Category: Investing Insight

Investing insight to make you a better investor.

Trending Momo Models

Previously, we discussed how applying a trend filter to a midcap momentum index could make sense. Then, we extended that to our homegrown momentum models. In both cases, there are certain situations where trended momentum side-steps deep drawdowns. However, if you are only looking at “raw” returns, you would be better off with monthly rebalanced momentum versions.

In this post, we run a similar test on the Momo versions of our homegrown momentum models.

Momentum (momo)
Velocity (momo)
Acceleration (momo)

The problem with high turnover strategies, beyond transaction costs, is the higher operational risk it entails. You could probably get away with postponing trades by a day or two in the monthly rebalance strategies but with these, you need automated trading systems.

You can track these strategies here: Tactical Momo (Momentum), Tactical Momo (Velocity) and Tactical Momo (Acceleration).

Code and charts: github

Trending Momentum Models

Momentum is known to trend. Our previous post explored trend overlays on momentum indices. The question now is, does it make sense to do the same to our own homegrown momentum models?

Our Momentum, Velocity and Acceleration models created between 2013 and 2015 have a monthly rebalance schedule. As a risk management measure, trailing stop-losses were introduced to them in 2016 and their momo versions – Momo (Relative) v1.1, Momo (Velocity) v1.0 and Momo (Acceleration) v1.0 – were born.

While these momo strategies do well with sudden market crashes, the problem has always been markets that grind down. Does a trend overlay on the original strategies perform better than momos after transaction costs?


The trend-overlay strategies seem to avoid drawdowns and perform better than their momo counterparts. The post-2020 Corona Crash market rally was one for the record books. So, a strategy that sidesteps the crash may not necessarily perform better during the rally but the full dataset will show superior performance. What if we took the crash data out of the picture?


It looks like Momentum and Acceleration strategies saw big pickups in performance. A trend overlay on Velocity resulted in lower drawdowns but that came with a big performance penalty.

You can track these strategies here: Tactical Momentum, Tactical Velocity and Tactical Acceleration.

Code and charts: github

Trending Momentum

Can a simple moving-average be used to time momentum indices? Returns from 2010 through 2015 of NIFTY MIDCAP150 MOMENTUM 50 TR and NIFTY200 MOMENTUM 30 TR under different SMA strategies look like this:

It appears the moving averages with short lookbacks can at least help reduce drawdowns, if not boost returns. If you pick the “best” config from the dataset and apply it across data from 2016 through 2022, it looks promising.

Should expect trend returns to be much lower after incorporating taxes and transaction costs but the lower drawdowns merit a closer look.

Given how our trend-midcap strategy has performed, we expect trend effects to be stronger in midcap-momentum than in the largecap version.

Code and charts: github

Volatility and Returns of Momentum Indices

The standard deviation over 200-days and future 20-day returns from 2010 through 2015 of NIFTY MIDCAP150 MOMENTUM 50 TR and NIFTY200 MOMENTUM 30 TR looks like this:

Can historical volatility, as measured by standard deviation, be used to enter and exit momentum strategies?

On a rolling basis, there doesn’t seem to be a strong correlation between historical volatility and future returns. Back-tests over this period might give you a config that might look like it works but it is probably a fluke.

Given that liquid ETFs for these indices are not available and we are stuck with index funds for the foreseeable future, we setup a back-test to calculate the 200-day std. dev. at the end of each month to decide whether to hold it for the next month. Needless to say, the results were pretty lackluster.

We chose the 2010-2015 period because it avoids the 2008 crash and the subsequent recovery. The back-tests look phenomenal when you include that data but we wanted to see how such a strategy would perform in “normal” markets before stress-testing it. We don’t want to be the generals always fighting the last war.

Code & charts: github


Adventures in Pattern Matching, Part I

Hypothesis: If a price segment is similar to a set of historical segments, then there should be a correlation between historical and future returns.

Setup: Take 60-minute segments of 1-minute bars. Match them with 200 of the most recent days’ with times aligned. Take the top 40 days with the closest match and calculate their median next 30-minute returns. Plot the median vs. actual.


  1. Non-overlapping segments. Its can be argued that patterns don’t start and stop and perfectly aligned boundaries.
  2. Most-recent 200 day look-back period. What if that’s not enough? Or too much?
  3. 30-minute prediction. Too short? Too long?

Verdict: There is no discernable relationship.

Code: github

Related: Euclidean Distance for Pattern Matching