# Quantifying and Combining Crypto Alphas

In this article, I’ll take some crypto stat arb features from our recent brainstorming article and show you how you might quantify their strength and decay characteristics and then combine them into a trading signal.

This article continues our recent articles on stat arb:

Ultimately, combining signals is about making sensible decisions about how you want each signal to contribute to the calculation of portfolio weights. And to make those decisions, you need to understand your signals:

• Do they need to be scaled or transformed in some way to be useful?
• How strongly are they predictive of forward returns?
• How quickly does this predictive power decay?
• How noisy is the signal? Does it bounce around all over the place or is it relatively stable?
• How correlated are your signals to one another?

One you understand these questions, you can start to consider how to combine your signals. And there are no right or wrong answers, just different paths through the various trade-offs depending on your goals and constraints. When considering costs, the questions around signal decay and stability become critical.

And you might combine your signals using simple heuristics (equal weight, equal volatility contribution), or by selecting coefficients based on your understanding of how the signals behave. You can also use explicit optimisation techniques, but you don’t have to.

In this article, we’ll combine some cross-sectional signals with a time-series one, which has the effect of tipping the portfolio net long or short – which may or may not be acceptable depending on your constraints.

I’ll include all the code and link to the data so that you can follow along if you like.

Let’s get to it.

First, load some libraries and set our session options:

# session  options
options(repr.plot.width = 14, repr.plot.height=7, warn = -1)

library(tidyverse)
library(tibbletime)
library(roll)
library(patchwork)

# chart options
theme_set(theme_bw())
theme_update(text = element_text(size = 20))
â”€â”€ Attaching core tidyverse packages â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€ tidyverse 2.0.0 â”€â”€
âœ” dplyr     1.1.4     âœ” readr     2.1.4
âœ” forcats   1.0.0     âœ” stringr   1.5.1
âœ” ggplot2   3.4.4     âœ” tibble    3.2.1
âœ” lubridate 1.9.3     âœ” tidyr     1.3.1
âœ” purrr     1.0.2
â”€â”€ Conflicts â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€ tidyverse_conflicts() â”€â”€
âœ– dplyr::filter() masks stats::filter()
âœ– dplyr::lag()    masks stats::lag()
â„¹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Attaching package: 'tibbletime'

The following object is masked from 'package:stats':

filter



Next load some data. This dataset includes daily price, volume, and funding data for crypto perpetual futures contracts traded on Binance since late 2019.

You can get the data here.

perps <- read_csv("https://github.com/Robot-Wealth/r-quant-recipes/raw/master/quantifying-combining-alphas/binance_perp_daily.csv")
head(perps)
Rows: 187251 Columns: 11
â”€â”€ Column specification â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€â”€
Delimiter: ","
chr  (1): ticker
dbl  (9): open, high, low, close, dollar_volume, num_trades, taker_buy_volum...
date (1): date

â„¹ Use spec() to retrieve the full column specification for this data.
â„¹ Specify the column types or set show_col_types = FALSE to quiet this message.

A tibble: 6 Ã— 11
<chr><date><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl>
BTCUSDT2019-09-1110172.1310293.11 9884.31 9991.84 8595536910928 5169.153 52110075-3e-04
BTCUSDT2019-09-12 9992.1810365.15 9934.1110326.581572234981938411822.980119810012-3e-04
BTCUSDT2019-09-1310327.2510450.1310239.4210296.5718905512925370 9198.551 94983470-3e-04
BTCUSDT2019-09-1410294.8110396.4010153.5110358.0020603134931494 9761.462100482121-3e-04
BTCUSDT2019-09-1510355.6110419.9710024.8110306.3721132687427512 7418.716 76577710-3e-04
BTCUSDT2019-09-1610306.7910353.8110115.0010120.0720821137629030 7564.376 77673986-3e-04

First, let’s constrain our universe by just removing the bottom 20% by trailing 30-day dollar volume.

How many perps are in our universe?

universe <- perps %>%
group_by(ticker) %>%
mutate(trail_volume = roll_mean(dollar_volume, 30)) %>%
# also calculate returns for later
mutate(
total_fwd_return_simple = dplyr::lead(funding_rate, 1) + (dplyr::lead(close, 1) - close)/close,  # next day's simple return
total_fwd_return_simple_2 = dplyr::lead(total_fwd_return_simple, 1),  # next day again simple return
total_fwd_return_log = log(1 + total_fwd_return_simple)
) %>%
na.omit() %>%
group_by(date) %>%
mutate(
volume_decile = ntile(trail_volume, 10),
is_universe = volume_decile >= 3
)

universe %>%
group_by(date, is_universe) %>%
summarize(count = n(), .groups = "drop") %>%
ggplot(aes(x=date, y=count, color = is_universe)) +
geom_line() +
labs(
title = 'Universe size'
)

## Some simple features

We’ll look at three very simple features implied by our brainstorming session.

• Breakout – closeness to recent 20 day highs: (9.5 = new highs today / -9.5 = new highs 20 days ago)
• Carry – funding over the last 24 hours
• Momentum – how much has price changed over the last 10 days

My intent is to use the carry and momentum features as cross-sectional predictors of returns. That is, does relative carry/momentum predict out/under-performance?

And I’ll use the breakout feature as a time-series overlay. I hope to use it to predict forward returns for each asset in the time-series, not in the cross-section.

This is mostly to demonstrate how to combine cross-sectional and time-series features into a single long-short strategy. Adding time-series features to a long-short stat arb basket has the effect of making the basket net long or net short rather than delta neutral, so you may not want to do it, depending on your constraints.

First we calculate the raw features and lag them by one observation:

rolling_days_since_high_20 <- purrr::possibly(
tibbletime::rollify(
function(x) {
idx_of_high <- which.max(x)
days_since_high <- length(x) - idx_of_high
days_since_high
},
window = 20, na_value = NA),
otherwise = NA
)

features <- universe %>%
group_by(ticker) %>%
arrange(date) %>%
mutate(
# we won't lag features here because we're using forward returns
breakout = 9.5 - rolling_days_since_high_20(close),  # puts this feature on a scale -9.5 to +9.5 (9.5 = high was today)
momo = close - lag(close, 10)/close,
carry = funding_rate
) %>%
ungroup() %>%
na.omit()

head(features)
A tibble: 6 Ã— 20
<chr><date><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><dbl><int><lgl><dbl><dbl><dbl>
BTCUSDT2019-10-299389.989569.109156.039354.63120749620123844063093.62592624390-0.00117130640692193-0.02797496 0.011791818-0.028373711FALSE7.59353.776-0.00117130
BTCUSDT2019-10-309352.659492.868975.729102.91108960600626784458062.12537610627-0.00106636660931006 0.01179182-0.015317479 0.011722841FALSE6.59102.029-0.00106636
BTCUSDT2019-10-319103.869438.648933.009218.70 85180209422921646234.79423883668-0.00092829680429873-0.01531748 0.025637957-0.015436001FALSE5.59217.811-0.00092829
BTCUSDT2019-11-019218.709280.009011.009084.37 81600115923309143506.69398347973-0.00074601698120233 0.02563796-0.015217382 0.025314821FALSE4.59083.466-0.00074601
BTCUSDT2019-11-029084.379375.009050.279320.00 65353954320433835617.67328788833-0.00030000710903610-0.01521738 0.011864661-0.015334351FALSE3.59319.204-0.00030000
BTCUSDT2019-11-039319.009366.699105.009180.97 60923750121966231698.32292888566-0.00030000722483703 0.01186466 0.008212094 0.011794831FALSE2.59180.160-0.00030000

## Exploring our features

The first thing you’ll generally want to do is get a handle on the characteristics of our raw features. How are they distributed? Do their distributions imply the need for scaling?

features %>%
filter(is_universe) %>%
pivot_longer(c(breakout, momo, carry), names_to = "feature") %>%
ggplot(aes(x = value, colour = feature)) +
geom_density() +
facet_wrap(~feature, scales = "free")

We see that the breakout feature is not too badly behaved. Carry and momentum both have significant tails however, so we’re going to want to scale those features somehow.

We’ll scale them cross sectionally by:

• calculating a zscore: the number of standard deviations each asset is away from the daily mean
• sorting into deciles: sort the assets into 10 equal buckets by the value of the feature each day – this collapses the feature value down to a rank for each asset each day (multiple assets will have the same rank).

We’ll also calculate relative returns so that we can explore our features’ predictive utility in the time-series and the cross-section.

features_scaled <- features %>%
filter(is_universe) %>%
group_by(date) %>%
mutate(
demeaned_fwd_returns = total_fwd_return_simple - mean(total_fwd_return_simple),
zscore_carry = (carry - mean(carry, na.rm = TRUE)) / sd(carry, na.rm = TRUE),
decile_carry = ntile(carry, 10),
zscore_momo = (momo - mean(momo, na.rm = TRUE)) / sd(momo, na.rm = TRUE),
decile_momo = ntile(momo, 10),
) %>%
na.omit() %>%
ungroup()

Next we would plot our features against next day returns. For example, here’s a factor plot of the decile_carry feature against next day relative returns:

features_scaled %>%
group_by(decile_carry) %>%
summarise(
mean_return = mean(mean(demeaned_fwd_returns))
) %>%
ggplot(aes(x = factor(decile_carry), y = mean_return)) +
geom_bar(stat = "identity") +
labs(
x = "Carry Decile",
y = "Cross-Sectional Return",
title = "Carry decile feature vs next-day cross-sectional return"
)

We see that it’s a good discriminator of the sign of next day returns, and the relationship is noisily linear, but much stronger in the tails.

And here’s a plot of the breakout feature against mean next day time-series returns:

features_scaled %>%
group_by(breakout) %>%
summarise(
mean_return = mean(total_fwd_return_simple)
) %>%
ggplot(aes(x = breakout, y = mean_return)) +
geom_bar(stat = "identity") +
labs(
x = "Breakout Feature Value",
y = "Return",
title = "Breakout feature vs mean next-day time-series return"
)

We see a pretty noisy and non-linear relationship, but high values of the breakout feature are generally associated with higher returns.

Here’s the momentum factor:

features_scaled %>%
group_by(decile_momo) %>%
summarise(
mean_return = mean(demeaned_fwd_returns)
) %>%
ggplot(aes(x = factor(decile_momo), y = mean_return)) +
geom_bar(stat = "identity") +
labs(
x = "Momo Decile",
y = "Cross-Sectional Return",
title = "Momentum decile feature vs next-day cross-sectional return"
)

We see a very noisy relationship between 10-day cross-sectional momentum and next day relative returns. Interestingly, we see a roughly negative relationship, implying a slight mean-reversion effect rather than momentum.

### More work to do

There are all sorts of ways you can view your features.

Here, we’ve only looked at the mean returns to each discrete feature value, which is useful because by aggregating returns, we get a clear view of the on-average relationships. On the downside however, this approach hides nearly all of the variance. So in practice you’d also rely on other tools – for instance scatterplots and timeseries plots of cumulative next day returns to the feature.

We’ve also not looked at turnover implied by our signals. This is crucial when you start to consider costs.

You’d also want to look at look at the stability of the factors’ performance over time. For example, you may want to down-weight factors that outperformed in the past but have levelled off recently. What we’ve done here hides that level of detail.

## Information coefficient

The information coefficient is simply the correlation of the feature with next day returns. It provides a single metric for quantifying the strength of the signal. We can calculate it for time-series returns and cross-sectional returns:

features_scaled %>%
pivot_longer(c(breakout, zscore_carry, zscore_momo, decile_carry, decile_momo), names_to = "feature") %>%
group_by(feature) %>%
summarize(IC = cor(value, demeaned_fwd_returns))  %>%
ggplot(aes(x = factor(feature, levels = c('breakout', 'zscore_carry', 'decile_carry', 'zscore_momo', 'decile_momo')), y = IC)) +
geom_bar(stat = "identity") +
labs(
x = "Feature",
y = "IC",
title = "Relative Return Information Coefficient"
)
features_scaled %>%
pivot_longer(c(breakout, zscore_carry, zscore_momo, decile_carry, decile_momo), names_to = "feature") %>%
group_by(feature) %>%
summarize(IC = cor(value, total_fwd_return_simple))  %>%
ggplot(aes(x = factor(feature, levels = c('breakout', 'zscore_carry', 'decile_carry', 'zscore_momo', 'decile_momo')), y = IC)) +
geom_bar(stat = "identity") +
labs(
x = "Feature",
y = "IC",
title = "Time-Series Information Coefficient"
)

## Decay characteristics

Next, it’s useful to understand how quickly your signals decay. Signals that decay quickly need to be acted upon without delay, while those that decay slower are more forgiving to execute.

We’ll look at relative return IC for our cross-sectional features and the time-series return IC for our breakout feature.

features_scaled %>%
filter(is_universe) %>%
group_by(ticker) %>%
arrange(date) %>%
mutate(
demeaned_fwd_returns_2 = lead(demeaned_fwd_returns, 1),
demeaned_fwd_returns_3 = lead(demeaned_fwd_returns, 2),
demeaned_fwd_returns_4 = lead(demeaned_fwd_returns, 3),
demeaned_fwd_returns_5 = lead(demeaned_fwd_returns, 4),
demeaned_fwd_returns_6 = lead(demeaned_fwd_returns, 5),
) %>%
na.omit() %>%
ungroup() %>%
pivot_longer(c(zscore_carry, zscore_momo, decile_carry, decile_momo), names_to = "feature") %>%
group_by(feature) %>%
summarize(
IC_1 = cor(value, demeaned_fwd_returns),
IC_2 = cor(value, demeaned_fwd_returns_2),
IC_3 = cor(value, demeaned_fwd_returns_3),
IC_4 = cor(value, demeaned_fwd_returns_4),
IC_5 = cor(value, demeaned_fwd_returns_5),
IC_6 = cor(value, demeaned_fwd_returns_6),
)  %>%
pivot_longer(-feature, names_to = "IC_period", values_to = "IC") %>%
ggplot(aes(x = factor(IC_period), y = IC, colour = feature, group = feature)) +
geom_line() +
geom_point() +
labs(
title = "IC by forward period against relative returns",
x = "IC period"
)

We see some potentially interesting things:

• Our momentum decile feature shows only a slightly negative IC on day 1, but it decays quite slowly.
• Our carry decile feature on the other hand has a significant and sticky IC.
features_scaled %>%
group_by(ticker) %>%
mutate(
fwd_returns_2 = lead(total_fwd_return_simple, 1),
fwd_returns_3 = lead(total_fwd_return_simple, 2),
fwd_returns_4 = lead(total_fwd_return_simple, 3),
fwd_returns_5 = lead(total_fwd_return_simple, 4),
fwd_returns_6 = lead(total_fwd_return_simple, 5),
) %>%
na.omit() %>%
ungroup() %>%
pivot_longer(c(breakout), names_to = "feature") %>%
group_by(feature) %>%
summarize(
IC_1 = cor(value, total_fwd_return_simple),
IC_2 = cor(value, fwd_returns_2),
IC_3 = cor(value, fwd_returns_3),
IC_4 = cor(value, fwd_returns_4),
IC_5 = cor(value, fwd_returns_5),
IC_6 = cor(value, fwd_returns_6),
)  %>%
pivot_longer(-feature, names_to = "IC_period", values_to = "IC") %>%
ggplot(aes(x = factor(IC_period), y = IC, colour = feature, group = feature)) +
geom_line() +
geom_point() +
labs(
title = "IC by forward period against time series returns",
x = "IC period"
)

The breakout feature decays quite slowly too. It’s has a larger IC value than momentum, but less than carry.

## Feature correlation

Next let’s look at the correlation between our features. Ideally, they’ll be uncorrelated or anti-correlated to provide a diversification effect.

features_scaled %>%
select('breakout', 'momo', 'carry', 'zscore_carry', 'decile_carry', 'zscore_momo', 'decile_momo') %>%
as.matrix() %>%
cor() %>%
as.data.frame() %>%
rownames_to_column("feature1") %>%
pivot_longer(-feature1, names_to = "feature2", values_to = "corr") %>%
ggplot(aes(x = feature1 , y = feature2, fill = corr)) +
geom_tile() +
low = "blue",
high = "orange",
mid = "white",
midpoint = 0,
limit = c(-1,1),
name="Pearson\nCorrelation"
) +
labs(
x = "",
y = "",
title = "Feature correlations"
) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, size = 16, hjust = 1)) 

This isn’t a bad result. We see that our breakout and carry features are negatively correlated, and our momentum feature is almost uncorrelated with everything. That means that we should get some nice diversification from combining these signals.

## Combining our signals

Based on what we’ve seen and what we understand about our signals, I think it makes sense to:

• Overweight the decile_carry signal, as it has a high IC, decays slowly, and is anti-correlated with our other cross-sectional feature.
• Underweight the decile_momo signal, as it has a small negative IC.
• Overweight the breakout signal slightly. It has a moderate IC, decays slowly, and is uncorrelated with the other signals. Note that it also tilts the portfolio net long or short since it is used in the time series, not the cross-section.

You might want to weight these signals differently depending on your objectives and constraints, as well as what you understood from a more thorough analysis of each signal.

In any event, here’s a plot of the cumulative returns to our weighted signals over time.

I want to stress that this isn’t a backtest – it makes zero attempt to address the real-world issues such as costs, turnover, or universe restriction. It simply plots the returns to our combined features as if you could do so frictionlessly.

# start simulation from date we first have n tickers in the universe
start_date <- features %>%
group_by(date, is_universe) %>%
summarize(count = n(), .groups = "drop") %>%
filter(count >= min_trading_universe_size) %>%
pull(date)

model_df <- features %>%
filter(is_universe) %>%
filter(date >= start_date) %>%
group_by(date) %>%
mutate(
carry_decile = ntile(carry, 10),
carry_weight = (carry_decile - 5.5),  # will run -4.5 to 4.5
momo_decile = ntile(momo, 10),
momo_weight = -(momo_decile - 5.5),  # will run -4.5 to 4.5
breakout_weight = breakout / 2,   # TODO: probably don't actually want to allow a short position based on the breakout feature
combined_weight = (0.5*carry_weight + 0.2*momo_weight + 0.3*breakout_weight),
# scale weights so that abs values sum to 1 - no leverage condition
scaled_weight = if_else(combined_weight == 0, 0, combined_weight/sum(abs(combined_weight)))
)

returns_plot <- model_df %>%
summarize(returns = scaled_weight * total_fwd_return_simple, .groups = "drop") %>%
mutate(logreturns = log(returns+1)) %>%
ggplot(aes(x=date, y=cumsum(logreturns))) +
geom_line() +
labs(
title = 'Combined Carry, Momentum, and Trend Model on top 80% Perp Universe',
subtitle = "Unleveraged returns",
x = "",
y = "Cumulative return"
)

weights_plot <- model_df %>%
group_by(date) %>%
summarise(total_weight = sum(scaled_weight)) %>%
ggplot(aes(x = date, y = total_weight)) +
geom_line() +
labs(x = "Date", y = "Portfolio net weight")

returns_plot / weights_plot + plot_layout(heights = c(2,1))

The portfolio net weight plot shows that the breakout feature tilts the portfolio net long or short.

Note that because of the way we’ve aligned features and returns, the plot assumes you calculate the feature values at the daily close and act on them at that same price. While this is flattering, it’s not entirely inaccurate for a 24-7 market like crypto. For stocks, you’d definitely want to put another day’s gap between your feature and your forward return. This is illustrative for crypto too. Here’s what the returns would look like if we delayed acting on them for 24 hours:

returns_plot <- model_df %>%
summarize(returns = scaled_weight * total_fwd_return_simple_2, .groups = "drop") %>%
mutate(logreturns = log(returns+1)) %>%
ggplot(aes(x=date, y=cumsum(logreturns))) +
geom_line() +
labs(
title = 'Combined Carry, Momentum, and Trend Model on top 80% Perp Universe',
subtitle = "Unleveraged returns, delay execution by 24 hours",
x = "",
y = "Cumulative return"
)

weights_plot <- model_df %>%
group_by(date) %>%
summarise(total_weight = sum(scaled_weight)) %>%
ggplot(aes(x = date, y = total_weight)) +
geom_line() +
labs(x = "Date", y = "Portfolio net weight")

returns_plot / weights_plot + plot_layout(heights = c(2,1))

## Conclusions

In this article, we looked at ways to understand our signals, and to combine them using a weighting scheme derived from our understanding.

And let’s be honest – the work we did here to understand our signals was very shallow. There’s much more you can and should do in order to understand your signals. For example:

• Scatterplots and time-series plots that don’t hide the variance
• Stability of factor ICs over time
• Stability of the signal itself and the implications for turnover
• Impact of taking the tails only
• etc

In the next article, we’ll tackle the questions of turnover and cost and use a heuristic approach to help us navigate these trade-offs. We’ll also be a little more careful with universe selection (we don’t want to be trading the top 80% of perpetual contracts).

In a future article, we’ll also explore how we can use mean-variance optimisation to achieve the same goal.

Revision history:

• 10 Feb 2024: return plot updated to correct an error in the calculation of unleveraged portfolio weights.
• 15 Feb 2024: updated to reflect bug fix in universe selection.
• 18 Feb 2024: removed one-day gap between feature calculation and position entry, but added this as an example of delayed execution.
• 21 Feb 2024: removed non-USDT denominated contracts from tradeable universe. Scale breakout feature by dividing by 2, not 5.

### 19 thoughts on “Quantifying and Combining Crypto Alphas”

1. Thank you for the post! What does “is_universe” mean?

• is_universe defines which tickers were in our analysis universe on a given date. I essentially just ditched the worst of the worst by:

• excluding anything with a price < 0.01 (arbitrary)
• excluding remaining 20% with lowest trailing monthly volume
2. This is a *phenomenal* article Kris

• Thanks so much Scott!

3. Hey, thanks for all those blogs, very insightfull.
One question: Correlations to the time-serie&cross returns are at most 0.02 in absolute value.
What would you say is a threshold from which you’d start to be interested in going further with the feature?
I must admit 0.02 seems pretty low, I would have thrown all those ideas away already

• Good question. 0.02 seems very low, but you must remember, this is daily financial data we’re dealing with! It’s noisy as hell and any effects that show up will be tiny in comparison to the noise.

When you aggregate the returns to those features by decile of the feature value, you can see that they’re actually reasonable discriminators of the sign of returns – at least in aggregate. But look at a scatterplot of the raw feature values vs next day returns and you’ll see so much noise that you barely notice an effect. That’s why the correlation values are so low.

Any effect that we find will only explain a tiny fraction of the variance – this is just a feature of financial returns. But we can still potentially make money from such effects. If you look at higher frequency data, you can find features with higher correlations, but you run into a bunch of other awkward trade-offs around turnover, execution, etc.

So the message is don’t worry too much about a threshold correlation value. Worry more about understanding your features as deeply as you can. For instnace, look at your features from many different angles (factor plots, scatter plots, time series plots, correlation to expected returns, decay characteristics, turnover, etc), and make sure there’s a logical reason for them to be predictive.

4. Kris – is there a smart way to look at IC of a feature over a period of time longer than the next period? let’s say i have a feature that doesn’t exhibit a positive IC on day one (or two), but it shows a positive IC on average over the next ten days. looking at average returns over ten days versus one feature value doesn’t work as the ten day returns are not independent, so you can’t accurately model your features as described in your post.

• If you have a positive IC over days 3-10 but not on days 1-2, then that suggests that you’re missing something in your model. For example, say you’re looking at a trend feature. It shows negative IC on day 1, but a positive IC on days 2, 3, 4. That would suggest a short-term mean-reversion effect that you would think about modellings explicitly.

I also think you’re fine to use the ten-day return as your target variable as well if you want to – you just need to be aware that you’re hiding some granularity.

5. Hi Kris,

Is there a reason why you choose breakout as time-series return and momentum and carry as cross-sectional return predictors?

• Mostly because I’ve traded them in that way as part of a crypto strategy we run inside RW Pro, and we’d therefore done a bunch of prior research, and I knew they were reasonable predictors! So a little bit of strategic cherry picking.

But if you look at the bar plots that show the IC calculated on cross-sectional returns and on time-series returns, you see that carry and momentum have a higher IC as cross-sectional predictors, and the breakout feature has a higher IC as a time-series predictor. And I think this makes intuitive sense given what these features represent.

Carry is an interesting one though – I think it makes sense both as a cross-sectional predictor and a time-series predictor.

But in this case, for the sake of the example, I wanted something that we could trade long-short and nudge the net portfolio position around a little. So I chose two cross-sectional and one time-series predictor.

• I see! Thanks Kris. Currently implementing this in Python as practice. ðŸ™‚

6. Hi Kris,

One more question the momentum feature, in your code it is calculated as

momo = close – lag(close, 10)/close

An example would be 100 – 90/100 = 100 – 0.9 = 99.1. Shouldn’t it be

momo = (close – lag(close, 10))/close

Which would give (100 – 90)/100 = 10/100 = 10% momentum? I’m confused on this. Or am I interpreting the R code wrongly?