Pair Trading with Token Baskets

Bitcoin Dominance (BTC.d or BTCDOM) is Bitcoin's market share relative to the whole crypto market share. It’s calculated by dividing Bitcoin's market capitalization by the total market capitalization of all crypto currencies and multiplying it by 100.

For example: If Bitcoin's market capitalization is 2 Trillion and the market capitalization of all crypto currencies is 3.4 Trillion, BTC dominance would be: (2 / 3.4) * 100 = 58.8

In other words, Bitcoin makes up 58.8% of all crypto currencies.

Why is this interesting?

Bitcoin dominance gives you an idea where we could be in the market cycle.

If it rises it could either mean that Bitcoin price is going up and altcoins are staying flat or even declining.

If it goes down that either means Bitcoin price is going down or altcoins are growing in value.

Changes in the BTC.d could either mean that money is flowing from BTC to alts or vice versa or that new money is coming in for one of these sectors.

Historically, at the start of a bull market cycle, money flows into Bitcoin and Bitcoin dominance rises. At some point it tops and through a wealth effect it flows into alt coins, which leads to a further decline in BTC.d

Trading BTC.d index

What if there was a way to put money on BTC.d going up or down? This index is traded on Binance and Bitfinex and is also available as a long or short leg on the INTENT engine on Pear Protocol:

https://intent.pear.garden/trade/BTCDOM-USDC

How to use it for trading?

A) Long BTCDOM

Longing BTCDOM vs. USDC results in a straight long position

B) Short BTCDOM

Shorting BTCDOM vs. USDC results in a short position and is betting on altcoins to rise

C) As a short leg: Betting on the beginning of an altcoin season AND ethereum profiting

For example: Long ETH / Short BTCDOM 

D) As a long leg: Betting on the decline of altcoins and BTCs growth.

For example: When Bitcoin is showing strength and a small correction in Bitcoin lets altcoins correct 20-30%, Long BTCDOM / Short Meme coins

Recent BTCDOM trades and ideas

• BTC / BTCDOM

• SOL / BTCDOM

• BTCDOM / USDC

• USDC / BTCDOM

• DOGE / BTCDOM

• ETH / BTCDOM

• BTCDOM / XLM

by Yuvi (@crypto_yuvi), Conclave Head of DeFi

I recently started placing trades on Pear Protocol because I believe that pair trading is a fantastic way to bet on relative outperformance while mitigating the influence of extraneous variables. You can long one asset and short another, and if the market is influenced by something you had not considered, they are more likely to move together and allow you to focus on relative outperformance of the specific pair.

In doing so, I found myself swamped with tickers and ideas, and decision paralysis meant that I could never settle on a pair. So I set out to build a process that could help me synthesize the volumes of data that are available and find indicators for pair trading ideas that I could consider.

I shared some outputs with a few friends (s/o HanSolar) who helped me come up with ideas and indicators, so in this article I would like to share one particular tool that I have been using, and how my use developed in response to some losing trades.

The hypothesis is simple; tokens with large upcoming unlocks will not see commensurate demand to absorb supply.

To begin, I looked at the DefiLlama Unlocks dashboard (s/o DefiLlama). By downloading the same data that informs the unlock dashboard, I built a python script to parse it for my own dashboard.

Unlocks dashboard as at 07 Oct 24


Since this was a problem of supply and demand, I thought a good way to present the data would be an ‘average return over 14 days’ relative to the ‘next 30 day inflation rate’ per any unlocks occurring in the next 30 days, compared to the ‘expected analytical price action’.

The expected analytical price action is simply an inflation curve whereby no unlocks means no price movement, and 100% supply inflation corresponds with -50% price movement (if you double the supply, you half the value).

Output at 07 Oct 24


This simply provides another view of the unlock data, however an edge may lie in assessing market activity. In order to see if unlocks were priced in, I used the Binance public API to fetch the last two weeks of price action for each asset, and compare the average daily price movement to the expected price movement caused by upcoming inflation events. For any inflation rate caused by unlocks, the average price movement of the asset should correspond with the analytical expected price action curve, if the unlock is being priced in.

Lesson 1: Timeline. Unlocks will occur in the future, whereas the data I am looking at occurred in the past. Since the supply hasn’t hit the market yet, there is no material impetus for the price action of the asset to align with the expected curve. However, the opportunity here is to find the relative outperformers and look for likely cases of reversion.

It is critical to understand the context of these assets, what ‘unlock’ means, and what kind of liquidity they have. Having narrowed down opportunities to a few, I could go back to the unlock dashboard and take a look at the purpose of the unlock and what that supply was doing. Unlocking treasury assets? Less likely to get sold off. Early investor or advisor unlocks? More likely to get sold off. Similarly, a 2% depth calculation from the Binance API data could reveal illiquid assets. The context of an unlock is important to understand the effect it has on supply.

Output at 07 Oct 24



So now I had narrowed down my picks to a few assets whose unlocks I thought would hit the market, and whose price action I thought was not reflecting the upcoming influx.

Lesson 2:
Thematic pairs. Initially I was only looking for shorts and ignored the long side. I paired with majors because I assumed they would be less volatile, allowing me to focus on the inflating assets. I realized that this introduced more variables into the question of relative outperformance, because now network effects came into play. There is definitely a time and place for this, but I decided that I wanted even fewer variables influencing my trades, and I think this is where Pear protocol really sets itself apart. If the short token was an AI token, and the long was BTC, there were cases where the AI narrative saw a tailwind in the order books and even the lowest tier assets caught huge bids. Now, when I select a short leg based on this approach, I look for a theme-aligned long pair. If the short asset is a gaming token, I look for a relatively stronger looking gaming token. If the short asset is an AI token, I look for a relatively stronger looking AI token.

Narratives catching a bid was one hurdle to navigate, but has been easy enough to mitigate. Other hurdles have presented themselves with root causes that were much harder to identify. I recently opened a pair trade shorting $BIGTIME, before it launched up over 50% in a matter of days. I could not work out why. No other gaming tokens caught a bid, no other network tokens were up, and volume on Binance had barely moved.

Lesson 3:
Geography. Binance may have the deepest liquidity and typically the most volume, but it is not the only exchange. In the case of $BIGTIME, trading volume on Bithumb and Upbit, largely driven by Korean traders, had elevated to beyond BTC volumes. After losing a few trades to the Korean markets, I integrated Bithumb and Upbit volume data into my dashboard to help me identify cases where Korean market momentum might catch me off guard. Fool me twice, shame on me.

Output at 07 Oct 24


It’s not all pair trading, and I have other tools and indicators that I am using to inform my trades and help me generate ideas. My trading accounts are still small and I have gone from losing money to losing less money, which means I am trending towards not losing money, and perhaps will go as far as making money. One thing that is certain is that I will lose more trades. I know that and I am okay with it. My goal is to develop a process by which I win more trades than I lose, and hopefully in larger sizes. My pair trading process has evolved from ‘short token unlocks’, to quantitatively analyzing data and identifying relative outperformance to trade against.

by Chris Newhouse of Cumberland Labs (@CumberlandLabs)

Advisor to Pear Protocol

Pair trading has become a staple strategy for navigating volatile crypto markets. While it may sound simple—long one asset, short another—the success of this approach often hinges on selecting the correct leg to short. The goal of the short leg is to hedge the market risk while amplifying the relative outperformance of the long leg. In this article, we explore how to select the short leg, why it matters, and how to utilize a data-driven approach for success.

Why Choosing the Short Leg Matters

At the core of pair trading is the desire to exploit the relative movement between two assets. Ideally, the long asset should outperform, while the short asset either declines or remains neutral. This creates profit on both sides: gains on the long leg and losses (or minimal gains) on the short leg. The wrong short leg can negate the gains of the long leg, which is why careful selection is crucial.

For instance, in a typical Bitcoin (BTC) and Worldcoin (WLD) pair, if BTC rallies significantly while WLD stays flat or falls, the trader can capitalize on the BTC outperformance while minimizing overall market risk. However, if WLD suddenly outperforms BTC due to unforeseen events, the trade may underperform or even result in losses.

Data-Driven Criteria for Selecting the Short Leg

The choice of the short leg involves balancing several factors, all of which can be informed by data:

1. Correlation and Volatility

Pair trading relies on a certain level of correlation between the two assets. Highly correlated pairs tend to move together, which minimizes the risk of one leg significantly diverging from the other. For example, in high-frequency data, correlations between major blue-chip cryptos like BTC and ETH can exceed 90%. Ideally, the short leg should have a high correlation but exhibit greater downside volatility.

Data Tip: Look at historical correlation and volatility metrics over different time frames (e.g., daily, weekly, and monthly). This can be easily observed using pair charts, like ETH/BTC, to visualize how one asset performs relative to the other during different market conditions, as well as more sophisticated tools like Crypto Wizards.

2. Liquidity Considerations

The short leg should be liquid enough to handle large trades without significant slippage. Illiquid assets can be risky to short as sudden spikes in trading volume or market-moving news can drive sharp upward price movements.

Data Tip: Use liquidity metrics, such as order book depth and average daily trading volume, across popular exchanges like Binance (which supports over 250 trading pairs). Filtering the least liquid tokens out of your shortlist for the short leg will reduce the risk of price manipulation or large price movements due to thin order books.




3. Idiosyncratic Risks: Avoid Event-Driven Moves

Before choosing the short leg, it is critical to assess whether there are any upcoming events that could disproportionately affect its price. Regulatory changes, network upgrades, or high-profile partnerships can drive unpredictable volatility.

Data Tip: Keep a close eye on news feeds and calendar events. For example, if there’s a significant development like a protocol upgrade or token burn event, it may not be suitable for a short position. Using platforms that aggregate news and on-chain analytics will help traders avoid getting caught by surprise(Pear Things to Consider).

4. Relative Strength

An asset that shows signs of persistent weakness compared to its pair is often a good candidate for the short leg. This weakness can be gauged through technical indicators such as Relative Strength Index (RSI) or Moving Averages. If the asset is underperforming over multiple time frames, it signals a potential decline.

Data Tip: RSI and moving average crossovers (e.g., 50-day vs. 200-day) provide clear signals of relative weakness. Traders should also monitor whether the short leg has repeatedly failed to break through key resistance levels.

5. Funding Costs and Arbitrage Considerations

In perpetual futures markets, the funding rate is a critical consideration. A positive funding rate (where shorts receive funding and longs pay) can offer an additional layer of profitability. For example, if you are short ETH and the funding rate is positive, you receive a payment for holding the short position, which enhances your return.

Data Tip: Calculate net funding across the selected pair and include this in your decision-making process. Using funding heatmaps available on Binance and other exchanges can help identify pairs where shorting is more cost-effective, especially when the funding rate is positive.

Future Features for Data-Driven Trading on Pear Protocol

As pair trading evolves, so must the tools that support it. Pear Protocol has already established itself as a streamlined platform for executing pair trades, but there are several key features that could further enhance the trading experience and provide users with a more data-driven edge. Here are three features that I, as a trader, would like to see integrated into Pear Protocol in the future:

1. Screener for Long/Short Pair Selection

A robust screener that automatically suggests long and short pairs based on the criteria we've discussed—correlation, volatility, liquidity, and funding rates—would be a game-changer for traders. By integrating data analytics directly into the platform, Pear Protocol can offer traders the ability to quickly identify ideal pairs, removing much of the manual analysis required today. A screener could:

• Provide a ranked list of potential pairs based on customizable filters.

• Display real-time metrics such as correlation and historical volatility.

• Highlight optimal short-leg candidates with favorable funding rates or weaker relative strength.

This feature would empower traders to make quicker, more informed decisions and seamlessly find pairs that fit their trading strategy.

2. Market Overview Page with Emissions and Funding Data

Understanding tokenomics and funding rates is crucial for successful pair trading. A market page that provides real-time data on token emissions, alongside funding rates across popular exchanges, would offer valuable insights to traders looking to short tokens with high emissions or negative funding rates. Such a page could:

• Track tokens with low vs. high emissions, helping traders gauge potential supply pressure.

• Show funding rates in real-time, allowing traders to capitalize on favorable conditions for short positions.

• Include customizable alerts for funding rate changes or emissions announcements.

This would make Pear Protocol a one-stop hub for tokenomics and funding data, increasing its appeal to traders seeking data-driven advantages.

3. Unified Platform for Data and Analytics

The ultimate goal for Pear Protocol should be to become a fully integrated platform that seamlessly blends trading execution with powerful analytics. By consolidating all relevant data—correlations, volatility, liquidity, news events, and funding rates—Pear can become a go-to destination for traders who want not only to execute trades but also to analyze market conditions in real-time. Imagine having a dashboard that:

• Displays all the key data points needed for pair trading in one place.

• Provides predictive analytics based on past market behavior, helping traders anticipate trends.

• Allows for in-depth back-testing of pair trading strategies using historical data.

  
  

By evolving into a unified platform for trading and analytics, Pear Protocol can set itself apart from other platforms and attract a broader base of sophisticated traders.