AI trading’s biggest user need: make more money, with results they can verify.
Spent time in London discussing PMF with @jenniekusu, who introduced me to @MojoAI_HQ.
As a former trading intern at High-Flyer ( where DeepSeek originated)—my only quant-firm experience—I see 3 paths:
1/ The best tools: an AI version of @gmgnai . Fast, easy trading.
2/ AI trading harnesses built by top traders: automate specific parts of the workflow for intermediate and advanced traders. @minara
3/ Professional quant strategies: @QFEX’s Magic Strategy or @aleabitoreddit AI onchain quant fund. Verifiable track records, but often limited capacity or closed access. QFEX currently reports 29% unlevered APY.
I still see no clear breakout AI trading project. Maybe nobody has fully delivered what users want: making money, with proof.
Who’s closest?
Spent time in London discussing PMF with @jenniekusu, who introduced me to @MojoAI_HQ.
As a former trading intern at High-Flyer ( where DeepSeek originated)—my only quant-firm experience—I see 3 paths:
1/ The best tools: an AI version of @gmgnai . Fast, easy trading.
2/ AI trading harnesses built by top traders: automate specific parts of the workflow for intermediate and advanced traders. @minara
3/ Professional quant strategies: @QFEX’s Magic Strategy or @aleabitoreddit AI onchain quant fund. Verifiable track records, but often limited capacity or closed access. QFEX currently reports 29% unlevered APY.
I still see no clear breakout AI trading project. Maybe nobody has fully delivered what users want: making money, with proof.
Who’s closest?
"Greed makes markets."View quoted post on X
Annanay Kapila (@annanay) spent years as an HFT quant at Flow Traders and Tower Research before founding QFEX (@QFEX), a perpetual futures exchange for equities, commodities & FX. Cambridge Maths.
At his last job, the desk traded $10B+ of volume a day, run almost entirely by algorithms.
In this lecture, he builds market making from first principles:
- The map of systematic trading: prop money vs. other people's money, HFT vs. mid-frequency, and why every strategy lives in one quadrant
- Why prop firms can return 50-100% a year while hedge funds can't: HFT never leaves you holding a big position
- The order book from scratch: bids, offers, limit orders, and why the first order on a price level gets the trade
- No free lunch: how arbitrageurs force every order book to reveal a fair price without anyone trying to
- The entire strategy in one line: bid below fair value, offer above it, collect the difference forever
- Market making is "time-spreading risk," warehousing positions between a seller who shows up now and a buyer who shows up later
- Adverse selection: you're most likely to get filled precisely when your price is most wrong
- Why market makers are completely self-serving, and why that's exactly what makes markets work
- The competitive race that compresses spreads: greed among market makers means lower costs for everyone else
- Thick books vs. thin books, and the vicious loop that keeps illiquid markets illiquid
Highlights:
(00:45) The systematic trading map: capital vs speed
(01:45) Prop firms vs hedge funds: return-on-capital economics
(06:29) A message from Onyx
(07:55) Where market making sits: high-Sharpe prop HFT
(08:55) Fungibility and why exchanges exist
(11:00) How a central limit order book actually works
(17:40) No free lunch: arbitrage-free pricing in the book
(19:55) Theo: reading fair value from the mid
(22:45) Who really posts the book (not retail)
(24:35) Market making as warehousing risk across time
(27:20) Edge: quoting around your fair value
(29:45) Queue priority and the adverse selection problem
(33:05) How competition compresses the bid-offer spread
(37:40) Thick vs thin books: liquidity begets liquidity
(43:55) Scaling to $10B a day on algorithms