# dYdX: Liquidity Providers’ Incentive Programme Review

Colin Chan

(June 2023)

###### Abstract

Liquidity providers are currently incentivised to provide liquidity through
the LP Incentives Programme. Based on the various parameters - makerVolume,
depths and spreads, they are rewarded accordingly based on their activities.
Given the maturity of the BTC and ETH markets, alongside other altcoins which
enjoys a consistent amount of liquidity, this paper aims to update the formula
to encourage more active and efficient liquidity, improving the overall
trading experience. In this research, I begin by providing a basic
understanding of spread management, before introducing the methodology with
the various metrics and conditions. This includes gathering orderbooks on a
minute interval and reconstructing the depths based on historical trades to
establish an upper bound. I end off by providing recommendations to update the
maxSpread parameter and alternative mechanisms/solutions to improve the
existing market structures. 111This research was sponsored by the dYdX Grants
Programme. Thank you to the dYdX Trading Team, Foundation and Grants Team for
the opportunity and their support.

###### Contents

  1. 1 Introduction
  2. 2 Deconstructing the Spread
  3. 3 Understanding DYDX LP Rewards Incentives
    1. 3.1 Different Parameters - Types of Liquidity Needed
    2. 3.2 Peer Analysis
      1. 3.2.1 CEXs’ LP Programmes
      2. 3.2.2 Orderbook Snapshot
  4. 4 Methodology
    1. 4.1 Data Collection
      1. 4.1.1 Orderbook Data
      2. 4.1.2 Trade Data
      3. 4.1.3 Data Interpretation Measures
    2. 4.2 Metrics
      1. 4.2.1 Average Trade Size, Max Trade Size, Number of Trades above a certain amount on a daily basis
      2. 4.2.2 Time to Recovery
      3. 4.2.3 (Upper Bound) : Estimated Depth Required
    3. 4.3 Conditions
      1. 4.3.1 Order book and Trade Dynamics
  5. 5 Results
    1. 5.1 Descriptive Statistics
      1. 5.1.1 Depth and Spread (Altcoins)
      2. 5.1.2 Trade Statistics
    2. 5.2 Event Study: High Market Impact Events
      1. 5.2.1 Orderbook Resiliency
      2. 5.2.2 Reconstructed Depths vs maxSpread
  6. 6 Discussion
    1. 6.1 Market Observations
      1. 6.1.1 Tick Constrained Markets
      2. 6.1.2 Available liquidity
      3. 6.1.3 Majors : BTC, ETH
    2. 6.2 Suggested maxSpread parameters
  7. 7 Further Explorations for Alternative LP Reward Mechanisms
    1. 7.1 Suggestion 1: Simplify the LP reward formula for mature markets to a volume parameter
    2. 7.2 Suggestion 2: Implementing the LP Rebate Scheme
      1. 7.2.1 Replace the LP Incentive Programme for BTC and ETH with the Rebates Scheme
      2. 7.2.2 Enhanced Rebates for long tail / new markets
    3. 7.3 Suggestion 3: Increase the makerVolume weightage for non-BTC/ETH markets to 0.85
    4. 7.4 Suggestion 4: Assigning DMMs for markets per epoch
      1. 7.4.1 Start of Epoch
      2. 7.4.2 During Epoch
      3. 7.4.3 End Epoch
    5. 7.5 Suggestion 5: Sunset or reduce rewards for certain existing v3 markets
    6. 7.6 Other Suggestions:
      1. 7.6.1 Expand the LP Rewards Formula - Reward Multiplier for Volatile Periods
      2. 7.6.2 Implement a liquidity mining campaign with Hummingbot to provide targeted liquidity
  8. 8 Conclusion
  9. 9 References
  10. 10 Appendix
    1. 10.1 Min Tick Size vs Spread
    2. 10.2 Average Orderbook Depths Recorded
    3. 10.3 Average Daily Trade Statistics - Between April 30 and May 19 2023
    4. 10.4 Average Daily Trade Statistics - Between May 20 and May 24 2023
    5. 10.5 PPI Release with Tighter Spreads
    6. 10.6 US Initial Jobless Claims
    7. 10.7 FOMC Meeting Minutes Release
    8. 10.8 Fees Generated

##  1 Introduction

Exchanges provide a medium for buyers and sellers to come together and trade
at a common price based on each agents’ perspective. With the advent of
electronic trading, we witness the emergence of liquidity providers (LPs) who
provide transparent two-sided markets at all times. They play a critical role
to ensure a healthy flow of liquidity for the price discovery process and
enable a seamless trading experience.

Through the years, a primary aim of exchanges is to attract and reward these
LPs. The objective is to establish a mechanism to encourage a consistent of
liquidity through rebates and incentives. For instance, NYSE establishes
designated Market Makers (DMMs) where they are specially appointed and obliged
to maintain quotes and facilitate transactions for an allocated set of stocks.
Deutsche Boerse has also implemented the Xetra Liquidity Provider Programme to
reward LPs based on a set of parameters. Especially for margin trading which
relies more on liquidity depth to absorb delinquent traders, exchanges have to
find a balance between tighter spreads and deeper depths. As such, to
incentivise LPs, it is critical to understand their considerations in quoting
buy and sell orders to the book (BIS, 2015):

Revenue: Bid ask spread, Delta market value from position

Costs: Funding, Maker fees

They primarily profit by arbitraging the differences in spread and thus
compete on the order execution and turnover to build more volume. In fact,
spread behaviour is a crucial focus for all market participants:

* Takers: Tighter spreads improve the price discovery mechanism as takers can execute orders closer to the fair price and institutional investors can put on larger blocks of trade without suffering from excessive slippage.

* Makers (LPs): These providers constantly face a dilemma between turnover and spreads as many attempt to identify the optimal spread management. For instance, lower spread means that the LPs’ quotes are more likely to get hit, resulting in higher turnover rates. However, this will also entail a lower income per trading cycle since the profit potential is lower each time.

Therefore, the paper will begin by unravelling the LP rewards scheme on dYdX,
in particular the spread management aspects. Rather than just focussing on
historical order book data and depths, I will empirically analyse the trading
data via the dYdX API. In particular, this will seek to understand the exact
volume of trades happening on the volume and provide more granular insights
into the order book resiliency, especially after liquidity shocks initiated by
anomalous market orders. The aim of this paper would be to:

_Enhance the market efficiency by ensuring sufficient liquidity in the
orderbook and tightening the maxSpread parameter, while being aligned with
current liquidity provision strategies by LPs and trading statistics on the
exchange_

##  2 Deconstructing the Spread

The bid-ask spread (quoted spread) can be defined as:

$QuotedSpread=LowestAsk-HighestBid$ (1)

This is the simplest form of measurement taken directly from quotes at the top
of the orderbook. However, the majority of trades usually occur beyond this
point given varying transaction sizes.

Prior empirical research by Demsetz (1968) provides the foundation for
identifying the determinants of the spread based on the NYSE - inventory,
transaction, information costs and level of competition based on the number of
LPs providing liquidity. This was further elaborated by Copeland and Galai
(1983) in emphasising the variables of volatility and trading activities which
are extrinsic risks faced by liquidity providers. More recent studies on the
NYSE have also revealed that volume exerts statistically significant inverse
relationship with spreads while the impact of price on spreads returns mixed
results depending on the sectors as it incurs different variations in the
market making costs. Furthermore, greater volatility induces more risks for
LPs, who may widen spreads to price in the uncertainties. As a result,
different exchanges have curated LP Programmes which actively encourage volume
and spreads for designated LPs. For instance, the Xetra Liquidity Provider
service agreement includes volatility within its scores, where the achieved
best bid-offer (BBO) presence is doubled in times of volatility, on which the
EUR index exceeds a range of 3%.

Therefore, the spread can be represented with a simplified equation where:

$Spread=f(l,v,p)$ (2)

l = liquidity, v = volatility, p = price impact

It is commonly expected that the long tail markets will have a wider spread,
reflecting greater adverse selection. Hence, compensating LPs for assuming
delta risk in being exposed to these price movements.

##  3 Understanding DYDX LP Rewards Incentives

To incentivize two-sided liquidity, the LP rewards programme was introduced to
distribute $DYDX to LPs - makerVolume, depth and spread vs mid market, and
uptime on provision. The general formula can be summarised with the
participant’s Q score per epoch:

$Q_{\text{Final}}=\left[Q_{\text{min}}(\sum_{i=1}^{n}\frac{\text{BidDepth}_{i}}{\text{Spread}{{}_{i}}}+\sum_{j=1}^{m}\frac{\text{AskDepth}_{j}}{\text{Spread}_{j}})\right]^{y}\times\left[\left(\sum{{Q_{\text{min}}(N)>0}}\right)\right]^{5}\times\text{makerVolume}^{z}$
(3)

where:

$y=0.15$ if BTC/ETH, $y=0.35$ for the remaining.

$z=0.85$ for BTC/ETH, $z=0.65$ for the remaining.

Previous community discussions have centred around adjusting the weights for
the overall formula. For instance, LPs who generated lower makerVolumes
received the majority of the LP rewards which contradicted the purpose of this
programme.

###  3.1 Different Parameters - Types of Liquidity Needed

In the formula, the paper will expound deeper into the scoring mechanism based
on Depth/Spread located within. The programme has specified a MaxSpread
parameter where providers will have to provide bids and asks within this
spread for the markets in order to qualify for the rewards.

Based on the formula, there are 2 segments that focus on spread management -
depth/spread ratios and Qmin, while a weightage is placed on makerVolume.
These parameters represent different interests from LPs:

* Deep Liquidity: LPs who constantly provide 2 sided liquidity within the maxSpread will be eligible for these rewards, even though some of these remain as passive orders in the book. This influences their uptime scores which is raised to the power of 5.

Table 1: MaxSpread parameter Markets | Max Spread vs Mid Market
---|---
BTC, ETH | 20 bps
Remaining | 40 bps

* Active Liquidity: makerVolume refers to the amount of liquidity provided by LPs which were successfully taken by counteracting trades. These ’active liquidities’ have a higher probability of being near the BBO, away from the maxSpread, providing a favourable environment for takers.

Thus, a key focus is to incentivise LPs to provide active and deep liquidity
at competitive prices.

###  3.2 Peer Analysis

####  3.2.1 CEXs’ LP Programmes

CEXs such as Binance and Okx implement LP programmes, which primarily
incentivises LPs to trade through lower to negative maker fees. The main
criteria includes the amount of assets held on the exchange, monthly trading
volume and/or amount of CEX native tokens. For Binance, the exchange also
implements weekly performance reviews, looking at makerVolumes, Bid/offer
spreads, total order sizes, duration and market making time.

In traditional exchanges such as the NASDAQ, the Designated Liquidity Provider
(DLP) Programme was implemented, aimed to encourage LPs to support a broader
range of ETPs. This is mainly through tiered rebates (i.e. negative maker
fees). Certain applicable criteria include time @NBBO (national best bid
offer, across exchanges), time within 5bps of NBBO, notional depth and average
spread. In particular, the focus is on ETFs that ’are under the 250,000 share
threshold for average daily volume’, where the exchange gives LPs a stipend on
top of the existing rebates. These have yielded positive results with tighter
spreads and depths around the NBBO.

####  3.2.2 Orderbook Snapshot

As a comparison across exchanges, a snapshot of the orderbooks on dYdX and the
most liquid exchange, Binance, were taken on May 4 across all the markets
supported using USDT as the base asset. UMA-USDT is not supported on Binance.

It can be seen that the BBO spreads for certain markets such as ZRX-USD and
UMA-USD have been $consistently\geq 40bps$ and likely suggests that LPs are
not incentivised to provide liquidity in these markets. This observation is
supported by the constant readjustments in margin fractions due to the lack of
liquidity and volatility experienced.

Figure 1: BBO Spreads on dYdX vs Binance (where bid spread = ask spread)

A closer look at the liquidity distribution in the order book highlights the
positioning of the limit orders. This is analogous to a competitive (non-
cooperative) game where LPs aim to minimize the spreads to attract trading
volume and increase the likelihood of executing trades based on the
probability density function for the different price levels. Ultimately, a LP
seeks to position themselves around the tightest spreads possible, thereby
influencing other LPs to compete around the BBO spreads to have higher
execution rate. The following shows a snapshot of the order books for BTC:

Table 2: BTC-USD on dYdX Original maxSpread = 20bps | maxSpread = 10bps
---|---
|
Table 3: BTC-USDT on Binance Original maxSpread = 20bps | maxSpread = 10bps
---|---
|

From an absolute volume perspective, the majority of the volumes are found
within 10bps from the midPrice for BTC-USD market. In this case, passive
orders far away from the BBOs have a lower probability to get hit but yet LPs
still continue to be eligible for these rewards should the depths hit the
minimum requirements within the maxSpread.

Therefore, in determining the maxSpread parameter, it is crucial to understand
the order book dynamics and contextualise it with the trades on the exchange.
For instance, if majority of trades are low in volume, then perhaps
incentivising too wide a spread may be unncessary.

##  4 Methodology

###  4.1 Data Collection

####  4.1.1 Orderbook Data

Minute by minute orderbook data was collected from the dYdX API for 2 weeks
from May 11 (CPI Release) to May 24. This period was deliberately chosen given
the harsh regulatory and economic climate (e.g. significant pullback of
liquidity on CEXs). Specific events were also identified in which the orders
removed a significant amount of liquidity from the market:

Table 4: High Market Impact Events Date | Event
---|---
PPI Release | Start : 1683804600, End: 1683811800
US initial Jobless Claims | Start : 1684409400, End: 1684416600
FOMC Meeting Minutes Release | Start : 1684947600, End : 1684954800

The order book data was manually collected on a minute by minute basis to
determine the depths within the range of 10bps and 50bps. I note that the data
provided by the API does not distinguish between the different types of orders
and hence, trade data was also retrieved to supplement the takers’ behaviors
.The following notation is used for the data within the order book at time
$t$:

*$m_{t}=\frac{P_{bid,0}+P_{ask,0}}{2}$ — mid price using the lowest ask and highest bid prices

* $p_{x,l},v_{x,l}$ — depicting the price and volume respectively for the $l$th level in the book, and the side (x = bid, ask)

* $ms_{x}=\text{midSpread}$ — where bidSpread = askSpread in a symmetric book

* $liquidity_{pre},liquidity_{post}$ — depicts the amount of liquidity present before and after the market impact events

####  4.1.2 Trade Data

All trades executed between April 30 and May 24 were retrieved as well from
the dYdX API. This contained the side, size, price and liquidation parameters
within each trade executed. Subsequently, this will be grouped at the minute
level to estimate the bid and ask depths necessary, effectively reconstructing
the order book to suggest the tightest spreads possible based on this
empirical analysis.

####  4.1.3 Data Interpretation Measures

Descriptive statistics are provided regarding the state of liquidity, to
understand the changes in order book depths and if there was sufficient buffer
to absorb these trades. Furthermore, this will also summarize the daily trade
statistics for each market, by identifying the following - number of trades
above $50,000, $100,000, $250,000, $500,000 and $1.5m, mean, standard
deviation and maximum trade size.

In particular, an analysis of LP behavior in highly stressed environments is
critical to provide an initial understanding into the order book dynamics
during these circumstances. For instance, LPs will exit markets when the risks
of liquidity provision outweigh the benefits, and hence, the incentives scheme
is necessary to retain LPs regardless of volatility. The resiliency analysis (
t - 1, t + 1 hours of Market Impact events) based on the identified events
will visualise the changes in order book depths before and after the interval
to understand the changes in volumes and how quick LPs were in enabling the
order book to recover back to average levels.

###  4.2 Metrics

####  4.2.1 Average Trade Size, Max Trade Size, Number of Trades above a
certain amount on a daily basis

This identifies the amount of liquidity needed by takers to execute trades in
a low slippage environment. Sufficient liquidity buffer will have to be
available to fill these orders in an orderly fashion.

####  4.2.2 Time to Recovery

For these Market Impact Events, a more granular perspective is adopted to
fully understand the LPs’ reaction. This will contribute to the general
understanding in the order book resiliency under such stress tests as it is
crucial to have a good liquidity recovery process in these instances. For a
general perspective, these were calculated in minutes to feature the duration
of time before the amount of liquidity returns to 75% of the average level.
The metric was adapted from a study that emphasised the importance of
resiliency as a market attribute in the Australian equity market. (Lo and
Hall, 2015)

####  4.2.3 (Upper Bound) : Estimated Depth Required

Based on the trade data for each market, the estimated depth required will be
calculated on each side of the order book. This is based on the
reconstruction, aggregated per minute level, creating an upper bound.

In the BTC-USD market, if a trade with ’BUY” was recorded, the notional value
of the trade (i.e. price * size) is calculated. The trade time will be rounded
off to the nearest minute based on ’createdAt’. This process is repeated for
all trades. The trades are then grouped by hour and minute for each day, and
added to the order book. In this case, it will be added to the ’Ask’ side.
Through this iterative approach, it creates an initial set of parameters for
backtesting, to determine the upper bound of the recommended depths. A sample
of the algorithm can be found below, which continuously updates the local
state of the order book defined by the OrderBook class.

Algorithm 1 Reconstruction of Historical Order Book (Aggregated Per Minute)

1:$Global:\text{order\\_books},\text{times}$

2:

3:procedure Main(Updating Orderbooks - Consolidates bids and asks)

4: for _ , trades_minute in trades_df.groupby([trades_df[’hour’],
trades_df[’min’]]) do

5: $\text{trades\\_minute}\leftarrow\text{trades\\_minute[['price', 'size',
'side']].values.tolist()}$

6: $\text{order\\_book}\leftarrow\text{OrderBook()}$

7: $\text{order\\_book.reconstruct\\_order\\_book}(\text{trades\\_minute})$

8: $\text{times.append}(\\_)$

9: $\text{order\\_books.append}(\text{order\\_book})$

10: end for

11:end procedure

12:

13:function Helper Class(Orderbook)

14: def reconstruct_order_book(self, trades):

15: self.bids $\leftarrow\\{\\}$

16: self.asks $\leftarrow\\{\\}$

17:end function

###  4.3 Conditions

In deciding the maxSpread parameters, the following conditions will need to be
fulfilled:

####  4.3.1 Order book and Trade Dynamics

Condition 1 (Lower Bound) : $MinTickSize$ >$Price\times Spread$

For instance, ETC has a tick size of 0.01. At a price of $18.33, should an LP
quote at $18.34, this is already equivalent to 5.5bps. This plays a critical
role in influencing how LPs quote in the order book. (Refer to Appendix for
Bps requirements). Therefore, if an LP who is quoting for UMA already incurred
42.8bps for just a 1 tick difference, then it will remain in the 40bps
maxSpread basket. This represents a suggested lower bound for the market to
remain attractive to LPs.

Condition 2 : $Mean_{depthatchosenspread}$ $\geq$
$Mean_{volumeateachside}(Perminute)$

Using historical trade data, the average size for each side of the order book
is then compared against the depths retrieved in intervals of 10bps. The
reconstructed depths will have to be greater than the 95th percentile of daily
historical volumes, recorded on a minute by minute basis.

Based on the isolated periods of volatility, the depths at the chosen spread
will then be plotted against the trade volume recorded for that minute. This
stress test will reveal if there’s empirically sufficient liquidity buffer in
Section 5.2.

Condition 3 : Depth at chosen spread exhibits mean reverting behavior

The actual depth based on the chosen spread that has fulfilled Conditions 1
and 2 is chosen and this undergoes the statistical ADF test if the series is
stationary - mean, variance, covariance and standard deviation are not a
function of time, without trend or seasonal components. This will show that
these liquidity depths are consistent with present LP behaviors and aren’t
mere coincidences.

Condition 4 : $Depth_{spread,x}$ $\geq$ $Estimated_{V}olume_{x,t}(Perminute)$,
where x = bid, ask

This is based on the event study for high market impact events in the later
section.

##  5 Results

###  5.1 Descriptive Statistics

####  5.1.1 Depth and Spread (Altcoins)

Based on the reconstruction of the orderbook, aggregated per minute level,
there is a distinct segregation amongst the different markets. During the
recorded periods where large trade sizes are executed,

– SOL leads the pack, requiring the largest upper bound of between $300,000
and $680,000.

– AVAX, DOGE, MATIC, LINK, LTC, FIL, ATOM are in the next bracket, requiring a
relatively larger amount of depths in the support trading activities, $150,000
up to $300,000.

– Next, 1INCH, AAVE, ADA, BCH, CRV, EOS, ICP, SUSHI, SNX, XTZ, COMP, MKR saw
an estimated depth of under $150,000, while the remaining markets require
depths of less than $100,000 and some lower than $50,000.

However, it should be recognised that this means on the majority of occasions,
the exchange may possibly be overtly incentivising for liquidity provision on
normal days. This can be seen from the 95th percentile of bid and ask depths
required in Appendix 10.3 and 10.4.

** Note that the mean is based on the number of minutes where trades are
recorded.

####  5.1.2 Trade Statistics

By observing individual trades, the number of trades that have a size of over
$50,000, $100,000, $250,000, $500,000, $1.5 million are retrieved for each
market, alongside the mean, standard deviation and maximum trades. The
following observations can be noted:

Between April 30 and May 19:

– Markets hardly recorded any trades with a notional value of above $50,000,
with the exception of SOL which had 44 trades of this size.

– By narrowing down on those with trades above $100,000, the following markets
are observed with the maximum recorded of 6 in the ATOM-USD market on May 8.

Table 5: Number of trades above the amounts - $50k and $100k Trades above $50,000 | Trades above $100,000
---|---
|

Between May 20 and May 24:

– A similar observation is recorded, with SOL leading once again - 39 trades
with a notional value of above $50,000.

– By narrowing down on those with trades above $100,000, the following markets
are observed with the maximum recorded in the SOL-USD market on May 23. In
fact, that day presented 4 successive trades of abnormally large notional
values, with 1 trade ’2023-05-23T16:37:47.530Z’ recording over $500,000.

Table 6: Number of trades above the amounts - $50k and $100k Trades above $50,000 | Trades above $100,000
---|---
|
Table 7: Trades in SOL USD on May 23 side | size | price | createdAt | liquidation | day | notional
---|---|---|---|---|---|---
BUY | 14087.6 | 20.054 | 2023-05-23T16:38:45.563Z | False | 2023-05-23 | 282512.7304
BUY | 10486.5 | 20.054 | 2023-05-23T16:38:37.680Z | False | 2023-05-23 | 210296.27099999998
BUY | 29260.0 | 20.059 | 2023-05-23T16:37:47.530Z | False | 2023-05-23 | 586926.3400000001
BUY | 19506.7 | 20.045 | 2023-05-23T16:36:38.840Z | False | 2023-05-23 | 391011.80150000006
SELL | 5428.0 | 19.97 | 2023-05-23T03:00:55.158Z | False | 2023-05-23 | 108397.15999999999

Overall, there are very few ’large sized’ orders, suggesting the lack of
’anomalous’ trades which may severely dent liquidity. However, extra caution
has to be paid to the SOL market given its historically higher volumes
recorded on dYdX and larger trade sizes executed.

###  5.2 Event Study: High Market Impact Events

In the aforementioned highlighted high market impact events, an event study
was conducted to check the liquidity depths against the reconstructed order
book at the same interval. By merging both order book and trades data into a
common dataframe at a minute interval, this will feature the order book
behavior, contextualized against the trades which occurred.

####  5.2.1 Orderbook Resiliency

Based on the derived maxSpreads from the earlier section, these are then
visualised and plotted against the high market impact events, to observe
$liquidity_{pre},liquidity_{post}$ of market shocks. The duration will be
critical to understand how LPs respond to these changes in liquidity and
estimate the liquidity replenishment process for the market’s self-correcting
ability. A threshold of 75% is set where the estimated time taken for the
initial effect of the liquidity shock to dissipate, and liquidity to return to
75% of the average level is calculated.

– Consistent with literature, liquidity generally recovered much faster at
wider spreads during these times of volatility, highlighting that LPs and
traders are positioning further away from mid price.

– For majors, liquidity recovered within 1 and 2 minutes for ETH and BTC
respectively for 20bps and below, suggesting that these books are robust and
mature enough to attract liquidity in a short period of time.

– Markets (LINK, ATOM, ALGO, MATIC, ETC, AVAX, LTC, NEAR) show a consistent
quick rate of recovery at 20bps and 30bps in under 3 minutes.

– Meanwhile, the remaining markets exhibit some variation in their recovery
times. For instance, SUSHI, MKR and ICP takes over 10 minutes to recover at
20bps and below but at 30bps, these assets enjoy a less than 5 minute
recovery. On the contrary, ENJ and ZRX see a slower rate of recovery, between
7 and 15 minutes, suggesting that the rate of liquidity uptake far exceeds the
liquidity provision under these circumstances. This is similar to the findings
of Anand and Venkataraman (2012) where liquidity provision in smaller stocks
can be ’sparse and opportunistic’. These results also point to the suggestion
to establish DMMs with a strong presence in these less actively traded markets
to improve resiliency.

* The relevant bar charts can be found in the Appendix.

####  5.2.2 Reconstructed Depths vs maxSpread

The reconstructed depths were compared with each maxSpread (in intervals
between 5bps to 50bps). Should the reconstructed depth exceed the order book
depth then, this would suggest that the depth at that particular maxSpread
will be insufficient and hence, a higher maxSpread will be checked against to
ensure sufficient liquidity buffer. This iterative process is repeated for all
markets.

Table 8: Historical Orderbook vs Trades (Reconstructed) for FOMC Meeting Minutes Release (t-1, t+1) ETH-USD | LTC-USD
---|---
|

##  6 Discussion

Markets exhibited a distinct segmentation in behavior based on liquidity
provision and volume. By establishing the relevant lower bounds (based on tick
sizes) and upper bounds (based on reconstructed orderbooks), it can be
observed that the exchange had a sufficient amoun tof liquidity in the
orderbooks to handle trade flows. This is further supported by the trade data
where few huge sized orders are recorded. In particular, markets on dYdX can
be further classified into the following categories based on theses
characteristics - tick constraints, liquidity and price impacts:

###  6.1 Market Observations

####  6.1.1 Tick Constrained Markets

Based on Condition 1, markets identified to be tick constrained include UMA
and ZRX where a 1 tick difference already incurs more than 40bps. These make
it more difficult to trade for LPs given the higher spread costs incurred. For
instance, at a 1 tick difference, UMA records 42.8bps for a quote, beyond the
maxSpread defined. As a result, due to the lack of order flows and economic
costs, these can remain status quo in the 40bps bracket to compensate the risk
borne by LPs by possibly quoting at BBO.

####  6.1.2 Available liquidity

By observing the historical orderbook snapshots, markets such as COMP, ADA and
ENJ recorded similar depth levels at 30bps and 40bps on majority of the
occasions, thus remaining in the upper limits of 40bps.

On the other hand, more mature altcoin markets with higher volumes and
interest can have tighter maxSpreads. It is noteworthy to recognise that
majority, if not all of these orders can be absorbed within 20bps of each side
on normal days. However, some form of variation exists in their demands and
order book response especially from the high market impact events. Markets
such as DOT, FIL, CELO, RUNE, DOGE, 1INCH and MKR exhibit rather thinly-traded
books especially during these volatile periods, with a distinct difference in
liquidity at each depth level. For instance, the ICP market had a significant
number of instances where the depths were less than $50,000 at a tighter
maxSpread (30bps). Therefore, these are likely to result in greater price
impacts which can be seen from the longer duration in the liquidity recovery
process. In the meanwhile, order books of LINK, AVAX, ATOM, ETC, UNI AND LTC
were able to sustain sudden influxes in volumes. These markets have also shown
strong self correcting abilities to respond to shocks and their liquidity can
easily cushion against these events. However, for SOL and MATIC, an evident
dent is observed and 30bps may be generally preferred, especially since these
are the top 5 most traded markets on dYdX, by historical volume. Therefore,
sufficient liquidity buffer is necessary to dampen potential market shocks.

####  6.1.3 Majors : BTC, ETH

10 bps would be ideal given the maturity of BTC and ETH markets. Historical
data has also revealed the consistent tight spreads around the mid price, with
deep books on each side. However, given the relatively higher trading volumes
on dYdX, 15bps is suggested to provide more buffer. Nonetheless, I will
contend in the next section that this part of the formula can be removed.

###  6.2 Suggested maxSpread parameters

Table 9: Suggested maxSpread Parameters Number of Markets |  Markets |  Original maxSpread parameters |  Revised maxSpread parameters
---|---|---|---
2 |  BTC, ETH |  20bps |  15bps (suggested to be removed in Section 7.1)
6 |  ETC, LTC, AVAX, LINK, ATOM, UNI |  40bps |  20bps
15 |  MATIC, SOL, NEAR, CRV, SNX, YFI, XTZ, XMR, SUSHI, ALGO, XLM, AAVE, BCH, DOGE, TRX |  40bps |  30bps
14 |  MKR, ICP, COMP, ENJ, EOS, 1INCH, ZRX, UMA, ZEC, FIL, CELO, DOT, RUNE, ADA |  40bps |  40bps

Note: Markets such as UMA remain in the 40bps bracket since 1 tick away is
essentially over 40bps.

Note: MATIC and SOL were shifted to the 30bps bracket given its demand on dYdX
and the general lack of liquidity depth at 20bps during key events.
Nonetheless, there is more than sufficient liquidity to handle daily trades.

Assets in in each basket should be re-evaluated monthly, due to differing
liquidity conditions. The tick sizes should also be re-calibrated to enable
better operating spreads. Nonetheless, the exchange and community should
consider implementing a rebates scheme to encourage tighter spreads, and the
liquidity rewards to supplement these rebates by incentivising deeper books in
the long term.

##  7 Further Explorations for Alternative LP Reward Mechanisms

###  7.1 Suggestion 1: Simplify the LP reward formula for mature markets to a
volume parameter

The consistent volume and liquidity depths that these 2 markets attract are
testament to the maturity of BTC and ETH in receiving healthy flows. This
follows the previous proposal where the weights for makerVolumes were updated
to 0.85, and there hasn’t been an overt compromise in depth levels on the
exchange. A sample from the US Initial Jobless Claims revealed that there was
sufficient liquidity available in both ETH and BTC markets. In this case, an
average of $3.5m in BTC and $4.3m in ETH of liquidity stands within 15bps of
the midPrice and these were quick to recover to average levels within 2
minutes.

Table 10: Historical Orderbook vs Trades (Reconstructed) for US Initial Jobless Claims ETH-USD | BTC-USD
---|---
|

Another observation is that LPs churning far lower volumes still continue to
receive a higher amount of rewards. Despite producing 15% of makerVolume, the
account (0xa615) only received 4.67% of the rewards, much lesser than the
accounts above which $produced\leq 10\%$, (mostly hovering around 5 to 7 % for
BTC or as low as 2 % for ETH markets).

Epoch 22: May 9 (BTC Rewards)
Account Reward Share Maker volume Uptime (%) 0xd332 38.2 25.67 95.4 0xdade
14.22 9.85 99.3 0xc698 12.17 7.28 98.4 0xfe85 6.78 6.5 91.9 0xddc9 6.16 5.33
94 0x66e4 4.98 3.28 97.6 0xa615 4.67 15.39 75.5

Epoch 22: May 9 (ETH Rewards)
Account Reward Share Maker volume Uptime (%) 0xd332 36.18 21.47 97.4 0xc698
16.86 11.23 96.7 0xdade 15.33 11.07 99 0xddc9 6.38 5.48 94.7 0x66e4 4.66 2.76
98.8 0x2002 4.14 2.89 96.5 0xa615 4.12 14.85 74.8

While it can be argued that the other accounts had a much higher uptime (more
orders within the maxSpread parameter), they have disproportionately lower
volumes which suggest that these orders are not near the BBO. In particular, a
primary point of contention is that there should no longer be the need for the
exchange to still pay for passive liquidity in these mature markets given the
demand across exchanges. LPs have naturally provided liquidity within the
maxSpreads and (even beyond) to compete for order flow as seen from how quick
liquidity recovery occurs around the BBO during high market impact events.
Furthermore, by incentivising volumes linearly, this would mark the first step
to aligning the scheme with CEXs, before gradually introducing it to other
markets.

As such, the formula for BTC/ETH markets can be adjusted to:

$Q_{x}=\text{makerVolume},x=BTC,ETH$

_Further Implementations:_ Check for wash trading between LPs to ensure that
they are not trading between each other to churn maker volumes. Given the
fewer number of LPs relative to takers, this would be feasible implementation
by the team in designing the algorithm to catch these accounts. This includes
graph techniques to identify strongly connected components in the trade graph
and matching of position changes across accounts in the interval. The
suggested pseudocode and explanation can be found here. Alternatively, other
suggestions can be found in this paper which highlighted 3 main suggestions -
Using Benford’s law to examine the distribution of the first significant
digits in the trade data set, Clustering of transaction sizes at round numbers
and check if the observed trade size distributions have fat tails based on the
power law distribution.

###  7.2 Suggestion 2: Implementing the LP Rebate Scheme

Wintermute previously introduced a successful snapshot \- ’Maker Maker Rebate
Programme to reduce the reliance on LP rewards and incentivise liquidity in a
natural way’. This would mean that LPs’ rewards are now ’directly proportional
to their maker volume’, a similar measure by many exchanges. Therefore, one
can easily quantify the rewards that can be possibly earned by LPs and even
conserve the allocated DYDX rewards for future use (such as boostrapping new
markets). Furthermore, LPs are given certainty since rebates are denominated
in USDC, reducing delta exposure to DYDX prices.

However, one has to consider if the rebate scheme is sufficient to overhaul
the LP scheme. Based on Epoch 22, there were a total of 74 LPs with a volume
$\geq$ 0.25% of the exchange’s volume. Using the suggested fee schedule, 19
LPs will be eligible for Tier 5 as they collectively provide for 71.4% of
exchange’s volume. At a 0.0100% rebate, this equates to $2,164,061 in USDC
already given to this group of LPs. In comparison, the LP programme emits
$2,301,370 (1,150,685 DYDX tokens at $2 each) per epoch.

Figure 2: Epoch 22 Maker Volume Distribution

In this case, it is important to recognise that majority of the volume is
driven by the majors222Majors = BTC, ETH(8̃0%). As such, a pure rebate scheme
for the altcoin markets will likely prove ineffective since these rebates will
be significantly lower than the present LP incentives. In Epoch 22, altcoin
volumes recorded $6,276,767,740.46. Assuming all LPs who provided in these
markets receive the maximum 0.0100% rebate, this is equivalent to $627,676.77
(current LP allocation for altcoins = $1,841,096.0 at $2 each). This can be
observed from the following simulation:

a. Altcoin fee revenue is approximately $1.13M, Altcoin Volume is estimated to
be $6.3B.

b. This equates to 0.0179% earned by the exchange through the current maker
and taker fees. Therefore, an upper bound of rebates is set at this rate to
ensure the exchange remains profitable. Likewise, the lower bound of rebates
will be at the highest tier in Wintermute’s proposed fee schedule of 0.0100%.

c. Plotting out the volumes, rebates and present LP rewards scheme, it can be
seen that the rebates are relatively lower in value, unless volumes scale
above $11B.

Figure 3: Rebates vs Rewards (For Altcoins)

Henceforth, this will require a careful calibration to implement the rebate
scheme and adjust the rewards segment moving forward. After all, since this
will directly incentivise active liquidity, the changes in depth will have to
be closely monitored to ensure deep liquidity still exists.

_Consideration: Lower Exchange Revenue but more DYDX Tokens._ Presently, most
of the fee revenue is driven by the majors with $3,472,552.65 while the
altcoins rake in $1,128,679.0733318 May - 17 June as this data was added in at
a later date. The next 2 suggestions will be focussing on possible rebates
schemes but these will result in a contraction in overall revenue, should
volumes not increase proportionally.

####  7.2.1 Replace the LP Incentive Programme for BTC and ETH with the
Rebates Scheme

Should the formula be linearized to a volume parameter for the majors, then
the rebates scheme can be implemented, re-allocating the 20% of DYDX rewards.

Based on epoch 22, BTC and ETH recorded $2̃5,059,174,253 in volume.

1\. The volumes can be rounded off to $25B for calculation.

2\. Using the suggested fee schedule and the previous epoch data (where 19 LPs
are eligible), they provide for an estimated 90% of volumes for the majors.

Table 11: Maker Volume Distribution for BTC and ETH |
---|---

** Other LPs with $\leq 1\%$ volumes are not included in these pie charts.

3\. With the 0.01% fee rebate, this equates to approximately $2.25M of USDC
rebates ($25B * 90% * 0.01%), significantly higher than the present DYDX
allocated to these markets (230,127 DYDX Tokens = $460,274 at a price of $2).

4\. Over time, as more LPs are attracted to provide liquidity, this may level
off the playing field in volume distributions, encouraging the formation of a
more healthy and mature market.

Supported by the previous Section 7.1, the formula can be updated to solely
incentivise makerVolumes with a strong self-correcting market and deep
liquidity. Since LPs and the community have already signalled the intention
that they are agreeable to a rebate scheme, this can be experimented starting
from the majors. It not only conserves the DYDX treasury, but LPs are likely
to reap better economic benefits and certainties from providing liquidity in
these markets. This begets liquidity demand, enabling a virtuous cycle of
market activities.

####  7.2.2 Enhanced Rebates for long tail / new markets

Currently, a one-size-fit-all rebate scheme is suggested across the exchange,
which may not necessarily incentivise LPs to provide for newly listed and
existing thinly traded markets. Inspired by the NASDAQ DLP Programme, an
enhanced rebates scheme can be considered. This was used to incentivise LPs to
’support a broader array of ETPs, building the foundation for emerging ETPs’
volume..’. A similar programme, on top of the suggested rebate scheme can be
implemented to attract LPs and potentially reduce the amount of DYDX rewards.

This can be seen from the reward coeffecient which displays the number of DYDX
earned for a constant level of liquidity (depth and spread) provided. A higher
coeffecient will attract LPs to provide liquidity into these markets. However,
market such as UMA (4.70), ZRX (3.86), ZEC (3.56), ENJ (2.67) have high
coeffecients based on the past 3 months and yet they experience relatively
thin orderbooks with deviant spreads. Evidently, LPs are not attracted to
provide liquidity in these areas to begin with.

Table 12: Suggested Rebate Scheme for all markets (By Wintermute) | Maker Volume (% of 30D Exchange Volume) | Normal Rebates
---|---|---
Tier 1 |  $\geq$ 0.1% | -0.0025%
Tier 2 |  $\geq$ 0.25% | -0.0040%
Tier 3 |  $\geq$ 0.5% | -0.0050%
Tier 4 |  $\geq$ 0.75% | -0.0065%
Tier 5 |  $\geq$ 1% | -0.0100%
Table 13: Enhanced Rebates Scheme for Long Tail / New Markets | Maker Volume (% of 30D Market Volume) | Enhanced Rebates
---|---|---
Tier 1 |  $\geq$ 5% | -0.0125%
Tier 2 |  $\geq$ 10% | -0.0150%

Currently, there are very few LPs (within 3 per market) that contribute at
least 10% of the market’s volume in these long tail markets and thus, a higher
level of rebates may possibly encourage other LPs to move up the tiers and
participate in the liquidity provision process. These should be trialled to
observe the improvements in orderbook depths while gradually calibrating the
incentives scheme.

###  7.3 Suggestion 3: Increase the makerVolume weightage for non-BTC/ETH
markets to 0.85

The present LP rewards formula heavily emphasises on depths and spreads given
the 2 components (bid, spread and Qmin) scores, with just 0.65 for
makerVolume. Especially with the persistently low volumes and wide spreads,
the system can be easily gamed by LPs who provider wider and passive orders
which hardly get hit. This drives the scenario where quoting at 40bps away
from midPrice for the SOL market still returns a sizeable amount of rewards.

A concern is the ratio of volumes for these markets relative to other CEXs.
Altcoins vs BTC/ETH on dYdX presents an extremely disoriented picture where
there is an overt amount of trading activities in the majors. The lack of
volume proves worrying and this can be seen from the relatively wide quotes
away from the BBO (earlier snapshot on May 4), causing traders to suffer from
worse slippages, deteriorating the experience. In fact, many of these markets
offer deeper books relative to the average volumes recorded on dYdX,
suggesting that there should be a greater emphasis on makerVolumes to drive
active liquidity.

In particular, from a LP rewards perspective, similar circumstances are
observed where LPs with higher makerVolumes receive a disproportionately lower
amount of rewards:

_Epoch 22: Top 5 LPs_

1INCH |  Reward |  Vol |  Uptime |  ALGO |  Reward |  Vol |  Uptime
---|---|---|---|---|---|---|---
0x66e4 |  42.29 |  32.7 |  99.3 |  0x66e4 |  28.23 |  22.61 |  99.2
0xd332 |  14.25 |  13.69 |  98.2 |  0xc698 |  26.16 |  16.15 |  98.9
0xa615 |  6.45 |  13.56 |  98.1 |  0x57c4 |  12.84 |  17.36 |  99.3
0xc698 |  12.38 |  6.73 |  99.5 |  0xd332 |  10.2 |  8.39 |  96.5
0xddc9 |  10.68 |  8.15 |  98.0 |  0xddc9 |  10.19 |  6.77 |  96.5

AAVE |  Reward |  Vol |  Uptime |  MATIC |  Reward |  Vol |  Uptime
---|---|---|---|---|---|---|---
0xd332 |  41.72 |  36.66 |  98.9 |  0xd332 |  30.16 |  24.29 |  98.7
0x66e4 |  20.88 |  18.59 |  98.1 |  0xa615 |  7.56 |  16.72 |  94.7
0xc698 |  13.85 |  9.04 |  98.7 |  0xc698 |  18.62 |  12.91 |  98.8
0xddc9 |  11.25 |  8.38 |  97.6 |  0x66e4 |  14.18 |  13.91 |  98.4
0xa615 |  4.1 |  7.18 |  96.5 |  0xddc9 |  9.68 |  7.45 |  97.5
0xfe85 |  1.13 |  10.57 |  63.7 |  - |  - |  - |  -

SOL |  Reward |  Vol |  Uptime |  ADA |  Reward |  Vol |  Uptime
---|---|---|---|---|---|---|---
0xd332 |  48.02 |  41.03 |  98.9 |  0xc698 |  25.98 |  15.79 |  98.7
0xc698 |  16.99 |  9.98 |  98.2 |  0xd332 |  18.95 |  9.06 |  98.4
0xddc9 |  9.59 |  6.98 |  97.9 |  0xa615 |  17.72 |  29.13 |  97.3
0xa615 |  7.67 |  15.88 |  93.2 |  0xfe85 |  8.73 |  4.41 |  97.8
0xefa |  4.66 |  3.87 |  89.1 |  0xbce6 |  6.74 |  17.03 |  90.7

Despite producing nearly 16% of makerVolume in SOL, account 0xa615 has only
received 7.67% of rewards which is disproportionately lesser than the above
account with over half of the makerVolume. This observation is similarly seen
across multiple markets such as 1inch, AAVE, MATIC and ADA (as seen in the
tables above). While sufficient liquidity buffer is necessary, makerVolumes
should also be incentivised to provide tighter quoting. Some of these altcoin
markets are also primarily dominated by the same group of LPs and would have
already witnessed a relatively high uptime. Thus, a performance breaker would
be focussing on the makerVolume to reward those for providing liquidity in the
active ranges. This suggestion has witnessed similar success previously with
the BTC/ETH markets where the weightage rose to 0.85 and hence, should be
trialled in the altcoin markets.

The updated weightage should be ceded for discussion. However, I would
recommend this to be only implemented after the maxSpread parameters have been
revised to ensure that depths have not been not overly penalized.

###  7.4 Suggestion 4: Assigning DMMs for markets per epoch

DMMs are a common implementation in traditional exchanges where they are
appointed by the exchange to provide liquidity in certain markets based on a
set of criteria. This may include a minimum BBO presence, minimum passive
volume share and passive volume ratio. For instance, these DMMs will be
required to provide orders for a percentage of their trading time within the
BBO while hitting a required share of their passively executed volume which
remains in the order book. A similar programme can be implemented for the
different markets.

The main rationale would be that every LP has a particular focus on certain
markets based on the products they are proficient in. Especially in v4, where
permissionless market listings become possible, this provides an avenue for
LPs who specialize in these new markets to actively provide liquidity. This
phenomenon can already be observed on dYdX where the markets are primarily
dominated by a group of different LPs which record disproportionately higher
makerVolume and rewards (as seen in Section 7.3). Furthermore, the irregular
orderbook depths can be observed in the long tail markets during high market
impact events.

Table 14: Historical Orderbook Depth Snapshot from FOMC Minutes Meeting Release (at 40bps) ADA-USD | ENJ-USD
---|---
|

Therefore, to compensate LPs for the risk of trading in these new / ’higher-
risk’ markets, a trial can be established in the low volume altcoin markets to
model the likely improvement in liquidity. A suggested model can be seen
below:

Figure 4: DMM Structure Per Epoch

A minimum requirement can be implemented for existing and new markets where
the weighted score will be calculated for each LP:

Table 15: Suggested DMM Bidding Requirements | Conditions | Existing Market | New Market
---|---|---|---
1 | Number of Epochs the LP has been providing liquidity | Y | Y
2 |  $\geq$ 2% of total volume traded | Y | Y
3 | Top 3 by makerVolume share OR $\geq$ 20% of makerVolume | Y | Y
4 |  $\geq$ 95% Uptime | Y | Y
5 | Top 3 by Per Minute Depth-Spread Score | Y | Y
6 | Amount of liquidity provision intended (Commitment) | x | Y
7 | maxSpread relative to midPrice (Max of 40bps) | x | Y
8 | Max Price Deviation (Volatile Events) | 10% | 10%

** Condition 3 : Whichever condition entails a lower makerVolume. For
instance, certain markets may be dominated by a LP providing over 50% of
makerVolume. Hence, to encourage more LPs to participate, the parameter is
widened.

** Since these are new markets with uncertain volumes, DMMs are expected to
provide a minimal amount of liquidity into the market and assist in the price
discovery process. maxSpreads should be ideally as tight as possible to
facilitate flows. Therefore, these are additional requirements above the usual
metrics.

** These numbers are based on the scores observed on the long tail markets.

$Score_{LP}=\sum_{i=1}^{n}\frac{x_{i}}{total_{i}}$

Table 16: Sample Calculation for Average Weighted Scores | Number of Epochs | % of Total Volume Traded | ..
---|---|---|---
LP 1 | 10 | 30 | ..
LP 2 | 6 | 10 | ..
Total Number | 16 | 40% |

Therefore, LP 1’s score = $(10\div 16)+(30\div 10)+$ ….

Notations: M = Minimum Assets Requirement on dYdX, A = Penalty factor, R =
Reward multiplier

####  7.4.1 Start of Epoch

Each LP can submit their intended functioning parameters based on the
aforementioned metrics, and indicate the markets which they intend to apply
for while having this minimum amount on the platform as part of this process.
A simplified formula is proposed where $M=Liquidity_{Epoch}*0.02\%$. This is
akin to the net liquid assets requirements for DMMs in traditional exchanges.
It is critical that the DMM unit maintains sufficient liquidity, to ensure an
orderly market in these assigned markets especially during market stress.

eg. ** If the market records a daily average of $5M, M required = $5M * 28 *
0.02% = $28,000 USDC.

With reference to the metrics, should these be existing markets, liquidity
will refer to the rolling average for the past 3 epochs of historical volume.
For new markets, liquidity will be the amount committed by the LPs based on
their bids. Using the equally weighted formula, the LP with the highest score
will be the DMM for that market and will be obliged to provide liquidity at
their submitted parameters.

####  7.4.2 During Epoch

Weekly reviews will be conducted (e.g. by the LP/Validator subDAO ) to ensure
that the DMM meets the minimum obligations. This can be based on the existing
LP rewards dashboard visualized on datahog.

_Failure to Meet Obligation_ \- Should the DMM fail to meet their obligations
for 1 day, the DMM will be required to account for the discrepancies on the
forums. Otherwise, the DMM will be suspended and removed from the programme.
It will then not be allowed to participate as a DMM in the subsequent 3
epochs. The cycle repeats again with LPs bidding for the position to replace
the DMM.

_Successfully Meets Obligation_ \- During this period, the DMM will be
eligible for a different maker fee structure that confers preferential
treatment to this group of LPs. In particular, the markets on the exchange can
be classified similarly into ’More Active Markets’ and ’Less Active Markets’
based on their historical volumes and spreads.

In determining the structure, the following equation was used, where the fees
and volumes are summed up for all markets in each tier, and assuming 50%
represents the makerVolume share of the DMM:

$FeeRevenue-(Volumes\times Rebates\times 50\%)$ (4)

This would represent the approximated margins earned by DYDX. The shortfall
will be compensated with these suggested rewards at a price of $2, acting as a
’stipend’.

Table 17: DMM Rebates and Rewards Structure | Markets (excl BTC/ETH) | Fees | DYDX
---|---|---|---
More Active Markets | SOL, MATIC, LTC, AVAX, ADA, DOGE, ATOM | -0.0125% | 7500
Less Active Markets | XLM, COMP, CELO, ENJ, ZRX, ZEC, RUNE, UMA | -0.0150% | 2500
Active Markets | Remaining Markets | -0.0150% | 5000
| Total Emissions |  | 172500

** ’More Active Markets’ tend to be dominated by the top LPs and will likely
be eligible for the 0.0100% rebates proposed previously and incur 0 maker
fees. Hence, raising this to 0.0125%. The exchange still continues to earn fee
revenue given that the lowest taker fee is at 0.0200%.

####  7.4.3 End Epoch

Should the chosen LP be willing to continue as the DMM for the assigned
market/markets and have successfully met its obligations for the epoch, there
will be no need to restart the bidding period. However, if it chooses to
discontinue, then other LPs will be invited to bid and take over as the DMM.

Ultimately, this opens up a new mechanism for specific LPs to provide
liquidity to their preferred products without overtly spreading across
multiple markets. Especially for new markets, this aids in the bootstrapping
of liquidity by encouraging LPs to aggressively provide better quotes and
create a healthy orderbook flow. As a result, improving the market quality
given the increased competition through a pragmatic way of enjoying better
maker rebates.

_Further Implementations:_ Create a group of DMMs based on market segmentation
(of volume). Since certain markets are primarily dominated by a group of LPs,
markets can be grouped together to reduce the complexity of this structure.
This will involve the exchange identifying commonalities - eg. SOL, MATIC and
AVAX may share a group of 2 to 3 DMMs subject to the aforementioned criteria.

###  7.5 Suggestion 5: Sunset or reduce rewards for certain existing v3
markets

Another consideration would be to study the profit margins for each market:
Fee Revenue - DYDX Rewards allocated. Based on historical observations when
benchmarked against other CEXs, we can observe the relatively lower volumes
across the space and the minute fee revenues generated from activity in these
markets. Furthermore, while these rewards are distributed based on the number
of Qminsamples, it still effectively categorises extremely long tail markets
such as UMA into the same basket with the SOL market. A look at the historical
volumes (excluding BTC/ETH) highlights the disparity across markets, where SOL
reached as high as 12% of total volume. As such, rather than continuously
allocating rewards to these ’low profit’ markets, the exchange can better
utilize its rewards to drive volume in more active markets.

Based on the dYdX API, the fees generated from each market can be seen below
(Statistics can be found in Appendix 10.8)44418 May - 17 June as this data was
added in at a later date:

Table 18: Fees Generated (30 Days) All markets on dYdX | Altcoin Markets on dYdX
---|---
|

Altcoin markets have brought in a fee revenue of $1.13M over the 30 days.
(This accounts for total fees paid by users - including LPs and takers). In
comparison, the 80% of LP rewards allocated, (at price of $2), stands at
$1.84M. In deciding the allocations for each market, we can consider the
following:

1\. Historical fee revenue generated from the market should be considered.
Should it produce relatively low fees, then the market should receive a lower
amount of rewards.

2\. Markets should only be incentivised if they see continuous demand on the
exchange and across other CEXs. A comparison in historical volumes can be done
with Binance, Coinbase and Okx and historically low volume market can be
considered for delisting.

An initial suggestion would be to classify the following markets into these
tiers based on the fee revenue and volume.

Table 19: Suggested Tiers | Markets
---|---
Tier 1 | SOL, MATIC, LTC, AVAX, ADA, DOGE, ATOM
Tier 2 | CRV, FIL, UNI, LINK, SNX, AAVE, TRX, NEAR, EOS, DOT
Tier 3 | ICP, ALGO, 1INCH, XTZ, BCH, MKR, XMR, YFI, SUSHI, ETC
Tier 4 | XLM, COMP, CELO, ENJ, ZRX, ZEC, RUNE, UMA
Table 20: Suggested Allocation  | Fee Revenue | Allocation (if 80%) | Allocation (if 100%)
---|---|---|---
Tier 1 | 555494.79 | 40% | 50%
Tier 2 | 276592.38 | 20% | 30%
Tier 3 | 199288.70 | 15% | 15%
Tier 4 | 97303.20 | 5% | 5%

Alternatively, we can consider capping the reward allocations for the Tier 4
markets to 5% as highlighted above given their historically low volumes and
fees generated.

However, future studies should be done to provide a granular understanding of
the current reward allocation to each of the altcoin markets (should the data
be made available in the future).

###  7.6 Other Suggestions:

####  7.6.1 Expand the LP Rewards Formula - Reward Multiplier for Volatile
Periods

_Volatile periods can be defined as periods of time where prices fluctuate,
presenting much uncertainty due to asymmetric information from a mixture of
informed and uninformed traders._

In markets with high volatility / low volume, investors and LPs will want to
place orders far away from trading levels to benefit from violent movements.
This results in a fatter tail as the spreads are noticeably wider. LPs can
also simply withdraw their liquidity during that short period of time, which
does not really affect their uptime score. Hence, LPs with a high uptime
during the volatile periods should be adequately rewarded based on a
multiplier to further distil the sticky behaviour regardless of market
environments.

This is also supported by the excess liquidity paid for by the exchange on
normal days and hence, the scheme should be recalibrated to optimize the LP
rewards. As seen in the Xetra Liquidity Provider Programme, an initial
constant of 2 is suggested to be multiplied to Q scores on volatile days.

Table 21: Extracted from Xetra Liquidity Programme Agreement
---

However, due to the lack of readily available historical data of Q scores for
individual markets, the impacts of these should be revisited in the future.

####  7.6.2 Implement a liquidity mining campaign with Hummingbot to provide
targeted liquidity

Since the grant for Hummingbot from Q4 2022, this has brought over $10m of
liquidity to dYdX. Despite receiving no publicity, it can be argued that the
partnership has achieved moderate success in attracting LPs to the platform. A
liquidity mining campaign can be organized to drive liquidity to long tail
markets. This partnership has been similarly implemented on other CEXs such as
Binance, KuCoin and Gate.io.

##  8 Conclusion

Ultimately, the LP incentives scheme plays a critical role in supporting
vibrant markets on dYdX. The parameters have to be carefully calibrated to
seek a fine balance between LPs (profitability vs risk) and takers
(slippages), which can be further seen from the perspective of the operating
spreads and depths permissible. The ideal state of the exchange would be when
LPs instinctively compete at the BBO, ensuring a healthy flow of liquidity and
this can be effectively witnessed in mature markets (BTC/ETH) given the high
volumes, tighter spreads and lower inherent risks.

Through multiple series of backtesting on historical and more recent data
using different metrics, the adjusted maxSpreads have been suggested in
Section 6, directing incentives to a tighter active range in majority of the
markets. At the same time, alternatives have been explored to improve the LP
rewards mechanism based on the recent end of epoch data. The following are
recommended as the immediate steps:

1\. Revise the LP rewards scheme for BTC/ETH markets to a volume parameter.

2\. Replace the LP rewards scheme for BTC/ETH markets with the rebates
suggested by Wintermute

3\. Increase the makerVolume weightage for non-BTC/ETH markets from 0.65 to
0.85, following the path taken by BTC/ETH last year.

4\. Implement Enhanced Rebates for Long Tail / New Markets

The impacts on liquidity dynamics will then have to be monitored before
adjusting the rewards allocations and introducing a reward multiplier for
periods of volatility. The implementation of DMMs for v4 will also entail an
interesting discussion for permissionless listings when more information is
available.

##  9 References

1) Anand, A., Venkataraman, K. (2010, September 7). Should Exchanges impose
Market Maker obligations?
https://www.sec.gov/divisions/riskfin/seminar/venkataraman0313.pdf

2) BIS. (2015, March 18). The economics of market making. The Bank for
International Settlements.
https://www.bis.org/publ/qtrpdf/r_qt1503y.html

3) Callen (2022, April 15). A step towards a more equitable liquidity provider
reward structure. Commonwealth.
https://forums.dydx.community/discussion/4407-a-step-towards-a-more-equitable-
liquidity-provider-reward-structure

4) Copeland, T. E., Galai, D. (1983). Information effects on the bid–ask
spread. The Journal of Finance, 38(5), 1457 –1469.
https://doi.org/10.1111/j.1540-6261.1983.tb03834.x

5) Demsetz, H. (1968). The cost of transacting. The Quarterly Journal of
Economics, 82(1), 33–53.
https://doi.org/10.2307/1882244

6) dYdX. (n.d.). Liquidity Provider Rewards - Governance Documentation.
https://docs.dydx.community/dydx-governance/rewards/liquidity-provider-rewards

7) Lo, D. K., Hall, A. D. (2015, October 9). Resiliency of the Limit Order
Book.
https://opus.lib.uts.edu.au/bitstream/10453/98964/1/
Lo_Hall_Resiliency_of_the_limit_order_book_Accepted_Manuscript.pdf

8) Müller (2021, November 13). Incentivize liquidity providers on spread.
Commonwealth.
https://forums.dydx.community/discussion/2519-incentivize-liquidity-providers-
on-spread

9) SLN Capital. (2022, July 20). Discussion- revisions to improve the LP
reward formula. Commonwealth.
https://forums.dydx.community/discussion/6322-discussion-revisions-to-improve-
the-lp-reward-formula

##  10 Appendix

###  10.1 Min Tick Size vs Spread

Market | tickSize | indexPrice | Min Bps
---|---|---|---
CELO-USD | 0.0010 | 0.5306 | 0.188
LINK-USD | 0.0010 | 6.6375 | 0.015
DOGE-USD | 0.0001 | 0.0725 | 0.138
1INCH-USD | 0.0010 | 0.4171 | 0.240
XMR-USD | 0.1000 | 153.8950 | 0.065
FIL-USD | 0.0100 | 4.5030 | 0.222
ETH-USD | 0.1000 | 1826.0380 | 0.005
AAVE-USD | 0.0100 | 63.6962 | 0.016
ATOM-USD | 0.0010 | 10.9800 | 0.009
MKR-USD | 1.0000 | 637.6600 | 0.157
EOS-USD | 0.0010 | 0.8899 | 0.112
COMP-USD | 0.1000 | 35.2400 | 0.284
ALGO-USD | 0.0001 | 0.1660 | 0.060
XTZ-USD | 0.0010 | 0.8963 | 0.112
UNI-USD | 0.0010 | 5.1756 | 0.019
ADA-USD | 0.0010 | 0.3700 | 0.270
ZRX-USD | 0.0010 | 0.2232 | 0.448
YFI-USD | 1.0000 | 6975.0200 | 0.014
MATIC-USD | 0.0001 | 0.8688 | 0.012
ETC-USD | 0.0100 | 18.3297 | 0.055
AVAX-USD | 0.0100 | 15.2459 | 0.066
LTC-USD | 0.1000 | 86.5360 | 0.116
ENJ-USD | 0.0010 | 0.3414 | 0.293
DOT-USD | 0.0100 | 5.3837 | 0.186
SNX-USD | 0.0010 | 2.1035 | 0.048
RUNE-USD | 0.0010 | 1.1918 | 0.084
XLM-USD | 0.0001 | 0.0884 | 0.113
BCH-USD | 0.1000 | 118.6310 | 0.084
TRX-USD | 0.0001 | 0.0704 | 0.142
BTC-USD | 1.0000 | 27369.0000 | 0.004
UMA-USD | 0.0100 | 2.3360 | 0.428
NEAR-USD | 0.0010 | 1.6745 | 0.060
ZEC-USD | 0.1000 | 33.2750 | 0.301
SOL-USD | 0.0010 | 21.3291 | 0.005
SUSHI-USD | 0.0010 | 0.8999 | 0.111
ICP-USD | 0.0100 | 5.3205 | 0.188
CRV-USD | 0.0001 | 0.8171 | 0.012

###  10.2 Average Orderbook Depths Recorded

| Market | 10bps | -10bps | 15bps | -15bps | 20bps | -20bps | 30bps | -30bps | 40bps | -40bps
---|---|---|---|---|---|---|---|---|---|---|---
0 | CELO-USD | 28787.73 | 35185.46 | 29261.51 | 35850.78 | 29669.73 | 36290.08 | 191409.78 | 188850.3 | 195142.8 | 192413.53
1 | LINK-USD | 161107.56 | 134163.24 | 303082.62 | 286661.05 | 431983.09 | 436506.34 | 609090.46 | 633756.96 | 771597.82 | 817821.25
2 | DOGE-USD | 157078.05 | 168368.21 | 157180.16 | 168495.85 | 157588.06 | 168992.95 | 515988.06 | 549654.67 | 738602.04 | 801575.77
3 | 1INCH-USD | 0.0 | 0.0 | 109029.13 | 134063.86 | 109029.13 | 134063.86 | 109029.13 | 134063.86 | 576846.92 | 609875.06
4 | XMR-USD | 92587.85 | 83628.09 | 114543.19 | 103350.03 | 228441.84 | 221479.12 | 411085.52 | 445251.56 | 548605.38 | 611941.5
5 | FIL-USD | 0.0 | 0.0 | 251647.17 | 239890.42 | 251647.17 | 239890.42 | 251725.48 | 240029.29 | 850872.67 | 879431.32
6 | ETH-USD | 2546580.58 | 2561040.27 | 4245665.63 | 4397182.39 | 5708734.8 | 5890386.79 | 6599574.08 | 6788933.85 | 7187918.98 | 7426181.35
7 | AAVE-USD | 49697.67 | 70134.63 | 130893.5 | 136773.32 | 216724.07 | 213279.09 | 341797.6 | 368186.34 | 638373.2 | 681698.27
8 | ATOM-USD | 115717.71 | 110604.11 | 219229.62 | 214303.62 | 297760.16 | 316492.55 | 405596.36 | 443282.39 | 685211.8 | 735087.55
9 | MKR-USD | 34586.36 | 32854.05 | 34586.36 | 32854.05 | 34635.74 | 32886.78 | 250517.94 | 247363.69 | 322585.42 | 330185.33
10 | EOS-USD | 55997.53 | 51212.27 | 56538.9 | 51618.22 | 290392.09 | 313614.02 | 651776.29 | 670471.04 | 734216.44 | 745643.36
11 | COMP-USD | 0.0 | 0.0 | 100175.95 | 128884.87 | 100175.95 | 128884.87 | 100180.43 | 128894.51 | 100180.43 | 128894.51
12 | ALGO-USD | 74774.17 | 65591.89 | 105661.85 | 90547.56 | 186312.39 | 164822.37 | 312329.76 | 334672.3 | 562642.95 | 598113.26
13 | XTZ-USD | 52945.44 | 38800.14 | 64942.87 | 50372.23 | 265699.24 | 237051.89 | 617995.99 | 630561.02 | 690939.83 | 729702.0
14 | UNI-USD | 67701.46 | 65159.16 | 169719.46 | 164895.65 | 259421.98 | 262384.3 | 387069.44 | 427436.78 | 692131.05 | 753413.52
15 | ADA-USD | 0.0 | 0.0 | 266608.77 | 249105.21 | 266608.77 | 249105.21 | 266708.14 | 249188.85 | 300039.47 | 278033.12
16 | ZRX-USD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 122900.49 | 107576.54 | 122900.49 | 107576.54
17 | YFI-USD | 54557.0 | 41524.0 | 118384.17 | 92580.62 | 172234.09 | 150188.31 | 281996.09 | 299596.37 | 540530.75 | 567477.19
18 | MATIC-USD | 155602.0 | 153258.56 | 299957.34 | 310325.85 | 459757.23 | 500703.95 | 709877.37 | 768827.86 | 867585.38 | 903852.39
19 | ETC-USD | 83555.65 | 80285.83 | 249551.01 | 196636.3 | 350814.05 | 288537.03 | 461066.18 | 424340.71 | 652954.32 | 620185.48
20 | AVAX-USD | 71156.85 | 65520.86 | 245219.77 | 209706.55 | 447134.5 | 428839.9 | 582101.62 | 587861.68 | 868630.57 | 918474.46
21 | LTC-USD | 104308.24 | 121798.89 | 104483.89 | 122024.11 | 455209.41 | 439667.45 | 769702.73 | 733563.39 | 877667.67 | 821924.51
22 | ENJ-USD | 0.0 | 0.0 | 99556.63 | 97717.75 | 115190.17 | 113647.88 | 115227.99 | 113691.18 | 115231.37 | 113696.72
23 | DOT-USD | 248586.62 | 183757.21 | 248586.62 | 183757.21 | 248710.7 | 183839.72 | 998962.68 | 744084.09 | 999031.7 | 744151.4
24 | SNX-USD | 27141.83 | 25747.3 | 86026.61 | 78815.72 | 125804.03 | 113801.21 | 203463.87 | 190677.65 | 392287.28 | 397621.78
25 | RUNE-USD | 13124.4 | 14237.15 | 70953.94 | 74786.44 | 80483.43 | 82964.79 | 235049.24 | 233721.19 | 483292.95 | 511799.25
26 | XLM-USD | 66146.3 | 68276.43 | 66280.72 | 68429.97 | 306274.56 | 342618.05 | 684292.57 | 717040.94 | 742069.3 | 791883.39
27 | BCH-USD | 48738.17 | 54009.16 | 275395.6 | 281682.72 | 276658.22 | 282958.93 | 527205.42 | 557679.96 | 792609.95 | 853019.14
28 | TRX-USD | 108412.16 | 113707.18 | 108427.64 | 113716.17 | 226815.75 | 228915.3 | 520635.16 | 478520.6 | 857417.44 | 793290.23
29 | BTC-USD | 2276943.17 | 2174704.49 | 3510947.24 | 3607486.39 | 4567733.64 | 4749037.02 | 5275372.07 | 5471668.8 | 5844803.31 | 5974759.74
30 | UMA-USD | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 34135.46 | 37805.43 | 34135.46 | 37805.43
31 | NEAR-USD | 121450.82 | 113877.07 | 171431.81 | 170266.38 | 311177.15 | 313344.63 | 502781.99 | 544324.1 | 662886.96 | 713059.54
32 | ZEC-USD | 0.0 | 0.0 | 44215.3 | 48066.66 | 137469.38 | 155516.76 | 137469.38 | 155516.76 | 137469.38 | 155516.76
33 | SOL-USD | 185227.36 | 164849.59 | 363538.86 | 333888.11 | 519990.69 | 477412.04 | 739261.33 | 728070.08 | 896721.74 | 899660.61
34 | SUSHI-USD | 20361.69 | 19384.7 | 21399.07 | 20517.44 | 108619.89 | 97515.15 | 407091.82 | 419850.96 | 459266.71 | 484903.72
35 | ICP-USD | 104987.07 | 102524.32 | 124937.43 | 121535.02 | 125132.25 | 121722.91 | 501373.47 | 552225.15 | 577716.97 | 636908.5
36 | CRV-USD | 43927.14 | 28278.95 | 94219.46 | 77564.36 | 154005.77 | 155217.57 | 268398.71 | 306948.46 | 540221.98 | 591512.26

###  10.3 Average Daily Trade Statistics - Between April 30 and May 19 2023

Market | avg_depth_bid | avg_depth_ask | median_depth_bid | median_depth_ask | max_depth_bid | max_depth_ask | ninetyfive_depth_ask | ninetyfive_depth_bid
---|---|---|---|---|---|---|---|---
1INCH-USD | 2789.27 | 2755.95 | 893.45 | 931.68 | 93212.9 | 96751.7 | 9527.51 | 9160.68
AAVE-USD | 1893.59 | 1814.02 | 286.9 | 299.58 | 118885.34 | 94218.78 | 8007.21 | 7894.17
ADA-USD | 4166.26 | 4056.73 | 1041.31 | 942.8 | 158795.53 | 182847.56 | 15050.15 | 15778.71
ALGO-USD | 1790.31 | 1833.26 | 191.71 | 395.51 | 101664.74 | 78072.64 | 7312.07 | 7004.38
ATOM-USD | 2437.53 | 2319.11 | 894.63 | 899.81 | 235158.65 | 190300.3 | 7237.29 | 7012.76
AVAX-USD | 3034.81 | 2802.44 | 310.16 | 315.11 | 287715.82 | 248930.04 | 10801.78 | 10671.71
BCH-USD | 2371.36 | 2387.52 | 1066.53 | 1002.29 | 124514.71 | 124993.46 | 7882.95 | 8067.13
CELO-USD | 1852.56 | 1761.57 | 320.74 | 289.63 | 86065.32 | 72867.64 | 7038.69 | 7180.7
COMP-USD | 3682.5 | 3702.15 | 956.42 | 1115.38 | 128628.5 | 110899.83 | 14876.44 | 15285.51
CRV-USD | 2552.19 | 2385.84 | 727.5 | 693.15 | 145151.7 | 127082.33 | 9255.79 | 9584.03
DOGE-USD | 4192.52 | 4011.55 | 221.07 | 128.25 | 248201.33 | 201763.27 | 19024.23 | 19214.49
DOT-USD | 2406.03 | 2341.25 | 519.28 | 482.92 | 156940.46 | 147332.58 | 7979.97 | 8443.82
ENJ-USD | 2805.47 | 2793.77 | 180.27 | 192.81 | 110451.14 | 92532.19 | 13127.74 | 12650.09
EOS-USD | 2178.94 | 1965.5 | 648.4 | 563.09 | 134356.3 | 92423.47 | 7677.63 | 7771.4
ETC-USD | 1397.04 | 1366.84 | 527.37 | 527.47 | 85268.71 | 97027.91 | 3889.54 | 3877.27
FIL-USD | 3812.48 | 3626.75 | 870.18 | 867.42 | 188564.73 | 147028.17 | 13623.9 | 13411.81
ICP-USD | 3129.93 | 2718.67 | 1294.17 | 918.09 | 160428.42 | 154460.42 | 8831.99 | 10070.65
LINK-USD | 2079.48 | 2096.95 | 505.05 | 497.53 | 136568.44 | 134693.74 | 8414.11 | 8144.7
LTC-USD | 3060.99 | 3009.72 | 788.42 | 714.4 | 315470.91 | 274313.07 | 8926.51 | 8711.25
MATIC-USD | 4966.33 | 5150.44 | 761.81 | 801.63 | 335618.61 | 339231.69 | 20824.43 | 19964.91
MKR-USD | 3057.52 | 3114.05 | 696.84 | 773.58 | 166510.62 | 158854.52 | 11223.02 | 9785.69
NEAR-USD | 2215.09 | 2174.38 | 412.18 | 416.58 | 142771.98 | 128762.89 | 7666.1 | 7299.51
RUNE-USD | 2067.09 | 2088.74 | 380.08 | 396.84 | 71298.52 | 92186.51 | 7256.69 | 7294.08
SNX-USD | 1370.81 | 1314 | 301.61 | 294.16 | 98496.77 | 71231.53 | 5312.83 | 5186.17
SOL-USD | 9823.7 | 8276.23 | 2806.47 | 2542.62 | 677403.74 | 434800 | 31304.66 | 32501.9
SUSHI-USD | 2625.56 | 2548.38 | 577.01 | 526.65 | 150481.78 | 122771.27 | 10003.4 | 9857.84
TRX-USD | 1576.53 | 1748.46 | 8.85 | 15.85 | 58391.89 | 74238.7 | 7623.28 | 6756.08
UMA-USD | 2805.17 | 2696.41 | 583.65 | 430.85 | 54885.67 | 47593.37 | 11875.47 | 11797.12
UNI-USD | 1835.82 | 1782.74 | 514.12 | 527.07 | 100119.74 | 91063.31 | 6371.7 | 6258.08
XLM-USD | 1414.41 | 1436.1 | 109.73 | 137.59 | 90328.01 | 75175.22 | 5474.3 | 5029.96
XMR-USD | 2001.54 | 2012.58 | 543.64 | 456.39 | 71020.94 | 74122.01 | 7350.28 | 7008.55
XTZ-USD | 2870.74 | 3167.37 | 613.34 | 1273.41 | 98378.35 | 96117.9 | 12174.94 | 11342.99
YFI-USD | 2027.92 | 1984.43 | 571.36 | 557.04 | 58333.59 | 59568.33 | 7148.84 | 7249.9
ZEC-USD | 2783.02 | 2742.66 | 293.72 | 326.47 | 88542.51 | 72485.04 | 11941.19 | 12102.72
ZRX-USD | 1903.7 | 1869.52 | 4.08 | 2.79 | 48038.94 | 40706.58 | 10648.94 | 10867.31

###  10.4 Average Daily Trade Statistics - Between May 20 and May 24 2023

Market | avg_depth_bid | avg_depth_ask | median_depth_bid | median_depth_ask | max_depth_bid | max_depth_ask | ninetyfive_depth_ask | ninetyfive_depth_bid
---|---|---|---|---|---|---|---|---
1INCH-USD | 2290.579044 | 2460.290254 | 361.4916 | 563.6371 | 109632.3542 | 121802.8124 | 7198.18136 | 6888.13844
AAVE-USD | 775.6549121 | 761.5169433 | 51.07588 | 58.70494 | 73446.60082 | 69163.46306 | 3757.164317 | 4136.08648
ADA-USD | 3820.347317 | 3857.431758 | 357.3074 | 251.37 | 127320.7402 | 111137.0242 | 15085.7699 | 14702.23388
ALGO-USD | 922.6858346 | 984.4883105 | 4.93128 | 97.93578 | 70966.42644 | 53629.45504 | 3538.859346 | 3389.38457
ATOM-USD | 1121.430254 | 1064.538328 | 589.58854 | 586.63624 | 80743.50236 | 67970.915 | 2984.20342 | 3054.897074
AVAX-USD | 2177.6453 | 2176.558451 | 298.0914 | 293.0652 | 172432.2334 | 147763.7586 | 8250.7979 | 7662.60364
BCH-USD | 1845.875327 | 1866.546088 | 248.158 | 191.2816 | 121276.1044 | 130084.8248 | 7101.93904 | 7143.54892
BTC-USD | 56450.69792 | 57942.51278 | 9511.76117 | 10006.6117 | 2830382.275 | 2180924.896 | 240256.2621 | 234561.5017
CELO-USD | 1637.066263 | 1563.510474 | 209.1639 | 161.5299 | 84141.8776 | 62433.8058 | 6429.0748 | 6672.86464
COMP-USD | 1930.978384 | 2277.769704 | 24.3419 | 102.9572 | 52415.2262 | 71506.9772 | 10811.32928 | 10575.63307
CRV-USD | 1558.056675 | 1439.200968 | 391.18707 | 358.74766 | 123526.1518 | 77974.43934 | 5184.467106 | 4880.274604
DOGE-USD | 3332.401806 | 3567.755459 | 70.8678 | 87.2766 | 162941.9924 | 142367.026 | 19198.87974 | 16649.4005
DOT-USD | 1937.306672 | 1924.97545 | 96.1776 | 17.4346 | 148426.7554 | 128115.0674 | 7946.63104 | 7497.7291
ENJ-USD | 1629.321883 | 1680.924461 | 206.7679 | 189.5885 | 44442.1874 | 34013.5104 | 8639.21774 | 8123.93421
EOS-USD | 1615.56714 | 1489.637366 | 168.0852 | 173.325 | 112854.525 | 107807.2018 | 5214.70379 | 6477.58528
ETC-USD | 821.335813 | 834.5595786 | 269.6208 | 355.525 | 97193.5846 | 89041.3986 | 2400.02366 | 2506.95224
ETH-USD | 72858.82878 | 76748.17239 | 13473.43308 | 13180.07441 | 3367337.936 | 2951386.36 | 347178.9095 | 332066.827
FIL-USD | 2845.570969 | 2375.42143 | 242.1354 | 306.0274 | 283558.3276 | 178139.4616 | 6862.59637 | 7448.04701
ICP-USD | 1495.919987 | 1380.981044 | 140.767 | 235.812 | 138953.3468 | 75877.6646 | 4459.65027 | 4107.79114
LINK-USD | 1420.933924 | 1464.899258 | 278.73686 | 197.37878 | 121695.0593 | 87505.56024 | 5385.099861 | 4959.656777
LTC-USD | 2662.874788 | 2607.647309 | 529.7208 | 491.9911 | 243949.2008 | 281985.1228 | 8204.50186 | 8422.61403
MATIC-USD | 2536.885646 | 2830.372519 | 343.54898 | 344.19116 | 137469.4662 | 316325.6192 | 10919.27501 | 10208.46728
MKR-USD | 1750.639831 | 1732.809627 | 198.5781 | 35.5703 | 117634.958 | 155068.8478 | 4622.26376 | 4199.58468
NEAR-USD | 1675.165519 | 1562.516707 | 253.3468 | 165.22656 | 88399.77032 | 95291.04754 | 5112.051726 | 5469.60348
RUNE-USD | 1405.301793 | 1236.978801 | 16.3594 | 14.192 | 95479.7172 | 35884.6144 | 4874.90012 | 5169.0742
SNX-USD | 1176.601377 | 1181.77794 | 175.40392 | 157.22626 | 74976.70678 | 63317.19966 | 4317.679256 | 4539.195001
SOL-USD | 5593.702429 | 4965.523614 | 598.20531 | 553.09842 | 510629.164 | 325720.7934 | 19811.2121 | 17715.71406
SUSHI-USD | 1742.433093 | 1702.580362 | 451.14212 | 363.43854 | 119696.5731 | 97862.32946 | 6188.750554 | 5853.849843
TRX-USD | 2020.263181 | 2206.362237 | 207.119 | 186.352 | 112130.8632 | 100583.9748 | 8609.58809 | 7762.54352
UMA-USD | 1674.146218 | 1867.435641 | 1.661 | 2.3113 | 27597.5654 | 25958.7702 | 10339.02843 | 9421.88208
UNI-USD | 1228.737573 | 1290.555566 | 19.93088 | 110.55462 | 60221.73284 | 79114.6061 | 5118.353054 | 4848.283492
XLM-USD | 945.8556346 | 1056.661455 | 14.4154 | 11.2268 | 27968.5548 | 33075.7324 | 4160.83337 | 3593.48083
XMR-USD | 1241.302452 | 1311.831047 | 149.1158 | 284.344 | 60161.3878 | 47362.076 | 4184.67491 | 3944.77843
XTZ-USD | 2535.35591 | 2924.513667 | 25.3692 | 654.9378 | 118821.1654 | 116389.5882 | 12495.11196 | 11254.26466
YFI-USD | 1141.032228 | 1067.767459 | 30.76762 | 74.41496 | 57176.42934 | 51629.6775 | 4650.909977 | 4621.203557
ZEC-USD | 1498.67572 | 1417.133854 | 3.5894 | 4.6175 | 46219.6362 | 43259.5068 | 7728.24865 | 7725.8727
ZRX-USD | 1095.318171 | 1103.402167 | 1.8322 | 3.1085 | 17729.5864 | 18748.5542 | 7004.15086 | 6953.47491

###  10.5 PPI Release with Tighter Spreads

###  10.6 US Initial Jobless Claims

###  10.7 FOMC Meeting Minutes Release

###  10.8 Fees Generated

Market | Fees
---|---
UMA-USD | 5587.050283
RUNE-USD | 11309.512089
ZEC-USD | 11740.140657
ZRX-USD | 12358.879347
ENJ-USD | 13255.440886
CELO-USD | 13408.904137
COMP-USD | 14309.80742
XLM-USD | 15333.464136
ETC-USD | 15570.710288
UNI-USD | 16431.278735
YFI-USD | 16512.778395
XMR-USD | 16620.451919
MKR-USD | 17944.658857
BCH-USD | 18684.292511
XTZ-USD | 19308.532013
1INCH-USD | 19428.0062
ALGO-USD | 19914.182586
ICP-USD | 22817.903744
DOT-USD | 23420.763145
EOS-USD | 23538.252361
NEAR-USD | 23540.184115
TRX-USD | 24342.576194
AAVE-USD | 24537.743053
SNX-USD | 25241.952879
LINK-USD | 31594.305213
SUSHI-USD | 32487.182111
ATOM-USD | 32988.331918
FIL-USD | 40869.382344
CRV-USD | 43075.945467
DOGE-USD | 43079.828927
ADA-USD | 47467.200754
AVAX-USD | 52823.149907
LTC-USD | 67774.840313
MATIC-USD | 128119.256386
SOL-USD | 183242.177007
BTC-USD | 1527581.557764
ETH-USD | 1944971.095694