Crypto-Trading for MFRM 2024-25

Traditional Markets

What is Market Microstructure?

How do trading institutions affect market outcomes, e.g.
- trading costs
- information efficiency
- allocative efficiency
A lot of tricky issues
- no one model can do it all
- complex theoretical problems (e.g., infinite regress)
- large datasets

Broker

Exchange

Internalizer

Wholeseller

Darkpool

Venue

Settlement

Traditional Institutions

Investors

Trading Arrangements

central limit order book

complexity

price impact of trades

anonymity

price discovery

centralized auction

bilateral
negotiation

Request
for Quote

open
outcry

Broker

Exchange

Internalizer

Wholeseller

Darkpool

Venue

Settlement

Investor

Who trades?

Most of the time there is no "coincidence of wants"
\(\Rightarrow\) Need intermediating liquidity providers!

What's there first?
- trader's order \(\equiv\) liquidity provision
- trader's order \(\to\) liquidity provision
- liquidity provision \(\to\) trader's orders
Deeply affects models and modelling choices

Key question for liquidity provision

Why is there trading?
- diversification/liquidity shocks
- private information
- arbitrage

Seminal papers

trader's order \(\equiv\) liquidity provision
trader's order \(\to\) liquidity provision
liquidity provision \(\to\) trader's orders

What's there first? Orders or Liquidity?

Seminal papers

trader's order \(\equiv\) liquidity provision
- Grossman & Stiglitz (AER 1980)
trader's order \(\to\) liquidity provision
- Kyle (ECTA 1985)
- Grossman & Miller (JF 1988)
liquidity provision \(\to\) trader's orders
- Glosten & Milgrom (JFE 1986)
- Foucault (JFM 1999)

people "buy" noisy information
demand = supply
price reveals information
marginal value of info = cost of info

competitive liquidity provider receives order flow and sets zero-profit price
noise trader order flow
monopolist informed trader cautiously trades over time to conceal info
slope of the price curve measures liquidity

risk-averse market makers require compensation for risk-taking (position risk)
liquidity-motivated traders submits order
what determines the price slope?

informed and uninformed arrive in random sequence at the market
market maker posts bid and ask prices that account for info-driven loss
what determines the spread?

submit market vs limit order?
post limit order = trading option for others
fundamental may change and may not be able to adjust limit order
gives rise to price slope and spread

Basics of liquidity provision under value uncertainty

fundamental value up
- \(\Rightarrow\) price up
  - \(\Rightarrow\) sell the asset for less than its worth
fundamental value down
- \(\Rightarrow\) price up
  - \(\Rightarrow\) buy the asset for more than its worth

Questions:

What are the fees/cash sources of income?
When does/did the value change?
When do I assess the positional gain/loss?

liquidity provision = accept a risk
risk = fundamental value may change (or may have already changed and you don't know it)

every model has some form of structure like

\[\text{trading income (fees, spreads, etc)} +\underbrace{\text{what I sold it for}-\text{value of net position}}_{\text{positional gain/loss}} \ge \text{outside option} \]

Example 1: Grossman/Miller

Any position is a risk \(\to\) cost
always over-price the trade to compensate for risk

Example 2: Kyle or Glosten-Milgrom

trade with informed:
- receive too little/pay too much
trade with uninformed:
- pay too little/receive too much
trade-by-trade view
- can either trade on "other" market
- hold asset to maturity

Example 3: Limit order market (a la Glosten 1994)

trade with uninformed:
- overprice small orders
trade with informed:
- underprice small orders

Centralized Trading

Issues and concerns with CEX trading

No shortage of drama!
A short list of issues
1. expensive arbitrage
2. price manipulation
3. volume manipulation
4. security risk
5. Google "Lazarus Group"
6. Are they unlicensed securities exchanges?

Costly Arbitrage

BTC/USD

ask: 7,600

bid: 7,550

BTC/USD

ask: 7,500

bid: 7,450

buy BTC

sell BTC

move BTC to Kraken

=> arbitrage = commit capital on multiple exchanges

Crypto Wash Trading, Lin William Cong, Xi Li, Ke Tang, Yang Yang

systematic tests:
- robust statistical and behavioral patterns in trading to detect fake transactions on 29 cryptocurrency exchanges.
- Regulated exchanges are OK
- unregulated exchanges: rampant manipulations
- wash trading on each unregulated exchange:
  - on average over 70% of the reported volume
  - improve exchange ranking
  - temporarily distort prices

Volume Manipulation

Cryptocurrency Pump-and-Dump Schemes
Tao Li, Donghwa Shin, and Baolian Wang, 2020

What is pump and dump?

arranged via Telegram Channels

Price Manipulation: Pump & Dump

August 2016

Security Risk

Decentralized Trading

where do I find these plots? theblock.co/data/

... 300 lines of code ...

Can we run a limit order book directly on the blockchain? Yes, but ...

for reference: NASDAQ creates several peta-bytes worth of data each month!
\(\to\) on-chain order book is a really bad idea!
\(\to\) Data-intensive, computation intensive, costly, inefficient

How do people trade on-chain?

Decentralized trading using automated market makers (AMM)

Liquidity providers

Liquidity demander

Liquidity Pool

AMM pricing is mechanical:

determined by the amounts of deposits
most common:
- constant product
- #USDC \(\times\) #ETH = const

No effect on the marginal price

	limit order book	periodic auctions	AMM
continuous trading
price discovery with orders
risk sharing
passive liquidity provision
price continuity
continuous liquidity
sniping prevented

AMM Theory: The Price Function

Basic Requirements

Two assets:
- cash and an asset
- deposits in pool of \(c\) cash and \(a\) asset
AMMs should be self-contained: pricing rule based on
- deposits \(a,c\) and
- liquidity demanded \(q\)
implied value of the asset at deposit: \[p=\frac{c}{a}.\]
marginal price: an infinitesimal unit should cost \[p^m=p\]

What does an AMM need?

trades create linear changes:
- a trade of \(q\in(-\infty,a),\) will cost \(\Delta c(q)\in(-c,\infty)\)
- liquidity changes
  - \(a\) \(\to\) \(a-q\)
  - \(c\) \(\to\) \(c+\Delta c(q)\)
- \(\to\) marginal price after trade of \(q\) is
  
  \[p^m(0)=\frac{c}{a}~~\to~~p^m(q)=\frac{c+\Delta c(q)}{a-q}.\]

quantity

price

\(q\)

\(p^m(q)\)

\(\Delta c(q)(Q)=p(q)^m\times q\)

Traditional pricing: auctions/open outcry/RFQ: uniform price

idea: cost of \(q=\) price\(\times\) quantity

Some Pricing Rules from Traditional Markets: Uniform Price

assume cost is price \(\times\) quantity: \[\Delta c(q)=q\cdot p(q)\]
plus assume average price \(=\) marginal price after trade

\[p(q)=\frac{c+q\cdot p(q)}{a-q}~~\Leftrightarrow~~p(q)=\frac{c}{a-2q}.\]
formalized and analyzed by Canidio and Fritsch (2023) and implemented in COW-Swap
theoretical/implementation problem: splitting an order into smaller units is profitable!

\(q=2\)

\(\Delta c(q)= q\times p^m(q)=2\times 15.5\)

Main pricing rule in stock exchanges: limit order book

quantity

price

\(q\)

\(p^m(q)\)

\(\Delta c(q)=\int_0^qp^m(s)~ds\)

Some Pricing Rules from Traditional Markets: Limit Order Book

For AMM: assume continuity \(\to\) price of "final" marginal unit coincides with price of "first" marginal unit of next trade \[\Delta c(q)=\int_0^q \rho(s) ds~\to~\Delta c'(q)=\rho(q)=p(q).\]
Also "first" unit costs nothing \(\Delta c(0)=0\)
This brings differential equation
\[p(q)=\frac{c+\int_0^q \rho(s) ds}{a-q}~~\Leftrightarrow~~\Delta c'(q)=\frac{c+\Delta c(q)}{a-q},\]
with solution \[\Delta c(q)=q\cdot\frac{c}{a-q}.\]
Or, we can write \[\Delta c(q)=\int\limits_0^q p(s)ds=\int\limits_0^q\frac{ac}{(a-q)^2}ds=\frac{cq}{a-q}.\]

Idea of pricing: liquidity before trade \(=\) after trade
\[L(a,c)=L(a-q,c+\Delta c)\]

Most Common Pricing Rule in DeFi: Constant Product

With \[a\cdot c= (a-q) (c+\Delta c(q))\]
we compute cost \[\Delta c(q)=q\cdot \frac{c}{a-q},\]
average price \[p(q)=\frac{c}{a-q}\]
and marginal price \[p^m(q)=\frac{c+\frac{cq}{a-q}}{a-q}=\frac{ac}{(a-q)^2}.\]

Insight: AMM pricing function is the same as a limit order book when we require

that prices are continuous and
that prices are determined by liquidity deposits only

price \(\times\) quantity: \[p(q)=\frac{c}{a-2q}\]
limit order book: \[p(q)=\frac{c}{a-q}.\]
constant product: \[p(q)=\frac{c}{a-q}.\]

Some insights on pricing functions

Price \(\times\) quantity
- nice feature:
  - regret-free price: if price move is due to information, you have no positional loss
- poor feature:
  - you can always save by order-splitting
  - limit price for splitting = limit order book price

LOB/CP
- nice feature:
  - order splitting doesn't pay
- poor feature:
  - prices not regret-free: if price move is due to information, you always lose

The Pricing Function

Most common form of AMM liquidity rule is Constant Product Pricing
\[L(a,c)=a\cdot c~\Rightarrow~a\cdot c= (a-q)\cdot (c+\Delta c).\]
Total cost of trading \(q\) \[\Delta c=\frac{cq}{a-q}.\]
Price per unit \[p(q)=\frac{c}{a-q}.\]
Average spread paid\[\frac{p(q)}{p(0)}-1=\frac{q}{a-q}.\]

liquidity provider makes asset and cash deposit
more deposits flatten price curve
- may attract more volume
- but larger "positional" dollar loss when prices move

larger liquidity deposits \(a\) \(\Rightarrow\)
- lower costs (price impact) for liquidity demanders

Liquidity Supply and Demand in an Automated Market Maker

Liquidity providers: positional losses

Deposit asset & cash when the asset price is \(p\)
Withdraw at price \(p'\ne p\)

Buy and hold

Provided liquidity

in the pool

Why?
- adverse selection losses
- arbitrageurs trade to rebalance the pool

\(\to\) always positional loss relative to a "buy-and-hold"

Two views

You may trade with uninformed or informed.
- you may have a position gain (against uninformed) or loss (against informed)
- example: Aoyagi and Ito (2021) "Coexisting Exchange Platforms: Limit Order Books and Automated Market Makers", Cartea, Drissi, Monga (2023)
You trade over time with informed and uninformed
- you always lose but get compensated over time
- examples: Lehar and Parlour (2021), Hasbrouck, Riviera, Saleh (2023), Park (2021), Malinova and Park (2023)
- Lehar and Parlour (2021) assume that every uninformed trade is off-set by an arbitrageur \(\to\) more fee income

between deposit and withdrawal, asset value changes from \(V_0\to V_t\) and price \(p_0\to p_t\)
efficient pricing: \(p_t=V_t\)
gross return \(R=p_t/p_0\)
in AMM, only trades move price
between deposit and withdrawal, trade with informed and uninformed

Basics of Liquidity Provision

\[\underbrace{F \int DV \mu(DV) }_{\text{fees earned on}\atop \text{balanced flow}}+\int_0^\infty\underbrace{(\Delta c(q^*)-q^*p_t(R)}_{\text{adverse selection loss} \atop \text{when the return is {\it R}}} +\underbrace{F \cdot \Delta c(q^*))}_{\text{fees earned}\atop \text{from arbitrageurs}}~\phi(R)dR \ge 0.\]

\(q^* \) is what arbitrageurs trade to move the price to reflect \(R\)

Basic idea of liquidity provision: earn more on balanced flow than what you lose on price movement

\[\text{fee income} +\underbrace{\text{what I sold it for}-\text{value of net position}}_{\text{adverse selection loss}} \ge \text{cost of capital} \]

in AMMs:
protocol fee

in tradFi: bid-ask spread

in Malinova & Park (2023): express equation with returns
single variable decision problem as fraction of shares outstanding
allows calibration to any market

Theory Literature on AMM

Lehar & Parlour (JF 2024): compares LOB with CP for theory and empirics
Barbon & Ranaldo (2022): empirics on trading costs; closed form solution to loss
Milionis, Moallemi, Roughgarden, and Zhang (2022) have dynamic analysis of impermanent loss (or, rather, "loss vs rebalancing" (LVR))
Hasbrouck, Riviera, Saleh (2023): optimal trading fee \(\not=\) 0
Park (2023): analysis of price functions properties
Malinova & Park (2023): calibrate AMM to equity markets

Sidebar: we can quantify how much a PASSIVE LP loses when the price moves by \(R\)

for orientation:

If the stock price drops by 10% the incremental loss for liquidity providers is 13 basis points on their deposit
- \(\to\) total loss=-10.13%
If the stock price rises by 10%, the liquidity provider gains 12 basis points less on the deposit
- \(\to\) total gain =9.88%

\[\frac{\text{adverse selection loss when the return is \(R\)}}{\text{initial deposit}}=\sqrt{R}-\frac{1}{2}(R+1)\]

see Barbon & Ranaldo (2022)

Liquidity Demander's Decision & (optimal) AMM Fees

Two opposing forces in equilibrium for fee \(F\nearrow\)
1. more liquidity provision
  \(\to\) lower price impact
2. more fees to pay

Result:

competitive liq provision\(\to\) there exists an optimal (min trading costs) fee \(>0\)

\(\to\) derive closed form solution for competitive liquidity provision
depends on return distribution, balanced volume, quantity demanded

Similar to Lehar&Parlour (2023) and Hasbrouck, Riviera, Saleh (2023)

For trading of a fixed quantity:

\[\text{LD cost}=\text{AMM price impact} +\text{AMM fee} .\]

\[F^\pi=\frac{1}{E[|\sqrt{R}-1|/2]+V}\left(-2q\ E[\text{position loss}]+ \sqrt{-2qV\ E[\text{position loss}]}\right).\]

assume: liquidity providers add liquidity until they break even in expectation

Empirical Work

Lehar and Parlour (2021)

Barbon & Ranaldo (2023)

Other notables:

Lehar, Parlour, Zoican (2022):
- study and document fragmentation of liquidity across different fee levels
- most intense provision-withdrawal activity for low fees \(\to\) UniSwap v3 starts mimicking tradfi
Capponi & Jia (2022) study impact of curvature of pricing function
- Theory: curvature of optimal pricing curve \(\nearrow\) volatility
- Empirics: cost volatility is 60% lower for stable-coin pairs
- corr(deposit inflows ,volatility)<0
  corr(deposit inflows ,volume)>0

Malinova & Park (2023): AMM applied to equities would reduce trading costs by 30%

UniSwap v3

UniSwap v3 has "concentrated liquidity provision"

liquidity provision choice in v2:
- submit \(a,c\) at the marginal \(p_0=\frac{c}{a}\)
- \(\to\) for given \(p_0\), there is ONE degree of freedom
- provide liquidity
  - for all prices \(p\in(0,\infty)\)
  - all asset amounts \(q\in(0,a)\)
  - all cash amounts \(\Delta c\in (0,c)\)

liquidity provision choice in v3:
- submit \(u,\Delta c (d)\)
- choose price range \([p_d,p_u]\)
- provide liquidity
  - for all prices \(p\in[p_d,p_u]\)
  - all asset amounts \(q\in(-\infty,u)\)
  - all cash amounts \(\Delta c\in (-\Delta c(d),\infty)\)

Just a little more institutional details

users submit liquidity positions \((u,d,p_u,p_d)\) and receive an ERC-721 (=NFT) token as a receipt
the code segments liquidity into discrete exponential intervals \([p_k,p_{k+1}]\) with \[p_k=(1+\delta)^k. ~~~(\text{numerically: }p(k)=1.0001^k)\]
it aggregates liquidity over these intervals
the pricing curve for each interval is determined by the constant product rule
intervals may be "empty"

How the price is determined

In v2, for given \(p_0=\frac{c}{a}\), users have ONE degree of freedom.

for instance, when you pick prices \(p_u,p_d\) and the quantity \(u\) that you are willing to supply, then the amount of cash that you have to contribute is pre-determined.
You can pick \(p_d=p_0\) so that \(\Delta c(d)=0\)

In v3, for given \(p_0\), users have THREE degree of freedom.

How the price is determined

some more facts
- if the marginal price \(p_u\) is reached, you will sell \(u\) units of the asset
- the price is still using constant product
- but we use the term "virtual" liquidity
- its the v2 liquidity that would pertain if you'd supply liquidity on the entire interval of prices

\(p_d\)

\(p_u\)

\(p_0\)

we know this curve has functional form \[p^m(q)=\frac{\tilde{a}c}{(\tilde{a}-c)^2}\]

where \(\tilde{a}\) is the virtual liquidity

quick disclaimer: what follows is not how UniSwap is explained on its website etc. But the resulting maths are the same

Trading Infrastructure

payments network

Stock Exchange

Clearing House

custodian

beneficial ownership record

seller

buyer

Broker

IS BITCOIN REALLY UN-TETHERED? JOHN M. GRIFFIN and AMIN SHAMS
Journal of Finance 2020

Tether = ‘pushed’
- print an unbacked digital dollar to purchase Bitcoin.
- \(\to\) additional supply of Tether creates unwarranted inflation in Bitcoin price

vs.

Tether = ‘pulled’
- driven by legitimate demand from investors who use Tether as a medium of exchange
- \(\to\) the price impact of Tether reflects natural market demand

Price Manipulation: Pumping Bitcoin

Figure 1. Aggregate Flow of Tether between Major Addresses

marginal pricing function

quantity \(q\)

price \(p^m(q)\)

Illustration of pricing

Some Pricing Rules from Traditional Markets: Limit Order Book

Liquidity providers submit sell orders \(s_1(p_1,q_1),\ldots,s(p_n,q_n)\)
Matching engine sorts order increasing by price
someone who wants to buy quantity

\[q\in\left(\sum_{i=1}^{m-1} q_i,\sum_{i=1}^{m} q_i\right]~~~\text{pays}~~\Delta c(q)=\sum_{i=1}^mq_i\cdot p_i.\]
For a simpler exposition, assume that the price-quantity curve can be expressed by a continuous marginal price function \(\rho(s)\) so that

\[\Delta c(q)=\int_0^q\rho(s) ds.\]

Returns to Liquidity Provision

For fixed balanced volume \(V\) and fee \(F\)

Larger pool \(\to\) smaller share of fees
\(\to\) LP expected return \(\searrow\) pool size

Competitive liquidity provision

\(\Rightarrow\) upper bound on pool size above which LPs lose money
Here: characterize this amount by the asset's market cap to be deposited to the pool (notation: \(\overline{\alpha}\))

Basics of Liquidity Provision

\[\text{LP payoff}=\text{what I sold it for}-\text{value of net position}+\text{fee income}\]

\(R\) = asset return
F = trading fee
V = balanced volume
a = size of the liquidity pool

see Lehar and Parlour (2023), Barbon & Ranaldo (2022).

(incremental) adverse selection loss when the return is \(R\)

fees earned

on informed

fees earned

on balanced flow

for reference:

If the asset price drops by 10% the incremental loss for liquidity providers is 13 basis points on their deposit
- \(\to\) total loss=-10.13%
If the asset price rises by 10%, the liquidity provider gains 12 basis points less on the deposit
- \(\to\) total gain =9.88%

positional loss

Basics of Liquidity Provision

\[\frac{1}{\text{initial deposit}}\int_0^\infty(\Delta c(q^*)-q^*p_t(R)+F \cdot \Delta c(q^*))~\phi(R)dR +\frac{F p_0 V}{\text{initial deposit}}\ge 0\]

\[\int_0^\infty\left(\frac{\Delta c(q^*)-q^*p_t(R)}{\text{initial deposit}} +F \cdot \frac{\Delta c(q^*)}{\text{initial deposit}}\right)~\phi(R)dR +\frac{F p_0 V}{\text{initial deposit}}\ge 0\]

closed form functions of \(R\) only
(see Barbon & Ranaldo (2022))

\[\underbrace{F \int DV \mu(DV) }_{\text{fees earned on}\atop \text{balanced flow}}+\int_0^\infty\underbrace{(\Delta c(q^*)-q^*p_t(R)}_{\text{adverse selection loss} \atop \text{when the return is {\it R}}} +\underbrace{F \cdot \Delta c(q^*))}_{\text{fees earned}\atop \text{from arbitrageurs}}~\phi(R)dR \ge 0.\]

Basics of Liquidity Provision

\(\Rightarrow\) \(\Delta c(q^*)-q^*p_t(R)\) is also referred to as the "impermanent loss" or "divergence loss"

\(\Delta c(q^*)-q^*p_t(R)=\underbrace{p_t(R)\times(a-q^*) +c+\Delta c(q^*)}_{\text{value of liquidity deposit}}-\underbrace{(p_t(R)a+c)}_{\text{value of buy-} \atop \text{and-hold position}}\)

see Milionis, Moallemi, Roughgarden, and Zhang (2022) for a dynamic analysis of impermanent loss

Basics of Liquidity Provision

Liquidity provision measured as "collective" deposit \(\alpha\) of token's market cap as function of

balanced volume
return distribution
(some authors use explicit functions so they can relate it to asset volatility)
fee

\[E[\text{DL}(R)]+F\cdot E[\text{another function of }R]+F\cdot \frac{\text{E[dollar volume]}}{\text{initial deposit}}\ge 0.\]

\[\text{what I sold it for}-\text{value of net position}+\text{fee income} \ge 0 \]

The Decision of the Liquidity Demander

Wants to trade some quantity \(q\) at cost

\[\text{AMM price impact} +\text{AMM fee}.\]

two opposing forces for \(F\nearrow\) for liquidity demand
- more liquidity provision
  \(\to\) lower price impact
- more fees to pay
Finding: There is a minimal cost fee:

\[F^\pi=\frac{1}{E[|\sqrt{R}-1|/2]+E[DV]}\left(-2q\ E[\text{DL}]+ \sqrt{-2qE[DV]\ E[\text{DL}]}\right).\]

this is from Malinova and Park (2023); similar result is in Hasbrouk, Riviera, Saleh (2023)

Model Summary

Have: equilibrium choice for liquidity provision.
Useful feature: can express liquidity by % market cap of token/asset
Can measure cost for liquidity demanders to use AMM.
Know: fee that maximizes liquidity demander benefit (\(\not=0\))

The Pricing Function

Liquidity Deposit \(\Rightarrow\) slope of the price curve

Most common form of AMM liquidity rule is Constant Product Pricing
\[L(a,c)=a\cdot c~\Rightarrow~a\cdot c= (a-q)\cdot (c+\Delta c).\]
Total cost of trading \(q\) \[\Delta c=\frac{cq}{a-q}.\]
Price per unit \[p(q)=\frac{c}{a-q}.\]
Average spread paid\[\frac{p(q)}{p(0)}-1=\frac{q}{a-q}.\]

makes asset and cash deposit
more deposits flatter price curve
- attracts more volume
- but larger "positional" loss when prices move

The Pricing Function (just a little more)

A limit order book has a marginal price function \(\rho(q)\) so that \[\Delta c(q)=\int_0^q\rho(s) ds.\]
We can do that here, too: for
\[\rho(q)=\frac{ac}{(a - q)^2},\]
we have \[\int_0^q \frac{ac}{(a - s)^2}ds=\frac{cq}{a-q}.\]
\(\to\) Constant Product = special case of a limit order book.

The Pricing Function (almost done, just one more thing)

Suppose we start with a price \(P\) and end with a price \(P'=R\cdot P\).

We know by definition
\[P=\frac{c}{a},~P'=\frac{c'}{a'},~L=\sqrt{ac},~L=\sqrt{a'c'}.\]
Then it holds that \[a=\frac{L}{\sqrt{P}},~c=L\sqrt{P},~a'=\frac{L}{\sqrt{P'}},~c'=L\sqrt{P'}.\]

For the price to move from \(P\) to \(P'\), quantities \(q,\Delta c\) change hands.
Therefore \(a'=a-q,~c'=c+\Delta c\)

Combine with the above \[a-q=\frac{L}{\sqrt{P'}}~\Leftrightarrow~q=L\left(\frac{1}{\sqrt{P}}-\frac{1}{\sqrt{P'}}\right).\]
do the same for \(\Delta c\) \[\Delta c= L\left(\sqrt{P'}-\sqrt{P}\right).\]

the per unit price for the trade of \(\Delta x\) is \[\frac{\Delta c}{q}=\sqrt{PP'}=P\sqrt{R}.\]
\(\to\) avg price is the geometric mean of the prices

AMMs continue to evolve: UniSwap v3

Basic idea:

allow different fees (let's ignore)
don't supply liquidity for entire price range \((0,\infty)\) but only \([p_a,p_b]\)

idea: project the iso-liquidity curve back to the axes
price in this range is determined by constant product rule

Source:" Uniswap v3 Core," Adams, Zinsmeister, Salem, Keefer, Robinson (2021)

current price at point \(c\): \(p_c\)
\(x_{\text{real}}=\) max of \(X\) you buy
\(y_{\text{real}}=\) max of \(Y\) you buy

pricing effectively occurs on a grid
it's not straightforward to describe the pricing function without lots of new notation

Some UniSwap v3 maths (Barbon & Ranaldo 2023)

Source: Elsts (2021) "Liquidity Math in UniSwap v3"

We are interested in "virtual limit orders"
- for selling known \(x\Rightarrow y=0\) (only sell) have known \(P,x,y,p_a\):
  - what is the (ask) price \(p_b\)?
- for selling up to price \(p_b\) \(\Rightarrow y=0\) (only sell) have known \(P,y,p_a,p_b\):
  - what is the quantity \(x\)?

When the price is in interior of \((p_a,p_b)\) liquidity provided "to the left" of the price must coincide with liquidity provided to the right.
We can derive conditions \[x=L\ \frac{\sqrt{p_b}-\sqrt{P}}{\sqrt{p_b}\sqrt{P}}\]
\[y=L\ (\sqrt{P}-\sqrt{p_a})\]

\(\tilde{x},\tilde{y}\) the "real" reserves, \(x,y\) the virtual reserves
\(P\) the price of "\(x\)" (asset) in "\(y\)" (cash), with \(P\in[p_a,p_b]\)
\(L\) the liquidity
Constant product function: \(L=\sqrt{xy}\)
And then \[L^2=\left(\tilde{x}+\frac{L}{\sqrt{p_b}}\right)\left(\tilde{y}+L\sqrt{p_a}\right).\]

At the price bounds, the real amounts are either \(x=0\) or \(y=0\). We can derive conditions \[x=L\ \frac{\sqrt{p_b}-\sqrt{p_a}}{\sqrt{p_b}\sqrt{p_b}}\]
\[y=L\ (\sqrt{p_b}-\sqrt{p_a})\]

Problem: MEMPOOL Frontrunning is intrinsically profitable

\(X\)

\(Y\)

normal trade: sell \(x\) \(\to\) get \(y'\)

\(Y-y'\)

\(X+x\)

front-running:

front-runner: sells \(x\) \(\to\) gets \(y'\)
front-run: sells \(x\) \(\to\) gets \(y''\)
front-runner: buys \(x\) \(\to\) pays \(y''\)

\(Y-y'-y''\)

\(X+2x\)

\(y'>y''~\Rightarrow\)

front-running is intrinsically profitable

Disclaimer:

this problem is well-known
fees can mitigate it
several protocols such as the latest iteration by Balancer try to combat it

Dark side of DEx trading: Miner extractable value

Application 1: decentralized trading with automated market makers

Problems:

How to get liquidity?
How do you attract traders?

lesser problem because

tokens trade elsewhere
arbitrage creates activity

Common solution: create a reward token! Here's how this works

Step 4: users receive a reward token based on the time that they lock up the "receipt" token

Step 3: users lock up the "receipt" token in a smart contract

Step 2: users contribute liquidity and get a "receipt" token

Step 1: create reward tokens and deposit into a smart contract

borrow

provide collateral

Application: Pool-based borrowing and lending

Application 2: decentralized Borrowing and Lending

Same problems as with trading:

How to get liquidity?
How do you attract borrowers?

But: in contrast to trading, here you need both!

liquidity \(\nearrow\)

volume \(\nearrow\)

protocol fees \(\nearrow\)

token value \(\nearrow\)

Platform economics is tricky:

What's the product?
How do you get it started?
How do you get people to contribute?
How do you earn money?

Without intermediaries:
platform economics!

incentives for both?

What value do these tokens have?

Functionality:
- fee levels for AMMs
- decision to deploy on other chains
- key threshold values for lending
Monetary value
- implicit claims on "protocol" income
- implicit claim on reserve pools

Vampire Attacks and Other Shenanigans

Source: https://finematics.com/vampire-attack-sushiswap-explained/

Vampire Attack:
- syphon liquidity from a different protocol
- using your own token!!!
Mechanism:
- deposit in UniSwap, receive LP token
- deposit LP token to Sushi, receive rewards
- liquidate LP token and migrate original liquidity to Sushi = "suck out the blood from UniSwap"

another common trick:

pump the price of the reward tokens
\(\Rightarrow\) "yields" often exceed 500%

The Microstructure of Crypto-Asset Trading

Issues and concerns with CEX trading

Costly Arbitrage

=> arbitrage = commit capital on multiple exchanges

Volume Manipulation

Cryptocurrency Pump-and-Dump Schemes Tao Li, Donghwa Shin, and Baolian Wang, 2020

Price Manipulation: Pump & Dump

Security Risk

... 300 lines of code ...

Can we run a limit order book directly on the blockchain? Yes, but ...

How do people trade on-chain?

Decentralized trading using automated market makers (AMM)

Trading Infrastructure

vs.

Price Manipulation: Pumping Bitcoin

Price Manipulation: Pumping Bitcoin

Problem: MEMPOOL Frontrunning is intrinsically profitable

Dark side of DEx trading: Miner extractable value

Application 1: decentralized trading with automated market makers

Application 2: decentralized Borrowing and Lending

The Microstructure of
Crypto-Asset Trading

Cryptocurrency Pump-and-Dump Schemes
Tao Li, Donghwa Shin, and Baolian Wang, 2020