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Home Altcoin

Dynamic Fees in AMMs: When the Signal Matters

Digital Pulse by Digital Pulse
August 1, 2026
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Dynamic Fees in AMMs: When the Signal Matters
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Half I requested whether or not an AMM floor shaped. Half II ran pool state ahead. Half III contrasted reserves with ebook depth. This piece runs the AMM protection layer: fastened price vs state-responsive price.

Overview. Campbell-style CPMM simulation with interchangeable price insurance policies. Illustrative parameters; not calibrated to a dwell pool.

Half III ended on dynamic charges as proof that the fixed-fee pool was under-defended-not as proof that hooks beat a ebook. Uniswap v4 dynamic charges make the protection layer callable: a hook can learn pool state and set the price per swap.

The design query is narrower than “can the hook observe extra fields?” Below this constant-function market maker (CFMM) setup — particularly a constant-product market maker (CPMM) — does an additional discipline add info, or solely rescale a response the invariant already implies?

I put Campbell et al. (2025) and Baggiani et al. (2025) into the identical Rust lab. Campbell et al. provide the fixed-fee LP goal and the routing mechanism: when CEX buying and selling is dear, a stale AMM can nonetheless appeal to basic circulate by charging a low sufficient price. Baggiani et al. provide the coverage query — whether or not observation-based charges can beat a static optimum. 4 insurance policies share one FeePolicy interface and the identical Monte Carlo paths: fastened 6 bps, fastened 10 bps, oracle-gap, and inventory-gap.

The bridge is market-making management. Campbell et al. outline what the pool is making an attempt to guard: price income web of stale-price loss. Baggiani et al. change the price from a scalar parameter right into a coverage over market state. A CLOB maker does this by way of spreads, measurement, cancels, and publicity limits when circulate turns poisonous. A programmable AMM can not reproduce the entire ebook, however price is its smallest spread-like protection.

The Campbell baseline

It is a baseline verify, not a fee-design breakthrough. A CEX or oracle reference strikes earlier than the pool worth does. Arbitrage closes the hole; the LP absorbs LVR. Campbell et al. formalize that setting with arbitrageurs, basic merchants, and a passive LP. Hedged PnL is price income minus monitoring error.

Within the lab, oracle_gap_bps is pool worth vs CEX worth in bps-the publicity window earlier than arb trades. arb_delta closes the hole every step; hedged_pnl information what the LP stored web of LVR.

With a ten bps CEX price, the optimum AMM price lands close to 6 bps. Increased charges extract extra per arb commerce however widen the no-trade band and lose basic circulate. I reproduced the sweep in Rust-1 to 100 bps on one GBM path, then 500-path Monte Carlo.

Panel A: hedged PnL vs AMM fee on a single GBM path. Panel B: Monte Carlo mean with shaded ±1 std band, zoomed to 1–30 bps.
Fig. 1. The fixed-fee optimum stays close to 6 bps below this setup. Going under it leaves LVR uncovered; going above it costs out circulate. Panel A: one GBM path. Panel B: 500-path imply (±1 std, 1–30 bps). Supply: amm-lab.

Beneath 6 bps, arb retains buying and selling however the price doesn’t cowl LVR. Above 6 bps, the pool retains some arb income however loses basic circulate to the CEX.

Payment as a coverage

Dynamic charges are management legal guidelines over observations. Manufacturing hooks override the price in beforeSwap or through updateDynamicLPFee. The simulation makes use of the identical floor: observe state, select price, run arb and basic trades.

FeeObservation carries oracle hole (bps), stock skew, and up to date realized volatility. On this comparability, realized volatility is recorded however not used within the price rule. Every step calls coverage.price(&obs) earlier than trades execute.

FixedFeePolicy reproduces the Campbell baseline. In these runs, price is saved as a fraction ( 1 bps = 0.0001, so base_fee = 0.0006). The 2 dynamic insurance policies are:

OracleGapFeePolicy: price = base_fee + 0.04 × |oracle_gap_bps| / 10 000, clamped to [1, 20] bpsInventoryGapFeePolicy: price = base_fee + 0.01 × |inventory_skew|, clamped to [1, 20] bps

Oracle-gap prices extra when the pool has drifted from the CEX. Stock-gap prices extra when reserve composition is imbalanced. Identical interface; totally different acknowledged sign.

Oracle-gap beats fastened 6 bps on each path

Throughout 500 paired Monte Carlo paths, oracle-gap beat fastened 6 bps on each path: imply Δ hedged PnL +15.2, minimal +8.

Dot-interval chart showing hedged PnL p05–mean–p95 for four policies across 500 paths.
Fig. 2. Mounted 10 bps underperforms for a similar purpose as Fig. 1: it overcharges and loses circulate. Each dynamic insurance policies beat fastened 6 bps, however solely oracle-gap delivers materials raise (p05–imply–p95, 500 paths).

The comparability is paired by seed: fastened, oracle-gap, and inventory-gap run on the identical GBM path and demand attracts. The delta distribution is the helpful statistic as a result of it asks what modified when solely the price rule modified.

Fig. 3. Identical signal, totally different magnitude: oracle-gap provides +8 to +22 per path; inventory-gap provides +0.5 to +2.5 (500 seeds). The coverage rating shouldn’t be shut.

Stock-gap additionally beat fastened 6 bps on each path, however imply acquire was solely +1.8 -roughly one-eighth of oracle-gap’s benefit.

Stock skew collapses into oracle hole

Stock skew shouldn’t be a brand new sign right here. In a CPMM, each fields are monotone capabilities of the identical quantity-deviation between AMM and CEX worth:

oracle_gap_bps = (P_amm − P_cex) / P_cex × 10 000inventory_skew = (P_amm − P_cex) / (P_amm + P_cex)

When arb retains the pool close to the CEX worth, P_amm ≈ P_cex and the skew denominator is roughly 2 × P_cex:

inventory_skew ≈ oracle_gap_bps / 20 000

Scatter plot of oracle gap (bps) vs inventory skew × 10 000, showing near-perfect linear relationship.
Fig. 4. These variables are successfully the identical coordinate on this mannequin. Close to-linear match (slope ≈ 0.5, Pearson r = 1.000 to 4 decimals) exhibits inventory-skew coverage is usually oracle-gap coverage with decrease acquire.

InventoryGapFeePolicy applies the identical sign at decrease acquire—the step-level audit within the repo exhibits why. The +1.8 imply acquire is amplitude, not a unique sign.

That is particular to constant-product swimming pools the place arb constantly aligns pool worth with the exterior reference. In pegged-pool designs, reserve imbalance can transfer whereas marginal worth stays close to peg, so stock can carry info not captured by present oracle hole. Curve’s offpeg_fee_multiplier is aimed toward that regime, not at CPMM-style rescaling.

What a hook ought to take a look at subsequent

If the function is reserve-derived in CPMM, assume redundancy till proven in any other case. Present oracle hole is the primitive; stock skew, reserve ratio, and pool worth are capabilities of it. Including them doesn’t develop the knowledge set. It rescales the response.

The following candidate shouldn’t be one other reserve-derived discipline. It’s path-derived or exterior:

Realized volatility: not recoverable from instantaneous CPMM stateOrder-flow imbalance: directional stress inside a block, seen earlier than the swap executesOracle latency: time since final dependable exterior updateToxic-flow proxy: share of current quantity from arb vs basic demand

These break collinearity as a result of the invariant doesn’t encode them. The take a look at: does the function add info past present oracle hole below the identical path and demand assumptions?

Closing

Half II ran two settings: a seeded path the place charges beat LVR, and a deterministic shock the place LP-vs-hold stayed detrimental. The coverage layer issues right here: oracle-gap dynamic charges beat fastened 6 bps on each sampled path, whereas inventory-gap provides solely a small raise as a result of the 2 alerts are practically collinear.

That doesn’t show dynamic charges rescue LP economics in manufacturing. The run makes use of illustrative parameters (σ = 4%, μ = 0, 100 Y basic demand per step, CEX price = 10 bps). The acquire nonetheless is determined by multiplier, demand, and volatility regime, even when the sign relationship is structurally redundant below the CPMM invariant.

Manufacturing needs to be stricter than simulation. This reduced-form world has no fuel public sale, block latency, queue dynamics, or LP repositioning price. A hook that freely explores price settings with LP capital can change into statistical playing as soon as these frictions enter. The safer model begins from a champion rule, deviates solely when edge clears price and uncertainty, and may abstain.

Below CPMM, most price innovation is management regulation, not info. The following take a look at is strict: add a path-derived function (realized volatility or order-flow proxy), verify orthogonality to oracle hole on the identical paths, then hold it provided that paired PnL improves at matched danger.

Appendix: supply

Half I: The token appeared twice. The AMM market shaped as soon as.Half II: Earlier than MEV, I Constructed a Rust AMM Lab to Measure Pool State.Half III: Hyperliquid Reveals What a Market Appears Like With out an AMM Pool.Github Repo: amm-lab, Campbell simulation and dynamic-fee coverage runsCampbell: Campbell, S., Bergault, P., Milionis, J., and Nutz, M. (2025). arXiv:2508.08152.Baggiani: Baggiani, L., Herdegen, M., and Sánchez-Betancourt, L. (2025). arXiv:2506.02869.

This publish was initially printed on my private weblog: https://egpivo.github.io/2026/07/21/dynamic-fees-amm-signal-matters.html

Dynamic Charges in AMMs: When the Sign Issues was initially printed in The Capital on Medium, the place persons are persevering with the dialog by highlighting and responding to this story.



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