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719 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26119.1 Motivation for Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . 26119.2 Advantages and Disadvantages of SVMs . . . . . . . . . . . . . . . . . . . . . . . 26219.2.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26219.2.2 Disadvantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26219.3 Linear Separating Hyperplanes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26319.4 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26519.5 Deriving the Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26619.6 Constructing the Maximal Margin Classifier . . . . . . . . . . . . . . . . . . . . . 26719.7 Support Vector Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26819.8 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27119.8.1 Biblographic Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27320 Model Selection and Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . 27520.1 Bias-Variance Trade-Off . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27520.1.1 Machine Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 27520.1.2 Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27620.1.3 The Bias-Variance Tradeoff . . . . . . . . . . . . . . . . . . . . . . . . . . 27820.2 Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28120.2.1 Overview of Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . 28220.2.2 Forecasting Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28220.2.3 Validation Set Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28320.2.4 k-Fold Cross Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28420.2.5 Python Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28520.2.6 k-Fold Cross Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28920.2.7 Full Python Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29221 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30121.1 High Dimensional Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30221.2 Mathematical Overview of Unsupervised Learning . . . . . . . . . . . . . . . . . 30221.3 Unsupervised Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . 30321.3.1 Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 30321.3.2 Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30321.4 Bibliographic Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30422 Clustering Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30522.1 K-Means Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30522.1.1 The Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30622.1.2 Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30722.1.3 Simulated Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30822.1.4 OHLC Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31022.2 Bibliographic Note . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31822.3 Full Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31823 Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32523.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
823.2 Supervised Document Classification . . . . . . . . . . . . . . . . . . . . . . . . . 32623.3 Preparing a Dataset for Classification . . . . . . . . . . . . . . . . . . . . . . . . 32623.4 Vectorisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33823.5 Term-Frequency Inverse Document-Frequency . . . . . . . . . . . . . . . . . . . . 33823.6 Training the Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . . 34023.7 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34223.8 Full Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344V Quantitative Trading Techniques 34924 Introduction to QSTrader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35124.1 Motivation for QSTrader . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35124.2 Design Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35224.3 Installation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35425 Introductory Portfolio Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . 35525.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35525.2 The Trading Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35525.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35625.4 Python QSTrader Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 35725.4.1 MonthlyLiquidateRebalanceStrategy . . . . . . . . . . . . . . . . . . . . . 35825.4.2 LiquidateRebalancePositionSizer . . . . . . . . . . . . . . . . . . . . . . . 35925.4.3 Backtest Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36025.5 Strategy Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36125.5.1 Transaction Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36125.5.2 US Equities/Bonds 60/40 ETF Portfolio . . . . . . . . . . . . . . . . . . . 36125.5.3 "Strategic" Weight ETF Portfolio . . . . . . . . . . . . . . . . . . . . . . 36225.5.4 Equal Weight ETF Portfolio . . . . . . . . . . . . . . . . . . . . . . . . . 36425.6 Full Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36526 ARIMA+GARCH Trading Strategy on Stock Market Indexes Using R . . . 36926.1 Strategy Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36926.2 Strategy Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37026.3 Strategy Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37326.4 Full Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37627 Cointegration-Based Pairs Trading using QSTrader . . . . . . . . . . . . . . . 38127.1 The Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38127.2 Cointegration Tests in R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38227.3 The Trading Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38427.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38527.5 Python QSTrader Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . 38627.6 Strategy Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39127.6.1 Transaction Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39127.6.2 Tearsheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39227.7 Full Code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393
Page 2 and 3: ContentsI Introduction 11 Introduct
Page 4 and 5: 38.2 Correlation . . . . . . . . .
Page 6 and 7: 513.2 The Kalman Filter . . . . . .
Page 10 and 11: 928 Kalman Filter-Based Pairs Tradi
Page 12 and 13: Limit of Liability/Disclaimer ofWar
Page 14: Part IIntroduction1
Page 17 and 18: 4The aim of this book is to provide
Page 19 and 20: 61.3 How Is The Book Laid Out?The b
Page 21 and 22: 81.5 How Does This Book Differ From
Page 23 and 24: 101.7.1 AlternativesThere are many
Page 26 and 27: Chapter 2Introduction to Bayesian S
Page 28 and 29: 15Table 2.1: Comparison of Frequent
Page 30 and 31: 172.2 Applying Bayes’ Rule for Ba
Page 32 and 33: 19the next chapter. At this stage,
Page 34 and 35: 21# Create and plot a Beta distribu
Page 36 and 37: Chapter 3Bayesian Inference of a Bi
Page 38 and 39: 25• The fairness of the coin does
Page 40 and 41: 273.4.3 Multiple Flips of the CoinN
Page 42 and 43: 29x = np.linspace(0, 1, 100)params
Page 44 and 45: 31Hence, all we need to do is re-ar
Page 46 and 47: 33Figure 3.3: The prior and posteri
Page 48 and 49: Chapter 4Markov Chain Monte CarloIn
Page 50 and 51: 374.2.1 Markov Chain Monte Carlo Al
Page 52 and 53: 39We discussed the fact that we cou
Page 54 and 55: 41Finally we specify the Metropolis
Page 56 and 57: 43we do not need to compute 100,000
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45x, stats.beta.pdf(x, alpha, beta)
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Chapter 5Bayesian Linear Regression
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49non-informative priors.• Poster
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51# Use a linear model (y ~ beta_0
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53# Use the No-U-Turn Samplerstep =
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55plt.show()We can see the sampled
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57"""Calculates the Markov Chain Mo
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Chapter 6Bayesian Stochastic Volati
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61plt.show()Figure 6.1: Different r
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63( )yilog ∼ t(ν, 0, exp(−2s i
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656.3.2 Model Specification in PyMC
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67Figure 6.5: Overlay of samples fr
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69plt.xlabel(’Time’)plt.ylabel(
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Part IIITime Series Analysishttps:/
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74• Seasonal Variation - Many tim
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767.5 How Does This Relate to Other
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78Definition 8.1.1. Expectation. Th
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808.2 CorrelationCorrelation is a d
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82Definition 8.3.4. Stationary in t
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84Figure 8.2: Correlogram plotted i
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868.6 Next StepsNow that we’ve di
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88• Use our knowledge of time ser
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909.3.2 CorrelogramWe can also plot
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929.4.2 CorrelogramThe autocorrelat
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94created the difference series, we
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96there are a few that are marginal
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9810.1 How Will We Proceed?In this
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10010.4.1 RationaleIn the previous
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10210.4.4 Simulations and Correlogr
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104Figure 10.2: Realisation of AR(1
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10610.4.5 Financial DataAmazon Inc.
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108Figure 10.6: Correlogram of Firs
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110> plot(gspcrt)Figure 10.8: First
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112model, as well as the conditiona
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114Figure 10.10: Realisation of MA(
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116Coefficients:ma1 intercept-0.729
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118> require(quantmod)> getSymbols(
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120> amznrt.maCall:arima(x = amznrt
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122Figure 10.16: Residuals of MA(1)
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124Figure 10.18: Residuals of MA(3)
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12610.6.3 RationaleTo date we have
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128Figure 10.20: Correlogram of an
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130Figure 10.22: Correlogram of an
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132Figure 10.23: Correlogram of the
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134Notice that there are some signi
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136I would like to thank you for be
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138Figure 11.1: Plot of simulated A
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140> amzn = diff(log(Cl(AMZN)))As i
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142We fit an ARIMA model by looping
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144being able to create strategy in
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14611.5.2 Why Does This Model Volat
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148ɛ t = σ t w t (11.17)σ 2 t =
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1502.5 % 97.5 %a0 0.1786255 0.21726
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152Figure 11.10: Residuals of an AR
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Figure 11.13: Squared residuals of
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156mean, but due to its stationarit
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158x t = pz t + w x,t (12.1)y t = q
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160Figure 12.3: Plot of x t and y t
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162> layout(1:2)> plot(badcomb, typ
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164We then created two subsequent s
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166Figure 12.6: Backward-adjusted c
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168Coefficients:(Intercept) ewaAdj3
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170> plot(rdsaAdj, rdsbAdj,xlab="RD
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172set.seed(123)## SIMULATED DATA##
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174plot(comb1$residuals, type="l",x
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176> q <- 0.6*z + rnorm(10000)> r <
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178Figure 12.12: Plot of s t , the
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180EWA.Adjusted.l2 EWC.Adjusted.l2
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182library("quantmod")library("tser
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184
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186• Smoothing - Estimating the p
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18813.2.1 A Bayesian ApproachRecall
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190E[y t+1 |D t ] = E[Ft+1θ T t +
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192of our linear regression. The ne
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194Figure 13.1: Scatterplot of the
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196Figure 13.2: Time-varying slope
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198delta = 1e-5trans_cov = delta /
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200
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202"risk manager". They will be use
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204Figure 14.1: Two-state Markov Ch
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206Figure 14.2: Hidden Markov Model
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208> bull_var <- 0.1> bear_mean <-
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210historical financial data.14.3.3
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212The same process will now be car
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214install.packages(’quantmod’)
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216
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Chapter 15Introduction to Machine L
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22115.3.1 Linear RegressionAn eleme
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223While seemingly not a traditiona
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Chapter 16Supervised LearningSuperv
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227ŷ = ˆf(x) = argmax z∈R p(y =
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Chapter 17Linear RegressionIn this
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231if __name__ == "__main__":# Set
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23317.3 Maximum Likelihood Estimati
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235To simplify the notation the lat
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237Once the full dataset is created
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239if __name__ == "__main__":# Set
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241))lr_model.coef_[0]# Create a sc
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Chapter 18Tree-Based MethodsIn this
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245Figure 18.2: The resulting parti
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24718.3 Decision Trees for Classifi
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249In quantitative finance applicat
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251(a) Grow a tree ˆf b with k spl
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253tslag["Lag%s" % str(i+1)] = ts["
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255The same approach is carried out
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257seen whether it is feasible to p
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259amzn = create_lagged_series("AMZ
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Chapter 19Support Vector MachinesIn
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263• Non-Probabilistic - Since th
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265The key point here is that it is
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267classification. Overfitting mean
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269Figure 19.6: Addition of a singl
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27119.8 Support Vector MachinesThe
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273p∑K(x i , x k ) = (1 + x ij x
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Chapter 20Model Selection andCross-
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277This motivates the concept of a
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279Figure 20.1: Various estimates o
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281error for a particular model est
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283Figure 20.3: Amazon, Inc. - Pric
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28520.2.5 Python ImplementationWe a
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287At this stage we have the necess
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289..import pylab as plt....def plo
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291fold = 0# Loop over the k-foldsf
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293Figure 20.5: Test MSE curves for
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295return tsretdef validation_set_p
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297model = Pipeline([("polynomial_f
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299X = lags[["Lag1", "Lag2", "Lag3"
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Chapter 21Unsupervised LearningThe
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303parameters of the model, θ.Conv
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Chapter 22Clustering MethodsIn this
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3072. Iterate the following until c
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309]# Generate a list of the 2D clu
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311clustered using K-Means and then
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313ax.set_axis_bgcolor((1,1,0.9))ax
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315ClusterTomorrow to contain tomor
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317Figure 22.4: 3D scatterplot of n
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319# Apply the K-Means Algorithm fo
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321# up days and red for down daysc
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323sp500["ClusterMatrix"] = list(zi
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Chapter 23Natural Language Processi
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327The Reuters 21578 dataset can be
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329for export as shippers are now e
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331tungtung-oilveg-oilwheatwoolwpiy
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333in order to minimise memory usag
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335’The Tokyo Grain Exchange said
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337for t in d[0]:if t in topics:d_t
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339This would mean that we are givi
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341partitions into the training and
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3430.660194174757[[21 0 0 0 2 3 0 0
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345self.encoding = encodingdef _res
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347"""topics = open("data/all-topic
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Part VQuantitative Trading Techniqu
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352While vectorised backtests are s
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354• Backtest - Encapsulates the
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356The second strategy provides a 3
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35825.4.1 MonthlyLiquidateRebalance
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360"""Size the order to reflect the
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362Figure 25.1: US Equities/Bonds 6
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364five years relative to US equiti
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366# Use the monthly liquidate and
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368import datetimefrom qstrader imp
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37026.2 Strategy ImplementationTo i
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372final.order[1],final.order[3]),
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374> spIntersect = merge( spArimaGa
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376enters what looks to be more a s
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378# Output the CSV file to "foreca
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380
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382natural gas.The hypothesis prese
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384## Carry out linear regression o
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38627.5 Python QSTrader Implementat
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388that arrive out of order in the
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390if self.invested is not None:if
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392take into account commission dif
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394adf.test(comb$residuals, k=1)# c
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396def go_short_units(self):"""Go s
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398from qstrader.trading_session.ba
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400default=’’,help=’Pickle (.
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402Hence we can "long the spread" i
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404class KalmanPairsTradingStrategy
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406if all(self.latest_prices > -1.0
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408It also changes the FixedPositio
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410’--testing/--no-testing’, de
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412• Asset Selection - Choosing a
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414if self.R is not None:self.R = s
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416)self.events_queue.put(SignalEve
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418@click.option(’--testing/--no-
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420• Short-Term Trend - Predict m
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422It takes the CSV file path as an
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424produce a final truth column whe
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426bias-variance trade-off of the m
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428used by the machine learning mod
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430IQFeedIntradayCsvBarPriceHandler
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432’--filename’,default=’’,
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Figure 29.2: Tearsheet for Random F
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436):lookforward_minutes=5,up_down_
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438#ts["UpDown"] = down_tot & up_to
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440events_queue - A handle to the s
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442from intraday_ml_strategy import
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444if __name__ == "__main__":main()
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446sentiment.In recent years there
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448Ticker Name Period LinkMSFT Micr
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450self.statistics.update(event.tim
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452if self.end_date is not None:sen
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454a sent_sell corresponding exit t
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456sentiment_handler = SentdexSenti
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458Figure 30.2:volatile, posting mo
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460fication by adding many more sto
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462events_queue = queue.Queue()csv_
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464def main(config, testing, ticker
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466In quantitative trading this pro
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468self.tickers[ticker]["timestamp"
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470df["Adj Close"][mask],".", lines
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472from qstrader.event import (Sign
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474class RegimeHMMRiskManager(Abstr
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476# If in the undesirable regime,
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47831.5 Strategy Results31.5.1 Tran
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480Figure 31.3: Trend Following Reg
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482ax.grid(True)plt.show()if __name
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484elif short_sma < long_sma and se
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486elif action == "SLD":if self.inv
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488backtest = Backtest(price_handle
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490holding an instrument representi
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492rolling_sharpe_s = np.sqrt(self.
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494In order to use the annualised r
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496Figure 32.2: Aluminum Smelting S
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498
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500[18] Wikipedia: Viterbi algorith
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502[56] Investor, S. Regime detecti
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504[92] Sinclair, E. Volatility Tra
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