Developer Self-Service & Internal Developer Platforms
Engineers who build trading infrastructure quickly realise that the models on the other side of their APIs are not simple. Active traders use a vocabulary of strategies and instruments that is specific, consistent, and worth understanding if you want to build systems that serve them well. This is a tour through five of the most widely used.
Futures markets exist for a wide range of commodities and financial instruments, and each market has a "term structure" — the relationship between contract prices at different delivery dates. When near-term prices exceed far-term prices, the market is said to be in backwardation in futures markets. This condition typically signals acute immediate demand or supply scarcity. In crude oil, for example, a market in backwardation tells refiners that today's barrel is worth more than a barrel delivered six months from now, which incentivises drawdown of inventories and discourages new storage. Commodity traders who hold long futures positions benefit structurally from backwardation because each contract roll happens at a lower price than the expiring one, generating a positive roll yield over time.
Options traders who expect a period of relative calm in a stock or index often reach for calendar spreads — selling a near-dated option while buying a further-dated one at the same strike. The trade exploits differential time decay: near-dated options lose their time value faster. If the underlying stays close to the strike through the near expiry, the short leg expires worthless and the trader retains the longer-dated option, which still has significant time value. Calendar spreads are a relative-value structure, similar in spirit to backwardation trades — in both cases, the trader is exploiting the different valuations placed on the same asset at different points in time.
Market-neutral equity strategies often centre on betting on the spread between two related stocks. Two companies in the same industry — same customers, similar cost structures, comparable regulatory exposure — should trade in a correlated fashion most of the time. When one diverges significantly from the other, a pairs trader goes long the cheaper one and short the more expensive one, expecting mean reversion. The position is designed to be immunised against broad market moves: a market selloff should hurt both legs roughly equally, leaving only the spread exposure. Like calendar spreads, pairs trading is fundamentally a bet on reversion to a historical relationship rather than on directional price movement.
When a security spends time oscillating between identifiable support and resistance levels, buying support and selling resistance becomes a systematic approach. Range traders wait for prices to reach the lower boundary of the range and buy, then wait for the upper boundary and sell. The strategy fails decisively when a breakout occurs — which means discipline about position sizing and stop losses is essential. Range trading requires accepting that a significant portion of trades will be stopped out by genuine breakouts, and that the expected return comes from winning more on the majority of range-bound trades than is lost on breakouts.
Technical indicators that combine price and volume often generate more reliable signals than price alone. On-balance volume (OBV) is among the simplest and most durable of these. It accumulates volume on up-price-days and subtracts it on down-days, creating a running total whose trend can be compared to the price trend. When OBV is rising but price is flat, it suggests that buying pressure is building beneath the surface — a potential setup for a breakout. When price hits a new high but OBV lags, the rally may be thinning out. In range trading, an OBV divergence near support levels is often the earliest available signal that the range is about to break rather than bounce.
These five approaches — backwardation, calendar spreads, pairs trading, range trading and OBV — are not exotic or obscure. They appear across Bloomberg terminals, quantitative hedge funds and retail platforms alike. For engineers building trading infrastructure, recognising these patterns in user behaviour and order flow is the first step toward building systems that serve professional traders accurately and efficiently.