Whoa!
I’m coming at this from years in the pit and in the code — market making isn’t a theory class exercise. Medium-term liquidity decisions kill or make returns, and you feel that in your P&L every single day.
At first blush it looks simple: tighten spreads, add volume, collect fees. But actually, wait—let me rephrase that: tightening spreads without regard for inventory, adverse selection, and latency is how you get squeezed out faster than you can blink.
The good news is that the tools we have now — smarter algorithms, better access to on-chain order books, lower fees on certain DEX rails — change the calculus in practical ways that pro traders can exploit.
Really?
Yeah. Here’s the thing.
I want to talk about three linked topics: market-making strategy, trading algorithms that actually work live, and order-book mechanics at scale. I’ll be candid about what I use, what bugs me, and what still keeps me up at night.
On one hand, a market maker’s job is obvious — provide liquidity — though actually it’s a complex interplay of risk management, prediction, and order placement technique that often looks like busywork until a move happens and then you see the whole thing for what it is.
Hmm…
System 1 reaction: you’re either a human or a bot. My instinct said: if you’re human, you want robust heuristics that survive stress. Something felt off about strategies that ignore rare stress events.
System 2: dig deeper — model the inventory dynamics, test the alg across varying spreads and depth, simulate latency and fee regimes. Initially I thought you could just scale a single strat; but then realized markets are non-stationary, and you need adaptive policies.
Oh, and by the way, latency matters less than you’d think when your tick management is proactive — but it still matters for tight spreads.
Wow!
So let’s break this down.
Start with the order book. A live order book represents not just prices but intentions — layers of standing orders that show how other participants are likely to react, and that’s your raw signal for microstructure-aware decision-making.
Longer thought: if you treat the book as a static snapshot, you’re missing the temporal patterns — iceberging, cancels, and refill behavior — which are all short-term predictors of adverse selection risk if you don’t account for them appropriately while posting passive liquidity.
Seriously?
Yes. And here’s a practical first rule: quantify your acceptable inventory range, but make it symmetric only if the asset’s funding and drift justify that symmetry; otherwise bias inventory toward the hedge direction (e.g., delta-hedge bias in volatile tokens).
That means your algo should do two things quickly: adjust spread asymmetrically and skew order sizes across the book, not just post equal bids and asks.
On one hand, symmetric posting looks neat in backtests; though actually live flows punish neatness because real counterparties exploit predictability.
Whoa!
Next: algorithms. Don’t overfit. Don’t be cute.
My baseline stack: an adaptive quoting engine, an event-driven repricer tied to on-chain order book events, and a lightweight risk controller that enforces time- and event-based stop-outs. Initially I wired in fancy ML features; then I stripped them back because the marginal gains didn’t justify the fragility in regime shifts.
Actually, wait—let me rephrase that—ML can add value when used as a signal filter rather than as the final decision-maker.
Hmm…
Latency mitigation is partly infra and partly design. Collocation can’t be done on-chain, obviously, but smart batching of cancels and replaces, predictive TTL (time-to-live) for orders, and local queuing where you shadow orders until confirmations arrive are practical mitigations that keep the book honest.
Longer thought: in block-based DEXs, bundle timing and reorder protection mechanisms (MEV-aware handling) matter immensely — ignore them at your peril. This is where protocol-level differences translate directly to P&L.
Wow!
Fees change the math. Very very important to model taker vs maker fees, rebates, and potential rebate caps per epoch. Some DEXs reward volume in ways that fundamentally alter the optimal spread. I’m biased, but fee-aware dynamic spread is the single easiest win for an experienced quant.
Also, don’t forget cross-pair flows. Many times you hedge via a related pair and end up with basis risk that shows up two days later when funding flips.
Whoa!
Let me give a concrete workflow I use for launching a new market making bot on an order-book DEX:
1) Run a microstructure scan: fill and cancel rates, visible depth, historical spreads during volatile periods.
2) Simulate inventory paths with realistic counterparties (not IID fills), including tail events.
3) Choose defensive defaults: asymmetric spreads, dynamic position limits, and time-decayed price anchors.
4) Start on small size, monitor slippage and refilling behavior, then scale up gradually with live A/B tests.
Really?
Yep — you scale into markets that scale back at you. The market tells you what’s allowed. Learn that fast.
One surprising detail: adaptive TTLs that lengthen after repeated cancels reduce cancel-stomp costs and tame cancels chasing, which lowers your visible adverse selection. That’s subtle but effective.
Hmm…
Now, for those who prefer an on-chain order-book DEX with the kind of throughput and fee structure that lets you execute these strategies without constant infrastructure drag, I’ve been experimenting with newer rails. One of them, hyperliquid, fits the bill for many of my smaller-to-mid ticket strategies — low fees, order-book model, and features that let you manage batches of orders programmatically with predictable settlement patterns.
I’m not endorsing blindly, but if you want to test order-book MM in a low-cost environment, check out hyperliquid and see how its fee curve matches up to your edge.
Whoa!
Risk controls: you need both reactive and proactive layers. Reactive = immediate deltas, stop placement, and volatility gates; proactive = re-rating spreads ahead of expected news and piggybacking on predicted flow from order flow signals.
Inventory rebalancing should be partly scheduled and partly opportunistic, and you should be ready to flatten quickly when correlation across tokens spikes.
Long thought: design your risk controller so that it degrades performance gracefully — reduced quoting frequency and wider spreads are better than a hard stop that forces liquidation into a moving market.
Wow!
Execution monitoring: track more than fills. Track queue position, cancel rates, and “ghost liquidity” (orders that sit then vanish on large ticks). These metrics are early-warning signals.
Also, track adversarial behavior — skimming bots, pingers, and sandwich attempts; they leave fingerprints, and you can adapt by randomizing order sizes and posting patterns.
I’m not 100% sure where the edge ends and the arms race begins, but you can preserve a lot of alpha by keeping your algos boring and robust.
Putting it together: a mental checklist
Okay, so check this out—before you deploy a market maker on an order-book DEX, run this checklist. First, microstructure audit (fills, cancels, depth). Second, fee mapping (maker/taker and rebates). Third, inventory policy and asymmetric spread logic. Fourth, latency/design mitigations. Fifth, risk controllers with graceful degradation. Sixth, continuous monitoring for adversarial patterns.
You’ll refine this in live runs, but having the structure keeps you from overreacting when the market noisily disagrees with you. I’m biased toward small iterative changes rather than big swings, because big swings invite big losses.
FAQ
Q: Is order-book market making still better than AMMs for pros?
A: It depends. Order-book MM wins when you need precision — tight spreads, control over size at price, and the ability to adapt quickly to asymmetric inventory. AMMs scale broader liquidity and are simpler, but they suffer from impermanent loss and have limited control over execution. For pro traders wanting low fees and tight control, order books often give the edge — provided you can manage infra and adverse selection.
Q: How do I start testing without huge capital?
A: Use low-fee DEX rails (like the one I mentioned earlier), start with micro-size quoting, instrument everything, and run stress scenarios. Simulate fills with noisy counterparties. Scale only when your live metrics match simulated expectations. And yes, backtests lie sometimes — trust live small experiments more.
Q: What’s the single most common rookie mistake?
A: Posting symmetric tight spreads with no skew or adaptive inventory control. It looks cute on paper, but it’s a magnet for informed takers. If you’re not adjusting for flow and skew, you’re basically donating spread to the market.
