Algorithmic Trading & High Frequency Trading
-
Upload
tezer-yelkenci -
Category
Economy & Finance
-
view
601 -
download
23
description
Transcript of Algorithmic Trading & High Frequency Trading
How Algorithmic Trading Works?:
High Frequency
“Tezer YELKENCİ”
COMPUTER PROGRAMS FOR ENTERING TRADING ORDERS, WITH THE COMPUTER ALGORITHM DECIDING ON ASPECTS OF THE ORDER.
The computer program decides according to aspects of the orders which are:
Timing
Price
Quantity
All other elements of Quantitative Approach
Asp
ect
of
the O
rders
Develo
pm
en
t of
Alg
ori
thm
ic T
rad
ing
Algorithmic trading have been growing rapidly since mid 1990s and it is expected to account for over 40% of all trading volume by 2015 worlwide. Today, algorithmic trading is widely used by:
Buy Side
Pension Funds
Mutual Funds
Buy-Side Institutional Traders
Divide large trades into several smaller trades
Manage market impact, and risk
Sell Side
Market Makers
Hedge Funds
Provide liquidity to the market
High-frequency trading is the execution of computerized trading strategies characterized by extremely short position-holding periods. In high-frequency trading, programs running on high speed computers analyze market data, using algorithms to utilize trading opportunities that may open up for only a fraction of a second to several hours. High-frequency trading, often abbreviated HFT, uses quantitative investment computer programs to hold short-term positions in equities, options, futures, ETFs, currencies, and all other financial instruments that possess electronic trading capability.
Differences Between Algo and HFT
Algo-trade refers to any computerized trading strategy and can include the holding of assets for long periods,whereas HFT is sub-class that aims for very short holding periods.
Hig
h F
requ
en
cy T
radin
g
Biggest “Cash Cow” on Wall Street
It generates approximately $15 - $25 billion revenue.
The speed factor in trading is “latency”. “Ultra-low latency” is trading at . speeds of less than 1 microsecond.
Th
e S
ituati
on o
f H
FT f
or
Tod
ay
Basi
c Exe
cuti
on
Alg
ori
thm
sBenchmarking
Market Making
Arbitrage
MOC
TWAP
VWAP
Participation
Bench
mark
ing &
Mark
et
Maki
ng Benchmarking:
Market Making: Placing a limit order to sell above the current market price. A buy limit order below the current price in order to benefit from the bid-ask spread.
Traders attemping to mimic an index return. Electronically traded funds (EFT) are mostly traded using variation benchmarking algorithms.
MO
C (
Mark
et
on C
lose
) MOC:
MOC order is to buy or sell stocks or futures and options contracts as near as possible to when the market closes for the day.
TW
AP &
VW
AP
VWAP:
TWAP:
The average price of contracts or shares over a specified time. High-volume traders use TWAP to execute their orders over a specific time so they trade to keep the price close to that which reflects the true market price.
Many pension funds and some mutual funds, fall into this category. It is a measure of the average price a stock traded at over the trading horizon.
TWAP & VWAP
Part
icip
ati
on
& A
rbit
rag
e
Arbitrage:
Determines a relation between :
The price of domestic bond Bond deneminated in a foreing currency The spot price of the currency and A price of a foward contract on the country
Participation:
How much the orders that it will place will move the price It aims at quickly executing orders to optimize the trade-off between price impact and exposure to adverse price movements or opportunity cost.
Hig
h F
requ
en
cy T
radin
gIs it Good or Bad for the Market?
Positive Effects: Adds liquidity to the markets Speeds execution time Narrows the price spreads between markets and exchanges
Negative Effects: The low to zero capital requirements that these so-claimed “liquidity providers” carry is problematic and speculation over the danger that unchecked high frequency trading could cause to the entire system is truely awful.
Reel Exam
ple
for
Pro
fita
ble
A
lgori
thm
ic
Trad
ing
The algorithm will allow you to profit from small changes in market price, by getting in and out as fast as possible. The Shake Algorithm is designed to reach maximum profitability based on the amount of time it is running in the market, rather then a specific market direction.
Rece
nt
Exam
ple
for
Dan
ger
of
Hig
h
Freq
uen
cy T
rad
ing
Flasch Crash – May 6, 2010
It was a United States stock market crash which the Dow Jones Industrial Average plunged about 900 points only to recover those losses within minutes.
Flash
Cra
sh
Th
reats
of
Hig
h F
req
uen
cy T
radin
g
1.The fat-finger theory
2.Impact of High Frequency traders
3.Large directional bets
4.Changes in market structure
5.Technical glitches
Thank YouFor
LISTENNINGTh
ank
You