Some thoughts on market makers, transparency & the @Forgd_ leaderboard: Yesterday, a ranking of crypto market makers made the rounds on X and generated a lot of debate. For context, the ranking was copy + pasted directly from a leaderboard built by @Forgd_, hosted on @DefiLlama & @Tokenomist_ai. Importantly, this leaderboard benchmarks market makers engaged by blockchain projects to support liquidity for their native tokens – not prop trading firms deploying their own balance sheets. It grades MMs based on a combination of: → Historical trade activity → KPI adherence → Venue coverage & treasury management capabilities → Size of client base It’s also only one of several leaderboards we publish. You can separately see which desks historically provide the deepest liquidity, generate the most maker volume, maintain the tightest spreads, and demonstrate the strongest uptime. Is the methodology perfect? Absolutely not. But there are far too many people in crypto claiming to have alpha on “who the best market makers are” based almost entirely on gut instinct and anecdotal evidence. After spending more than a decade in crypto (including time working at CEXs & market makers myself) I’ve probably been asked “Who are the best MMs?” hundreds of times. I wanted to take a shot at answering that question with actual data. So, some context on why we built these tools, how the data works, what the leaderboard does and does NOT tell you, and where I think we can improve 👇 1. Why we built it One thing worth acknowledging: Forgd wasn’t the first company to recognize this problem. Before we launched, other firms were already helping projects connect with market makers, structure deals, and better understand how their liquidity providers were performing. I think they deserve credit for helping validate an important thesis: market making was an enormous black box for most token issuers. Where we wanted to take a different approach was accessibility. We didn’t think projects should need to pay large advisory fees or recurring SaaS subscriptions simply to understand whether their market maker was doing its job. So we built self-service tools that projects can use completely free of charge to: → Monitor market maker performance → Run competitive MM RFQs → Compare proposals → Benchmark historical MM performance → Make more informed decisions Our belief was pretty simple: GTM infrastructure should be broadly accessible. Today, hundreds of projects across virtually every sector and stage use Forgd’s infrastructure. Some projects like @zama, @MeteoraAG and @metaplex have taken that a step further by making their market maker performance & engagement structures publicly transparent through products like “Universal Token Ratings”. We think that’s a good thing. The more transparency projects are willing to provide around how their market makers trade, where they provide liquidity, and how engagements are structured, the better informed the entire industry becomes. And because the tools are free, adoption has given us something extremely valuable: sample size. Since launching in Q4 2024, we’ve tracked nearly 600 market maker engagements across 30+ market makers, creating what we believe is one of the largest structured datasets for understanding how crypto market makers actually perform across real engagements. 2. How does the methodology actually work? TL;DR: i. Projects use Forgd to monitor their market makers. For every integrated engagement, the market maker transmits trading activity to Forgd through a standardized API integration. ii. Through these integrations, we capture things like maker fill volume, liquidity depth, spreads, KPI adherence and other engagement-specific data on a per-project, per-exchange and per-trading-pair basis. iii. Separately, Forgd independently consumes market-wide data directly from exchanges and on-chain infrastructure. That means we can observe not only what the market maker says it did, but what was happening across the broader market. put simply... → The API tells us what the market maker did. → Our independent market infrastructure tells us what the broader market looked like. We can then calculate things like the MM’s share of total maker volume or total order book depth and evaluate its activity in the context of the entire market rather than looking at an isolated number. We aggregate those observations at the project level and then across each market maker’s engagements to understand how that desk performs on average for the projects we monitor. That allows us to compare things like: → Average depth of liquidity → Average maker / taker fill volume → Average spreads → KPI adherence / uptime → Engagement structures → Average loan sizes → Loan + option vs. retainer + working capital usage → Other commercial considerations If you want to nerd out on the actual methodology, including examples of how raw data is transformed into project-level and MM-level observations, we published the full documentation here: Some people will like the methodology. Some will hate it. That’s totally fine! I’d much rather publish the methodology, expose it to criticism, and continuously improve it than pretend we’ve somehow solved market maker benchmarking forever! 3. There are also important limitations. Our dataset is large, but it is not comprehensive. Forgd has tracked nearly 600 market maker engagements since launching, but we obviously don’t capture every token project or every market maker in crypto. Our software is free to use by design. We wanted to democratize access to tools that let projects monitor their market makers, understand how they’re trading, and make better-informed decisions without putting that infrastructure behind a paywall. That model has given us a very robust sample size, but we can’t force every project to use it. The same applies to market makers. Desks like @wintermute_t, @CumberlandSays, @jump_ , and @FalconXGlobal aren’t currently included because they aren’t integrated with our platform. Does that mean they’re bad market makers? Or that they’re “hiding something”? Absolutely not. My experiences with these desks throughout my career has been extremely positive, and I think they can be fantastic partners for projects launching tokens. Integration takes engineering resources, and these firms have their own development priorities. Hopefully we can convince more of them to integrate over time and make the dataset even stronger. On the flip side, I want to give credit to desks like @flowdesk_co, @GSR_io, @ambergroup_io, @SeliniCapital, @keyrock, @jpegtrading, @tophash and others that have invested time integrating with Forgd and working with our team to improve the infrastructure. Longer term, I think transparency around token liquidity is only going to become more important. I’d love to see a world where projects proactively disclose who their market makers are, how those engagements are structured, and (retroactively, T + 1) how those desks actually traded across different venues. That kind of transparency would give the investing public much better visibility into a token’s market structure, including the ability to distinguish organic liquidity and volume from liquidity and volume being subsidized through formal market maker engagements. We’re obviously not there yet. But the more projects & MMs that participate in initiatives like Universal Token Ratings ( & Token Transparency Framework by @Blockworks / @JasonYanowitz, the more complete the picture becomes. Our goal is to build the free infrastructure that makes providing that transparency as easy as possible. 4. A note on competition I’ve seen plenty of criticism and debate around the methodology. That’s okay. In fact, I think it’s healthy. We’re always looking for ways to improve the methodology, and thoughtful criticism helps us do that. More broadly, competition is one of the healthiest forces in any industry. It forces everyone to build better products, serve customers better and continuously raise the standard. Web3 market infrastructure should be no different. In fact, some of the companies we now compete with helped validate the original problem we set out to solve. They demonstrated that projects wanted better ways to select market makers, structure engagements and understand performance. We simply had a different thesis around how those products should be delivered. Most notably, that critical infrastructure like MM monitoring and RFQs shouldn’t be locked behind SaaS paywalls or expensive advisory engagements. And I don’t think any of this needs to be zero-sum. There is plenty of room for multiple companies to build great products in the same category. Personally, I’d rather spend our time shipping better software than publicly tearing down other builders. If another platform develops a better methodology, feature, dataset or product, that should motivate us to improve ours. That’s how competition is supposed to work. And we’re building well beyond market maker analytics. MM monitoring and RFQs are only one part of Forgd. We’ve expanded the platform into tokenomics and protocol design, exchange listing intelligence, token unlock tracking, market structure analytics and novel institutional-grade research. These problems are interconnected, and we ultimately think better data leads to better decisions throughout a token’s lifecycle. 5. I’ll end with a question. What actually makes someone the “best” market maker? - Is it the desk that consistently provides the most depth? - The most maker volume? - The tightest spreads? - The strongest contractual KPI adherence? - The best treasury management capabilities? - The broadest exchange coverage? - The projects with the best post-TGE price performance? - Something else entirely? @mdudas and anyone else who spends time thinking about market structure — I’d genuinely love to hear how you’d approach it. cc @hosseeb @BrianInCrypto @ahboyash Our leaderboard will continue to evolve as the dataset grows and we get better feedback. The goal isn’t to declare that we’ve created the definitive answer to “Who is the best market maker?” The goal is to take one of the biggest black boxes in crypto and make it a little more transparent, measurable and data-driven. We’ll keep publishing the methodology, listening to constructive criticism, improving the product, and letting the data speak for itself.