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EXANTE Algorithmic Trading: Practical Aspects

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Slides for speech of EXANTE Managing Partners Vladimir Maslyakov and Anatoliy Knyaze , entitled "Practical aspects of algorithmic trading and high-frequency trading", on TradeTech Russia …

Slides for speech of EXANTE Managing Partners Vladimir Maslyakov and Anatoliy Knyaze , entitled "Practical aspects of algorithmic trading and high-frequency trading", on TradeTech Russia 2011

Presentation highlights the problems associated with the development of a model (pre-trade analysis), the launch of the strategy (trading) and the post-trade analysis, as well as an overview of the algorithmic trading in general, and a small glimpse into the future.

Published in: Economy & Finance, Business
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  • 1. Algorithmic trading: practical aspects EXANTE Ltd. exante.com.mt info@exante.com.mtMoscow 2011
  • 2. I. Algorithmic tradingII. Develop the modelIII. Launch the strategyIV. Analyze the resultsV. Trends
  • 3. Algorithmic trading Automated trading HFT
  • 4. Arbitrage Pricing Automated trading Buy-side Sell-sideTrendfollowing Smart order routingStat arbitrage Market Making / HFT VWAP
  • 5. I. Algorithmic tradingII. Develop the modelIII. Launch the strategyIV. Analyze the resultsV. Trends
  • 6. Data Hypothesis Model Testing
  • 7. Historical Data Width Depth CorrectnessInstruments Past period Splits and divs Venues Resolution GapsCorp. actions Order book Timestamps News Counterparties Validation
  • 8. Data Hypothesis Model Testing
  • 9. A priori knowledgeFundamental Empirical Gut feeling
  • 10. Visualization Datavolume Math Speed Иллюстрация с panopticon.com
  • 11. RTS Index and S&P Index, 2010-10-11 RTSI SPX16:40 16:50 17:00 17:10 17:20 17:30 17:40
  • 12. Data Hypothesis Model Testing
  • 13. ModelAlpha Risks Transaction costs
  • 14. EDGE ?
  • 15. Model: math Prototype
  • 16. Our experience: RDomain Libraries Open and free Slow No realtime Open and free
  • 17. Data Hypothesis Model Testing
  • 18. TestingData Prototype Results• Historical data • R / Python/ • Alpha• Modeling Java • Risks market impact • Cluster / cloud • Transaction and order flow • GPU costs• Realtime
  • 19. I. Algorithmic tradingII. Develop the modelIII. Launch the strategyIV. Analyze the resultsV. Trends
  • 20. Инфраструктура
  • 21. Realtime data Speed Depth Coverage Low-latency L1 AmericasUltra low-latency L2 EuropeSub millisecond Raw Asia
  • 22. Strategy Language Infrastructure ControlHigh-level (C++, Java, Client Manual C#, etc) DSL (Slang, etc) Server Automatic Visual (diagrams) Cloud GUI Strategy sandbox Data OrdersNYSE MFG Robot 1 Robot 2 Robot 3 Robot 4 LSE JP
  • 23. Arbitrage example London Server (Telehouse) Arbitrage strategyGAZPRU On new tick: LIMIT (LSE)(MICEX) ogzd_rub = convert(ogzd, usd_rub) spread = normalize(ogzd_rub/gazpru) Filled (size) changedSpread() OGZD (LSE) On change spread: if (spread > threshold) MARKET (MICEX) place_limit(OGZD, price, size)USD/RUB Filled (price)(FOREX) On limit fill: If (limit_is_filled) place_market(GAZPRU, size) Parameters: threshold
  • 24. VWAP example Moscow Server (MacomNet) VWAP strategySBER bid/ask On new tick:(MICEX) vwap = recalculateVwap(trades) execute_vol = recalculate(average_volume, volume) MARKET (MICEX)SBER volume executeOrder(execute_vol)(MICEX) Filled (size) on market fill:SBER trades(MICEX) our_vwap = update(price, size) vwap_delta = our_vwap - vwap Parameters: average_volume
  • 25. I. Algorithmic tradingII. Develop the modelIII. Launch the strategyIV. Analyze the resultsV. Trends
  • 26. Gather results dataMarket snapshot Orders Data Latency Strategy parameters
  • 27. Export the results data Excel R, Matlab ExportVisualization Model
  • 28. Compare with the model
  • 29. Optimize the parameters Model Results Testing Trading
  • 30. I. Algorithmic tradingII. Develop the modelIII. Launch the strategyIV. Analyze the resultsV. Trends
  • 31. Adoption100 FORTS CME GLOBEX Vol, % Msgs, % 90 E-mini S&P 500 Futures 51.66 69.9390 EuroFX Futures 69.32 83.4180 Eurodollar Futures 51.29 64.4670 60 Crude Oil Futures 35.34 71.2460 Algorithmic Trading and Market Dynamics July 15, 20105040 Foreign Exchange Buy-side, % Sell-side, %30 Order Routing 25 9220 Time-slice 25 1510 Liquidity 42 46 0 Alpha 92 39 Vol, % Msg, % FX Hedging 25 39 Estimated by FORTS 09.2011 Streambase 2011 Special Report on FX
  • 32. Dodd-Frank Swap Execution Facility.SEC 15c3-5 Eliminate naked access to exchange.MiFID II Crossing networks, derivatives, HFT.
  • 33. Algotrader: a new breed MathematicsTechnology Finance
  • 34. Strategies Multi-assetULL DMA HFT trading Buy-side or sell- FX, Eqty, Debt, 5μs / km side? Derivs Market making Europe, USA, Asia< 100μs / algo Liquidity search Feeds and and aggregation execution106 msg / sec Fast and reliable Stat arb data
  • 35. TechnologiesSoftware Overclocking FPGAMulti-core GPGPU Cloud x32 x200 x30000
  • 36. Our experience: cloud Power Diversity Cost controlEngineering Compliance Latency
  • 37. Service!
  • 38. Anatoliy Knyazev ak@exante.com.mtVladimir Maslyakov vm@exante.com.mt

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