Input/Output datas

What datas are useful for the Lobster algorithm to work properly?

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Last updated 10 months ago

Input/Output datas

What datas are useful for the Lobster algorithm to work properly?

Input data ▶️🔚

For our algorithms to work properly, we need a certain amount of market data for the algorithm to take the best decisions.

As an example, here are all the datas used by our algorithm for the BTC strategy on the BTC/ETH pool. This data is retrieved in real-time by the algorithm directly on-chain. We have also retrieved all the historical data since the creation of the pool in order to backtest the performance that the strategy would have given if it had been launched earlier.

Here are the input data used for the Lobster algorithm:

Output data 🔚

The algorithm's output data is simpler than the input data. In fact, there are only 3:

The concentration chosen for our liquidity-providing position on Uniswap v3. This corresponds to a figure between 1 for full range liquidity, and 40,000 for extremely concentrated liquidity on a single tick.
The exposure of the deposit token in relation to our overall position. If we create a liquidity range where one of the edges is close to the current price, the exposure will be either 0% or 100%. This variable is therefore also bounded between these two values.
The borrow ratio on the lending protocol, between the deposit token and the borrowing token. Borrowing more or less from one token or the other will modify the strike price of the Uniswap v3 call option. A liquidity providing position is equivalent to buying a long gamma option. To modify the strike price of our position (and therefore the price at which our profit is maximized), we borrow more or less token. This variable ranges from 0%, when we don't want to borrow, to the liquidation limit, which is often around 80%.

These three parameters enable us to regulate our entire pseudo-market neutral position on a liquidity pool. They are then translated into actions to be executed on the chain via the smart contracts of the underlying protocols by our arbitration workflow.

PreviousRebalancing frequency NextArbitration workflow

Last updated 10 months ago

Input data ▶️🔚

For our algorithms to work properly, we need a certain amount of market data for the algorithm to take the best decisions.

Here are the input data used for the Lobster algorithm:

Output data 🔚

The algorithm's output data is simpler than the input data. In fact, there are only 3:

The concentration chosen for our liquidity-providing position on Uniswap v3. This corresponds to a figure between 1 for full range liquidity, and 40,000 for extremely concentrated liquidity on a single tick.
The exposure of the deposit token in relation to our overall position. If we create a liquidity range where one of the edges is close to the current price, the exposure will be either 0% or 100%. This variable is therefore also bounded between these two values.
The borrow ratio on the lending protocol, between the deposit token and the borrowing token. Borrowing more or less from one token or the other will modify the strike price of the Uniswap v3 call option. A liquidity providing position is equivalent to buying a long gamma option. To modify the strike price of our position (and therefore the price at which our profit is maximized), we borrow more or less token. This variable ranges from 0%, when we don't want to borrow, to the liquidation limit, which is often around 80%.