Terms like “quant trading,” “systematic trading,” “market-making,” and “high-frequency trading” get thrown around loosely. This article breaks down the two core strategies that proprietary trading firms run, explains how each one makes money, and helps you figure out which side of the business might suit you.
First, some context
Proprietary trading firms trade their own capital. No client money, no management fees. If the strategies work, the firm profits. If they don’t, the firm absorbs the loss. At VivCourt, we run a multi-strategy approach across equities, ETFs, options, fixed income, FX, cryptocurrency, and energy. Our strategies fall into two main categories: systematic (quant) trading and market-making.
Let’s start with the one people tend to find more intuitive.
What is systematic trading?
Systematic trading, also known as quantitative trading or quant trading, utilises data and statistical models to identify repeatable patterns in financial markets. It then builds strategies, with varying degrees of automation, to trade on these patterns.
“Systematic” means the strategy follows a defined, testable set of rules rather than a trader’s gut feeling. “Quantitative” points to the statistical modelling that powers those rules. At a firm like ours, all of our strategies are quantitative in nature, but the industry typically uses “quant trading” or “systematic trading” to describe this particular category of predictive, model-driven strategy.
How does systematic trading make money? The goal is to generate alpha: returns from a genuine predictive edge, not just the market going up. Researchers analyse market data (prices, volumes, order book dynamics), looking for statistical relationships that predict future price movements. Those signals get combined into a strategy with entry and exit rules, position sizing, and risk limits, then backtested against historical data. Once deployed, the strategy trades according to its criteria while humans monitor, refine, and improve it over time.
Holding periods are typically minutes to days, which is what the industry refers to as mid-frequency trading. The edge is statistical, not guaranteed on any single trade. A strategy might be right only slightly more often than it’s wrong, but across thousands of trades with disciplined risk management, a small edge compounds into consistent returns.
An illustrative example: Consider a hypothetical pairs strategy in Australian equities. The model identifies two mining companies whose share prices historically move together. When prices diverge beyond what the model considers normal, the strategy buys the relatively cheaper stock and short-sells the more expensive one, expecting the relationship to revert. The model doesn’t know why the divergence happened. What it knows is that historically, similar divergences have tended to close within a certain time window, and the expected profit outweighs the expected risk. No single trade is the point – the edge plays out across hundreds of similar situations.
What’s required: You could build a basic systematic strategy on a laptop. But the gap between a backtest and a strategy that generates consistent returns in live markets is enormous. It requires clean, high-resolution data; execution infrastructure that minimises slippage and market impact; real-time risk monitoring with kill switches; significant capital to trade at meaningful scale; and dedicated research teams testing many hypotheses to find the few that survive. Each of these is hard enough on its own – together, they form a barrier that’s difficult to replicate outside an institutional setup.
What is market-making?
Where a systematic trader tries to predict where prices will go, a market-maker’s job is to be available for other people to trade with, regardless of direction. For a given product, a market-maker continuously quotes two prices: a bid (the price they’ll buy at) and an ask (the price they’ll sell at). The ask is always higher than the bid. The gap between them is the spread.
Market-making is a significant part of today’s financial infrastructure, with HFT as a whole estimated to represent around half of US equity trading volume.
How does market-making make money? Buy at the bid, sell at the ask, pocket the spread. A market-maker quoting $99.95 / $100.05 earns $0.10 per completed round trip.
Per-trade margins are razor-thin: research on European exchanges found gross profits of less than €1 per trade, with much of the spread revenue offset by the cost of holding inventory between trades (Menkveld, 2013). The hard part isn’t earning the spread – it’s keeping it.
A key challenge – inventory risk: To earn the spread, a market-maker has to temporarily hold what they’ve just bought or sold, and that inventory is exposed to price movement. Buy at $99.95, and if the stock drops to $99.00 before you can sell, you’ve lost $0.95 on a trade. This is the fundamental tension. Market-makers profit from short-term price fluctuations, but because they’re continuously trading on both sides, any strong directional trend leaves them exposed on one side of their book faster than the spread can compensate. Almost everything about how a market-making operation is built exists to manage this problem.
Managing inventory risk involves several layers:
- Speed. The faster you trade, the less time you’re exposed. This is why market-making is closely associated with high-frequency trading (HFT) – though HFT is a broader speed category that includes other strategies too. Market-making at firms like ours uses HFT technology (specialised hardware, co-located servers, finely optimised software), but the two terms aren’t interchangeable.
- Hedging. If a market-maker accumulates more of a position than they’d like, they offset risk using correlated instruments – shorting an ETF, selling futures, or trading options.
- Dynamic pricing. Quotes aren’t static. In volatile markets, spreads widen. When inventory builds on one side, quotes shift to encourage offsetting flow.
- Fair-value estimation. The most important decision: “What is this thing actually worth right now?”. Bids and asks are set relative to a real-time model processing recent trades, order book dynamics, and price movements in related instruments. If the estimate is consistently wrong, no amount of spread will save you. Machine learning plays a role here too, and it’s an area we expect to keep developing as the capabilities improve.
An illustrative example: To give a simplified picture: imagine a firm making a market in an ASX-listed ETF tracking the S&P/ASX 200. Its systems constantly recalculate the ETF’s fair value based on underlying stock prices, adjusted for currency and dividends. If BHP drops, quotes update within microseconds. A fund manager sells a large block, hitting the bid. The firm now owns more than it wants, so the system lowers the bid, begins hedging via index futures, and sells inventory as other buyers arrive. The profit on this sequence might be fractions of a cent per unit – but across thousands of daily transactions and many products, it compounds.
What’s required: Market-making needs everything systematic trading needs – plus extreme speed and reliability. Specialised hardware (including FPGAs), exchange co-location, and heavily optimised software are all necessary – alongside formal exchange relationships.
Which role might suit you?
Systematic trading tends to attract people who enjoy working with data over longer time horizons: exploring hypotheses, building models, and seeing whether ideas survive contact with real markets. Strong foundations in statistics, maths, or quantitative science are common, and comfort with Python helps.
Market-making tends to attract people energised by speed, competition, and real-time decision-making. Numerical fluency and a natural instinct for thinking in probabilities matter. The feedback is immediate.
Whether it’s systematic trading or market-making, at VivCourt, many of our trading roles combine elements of both research and execution. And while the strategies differ, the underlying craft is the same: using data and technology to make better decisions than the next person. What matters is quantitative thinking, curiosity, a willingness to be wrong, and the drive to figure things out.
REFERENCES
- Breckenfelder, J. (2019). Competition among high-frequency traders and market liquidity. ECB Working Paper Series.
- Chakraborty, T. & Kearns, M. (2011). Market making and mean reversion. Proceedings of the 12th ACM Conference on Electronic Commerce.
- Hagströmer, B. & Nordén, L. (2013). The diversity of high-frequency traders. Journal of Financial Markets, 16(4), 741–770.
- Menkveld, A. J. (2013). High frequency trading and the new market makers. Journal of Financial Markets, 16(4), 712–740.
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