Category: Crypto

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.

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.

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)