1. Session 5: Review of the Scientific Method and The ATS Development Process 1

2. Agenda 2 Education Session 1: Industry Introduction and Derivatives Overview Session 2: Overview of Market Microstructure Session 3: Prerequisites for Algorithmic Trading System (ATS) Development and Selecting a Platform Session 4: Model Development and Stylized Facts Session 5: Review of the Scientific Method and the ATS Development Process Session 6: Formulation and Specification of a Strategy Research Session 1: Workshop Session 2 Workshop Competition - 2 weeks (10 days)

3. Loyola Algorithmic Trading Championship 3 Teams: 1-4 members per team Platform: NinjaTrader April 16 – April 27 (2 Weeks) WTC 1 st 2 nd 3 rd, LSC 1 st 2 nd 3 rd, 2011-2012 Loyola Algorithmic Trading Champion

4. The Scientific Method 4  The scientific method is a process for experimentation that is used to explore observations and answer questions.  Scientists use the scientific method to search for cause and effect relationships in nature.

5. The Scientific Method 5  Scientific inquiry is generally intended to be as objective as possible so as to reduce biased interpretations of results.  Another basic expectation is to document, archive and share all data and methodology so they are available for careful scrutiny by other scientists, giving them the opportunity to verify results by attempting to reproduce them.

6. The Scientific Method 6  Begins with the null hypothesis, which typically corresponds to a default position. For example, the null hypothesis might be that there is no relationship between two measured phenomena or that a potential treatment has no effect.

7. The Scientific Method 7

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9. 9 Beliefs and Biases  Beliefs can alter observation  Ex – Confirmation Bias  This is why the scientific method calls for experiment in a controlled setting that can be replicated by others; the goal is to remove cognitive bias.

10. The Scientific Method 10 Certainty and Myth  Any scientific theory is closely tied to empirical findings and always remains subject to falsification if new experimental observation incompatible with it is found.  In contrast to this, a myth can be believed and acted upon irrespective of its truth.

11. The Scientific Method 11 Uncertainty  Measurements in scientific work are also usually accompanied by estimates of their uncertainty.  Ex - Counts may represent a sample of desired quantities, with an uncertainty that depends upon the sampling method used and the number of samples taken.

12. The Scientific Method 12 Uncertainty  Measurements in scientific work are also usually accompanied by estimates of their uncertainty.  Ex - Counts may represent a sample of desired quantities, with an uncertainty that depends upon the sampling method used and the number of samples taken.

13. The Scientific Method 13

14. The Scientific Method 14  It is with this scientific approach that we go forward into developing strategies for algorithmic trading.  We use the approach based on the scientific method because it is objective, it is systematic, and – if used properly – is powerful.

15. Algorithmic Trading Strategy Development 15 Based on the Scientific Method. Generally, 6 stages :  Formulation and Specification  Backtesting  Optimization  Walk - Forward Analysis  Trading the Strategy Live  Refinement and Evolution

16. Algorithmic Trading Strategy Development 16 Step 1: Formulation and Specification of the Trading Strategy  Like anything, a trading strategy begins as an idea. The rules that compose that strategy must be laid out one at a time.  Must be reduced to a set of precise rules and formulae. This is done using logic ( and, or ), comparison ( greater than, less than ), and conditional operators ( if - then, if - then - else )

17. Algorithmic Trading Strategy Development 17 Step 1: Formulation and Specification of the Trading Strategy Can be based on indicators.  Trend Following Indicators : Allow to detect and follow major market trends  Overbought and Oversold Indicators : Allow to detect important market turning points

18. Algorithmic Trading Strategy Development 18 Step 1: Formulation and Specification of the Trading Strategy Can be based on indicators.  Cycle Indicators : Allow to detect periodic market fluctuations  Timing Indicators : Allow to detect when things will happen ( ie seasonality )

19. Algorithmic Trading Strategy Development 19 Step 1: Formulation and Specification of the Trading Strategy All strategies have three major components : 1) Entry and Exit 2) Risk Management 3) Position Sizing

20. Algorithmic Trading Strategy Development 20 Step 1: Formulation and Specification of the Trading Strategy 1) Entry and Exit  A strategy enters and exits using various orders  A strategy can employ more then one entry and exit ( scaling )

21. Algorithmic Trading Strategy Development 21 Step 1: Formulation and Specification of the Trading Strategy  A strategy’s buy and sell conditions can be symmetrical : Ex - Buy when price rises through a three day high and sell when price breaks three day low.

22. Algorithmic Trading Strategy Development 22 Step 1: Formulation and Specification of the Trading Strategy  A strategy’s buy and sell conditions can also be asymmetrical : Ex - Buy when a five day high is broken and sell when a 5- day moving average falls below the 20 day moving average.

23. Algorithmic Trading Strategy Development 23 Step 1: Formulation and Specification of the Trading Strategy  Entry Rule : Rule that initiates new long or short position  Exit Rule : Rule that closes out a current long or short position  Reversal Rule : Rule that closes out position and initiates a new position in the opposite direction

24. Algorithmic Trading Strategy Development 24 Step 1: Formulation and Specification of the Trading Strategy 2) Risk Management  With respect to strategies, risk is defined as the possibility of financial loss  There are trading risks ( losing on a trade ), strategy risks ( losing on a strategy ), and portfolio risks ( losing on a portfolio, potentially multi - strategy )

25. Algorithmic Trading Strategy Development 25 Step 1: Formulation and Specification of the Trading Strategy  A good way to think about risk management : “What is the least amount of money necessary to lose to allow the trading strategy to achieve maximum trading profit ? ”

26. Algorithmic Trading Strategy Development 26 Step 1: Formulation and Specification of the Trading Strategy  Risk : Reward Ratio – “I am willing to risk 20 ticks to make 20 ticks” – 1:1  You set how much you are willing to risk with a stop - loss order  You set the stop - loss order at the maximum, yet subject to slippage, loss to be taken on a trade

27. Algorithmic Trading Strategy Development 27 Step 1: Formulation and Specification of the Trading Strategy 3) Position Sizing  A trading strategy can either trade a fixed number of contracts in each position or it can vary how many contracts to trade according to some rules or principles  “Scaling - in”, “Scaling - out”

28. Algorithmic Trading Strategy Development 28 Step 2: Backtesting  This stage tests the strategy over a past amount of data  This process is used to ensure that the trading rules are calculated correctly and give a preliminary estimate as to the performance and profitability

29. Algorithmic Trading Strategy Development 29 Step 2: Backtesting  The four features of a robust strategy : Strategy is profitable under : 1) Different range of parameter sets 2) A wide - basket of diverse markets 3) A wide - range of market types and conditions 4) Long and short trades

30. Algorithmic Trading Strategy Development 30 Step 2: Backtesting Determining minimum needed to trade a strategy :

31. Algorithmic Trading Strategy Development 31 Step 2: Backtesting  Performance Summary ( Check Email )

32. Algorithmic Trading Strategy Development 32 Step 3: Optimization  After completing the backtest, we complete an optimization.  To optimize means we try to “make the best or most effective use of. ”

33. Algorithmic Trading Strategy Development 33 Step 3: Optimization  The purpose of optimization is to make a robust trading model, not to overfit  Overfitting is the opposite of making a strategy robust – it curve fits and makes it difficult for a strategy to do well in future market conditions Ex - Human Beings are Currently ‘Overfitted’ to Earth

34. Algorithmic Trading Strategy Development 34 Step 4: Walk - Forward Analysis  Walk - Forward Analysis – test the model on real - time data as it comes in  This can facilitate the optimization process in developing your trading strategy

35. Algorithmic Trading Strategy Development 35 Step 4: Walk - Forward Analysis  Walk - Forward Analysis – test the model on real - time data as it comes in  This can facilitate the optimization process in developing your trading strategy

36. Algorithmic Trading Strategy Development 36 Step 5: Trade Live Step 6: Evolve  Finally, if you are satisfied with the optimization, and the walk - forward analysis looks good, you can turn your trading strategy on to real - time  You must watch over your strategy still and monitor how it evolves. The market will eventually change, you must determine when to hit ‘off’.