Category: Crypto

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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Hyperliquid Quotes vs. the Consolidate Tape

Data is the lifeblood of quantitative research and trading. The first step is to understand the benefits and shortcomings of different data sources and mapping out their use for the tasks at hand.

For example, Tiingo does a fantastic job of consolidating prices from different exchanges and presenting it through an easy to use API. While the consolidated tape is a decent starting point for developing trading strategies, you can’t trade the consolidated tape – you can trade only at a handful of venues, mostly just one.

How do Hyperliquid quotes compare with the Tiingo consolidated tape? Most of the time, the differences are within a tight range (zero mean and median). However, there are certain times when the quotes are way off even for the most liquid coins.

There have been instances where the quoted mid was off more than 10% from what Tiingo reported.

Given that there are dozens of crypto exchanges and the volatile nature of the coins themselves, some of these differences are inevitable. However, the data highlights an inefficiency and the need to have multiple exchange feeds so that you don’t shoot yourself in the foot while trading.

Code and charts are up on github.

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Simple Trend-following

Our introduction to trend-following posts on Zerodha Varsity (Part I, II, III, IV and V) used tradfi instruments to build a basic model. What if we applied the same principles on crypto assets?

To keep things simple, we’ll pick only two assets: BTC and ETH. The portfolio is evenly split between the two. Since crypto markets are 24/7/365, we’ll divide each day into 24 hour slots and construct a daily series based on the closing prices at each hour. The portfolio is further split into 24 parts each. Each position is an average of a binary trend signal.

Individually, trend-following boosts the Sharpe ratio of each asset.

You may not have captured the absolute highs but you would have avoided the steep drawdowns.

They are stronger together than individually.

Needless to say, leverage in this scenario would be fatal.

Code on github.

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