Tag: sma

SMA Strategies, Part III

In Part II of SMA Strategies, we saw how we could reduce drawdowns by making sure that we go long only when the slope of the SMA is positive. i.e., when the SMA is trending higher. Here, we will look at cross-overs.

While previous strategies compared the current value of the index vs. its SMA, a cross-over strategy uses a smaller look-back SMA instead of the index. Essentially, go long if SMA(N/4) > SMA(N).

NIFTY 50 Cumulative Returns

Cross-over only

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Cross-over with slope check

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The stand-alone slope check from Part II has lower peak drawdowns than the cross-over versions. The additional averaging of recent prices leads to a lagged response. Given the proclivity of our markets to cliff dive, a lagged response will result in higher drawdowns. It could, however, lead to lower transaction costs by papering over short-term mean-reverting moves.

Code and additional charts are on github.

SMA Strategies, Part II

In Part I we saw how a simple tactical strategy that can be implemented by ETfs out-performs an actively managed mutual fund even after transaction costs. However, there are more than a million ways to implement an SMA strategy. Everything from picking the lookback period, cross-overs and enveloping are all open questions. There is no single “best” way to do it. Here, we add a simple check that makes sure that the SMA is trending higher before going long.

Quite simply, for an N-day SMA, we compare Nth-day to N/2th-day. If it is higher, then we go long.

Cumulative returns

NIFTY 50

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NIFTY MIDCAP 100

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NIFTY SMLCAP 100

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Take-away

The gross returns are lower than the “raw” strategy that we saw in Part I. However, the drawdowns for the 10-day SMA are a lot shallower. Shallower drawdowns allow a bit of leverage to be employed. This could be a good starting point for a NIFTY futures trading strategy.

In Part III, we look at how cross-over strategies perform.

Code and charts are on github.

SMA Strategies using ETFs

A simple moving average of an index is nothing but the average of closing prices of that index over a specified period of time. We did a quick backtest to see how an SMA based toggle between going long an index vs. cash performed.

Cumulative returns

NIFTY 50

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NIFTY MIDCAP 100

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NIFTY SMLCAP 100

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Feasibility

The backtest, unsurprisingly, shows that shorter the SMA look-back period, better the performance. However, the boost in performance comes at the expense of higher number of trades. Lower look-backs are only viable now thanks to brokerages where you would pay zero for these trades (however, you still pay the securities transaction tax.) To see how this would shake out in the real world, have a look at how our Tactical Midcap 100 Theme has performed in the last ~2 years:

The Theme used the M100 ETF (Motilal Oswal Midcap 100 ETF) with a 10-day SMA toggle to switch between the ETF and LIQUIDBEES. The blue line represents zero brokerage and 0.1% STT and the green line represents a brokerage of 5p and 0.1% STT. The chart shows it beating an actively managed midcap fund across all transaction fee scenarios.

The snag is that this strategy is tough to scale. The M100 ETF just doesn’t trade enough for this strategy to absorb more than Rs. 10 lakhs. And there is no small cap ETF on the horizon to implement the strategy in that space.

The second problem is that M100 trades to a wide premium/discount to NAV (see: ETF Premium/Discount to NAV.) This is another layer of risk that an investor could do without.

However, things seem to be moving in the right direction. Reliance Capital launched a new ETF recently that tracks the NIFTY MIDCAP 150 index. Their ETFs usually trade better – tighter spreads, narrower tracking errors, better liquidity. Hopefully, it will emerge as a stronger alternative to M100 and allow these strategies to scale. We setup the Tactical Midcap 150 Theme that uses the RETFMID150 ETF instead of the M100 ETF for those who are interested.

In Part II, we will see how adding a simple check on the SMA can reduce drawdowns.

Code and charts are on github.

SMA Distance, Part III – Backtest

In Part II, we saw that when the 50- and 100-day SMA Distance is in the first quintile, subsequent 20-day returns have smaller left tails. Can that observation be turned into a market-timing system?

The backtest

We setup two long-only portfolios: one that goes long S&P 500 if either of the 50-day or 100-day SMA Distance is in the first quintile and another that, in addition to the 50- and 100-day being in the first quintile, also makes sure that the 200-day SMA Distance is not in the first quintile. These are L1 and L2 in the chart below:

Using SMA Distance is a poor long-term strategy. However, it does help avoid deep drawdowns. It is not very useful as a standalone indicator but perhaps could be used to confirm other signals.

Code and charts are on github.

SMA Distance, Part II

Part I introduced the concept of SMA Distance – the distance between a Simple Moving Average and the index. What can the current SMA Distance tell us about future returns?

Distribution of future returns

To answer this question, we will bucket the SMA Distance into 5 bins (quintiles). The first quintile will have the lowest distance and the fifth one will have the highest. Since a rising market will have negative distances, the first quintile will mark a rising market and the last quintile will mark falling markets. We will bin 50-, 100- and 200-day SMA distances into quintiles, then plot the distribution of the subsequent 20-, 50- and 100-day returns for each.

50-day SMA distance quintiles vs. subseqent 20-, 50- and 100-day returns:

100-day SMA distance quintiles vs. subseqent 20-, 50- and 100-day returns:

200-day SMA distance quintiles vs. subseqent 20-, 50- and 100-day returns:

The negative tails on the 20-day returns are interesting. On the 50-day and 100-day charts, you will notice that the negative tails on 20-day returns are lot more on the 5th quintile than on the first – showing that in the short term, markets trending higher tend not to reverse course sharply. But if the market is way off its 200-day SMA (the last chart,) the first quintile seems to represent over-extended markets that are prone to steeper drops.

We will test this theory on data from 2006 onward in our next post.

Code and charts are on github.