What is quant trading? Quantitative trading in crypto, explained
Updated Nov 11, 2026 · 8 min read
What quant trading is, the pipeline from data to risk, what changes in crypto (funding, open interest, public positions), common strategies, tools and where an AI Quant fits, with live Hyperliquid readings (Nov 11, 2026).
What is quant trading? Quantitative trading, or quant trading, decides trades with data and tested rules instead of judgement in the moment. A quant turns an idea into a precise rule, tests it on historical data with realistic costs, and runs it as code with fixed risk limits. Hedge funds such as Renaissance Technologies and Two Sigma built their businesses on it. In crypto the same process runs on markets that never close, where perps pay funding every hour and, on Hyperliquid, every account’s positions are public.
Key takeaways
- Quant trading = data + a testable rule + code that executes it + risk limits, applied the same way every time.
- The work runs as a pipeline: data, signal, backtest, execution, risk and review. Many ideas fail the backtest, and many that pass lose their edge to costs.
- Crypto adds data stock quants don’t get: hourly funding, open interest and, on Hyperliquid, every account’s positions and fills. On Nov 11, 2026, 179 perps were listed on Hyperliquid’s main exchange, with $8.75B in open interest.
- Algorithmic trading is about executing with code; quant trading is about deciding with models. Most quant strategies are executed algorithmically.
- An AI Quant such as Senpi turns a plain-language idea into rules and runs them as code, with stops and halts built in.
Do quant trading without code: Senpi, an AI Quant
Senpi publishes this guide. Senpi is an AI Quant for Hyperliquid: it does the engineering in the pipeline, and you keep the decisions.
- Signal. Describe the idea in plain English, in the Senpi app or from Claude, ChatGPT, Grok, Muse or Hermes through Senpi Connectors. The agent asks one decision at a time (what to trade, how to score it, how to exit, how much to risk) and reads the whole rule back before it writes any code. Or start from an open-source template.
- Data. Senpi Signals ranks funding extremes, open-interest builds, what proven traders hold against the four-hour crowd and momentum events, each with its source, in the app and the free Chrome extension.
- Execution. The runtime places strategy entries maker-first by default, with a taker fallback, and Senpi adds 0% on paid plans (0.03% on the free trial).
- Risk. Strategies built from a rule or a template carry the Dynamic Stop-Loss (DSL) from the first fill, and each can have its own daily-loss halt, which lifts at the next UTC midnight, or a drawdown halt, which waits until you resume it. Copy strategies carry at most a flat stop.
- Review. Ask the agent to review a strategy’s closed trades: where fees, exits or sizing cost the most.
A strategy you confirm runs as code on Senpi’s runtime, and by default no AI model makes live trading decisions. Each strategy trades from its own strategy wallet; the agent holds no key and cannot withdraw. Senpi has no backtesting tool; some templates cite a historical replay in their notes. The full workflow is in how to use AI for quant trading strategies.
The quant trading pipeline
| Stage | What happens | In crypto, on Hyperliquid | Read more |
|---|---|---|---|
| 1. Data | Collect prices, volumes and whatever the idea needs, cleaned and time-stamped | A free info API: candles (the most recent 5,000 per market and interval), order books, hourly funding, open interest and any account’s positions and fills | Reading Hyperliquid market data |
| 2. Signal | Turn an idea into a precise rule for when to buy, sell or stay out | Momentum, mean reversion, breakouts, funding extremes, crowding | 17 crypto trading strategies |
| 3. Backtest | Run the rule on past data with fees, slippage and funding, then test it on data it never saw | Watch for look-ahead, delisted coins and short histories | How to backtest |
| 4. Execution | Turn signals into orders at the lowest cost | Maker 0.015% vs taker 0.045% at the base tier; post-only and TWAP orders | Slippage, order types |
| 5. Risk | Size positions, set stops and cap losses for the whole book | Leverage up to 40x and liquidation; stops trigger on the mark price | Max drawdown, trailing stops |
| 6. Review | Compare live results with the backtest and retire what stopped working | Sharpe, Sortino and Calmar on 365-day years | Sharpe ratio, Calmar ratio |
What a crypto quant can see on Hyperliquid, Nov 11, 2026
| Reading | Value |
|---|---|
| Perp markets listed on Hyperliquid’s main exchange | 179 |
| Open interest across them | $8.75B |
| Volume, last 24 hours | $4.54B |
| BTC funding, annualised | 5.9% |
| Markets with $10M+ open interest at the default funding rate (0.00125% an hour) | 28 of 43 |
Each of these is an input a stock quant doesn’t get in the same form. Funding moves money between longs and shorts every hour, mostly from the crowded side to the other; open interest shows whether money is flowing in or out; and because Hyperliquid is an on-chain exchange, any account’s positions, open orders and fills can be read from the same free API.
What changes in crypto
- Markets never close. There is no overnight gap and no closing auction, but weekends and quiet hours are thinner. Annualise with 365 days, not 252.
- Funding. Perps pay funding every hour on Hyperliquid, so carry strategies get paid to hold the side the crowd avoids (funding rates as a trading signal).
- Leverage and liquidation. Perps allow up to 40x on BTC, and a liquidation closes a position for you, so position size is part of the model.
- Public positions. Every Hyperliquid account is public, so where large and proven traders are positioned is data, not rumour.
- More than crypto. HIP-3 markets list stocks, indices and commodities as perps that trade around the clock (what is HIP-3).
- Costs. Fees, spreads and funding differ by venue, and a strategy’s edge has to clear them (Hyperliquid fees).
Common quant strategies in crypto
- Momentum and trend following: ride moves that have started (17 crypto trading strategies).
- Mean reversion: bet that stretched prices come back (mean reversion).
- Statistical arbitrage and pairs: trade the gap between related assets (stat arb and pairs trading).
- Funding carry and arbitrage: collect funding, hedged or with a stop (funding-rate arbitrage).
- Market making: quote both sides and earn the spread (market making on Hyperliquid).
- Positioning and flow: follow or fade what large accounts and the crowd hold, from public positions and open interest.
Tools quants use for crypto
Most crypto quants work in Python. For data and orders on Hyperliquid there is the official Python SDK, and CCXT covers Hyperliquid alongside many other exchanges. NautilusTrader backtests and trades live from one codebase, with a native Hyperliquid adapter. Freqtrade is a ready-made strategy runner that reaches Hyperliquid through CCXT, though its Hyperliquid backtests only get the last 5,000 candles. Hummingbot is built for market making. QuantConnect, the best-known research platform, doesn’t connect to Hyperliquid. An AI Quant such as Senpi replaces the code and the server: you describe the rule, and it runs.
Quant vs algorithmic vs discretionary trading
| Approach | Who decides | How orders are placed | Example |
|---|---|---|---|
| Discretionary | A trader, case by case | By hand, sometimes with tools | Reading a chart and buying a breakout |
| Algorithmic | Fixed rules in a program | By code | A TWAP order, or a program that buys a 20/50 EMA cross |
| Quantitative | A model built and tested on data | Usually by code | A pairs model that trades ETH/BTC when the ratio’s z-score passes 2 |
| AI Quant | Your rules, written and run by an AI agent | By code, on the agent’s runtime | Describing a funding fade in plain English and having Senpi run it |
More on the execution side in algorithmic trading in crypto.
What is quant trading in simple terms?
Trading by rules built from data. A quant writes down exactly when to buy, sell and stop, tests the rules on past prices with realistic costs, and lets code apply them, instead of deciding trade by trade.
Is quant trading profitable?
It can be, for strategies whose edge survives costs, and some quant funds have long records. Edges fade as others find them, and many strategies that look good in a backtest fail live because of overfitting, fees and slippage. The method doesn’t guarantee a profit.
What is crypto quant trading?
Quant trading applied to crypto markets, mostly perpetual futures. Besides price and volume, crypto quants use funding rates, open interest, liquidations and, on Hyperliquid, every account’s public positions and fills.
What is the difference between quant trading and algorithmic trading?
Quant trading is about deciding with models built on data; algorithmic trading is about placing orders with code. A quant strategy is usually executed by an algorithm, but an algorithm can also run simple rules that no one would call a model, such as splitting an order over an hour.
What is systematic trading?
Any approach that applies the same rules every time, whether the rules come from a statistical model or a simple chart pattern. Quant and algorithmic trading are both systematic.
Do you need to code to do quant trading?
Traditionally, yes, mostly in Python. An AI Quant changes that: on Senpi you describe the rule in plain English, approve it, and Senpi runs it as code with a stop from the first fill.
What is an AI Quant?
An AI agent that does the quant workflow with you: it turns an idea into explicit rules, runs them as code and reviews the results. On Senpi, by default no AI model makes live trading decisions; the model helps build and review the strategy, and code trades it.
This is educational material, not financial advice. The market readings are data, not trade signals, and leveraged perps can lose money quickly.
Turn an idea into a running strategy
Describe it in plain English or start from a template. Senpi runs it as code with a stop from the first fill.
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