advanced-algorithmic-trading

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151> ft <- as.numeric(ftrt)> ft <- ft[!is.na(ft)]The next task is to fit a suitable ARIMA(p,d,q) model. We saw how to do that in the previouschapter so the procedure will not be repeated here. Instead the code will simply be provided:> ftfinal.aic <- Inf> ftfinal.order <- c(0,0,0)> for (p in 1:4) for (d in 0:1) for (q in 1:4) {> ftcurrent.aic <- AIC(arima(ft, order=c(p, d, q)))> if (ftcurrent.aic < ftfinal.aic) {> ftfinal.aic <- ftcurrent.aic> ftfinal.order <- c(p, d, q)> ftfinal.arima <- arima(ft, order=ftfinal.order)> }> }Since we have already differenced the FTSE returns once we should expect our integratedcomponent d to equal zero, which it does:> ftfinal.order[1] 4 0 4Thus we receive an ARIMA(4,0,4) model. This contains four autoregressive parameters andfour moving average parameters.We are now in a position to decide whether the residuals of this model fit possess evidenceof conditional heteroskedastic behaviour. To test this we need to plot the correlogram of theresiduals, as given in Figure 11.10.> acf(resid(ftfinal.arima))This looks like a realisation of a discrete white noise process indicating that we have achieveda good fit with the ARIMA(4,0,4) model.To test for conditional heteroskedastic behaviour we need to square the residuals and plotthe corresponding correlogram, as given in Figure 11.11.> acf(resid(ftfinal.arima)^2)We can see clear evidence of serial correlation in the squared residuals, leading us to theconclusion that conditional heteroskedastic behaviour is present in the diff log return series ofthe FTSE100.We are now in a position to fit a GARCH model using the tseries library.The first command actually fits an appropriate GARCH model, with the trace=F parametertelling R to suppress excessive output.The second command removes the first element of the residuals, since it is NA:> ft.garch <- garch(ft, trace=F)> ft.res <- ft.garch$res[-1]To test for a good fit we can plot the correlogram of the GARCH residuals and the squareGARCH residuals as given in Figure 11.12.> acf(ft.res)
152Figure 11.10: Residuals of an ARIMA(4,0,4) fit to the FTSE100 diff log returnsFigure 11.11: Squared residuals of an ARIMA(4,0,4) fit to the FTSE100 diff log returnsThe correlogram looks like a realisation of a discrete white noise process indicating a goodfit. Let us now try the squared residuals as given in Figure 11.13.
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ContentsI Introduction 11 Introduct
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38.2 Correlation . . . . . . . . .
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513.2 The Kalman Filter . . . . . .
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719 Support Vector Machines . . . .
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928 Kalman Filter-Based Pairs Tradi
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Limit of Liability/Disclaimer ofWar
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Part IIntroduction1
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4The aim of this book is to provide
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61.3 How Is The Book Laid Out?The b
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81.5 How Does This Book Differ From
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101.7.1 AlternativesThere are many
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Chapter 2Introduction to Bayesian S
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15Table 2.1: Comparison of Frequent
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172.2 Applying Bayes’ Rule for Ba
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19the next chapter. At this stage,
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21# Create and plot a Beta distribu
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Chapter 3Bayesian Inference of a Bi
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25• The fairness of the coin does
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273.4.3 Multiple Flips of the CoinN
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29x = np.linspace(0, 1, 100)params
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31Hence, all we need to do is re-ar
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33Figure 3.3: The prior and posteri
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Chapter 4Markov Chain Monte CarloIn
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374.2.1 Markov Chain Monte Carlo Al
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39We discussed the fact that we cou
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41Finally we specify the Metropolis
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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
Page 113 and 114: 10010.4.1 RationaleIn the previous
Page 115 and 116: 10210.4.4 Simulations and Correlogr
Page 117 and 118: 104Figure 10.2: Realisation of AR(1
Page 119 and 120: 10610.4.5 Financial DataAmazon Inc.
Page 121 and 122: 108Figure 10.6: Correlogram of Firs
Page 123 and 124: 110> plot(gspcrt)Figure 10.8: First
Page 125 and 126: 112model, as well as the conditiona
Page 127 and 128: 114Figure 10.10: Realisation of MA(
Page 129 and 130: 116Coefficients:ma1 intercept-0.729
Page 131 and 132: 118> require(quantmod)> getSymbols(
Page 133 and 134: 120> amznrt.maCall:arima(x = amznrt
Page 135 and 136: 122Figure 10.16: Residuals of MA(1)
Page 137 and 138: 124Figure 10.18: Residuals of MA(3)
Page 139 and 140: 12610.6.3 RationaleTo date we have
Page 141 and 142: 128Figure 10.20: Correlogram of an
Page 143 and 144: 130Figure 10.22: Correlogram of an
Page 145 and 146: 132Figure 10.23: Correlogram of the
Page 147 and 148: 134Notice that there are some signi
Page 149 and 150: 136I would like to thank you for be
Page 151 and 152: 138Figure 11.1: Plot of simulated A
Page 153 and 154: 140> amzn = diff(log(Cl(AMZN)))As i
Page 155 and 156: 142We fit an ARIMA model by looping
Page 157 and 158: 144being able to create strategy in
Page 159 and 160: 14611.5.2 Why Does This Model Volat
Page 161 and 162: 148ɛ t = σ t w t (11.17)σ 2 t =
Page 163: 1502.5 % 97.5 %a0 0.1786255 0.21726
Page 167 and 168: Figure 11.13: Squared residuals of
Page 169 and 170: 156mean, but due to its stationarit
Page 171 and 172: 158x t = pz t + w x,t (12.1)y t = q
Page 173 and 174: 160Figure 12.3: Plot of x t and y t
Page 175 and 176: 162> layout(1:2)> plot(badcomb, typ
Page 177 and 178: 164We then created two subsequent s
Page 179 and 180: 166Figure 12.6: Backward-adjusted c
Page 181 and 182: 168Coefficients:(Intercept) ewaAdj3
Page 183 and 184: 170> plot(rdsaAdj, rdsbAdj,xlab="RD
Page 185 and 186: 172set.seed(123)## SIMULATED DATA##
Page 187 and 188: 174plot(comb1$residuals, type="l",x
Page 189 and 190: 176> q <- 0.6*z + rnorm(10000)> r <
Page 191 and 192: 178Figure 12.12: Plot of s t , the
Page 193 and 194: 180EWA.Adjusted.l2 EWC.Adjusted.l2
Page 195 and 196: 182library("quantmod")library("tser
Page 197 and 198: 184
Page 199 and 200: 186• Smoothing - Estimating the p
Page 201 and 202: 18813.2.1 A Bayesian ApproachRecall
Page 203 and 204: 190E[y t+1 |D t ] = E[Ft+1θ T t +
Page 205 and 206: 192of our linear regression. The ne
Page 207 and 208: 194Figure 13.1: Scatterplot of the
Page 209 and 210: 196Figure 13.2: Time-varying slope
Page 211 and 212: 198delta = 1e-5trans_cov = delta /
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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
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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