columns
permlist = []
for asset1 in asset_names:
for asset2 in asset_names:
if asset1==asset2:
continue
pairing = [asset1, asset2]
if pairing in permlist:
continue
pairing.reverse()
if pairing in permlist:
continue
permlist.append(pairing)
return permlist
def get_rolling_corr_for_perm_pair(perm_pair, roll_df):
return roll_df[perm_pair[0]][:,perm_pair[1]]
Just for fun, here are some one year rolling correlations for the three types of portfolios. I've used the returns for US10, US5 and SP500. For trading rules I've used the returns of EWMAC16, EWMAC32 and carry (for S&P 500 as it happens, but the results will be pretty similar on any instrument).
get_rolling_correlations(use_returns, 52).plot()
First underlying returns:
Now trading subsystems:
Finally trading rules:
I leave the interpretation of these plots as an exercise for the reader. But the main point is that correlations do move around and there are some clustering states; whether they are predictable by using more frequent estimates is a good question.
Now let's return to the question of how much data we should use when predicting correlations:
Nweeks_forward = 52 # use 13 weeks for underlying returns, 52 for others
import statsmodels.api as sm
import matplotlib.pyplot as pyplot
pyplot.rcParams.update({'font.size': 16})
Nweeks_list = [4, 7, 13, 26,52, 104, 208, 520]
r_squared = []
for Nweeks_back in Nweeks_list:
print(Nweeks_back)
pd_result = get_forecast_and_future_corr(Nweeks_back, Nweeks_forward)
pd_result = pd_result.dropna()
x = pd_result.forecast
x = sm.add_constant(x)
y = pd_result.turnout
est = sm.OLS(y, x).fit()
r2 = est.rsquared_adj
r_squared.append(r2)
ax = pyplot.gca()
ax.scatter(Nweeks_list, r_squared)
ax.set_xscale('log')
Here are the results for underlying assets (perc_returns_df, Nweeks_forward = 13):
To explain: the x-axis is the number of weeks we're looking back (log scale), whilst doing a regression on how correlation ends up being in the next 13 weeks, on what correlation was in the lookback period. The y-axis is the resulting R squared of the regression. Higher R squared is good! And the results are interesting: it looks like around 2 years (100 weeks) is the optimum length of time to predict 13 weeks correlations, although anything from 1 year upwards is fine.
And here's subsystem returns (subsys_returns_df, Nweeks_forward = 52):
Again, it looks like we should use at least a couple of years of data, although there is a benefit from using even more (which makes sense since we're trying to forecast a year ahead rather than just 3 months). And finally here are the trading rules (rule_returns_df, Nweeks_forward = 52), for which we need to tweak our code to use the average R squared across instruments:
Nweeks_forward = 52 # use 13 weeks for underyling returns, 52 for others
r_squared = []
for Nweeks_back in Nweeks_list:
print(Nweeks_back)
all_r2 = []
for instrument_code in instrument_list:
print("%s/%s" % (instrument_code, Nweeks_back))
use_returns = get_rule_returns(instrument_code)
use_returns = use_returns[pd.datetime(1998, 1, 1):] # common timestamp
use_returns = use_returns.resample("5B").sum() # good enough approx for weekly returns
pd_result = get_forecast_and_future_corr(Nweeks_back, Nweeks_forward)
pd_result = pd_result.replace([np.inf, -np.inf], np.nan)
pd_result = pd_result.dropna()
x = pd_result.forecast
x = sm.add_constant(x)
y = pd_result.turnout
est = sm.OLS(y, x).fit()
r2_this_instr = est.rsquared_adj
all_r2.append(r2_this_instr)
r2 = np.mean(all_r2)
r_squared.append(r2)
Again we need to use at least 5 years, and perhaps 10 or more years of data.
Let's recap our results. We're primarily interested here in setting instrument and rule weights, so we'll put the underlying instrument results to one side (although they're not that different - the sweet spot is 2 years):
- A lookback of less than 2 years is likely to do a relatively poor job of forecasting future correlations over the next year
- Lookbacks of more than 10 years may do a little better at forecasting, but from above we know they will produce weights that aren't as robust even after applying a fudge factor
- Shorter lookbacks may also result in assets moving between groups, which will produce changes in weights regardless of how robust our method is.
In conclusion then I'd recommend using 10 years of data, or say 500 data points, in the handcrafting correlation estimates.
Update 9th November 2020:
In the original post I suggested using a 2 year window. The only real reason to use a shorter window was because I was uncomfortable with the zero weights produced by the longer windows. But I already 'solved' that with the minimum weight. In fact, the data is telling me that with 10 years of data you can be darn sure that the correct portfolio does indeed have a zero weight in some cases. It strikes me as better to use the explicit hack, rather than achieving it through the back door and pretending it's the correct thing to do.
Note that where we to consider the uncertainty of Sharpe ratios and correlations jointly,that would justify having at least some weight even when an asset is highly correlated as there would be some outcomes when having a zero weight would be suboptimal. This could be shown by doing a double pass; stepping through SR values and correlation values together, and optimising across them all. Whilst this is a neat idea it would slow things down a lot and we'd lose the intuition of the two step process (here are the weights for this correlation matrix. now lets' adjust them for SR). Something for a future blogpost.
Plugging into pysystemtrade
Let's now plug the new code into pysystemtrade, replacing the original methodology. Incidentally, the python code I've used so far can be found
here.
Basically in the
handcrafting code we replace
get_weights_using_candidate_method(cmatrix) with code we've already developed (although there are a couple of minor tweaks because it reuses existing optimisation functions). I've also increased the p-step to 0.25 since it doesn't affect the results, and results in a 5^3 improvement in speed (we only do 9 optimisations per correlation matrix; at confidence intervals of 0.25, 0.5 and 0.75 per each of the three assets).
The optimisation code is a bit slow, but I need to refactor it first before I try and speed it up.
Does it work? Let's try with a quick and dirty 3 asset portfolio.
To make the results more interesting I am using a 2 year lookback here, rather than the 10 years suggested above.
First some forecast weights:
from systems.provided.futures_chapter15.estimatedsystem import futures_system
system = futures_system()
system.config.instruments = ['US10', 'US5', 'SP500']
system = futures_system(config = system.config)
x=system.combForecast.get_forecast_weights("US10")
x.plot()
These seem kind of sensible. The weight to the middle EWMAC is lower, because it is in a 3 asset group, and it is highly correlated with the other two EWMAC. It looks like the minimum weight is kicking here at times. There is some movement from year to year, possibly due to the shorter lookback on correlations, but nothing too extreme; however it will be more stable once we move to the recommended 10 year lookback.
Now lookee at some instrument weights:
Prety much what you'd expect (bearing in mind the different data lengths).
Summary
That's it. I think the handcrafting method is probably about as good as I can get it now. Hope it's been an interesting journey.
Note: I will be updating the earlier posts on handcrafting to reflect this new methodology.
