PEPE Token: $15K to $1.2M Daily Volume on Uniswap

Executive Summary

This case study documents every aspect of the campaign: the token project's situation before volume boosting, how the OpenLiquid bot was configured, the hour-by-hour results, the technical infrastructure that made it possible, and a transparent cost breakdown. Every transaction referenced in this study is verifiable on Etherscan.

Ethereum is the most expensive chain to run a volume bot on, but it is also the chain where volume visibility carries the most weight. Trending on DexScreener's Ethereum page puts a token in front of the largest and most active DeFi trading audience in the world. This case study shows that even with $2-5 gas costs per swap, the return on investment from Ethereum exposure can be extraordinary.

The Challenge

The PEPE token in this case study was a derivative memecoin project launched three weeks before the volume campaign began. Despite having a well-designed smart contract, a growing Telegram community of 2,400 members, and over $180,000 in locked liquidity on Uniswap V3, the project was struggling with a fundamental visibility problem.

Daily trading volume had plateaued at approximately $15,000. On Ethereum, where hundreds of tokens launch weekly and DexScreener's trending page requires at least $500,000 in 24-hour volume to appear, $15,000 was effectively invisible. The token did not appear on any DexScreener ranking page, was not listed on DexTools' hot pairs, and had no presence on CoinGecko or CoinMarketCap.

Specific Problems the Project Faced

The project team identified four interrelated problems that volume could solve.

First, the token's chart on DexScreener showed long periods of inactivity between trades. When prospective buyers found the token through community links, they saw a chart with gaps — sometimes 30-60 minutes between candles. This created an immediate impression that the token was dead or abandoned, even though the community was active.

Second, the low volume meant the token had zero discovery potential. DexScreener, DexTools, CoinGecko, and every other aggregator surfaces tokens based on volume and activity. With $15,000 in daily volume, the token was ranked below thousands of other Ethereum pairs. No organic trader would ever stumble across it.

Third, the project team had been in discussions with two mid-tier centralized exchanges about listing. Both exchanges required a minimum of $500,000 in average daily volume over 14 consecutive days before they would proceed with the listing review. At $15,000 per day, the project was orders of magnitude below this threshold.

Fourth, community growth had stalled. The Telegram group had been stuck at approximately 2,400 members for over a week. New members trickled in at 10-20 per day, mostly from paid marketing campaigns. The team needed a visibility catalyst that would bring in organic community members — people who discovered the token through DexScreener or DexTools and joined the community because they were interested in trading it.

The project team considered three options: hiring a professional market maker ($15,000-$50,000 per month minimum), attempting to coordinate community buy events (risky and unreliable), or using a volume bot to generate the initial momentum that would attract organic attention. They chose OpenLiquid because of its flat 1% fee structure, Telegram-based simplicity, and multi-wallet rotation that creates organic-looking trading patterns.

The Strategy

The PEPE project team configured their OpenLiquid volume campaign through the Telegram bot interface. The entire setup took less than 10 minutes. Here is a detailed breakdown of every parameter they set and why.

Target Volume and Timeline

The team set a target of $1,000,000 in daily volume, maintained for a 48-hour initial window. This target was chosen based on DexScreener's Ethereum trending thresholds at the time: the top 10 Ethereum pairs typically required $800,000-$1.5M in 24-hour volume, while the top 20 required $400,000-$800,000. By targeting $1M, the team ensured they would land in the top 10, maximizing visibility during the critical first exposure window.

The 48-hour window was strategic. The first 24 hours would establish the trending position. The second 24 hours would sustain it long enough for organic momentum to build. Many projects make the mistake of running a short 6-12 hour burst that trends briefly but loses the position before organic traders have time to discover and act on the opportunity.

Multi-Wallet Rotation

OpenLiquid deployed 65 unique wallet addresses for this campaign. Each wallet was freshly generated with no prior transaction history. The wallets were funded with varied amounts of ETH — ranging from 0.05 ETH to 0.8 ETH per wallet — to simulate the natural variation you see in real trader wallets.

The 65-wallet count was determined by OpenLiquid's algorithm based on the target volume, the Ethereum network, and the campaign duration. On Ethereum, where on-chain analytics tools like Arkham Intelligence and Nansen actively track wallet clusters, using a larger number of wallets with lower individual volume is critical. A single wallet generating $1M in trades would be immediately flagged as a bot. Sixty-five wallets, each generating $7,000-$20,000 over 48 hours, falls well within normal trader behavior patterns.

The wallets were organized into rotation groups. At any given time, approximately 15-25 wallets were active, with the active set rotating every 2-4 hours. This created a natural pattern where "traders" appeared to come and go, with some wallets being more active during certain hours — mimicking real trading behavior where individuals have different schedules and time zones.

Trade Size Randomization

Individual trade sizes ranged from $5 to $500, with the distribution weighted toward smaller trades. Approximately 60% of trades were between $5 and $100, 30% were between $100 and $300, and 10% were between $300 and $500. This distribution mirrors organic memecoin trading patterns, where most traders make small speculative bets and only a few make larger entries.

The randomization extended to the buy/sell ratio within each time window. While the overall session maintained a roughly 50/50 buy/sell balance to avoid price manipulation, individual 15-minute windows could vary from 60/40 to 40/60 buy/sell. This created realistic green and red candles on the chart, with occasional small pumps and dips that looked like normal market activity.

Timing Patterns

Trades were executed at randomized intervals averaging 15-45 seconds between transactions. During configured "peak hours" (8:00-11:00 UTC and 14:00-18:00 UTC, corresponding to European and US trading sessions), trade frequency increased by approximately 40%. During "quiet hours" (2:00-6:00 UTC), frequency decreased by 30%. This created a realistic daily volume curve that matched the natural ebb and flow of Ethereum trading activity.

The peak hour configuration was critical for DexScreener trending. DexScreener's trending algorithm weighs recent activity more heavily than older activity. By concentrating higher volume during peak trading hours when more users are actively browsing the trending page, the campaign maximized the number of eyeballs that saw the PEPE token during its highest-ranked moments.

Anti-MEV Protection

Anti-MEV protection was enabled by default for this Ethereum campaign. OpenLiquid routes all Ethereum transactions through Flashbots Protect, which submits transactions to block builders through a private channel rather than the public mempool. This means sandwich bots — which scan the public mempool for pending trades to front-run — never see the transactions until they are already included in a confirmed block.

On Ethereum specifically, MEV protection is not optional — it is essential. Without it, the team estimated they would have lost 8-15% of their campaign budget to sandwich attacks, based on the average MEV extraction rates on Uniswap pairs with similar liquidity depth. On a $12,000 budget, that represents $960-$1,800 in preventable losses.

Smart Routing Across Uniswap V2 and V3

The PEPE token had liquidity pools on both Uniswap V2 and Uniswap V3. OpenLiquid's routing algorithm analyzed pool depth on both versions and distributed volume accordingly. The V3 concentrated liquidity pool had approximately 3x deeper liquidity at the current price, so 70% of trades were routed through V3 and 30% through V2.

Splitting volume across both DEX versions served two purposes. First, it created activity on both pools, making the trading pattern look more organic — real traders use both V2 and V3 depending on their tools and preferences. Second, DexScreener aggregates volume from all Uniswap versions, so the combined volume from both pools contributed to a single, larger trending score.

The Results

The campaign exceeded expectations across every metric the team was tracking. Here is a detailed breakdown of the results, with timestamps and verifiable on-chain data points.

Volume Growth Timeline

Within the first hour of the campaign starting, 24-hour rolling volume had already increased from $15,000 to approximately $85,000. By hour 3, volume crossed the $200,000 mark. By hour 6, the token was generating over $500,000 in rolling 24-hour volume, and it first appeared on DexScreener's Ethereum trending page at position #14.

By hour 12, volume had reached $850,000 and the token climbed to the top 8 on DexScreener. At this point, something significant happened: organic trading volume began to materially increase. The team could distinguish organic trades from bot trades by tracking wallets not in the OpenLiquid rotation set. Organic volume went from approximately $5,000 per day to over $40,000 per day within the first 12 hours — an 8x increase.

By hour 24, total daily volume peaked at $1.2M. The token sat at position #4 on DexScreener's Ethereum trending page. Organic volume alone had reached $120,000 — nearly 10x the total volume before the campaign started.

During the second 24-hour window, the team reduced bot volume by approximately 20% while organic volume continued to climb. By the end of the 48-hour campaign, organic volume represented approximately 35% of total daily volume, up from less than 2% before the campaign.

DexScreener Trending Performance

The token first appeared on DexScreener's trending page at hour 6, entering at position #14. It climbed steadily over the next 6 hours, reaching the top 5 at hour 12 and peaking at position #3 at hour 20. The token maintained a top-10 position for 36 consecutive hours — far exceeding the minimum threshold the team needed for maximum exposure.

DexScreener's Ethereum trending page is viewed by an estimated 50,000-100,000 unique traders daily. Each of those viewers saw the PEPE token's name, price chart, and volume metrics during the 36-hour trending window. This kind of passive exposure is essentially impossible to buy through any other channel at comparable cost.

DexTools Hot Pairs Listing

In addition to DexScreener, the token appeared on DexTools' hot pairs list within 8 hours of the campaign starting. DexTools uses a slightly different algorithm that weighs unique wallet count and transaction frequency alongside raw volume. The 65-wallet rotation setup from OpenLiquid naturally satisfied all three criteria. The token remained on DexTools' hot pairs for 28 hours.

Wallet Distribution Pattern

The 65 rotating wallets executed a total of 4,780 trades over the 48-hour campaign. The average wallet executed 73 trades, with the range spanning from 22 trades (quieter wallets) to 142 trades (more active wallets). This variation was intentional — in real trading populations, activity follows a Pareto-like distribution where a minority of traders are responsible for a disproportionate share of volume.

On-chain analysis of the wallet distribution showed no detectable clustering patterns. Each wallet had a unique funding amount, unique trade timing patterns, and unique trade size distributions. When the team ran the wallet set through Arkham Intelligence's cluster detection tool post-campaign, none of the 65 wallets were flagged as belonging to the same entity.

Organic Buyer Increase

The most important metric for the project team was the increase in organic buying activity. Before the campaign, the token averaged 45-60 unique buying wallets per day. During the campaign's peak, 380+ unique wallets that were not part of the OpenLiquid rotation made purchases. In the week following the campaign, organic unique buyers stabilized at approximately 180-220 per day — a 3.5x sustained increase over pre-campaign levels.

The 8x peak increase in organic buyers can be attributed almost entirely to DexScreener and DexTools discovery. Post-campaign surveys in the Telegram group showed that 72% of new members found the token through DexScreener's trending page, 15% through DexTools, and 13% through Twitter posts that referenced the token's trending status.

Holder Count Growth

The token's holder count increased from 1,850 at campaign start to 4,200 at the end of the 48-hour window. Two weeks after the campaign, the holder count had reached 6,800. This growth was almost entirely organic — the volume bot's balanced buy/sell execution means it does not create new holders (the same wallets buy and sell). The 2,350 new holders during the campaign window came from organic traders who discovered the token through trending pages and decided to buy and hold.

Community Growth

The project's Telegram group grew from 2,400 members at campaign start to 5,100 by the end of the 48-hour window. Over the following two weeks, it reached 11,800 members. The daily join rate went from 10-20 pre-campaign to 150-300 during the campaign peak, settling at 80-120 per day in the weeks afterward.

This community growth was the most valuable long-term outcome. Unlike trading volume, which can be temporary, an engaged Telegram community continues to drive organic activity, word-of-mouth marketing, and holder retention indefinitely. The team estimated that achieving the same community growth through paid Telegram marketing would have cost $25,000-$40,000.

Technical Deep Dive

Understanding the technical infrastructure behind this campaign helps explain why the results were achievable and why the volume pattern passed organic analysis checks.

Anti-MEV via Flashbots Protect

Every transaction in this campaign was submitted through Flashbots Protect, Ethereum's private transaction relay network. Instead of broadcasting transactions to the public mempool where thousands of MEV bots scan for sandwich opportunities, OpenLiquid sends transactions directly to block builders who include them in blocks without public visibility until confirmation.

The technical flow works as follows: OpenLiquid's execution engine constructs the swap transaction, signs it with the rotating wallet's private key, and submits it to the Flashbots RPC endpoint. Flashbots forwards the transaction to its network of partnered block builders, who include it in the next block they produce. The transaction is never visible in the public mempool, so sandwich bots cannot detect or front-run it.

For this campaign, Flashbots protection saved an estimated $1,400-$2,500 compared to public mempool submission. This estimate is based on the average MEV extraction rate on Uniswap pairs with comparable liquidity depth during the campaign period. On a per-trade basis, anti-MEV protection saved approximately $0.30-$0.50 per trade — which accumulates significantly across 4,780 total trades.

Gas Optimization

Ethereum gas prices fluctuated between 12 and 45 gwei during the campaign period. OpenLiquid's execution engine includes a gas optimization module that monitors current gas prices and adjusts trade timing to favor lower-gas windows. Trades were not delayed indefinitely — the maximum gas-related delay was 90 seconds — but by avoiding the highest spikes, the campaign saved approximately 15-20% on total gas costs.

The engine also batched wallet funding transactions during off-peak gas periods. All 65 wallets were funded in a 20-minute window when gas dropped below 15 gwei, saving approximately $200-$300 compared to funding during peak hours. After the campaign concluded, remaining ETH was consolidated back to the project wallet in a similar off-peak window.

Smart contract interaction was also optimized. On Uniswap V3, the bot used the ExactInputSingle function for straightforward swaps with minimal gas overhead. On V2, standard swapExactTokensForTokens calls were used. No multicall or complex routing was needed for single-pair volume generation, keeping per-swap gas costs in the $2-5 range rather than the $8-15 range that more complex DEX operations require.

Wallet Rotation Architecture

The 65 wallets were organized into three tiers based on their activity profile.

Tier 1 (10 wallets): High-frequency traders. These wallets executed 100-142 trades each, with an average of 5-8 minutes between trades. They represented the "degens" — the most active segment of any memecoin's trading population. Trade sizes for Tier 1 wallets skewed larger, averaging $150-400.

Tier 2 (25 wallets): Medium-frequency traders. These executed 50-99 trades each, with 15-30 minutes between trades. Trade sizes averaged $50-200. This tier represented the largest segment of the wallet population, matching the real-world distribution where moderate traders make up the bulk of any token's trading community.

Tier 3 (30 wallets): Low-frequency traders. These executed 22-49 trades each, with 30-90 minutes between trades. Trade sizes were the smallest, averaging $10-80. These wallets mimicked casual traders who check in periodically, make a few trades, and move on.

Each tier had its own active window rotation schedule. Tier 1 wallets were active in 4-6 hour blocks with 2-hour rest periods. Tier 2 wallets were active in 2-4 hour blocks with 4-hour rests. Tier 3 wallets had sporadic activity throughout the day with no consistent schedule. This multi-tier approach created a wallet activity distribution that closely mirrors real trading populations on Uniswap.

Cost Breakdown

Transparency about costs is essential for project teams evaluating whether a volume campaign is worth the investment. Here is a complete financial breakdown of this 48-hour Ethereum campaign.

Cost Category	Amount	Percentage
OpenLiquid Fee (1% of volume)	$12,000	80.0%
Gas Costs (4,780 trades at avg $2.80)	$2,384	15.9%
Wallet Funding/Consolidation Gas	$320	2.1%
Slippage (net, after balanced buy/sell)	$296	2.0%
Total Campaign Cost	$15,000	100%

The total campaign cost of $15,000 generated $1.2M in peak daily volume and $2.1M in total volume over 48 hours. In comparison, a professional market maker providing equivalent volume would cost $15,000-$50,000 per month with a minimum 3-month commitment. A Telegram-based volume service with subscription pricing would cost $5,000-$15,000 per month with less sophisticated execution.

The real ROI calculation, however, goes beyond the volume itself. The campaign produced:

• $120,000 in daily organic volume (sustained for 2+ weeks post-campaign)
• 4,950 new holder addresses (organic)
• 9,400 new Telegram community members (over 2 weeks)
• Top-5 DexScreener Ethereum trending for 36 hours
• DexTools hot pairs listing for 28 hours
• Qualification for CEX listing discussions (meeting the $500K daily volume threshold)

If the team had attempted to generate the same community growth through paid marketing alone, the estimated cost would have been $25,000-$40,000 based on current Telegram CPI (cost per install) rates of $2-4 for crypto-targeted audiences. The volume campaign achieved superior results at less than half the cost while simultaneously creating on-chain metrics that open doors for CEX listings and market maker partnerships.

Frequently Asked Questions