Like all great products perpetual futures (perps from here on) are a simple concept. Perps are instruments tracking the price of something. In this context token prices in USD.
Under the surface, perps rely on a complex incentive mechanism to keep their prices in line with their underlying token prices. This mechanism has been called the funding rate since Icbit launched crypto perpetual futures in 2011. While it shares similarities to funding in traditional finance, the name can be confusing. Unlike more traditional derivatives like fixed-term futures or margin traded products, there is no underlying asset using funding for leverage. The funding rate of perpetual futures links P&L on moves in the price of the underlying to P&L of the perp. More on that in the first piece.
This series is for those wanting to get into the weeds of the funding rate mechanism. We cover how it works, all the elements involved, how they contribute to keeping prices in line, how to optimise the process, and some future potential improvements.
Simple to advanced perpetual funding rates topics are all covered in detail. Whether you are a builder, arbitrageur, trader, or active community member, you’ll find everything you need about funding rates in greater detail than covered elsewhere. It is recommended that those not familiar with perpetual futures first read this introductory article.
Future posts will cover:
The series starts with an in depth examination of the forces that keep perpetual futures prices in line with their underlying indices. We answer the question of how and why the funding rate keeps perp prices close to their index prices.
The funding rate does not only introduce potential arbitrage. The funding rate guarantees that the return of the underlying index will be replicated by the perp over some period. This piece analyses the impact of funding rates on regular traders, market makers and arbitrageurs.
Examples how regular traders, market makers and arbitrageurs are all incentivised to converge markets help show how funding rates introduce a new aspect of active trading to perps.
Oracles are a well known and exploited attack vector in many DeFi protocols. Perps need to track the market price on their own exchange and an external oracle to calculate the funding rate. Manipulating these rates could allow a malicious actor to financially benefit.
This piece details best practices for measuring prices on the perp exchange and the external index price. Tradeoffs such as stability and manipulation resistance vs liveliness in different contexts is discussed.
Mitigation of price manipulation to benefit from funding rates is also discussed. It also covers calibration of these techniques in a way not covered since BitMex first implemented perps.
This piece will focus deeply on all the micro factors affecting the long-term natural rate for a perp. The level where on average traders will neither feel incentivised to be long nor short.
Exchanges today typically set one level for the interest rate component across all markets. We argue that this causes biases in the distribution of realised funding rates which idiosyncratic approach could help mitigate. This would have the benefit of less biases and smaller magnitudes in the funding rate premium, promoting healthier markets.
Many factors play a role in a no-arbitrage level for the equilibrium interest rate component. These include yield on the underlying token, availability of liquidity to take opposing long and short positions in the token externally and more.
This piece covers price adjustments to the index price that incentivise convergence and accurate marking.
Funding rates are typically paid out every one to eight hours. The rate used at the end of each funding period is the averaged observed rate. Previously sampled funding rates imply an accrued funding payment during a funding period. Not accounting for this can incentivise convergence to a price level not equal to the index price.
Prices used for margin and liquidations should undergo a slightly different adjustment to accurately price the true value of positions.
Adjustments to rates, commonly called basis adjustments, correct for this. This piece discusses the how, why, and the implications of doing so or not.
This piece extends the typical reference guide found on exchange help pages. All terminology and the overall formula are explained. The usual attention to detail is given to the following topics.
- CLAMP: Why a function normally used in computer graphics is part of many funding rate implementations?
- The impact of the funding rate period.
- The formula, some examples and a sensitivity analysis to see all the inputs come together.
- How all the inputs translate into rates and prices on trading front ends. This helps traders optimise their portfolios for funding rates.
- A glossary of all the terminology is included for reference.
Overview of the current implementations of BitMex, Binance, Deribit, ByBit, KuCoin and dYdX. The comparison to non-orderbook exchanges is also discussed.
Strategies harvesting funding rate differences between perpetual futures exchanges helps keep prices in line. Non-orderbook exchanges in particular can offer interesting offsets to funding traders due to their less correlated funding rate drivers.
Also covered is an interpretation of what this means for convergence and different stakeholders on these exchanges.
A formalisation of the objective function faced by arbitrageurs. The cases where arbitrage is happening against a spot instrument, the same perp on a different exchange and a fixed maturity future are all covered individually.
Anyone interested in this emerging derivative class can benefit from this deep dive into the technicalities of perpetual funding rates. Best practices in every aspect are made clear as well as the potential biases and profit opportunities when they are not followed.
The analysis focuses on perpetual futures, as the most liquid derivative currently. Everything discussed can be generalised to the entire family of perpetual derivatives.
The entire family of perpetual derivatives: perpetual futures, everlasting options, power perpetuals and more can be defined by their underlying index, payoff function and funding rate. Funding rate optimisation will be an important field for derivatives exchanges as they scale.