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High-Frequency Trading

Fast & Furious Algorithmic Trading

© Christopher Ting

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Algorithmic Trading Recalled

© Christopher Ting

Algorithmic trading (also called automated trading, black box trading and robotrading) is typically used to break up large orders into smaller orders to reduce execution risk, preserve anonymity and to minimize the price impact of a trade.

Orders are often partially revealed with iceberg or hidden-size orders

Hidden portions of large institutional orders are sometimes referred to as dark liquidity pools.

Image Source http://www.mooretechllc.com/

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Algorithmic Strategies

© Christopher Ting

Algorithmic trading results from mathematical models, which analyze quotes and trades, identify liquidity opportunities, and use this information to make intelligent trading decisions.

Some algo models seek to trade at or better than the average price over a day (e.g. VWAP, volume weighted average price)

Some seek to execute slowly so as to have minimal price impact.

Some seek to exploit arbitrage opportunities or price spreads between correlated securities.

Algorithms sometimes are set to produce more volume at market opens and closes when volume is high, and less during slower periods such as around lunch.

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Short Definition of Algo Trading

© Christopher Ting

Algorithmic trading strategies also includes “Alpha Models” to make trade decisions to generate trading profits and/or to control risk.

Thus, more generally, algorithmic trading can be defined as trading based on the use of computer programs and trading analytics to execute orders according to pre-defined strategies.

Image Source: http://www.tothetick.com/forex-trading-basics-tutorial-9-automated-forex-trading

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Risk and Caveat

© Christopher Ting

Algorithmic trading does have risks, including:

Leaks that might arise from competitors’ efforts to reverse engineer them.

lack of capacity to handle or respond to exceptional or rare events.

Thus, careful human supervision of algorithmic trading and other safeguards is still crucial.

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Insight!

© Christopher Ting

What is quantitative trading, really?

At the core it is about distilling useful information from among myriad sources in the “crowd” quickly so as to

Buy before the crowd buys.

Sell before the crowd sells.

So, it is about getting information fast and exploit your edge quickly.

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Technology Advancement

© Christopher Ting

How to

Obtain and gather information

Process and analyze information

Decide to trade or don’t trade

If trade, decide whether buy or sell, and also determine the trade size

Send the orders to the exchange

Receive the acknowledge and confirmation

as quickly as possible, and quicker than the crowd?

Answer: Technology

RESULT:

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CFTC’s Definition

HFT is a form of automated trading that employs:

A. algorithms for decision making, order initiation,

generation, routing, or execution, for each individual

transaction without human direction;

B. low-latency technology that is designed to minimize

response times, including the subscription to proximity

and co-location services;

C. high speed connections to markets for order entry;

D. recurring high message rates (orders, quotes or

cancellations) determined using one or more objective

forms of measurement, including cancel-to-fill ratios.

© Christopher Ting

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SEC’s Broad Definition of HFT

© Christopher Ting

Utilisation of high-speed, complex computer programs and equipment to effect trades

Utilisation of co-location services to reduce latencies

Extremely small time-frames for opening and closing positions

Placing numerous orders and as quickly cancelling them

Ending the trading day in as close to a flat position as possible

Remark: In the US equity market, HFT amounts to more than 50% of all trading activity by volume and represents a significant source of liquidity.

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Three Bare Essentials o HFT

Trading infrastructure Reliable and fast execution platforms

Trading strategies Comprehensive and accurate prediction models

Automated execution Decisions, risk management, and orders are completely

driven by quantitative models of the markets.

© Christopher Ting

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Relations between AT and HFT

© Christopher Ting

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Frequency and Latency

© Christopher Ting

High frequency reaction requires ultra-low latency in the pathway connecting the order generating machine to the exchange order matching system.

Category Reaction speed Trade duration

Very Low-Frequency >several hours > 1 week

Low-Frequency up to several minutes 1 day to 1 week

Medium Frequency up to several seconds 10 minutes to 1 day

High Frequency 100 milliseconds or less 10 seconds to 10 minutes

Ultra High-Frequency <1 millisecond <10 seconds

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Characteristics of HFT

Rapid submission and cancelation of orders

Fast opening and closing of a position

High turnover of capital

Automation

Millisecond speed

Ultra-low latency

© Christopher Ting

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Milliseconds

On Singapore’s exchange, the share of derivatives tradesaccounted for by HFT has risen from 10% to 30% in two years.

What about equities?

© Christopher Ting

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Algo Strategies that Need HFT

© Christopher Ting

Concept Description Example strategy Profit source

Liquidity provision

Provide bids and asks to the market. Update on new information.

- Market making Bid-ask spread

Arbitrage Determine ‘fair value’ and relationship between correlated products; buy cheap and sell expensive. Combines passive and aggressive orders .

- Spread trading- Index /ETF arb- Cross-exchange- Pairs trading- Statistical arbitrage

Short-term market inefficiencies

Short-term Momentum

Determine impact of newinformation (news, sharp market moves) and trade accordingly

- News based Short-termshift in fair value

Liquidity detection

Detect order flow that influences market direction

- Order book pressure

Short-termtrend

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Examples of HFT Strategies

© Christopher Ting

Spread trading: SPY vs E-Mini, Front month futures vs Back Month Futures. Near perfect correlation, same underlying.

Index/ETF arbitrage: Basket of stocks vs futures, ETF constituents vsETF if NAV deviates.

Cross exchange: In Europe Shell in NL & UK. In U.S. 13 exchanges Order inserted on one exchange impacts algos on all other exchanges.

Pairs trading: Correlated products – mean reversion. Big buyer in BHP moves price up. Sell BHP buy RIO – wait till reverse.

Stat arb: more complex version of pairs trading. Multiple instruments, e.g. relationships between 6 currencies and short-term interest rate futures. Based on real-time regression models, GARCH etc.

News based: Expected impact of economic figures. Link system to datafeed and insert orders when better/worse than expected. Link to datafeed and scan for keywords e.g. take-over, rights issue etc.

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What is Market Making?

Buyers and sellers’ orders arrive at the market at different time with varied order sizes.

Need a middle man to intermediate the buying and selling demand

Buy at the lower bid price and sell at the higher ask (also called the offer) price.

The bid-ask spread is the compensation for bearing market risk and liquidity provision

© Christopher Ting

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News + Financial Market Data

Data analysis News + Data quant models

Mainstream

News

Pre-News

Web 2.0

Social Media

Pre-Analysis Classifiers

Sentiment

Scores

(Numeric) financial

market data

Analysis Consolidated

News+Data

Updated beliefs,

Ex-ante view of market

environment

Quant Models

A. Return Predictions

B. Fund Management

C. Trading Decisions

D. Volatility estimates

and risk control

© Christopher Ting

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HFT Technology

Arms Race?

© Christopher Ting

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Front Running—Latency Arbitrage

© Christopher Ting

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Schematics of a HFT Infrastructures

© Christopher Ting

Orders

Exchange

FPGA/Co-Lo

Live feed Last Traded Price Trade confirmation

Ticker Plant

Other Co-Lo’s

Trader

Trading parameters

Order/Trade updates

Trading parameters

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Why Co-location?

Distance does

matter.

Speed of light

299,792,458 m/s

If a trader is 3,000

km away from the

exchange, the

signal takes at least

10 milliseconds to

reach the exchange.High-speed cable tray for co-location services at Equinix 4 market center, New Jersey.

© Christopher Ting

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ACTIV Financial

(Chicago)

ACTIV Financial

(New York)

NA

OPRA-NBBO

OPRA-FULL

UCSs

UCS:

DCS:

TSS:

CG:

UDP

OPRA

CTA,

CBOT,

CME,

etc

Option 2, the client has a backup circuit.

Option 1, the client has no backup circuit.

Option 3, the client DCS receives the

feed twice, diversely routed (and

arbitrates at a datagram level if possible

or processes one of the feeds if not).

NEWS

Republisher

ActivFeed Delivery Options

Client (Option 2)

Republisher

NA

OPRA-NBBO

OPRA-FULL

UCSs

UDP

OPRA

CTA,

CBOT,

CME,

etc

NEWS

DCS /

TSS / CG

API

Client

UDP TCP

Client (Option 1)

DCS /

TSS / CG

API

Client

UDP TCP

Client (Option 3)

DCS /

TSS / CG

API

Client

UDP TCP

Upstream Content Server

Downstream Content Server

Time Series Server

Content Gateway

DJNW

Comtex

etc.

DJNW

Comtex

etc.

© Christopher Ting

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Market Data Feed

DCs Across Asia:

Tokyo

Seoul

Hong Kong

Singapore

Efficient:

DCs are landing points for Pan-Asian network

No unnecessary hops

Distributed:

No central consolidation point

Point-to-point delivery

Lowest Latency

65ms

© Christopher Ting

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Technological Arms Race

© Christopher Ting

Software Platform ~300μsMPU 1st Generation ~40 μsMPU 2nd Generation 40 μs > 6 μs

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2nd Generation MPU - PCI-E

PCI-E Card: Full height half length Gen II PCI-E Can be deployed in Application Server Down to Single-Digit Microsecond Latency

© Christopher Ting

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Field-Programmable Gate Array

An integrated circuit designed to be configured by a customer or designer after manufacturing. The configuration is done via a hardware description language (i.e. changes to the electronic circuit to allow for automated analysis and simulation).

© Christopher Ting

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FPGA and High Frequency Trading

The Need For Speed

In HFT, money is made and lost in milliseconds (and even microseconds)

Speed provides the competitive advantage and the fastest players get the profits

Inadequacy of Computers

Computer hardware is designed to perform a myriad of tasks

Operating Systems Burden: The OS uses “time-slicing” to share the processor’s time between trading software and other applications

Protocol Stack Burden: There are many protocol layers between trading software and the market which results in delay

© Christopher Ting

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Why Use Specialized Hardware

Gain edge over competitors with faster speed

Don’t share processing time with other applications

Operate on lesser protocol layers

The FPGA Solution

Simple strategies (e.g. Order Flow strategy) can be coded onto the FPGA to be executed

Uses hardware to bypass the OS and protocol stack

Lightning fast connection to the market

© Christopher Ting

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Benefits of FPGA

Ultra low latency to get orders in the queue first

Spot arbitrage opportunities quickly before they are gone

Exit market quickly during “flash crash”

Inexpensive

© Christopher Ting

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More High-Tech

© Christopher Ting

Microwave

The first microwave connections between London and Frankfurt have been launched, cutting the time to send a trade by about 40% compared with optic fiber cables

Nasdaq-CME Microwave

Is it fair to let some market participants obtain quotes information tens of milliseconds earlier than others?

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Dark Pools

© Christopher Ting

Dark Pools, Internalization, and Equity Market Quality

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Why Dark Pools?

© Christopher Ting

Shares trading is not done exclusively at the exchange in the U.S.

In the past, block traders trading 10,000 shares and more trade them off the exchange to minimize the price impact while getting their orders filled completely.

Brokers and broker-dealers provide the service to match the buyer with the seller.

Recently, human brokers are being replaced by electronic systems.

Crossing Networks

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Dark Pools and the Changing Landscape

© Christopher Ting

The number of market makers on the NYSE had fallen to five from 25 since 2000 as the business of providing liquidity became dominated by hundreds of automated traders across markets with less stringent rules about when they must buy and sell.

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Market Fragmentation

© Christopher Ting

The U.S. equity market structure is fragmented. As of 2013, equity trading is dispersed across 13 exchanges, at least one electronic communications network (ECN), approximately 16 reporting dark pools, and more than 200 broker/dealers who internalize order flow.

Most exchanges and ECN are structured as electronic limit order book markets. Exchanges are generally both pre-trade and post-trade transparent:

Prices and trading interest are displayed prior to execution, and transaction details are publicly disseminated in real time.

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U.S. ``Market Centers’’

© Christopher Ting

Code New Name Old NameA NYSE MKT American Stock ExchangeB NASDAQ OMX BX Boston Stock ExchangeC National Stock Exchange Cincinnati Stock ExchangeD FINRA NASDJ EDGA ExchangeK EDGX ExchangeN NYSE Equities New York Stock ExchangeM Chicago Stock Exchange MidWest Stock ExchangeO CBOE Stock ExchangeP NYSE ARCA Pacific Stock Exchange

T, Q NASDAQ NASDAQX NASDAQ OMX PHLX Philadelphia Stock ExchangeY BATS Y ExchangeZ BATS Z Exchange

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“OTC” Markets

© Christopher Ting

OTC Markets Group operates open, transparent and financial marketplaces for 10,000 U.S. and global securities.

The OTCQX, OTCQB and OTC Pink marketplaces represent U.S. and global public companies that are traded in the U.S. off the exchanges.

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The OTC Bulletin Board®

© Christopher Ting

OTCBB is a regulated quotation service that displays real-time quotes, last-sale prices, and volume information in over-the-counter (OTC) equity securities. An OTC equity security generally is any equity that is not listed or traded on NASDAQ® or a national securities exchange.

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Why the Name Dark Pools?

© Christopher Ting

Quotes are not published for public, “unlit”

systematized execution facilities that operate with limited pre-trade transparency. The prices of orders entered into the dark pool are not displayed to other market participants and are matched anonymously against contra-side orders.

Anonymous liquidity pools

Classified as alternative trading systems by SEC

Dark pools provide an avenue for large institutional investors to trade anonymously with minimal market impact and can help operators improve pricing and reduce transaction costs for clients.

40 of 56 February 13, 2017 © Christopher Ting© Christopher Ting

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SEC versus Pipeline Trading Systems LLC

© Christopher Ting

The seven-year-old dark pool operator specializing in block trades, will pay $1.2 million to resolve U.S. claims it failed to provide the confidentiality and liquidity it advertised to customers. Sources: http://online.wsj.com/news/articles/SB10001424052970204777904576651111404462184

http://www.bloomberg.com/news/2011-10-24/pipeline-agrees-to-pay-1-million-over-sec-dark-pool-claims.html

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Predict What Customers Might Buy or Sell

© Christopher Ting

According to the SEC, New York-based Pipeline enlisted traders of its own to make its platform look more attractive to institutional clientele—but kept the arrangement hidden from customers.

Clients of Pipeline's private market nearly all the time wound up trading with an entity called MilstreamStrategy Group LLC, an affiliate of Pipeline.

Milstream, according to regulators, tried to predict what shares Pipeline's customers might buy or sell, and made similar trades on other markets before filling customer orders on the Pipeline platform.

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SEC versus eBX LLC

© Christopher Ting

eBX agreed to pay $800,000 to settle the SEC's allegations. It did so without admitting or denying wrongdoing.

Source:: http://online.wsj.com/news/articles/SB10000872396390444223104578034413566549332

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Transaction liquidity

Liquidity: How readily a security or asset is bought or sold at the current market price.

Does HF spread trading contribute to liquidity provision?

© Christopher Ting

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Volatility

How violent does a security price swing

Does HF spreading trading contribute to market volatility?

May 6, 2010 Flash crashes

© Christopher Ting

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CFTC-SEC Investigation

Kansas-based investment company Waddell & Reed Financial Inc.

At 2:32 p.m., the company triggered a cascade of selling by trying to unload 75,000 E-mini S&P 500 futures contracts

Waddell & Reed’s sell order was so large and hit the market so hard that it overwhelmed the available buyers. This sucked liquidity out of the market and allowed prices to go into free fall.

© Christopher Ting

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Many other flash crashes

59 wrong orders cause stock dive on NSE in Mumbai

End of the World for SIMSCI?

© Christopher Ting

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October 19, 1987 (Black Monday)

The 23 percent plunge in the Dow Jones Industrial Average came amid signs of a slowing economy, the threat of higher taxes and concern among individuals that trading was rigged for insiders.

Japan's Nikkei 225 Stock Average fell 2.4 percent before market opened. By midday, stocks in London were down 10 percent.

In New York, 11 of the 30 Dow components didn't open in the first hour of trading. The Dow went on to fall 508 points, while the S&P 500 tumbled to 224 from 282.

© Christopher Ting

49 of 56 February 13, 2017 © Christopher Ting

Sell! Sell! Sell!

© Christopher Ting

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Automated Exchange

© Christopher Ting

The NYSE, predominantly a market run by and for humans in 1987, is now an automated exchange with so-called designated market makers overseeing trading in their assigned stocks.

There are four main market makers on the exchange's trading floor, including Getco LLC, one of the largest automated trading firms, compared to more than 50 specialists at the time of the Black Monday crash.

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Myth of Market Making

© Christopher Ting

Financial market is inherently an unstable system. When a critical mass is reached, the chain reaction occurs spontaneously. On Black Monday, there was a lot of selling pressure and some (human) specialist firms in NYSE didn’t open to make the market, as they deemed the costs of market making were far too high.

When everyone realizes that everyone else wants to sell fast at the same time, panic strikes. It doesn’t matter whether humans or automated algorithms are making the market.

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Panic in UK

© Christopher Ting

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What Happened in Singapore?

© Christopher Ting

Straits Times 1987 Oct 20

Market Suffers 169-Point Drop

Straits Times 1987 Oct 21

Another 261 Points Down

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Exit Door Hasn’t Changed a Bit

© Christopher Ting

While Procter and Gamble Co. and McDonald's Corp. are up

more than 800 percent since 1987, protections adopted after the

crash couldn't stop unharnessed computer trading from erasing

almost $900 billion of value in less than 20 minutes on May 6,

2010, based on data compiled by Bloomberg.

E.E. "Buzzy" Geduld, 69, who oversaw about 60 equity traders 25

years ago at Herzog, Heine & Geduld Inc. and now runs

investment firm Cougar Trading LLC, says crashes happen when

investors become convinced they've lost control.

“In 1987 everybody tried to go to the exit at the same time, but the

exit door wasn't big enough," Geduld said. "You had literally a

panic. Fast forward to 2012. The volumes we can handle are

gigantic, but the exit door hasn't changed in size.”

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Circuit Breakers: Did They Work?

© Christopher Ting

“The theory of the circuit breakers was that if there were predetermined

stopping points for the market, then the market participants would

know this wasn’t a panic closing,” said Ruder.

BUT, the plunge in May 2010 didn't trigger those circuit breakers. The

Dow average fell 9.2 percent, most of it between 2:30 p.m. and 3 p.m.,

after aggressive selling of E-mini S&P 500 futures by a mutual fund

company caused a flight of liquidity. As equity market makers and other

providers of bids and offers withdrew, trades in individual stocks took

place at prices including fractions of 1 cent and $99,999.99.

While the flash crash wasn't caused by high-frequency traders, their

habit of buying and selling rapidly led to the sudden removal of

liquidity, kicking off a related plunge in stocks, a report by the SEC and

CFTC said on Sept. 30, 2010.

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Crash versus Flash Crash

© Christopher Ting

In 1987, panic spread on Wall Street by phone and ticker

tape. About $1 trillion in stock-market value was erased in

four days, according to a report by a task force led by

Treasury Secretary Nicholas Brady in January 1988.

It took more than a year to restore the 1987 crash, compared

with a week following the retreat on May 6, 2010.

Both these routs proved to be buying opportunities. Within

10 years of the 1987 crash the Dow average had quadrupled

and investors were enjoying the biggest bull market ever.

After falling 999 points on May 6, 2010, the gauge ended the

day down 348. The Dow rose 6.5 percent through the end of

the year.