Category: Crypto

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.

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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)

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Bitcoin Volatility Seasonality

Is Bitcoin Volatility seasonal? Yes.

There are calm months and there are frantic ones…

When you decompose the series, you can see the ebb and flow of monthly seasonality.

The pattern largely holds post-COVID as well — even after Bitcoin began its journey as an institutionally accepted asset.

Zooming in on the seasonal component alone, you can see how it troughs around October-November.

And this has tracked post-COVID as well.

The seasonal component has been negative during the months of July through December indicating that the volatility experienced during that time was idiosyncratic.

Code and charts on github.

Related: INDIA VIX Seasonality

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Buy Highs/Sell Lows

In equities, buying stocks that hit their All Time Highs is a decent strategy. When combined with a trailing stop loss, it beats the NIFTY 50 index with a Sharpe of around 1.8.

Can a similar long/short strategy work in crypto?

Since everything happens faster in crypto, we need to relax the “All-Time” constraint and consider shorter time-frames. For example, here’s the 200-day Highs stats, for returns of subsequent 1/5/10 & 20 days of L1 and L2 coins:

And here’s the same for 20-day Highs stats:

A similar thing plays out with 200- and 20-day lows.

Theoretically, you can go long coins making 20/50-day highs and go short coins making 20/50-day lows. Apply a reasonable trailing stop loss and you might have a decent strategy.

Code and charts on github.

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Binance Liquidity

Binance is one of the longest surviving crypto CEX (Centralized EXchange). At last count, they had around 3000 tokens listed. Just like how it is in tradfi exchanges, most of the liquidity is concentrated in the top 50% of tokens.

We use the bid/ask spread as a short-hand for liquidity.

If you want to keep your trading costs in check, then play in the top 5 deciles.

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