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Algorithmic Trading & High Frequency Trading


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Published in: Economy & Finance

Algorithmic Trading & High Frequency Trading

  1. 1. How Algorithmic Trading Works?: High Frequency “Tezer YELKENCİ”
  3. 3. The computer program decides according to aspects of the orders which are: Timing Price Quantity All other elements of Quantitative Approach AspectoftheOrders
  4. 4. DevelopmentofAlgorithmicTrading 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
  5. 5. 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. HighFrequencyTrading
  6. 6.  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. TheSituationofHFTforToday
  7. 7. BasicExecutionAlgorithms Benchmarking Market Making Arbitrage MOC TWAP VWAP Participation
  8. 8. Benchmarking&MarketMaking 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.
  9. 9. MOC(MarketonClose) 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.
  10. 10. TWAP&VWAP 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.
  11. 11. TWAP & VWAP
  12. 12. Participation&Arbitrage 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.
  13. 13. HighFrequencyTrading Is 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.
  14. 14. ReelExampleforProfitable AlgorithmicTrading 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.
  15. 15. RecentExampleforDangerof HighFrequencyTrading 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.
  16. 16. FlashCrash
  17. 17. ThreatsofHighFrequencyTrading 1.The fat-finger theory 2.Impact of High Frequency traders 3.Large directional bets 4.Changes in market structure 5.Technical glitches
  18. 18. ThankYou