Tag: quant

Standard breakout strategy

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The book Following the Trend: Diversified Managed Futures Trading, Andreas Clenow, describes a simple “breakout” strategy:

If today’s close is higher or equal to the highest close in the past 50 days, we buy tomorrow; if the close is below or equal to the lowest close for the past 50 days, we sell open tomorrow and go short. A similar logic is used to get out of positions, where a long trade is sold when the close reaches the lowest point in 25 days and a short trade is covered when the price makes a 25-day high.

The book was published more than a decade ago and traders would’ve moved on from the basic strategy it described. However, we were curious if it ever worked at all on Indian indices. So, we ran a backtest.

Turns out, it never did.

Layering a trend filter seems to help a little.

While the strategy avoids some really steep drawdowns, the backtest doesn’t consider trading costs, taxes, etc.

While you could data-mine and get to a bunch of parameters that might work for “trading breakouts”, there is no reason why it should continue to work in the future.

Code and charts for other indices are on github.

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Intraday Momentum, an Update

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Back in 2016, we ran a sniff test on Intraday Momentum: The First Half-Hour Return Predicts the Last Half-Hour Return (pdf). We promised an update so here it is (eight years later).

We ran the strategy with both the first 15min and 30min formations with and without considering gaps. It continues to not work with the three indices we used: NIFTY, BANK NIFTY and MID SELECT. Here’s the one for the NIFTY. The rest are on github.

Some strategies may benefit from becoming well known. However, a vast majority of them don’t. This one belongs to the former.

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Replication

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Sometimes, you many not be able to directly buy an index or access a strategy because of regulatory hurdles, mandates etc… In these situations, replicating it using a basket of accessible securities might make sense.

For example, you can replicate the S&P 500 index using Indian indices: NIFTY 50, MIDCAP SELECT and NIFTY BANK.

In fact, you can do for any reference timeseries (strategies/funds) and apply leverage as desired.

Also, the loadings give you and idea of the shifting relationship between the reference asset and the basket over time.

Code and images are on github.

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Market-cap Deciles and Circuit Limits

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Our previous post discussed how liquidity drops exponentially as the market cap gets smaller. This illiquidity also means that a lot of micro-cap stocks spend their time out of the market.

This is a problem for direct equity investors in micro-caps if they actually try to bank the price appreciation they might have seen in the stocks that they own. And a bigger problem for momentum algorithms in the small-cap space.

Index funds in the micro-cap space have yet to go through a test of the liquidity mismatch between allowing redemptions at daily NAV vs. not being able to trade the underlying stocks for days on end.

Bull markets allow us the luxury of coming up with a plan for something that has plenty of historical precedent.

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Market-cap Deciles and Illiquidity

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Our previous post used AMFI’s classification of stocks by market-cap to analyze liquidity dynamics. What if we broke down the universe of stocks into their market-cap deciles and then applied the same illiquidity metric to them?

If you look at the full sample, median liquidity tracks market-cap.

Mid/small caps have an embedded illiquidity premium. While index/mutual funds are obligated to honor their NAVs on redemption, there is no guarantee that direct equity investors can exit without taking a direct hit. Liquidity flees during market stress.

During bull markets, the taps are open. December 2017 was the absolute zenith of the mid/small cap mania. Liquidity was ample.

A month later, the hangover began. Erstwhile small-cap momentum stocks would hit their lower-circuits within a few seconds of the open. The market-clearing price for some of them were a cool 40%-50% away from where they finished 2017. It was weeks of watching the portfolio slowly bleed away.

All this to say, understanding liquidity dynamics is as important as understanding the fundamental and technical aspects of the stocks you own.

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