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High-Performance Futures Algorithms

Current industry algorithms are stale productivity tools with opaque heuristics and little focus on order placement and the unique aspects of futures market structure. At BestEx Research, our sophisticated order placement strategy shaves off every excess basis point of slippage. And our unique broker-neutral model allows you to use our high-performance algorithms while you trade with the broker or FCM you choose.

What does “high-performance” really mean?

We optimize every aspect of execution for each instrument’s market structure—trade planning, short-term price prediction, order placement, and order routing. Each child order our algorithms place or cancel—size, price, and venue—is based on a rigorous quantitative framework, backtested and re-optimized over thousands of simulations.

BestEx Research Global Multi-asset Coverage

We provide global, multi-asset coverage. Click below for the contracts we’re currently trading.

Take a deep dive into our algorithm design


Average Spread Costs

Most algorithms use simple heuristics to decide when to place orders and at what price, leading to high spread costs, especially in less liquid contracts.

The illustration above shows average spread for a variety of futures contracts.


BestEx Research Spread Savings

The heart of our algorithms is our multi-step fill probability model that prices each limit order as an option based on historical and real-time liquidity characteristics of the contract you’re trading, yielding higher spread savings. And it doesn’t matter whether you’re using a VWAP strategy or any other algorithm we offer.

Our simulated performance versus interval VWAP for a variety of futures contracts, with orders less than 1% of average daily volume. Performance vs. interval VWAP measures spread costs because an order’s market impact is incorporated into the VWAP price.

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Clock-Based Order Placement

Most algorithms have a serious design flaw—timing actions by the clock when the market’s memory is in volume. A trade can take a few minutes in an illiquid contract or less than one second in a highly liquid contract. A heuristic that cancels and replaces limit orders every 10-15 seconds will create extreme information leakage for less liquid contracts, while achieving very few passive fills.

A simplified limit order book evolving over 25 seconds. In each 5-second interval, the right side represents the back of the queue and the left side is the front. Here, 10 seconds was not enough time for a limit order in this contract to move to the front of the queue and execute. Similarly, 10 seconds was not enough time for an order placed at midpoint to be executed.


BestEx Research Cumulated Passive Fill Rate

Our algorithms make every decision—including cancel and replacement of limit orders or pausing after a fill—in contract-specific volume time, reducing information leakage and increasing passive fill rates.

This illustration shows simulated passive fill rates when BestEx Research trades a variety of futures contracts, with order sizes less than 1% of each contract’s average daily volume.

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Futures contracts Volume Is Noisy

Trading futures algorithmically requires high-quality volume, spread, and depth estimates, but these analytics are changing constantly for a given contract based on its position on the roll calendar. Poor estimates based only on the last several days of data are biased and deteriorate performance.

The chart above illustrates daily contract volumes for CL over a range of expiries. While the front contract has the highest volume, we can see volume in the next contract increasing as expirations approach. While volume appears to be noisy here, we can see that behaviors are remarkably similar over time.


We use long-range historical roll characteristics for each contract to reliably predict the volume, spread, and depth in your contracts each day and reduce the impact of your trading.

Here, we show our methodology for estimating roll period volume in each contract. Using long-range historical behaviors during the roll, we can estimate the speed of each contract’s roll and predict daily volume more reliably. CL contracts, above, appear to increase in volume quickly after the roll period begins, while other contracts may transition more slowly.

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Queue length varies based on tick size, price, volatility, and volume

Futures exchanges often have long queues waiting to trade highly liquid contracts. Waiting only 30 seconds or a few trades before canceling and replacing a limit order would never allow for passive fills in many contracts. Most brokers ignore this challenge and simply pay the spread when their standard wait times expire.

Time between trades for five example contracts. Eurodollar is a common example of a long-queue contract, for which traders must wait several times longer than is typical to execute limit orders. Limit orders placed at the bid for the E-mini or crude oil are likely to execute more quickly.


Order Books

Our algorithms naturally accommodate long queues, deviating from the schedule as needed and layering orders appropriately in the book to increase passive fills and reduce spread costs.

BestEx Research algorithms place limit orders (shown in green) according to contract-specific queue lengths, layering orders to optimize passive fills. Above, we show example order books for FIFO and pro-rata trading—with two very different behaviors. For FIFO we place multiple orders at the same price, but for pro-rata we place larger orders to get a larger share of executions.

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Most Aggressive Logic Ignores Changing Spreads

Most algorithms use aggressive logic only to catch up on a trade plan without accounting for varying depth and spread, resulting in higher costs for taking liquidity.

This image illustrates spreads widening and narrowing throughout the trading day. Overly simplistic aggressive logic executes market orders instantly as needed to get back on schedule, risking execution when spreads are at their widest and yielding high spread costs.


BestEx Research Takes When Spreads Are Narrow

Our algorithms monitor every quote change and utilize forecasts of spread, depth, and short-term alpha to take liquidity opportunistically, significantly minimizing your spread costs and temporary market impact.

BestEx Research forecasts spreads and depth to decide whether waiting to take will reduce spread costs. If spreads are likely to tighten, we’ll wait to take liquidity when the time is right, creating substantial savings.

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Adverse Selection for a Buy Order

While passive orders earn spread, they also tend to fill when prices are moving in your favor. Improved prices after your fills are referred to as “adverse selection” and the cost of adverse selection can be much higher than a half spread.

The price of this currency pair is moving down over time. This example shows adverse selection costs exceeding spread costs; prices are declining rapidly during your buy order, by more than the size of the quoted half spread.


Order Book for CL Front Contract

Our algorithms do what market makers do, canceling limit orders when our proprietary short-term alpha model points to a high probability that the price will move in your favor.

This illustration shows limit orders to buy and sell in the limit order book at an ECN. Our short-term alpha model detects the thinning liquidity at the BBO and below, and our algorithms will cancel those orders and replace with new limit orders deeper in the order book.

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