Okay, so check this out—I’ve been knee-deep in algo design for perpetual futures and market making for years. I trade, I build, and I’m biased toward systems that let me move fast without getting scalped by fees. Whoa! In practice you want tight spreads, low slippage, and rules that survive the ugly moments when funding blows out and leverage liquidates like crazy.
Short version: good architecture matters more than clever heuristics. Really? Yes. And here’s the thing. If your engine can’t adapt during regime shifts, it will bleed—very very quickly—because perpetuals punish complacency with cascading liquidations that flip the book on you when you least expect it.
On first glance, a market maker for perpetual futures looks a lot like a spot MM. Initially I thought the same. Hmm… But the dynamics are different. Actually, wait—let me rephrase that: funding and leverage create an extra feedback loop that changes optimal quoting behavior substantially, even if the orderbook superficially resembles spot markets.
Quick intuition: perpetuals carry funding; funding carries information. Wow! If longs pay shorts consistently, there’s an implied directional pressure. That pressure will widen spreads and tilt inventory. And if you ignore funding, you will misprice risk, plain and simple, and then wonder why your PnL is lopsided.
So what do pro traders actually implement? First tier: adaptive quoting with dynamic inventory skews. Seriously? Yep. You bias your quotes to offload inventory when skew is large, and tighten when neutral. Whoa! That bias should be a function of expected funding, realized volatility, and the tail risk of the underlying index (not just last trade). Long sentence, yes, but those components interact nonlinearly and deserve a unified model.
Risk control is not optional. Okay. Stop pretending it is. Your liquidation model must be baked into pricing. Hmm… Leverage amplifies shock; margin math is unforgiving. On one hand you can throttle size aggressively; on the other hand too much throttling kills profitability—so you need an intelligent taper that responds to both on-chain and off-chain signals.
Execution matters. Really. Not all DEX liquidity is equal. You can have quoted size, and you can have executable size—those are different things. Whoa! Fragmentation across AMMs, concentrated liquidity pools, and RFQ liquidity providers means you must evaluate slippage empirically, not theoretically. Long story short: run micro-simulations against live orderbooks and be ruthless about removing stale connectors.
Algorithm design patterns I lean on are pretty straightforward in principle. Here’s the thing. Use three layers: (1) a quoting engine that sets spread and skew; (2) a risk engine that limits position, sets max exposure per instrument, and enforces stop-loss style liquidity gates; (3) an execution manager that decides when to post, cancel, or hop to another venue. Whoa! When these layers talk cleanly, you get coherent behavior under stress; when they don’t, you get weird oscillations and then scrapes of margin calls.
Perpetual-specific levers you should consider. Hmm… Funding forecasting is number one. Really? Yes. Predict short-term funding to adjust quote mid-price. Whoa! Second: index and basis monitoring. If the perpetual drifts from the index beyond typical ranges, liquidity providers and arbitrageurs will hammer it—so make your quoting conditional on expected arbitrage volume and not just current spread.
Let’s get a bit more tactical without going into raw math. You can implement a skew function proportional to (position / target_inventory) times a volatility multiplier. Whoa! Add a funding term to shift mid-price: if expected funding is positive, nudge bids down to compensate. Okay, so that sounds simple—because it is—but the devil is in parameter tuning and latency handling. If your execution latency is variable, your ‘simple’ rules become brittle under stress.
On the infrastructure side, observability is everything. Seriously? Yes. You’ll want real-time metrics for fill rates, adverse selection, realized pnl by leg, funding exposures, and cross-venue deltas. Whoa! Alerts should trigger automated de-risking before teams even finish a coffee. (oh, and by the way…) humans should still own the escalation ladder—automation shouldn’t be an off switch for accountability.
Why DEX choice matters to pros. Hmm… Not all decentralized venues scale the same. Some have deep concentrated liquidity that looks great on chain but evaporates when taker flow hits hard. Whoa! That is where platforms built for tight perpetual markets shine. If you want to test a DEX that prioritizes low fees and large executable depth, check out the hyperliquid official site—I’ve routed flows there for sim trials and the latency and fee model have been compelling compared to many alternatives.
Market Making Playbook for Perps — Practical Rules
Start small and iterate. Really. Don’t deploy full-size until you run a stress battery. Whoa! Rule one: cap per-instrument exposure and aggregate cross-instrument delta. Rule two: fund-aware skewing of mid-price and fees. Rule three: a volatility-aware size throttle that reduces posted size when realized or implied vol spikes. Long sentence here because these rules interplay and your orchestration layer should reconcile them in a single risk budget.
Edge cases to watch. Hmm… Funding squeezes, oracle pauses, and index de-pegs. Whoa! In those windows, spreads should widen automatically and rebalancing should prefer liquidity-preserving actions over chasing PnL. I’m not 100% sure of every edge, but I’ve seen smart MMs survive by prioritizing solvency over market share during black swans.
On incentives and fee engineering. Here’s the thing. Fees change behavior. If maker fees are tiny and taker fees are punitive, you’ll get pinned-to-market strategies from other participants; if fees are symmetric, market quality shifts. Whoa! Choose venues and fee tiers where your strategy’s expected value remains positive after fees; sometimes paying a small maker rebate in exchange for reliable executable depth is worth it.
Operational checklist (practical): latency budget, fail-open/fail-closed modes, simulated liquidation drills, and a replay capability to rerun traumatic days. Whoa! Also, keep a cold path for manual intervention—automation is powerful, but sometimes you need a human to flatten positions and reconfigure risk quickly. Long sentence, yes, but trust me—those emergency modes save lives (and P&L).
FAQ
How do funding rates affect quoting?
Funding creates drift. If expected funding is high, short-side demand will be stronger and you should bias quotes to sell into that demand (i.e., lower bid). Conversely when funding favors shorts, buy-side pressure increases and you should widen or skew accordingly. This is simplified, but it captures the mechanics you’ll code into a funding-aware mid-price.
Can a single algorithm handle both spot and perps?
Technically yes, but operationally it’s cleaner to separate concerns. Perps require funding-aware inventory management and liquidation-aware limits that spot MMs don’t need. Many firms share core infrastructure but maintain distinct strategy layers for each product class.
What’s the single biggest mistake I see?
Overconfidence in backtests and underinvestment in stress testing. Whoa! Historic sims are useful but insufficient; real markets present microstructure surprises that only live testing and replay can reveal. Also, don’t ignore legal and counterparty implications when operating across decentralized rails.