London Futures Trader Spoofing Case: Orders Built to Move Prices and Avoid Trades

A London futures trader agreed to a $25,743,174 civil penalty and to return $12,871,587 in trading gains in a CFTC spoofing case over orders designed to move prices while avoiding execution. He admitted the allegations and the court’s findings: large orders created false supply or demand, other participants reacted, and genuine trades benefited from the distorted prices. For a programmer, trader or surveillance employee, the revealing detail is what the software was asked to do when those large orders came close to becoming real trades.

What the Civil Order Established

The Commodity Futures Trading Commission brought the trading-fraud enforcement action, resolved against the trader by the November 14, 2016 consent order. He was the owner, sole employee and trader of Nav Sarao Futures Limited PLC, operating from London. The order describes trading through the company and, later, through a personal account.

The business concentrated on the Chicago Mercantile Exchange’s E-mini S&P 500 futures contract. The trading pattern was fast and repetitive: establish a position, trade out of it, repeat, and normally finish the day holding no position. Both automated programs and manually entered orders supported the scheme.

The CFTC’s account of the complaint alleged that the layering algorithm ran on over 400 trading days between April 2010 and April 2015. The final order found artificial prices on multiple days. The Flash Crash was part of a repeated trading practice.

How Dynamic Layering Moved the Market

Dynamic layering kept large sell orders visible while moving them away from execution as prices changed. The trader’s program routinely placed four to six large orders at successive price levels, generally beginning at least three or four ticks from the best asking price. When the market moved, the orders moved together, preserving that distance before eventually disappearing.

The E-mini contract tracks the S&P 500 index and trades electronically on CME’s Globex system. The visible book combined individual orders into a total quantity at each price, ten ticks deep on each side. Participants could see the apparent buying and selling interest without ordinarily identifying the individual orders behind it.

That display helped other traders decide what to do. A heavy sell side could suggest an approaching price drop. Automated trading systems also read those imbalances. The layered orders supplied a false signal to both.

The trader used programmers to modify a commercial trading platform. His requests included cancellation as an order approached execution, multiple orders at different prices from one click, and immediate removal of the remaining quantity after a small fill. We see the absurdity in the design: a trading program whose requested improvements included keeping its own orders from trading. He also wanted different order sizes to make the pattern harder to recognize. The order quotes his explanation to a programmer:

The Genuine Trades Collected the Benefit

The genuine trades converted the false signal into money. The civil order found that the trader bought at artificially depressed prices or sold at artificially inflated ones. He could also use those prices to close a position he already held or reduce a potential loss.

The CFTC’s original complaint alleged a cycle that exploited both the downward pressure and the rebound after the layers disappeared:

The final order found that genuine trades were more profitable per contract when dynamic layering was active than when no spoof orders were active. The benefit showed up in the trades that filled, while the large displayed orders kept moving out of reach.

Back-of-Queue Orders Stayed Visible and Avoided the Front

The back-of-queue program exploited the matching rules at a single price. CME orders at the same price waited in the sequence received. Increasing an order’s quantity sent it to the back of that queue, as though it were a new order.

When another participant entered an order of a specified size at the same price, the program increased the trader’s spoof order by one contract. That amendment moved it behind the newcomer. The apparent buying or selling interest remained on the screen, with a reduced chance of becoming an executed trade.

These orders could sit at the best bid or ask. Dynamic layering kept its distance through price changes. The queue program kept giving up its place through quantity changes. Both let the trader influence what others saw while trying to avoid the trade his own order offered.

For someone who works with order messages, that is a concrete distinction. The price can stay the same while a quantity amendment changes the order’s position in line. Reading the amendments alongside the surrounding orders reveals what a final cancellation entry leaves out.

Manual Spoofing Reinforced the Programs

The trader also used large manual orders to support genuine trades on the opposite side. The order describes a 2,000-lot order placed at the best bid or offer, with a genuine trade executed while that order was active or within a second of its cancellation. If the displayed order moved the market away, its chance of filling fell. If it did not, the trader nearly always canceled it before execution.

A separate manual tactic placed resting spoof orders one or two price levels away from the best bid or offer. These could reinforce the automated layers, then disappear around the time the genuine trades filled.

On May 5, 2010, the trader ran dynamic layering while adding sell orders in sizes of 188 and 289 lots. Almost all of those resting orders disappeared without a fill. The sequence combined software-generated pressure with manual additions to the same side of the book.

The Panther Energy Trading consent order describes a related alleged pattern: large orders intended for cancellation helping smaller genuine orders execute on the opposite side. Here, the admitted findings show several ways to maintain that false interest, including coordinated price changes, deliberate queue movement and manual orders. The common question is which genuine trade the displayed orders served.

Layering and the Flash Crash Findings

The layering and Flash Crash findings connect the trader’s activity to an extreme imbalance in the E-mini order book on May 6, 2010. The order then describes a loss of liquidity that, together with other market events, contributed to the price crash.

The dynamic layering program ran for more than four hours and 25 minutes that day. Its orders moved repeatedly, with very little execution:

More than 81,000

modifications to the dynamic layering orders on the Flash Crash day.

Only 81 lots resulted in executed trades.

Federal court consent order, November 14, 2016

During an extended run, the order put the apparent downward pressure at approximately $170 million to over $200 million. The layers represented 20% to 29% of the entire sell side of the order book. By the time those layers were canceled, their quantity was almost equal to the entire buy side.

Manual resting orders added to that pressure. The court found that the trader’s conduct contributed to the extreme imbalance. It cited the CFTC and Securities and Exchange Commission’s preliminary findings connecting the imbalance to lost liquidity, and that liquidity loss, alongside other events, to the crash.

The court also found that the trader created artificial prices at multiple times that day and on other days. The case therefore reaches both the extraordinary disruption and the repeated market manipulation that preceded and followed it.

What the Criminal Plea Added

In his guilty plea, the trader admitted using an automated program and other techniques to create false supply and demand, induce other participants to trade, and profit through genuine orders placed close in time. The Justice Department’s November 9, 2016 announcement reports his plea to wire fraud and spoofing and his admission of at least $12.8 million in illicit gains.

The FBI’s Chicago Division investigated, and the Justice Department’s Fraud Section prosecuted the criminal case. The CFTC referred the matter and provided substantial assistance.

What Evidence Is Required to Prove Fraud?

In this spoofing case, the evidence connected software instructions, order changes and genuine fills to the trader’s purpose. The requested cancellation features explained why the layers retreated. Quantity amendments explained the queue movement. The genuine trades and positions showed where the benefit landed.

The 2016 Futures and Derivatives Law Report article The Exercise of Anti-Spoofing Authority in U.S. Futures Markets: Policy and Compliance Consequences explains why this work requires market expertise. Investigators face large amounts of data covering submissions, modifications, cancellations and executions. The paper also explains that public trading data can reveal suspicious activity while leaving the trading firms behind it unidentified.

That gap matters to the person who knows the system. A programmer may understand the purpose of a requested feature. A trader may know the position the orders were meant to help close. An operations employee may connect a displayed order with its amendments and fills. We look at those connections with you because each explains a part of the sequence that the public book alone cannot.

A 2024 working paper from a World Federation of Exchanges research project, An Analysis of Market Manipulation Definitions Around the World, describes surveillance systems flagging unusual patterns for specialist analysts to investigate. It also identifies reports from intermediaries, the public and whistleblowers as sources of detection. We bring the trading sequence and the insider’s explanation into the same examination: what the screen showed, what the software was told to do, and which trades benefited.

Whistleblower Rewards for Trading Information

The CFTC whistleblower program provides an aggregate award of 10% to 30% of qualifying monetary sanctions collected, shared among qualifying whistleblowers in an action. Our whistleblower experts build the CFTC case around what you know about the trades and the system behind them.

A whistleblower case under the False Claims Act can pay 15% to 30% of what the government recovers. That is a separate program from the CFTC’s. Our work with commodities fraud whistleblower lawyers starts with the trading conduct you can explain.

You Know Why the Orders Moved

If you worked on the code, traded beside the strategy or reviewed the order flow, speak to one of our Whistleblower Experts about what the system was built to do. We bring in commodities fraud whistleblower lawyers who understand trading systems and spoofing evidence, and stay beside you to build your strongest spoofing whistleblower case.