Impact of Automated Orders in Futures Markets 

A Report by Staff of the Market Intelligence Branch 

Division of Market Oversight 

U.S. Commodity Futures Trading Commission 

March 2019 

DISCLAIMER 

This is a report by staff of the U.S. Commodity Futures Trading Commission. Any views expressed in this report are 

solely the views of staff, and do not necessarily represent the position or views of any Commissioner or the 

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Table of Contents 

Executive Summary....................................................................................................................................... 4 

Automated and Manual Order Entry............................................................................................................5 

Level of Automation in Futures and Options Markets.................................................................................. 6 

Resting Time.................................................................................................................................................. 8 

Transaction Size ............................................................................................................................................ 9 

Types of Orders...........................................................................................................................................10 

Price Moves and Historical Volatility ..........................................................................................................11 

Price Volatility and Transactional Volume..................................................................................................14 

Conclusions and Takeaways........................................................................................................................15 

Staff Contributors .......................................................................................................................................16 

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List of Exhibits 

Exhibit 1: Commodity Groups and Corresponding Commodity Contracts ................................................... 6 

Exhibit 2: Share of Automated Futures and Options Transactions............................................................... 7 

Exhibit 3: Median Resting Time of Limit Orders ........................................................................................... 8 

Exhibit 4: Average Number of Contracts per Transaction ............................................................................ 9 

Exhibit 5: Futures Order Type Breakdown..................................................................................................10 

Exhibit 6: Average Number of Daily Price Moves and Price Volatility ........................................................11 

Exhibit 7: Correlation between Historical Volatility and Price moves.......................................................12 

Exhibit 8: Correlation between Historical Volatility and Share of Automated Orders...............................13 

Exhibit 9: Total Futures Volume, Number of Transactions, and Price Volatility.........................................14 

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Executive Summary 

The staff of the Market Intelligence Branch in the Division of Market Oversight (“DMO”)

conducted research on the entering of orders manually and automatically in commodity futures 

markets in the United States to determine how technological change is affecting futures 

trading. DMO staff used internal CFTC transactional data for thirty futures contracts during the 

period January 2013 – December 2018, and examined what effects, if any, the manual and 

automated order placement mechanisms had on these markets. 

The research produced the following findings: 

1. The percentage of automatically placed orders has increased for all commodity futures 

markets; 

2. Automated orders have smaller number of contracts per transaction than manual 

orders and their resting times are shorter than the resting times of orders placed 

manually; 

3. Automated orders are almost always limit orders; and 

4. Although the level of automation increased steadily each year, historical volatility of 

end-of-day prices did not exhibit the same trend.1 

1 End-of-day volatility is defined, in this report, as the statistical volatility calculated as a standard deviation of the 

natural logarithm of the end-of-day settlement price returns over a period of one year. 

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Automated and Manual Order Entry 

Automated and manual order entry refers to how an order is entered on the order entry 

message. Automated order entry refers to orders that are generated and/or routed without 

human intervention. This includes any order generated by a computer system as well as orders 

that are routed using functionality that manages order submission through automated means 

(i.e. an execution algorithm). Manual order entry refers to orders that are submitted to CME 

Globex by an individual directly entering the order into a front-end system, typically via 

keyboard, mouse, or touch screen, and which is routed in its entirety to the matching engine at 

the time of submission. 

Type of order entry is a self-identified tag, which market participants submit themselves. This 

tag is required only by the Chicago Mercantile Exchange (CME), therefore, DMO staff analysis is 

limited to CME contract markets. 2 

2 CME Market Regulation Advisory Notice, “Manual/Automated Trading Indicator (FIX Tag 1028),” Rule 536.B., 

September 2012, https://www.cmegroup.com/rulebook/files/cme-group-Rule-536-B-Tag1028.pdf. 

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Level of Automation in Futures and Options Markets 

DMO staff began the analysis by reviewing daily transactions in 30 futures contract markets. 

Staff classified the markets into eight commodity groups including: Currencies, Equities, 

Financials, Energies, Metals, Grain, Oilseeds, and Livestock. 

EXHIBIT 1: COMMODITY GROUPS AND CORRESPONDING COMMODITY CONTRACTS 

Currencies Metals 

Brazilian Real Futures COMEX Copper Futures 

British Pound Futures COMEX Gold Futures 

Euro FX Futures COMEX Silver Futures 

Mexican Peso Futures NYMEX Palladium Futures 

NYMEX Platinum Futures 

Equities 

E-mini NASDAQ 100 Futures Grains 

E-mini S&P 500 Futures Corn Futures 

NIKKEI 225 ($) Stock Futures KC Wheat Futures 

Rough Rice Futures 

Financials Wheat Futures 

10-YR Note Futures 

30-YR Bond Futures Oilseeds 

Eurodollar Futures Soybean Futures 

Federal Fund Futures Soybean Meal Futures 

Soybean Oil Futures 

Energies 

Natural Gas Henry Hub Futures Livestock 

NYMEX Crude Oil Futures Feeder Cattle Futures 

NYMEX Heating Oil Futures Lean Hog Futures 

NYMEX NY Harbor Gas (RBOB) Futures Live Cattle Futures 

Exhibit 1 is a list of futures contracts that DMO staff assigned to the eight commodity groups. 

Staff included the most actively traded futures contracts within each commodity group. 

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Within each DMO-assigned commodity group, staff calculated the total number of transactions 

that originated from either automatic (ATS) or manual outright and spread orders entered on 

CME Globex. Then, staff aggregated the individual markets’ total number of consummated

transactions for every year. 

EXHIBIT 2: SHARE OF AUTOMATED FUTURES AND OPTIONS TRANSACTIONS 

Exhibit 2 shows the share of ATS orders entered in futures markets. Overall, across all the 

commodity groups, the share of ATS orders3 increased from 2013 to 2018. On average, the share 

of ATS orders in Currencies, Equities, and Financials increased 7%. The average percentage 

increase was 19% for Energy, Metals, Gains, Oilseeds, and Livestock. 

Throughout the study period, the share of ATS orders was generally higher for financial products 

(i.e. Currencies, Equities, and Financials) than for physical commodities. After conducting 

interviews with market participants who trade futures and underlying cash products, DMO staff 

determined that a possible explanation for the higher level of automation in the financial 

products is the large transactional volume and low basis risk between the futures contracts and 

the underlying cash markets. Furthermore, the lower share of automation in the physical 

commodities may be attributed to the usually higher basis risk associated with delivery 

3 The analysis was based on the number of transactions regardless of the number of underlying contracts in each 

transaction. 

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specifications in the cash markets and, in some cases, slight differences in the futures contracts 

to the actual cash market. 

Resting Time 

DMO staff reviewed the time period during which limit orders were exposed to the market 

before being filled. The time between when an order is entered and the time when it is 

consummated is known as order resting time. DMO staff considers resting time to be a measure 

of the speed of trading. 

EXHIBIT 3: MEDIAN RESTING TIME OF LIMIT ORDERS 

Exhibit 3 depicts median resting times for limit orders over the period from 2013 to 2018. The 

red lines show the ATS orders and the blue lines show the manual orders. DMO staff calculated 

the median resting time within each commodity group by using the individual contract markets’ 

resting times, ordering them in value, and then finding the median for the entire group. In the 

groups with the white background, for all or most of the contract markets included in those 

groups, the exchange uses a first-in, first-out (FIFO) algorithm to match buy and sell orders; 

whereas for some of the contracts in the groups shaded in yellow, the matching algorithm 

prioritizes using order size. 

The graph above shows that manual orders were exposed to the market for a slightly longer 

time than ATS orders. Based on interviews conducted with market participants, DMO staff 

determined that one contributing factor for these longer resting times may be that, in general, 

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manual limit orders tend to be placed away from the market. The graph also shows that some 

commodity groups had shorter ATS order resting times than others. Based on the 

aforementioned interviews, DMO staff discovered that one explanation for the shorter resting 

times may be the significant high frequency trading activity in these commodity groups. 

Transaction Size 

DMO staff examined the average number of contracts per transaction during the period from 

2013 to 2018. To calculate the average number of contracts within each commodity group, staff 

divided the total number of contracts by the total number of transactions and trading days for 

every year. 

EXHIBIT 4: AVERAGE NUMBER OF CONTRACTS PER TRANSACTION 

Exhibit 4 depicts, on average, the number of contracts that were consummated in every ATS 

order (in red) and manual order (in blue). Across all commodity groups, contract sizes per 

transaction for ATS orders were slightly smaller than for manual orders. Both groups had an 

average transaction size between 1 and 2 contracts. However, contract sizes per transaction in 

the Equities and Financials groups tended to be larger. After examining the market participants 

listed in the CFTC trade capture report database, DMO staff determined that there were 

considerable numbers of big institutional traders in the Equities and Financials groups who 

generally consummated more contracts per transaction. 

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Types of Orders 

DMO staff examined the order type composition of automatically and manually entered orders. 

Staff categorized the order types simply based on whether they were limit, market, or stop 

orders. Limit orders define the maximum purchase price for buying and the minimum sale price 

for selling an instrument. Market orders get executed immediately at the current market price. 

Stop-loss orders do not immediately go on the book – they must be "triggered" at the price level 

submitted with the order. 

EXHIBIT 5: FUTURES ORDER TYPE BREAKDOWN 

Exhibit 5 breaks down the order composition for ATS orders (top panel) and manual orders 

(bottom panel). The different order types are marked as follows: limit in grey, market in purple, 

and stop in orange. Staff calculated the order type percentage breakdowns in each commodity 

group based on the total traded volumes of the individual contract markets within the group. As 

the graph shows, ATS orders were almost exclusively limit orders. Manual orders were stop-loss 

orders 4% and market orders 11% of the time. 

Based on interviews that DMO staff conducted with market participants who enter orders both 

manually or automatically, staff identified that a main reason for this difference is the ability of 

automated traders to replicate the functionality of stop-loss and market orders by relying on 

their speed in reading prices and placing limit orders instead. The implication of this finding is 

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that market events, in terms of excessive price movements, cannot be explained solely by 

investigating stop-loss orders that were entered during the event. To reflect this, the CME’s 

velocity halt logic includes both stop-loss and limit orders.4 

Price Moves and Historical Volatility 

DMO staff quantified the overall movement of commodity prices in two ways. First, staff 

counted the average number of daily price moves (up or down movements), in all contract 

markets within each commodity group. Second, staff calculated the standard deviation of a 252-

day window of one-day, natural logarithm price returns. The aforementioned price returns were 

derived from the end-of-day settlement prices and were normalized to an annual volatility 

measure. Staff first calculated this historical price volatility for the individual contract markets. 

Then, staff averaged these numbers within each commodity group to arrive to a common 

volatility representation for every year. This depiction of volatility is considered to be driven by 

market fundamentals because it involves the change in prices over long periods of time, in this 

case over years. 

Intra-day volatility, using pricing data within each trading date, from open to close, was not 

analyzed in this study. 

EXHIBIT 6: AVERAGE NUMBER OF DAILY PRICE MOVES AND PRICE VOLATILITY 

4 CME GLOBEX Reference Guide, March 2019, https://www.cmegroup.com/globexreferenceguide. 

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Exhibit 6 depicts the average number of daily price moves in the top panel, and the historical 

price volatility in the bottom panel of the graph. Based on this yearly depiction of the two price 

measurements, DMO staff concluded that for most of the commodity groups, when historical 

end-of-day volatility increased or decreased so did the number of daily price moves. 

To further investigate the relationship between the two price measurements, DMO staff 

performed a correlation analysis, depicted in Exhibit 7 below. Staff showed the degree and 

pattern of the relationships between the paired variables as a scatterplot. The numbers within 

the individual blocks represent the correlation coefficients. Most of the coefficients are above 

0.5, meaning that there is moderate to high positive correlation between the two price 

measurements. This observation suggests that, in general, the fundamentals-driven historical 

volatility is not disconnected from trading activity that drives the number of up or down price 

ticks each day. 

EXHIBIT 7: CORRELATION BETWEEN HISTORICAL VOLATILITY AND PRIC E MOVES 

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EXHIBIT 8: CORRELATION BETWEEN HISTORICAL VOLATILITY AND SHARE OF 

AUTOMATED ORDERS 

Exhibit 8 above shows a correlation analysis between historical end-of-day price volatility and 

share of automated orders. The numbers within the individual blocks represent the correlation 

coefficients. The majority of the correlation coefficients between these two variables are either 

around 0.1, which implies no relationship, or negative numbers, which implies a negative linear 

relationship. 

As discussed at the beginning of this report, the level of automated trading in futures markets 

has been increasing steadily over the period from 2013 to 2018. The aforementioned price 

analysis shows that historical end-of-day price volatility has not been equally increasing yearover-year. However, this does not imply that automated trading has not affected short term 

market events or intra-day price volatility which was not part of this study. 

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Price Volatility and Transactional Volume 

DMO staff also examined the volume traded, total number of transactions, and historical price 

volatility over the study period. 

EXHIBIT 9: TOTAL FUTURES VOLUME, NUMBER OF TRANSACTIONS, AND PRICE 

VOLATILITY 

Exhibit 9 superimposes the total volume traded (in blue), the total number of transactions (in 

red), and the historical price volatility (in black) for every commodity group. Based on this 

analysis, the graph shows that generally as historical volatility goes up, so does the trading 

volume and number of transactions. For example, the notable decrease in historical volatility 

from 2015 to 2016 and its subsequent increase in 2017, in the Equities commodity group, are 

similarly reflected in the changes in volumes for the same years. 

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Conclusions and Takeaways 

This research examined the effects that manually and automatically entered orders had on 

futures markets over a period of six years. DMO staff observed that automation has increased 

consistently over the study period. Furthermore, automatically submitted orders had smaller 

number of contracts per transaction and were exposed to the market for shorter periods of time 

compared to manually entered orders. DMO staff also observed that historical end-of-day price 

volatility was positively correlated with the average number of daily price changes. Lastly, 

although DMO staff did not analyze intra-day price volatility movements, staff did not find a 

systematic rise in end-of-day historical price volatility as the share of automation increased 

across all futures markets. 

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Staff Contributors 

Elitza Voeva-Kolev, Market Analyst, Market Intelligence Branch, DMO 

Rahul Varma, Associate Director, Market Intelligence Branch, DMO 

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