advanced-algorithmic-trading

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9All of the code in the Python sections of this book have been designed to be run usingAnaconda/Spyder for Python 2.7.x, 3.4.x and 3.5.x. However, many seasoned developers preferto work outside of the Anaconda environment, e.g. by using virtualenv. The code in this book willalso happily work in such virtual environments once the necessary libraries have been installed.If there are any questions about Python installation or the code in this book then pleaseemail support@quantstart.com.1.6.2 Installing RR is a little bit trickier to install than Anaconda but not hugely so. RStudio is an IDE for R thatprovides a similar interface to R as Spyder does for Python. RStudio has an R syntax-highlightingconsole and visualisation tools all in the same document interface.RStudio requires the underlying R software itself. R must first be downloaded prior tousage of RStudio. This can be done for Windows, Mac OS X or Linux from the following link:https://cran.rstudio.com/It is necessary to select the pre-compiled binary from the top of the page that fits the particularoperating system being utilised.Once R is successfully installed the next step is to download RStudio: https://www.rstudio.com/products/rstudio/download/Once again the version will need to be selected for the particular platform and operatingsystem type (32/64-bit) in use. It is necessary to select one of the links under "Installers forSupported Platforms".All of the code snippets in the R sections of this book have been designed to run on "vanilla"R and/or R Studio.If there are any questions about R installation or the code in this book then please emailsupport@quantstart.com.1.7 QSTrader Backtesting Simulation SoftwareThere is now a vast array of freely available tools for carrying out quantitative trading strategybacktests. New software (both open source and proprietary) appears every month.In this book extensive use will be made of QSTrader–QuantStart’s own open-source backtestingengine. The project has a GitHub page here: https://github.com/mhallsmoore/qstrader.QSTrader’s "philosophy" is to be highly modular with first-class status given to risk management,position-sizing, portfolio construction and execution. Brokerage fees and bid/ask spread(assuming available data) are turned on by default in all backtests. This provides a more realisticassessment of how a strategy is likely to perform under real trading conditions.QSTrader is currently under active development by a team of dedicated volunteers, includingmyself. The software remains in "alpha" mode, which means it is not ready for live-tradingdeployment yet. However it is sufficiently mature to allow comprehensive backtesting simulation.The majority of the quantitative trading strategies in this book have been implemented usingQSTrader, with full code provided within each respective chapter.More information about the software can be found in the chapter Introduction To QSTraderin the final section of this book.
101.7.1 AlternativesThere are many alternative backtesting environments available, some of which are listed below:• Quantopian - A well-regarded web-based backtesting and trading engine for equities markets:https://www.quantopian.com• Zipline - An open source backtesting library that powers the Quantopian web-based backtester:https://github.com/quantopian/zipline• PySystemTrade - Rob Carver’s futures backtesting system: https://github.com/robcarver17/pysystemtrade1.8 Where to Get HelpThe best place to look for help is the articles list on QuantStart.com found at QuantStart.com/articles.Over 200 articles about quantitative finance and algorithmic trading have been written to dateon the site.If you have a question that you feel is not answered by one of the current articles an alternativeis to contact QuantStart Support at support@quantstart.com.
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 8 and 9: 719 Support Vector Machines . . . .
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: 81.5 How Does This Book Differ From
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
Page 58 and 59: 45x, stats.beta.pdf(x, alpha, beta)
Page 60 and 61: Chapter 5Bayesian Linear Regression
Page 62 and 63: 49non-informative priors.• Poster
Page 64 and 65: 51# Use a linear model (y ~ beta_0
Page 66 and 67: 53# Use the No-U-Turn Samplerstep =
Page 68 and 69: 55plt.show()We can see the sampled
Page 70 and 71: 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
Page 113 and 114:
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
Page 221 and 222:
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
Page 274 and 275:
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
Page 358 and 359:
345self.encoding = encodingdef _res
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347"""topics = open("data/all-topic
Page 362:
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
Page 381 and 382:
368import datetimefrom qstrader imp
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37026.2 Strategy ImplementationTo i
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372final.order[1],final.order[3]),
Page 387 and 388:
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-
Page 433 and 434:
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
Page 489 and 490:
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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