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Algorithmic trading:
                 practical aspects

                      EXANTE Ltd.
                     exante.com.mt
                info@exante.com.mt
Moscow 2011
I. Algorithmic trading
II. Develop the model
III. Launch the strategy
IV. Analyze the results
V. Trends
Algorithmic trading

      Automated trading



                      HFT
Arbitrage                                         Pricing
                 Automated trading

                 Buy-side            Sell-side




Trend
following
                                                   Smart order
                                                   routing
Stat arbitrage
                     Market Making / HFT         VWAP
I. Algorithmic trading
II. Develop the model
III. Launch the strategy
IV. Analyze the results
V. Trends
Data   Hypothesis   Model   Testing
Historical Data

 Width                Depth          Correctness

Instruments          Past period      Splits and divs

  Venues              Resolution          Gaps

Corp. actions        Order book        Timestamps

   News             Counterparties      Validation
Data   Hypothesis   Model   Testing
A priori knowledge

Fundamental                 Empirical




              Gut feeling
Visualization


 Data
volume                          Math




            Speed


                         Иллюстрация с panopticon.com
RTS Index and S&P Index, 2010-10-11


                                                                  RTSI
                                                                  SPX




16:40   16:50        17:00         17:10          17:20   17:30          17:40
Data   Hypothesis   Model   Testing
Model
Alpha   Risks   Transaction
                   costs
EDGE ?
Model: math




  Prototype
Our experience: R

Domain        Libraries    Open and free



 Slow        No realtime   Open and free
Data   Hypothesis   Model   Testing
Testing


Data                Prototype           Results
• Historical data   • R / Python/       • Alpha
• Modeling            Java              • Risks
  market impact     • Cluster / cloud   • Transaction
  and order flow    • GPU                 costs
• Realtime
I. Algorithmic trading
II. Develop the model
III. Launch the strategy
IV. Analyze the results
V. Trends
Инфраструктура
Realtime data
    Speed               Depth       Coverage

  Low-latency             L1        Americas

Ultra low-latency         L2         Europe

Sub millisecond          Raw          Asia
Strategy
   Language              Infrastructure       Control
High-level (C++, Java,
                              Client           Manual
       C#, etc)

  DSL (Slang, etc)           Server           Automatic

  Visual (diagrams)           Cloud              GUI


                         Strategy sandbox
              Data                          Orders
NYSE                                                      MFG
                                Robot 1
                              Robot 2
                            Robot 3
                          Robot 4
 LSE                                                      JP
Arbitrage example
          London Server (Telehouse)
              Arbitrage strategy
GAZPRU        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
VWAP example
               Moscow Server (MacomNet)
                  VWAP strategy
SBER 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
I. Algorithmic trading
II. Develop the model
III. Launch the strategy
IV. Analyze the results
V. Trends
Gather results data

Market snapshot            Orders


                  Data


    Latency          Strategy parameters
Export the results data

   Excel                 R, Matlab


                Export


Visualization             Model
Compare with the model
Optimize the parameters

           Model


 Results             Testing


           Trading
I. Algorithmic trading
II. Develop the model
III. Launch the strategy
IV. Analyze the results
V. Trends
Adoption
100     FORTS                  CME GLOBEX                   Vol, %               Msgs, %
                    90         E-mini S&P 500 Futures             51.66                 69.93
90
                               EuroFX Futures                     69.32                 83.41
80
                               Eurodollar Futures                 51.29                 64.46
70
         60                    Crude Oil Futures                  35.34                 71.24
60                                                  Algorithmic Trading and Market Dynamics July 15, 2010
50
40
                               Foreign Exchange             Buy-side, %          Sell-side, %
30
                               Order Routing                        25                     92
20
                               Time-slice                           25                     15
10                             Liquidity                            42                     46
 0                             Alpha                                92                     39
      Vol, %      Msg, %       FX Hedging                           25                     39
  Estimated by FORTS 09.2011                                         Streambase 2011 Special Report on FX
Dodd-Frank
             Swap Execution Facility.



SEC 15c3-5
             Eliminate naked access to
             exchange.

MiFID II
             Crossing networks,
             derivatives, HFT.
Algotrader: a new breed

             Mathematics


Technology                 Finance
Strategies

                                      Multi-asset
ULL 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        execution
106 msg / sec                         Fast and reliable
                      Stat arb
                                            data
Technologies


Software          Overclocking     FPGA



Multi-core          GPGPU          Cloud
           x32              x200          x30000
Our experience: cloud

  Power        Diversity   Cost control


Engineering   Compliance     Latency
Service!
Anatoliy Knyazev
 ak@exante.com.mt

Vladimir Maslyakov
 vm@exante.com.mt

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Algorithmic Trading Strategies and Trends

  • 1. Algorithmic trading: practical aspects EXANTE Ltd. exante.com.mt info@exante.com.mt Moscow 2011
  • 2. I. Algorithmic trading II. Develop the model III. Launch the strategy IV. Analyze the results V. Trends
  • 3. Algorithmic trading Automated trading HFT
  • 4. Arbitrage Pricing Automated trading Buy-side Sell-side Trend following Smart order routing Stat arbitrage Market Making / HFT VWAP
  • 5.
  • 6. I. Algorithmic trading II. Develop the model III. Launch the strategy IV. Analyze the results V. Trends
  • 7. Data Hypothesis Model Testing
  • 8. Historical Data Width Depth Correctness Instruments Past period Splits and divs Venues Resolution Gaps Corp. actions Order book Timestamps News Counterparties Validation
  • 9. Data Hypothesis Model Testing
  • 10. A priori knowledge Fundamental Empirical Gut feeling
  • 11. Visualization Data volume Math Speed Иллюстрация с panopticon.com
  • 12. RTS Index and S&P Index, 2010-10-11 RTSI SPX 16:40 16:50 17:00 17:10 17:20 17:30 17:40
  • 13. Data Hypothesis Model Testing
  • 14. Model Alpha Risks Transaction costs
  • 16. Model: math Prototype
  • 17. Our experience: R Domain Libraries Open and free Slow No realtime Open and free
  • 18. Data Hypothesis Model Testing
  • 19. Testing Data Prototype Results • Historical data • R / Python/ • Alpha • Modeling Java • Risks market impact • Cluster / cloud • Transaction and order flow • GPU costs • Realtime
  • 20. I. Algorithmic trading II. Develop the model III. Launch the strategy IV. Analyze the results V. Trends
  • 22. Realtime data Speed Depth Coverage Low-latency L1 Americas Ultra low-latency L2 Europe Sub millisecond Raw Asia
  • 23. Strategy Language Infrastructure Control High-level (C++, Java, Client Manual C#, etc) DSL (Slang, etc) Server Automatic Visual (diagrams) Cloud GUI Strategy sandbox Data Orders NYSE MFG Robot 1 Robot 2 Robot 3 Robot 4 LSE JP
  • 24. Arbitrage example London Server (Telehouse) Arbitrage strategy GAZPRU 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
  • 25. VWAP example Moscow Server (MacomNet) VWAP strategy SBER 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
  • 26. I. Algorithmic trading II. Develop the model III. Launch the strategy IV. Analyze the results V. Trends
  • 27. Gather results data Market snapshot Orders Data Latency Strategy parameters
  • 28. Export the results data Excel R, Matlab Export Visualization Model
  • 30. Optimize the parameters Model Results Testing Trading
  • 31. I. Algorithmic trading II. Develop the model III. Launch the strategy IV. Analyze the results V. Trends
  • 32. Adoption 100 FORTS CME GLOBEX Vol, % Msgs, % 90 E-mini S&P 500 Futures 51.66 69.93 90 EuroFX Futures 69.32 83.41 80 Eurodollar Futures 51.29 64.46 70 60 Crude Oil Futures 35.34 71.24 60 Algorithmic Trading and Market Dynamics July 15, 2010 50 40 Foreign Exchange Buy-side, % Sell-side, % 30 Order Routing 25 92 20 Time-slice 25 15 10 Liquidity 42 46 0 Alpha 92 39 Vol, % Msg, % FX Hedging 25 39 Estimated by FORTS 09.2011 Streambase 2011 Special Report on FX
  • 33. Dodd-Frank Swap Execution Facility. SEC 15c3-5 Eliminate naked access to exchange. MiFID II Crossing networks, derivatives, HFT.
  • 34. Algotrader: a new breed Mathematics Technology Finance
  • 35. Strategies Multi-asset ULL 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 execution 106 msg / sec Fast and reliable Stat arb data
  • 36. Technologies Software Overclocking FPGA Multi-core GPGPU Cloud x32 x200 x30000
  • 37. Our experience: cloud Power Diversity Cost control Engineering Compliance Latency
  • 39. Anatoliy Knyazev ak@exante.com.mt Vladimir Maslyakov vm@exante.com.mt