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Algorithmic Trading: an Overview

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EXANTE's lecture at Stockholm School of Economics in Riga.
– Objectives of algorithmic trading
– Various types of algorithms
– The process of creating one
– Testing and evaluation
– Understanding the possible pitfalls (and solutions)

Published in: Economy & Finance
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Algorithmic Trading: an Overview

  1. 1. Algorithmic Trading: an Оverview Presented by Karlis Liepins Riga, 2017
  2. 2. All the buzz.. Algo-trading Automated trading systems High Frequency Trading/Low Latency Trading Black box Robots 50-85% of traded volume in US stocks is generated by robots Flash crash Robots take advantage of true investors?
  3. 3. Motivation Processing power Speed of execution (think 0.00001 seconds) Less human error Simplify repetitive tasks
  4. 4. What are the objectives? Smart execution/Hedging (min costs) Automating a strategy (make profit)
  5. 5. What are the objectives? Smart execution/Hedging (min costs) Automating a strategy (make profit) Brokers, Asset managers, etc. Used when placing large orders Disclosing information from the market Minimizing the impact on the price
  6. 6. Smart execution Example: Need to buy 100’000 shares of L’Oreal stock a) Place all in one order b) Divide into 100 orders, 1000 shares each c) Divide into 100 orders, 1000 shares each and place one every 5 minutes
  7. 7. What can be done? TWAP VWAP Percentage of Volume (POV) Minimal Impact (dark pools) Cost driven Other
  8. 8. What can be done? TWAP VWAP Percentage of Volume (POV) Minimal Impact (dark pools) Cost driven Other Time Weighted Average Price Attempt to match price over time 100’000 shares over 5hrs = 5’000 shares every 15 minutes Possible improvements: - more flexible schedule - make it less predictable
  9. 9. What can be done? TWAP VWAP Percentage of Volume (POV) Minimal Impact (dark pools) Cost driven Other Volume Weighted Average Price Large volume transaction have more impact on benchmark price Every 15 minutes place an order with a proportional size to the traded volume Relies on Historical trading volumes
  10. 10. VWAP More activity happening during around the opening and closing Attempt to get the price where the activity actually happened
  11. 11. What can be done? TWAP VWAP Percentage of Volume (POV) Minimal Impact (dark pools) Cost driven Other Example - trade 10% of each market trade A stock with original daily turnover of 1’000’000 shares should result in execution of 100’000 shares More dynamic than TWAP & VWAP
  12. 12. What can be done? TWAP VWAP Percentage of Volume (POV) Minimal Impact (dark pools) Cost driven Other Dark Pools - private exchanges for trading securities Not available to general public No transparency Came about to facilitate block trades when we want to minimize market impact Liquidity in Dark Pools is limited, thus usually combined with other strategies Other options: broker’s internal crossing networks, hidden order types (iceberg)
  13. 13. What can be done? TWAP VWAP Percentage of Volume (POV) Minimal Impact (dark pools) Cost driven Other Trading too aggressively may result in considerable market impact, while trading too passively incurs timing risk Balancing impact and timing Very poor liquidity / huge bid-ask spread Factors: - order size - time available for trading - asset specific (liquidity and volatility) - investor’s urgency or risk aversion
  14. 14. What can be done? TWAP VWAP Percentage of Volume (POV) Minimal Impact (dark pools) Cost driven Other Wait for specific market conditions More advanced/opportunistic algorithms - Momentum detection Hire someone to do it! Execution Services
  15. 15. What are the objectives? Smart execution (min costs) Automating a strategy (make profit) Market making Statistical arbitrage Front running Outsmarting the others
  16. 16. Automating a Strategy Market Making Statistical Arbitrage Front Running Outsmarting the others Keep Best Bid and Best Ask in the market Earn few cents per transaction Some exchanges offer rebates Improves the market Provides liquidity Must be very smart about risk management Think - casino/online poker
  17. 17. Automating a Strategy Market Making Statistical Arbitrage Front Running Outsmarting the others
  18. 18. Automating a Strategy Market Making Statistical Arbitrage Front Running Outsmarting the others Over long period of time/many transactions statistically gives near riskless profit Pair/Spread trading (Pepsi vs Coke, Stock vs Industry, fungible commodities) Comparing Index(ETF) to basket Mean reversion
  19. 19. General Motors vs Ford Motors
  20. 20. Automating a Strategy Market Making Statistical Arbitrage Front Running Outsmarting the others Guerilla algorithms to find Icebergs(liquidity): - Probabilistic models - Compare actual trades vs order book - Identify patterns HFT & price forecasting Signal/News trading Abusive strategies: e.g. flooding the system with orders to lower the latency for others
  21. 21. Automating a Strategy Market Making Statistical Arbitrage Front Running Outsmarting the others Pattern recognition: - Price/Volume /Order Book - Finding other algos (reverse engeneering) Market sentiment (e.g. processes Twitter mood) Fair price (derivatives) Be crative!
  22. 22. The Cycle of Automating a Strategy 1) Idea 2) Quantify the idea, Prototype an algorithm 3) Backtest 4) Test in safe environment 5) Production version/Babysitting 6) Constant improvement
  23. 23. The Cycle of Automating a Strategy 1) Idea 2) Quantify the idea, Prototype an algorithm 3) Backtest 4) Test in safe environment 5) Production version/Babysitting 6) Constant improvement Gut feeling or a practice Often comes from competence in other fields (market, sector, technology, processes…) General ideas are simple and public, it’s often the details and parameters that make the difference
  24. 24. The Cycle of Automating a Strategy 1) Idea 2) Quantify the idea, Prototype an algorithm 3) Backtest 4) Test in safe environment 5) Production version/Babysitting 6) Constant improvement Math : Trivial ~ Hardcore Building the correct model Example: Momentum following Idea: security on an uptrend/downtrend will continue on an uptrend/downtrend Implementation: take 1st and 2nd derivative of moving average, trade when specific threshold 1st/2nd is crossed
  25. 25. The Cycle of Automating a Strategy 1) Idea 2) Quantify the idea, Prototype an algorithm 3) Backtest 4) Test in safe environment 5) Production version/Babysitting 6) Constant improvement Getting good data (can be extremely expensive) Overfitting - can the past results be applied in the future trading Defining parameters/Testing them Risk evaluation (Sharpe ratio, Draw down, Expected shortfall, Value at Risk) Is the idea profitable?
  26. 26. The Cycle of Automating a Strategy 1) Idea 2) Quantify the idea, Prototype an algorithm 3) Backtest 4) Test in safe environment 5) Production version/Babysitting 6) Constant improvement Test on Demo/other assets/extremely small amounts Finding bugs Detecting corner cases What about trading costs? Is it still profitable?
  27. 27. The Cycle of Automating a Strategy 1) Idea 2) Quantify the idea, Prototype an algorithm 3) Backtest 4) Test in safe environment 5) Production version/Babysitting 6) Constant improvement Close to impossible to simulate the actual market Quality of execution (slippage) Speed Interaction with other algos/impact on market Is it still profitable? Identify the problems if not
  28. 28. The Cycle of Automating a Strategy 1) Idea 2) Quantify the idea, Prototype an algorithm 3) Backtest 4) Test in safe environment 5) Production version/Babysitting 6) Constant improvement Markets are constantly changing New participants, new competitors Strategies often stop being profitable after a while
  29. 29. Dangers of Algo-trading Robots do not understand if they’re doing something wrong, lots of pressure on the design (security, controls, limits…) More and more reliability on IT (networks, servers, hardware, data feed, latency) Very small margin of error Keeping your idea secret and protected The field is still rather new and proper regulation is being drafted as we speak Due to the above, there is a risk of systemic failures
  30. 30. Camber Energy example
  31. 31. Thank you!

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