The prevalent liquidity issues rooting from the recent delistings from exchanges like Binance and OKEX pushforth the reality of a *“liquidity crunch”*. Given the organisations unique orientation, prioritising market stability and concentrating liquidity on-chain is crucial for economic sustainability and open participation.

Over 1 year ago I conceptualised a strategy to diversify a proportion of the treasury; through leveraging proceeds from an auction with vesting. Deployed into a dual provisioning strategy between DAI and ETH to incentivise ring trades and capitalise off arbitage while saturating market volatility.

```
┌─────────────────────────┐ ┌─────────────────────────┐
│ │ │ │
aTORN │ │ LP │ │
┌───────────────── │ VESTING │ ┌─────────┤ UNIV2-DAI-TORN │ ◄────────┐
│ │ │ │ │ │ │
│ │ │ │ │ │ │
│ └─────────────────────────┘ │ └─────────────────────────┘ │
│ │ │
│ ▲ ▲ │ │
│ TORN │ │ TORN │ │
▼ │ ┌─────────────────────────┐ │
┌─────────────────────────┐ │ │ │ │
┌──────┐ │ │ │ LP │ │ │
│ │ ETH │ │ ├─────────┤ UNIV2-ETH-TORN │ │ DAI
│ USER │ ──────────► │ DUTCH AUCTION │ ◄───────┐ │ │ │ │
└──────┘ │ │ │ │ │ │ │
│ │ │ │ └─────────────────────────┘ │
└─────────────────────────┘ │ │ │
│ │ │
ETH │ │ ETH │ │ │
▼ ▼ TORN │ │ ┌─────────────────────────┐ │
│ │ │ │ │
┌─────────────────────────┐ │ │ │ │ │
│ │ │ │ │ UNIV2-ETH-DAI │ ──────────┘
│ │ │ │ │ │
│ TREASURY │ ────────┘ │ │ │
│ │ │ └─────────────────────────┘
│ │ │
└─────────────────────────┘ │ ▲
│ │
│ │ │ ETH
│ │ │
└───────────────────────────────┴──────────────────────┘
```

**Figure 1: Strategy flowchart**

# Benchmarking

These metrics and data series were computed in Rust, you can view the source code here. For more information on the higher-level concepts applied within you can read more here, and here.

## Methodology

Modern portofolio theory has found many ways to quantify the “risk” of any given financial strategy, through the means of primitive mathemathical pricipcals. Specifically through the application of subject mean-variance of any data time series.

Variance (VAR): is the rate of dispersion over an asset’s mean trajectory

Covariance (COV): is the relationship between two assets returns over a trajectory

**Figure 2: Covariance matrices**

Calculating covariances amongst a selection of assets can help identify which is the riskiest asset in any given portfolio. This is achieved by creating what is called a covariance matrix and computing the Eigenvalues (EV) and Eigenvectors (EVV) from.

An asset with the highest eigenvalue is known as the “market eigenvalue”, which is usually removed from strategising as it is the most correlated with general market returns and performances. For capital efficiency strategies should seek returns uncorrelated from the general market.

## Assumptions

- When the price of ETH moves more than
**2% daily**, simulate volume on the DAI pair - When the price of TORN moves more than
**2% daily**, simulate volume on the DAI pair - Volume simulation is approximately
**25% of daily**UNIV2-ETH-TORN pool volume - Fees are priced relative to latest index, since
**it is not a proactive strategy** - The UNIV2-DAI-TORN and UNIV2-ETH-TORN pools have equal reserves

## Results

Covariance plot (figure 3) match the expected assumptions, we see a clear correlation with TORN while ETH still is relative but slightly displaced suggesting a clear option of diversification.

**Figure 3: Covariance plot**

When analysing the values of TORN and UNIV2-FOLD eigenvalues (figure 4), we see the decision of removing TORN from the eigenportfolio to follow the general market eigenvalue thesis through exclusion.

**Figure 5: Eigenvalue plot**

While it’s clear the eigenvalue plot (figure 5) expresses volatility, there is clear indicaton of stability in UNIV2-2FOLD. With observations to it’s inverse relationship to both assets at varying time intervals, reforcing dynamics of market growth and depression of either provisioning asset’s capitalisiation.

**Figure 5: Eigenvector plot**

**Figure 6: ETH-USD, TORN-USD, eigenvalue plot**

**Figure 7: Volume, eigenvector plot**

When benchmarking culmative fees versus singular UNIV2-ETH-TORN provisioning (figure 8), the capital efficiency is proven; approximately $600,000 more fees are captured by UNIV2-2FOLD.

**Figure 8: Culmative fees plot**

# Parameters

Following criterion weighing of the associated eigenvectors we can find the optimal weighting for such strategy on the basis of covariance, let’s assume we deploy $500,000 out of the $5,000,000 dollars of the treasury’s current resources from the following parameters:

ASSET |
EV (λ) |
EVV |
---|---|---|

ETH | 3.219809892793882e-6 | 0.4359091422125294 |

TORN | 1.0587911840678754e-22 | 0.17171100046127025 |

UNIV2-2FOLD | -6.617444900424222e-24 | 0.3923798573262003 |

and deployed using eigenvector (EVV) weighing:

- $217,954.57 in ETH
- $85,855.50 in TORN
- $196,189.928 in UNIV2-2FOLD

# Conclusion

The best course of asset to address liquidity concerns is pointing to accelerating two-folded market making than single sided. When it comes to executing, the decision could be taken to:

- follow the eigenvector weighting as is, which can allow for resources to be rebalanced later without allocating more resources from the treasury
- remove the TORN weighing and allocate it equally
- remove the TORN weighing and allocate it to TORN or ETH

The simulation model could be drasticially optimised to model LP positions effectively, using Uniswap’s `x * y = k`

formula as currently it is flawed in that regard. While it would be beneficial, the data already indicates to the strategy being more optimal than a legacy approach.