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REVIEW 3 major objections 6 minor 36 references

Automated Market Makers: Toward More Profitable Liquidity Provisioning Strategies

T0 review · 3 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Using 700 days of Uniswap v3 positions, this paper establishes that long-term narrow-range liquidity provisioning earns the highest average returns in risky-risky pools, while short-term wide-range provisioning is the most profitable in…

desk verdict Honest descriptive study of Uniswap v3 LP returns, but the headline strategy rankings are conditioned on realized duration and are not implementable ex ante. read the letter →

arxiv 2501.07828 v1 pith:M6FOBPDW submitted 2025-01-14 q-fin.TR

Thanos Drossos , Daniel Kirste , Niclas Kannengie{ss}er , Ali Sunyaev This is my paper · ORCID
classification q-fin.TR
keywords automatedmarketmakersliquidityprovisioningstrategiesimpermanentlossloss-versus-holdingUniswapv3decentralizedfinancereturnsadverseselection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish which choices liquidity providers can make in a concentrated-liquidity automated market maker (Uniswap v3) actually move their profits. Using 700 days of historical position data from nine pools, the authors build a measurement model based on loss-versus-holding to separate realized impermanent loss from fee rewards and then rank four parameter combinations. They find that long-term (over 26.9 days) narrow-range positions earn the highest average return of 2.69% in risky-risky pools, while in stable-risky pools the short-term (under 1.12 days) wide-range strategy is the most profitable at 0.14%. The broader claim is that pool type, position duration, range size, and position size each influence returns, with range size and duration being the levers LPs can practically adjust.

What carries the argument

The carrying object is the loss-versus-holding measurement model: a formalization of impermanent loss for constant-product concentrated-liquidity AMMs, expressed by Equation (6), which gives realized IL as a function of the price change $d$ and the chosen lower and upper price bounds $p_a$ and $p_b$. The model compares the value of the deposited position with a buy-and-hold portfolio and computes LP returns as rewards minus realized IL. It is run on Uniswap v3 subgraph data, with positions matched deposit to withdrawal by FIFO, and strategies defined by cutting duration and range size at the 30th and 70th percentiles. This machinery lets the authors isolate each parameter's influence before comparing four strategy combinations.

What would settle it

Recompute average returns for the same pools after the analysis end date by also valuing still-open positions at current prices (unrealized IL plus accrued fees) and check whether the long-term narrow-range strategy still earns 2.69% and still beats the other three strategies.

Watch

Extended reading notes

Core claim

The central discovery is that liquidity provisioning returns on Uniswap v3 are systematically shaped by position duration and range size, and that the best strategy depends on pool token correlation. The authors' measurement model computes realized impermanent loss with the loss-versus-holding (LVH) metric and subtracts it from accumulated fee rewards to obtain LP returns. Applied to closed positions from May 2022 to April 2024, the model yields an average impermanent loss of -3.8% and shows that 49.5% of positions have negative returns. The strategy ranking is the paper's headline: long-term narrow-range positions average 2.69% returns in risky-risky pools, while short-term wide-range positions average 0.14% in stable-risky pools. From this the authors conclude that LPs should match duration and range size to the expected token price drift of the pool.

Load-bearing premise

The ranking's load-bearing premise is that the closed positions in the sample fairly represent all LP outcomes; if unprofitable positions stay open and are excluded, the reported averages, especially the long-term returns, are too high.

Editorial extensions

If this is right

  • If correct, LPs in risky-risky pools should favor narrow ranges and long holding periods to capture concentrated-liquidity fee income while correlated token prices limit impermanent loss.
  • If correct, LPs in stable-risky pools should avoid narrow short-term positions and use wide ranges over short horizons to reduce adverse selection losses.
  • The result that fee rewards grow faster than IL with duration implies that closing positions early, particularly within the first day, locks in losses and is rarely profitable.
  • Pool type and fee tier act as the primary screens: stable-stable pools give low but positive returns, stable-risky pools are negative on average, and risky-risky pools are positive only with the right duration and range combination.
  • Strategy guidance can be made concrete: choose long-term narrow-range in risky-risky pools and short-term wide-range in stable-risky pools, and avoid treating position size as a profitability lever.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable extension is to correct for the survivorship bias the authors flag: value still-open positions at current prices with unrealized IL and accrued fees, then re-rank the four strategies; the long-term narrow-range advantage may shrink or disappear.
  • The finding that wide-range short-term positions beat narrow-range short-term positions in stable-risky pools suggests a repeatable tactical rule, but only if gas fees are negligible, since the paper's model excludes them.
  • The same measurement model could be applied to other AMM designs or to a later time window to test whether the parameter ranking generalizes beyond the May 2022 to April 2024 sample.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 6 minor

Summary. The paper develops a measurement model based on loss-versus-holding (LVH) to compute realized impermanent loss and rewards for Uniswap v3 liquidity positions, applies it to 700 days of data from nine pools, and defines four liquidity provisioning strategies from position duration and range size. The central empirical claims are that the long-term narrow-range strategy yields the highest average returns (2.69%) in risky-risky pools and that the short-term wide-range strategy is the most profitable (0.14%) in stable-risky pools (Section 4.2). The paper concludes with parameter-level guidance for LPs on pool type, duration, and range size.

Significance. The paper addresses a practically important and timely question—how liquidity providers in concentrated-liquidity AMMs can improve profitability—and uses a substantial observational dataset. Its strengths include the use of a standard IL metric (LVH), a clear data collection and FIFO matching procedure, and an explicit acknowledgment of limitations such as survivorship bias and gas fees. If the strategy rankings were robust, they would offer actionable guidance. However, the central ranking is compromised by the ex-post definition of strategies based on realized position duration and in-sample percentile cutoffs, making the reported returns not directly implementable. The paper is a useful descriptive analysis, but the prescriptive conclusions require substantially more careful strategy definitions and robustness checks.

major comments (3)
  1. [Section 4.2 / Table 3] The four strategies are defined using realized position duration (e.g., long-term means duration > 26.90 days) and range-size cutoffs set at the 30th and 70th percentiles of the same sample on which performance is measured. Because duration is only known after a position is closed, the reported average return of 2.69% for the long-term narrow-range strategy is conditional on positions having survived for at least 27 days; it is not the expected return of an ex-ante strategy that commits to holding for 27 days. This is a look-ahead/survivorship bias distinct from the unclosed-position bias the authors acknowledge in Section 5.3. The in-sample percentile cutoffs compound the problem, since the thresholds are not knowable at the time a strategy is chosen. To support the prescriptive claim, the authors should redefine the strategies ex-ante (e.g., using intended holding periods or time-at-risk) or reframe Figure 6 as a descriptive, ex-post characterization rather than a ranking of implementable strategies.
  2. [Section 5.3] The paper excludes gas fees and acknowledges this in the limitations. This omission is load-bearing for the recommendation that short-term wide-range strategies are profitable in stable-risky pools, since the reported average return is only 0.14% with an average duration of 0.33 days (Section 4.2). For such short holding periods, network transaction costs and the costs of repeated position adjustments could easily exceed the gross return, possibly reversing the strategy ranking. The authors should quantify gas fees for representative position sizes or at least provide a break-even analysis so that LPs can judge whether the strategy is profitable after costs.
  3. [Section 4.2 / Figure 6] Average returns are computed per position without weighting by position size or position value. Since the paper aims to guide LPs in allocating capital, value-weighted returns are more decision-relevant. The current unweighted averages can be dominated by many small positions, which may not reflect the experience of a typical capital-sized LP. A sensitivity analysis using value-weighted returns should be reported to establish whether the strategy rankings are robust to the weighting scheme.
minor comments (6)
  1. [Section 3.3] The FIFO matching procedure for deposit and withdrawal events is described only briefly. It would be helpful to state explicitly how rewards are allocated when the price leaves the position's range and how partial withdrawals are matched to earlier deposits.
  2. [Section 4.1.1 / Figure 1] The text states that stable-risky pools exhibit higher IL than rewards and then reports negative average returns of -0.61%, while the figure shows distributions; please clarify whether these numbers refer to means or medians and how the statement relates to the figure.
  3. [Section 4.1.2 / Table 4] Table 4 presents median daily IL and rewards, but the surrounding text appears to describe mean values. Please state clearly which statistic is being reported in each part of the table and text.
  4. [Section 4.2 / Figure 6] The caption does not explain the subplot layout or the color/legend conventions. A brief description of the rows, columns, and what each panel displays would improve readability.
  5. [Section 3.2] The threshold for excluding pool types with less than USD 10,000 in total value locked is not justified. Please provide a rationale or a sensitivity check showing that the findings are not sensitive to this threshold.
  6. [Section 2.2] The term 'adverse selection' is used to frame the measurement model, but the paper measures LVH, which compares a position to buy-and-hold rather than isolating adverse selection costs. A short clarification of the relationship between LVH and adverse selection would avoid conceptual confusion.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the measurement model rests on an externally sourced LVH formula and the headline results are empirical summaries of Uniswap v3 positions, not predictions forced by construction.

full rationale

The derivation chain is self-contained and non-circular. The paper imports the LVH measure from prior literature (Eq. 6, citing works such as Hashemseresht and Pourpouneh [19]), computes realized IL by matching deposit and withdrawal events, and reports rewards, IL, and net returns as empirical statistics over closed Uniswap v3 positions. No fitted parameter is renamed as a prediction, and no central result is defined in terms of its own output. The four strategies in Table 3 are cross-tabulations of observed position duration and range size, with cutoffs taken from sample percentiles; the reported 2.69% and 0.14% returns are in-sample averages of those observed groups, not out-of-sample forecasts, so there is no statistical reduction by construction. The main validity threat is acknowledged by the authors: Section 4.2 states that few narrow-range positions reach long holding periods, and Section 5.3 warns that the sample 'might include survivorship bias since some unprofitable LPs might never have been closed.' These are legitimate external-validity and implementability limitations, and the skeptic's point about ex-post duration definitions is a real caveat for LP guidance, but it does not make the paper's derivation circular in the sense of X reducing to Y by construction. Self-citations ([22], [23], [24]) are used only for general background on AMMs and smart contracts and are not load-bearing for the measurement model or the empirical ranking.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The central claims depend on data-derived cutoff parameters and several domain assumptions about how IL and rewards are measured. No free parameters are fit to force the headline result, but the strategy boundaries themselves are fit to the same sample used for evaluation.

free parameters (5)
  • short-term duration upper cutoff = 1.12 days
    30th percentile of position durations in the sample; defines the short-term boundary for the four strategies (Table 3).
  • long-term duration lower cutoff = 26.90 days
    70th percentile of position durations; defines long-term boundary (Table 3).
  • narrow-range upper cutoff = 0.0467
    30th percentile of position range sizes; defines narrow-range boundary (Table 3).
  • wide-range lower cutoff = 0.2756
    70th percentile of position range sizes; defines wide-range boundary (Table 3).
  • TVL exclusion threshold = USD 10,000
    Pools with less than USD 10,000 total value locked were removed to reduce low-liquidity bias (Section 3.2).
assumptions (6)
  • domain assumption The LVH formula for concentrated liquidity (Eq. 6) correctly measures impermanent loss for Uniswap v3 positions.
    Equation 6 is imported from prior work [19] and is used throughout the analysis without independent derivation or validation on this dataset.
  • domain assumption Realized IL can be attributed to positions by FIFO matching of deposit and withdrawal events.
    Section 3.3 says liquidity additions/removals are handled with FIFO, but the exact event mapping is not specified or validated.
  • domain assumption Closed positions in the sample are representative of all LP outcomes.
    Section 5.3 acknowledges survivorship bias: unprofitable positions may remain open and are excluded.
  • domain assumption Trading fees collected by the pool are the only reward component; gas fees and other costs are negligible.
    Section 5.3 states gas fees are neglected, which affects small positions disproportionately.
  • ad hoc to paper 30th/70th percentile cutoffs provide a meaningful operationalization of short/long-term and narrow/wide-range strategies.
    The cutoff values are chosen from the same data and are not grounded in economic theory or external benchmarks.
  • domain assumption The Uniswap v3 subgraphs (Revert Finance and Uniswap) provide accurate and complete historical position data.
    All position-level data come from external subgraphs; no cross-validation against on-chain data is reported.

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Cite this review

Pith. "Pith review of Automated Market Makers: Toward More Profitable Liquidity Provisioning Strategies." pith.science (2026). https://pith.science/paper/M6FOBPDW

@misc{pith2026250107828,
  author       = {Pith},
  title        = {Pith review of: Automated Market Makers: Toward More Profitable Liquidity Provisioning Strategies},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M6FOBPDW}},
  note         = {Machine review of arXiv:2501.07828}
}
read the original abstract

To trade tokens in cryptoeconomic systems, automated market makers (AMMs) typically rely on liquidity providers (LPs) that deposit tokens in exchange for rewards. To profit from such rewards, LPs must use effective liquidity provisioning strategies. However, LPs lack guidance for developing such strategies, which often leads them to financial losses. We developed a measurement model based on impermanent loss to analyze the influences of key parameters (i.e., liquidity pool type, position duration, position range size, and position size) of liquidity provisioning strategies on LPs' returns. To reveal the influences of those key parameters on LPs' profits, we used the measurement model to analyze 700 days of historical liquidity provision data of Uniswap v3. By uncovering the influences of key parameters of liquidity provisioning strategies on profitability, this work supports LPs in developing more profitable strategies.

Figures

Figures reproduced from arXiv: 2501.07828 by the authors.

Figure 2
Figure 2. Rewards and IL by position duration. The data was [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 1
Figure 1. Distribution of IL (red) and rewards (blue) for all [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 3
Figure 3. Rewards and IL by position size [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Median position range size of all liquidity pools. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Daily rewards and IL by position range. 4.2 Liquidity Provisioning Strategy Analysis [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Returns of liquidity provisioning strategies for dif [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Reviewed August 10, 2026 · model on record in the stance chip above.