Author: shyam

Strategy 9 – Conclusion

In Rob Carver’s Advanced Futures Trading Strategies (Amazon,) there’s a chapter, “Strategy Nine: Multiple Trend Following Rules,” that uses composite trend-following rules to drive a long-short strategy. We explored the strategy through an Indian market participant’s lens.

We ran Strategy 9 across five increasingly broad universes — NIFTY indices, a 15-instrument multi-asset basket, 21 crypto coins, a dynamic walk-forward crypto universe, and MSCI country equity indices.

Scaled long-only is the only variant worth keeping

Across every experiment, scaled long-only (position size proportional to forecast strength) produced the best risk-adjusted returns. The short side of binary long-short strategies consistently loses money or adds drawdown without compensating Sharpe improvement. Binary long-only earns higher raw returns in some universes but with drawdowns that rule out leverage — making it strictly inferior to buy & hold on an absolute return basis.

As a retailer, scaling long-only cash positions is feasible if you automate everything. Strategy 9 is practical in that sense.

The strategy output is unleverageable

This is the central failure. The whole point of trend-following is to produce a return stream smooth enough that you can apply leverage and beat buy & hold. Strategy 9 never achieves this. Even its best variants have drawdowns in the 25–60% range. At 2× leverage, a 30% unlevered drawdown becomes 60% — a portfolio killer. Without the ability to lever up safely, you trail B&H on absolute returns.

Indians are anyway prohibited by regulations to take on leverage in international markets. However, there were only a couple of Indian indices with futures where leverage on Strategy 9 was workable. This is probably the most disappointing result of our backtests.

Expanding the universe yields diminishing returns

The Big 3 crypto coins (BTC, ETH, SOL) already capture most of what trend-following can extract from crypto. Adding 18 more coins, or dynamically re-selecting from the full universe each month, adds complexity without improving the portfolio. The best-performing 3–5 instruments drive the results; the rest contribute noise or outright negative returns.

Trading crypto for as an Indian is anyway not feasible given the current tax structure. Not sure if we lose out on much here.

Performance may be period-dependent

The MSCI equity experiment revealed that yearly returns effectively stopped working around 2009. What looks like a decent full-sample Sharpe may be entirely back-loaded — an artifact of pre-2009 returns that never recurred. The same question hangs over the other universes; we did not slice them the same way.

Cost screens hurt; inverse-vol weighting doesn’t help

Carver’s cost screen eliminates the faster filters (EWMAC2, EWMAC4), making the strategy sluggish and cutting returns 30–40% without meaningfully improving risk metrics. Inverse-volatility weighting, which should theoretically down-weight volatile losers and improve risk-adjusted returns, made no material difference versus simple equal-weight.

The best risk-adjusted result is narrow, scaled, long-only

A 50-50 equal-weight blend of Scaled Long-Only on NIFTY MIDCAP 150 and NIFTY SMALLCAP 250 produced a Sharpe of 1.18 with a 13.1% drawdown — the cleanest equity curve across all experiments. But even this trails B&H on unlevered absolute returns. At 2× leverage it earns 18.9% annualized with a 26% drawdown — the closest we got to a genuinely usable strategy.

Bottom line

Strategy 9 does not survive real-world scrutiny. It finds trends, it earns positive Sharpe, but it cannot produce a return stream smooth enough to lever into genuine outperformance. The drawdowns are always too deep, the universe expansion never helps, and the equity index variant suggests whatever edge existed may have expired. This is a strategy that looks promising in theory and in plots — and fails on closer inspection.

Code and charts on github.

Strategy 9 with Equity Indices

Previously, we ran Strategy 9 with Dynamic Universe Selection for crypto. Results were a bit underwhelming. You could argue that given crypto’s negative utility, their long-term returns should tend to zero and trend-following is not magic that can turn a basket of -EV assets into a stable return stream.

Here, we put MSCI country equity indices through the same strategy. On the face of it, a Binary Long-only strategy has a higher Sharpe than buy & hold.

However, it’s drawdown doesn’t make it leverage friendly. So, you end up trailing buy & hold returns. The bigger problem is that when you look at yearly returns, it appears that something stopped working in 2009.

The backtest performance could be back loaded.

Once again, Strategy 9 fails us.

Code and charts are on github.

Strategy 9 with Dynamic Universe Selection

Previously, we ran Carver’s Strategy 9 with Crypto listed before 2019. However, there was a ton of new coins listed in the early 2020’s and incorporating them in the backtest meant taking a walk-forward approach to universe selection.

Here’s what we did: at the end of every month, we looked at all coins that had at least 500 days of history and had a positive Sharpe Ratio when Strategy 9 was applied to it. We then applied Strategy 9 on those coins for the following month. The technical details can be found here.

The results were underwhelming, to say the least.

There is some literature on using the Hurst Exponent to filter for trending instruments, so we tried that out as well. However, using Hurst didn’t really make a big difference.

Code and charts on github.

Strategy 9 with Crypto

When we ran Carver’s Strategy 9 with 15 Instruments, we noticed how most of the returns were driven by crypto. However, that had only the three big coins – BTC, ETF and SOL. Since hand selecting instruments to trend-follow is also a form of overfitting, we expanded the universe to include all x-USDT coins listed in Binance since before the year 2019. There are 21 of those.

Once you expand the universe, the sheen wears off.

While the highest returns came from using a Binary Long-Only Equal-weight strategy, it came with a 60% drawdown, ruling out leverage.

Digging into the coin-level metrics, we see how a fair number of coins have negative contributions.

While the Big 3 coins had favorable trend-following returns, expanding the universe did not yield a better portfolio.

Code and charts on github.

Strategy 9 with 15 Instruments

Our previous post on Rob Carver’s Strategy 9 experimented with four major Indian indices. We saw that only two of them contributed to out-performance while the others dragged.

Can we just run those that worked and throw away the rest?

The whole point of using multiple moving averages is to avoid overfitting. Hand selecting instruments to trend-follow is also a form of overfitting. Carver repeatedly says that his approach works best on a large set of instruments (start with 100 and whittle down.) However, as an Indian retail trader, we do not have many options. Realistically, we can lay our hands on at most 15 different instruments.

With these 15, we played around with: scaled vs. binary x long-only vs. long-short x equal-weighted vs. inverse volatility weighted.

The results are sobering.

Long-only Equal-weight

Long-short Equal-weight

Long-only Inverse-volatility-weighting

Long-short Inverse-volatility-weighting

Of these, only the scaled long-only equal-weight setup looks promising. However, if you look at how individual instruments performed, it is hard to remain unbiased.

The largest contributor is crypto.

Charts and code are up on github (equal-weight, inverse-volatility-weight)