Sure, but first we need a clear mechanism, risk model, and data points to validate that volatility can be reliably turned into profit. Let me know the assumptions.

Looks solid. I’ll start pulling the 5‑minute spot and volatility feeds and compute the variance swap pricing model. I’ll also set up a 5‑minute VaR loop with a 0.3 % transaction‑cost buffer. If the implied vol data is sparse, we’ll need to interpolate, but that’s a minor tweak. Let me know which data feeds you’ll use for the order‑book snapshots. Once I have the initial dataset, we can run a backtest to confirm the spread targets hold up under the stress scenarios you outlined.

I don’t have the ability to pull live data or run backtests myself. I can walk you through setting up the queries for Uniswap v3, SushiSwap, and Curve’s subgraphs, or help you script the 5‑minute depth pulls and VaR loop if you want to do it on your end. Just let me know what you need next.

**Subgraph endpoints** - Uniswap v3 – https://api.thegraph.com/subgraphs/name/uniswap/uniswap-v3 - SushiSwap – https://api.thegraph.com/subgraphs/name/sushiswap/exchange - Curve – https://api.thegraph.com/subgraphs/name/curvefi/curve **GraphQL query templates** --- ### 1. Uniswap v3 *Get the top 10 tick levels above and below the current price (each tick gives a price and liquidity).* ```graphql query($poolAddress: Bytes!, $ticksAbove: Int!, $ticksBelow: Int!) { pool(id: $poolAddress) { tickCurrentIndex ticks(first: $ticksAbove, orderBy: tickIdx, orderDirection: asc, where: { tickIdx_gt: $tickCurrentIndex }) { tickIdx liquidityNet } ticks(first: $ticksBelow, orderBy: tickIdx, orderDirection: desc, where: { tickIdx_lt: $tickCurrentIndex }) { tickIdx liquidityNet } } } ``` *Variables example:* ```json { "poolAddress": "0x... (token‑token pool address)", "ticksAbove": 10, "ticksBelow": 10 } ``` *Post‑processing:* Convert each `tickIdx` to a price (price = 1.0001^tickIdx * sqrtPriceX96 / 2^96), then weight by `liquidityNet` to get the weighted average price for the ask and bid sides. --- ### 2. SushiSwap *Pool reserves give the best price (no depth in the subgraph). Use the `reserve0` and `reserve1` to calculate the price and then pull the on‑chain order book if needed.* ```graphql query($pairAddress: Bytes!) { pair(id: $pairAddress) { reserve0 reserve1 totalSupply token0 { id } token1 { id } } } ``` *Variables example:* ```json { "pairAddress": "0x... (pair address)" } ``` *Post‑processing:* Price = reserve1 / reserve0 (if token0 is the base). For depth, you’ll need an on‑chain call to the pool contract or a 3rd‑party service; the subgraph can’t give you top‑10 levels. --- ### 3. Curve *For a stablecoin pool, the subgraph lists the balances of each coin. The best price is the ratio of balances.* ```graphql query($poolAddress: Bytes!) { pool(id: $poolAddress) { balances coins totalSupply } } ``` *Variables example:* ```json { "poolAddress": "0x... (stablecoin pool address)" } ``` *Post‑processing:* Take `balances[i] / balances[j]` for the two coins of interest to get the spot price. Like SushiSwap, the subgraph doesn’t expose depth; you’ll need to query the contract or a depth provider for tick data. --- **Quick tip for your script** 1. Hit each endpoint with the query above every 5 minutes. 2. For Uniswap, map the ticks to prices and weight by liquidityNet to compute the weighted bid/ask average. 3. Store the timestamp, weighted bid, weighted ask, and the spread (`ask - bid`). 4. Feed that time‑series into your VaR loop. That’s all the payload you need—minimal data, enough for depth and spread estimation. Good luck!