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Intra-day De-Mark Plus Order-Flow Indicator (Indempofi) QuantCon Singapore 2016 Christopher Ting http://mysmu.edu.sg/faculty/christophert/ k [email protected] Lee Kong Chian School of Business Singapore Management University Novermber 11, 2016 Christopher Ting Quantitative Finance, SMU November 11, 2016 1 / 21 Table of Contents 1 Introduction 2 Tom DeMark’s Indicators 3 Order Flow 4 Back-Testing 5 Conclusions Christopher Ting Quantitative Finance, SMU November 11, 2016 2 / 21 Introduction Overview Trading Investment Systematic Discretionary Statistical Arbitrage Back-Testing Money/Risk Management Christopher Ting Quantitative Finance, SMU November 11, 2016 3 / 21 Introduction Industry Trend Trader Algo X Agency Emotional Christopher Ting X Principal Quantitative Finance, SMU Machine Operational November 11, 2016 4 / 21 Introduction Order Placement Strategies Source: Domowitz, I. and Yegerman, H. (2011). Sample Period: Q4 2009 to Q4 2010 Q4 Christopher Ting Quantitative Finance, SMU November 11, 2016 5 / 21 Introduction Aspects of An Algo Data Tests Algorithm Commission KPI Coding Christopher Ting Quantitative Finance, SMU November 11, 2016 6 / 21 Introduction Types of Trading Strategy Risk Mgmt Hedging Momentum Spread Neutral Factors Christopher Ting Trading Strategy Quantitative Finance, SMU Directional November 11, 2016 7 / 21 Tom DeMark’s Indicators Tom DeMark (TD) Main Feature • Market timing based solely on prices • Trend exhaustion • Spotting market high and market low Source: Tom Demark May 2016 Basic Building Block Closing price Pt today versus the closing price 4 days earlier Pt−4 Christopher Ting “Conventional indicators are typically trend followers whereas DeMark is designed specifically to anticipate trend reversals.” Quantitative Finance, SMU November 11, 2016 8 / 21 Tom DeMark’s Indicators Intra-Day TD Sequential: TD Setup (Buy) Buy Setup: consecutive Pt < Pt−4 nine times Christopher Ting Quantitative Finance, SMU November 11, 2016 9 / 21 Tom DeMark’s Indicators Intra-Day TD Sequential: TD Setup (Sell) Sell Setup: consecutive Pt > Pt−4 nine times Christopher Ting Quantitative Finance, SMU November 11, 2016 10 / 21 Order Flow Limit-Order Books 6E Mar16 Bid Qty Price Ask Qty 10907 54 10906 64 10905 49 10904 45 10903 10 8 10902 62 10901 46 10900 55 10899 38 10898 6E Jun16 Bid Qty Price Ask Qty 10938 12 10937 12 10936 14 10935 7 10934 1 1 10931 15 10930 14 10929 12 10928 52 10927 The back and front contracts of EUR/USD futures on December 17, 2015 7 PM EST. Christopher Ting Quantitative Finance, SMU November 11, 2016 11 / 21 Order Flow Date Time Type Price Contracts 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 2016-02-08 9:45:55 9:45:55 9:45:55 9:45:55 9:45:55 9:45:55 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 9:45:56 B A T A T A B A B A B A T T A B A B A B A T 16755 16760 16760 16760 16755 16760 16755 16760 16755 16760 16755 16760 16760 16755 16760 16755 16760 16755 16760 16755 16760 16760 210 144 1 143 1 150 209 149 219 149 213 149 3 2 146 211 146 212 146 212 146 2 Sign Signed Volume 1 1 -1 -1 1 -1 3 -2 1 2 Data of Small Nikkei 16H Futures from Bloomberg Christopher Ting Quantitative Finance, SMU November 11, 2016 12 / 21 Order Flow Features of Intra-Day Trading Christopher Ting Bid-Ask Spread buy at the ask and sell to the bid Slippage market depth, price impact Seasonality market “rush minutes” Trading Hours curtailed time horizon Quantitative Finance, SMU November 11, 2016 13 / 21 Order Flow Features of Intra-Day Trading Christopher Ting Survivorship no bias Corporate action no direct impact Macroeconomic news breakdown of TD? Quantitative Finance, SMU (cont’d) November 11, 2016 14 / 21 Order Flow FBI’s Announcement Effect on 6J Christopher Ting Quantitative Finance, SMU November 11, 2016 15 / 21 Order Flow FBI’s Announcement Effect on ES Christopher Ting Quantitative Finance, SMU November 11, 2016 16 / 21 Back-Testing Performance Measures for Back-Testing Winning Probability A := number of positive P&L number of round-trip trades Reward-to-Risk Ratio B := average gain average loss Trading Sharpe Ratio C := Christopher Ting average P&L standard deviation of P&L Quantitative Finance, SMU November 11, 2016 17 / 21 Back-Testing Performance Measures for Back-Testing (cont’d) t statistic • Average P&L (after costs) from n trades (e.g. n = 81) • Standard deviation of P&L (after costs) • Formula for t statistic t= √ n× average P&L standard deviation of P&L • If |t| is large (e.g. |t| > 2), then the average P&L is said to be statistically significant. Christopher Ting Quantitative Finance, SMU November 11, 2016 18 / 21 Back-Testing Parameter Robustness Christopher Ting Quantitative Finance, SMU November 11, 2016 19 / 21 Back-Testing Data Partition Past performance is not indicative of future performance. In-Sample Christopher Ting Out-ofSample Paper Trading Quantitative Finance, SMU Commission November 11, 2016 20 / 21 Conclusions Concluding Remarks • Applying TD in intraday trading requires the order flow threshold to increase the probability of winning and the t statistics of P&L. • Intra-day DeMark plus order flow indicator (Indempofi) has two parameters: holding-period and order-flow threshold. • It is important to separate in-sample data for “training” and out-of-sample data for more realistic expectation of Indempofi’s performance. • Cross-sectional scaling rather than position scaling is less risky: portfolio of indempofi (HPi , OFTi ). • t stat is useful to gauge the effectiveness of an algo on the rolling window basis. • Use t statistics as the “weights” for allocating capitals to indempofi’s. Christopher Ting Quantitative Finance, SMU November 11, 2016 21 / 21