Leveraging Machine Learning for High-Frequency Trading of

Commodity Futures

Value Propositions• Automatic high-frequency (enter/exit positions every second) trading of commodity futures

using multiple strategies.• Pricing of futures contracts based on convergence arbitrage model, correlating

spot/maturity to futures.• Presentation of market data from multiple exchanges (exchange source based on contract)

in OMS user interface via candlestick charts, pie charts• Margin account computation on a per-contract and per-portfolio basis, based on the

marking to market of open contracts• Fully-functional order entry with FIX (Financial Information Exchange) protocol data

translation.• Line handler for exchange connectivity runs as a separate process to parse route orders

from the database to the exchange and parse executions.• Robust historical data module which maps online contract prices to training vectors for the

regression model

Differentiation from Existing High-Frequency Trading Systems• Multiple algorithms (logistic regression, Naïve Bayes) for the training of the pricing

function from historical data. Both linear and quadratic degree for logistic regression• Large volume of data (ten years of pricing information) to both train and test both

algorithms against• Measurement of backtesting performance against Goldman Sachs Commodities Index• Margin computation to determine when margin calls will occur and portfolio P&L,

should position be closed now• Multiple automated strategies: Logistic regression entry/gain exit, Naïve Bayes

entry/gain exit, logistic regression entry/time exit, Naïve Bayes entry/time exit, logistic regression entry/stop loss exit, Naïve Bayes entry/stop loss exit

• Support of volatility options (gold/oil) and futures options will complement the futures trading functionality

4

Profit-Take Exit Strategy

• Entry into long position occurs if model price > market price• Entry into short position occurs if model price < market price• Exit long when contract rises by a percentage above threshold• Exit short when contract falls by a percentage below the threshold• When loss occurs (fall in long, rise in short), no action taken

Stop-Loss Exit Strategy

• Enter positions using same market/model comparision as profit-take• Exit long position when contract declines by percentage above

threshold• Exit short position when contract rises by percentage above threshold• If gain occurs or no change, no action taken

Time-Based Exit Strategy

• Enter positions by contrasting the regression model and market prices as in the case of profit-taking• Close contracts after a specified time (seconds) has elapsed since the

open• Indifferent to gain/loss (premise is that price incongruity will increase

in the imminent future)• Objective is to minimize the speed of order entry (no need to use

inference to determine when to close contract)

Parallel Computing Emphasis

• Minimal latency and maximal scale is required for order submission, execution processing, backtesting, market data updates, training of correlation functions, signal scanning• Separate thread pool at each stage of the high frequency trading,

market data download, and machine learning trading pipelines• In the future, leverage the graphics processing unit (GPU) for larger-

scale parallelization

Margin Computation

• Percent of notional required to be deposited in order to purchase futures is margin. • Initial and maintenance amounts are set by exchanges on a per contract and per portfolio

basis• As the futures price rises , the account is credited and, as it falls, the account is debited.• If the maintenance margin level is breached, a margin call is issued• Key benefit of the system is the initial/maintenance computation and the margin call level

computation (as the price of the contract moves)• A desirable feature would be connectivity to the clearinghouse to post margin deposits and

receive refunds electronically• The P&L effects on the portfolio are: Profit = final margin-initial margin-(deposits on margin

calls)• Another future benefit would be to render the underlying volatility of the contracts for which

the margin is computed

Historical Data for Training and Backtesting• Currently utilizing the MRCI database by parsing HTML (license fees

may apply later)• A different thread executes for each month over the range of years• Each thread downloads the HTML for a given day, extracts the spot

(front month futures contract) and futures (3-month, 6-month, etc. All months given) prices• This data is converted into training vectors in the database used to

calibrate the weights of the machine learning inference

High-Frequency Trading Signal Scan

• Iterate over the ticks table. For each underlying, find the current spot price (front month), contract maturity, and futures price• Input the spot, maturity, and underlying into the selected pricing algorithm

(Logistic Regression Linear, Logistic Regression Quadratic, Naïve Bayes)• If the market price>model price+band, generate the sell signal, and, if the market

price+band<model price, generate the buy signal• The emphasis is on the latency to find the mispriced contracts (minimize via

parallelism) and, given the discovered mispricing, to maximize the accuracy of the prediction (precision and recall) to minimize false positives and negatives. A false positive would be an interpretation of a market price that is not a buy (sell) signal as a buy (sell) signal, and a false negative would be the missing of a buy (sell) opportunity

Execution Report Processing (fills)

• Multiple thread pools to handle trade processing pipeline• Point is to quickly visualize firm’s position and margin obligations• Integrates with regulatory reporting, accounting, and customer reporting applications• In the future, straight-though-processing support via electronic wire transferring of

margin amounts to the clearinghouse is desired

Line handler parses TCP/IP

fills

Generate trade record in

database

Close offsetting open contracts

Compute margin, P&L

Line handler pool Database insert pool Trade processor pool Mark-to-market pool

High Frequency Trading Configuration• Selection one: Algorithm

• Logistic Regression (quadratic/linear)• Naïve Bayes

• Selection Two: Basis for reversal• Time (one hour expiry, 24-hour expiry)• Profit/Loss (close out on plus or minus 10% return)

• Selection Three: Running time of strategy• One day• One hour• Indefinitely (user stops manually)

• Selection Four: Basket Composition• Underlying (corn, soybeans, oil, etc)• Contract Month (January, March, etc)

• Algorithm is used to determine the mispricing – which contracts to open positions in• Basis for reversal is used to determine the signal which indicates when contracts in the HFT basket should be closed• Reversals can occur thousands of times per day for a given strategy

High-Frequency Decision Logic (time-based side for reversal and end)

Open Contracts

Time Check

Scan Thread Start

Reverse (close position)

Scan End Type

Check

Non-Reversal

Scan Time Check

End Scan

Continue Scan

Reversal Type

Check

Time-based reversal

Currtime-starttime>=reversal int

Currtime-starttime<reversal intTime-

based end

Currtime-starttime>=end int

Currtime-starttime<end int

High-Frequency Decision Logic (profit/loss-based side for reversal and termination)

Scan Thread StartReversal

Type Check

Profit/loss-based reversal Open

Contracts P/L Check

Reverse (close position)

Non-Reversal

CurrProfit-InitProfit>=reversal PL

CurrPL-startPL<reversal PL

Scan End Type

Check

Scan PL Check

End Scan

Continue Scan

P/L-based end

CurrPL-startPL>=end PL

Currtime-starttime<end PL

Portfolio Visualization

• Time series graph of profit/loss • Open contracts, closed contracts (accounting position)• Two and possibly three-dimensional line graphs will render the

portfolio time series• Point is to capture a snapshot of the firm’s current position (or the

fund’s return to date)

Profit and Loss Simulation

• Iterate market returns for a basket of commodity futures, commodity options, and volatility futures• Upon permuting the futures prices, compute the profit/loss of a

hypothetical portfolio every day over 100 days• Rebalance the portfolio according to the pricing algorithm and the

strategy parameters.• Plot the daily P&L and compute the final portfolio value on the last

day• Repeat the simulation 1000 times and compute the average –Monte

Carlo Simulation

Product Universe

• Commodity Futures – Corn, Wheat, Soybeans, Gold, Oil, Sugar• Options on Commodity Futures• Volatility – based on VIX volatility index computed by the Chicago

Board Options Exchange (CBOE)• Gold Volatility Index Futures (CME)• Crude Oil Volatility Index Futures (CME)

• Currently products traded on two exchanges (Chicago Mercantile Exchange and Intercontinental Exchange) are covered

Order Management System Demonstration Script• From the high-frequency demonstration UI, select the “+/-10% Linear Logistic Regression”,

with a time span of 1 minute for reversal, underlying of Sugar, expiry of 11/2014, quantity of 500

• Add the order to the basket• Start the high-frequency algorithm• After one minute, check that reversal occurred, namely that orders of the opposite side

appear in the database• Insert a 20% downtick order• Show new long position• Insert 20% uptick price• Wait one minute, then demonstrate closed contract• Demonstrate end of scan due to a 10% appreciation in basket holdings.