3 use cases for futures market data

In this article, we'll briefly cover the basics of futures before diving into three common trading strategies, the types of data needed to analyze and execute each effectively, and how to access futures data with Databento.

A futures contract is an obligation to either buy or sell an underlying asset at a predetermined price and date. There are many types of futures, but some of the most common categories are commodity, currency, and index futures. Futures trading is often motivated by two key factors: hedging and leverage.

Hedging is a mechanism used to mitigate the risk of an adverse price movement by purchasing an asset to potentially reduce or control the loss of another asset. For example, if you're a corn farmer, you can sell a corn future to lock in the current market price and protect against a potential loss due to declining corn prices.

Taking leverage enables a trader to control a substantial contract value with a relatively small amount of capital. Margin refers to the amount of capital a trader must deposit with a broker to open a leveraged position. It acts as collateral or security for the broker if the position goes against the trader. For example, if you have a 10% margin, you can control $1,000 worth of oil by only committing $100 of capital.

There are two types of futures margins: initial and maintenance.

Initial margin is the amount of money required to open a futures position and is usually a small percentage of the notional value of a contract.

Maintenance margin is the amount required to maintain the position. If the amount in your account drops below the maintenance margin, you will receive a margin call requesting that you add more funds to bring the balance back up to the initial margin.

Although this can be used as leverage to increase returns, it can also be used against you to worsen a loss.

Here are a few common futures trading strategies and the types of market data needed to analyze each one.

Arbitrage strategies aim to profit from price discrepancies between related assets and futures. This involves analyzing real-time price data that should include information like bid-ask spreads, order book depth, and execution speeds to find the right arbitrage opportunities.

Pairs trading is a strategy used to compare the historical prices of two correlated assets to understand their price relationship. For example, we can look at the relationship between the E-mini S&P 500 Futures and an ETF tracking the same index, such as SPY. In this case, the relationship between these two assets is linear, described as y = ax + b. You can buy the ETF and sell the future, or vice versa, when there's a discrepancy between the quoted value of the ETF and the actual price on the order book, effectively hedging your position. This is also known as a delta-neutral strategy.

A calendar spread is a strategy that involves buying and selling two different futures with different expiration dates. To execute this strategy, you will need data for the near-future and far-future contracts to understand the price difference accurately.

Databento offers live and historical futures data through our CME dataset—available in multiple data schemas such as MBO, MBP-10, TBBO, statistics, and more—to support a variety of strategies for your use case. You can access this data through our Python, Rust, and C++ client libraries and futures data APIs. Check out our docs page for more examples of trading strategies using futures and special conventions for futures at Databento.