├── Decision_tree └── information_gain.ipynb ├── Iceberg_Detection ├── add_iceberg.R ├── icebergPresent.jpg └── lookForIceberg.py ├── Logistic_Regression └── logistic_regression.py ├── Price Prediction with Machine Learning methods.pdf ├── README.md ├── Rebuild_orderbook ├── data process.py ├── dataProcess0412.py ├── merge.py ├── processData.r ├── readData.py └── takeLagBookValue.r ├── SVM └── SVM.R ├── Sample_data └── sample_ES_DATA_20150917.txt └── Visualization ├── AttributeVisulization.R ├── AttributesVisualization.py ├── PriceAndPredictionPlot.R ├── Rplot.pdf ├── iceberg.png ├── presentation.png ├── visualizeIceberg.r └── visualizeSpread.R /Decision_tree/information_gain.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 73, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "from math import log\n", 12 | "def calcShannonEnt(dataSet):\n", 13 | " numEntries = len(dataSet)\n", 14 | " labelCounts = {}\n", 15 | " for featVec in dataSet:\n", 16 | " currentLabel = featVec[-1]\n", 17 | " #print(featVec[-1])\n", 18 | " if currentLabel not in labelCounts.keys():\n", 19 | " labelCounts[currentLabel] = 0\n", 20 | " labelCounts[currentLabel] += 1\n", 21 | "\n", 22 | " shannonEnt = 0.0\n", 23 | " for key in labelCounts:\n", 24 | " #print(labelCounts[key],numEntries)\n", 25 | " prob = float(labelCounts[key])/numEntries\n", 26 | " shannonEnt -= prob * log(prob,2)\n", 27 | " return shannonEnt" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 74, 33 | "metadata": { 34 | "collapsed": false 35 | }, 36 | "outputs": [], 37 | "source": [ 38 | "def createDataSet():\n", 39 | " dataSet = [[1, 1, 'yes'],\n", 40 | " [1, 1, 'yes'],\n", 41 | " [1, 0, 'no'],\n", 42 | " [0, 1, 'no'],\n", 43 | " [0, 1, 'no']]\n", 44 | " labels = ['no surfacing','flippers']\n", 45 | " return dataSet, labels" 46 | ] 47 | }, 48 | { 49 | "cell_type": "code", 50 | "execution_count": 75, 51 | "metadata": { 52 | "collapsed": false 53 | }, 54 | "outputs": [ 55 | { 56 | "data": { 57 | "text/plain": [ 58 | "0.9709505944546686" 59 | ] 60 | }, 61 | "execution_count": 75, 62 | "metadata": {}, 63 | "output_type": "execute_result" 64 | } 65 | ], 66 | "source": [ 67 | "myDat,labels=createDataSet()\n", 68 | "calcShannonEnt(myDat)" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 76, 74 | "metadata": { 75 | "collapsed": false 76 | }, 77 | "outputs": [], 78 | "source": [ 79 | "def splitDataSet(dataSet, axis, value):\n", 80 | " retDataSet = []\n", 81 | " for featVec in dataSet:\n", 82 | " if featVec[axis] == value:# append all the features except axis\n", 83 | " reducedFeatVec = featVec[:axis]\n", 84 | " reducedFeatVec.extend(featVec[axis+1:])\n", 85 | " retDataSet.append(reducedFeatVec)\n", 86 | " return retDataSet" 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 77, 92 | "metadata": { 93 | "collapsed": false 94 | }, 95 | "outputs": [ 96 | { 97 | "data": { 98 | "text/plain": [ 99 | "[[1, 'no'], [1, 'no']]" 100 | ] 101 | }, 102 | "execution_count": 77, 103 | "metadata": {}, 104 | "output_type": "execute_result" 105 | } 106 | ], 107 | "source": [ 108 | "splitDataSet(myDat,0,0)" 109 | ] 110 | }, 111 | { 112 | "cell_type": "code", 113 | "execution_count": 80, 114 | "metadata": { 115 | "collapsed": false 116 | }, 117 | "outputs": [], 118 | "source": [ 119 | "def chooseBestFeatureToSplit(dataSet):\n", 120 | " numFeatures = len(dataSet[0]) - 1\n", 121 | " baseEntropy = calcShannonEnt(dataSet)\n", 122 | " bestInfoGain = 0.0; bestFeature = -1\n", 123 | " for i in range(numFeatures):\n", 124 | " featList = [example[i] for example in dataSet]\n", 125 | " #create a set to store unique values\n", 126 | " uniqueVals = set(featList)\n", 127 | " newEntropy = 0.0\n", 128 | " for value in uniqueVals:\n", 129 | " subDataSet = splitDataSet(dataSet, i, value)\n", 130 | " prob = len(subDataSet)/float(len(dataSet))\n", 131 | " newEntropy += prob * calcShannonEnt(subDataSet)\n", 132 | " infoGain = baseEntropy - newEntropy\n", 133 | " if (infoGain > bestInfoGain):\n", 134 | " bestInfoGain = infoGain \n", 135 | " bestFeature = i \n", 136 | " return bestFeature" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 81, 142 | "metadata": { 143 | "collapsed": false 144 | }, 145 | "outputs": [ 146 | { 147 | "name": "stdout", 148 | "output_type": "stream", 149 | "text": [ 150 | "[1, 1, 1, 0, 0]\n", 151 | "[1, 1, 0, 1, 1]\n" 152 | ] 153 | }, 154 | { 155 | "data": { 156 | "text/plain": [ 157 | "0" 158 | ] 159 | }, 160 | "execution_count": 81, 161 | "metadata": {}, 162 | "output_type": "execute_result" 163 | } 164 | ], 165 | "source": [ 166 | "chooseBestFeatureToSplit(myDat)" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": null, 172 | "metadata": { 173 | "collapsed": true 174 | }, 175 | "outputs": [], 176 | "source": [] 177 | } 178 | ], 179 | "metadata": { 180 | "kernelspec": { 181 | "display_name": "Python 3", 182 | "language": "python", 183 | "name": "python3" 184 | }, 185 | "language_info": { 186 | "codemirror_mode": { 187 | "name": "ipython", 188 | "version": 3 189 | }, 190 | "file_extension": ".py", 191 | "mimetype": "text/x-python", 192 | "name": "python", 193 | "nbconvert_exporter": "python", 194 | "pygments_lexer": "ipython3", 195 | "version": "3.5.1" 196 | } 197 | }, 198 | "nbformat": 4, 199 | "nbformat_minor": 0 200 | } 201 | -------------------------------------------------------------------------------- /Iceberg_Detection/add_iceberg.R: -------------------------------------------------------------------------------- 1 | data=read.csv('~/Dropbox/CME practicum/Apr 22nd/Data/with1BookRec.txt') 2 | iceberg=read.csv('~/Desktop/python/iceberg.txt') 3 | iceberg=iceberg[iceberg$type!='in the spread',] 4 | n = length(iceberg[,1]) 5 | for(i in 1:n){ 6 | 7 | if(iceberg$buy.sell) 8 | data$book1.best.ask.size[data$time.5.n==iceberg$time[i]] = iceberg$volume[i]+ 9 | data$book1.best.ask.size[data$time.5.n==iceberg$time[i]] 10 | else 11 | data$book1.best.bid.size[data$time.5.n==iceberg$time[i]] = iceberg$volume[i]+ 12 | data$book1.best.bid.size[data$time.5.n==iceberg$time[i]] 13 | 14 | } -------------------------------------------------------------------------------- /Iceberg_Detection/icebergPresent.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fushuyue/Ml_HFT/23145c9173c2dc358f0d2da8a15e1b84ca50f153/Iceberg_Detection/icebergPresent.jpg -------------------------------------------------------------------------------- /Iceberg_Detection/lookForIceberg.py: -------------------------------------------------------------------------------- 1 | import matplotlib as mpl 2 | import matplotlib.pyplot as plt 3 | import pandas as pd 4 | iceberg = [] 5 | dictionary = {} 6 | tradep = [] # temporary storage of trade price 7 | tradev = [] # temporary storage of trade volume 8 | a = [0,0,0,0] # initialize 9 | iceberg_flag = 0 10 | flag=0 11 | 12 | with open('/Users/fushuyue/Desktop/python/ESZ5_20150917.txt') as input_file: 13 | for line in input_file: 14 | 15 | # trade data structure: 0 time 1 name; 2 quantity; 3 price; 4 volume; 5 flag 16 | # keep track of the type of last update 17 | if a[1] == 'TRADEREC': 18 | flag = True 19 | else: 20 | flag = False 21 | 22 | # read data line by line 23 | a = line.strip().split(',') 24 | 25 | if a[1] == 'TRADEREC': 26 | 27 | date_temp1 = a[0][0:2]+a[0][3:5]+a[0][6:8]+a[0][9:12] 28 | 29 | if float(a[3]) > bid1: 30 | # buy order 31 | aggressor = 1 32 | levelOneAsk = [ask1, ask1_size] 33 | else: 34 | # sell order 35 | aggressor= 0 36 | levelOneBid = [bid1, bid1_size] 37 | 38 | # buy order when big price appear first, there is iceberg order 39 | # the opposite for sell order 40 | if len(tradep) != 0: 41 | if aggressor: 42 | if float(a[3]) < tradep[-1]: 43 | # iceberg.append([date_temp, a[3]]) 44 | iceberg_flag = 1 45 | iceberg_price = float(a[3]) 46 | iceberg_time = date_temp1 47 | else: 48 | if float(a[3]) > tradep[-1]: 49 | iceberg_flag = 1 50 | iceberg_price = float(a[3]) 51 | iceberg_time = date_temp1 52 | 53 | tradep.append(float(a[3])) 54 | tradev.append(float(a[2])) 55 | 56 | # one trade may have different price so 57 | # store trade price and volume in a dictionary 58 | # use trade price as key, trade volume as value 59 | 60 | if float(a[3]) not in dictionary.keys(): 61 | dictionary[float(a[3])] = float(a[2]) 62 | else: 63 | dictionary[float(a[3])] += float(a[2]) 64 | 65 | 66 | if a[1] == 'BOOK10DEEPREC': 67 | temp = [int(k) for k in a[2:62]] 68 | date_temp = a[0][0:2]+a[0][3:5]+a[0][6:8]+a[0][9:12] 69 | 70 | ask_size = temp[32:60:3] 71 | ask_price = temp[30:58:3] 72 | bid_size = temp[0:28:3] 73 | bid_price = temp[2:30:3] 74 | bid1=temp[29] 75 | ask1=temp[30] 76 | ask1_size = ask_size[0] 77 | bid1_size = bid_size[-1] 78 | 79 | # if this is the book updates right after a trade happens 80 | if flag: 81 | 82 | # if the trade price is in the spread, means there's iceberg order in the spread 83 | # cannot figure out the whole iceberg order size 84 | if aggressor: 85 | if levelOneAsk[0] not in dictionary.keys(): 86 | iceberg.append([date_temp1, min(dictionary.keys()), dictionary[min(dictionary.keys())], levelOneAsk[1], aggressor, 'in the spread']) 87 | continue 88 | else: 89 | if levelOneBid[0] not in dictionary.keys(): 90 | iceberg.append([date_temp1, max(dictionary.keys()), dictionary[max(dictionary.keys())], levelOneBid[1], aggressor, 'in the spread']) 91 | continue 92 | 93 | # if we already know there is iceberg order 94 | if iceberg_flag: 95 | if aggressor: 96 | # there's different price and larger price happens first 97 | if dictionary[iceberg_price] < levelOneAsk[1]: 98 | # if the displayed level one has not been fully executed 99 | iceberg.append([iceberg_time, iceberg_price, dictionary[iceberg_price], levelOneAsk[1], aggressor, 'part,need check']) 100 | else: 101 | if levelOneAsk[0] == ask1: 102 | # if the displayed level one has not been fully executed, but refreshed 103 | iceberg.append([iceberg_time, iceberg_price, dictionary[iceberg_price], levelOneAsk[1], aggressor, 'part']) 104 | else: 105 | # not refreshed, still needs to check next updates 106 | # if next bookupdates shows level one been refreshed, then needs to check for if bid level moves 107 | iceberg.append([iceberg_time, iceberg_price, dictionary[iceberg_price], levelOneAsk[1], aggressor, 'whole']) 108 | else: 109 | if dictionary[iceberg_price] < levelOneBid[1]: 110 | iceberg.append([iceberg_time, iceberg_price, dictionary[iceberg_price], levelOneBid[1], aggressor, 'part']) 111 | else: 112 | if levelOneBid[0] == bid1: 113 | iceberg.append([iceberg_time, iceberg_price, dictionary[iceberg_price], levelOneBid[1], aggressor, 'part']) 114 | else: 115 | iceberg.append([iceberg_time, iceberg_price, dictionary[iceberg_price], levelOneBid[1], aggressor, 'whole']) 116 | 117 | # if in a buy order, volume is larger than best ask size, we know there is iceberg 118 | else: 119 | if aggressor: 120 | if dictionary[levelOneAsk[0]] > levelOneAsk[1]: 121 | # if level one moves 122 | if levelOneAsk[0] != ask1: 123 | iceberg.append([date_temp1, levelOneAsk[0], dictionary[levelOneAsk[0]], levelOneAsk[1], aggressor, 'whole']) 124 | else: 125 | iceberg.append([date_temp1, levelOneAsk[0], dictionary[levelOneAsk[0]], levelOneAsk[1], aggressor, 'part']) 126 | 127 | else: 128 | if dictionary[levelOneBid[0]] > levelOneBid[1]: 129 | if levelOneBid[0] != bid1: 130 | iceberg.append([date_temp1, levelOneBid[0], dictionary[levelOneBid[0]], levelOneBid[1], aggressor, 'whole']) 131 | else: 132 | iceberg.append([date_temp1, levelOneBid[0], dictionary[levelOneBid[0]], levelOneBid[1], aggressor, 'part']) 133 | 134 | # empty the variable 135 | iceberg_flag = 0 136 | tradep = [] 137 | tradev = [] 138 | dictionary = {} 139 | 140 | 141 | 142 | 143 | df = pd.DataFrame(iceberg, columns = ['time', 'price', 'volume', 'displayed size', 'buy/sell', 'type']) 144 | 145 | df.to_csv('/Users/fushuyue/Desktop/iceberg.txt',index=False) 146 | 147 | -------------------------------------------------------------------------------- /Logistic_Regression/logistic_regression.py: -------------------------------------------------------------------------------- 1 | from numpy import * 2 | 3 | 4 | 5 | 6 | def getcomb(): 7 | import scipy.special as sp 8 | fr = open('/Users/shengdongliu/Downloads/0401.txt') 9 | for line in fr.readlines(): 10 | lineArr = line.strip().split() 11 | totalcolumn=len(lineArr)-2 12 | break 13 | number=sp.comb(totalcolumn,2,exact=False) 14 | return number 15 | 16 | def settwofactor(number): 17 | import itertools 18 | a=list(itertools.combinations('abcdefghijklmno',2)) 19 | col1=ord(str(a[number][0]))-97 20 | col2=ord(str(a[number][1]))-97 21 | #col3=ord(str(a[number][2]))-97 22 | return col1,col2 23 | 24 | def readheader(): 25 | import csv 26 | with open('/Users/shengdongliu/Downloads/data-2.csv', 'rb') as inputfile: 27 | reader = csv.reader(inputfile) 28 | head= list(reader) 29 | 30 | return head[0][1:16] 31 | 32 | 33 | def loadDataSet(col1,col2): 34 | dataMat = []; labelMat = [] 35 | fr = open('/Users/shengdongliu/Downloads/eminicsv.txt') 36 | for line in fr.readlines(): 37 | lineArr = line.strip().split('\t') 38 | # dataMat.append([1.0,float(lineArr[0]),float(lineArr[1]),float(lineArr[2]),float(lineArr[3]),float(lineArr[4])]) 39 | dataMat.append([1.0,float(lineArr[col1]),float(lineArr[col2])]) 40 | #,float(lineArr[col3])]) 41 | labelMat.append(int(lineArr[15])) 42 | return dataMat,labelMat 43 | 44 | def sigmoid(inX): 45 | return 1.0/(1+exp(-inX)) 46 | 47 | def gradAscent(dataMatIn, classLabels): 48 | dataMatrix = mat(dataMatIn) #convert to NumPy matrix 49 | labelMat = mat(classLabels).transpose() #convert to NumPy matrix 50 | m,n = shape(dataMatrix) 51 | alpha = 0.01 52 | maxCycles = 150 53 | weights = ones((n,1)) 54 | for k in range(maxCycles): #heavy on matrix operations 55 | h = sigmoid(dataMatrix*weights) #matrix mult 56 | error = (labelMat - h) #vector subtraction 57 | weights = weights+alpha * dataMatrix.transpose()* error #matrix mult 58 | return weights 59 | 60 | def plotBestFit(col1,col2): 61 | 62 | import matplotlib.pyplot as plt 63 | from mpl_toolkits.mplot3d import Axes3D 64 | # weights=wei.getA() 65 | dataMat,labelMat=loadDataSet(col1,col2) 66 | dataArr = array(dataMat) 67 | #print dataArr 68 | #weights=gradAscent(dataArr,labelMat) 69 | # w0=weights[0] 70 | # w1=weights[1] 71 | # w2=weights[2] 72 | 73 | n = shape(dataArr)[0] 74 | # print n 75 | xcord1 = []; ycord1 = [];zcord1=[] 76 | 77 | xcord2 = []; ycord2 = [];zcord2=[] 78 | for i in range(n): 79 | if int(labelMat[i])== 1: 80 | xcord1.append(dataArr[i,1]); ycord1.append(dataArr[i,2]) 81 | #zcord1.append(dataArr[i,3]) 82 | else: 83 | xcord2.append(dataArr[i,1]); ycord2.append(dataArr[i,2]) 84 | #zcord2.append(dataArr[i,3]) 85 | #print xcord1 86 | fig = plt.figure() 87 | ax = fig.add_subplot(111) 88 | #,projection='3D') 89 | ax.scatter(xcord1, ycord1,s=30, c='red', marker='s') 90 | 91 | ax.scatter(xcord2, ycord2, s=30, c='green') 92 | #x = arange(-30,50, 0.11) 93 | #print weights.item(0) 94 | #y = (-weights.item(0)-weights.item(1)*x)/weights.item(2) 95 | # print y 96 | # print x 97 | #ax.plot(x, y) 98 | #axes = plt.gca() 99 | # axes.set_xlim([-1,0.5]) 100 | #axes.set_ylim([-0.5,1]) 101 | #axes.set_ylim([0.5,1.5]) 102 | name=readheader() 103 | #print name 104 | #print name 105 | str1='emini_'+str(name[col1]) 106 | str2='emini_'+str(name[col2]) 107 | str1=str1.replace(".", "_") 108 | str2=str2.replace(".", "_") 109 | plt.xlabel(str1); plt.ylabel(str2); 110 | #plt.show() 111 | s=str1+'-'+str2 112 | plt.savefig(s+'.pdf') 113 | 114 | 115 | def stocGradAscent0(dataMatrix, classLabels): 116 | m,n = shape(dataMatrix) 117 | alpha = 0.01 118 | weights = ones((n,1)) #initialize to all ones 119 | for i in range(m): 120 | h = sigmoid(sum(dataMatrix[i]*weights)) 121 | error = classLabels[i] - h 122 | weights = weights + alpha * error * dataMatrix[i] 123 | return weights 124 | 125 | def stocGradAscent1(dataMatrix, classLabels, numIter=150): 126 | m,n = shape(dataMatrix) 127 | weights = ones(n) #initialize to all ones 128 | for j in range(numIter): 129 | dataIndex = range(m) 130 | for i in range(m): 131 | alpha = 4/(1.0+j+i)+0.001 #apha decreases with iteration, does not 132 | randIndex = int(random.uniform(0,len(dataIndex)))#go to 0 because of the constant 133 | h = sigmoid(sum(dataMatrix[randIndex]*weights)) 134 | error = classLabels[randIndex] - h 135 | weights = weights + alpha * error * dataMatrix[randIndex] 136 | del(dataIndex[randIndex]) 137 | return weights 138 | 139 | def classifyVector(inX, weights): 140 | prob = sigmoid(sum(inX*weights)) 141 | if prob > 0.5: return 1.0 142 | else: return 0.0 143 | 144 | def Test(): 145 | import csv 146 | frTrain = open('/Users/shengdongliu/Downloads/eminitrain.txt'); frTest = open('/Users/shengdongliu/Downloads/eminitest.txt') 147 | #print 1 148 | #frTrain = open('Training.txt'); frTest = open('Test.txt') 149 | column=15 150 | trainingSet = []; trainingLabels = [] 151 | for line in frTrain.readlines(): 152 | #print 2 153 | 154 | currLine = line.strip().split('\t') 155 | lineArr =[] 156 | for i in range(column): 157 | lineArr.append(float(currLine[i])) 158 | #print 3 159 | trainingSet.append(lineArr) 160 | trainingLabels.append(float(currLine[column])) 161 | trainWeights = stocGradAscent1(array(trainingSet), trainingLabels, 100) 162 | #print 4 163 | errorCount = 0; numTestVec = 0.0 164 | upcount=0.0;downcount=0.0; 165 | totalup=0.0;totaldown=0.0; 166 | predict=[] 167 | for line in frTest.readlines(): 168 | numTestVec += 1.0 169 | if int(currLine[column])==1: 170 | totalup+=1 171 | if int(currLine[column])==0: 172 | totaldown+=1 173 | currLine = line.strip().split('\t') 174 | lineArr =[] 175 | for i in range(column): 176 | lineArr.append(float(currLine[i])) 177 | 178 | if int(classifyVector(array(lineArr), trainWeights))!= int(currLine[column]): 179 | errorCount += 1 180 | if int(currLine[column])==1: 181 | upcount+=1 182 | if int(currLine[column])==0: 183 | downcount+=1 184 | 185 | predict.append(int(classifyVector(array(lineArr), trainWeights))) 186 | 187 | errorRate = (float(errorCount)/numTestVec) 188 | up_correct_Rate = 1-(float(upcount)/totalup) 189 | down_correct_Rate=1-(float(downcount)/totaldown) 190 | 191 | file = open("/Users/shengdongliu/Downloads/towu.txt", "w") 192 | for n in range(len(predict)) : 193 | file.write(str(predict[n])) 194 | file.write("\n") 195 | errorRate=1-errorRate 196 | print "correct rate is: %f" %errorRate 197 | print "correct up rate: %f" %up_correct_Rate 198 | print "correct down rate: %f" %down_correct_Rate 199 | 200 | return errorRate 201 | 202 | def multiTest(): 203 | numTests = 10; errorSum=0.0 204 | for k in range(numTests): 205 | errorSum += Test() 206 | print "after %d iterations the average error rate is: %f" % (numTests, errorSum/float(numTests)) 207 | #csvtotxt() 208 | Test() 209 | #multiTest() 210 | 211 | #readheader() 212 | # for n in range(int(getcomb())): 213 | # col1,col2=settwofactor(n) 214 | # plotBestFit(col1,col2) 215 | # 216 | # 217 | -------------------------------------------------------------------------------- /Price Prediction with Machine Learning methods.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fushuyue/Ml_HFT/23145c9173c2dc358f0d2da8a15e1b84ca50f153/Price Prediction with Machine Learning methods.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Price Prediction with Machine Learning Models 2 | This project proposed a machine learning (Logistic Regreesion and SVM) framework to predict price movement in a high frequency environment and build trading strategy based on this prediction.
3 | 4 | ###The project has three main parts:
5 | #####Part 1: Data processing and visualization 6 | - Data for this project is the limit order book of E-mini S&P500 future. A sample data is in sample_data folder.
7 | - Training set is created at every trade entry with attributes calculate from previous book entry.
8 | - Target value is the directional movement of next trade price.
9 | - 'ggplot' package is used for visualization 10 | 11 | #####Part 2: Model training and testing 12 | - Logistic Regression with nearly 78% accuracy 13 | - Support Vector Machines with nearly 87% accuracy 14 | 15 | #####Part 3: Iceberg Detection 16 | - Introduce the basic idea of iceberg order detection algorithm 17 | - Found more than 11,000 iceberg orders within one day 18 | 19 | 20 | --- 21 | > 22 | Shuyue Fu
23 | MSFE candidate at University of Illinois at Urbana-Champaign
24 | My Resume：[Shuyue Fu](https://github.com/fushuyue/Financial_Computing/raw/master/MyResume/MyResume.pdf)
25 | My Linkedln：[Linkedln](https://www.linkedin.com/in/shuyuefu)
26 | My Email：sfu11@illinois.edu 27 | -------------------------------------------------------------------------------- /Rebuild_orderbook/data process.py: -------------------------------------------------------------------------------- 1 | import matplotlib as mpl 2 | import matplotlib.pyplot as plt 3 | import pandas as pd 4 | 5 | data = [] 6 | tradep = [] # temporary storage of trade price 7 | tradev = [] # temporary storage of trade volume 8 | order = [] # storage for order data 9 | date = [] # storage for date 10 | date1 = [] 11 | price = [] # storage for price 12 | lots = [] # storage for lots of every order 13 | trade = [] 14 | a = [0,0,0,0] # initialize 15 | 16 | def weighted_mean(x,y): 17 | temp = [x[i]*y[i] for i in range(len(x))] 18 | return sum(temp)/float((sum(y))) 19 | 20 | flag=0 21 | 22 | with open('/Users/fushuyue/Dropbox/CME practicum/CMEdata/ESZ5_20150917.txt') as input_file: 23 | for line in input_file: 24 | if a[1] == 'TRADEREC': 25 | flag = True 26 | # trade data structure: 0 time 1 name; 2 quantity; 3 price; 4 volume; 5 flag 27 | elif a[1] == 'BOOK10DEEPREC': 28 | flag = False 29 | elif a[1] == 'IMPLIEDBOOKREC': 30 | continue 31 | # read data line by line 32 | a = line.strip().split(',') 33 | #if a[0] == '00:01:04.531': 34 | # break 35 | 36 | if a[1] == 'TRADEREC': 37 | # store trade price and volume 38 | tradep.append(float(a[3])) 39 | tradev.append(float(a[2])) 40 | 41 | if a[1] == 'BOOK10DEEPREC': 42 | temp = [int(k) for k in a[2:62]] 43 | date_temp = a[0][0:2]+a[0][3:5]+a[0][6:8]+a[0][9:12] 44 | 45 | ask_size = temp[32:44:3] 46 | ask_price = temp[30:42:3] 47 | bid_size = temp[15:28:3] 48 | bid_price = temp[17:30:3] 49 | 50 | average_ask_p = weighted_mean(ask_price, ask_size) 51 | average_bid_p = weighted_mean(bid_price, bid_size) 52 | spread = temp[30]-temp[29] 53 | 54 | volume_imbalance = sum(bid_size) - sum(ask_size) 55 | normalized_imbalance = volume_imbalance/(float(sum(bid_size) + sum(ask_size))) 56 | mid_price = (ask_price[-1]*ask_size[-1]+bid_price[0]*bid_size[0])/float(ask_size[-1]+bid_size[0]) 57 | 58 | weighted_price = (average_ask_p*sum(ask_size)+average_bid_p*sum(bid_size))/float(sum(bid_size)+sum(ask_size)) 59 | # 0=date; 1=bid price; 2=ask price;3=mid point; 4=spread; 5=imba; 6=normailize imba; 7=weighted price; 8=type; 60 | 61 | if flag: 62 | temp1=sum(tradev) 63 | lots.append(temp1) 64 | temp2=weighted_mean(tradep,tradev) 65 | order.append([date_temp, round(average_bid_p,5), round(average_ask_p,5),volume_imbalance, round(weighted_price,5),'trade',temp2 ]) 66 | tradep = [] 67 | tradev = [] 68 | trade.append([date_temp,temp1,temp2]) 69 | 70 | else: 71 | order.append([date_temp, round(average_bid_p,5), round(average_ask_p,5),volume_imbalance, round(weighted_price,5),'submit/cancel','NA']) 72 | print('finish reading') 73 | 74 | dtrade = pd.DataFrame(trade, columns=['date', 'volume', 'price']) 75 | dorder = pd.DataFrame(order, columns=['date', 'bid price', 'ask price', 'imba', 'weighted price', 'type', 'trade price']) 76 | ''' 77 | plt.plot(dtrade['price']) 78 | plt.ylim(198100,199000) 79 | plt.show() 80 | ''' 81 | 82 | plt.hist(lots,bins=10,normed=1) 83 | plt.show() 84 | 85 | print('finish plot') 86 | 87 | dorder.to_csv('OrderWholeDay.txt') 88 | dtrade.to_csv('TradeWholeDay.txt') 89 | 90 | ''' 91 | output=open("order.txt", "w") 92 | for i in range(len(order)): 93 | for k in range(len(order[i])): 94 | output.write(str(order[i][k])) 95 | if k != len(order[i])-1: 96 | output.write(",") 97 | output.write("\n") 98 | output.close() 99 | 100 | 101 | 102 | output1=open("trade.txt", "w") 103 | for i in range(len(trade)): 104 | for k in range(len(trade[i])): 105 | output1.write(str(trade[i][k])) 106 | output1.write(",") 107 | output1.write("\n") 108 | output1.close() 109 | ''' 110 | 111 | -------------------------------------------------------------------------------- /Rebuild_orderbook/dataProcess0412.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | ''' 3 | df = pd.read_csv("/Users/fushuyue/Dropbox/CME practicum/Apr 15th/Data/esOrderWholeDay.txt") 4 | df = df.iloc[::-1] 5 | df.to_table('/Users/fushuyue/Desktop/inverse.txt') 6 | mydata = [] 7 | numberBeforeTrade = 5 8 | nrow = df.shape[0] 9 | ncol = df.shape[1] 10 | 11 | for i in df.index: 12 | if df.ix[i,0] == 'trade': 13 | mydata.append(df.ix[i,:]) 14 | for j in range(numberBeforeTrade): 15 | if df.ix[i+1,0] != 'trade': 16 | mydata[-1]=mydata[-1]+df.ix[i,:] 17 | else: 18 | mydata[-1]=mydata[-1]+[] 19 | 20 | ''' 21 | mydata = [] 22 | numberBeforeTrade = 5 23 | flag = 0 24 | count = 0 25 | with open("/Users/fushuyue/Desktop/python/inverse.txt") as input_file: 26 | for line in input_file: 27 | a = line.strip().split(',') 28 | if flag: 29 | if a[1] == 'submit/cancel': 30 | mydata[-1] = mydata[-1] + a[3:11] 31 | count += 1 32 | else: 33 | mydata[-1] = mydata[-1] + [0]*(8*(numberBeforeTrade-count)) 34 | count = 0 35 | flag = 0 36 | if count == numberBeforeTrade: 37 | flag = 0 38 | count = 0 39 | 40 | if a[1] == 'trade': 41 | mydata.append(a[1:]) 42 | flag = 1 43 | 44 | # if a[2] == '235836949': 45 | # break 46 | 47 | header = ['type','time','best ask','best bid','average bid price','average ask price','ask size','bid size'\ 48 | ,'imbalance','weighted price','buy/sell','trade volume','trade price','best ask1','best bid1','average bid price1','average ask price1','ask size1','bid size1'\ 49 | ,'imbalance1','weighted price1','best ask2','best bid2','average bid price2','average ask price2','ask size2','bid size2'\ 50 | ,'imbalance2','weighted price2','best ask3','best bid3','average bid price3','average ask price3','ask size3','bid size3'\ 51 | ,'imbalance3','weighted price3','best ask4','best bid4','average bid price4','average ask price4','ask size4','bid size4'\ 52 | ,'imbalance4','weighted price4','best ask5','best bid5','average bid price5','average ask price5','ask size5','bid size5'\ 53 | ,'imbalance5','weighted price5'] 54 | 55 | header3 = ['type','time','best ask','best bid','average bid price','average ask price','ask size','bid size'\ 56 | ,'imbalance','weighted price','buy/sell','trade volume','trade price','best ask1','best bid1','average bid price1','average ask price1','ask size1','bid size1'\ 57 | ,'imbalance1','weighted price1','best ask2','best bid2','average bid price2','average ask price2','ask size2','bid size2'\ 58 | ,'imbalance2','weighted price2','best ask3','best bid3','average bid price3','average ask price3','ask size3','bid size3'\ 59 | ,'imbalance3','weighted price3'] 60 | 61 | header1 = ['type','time','best ask','best bid','average bid price','average ask price','ask size','bid size'\ 62 | ,'imbalance','weighted price','buy/sell','trade volume','trade price','best ask1','best bid1','average bid price1','average ask price1','ask size1','bid size1'\ 63 | ,'imbalance1','weighted price1'] 64 | 65 | 66 | df = pd.DataFrame(mydata,columns=header) 67 | df = df.ix[::-1] 68 | 69 | df.to_csv("keke1.txt") 70 | -------------------------------------------------------------------------------- /Rebuild_orderbook/merge.py: -------------------------------------------------------------------------------- 1 | order = [] 2 | trade = [] 3 | date2 = [] 4 | date1 = [] 5 | 6 | 7 | with open('order.txt') as input_file: 8 | for line in input_file: 9 | a = line.strip().split(',') 10 | if a[8]=='trade': 11 | order.append(a) 12 | 13 | with open('processed_trade.txt') as input_file: 14 | for line in input_file: 15 | trade.append(line.strip().split(',')) 16 | 17 | m = len(order) 18 | n = len(trade) 19 | 20 | print(m) 21 | print(n) 22 | 23 | 24 | 25 | ''' 26 | i = 0 27 | for j in range(m): 28 | # if already gone through every trade data, keep the price unchanged 29 | if i >= n-1: 30 | for k in range(j, m): 31 | order[k].append(trade[i][1]) 32 | order[k].append(0) 33 | break 34 | # match every trade with order data; if matched, goto next trade 35 | if order[j][0] >= trade[i][0]: 36 | order[j].append(trade[i][1]) 37 | order[j].append(trade[i][2]) 38 | i += 1 39 | # if not match, keep the price unchange 40 | else: 41 | order[j].append(trade[i-1][1]) 42 | order[j].append(0) 43 | 44 | 45 | 46 | print(i) 47 | print(n) 48 | 49 | output1=open("test.txt", "w") 50 | for i in range(len(order)): 51 | for k in range(len(order[i])): 52 | output1.write(str(order[i][k])) 53 | output1.write(",") 54 | output1.write("\n") 55 | output1.close() 56 | ''' -------------------------------------------------------------------------------- /Rebuild_orderbook/processData.r: -------------------------------------------------------------------------------- 1 | data=read.csv("/Users/fushuyue/Desktop/clOrderWholeDay.txt",header=T,sep=',') 2 | n=length(data[,1]) 3 | # price change from 1 to n-1 4 | pchange=data$trade.price[-1]-data$trade.price[-n] 5 | pchange=pchange[-1] 6 | # attri change from 2 to n 7 | attrichange=data[2:n,3:8]-data[1:(n-1),3:8] 8 | attrichange=attrichange[1:(length(attrichange[,1])-1),] 9 | imba=data$imba[2:(n-1)] 10 | vol=data$trade.volume[2:(n-1)] 11 | a=cbind(attrichange[,1:4],attrichange[,6],imba,vol,pchange) 12 | colnames(a)[1:5]=c('bid.price.change', 'ask.price.change', 'imba.change', 'weighted.price.change','vol.change') 13 | a$pchange=ifelse(a$pchange>0,1,ifelse(a$pchange<0,-1,0)) 14 | n=length(a[,1]) 15 | b=cbind(a[1:(n-4),6:7],a[2:(n-3),6:7],a[3:(n-2),6:7],a[4:(n-1),6:7],a[5:n,]) 16 | colnames(b)[1:8]=c('imba.lag4', 'vol.lag4', 'imba.lag3', 'vol.lag3', 'imba.lag2', 'vol.lag2', 'imba.lag1', 'vol.lag1') 17 | write.csv(b,"keke.csv") -------------------------------------------------------------------------------- /Rebuild_orderbook/readData.py: -------------------------------------------------------------------------------- 1 | import matplotlib as mpl 2 | import matplotlib.pyplot as plt 3 | import pandas as pd 4 | 5 | data = [] 6 | tradep = [] # temporary storage of trade price 7 | tradev = [] # temporary storage of trade volume 8 | order = [] # storage for order data 9 | date = [] # storage for date 10 | date1 = [] 11 | price = [] # storage for price 12 | lots = [] # storage for lots of every order 13 | trade = [] 14 | a = [0,0,0,0] # initialize 15 | 16 | def weighted_mean(x,y): 17 | temp = [x[i]*y[i] for i in range(len(x))] 18 | return sum(temp)/float((sum(y))) 19 | 20 | flag=0 21 | 22 | with open('/Users/fushuyue/Dropbox/CME practicum/CMEdata/ESZ5_20150917.txt') as input_file: 23 | for line in input_file: 24 | if a[1] == 'TRADEREC': 25 | flag = True 26 | # trade data structure: 0 time 1 name; 2 quantity; 3 price; 4 volume; 5 flag 27 | elif a[1] == 'BOOK10DEEPREC': 28 | flag = False 29 | # read data line by line 30 | a = line.strip().split(',') 31 | if a[1] == 'IMPLIEDBOOKREC': 32 | continue 33 | 34 | #if a[0] == '00:02:00.834': 35 | # break 36 | 37 | if a[1] == 'TRADEREC': 38 | # store trade price and volume 39 | tradep.append(float(a[3])) 40 | tradev.append(float(a[2])) 41 | 42 | if tradep[-1]>bid1: 43 | aggressor= 1 44 | else: 45 | aggressor= 0 46 | 47 | if a[1] == 'BOOK10DEEPREC': 48 | temp = [int(k) for k in a[2:62]] 49 | date_temp = a[0][0:2]+a[0][3:5]+a[0][6:8]+a[0][9:12] 50 | 51 | ask_size = temp[32:60:3] 52 | ask_price = temp[30:58:3] 53 | bid_size = temp[0:28:3] 54 | bid_price = temp[2:30:3] 55 | bid1=temp[29] 56 | ask1=temp[30] 57 | average_ask_p = weighted_mean(ask_price, ask_size) 58 | average_bid_p = weighted_mean(bid_price, bid_size) 59 | spread = temp[30]-temp[29] 60 | 61 | volume_imbalance = sum(bid_size) - sum(ask_size) 62 | normalized_imbalance = volume_imbalance/(float(sum(bid_size) + sum(ask_size))) 63 | mid_price = (ask_price[-1]*ask_size[-1]+bid_price[0]*bid_size[0])/float(ask_size[-1]+bid_size[0]) 64 | 65 | weighted_price = (average_ask_p*sum(ask_size)+average_bid_p*sum(bid_size))/float(sum(bid_size)+sum(ask_size)) 66 | # 0=date; 1=bid price; 2=ask price;3=mid point; 4=spread; 5=imba; 6=normailize imba; 7=weighted price; 8=type; 67 | 68 | if flag: 69 | temp1=sum(tradev) 70 | lots.append(temp1) 71 | temp2=weighted_mean(tradep,tradev) 72 | 73 | # aggressor type, 1=buy side aggressor; 0=sell side aggressor 74 | 75 | order.append(['trade',date_temp, round(average_bid_p,5), round(average_ask_p,5),volume_imbalance, round(weighted_price,5),aggressor,temp1,temp2 ]) 76 | tradep = [] 77 | tradev = [] 78 | trade.append([date_temp,temp1,aggressor,temp2]) 79 | #else: 80 | # order.append(['submit/cancel',date_temp, round(average_bid_p,5), round(average_ask_p,5),volume_imbalance, round(weighted_price,5),'NA','NA','NA']) 81 | 82 | print('finish reading') 83 | 84 | dtrade = pd.DataFrame(trade, columns=['time', 'volume', 'direction','price']) 85 | dorder = pd.DataFrame(order, columns=['type','time', 'bid price', 'ask price', 'imba', 'weighted price', 'buy/sell','trade volume','trade price']) 86 | 87 | 88 | 89 | ''' 90 | plt.plot(dtrade['price']) 91 | plt.ylim(198100,199000) 92 | plt.show() 93 | 94 | 95 | plt.hist(lots,bins=10,normed=1) 96 | plt.show() 97 | 98 | print('finish plot') 99 | ''' 100 | 101 | dorder.to_csv('esOrderWholeDay.txt',index=False) 102 | dtrade.to_csv('esTradeWholeDay.txt',index=False) 103 | 104 | ''' 105 | output=open("order.txt", "w") 106 | for i in range(len(order)): 107 | for k in range(len(order[i])): 108 | output.write(str(order[i][k])) 109 | if k != len(order[i])-1: 110 | output.write(",") 111 | output.write("\n") 112 | output.close() 113 | 114 | output1=open("trade.txt", "w") 115 | for i in range(len(trade)): 116 | for k in range(len(trade[i])): 117 | output1.write(str(trade[i][k])) 118 | output1.write(",") 119 | output1.write("\n") 120 | output1.close() 121 | ''' 122 | 123 | -------------------------------------------------------------------------------- /Rebuild_orderbook/takeLagBookValue.r: -------------------------------------------------------------------------------- 1 | data=read.csv("/Users/fushuyue/Desktop/clOrderWholeDay.txt",header=T,sep=',') 2 | ncol=length(data[1,])-1 3 | data=data[,2:(ncol+1)] 4 | n=length(data[,1]) 5 | # price change from 1 to n-1 6 | pchange=data$trade.price[-1]-data$trade.price[-n] 7 | pchange=pchange[-1] 8 | 9 | direction=data$buy.sell 10 | direction[direction==0]=-1 11 | nd=length(direction) 12 | for(i in 2:nd){ 13 | if(direction[i]*direction[i-1]>0) 14 | direction[i]=direction[i-1]+direction[i] 15 | } 16 | data$buy.sell=direction 17 | 18 | 19 | # trade attri change from 2 to n 20 | attrichange=data[2:n,5:14]-data[1:(n-1),5:14] 21 | attrichange=attrichange[1:(length(attrichange[,1])-1),] 22 | imba=data$imbalance[2:(n-1)] 23 | direction=data$buy.sell[2:(n-1)] 24 | vol=data$trade.volume[2:(n-1)] 25 | volchange=attrichange[,10] 26 | bestask=data$best.ask[2:(n-1)] 27 | bestbid=data$best.bid[2:(n-1)] 28 | asksize=data$ask.size[2:(n-1)] 29 | bidsize=data$bid.size[2:(n-1)] 30 | bestasksize=data$best.ask.size[2:(n-1)] 31 | bestbidsize=data$best.bid.size[2:(n-1)] 32 | time=data$time[2:(n-1)] 33 | a=cbind(asksize,bidsize,bestasksize,bestbidsize,imba,vol,volchange,direction,pchange,data$trade.price[2:(n-1)]) 34 | a=data.frame(attrichange[,1:8],a) 35 | colnames(a)[1:18]=c('trade.best.ask.size.change', 'trade.best.bid.size.change','trade.bid.price.change', 36 | 'trade.ask.price.change', 'trade.ask.size.change', 'trade.bid.size.change', 37 | 'trade.imba.change', 'trade.weighted.price.change','ask.size','bid.size','best.ask.size','best.bid.size', 38 | 'imba','vol','vol.change','direction','pchange','trade.price') 39 | 40 | a$pchange=ifelse(a$pchange>0,1,ifelse(a$pchange<0,-1,0)) 41 | n=length(a[,1]) 42 | b=cbind(time[5:n],a[2:(n-3),11:14],a[3:(n-2),11:14],a[4:(n-1),11:14],a[5:n,]) 43 | colnames(b)[2:13]=c('bestasksize.lag3','bestbidsize.lag3','imba.lag3', 'vol.lag3', 44 | 'bestasksize.lag2','bestbidsize.lag2','imba.lag2', 'vol.lag2', 45 | 'bestasksize.lag1','bestbidsize.lag1','imba.lag1', 'vol.lag1') 46 | #write.csv(b,"~/Desktop/withoutBookRec.txt") 47 | 48 | n=length(data[,1]) 49 | lag1=data[2:(n-1),18:25] 50 | diff1=data[2:(n-1),5:12]-lag1 51 | diff1=cbind(diff1,lag1[,1:2],lag1[,5:7]) 52 | colnames(diff1)=c('book1.best.ask.size.change', 'book1.best.bid.size.change','book1.bid.price.change', 'book1.ask.price.change', 53 | 'book1.ask.size.change', 'book1.bid.size.change','book1.imba.change', 'book1.weighted.price.change', 54 | 'book1.best.ask.size', 'book1.best.bid.size','book1.ask.size','book1.bid.size','book1.imbalance') 55 | 56 | dflag1=cbind(diff1[5:length(a[,1]),],b) 57 | write.csv(dflag1,"~/Desktop/with1BookRec.txt") 58 | 59 | lag2=data[2:(n-1),28:35] 60 | diff2=lag1-lag2 61 | diff2=cbind(diff2,lag2[,1:2],lag2[,5:7]) 62 | colnames(diff2)=c('book2.best.ask.size.change', 'book2.best.bid.size.change','book2.bid.price.change', 'book2.ask.price.change', 63 | 'book2.ask.size.change', 'book2.bid.size.change','book2.imba.change', 'book2.weighted.price.change', 64 | 'book2.best.ask.size', 'book2.best.bid.size','book2.ask.size','book2.bid.size','book2.imbalance') 65 | 66 | lag3=data[2:(n-1),38:45] 67 | diff3=lag2-lag3 68 | diff3=cbind(diff3,lag3[,1:2],lag3[,5:7]) 69 | colnames(diff3)=c('book3.best.ask.size.change', 'book3.best.bid.size.change','book3.bid.price.change', 'book3.ask.price.change', 70 | 'book3.ask.size.change', 'book3.bid.size.change','book3.imba.change', 'book3.weighted.price.change', 71 | 'book3.best.ask.size', 'book3.best.bid.size','book3.ask.size','book3.bid.size','book3.imbalance') 72 | 73 | dflag3=cbind(diff3[5:length(a[,1]),],diff2[5:length(a[,1]),],dflag1) 74 | write.csv(dflag3,"~/Desktop/with3BookRec.txt") 75 | 76 | lag4=data[2:(n-1),40:45] 77 | diff4=lag3-lag4 78 | diff4=cbind(diff4,lag4[,3:5]) 79 | colnames(diff4)=c('book4.bid.price.change', 'book4.ask.price.change', 'book4.ask.size.change', 'book4.bid.size.change', 80 | 'book4.imba.change', 'book4.weighted.price.change','book4.ask.size','book4.bid.size','book4.imbalance') 81 | 82 | lag5=data[2:(n-1),48:53] 83 | diff5=lag4-lag5 84 | diff5=cbind(diff5,lag5[,3:5]) 85 | colnames(diff5)=c('book5.bid.price.change', 'book5.ask.price.change', 'book5.ask.size.change', 'book5.bid.size.change', 86 | 'book5.imba.change', 'book5.weighted.price.change','book5.ask.size','book5.bid.size','book5.imbalance') 87 | 88 | 89 | dflag5=cbind(diff5[5:length(a[,1]),],diff4[5:length(a[,1]),],dflag3) 90 | 91 | write.csv(dflag5,"~/Desktop/with5BookRec.txt") 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | -------------------------------------------------------------------------------- /SVM/SVM.R: -------------------------------------------------------------------------------- 1 | setwd("/Users/Peng/Desktop/") 2 | data <-read.table("result.txt",header=T) 3 | data$mid_price_indicator. <-as.numeric(data$mid_price_indicator.) 4 | data$bid.ask_spread_size. <-as.numeric(data$bid.ask_spread_size.) 5 | data$price_indicator <-as.numeric(data$price_indicator) 6 | n <-nrow(data)*.8 7 | x <-data[1:n,1:2] 8 | y <-data[1:n,3] 9 | g=y[y!=0] 10 | f=x[y!=0,] 11 | model <-svm(f,g) 12 | training <-data[(n+1):nrow(data),1:2] 13 | z <-data[(n+1):nrow(data),3] 14 | t=training[z!=0,] 15 | z=z[z!=0] 16 | pred <-predict(model,training) 17 | u=ifelse(pred>0,1,-1) 18 | table(u,z) -------------------------------------------------------------------------------- /Sample_data/sample_ES_DATA_20150917.txt: -------------------------------------------------------------------------------- 1 | 00:00:00.413,BOOK10DEEPREC,47,28,198325,59,33,198350,42,26,198375,74,41,198400,64,36,198425,142,31,198450,67,36,198475,83,30,198500,28,22,198525,7,4,198550,198575,12,17,198600,26,43,198625,26,35,198650,37,51,198675,33,54,198700,37,111,198725,25,49,198750,30,86,198775,32,82,198800,42,263 2 | 00:00:00.413,BOOK10DEEPREC,47,28,198325,59,33,198350,42,26,198375,74,41,198400,64,36,198425,142,31,198450,67,36,198475,83,30,198500,28,22,198525,7,4,198550,198575,12,17,198600,26,43,198625,25,34,198650,37,51,198675,33,54,198700,38,113,198725,25,49,198750,30,86,198775,32,82,198800,42,263 3 | 00:00:00.432,BOOK10DEEPREC,47,28,198325,59,33,198350,42,26,198375,74,41,198400,63,35,198425,142,31,198450,67,36,198475,83,30,198500,28,22,198525,7,4,198550,198575,12,17,198600,26,43,198625,25,34,198650,37,51,198675,33,54,198700,38,113,198725,25,49,198750,30,86,198775,32,82,198800,42,263 4 | 00:00:00.481,BOOK10DEEPREC,47,28,198325,59,33,198350,42,26,198375,74,41,198400,63,35,198425,142,31,198450,67,36,198475,83,30,198500,28,22,198525,7,4,198550,198575,12,17,198600,25,41,198625,25,34,198650,37,51,198675,33,54,198700,38,113,198725,25,49,198750,30,86,198775,32,82,198800,42,263 5 | 00:00:00.512,BOOK10DEEPREC,47,28,198325,59,33,198350,42,26,198375,74,41,198400,63,35,198425,142,31,198450,68,37,198475,83,30,198500,28,22,198525,7,4,198550,198575,12,17,198600,25,41,198625,25,34,198650,37,51,198675,33,54,198700,38,113,198725,25,49,198750,30,86,198775,32,82,198800,42,263 6 | 00:00:00.932,BOOK10DEEPREC,47,28,198325,59,33,198350,42,26,198375,74,41,198400,63,35,198425,142,31,198450,68,37,198475,83,30,198500,28,22,198525,9,5,198550,198575,12,17,198600,25,41,198625,25,34,198650,37,51,198675,33,54,198700,38,113,198725,25,49,198750,30,86,198775,32,82,198800,42,263 7 | 00:00:01.048,BOOK10DEEPREC,47,28,198325,59,33,198350,42,26,198375,74,41,198400,63,35,198425,142,31,198450,68,37,198475,83,30,198500,28,22,198525,9,5,198550,198575,12,17,198600,25,41,198625,25,34,198650,37,52,198675,33,54,198700,38,113,198725,25,49,198750,30,86,198775,32,82,198800,42,263 8 | 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00:00:47.101,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,74,41,198400,66,36,198425,136,28,198450,95,38,198475,65,27,198500,34,22,198525,37,23,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 423 | 00:00:47.116,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,74,41,198400,66,36,198425,136,28,198450,95,38,198475,65,27,198500,35,23,198525,36,22,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 424 | 00:00:47.116,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,74,41,198400,66,36,198425,136,28,198450,95,38,198475,70,28,198500,30,22,198525,36,22,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 425 | 00:00:47.146,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,74,41,198400,66,36,198425,136,28,198450,95,38,198475,74,29,198500,30,22,198525,36,22,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 426 | 00:00:47.591,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,74,41,198400,66,36,198425,136,28,198450,95,38,198475,74,29,198500,31,23,198525,36,22,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 427 | 00:00:47.866,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,95,38,198475,74,29,198500,31,23,198525,36,22,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 428 | 00:00:52.578,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,95,38,198475,74,29,198500,31,23,198525,37,23,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 429 | 00:00:52.607,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,95,38,198475,74,29,198500,30,22,198525,38,24,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 430 | 00:00:52.607,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,95,38,198475,69,28,198500,35,23,198525,38,24,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 431 | 00:00:59.231,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,105,39,198475,69,28,198500,35,23,198525,38,24,198550,198600,23,37,198625,20,23,198650,35,47,198675,31,51,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 432 | 00:01:02.091,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,105,39,198475,69,28,198500,35,23,198525,38,24,198550,198600,23,37,198625,20,23,198650,35,47,198675,32,52,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263,198825,28,78 433 | 00:01:04.525,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,105,39,198475,69,28,198500,35,23,198525,38,24,198550,198575,1,1,198600,23,37,198625,20,23,198650,35,47,198675,32,52,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263 434 | 00:01:04.525,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,105,39,198475,69,28,198500,35,23,198525,38,24,198550,198575,2,2,198600,23,37,198625,20,23,198650,35,47,198675,32,52,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263 435 | 00:01:04.526,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,105,39,198475,69,28,198500,35,23,198525,37,23,198550,198575,2,2,198600,24,39,198625,20,23,198650,35,47,198675,32,52,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263 436 | 00:01:04.526,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,105,39,198475,69,28,198500,35,23,198525,37,23,198550,198575,2,2,198600,24,39,198625,21,27,198650,35,47,198675,32,52,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263 437 | 00:01:04.526,BOOK10DEEPREC,44,27,198325,56,32,198350,41,25,198375,71,40,198400,66,36,198425,136,28,198450,105,39,198475,69,28,198500,35,23,198525,35,21,198550,198575,2,2,198600,24,39,198625,21,27,198650,35,47,198675,32,52,198700,41,121,198725,26,52,198750,31,87,198775,32,82,198800,42,263 438 | -------------------------------------------------------------------------------- /Visualization/AttributeVisulization.R: -------------------------------------------------------------------------------- 1 | data = read.csv('~/Dropbox/CME practicum/Apr 22nd/Data/with1BookRec.txt') 2 | library('ggplot2') 3 | df = data[data$pchange != 0, ] 4 | df$pchange = ifelse(df$pchange < 0, 'down', 'up') 5 | ask = ggplot(data = df[1:1000,], aes(x = trade.best.ask.size.change, fill = factor(pchange))) + 6 | geom_histogram(alpha = 0.5, position = 'identity',bins = 50) + 7 | scale_fill_discrete(name='price change')+xlim(c(-100,100))+ylim(c(0,50))+guides(fill=FALSE) + 8 | ggtitle('histogram of best ask size change between trades') + 9 | theme(axis.title.x = element_text(colour="dark grey", size=10)) + ylab('') 10 | 11 | bid = ggplot(data = df[1:1000,], aes(x = trade.best.bid.size.change, fill = factor(pchange))) + 12 | geom_histogram(alpha = 0.5, position = 'identity',bins = 50) + 13 | scale_fill_discrete(name='price change')+xlim(c(-100,100))+ylim(c(0,50))+guides(fill=FALSE) + 14 | ggtitle('histogram of best bid size change between trades') + 15 | theme(axis.title.x = element_text(colour="dark grey", size=10)) + ylab('') 16 | 17 | askbidPoint = ggplot(data = df[1:150,], aes(x = trade.best.bid.size.change, y = trade.best.ask.size.change, 18 | colour = factor(pchange))) + geom_point() + scale_colour_discrete(name='price change') + 19 | xlim(c(-40,40))+ylim(c(-50,50)) 20 | 21 | imba = ggplot(data = df[1:10000,], aes(x = trade.imba.change, fill = factor(pchange))) + 22 | geom_histogram(alpha = 0.5, position = 'identity',bins = 100) + 23 | scale_fill_discrete(name='price change')+ylim(c(0,60))+xlim(c(-400,400)) + guides(fill=FALSE) + 24 | ggtitle('histogram of imbalance change between trades') + 25 | theme(axis.title.x = element_text(colour="dark grey", size=10)) + ylab('') 26 | 27 | imbaChange = ggplot(data = df[1:10000,], aes(x = book1.imba.change, fill = factor(pchange))) + 28 | geom_histogram(alpha = 0.5, position = 'identity',bins = 100) + 29 | scale_fill_discrete(name='price change')+guides(fill=FALSE)+ylim(c(0,60))+xlim(c(-400,400)) + 30 | ggtitle('histogram of imbalance change between books') + 31 | theme(axis.title.x = element_text(colour="dark grey", size=10)) + ylab('') 32 | 33 | imbaPoint = ggplot(data = df[1:500,], aes(x = trade.imba.change, y = book1.imba.change, 34 | colour = factor(pchange))) + geom_point() + scale_colour_discrete(name='price change') + 35 | xlim(c(-50,50))+ylim(c(-25,25))+ guides(fill=FALSE) 36 | 37 | volume = ggplot(data = df[1:500,], aes(x = vol.change, fill = factor(pchange))) + 38 | geom_histogram(alpha = 0.5, position = 'identity',bins = 70) + 39 | scale_fill_discrete(name='price change') + 40 | xlim(c(-30,30))+ylim(c(0,30)) + 41 | ggtitle('histogram of volume between trades') + 42 | theme(axis.title.x = element_text(colour="dark grey", size=10)) + ylab('') 43 | 44 | direction = ggplot(data = df[1:500,], aes(x = direction, fill = factor(pchange))) + 45 | geom_histogram(alpha = 0.5, position = 'identity',bins = 70) + 46 | scale_fill_discrete(name='price change') + 47 | xlim(c(-30,30))+ylim(c(0,30)) + xlab('number of consecutive direction trades') + 48 | ggtitle('histogram of bnumber of consecutive direction trades') + 49 | theme(axis.title.x = element_text(colour="dark grey", size=10)) + ylab('') 50 | 51 | strangePoint = ggplot(data = df[1:500,], aes(x = direction, y = vol.change, 52 | colour = factor(pchange))) + geom_point() + scale_colour_discrete(name='price change') + 53 | xlim(c(-25,25))+ylim(c(-25,25))+ xlab('number of consecutive direction trades') 54 | 55 | 56 | multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) { 57 | library(grid) 58 | 59 | # Make a list from the ... arguments and plotlist 60 | plots <- c(list(...), plotlist) 61 | 62 | numPlots = length(plots) 63 | 64 | # If layout is NULL, then use 'cols' to determine layout 65 | if (is.null(layout)) { 66 | # Make the panel 67 | # ncol: Number of columns of plots 68 | # nrow: Number of rows needed, calculated from # of cols 69 | layout <- matrix(seq(1, cols * ceiling(numPlots/cols)), 70 | ncol = cols, nrow = ceiling(numPlots/cols)) 71 | layout <- t(layout) 72 | } 73 | 74 | if (numPlots==1) { 75 | print(plots[[1]]) 76 | 77 | } else { 78 | # Set up the page 79 | grid.newpage() 80 | pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout)))) 81 | 82 | # Make each plot, in the correct location 83 | for (i in 1:numPlots) { 84 | # Get the i,j matrix positions of the regions that contain this subplot 85 | matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE)) 86 | 87 | print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row, 88 | layout.pos.col = matchidx$col)) 89 | } 90 | } 91 | } 92 | 93 | 94 | 95 | 96 | -------------------------------------------------------------------------------- /Visualization/AttributesVisualization.py: -------------------------------------------------------------------------------- 1 | def loadDataSet(): 2 | dataMat = []; labelMat = [] 3 | fr = open('testSet1.txt') 4 | for line in fr.readlines(): 5 | lineArr = line.strip().split() 6 | # dataMat.append([1.0,float(lineArr[0]),float(lineArr[1]),float(lineArr[2]),float(lineArr[3]),float(lineArr[4])]) 7 | dataMat.append([1.0,float(lineArr[2]),float(lineArr[4])]) 8 | labelMat.append(int(lineArr[5])) 9 | return dataMat,labelMat 10 | 11 | 12 | def sguess(): 13 | import matplotlib.pyplot as plt 14 | from mpl_toolkits.mplot3d import Axes3D 15 | import csv 16 | import numpy as np 17 | import matplotlib.pyplot as plt 18 | from matplotlib import style 19 | style.use("ggplot") 20 | from sklearn import svm 21 | 22 | 23 | dataMat = []; labelMat = [] 24 | n=0 25 | X = [] 26 | y= [] 27 | with open('/Users/shengdongliu/Desktop/testSet3.csv') as inputfile: 28 | reader = csv.reader(inputfile) 29 | lineArr = list(reader) 30 | for n in range(len( lineArr)) : 31 | 32 | # fr = open('testSet2.txt') 33 | # for line in fr.readlines(): 34 | print lineArr[n] 35 | 36 | # dataMat.append([1.0,float(lineArr[0]),float(lineArr[1]),float(lineArr[2]),float(lineArr[3]),float(lineArr[4])]) 37 | X.append([float(lineArr[n][0]),float(lineArr[n][1]),float(lineArr[n][2])]) 38 | y.append(int(lineArr[n][4])) 39 | n=n+1 40 | print n 41 | 42 | #dataMat,labelMat=loadDataSet() 43 | # x = [1, 5, 1.5, 8, 1, 9] 44 | # y = [2, 8, 1.8, 8, 0.6, 11] 45 | 46 | # plt.scatter(x,y) 47 | # plt.show() 48 | 49 | #X = np.array([[1,2], 50 | # [5,8], 51 | # [1.5,1.8], 52 | # [8,8], 53 | # [1,0.6], 54 | # [9,11]]) 55 | #y = [0,1,0,1,0,1] 56 | 57 | 58 | #clf = svm.SVC(kernel='linear', C = 1.0) 59 | #clf.fit(X,y) 60 | # print(clf.predict([0.58,0.76])) 61 | # print(clf.predict([10.58,10.76])) 62 | 63 | #w = clf.coef_[0] 64 | #print(w) 65 | 66 | #a = -w[0] / w[1] 67 | 68 | xx = np.linspace(-12,12) 69 | #yy = a * xx - clf.intercept_[0] / w[1] 70 | fig = plt.figure() 71 | ax = fig.add_subplot(111, projection='3d') 72 | #h0 = plt.plot(xx, yy, 'k-', label="non weighted div") 73 | for k in range(n-1): 74 | 75 | if(int(y[k])==1): 76 | u=ax.scatter(X[k][0], X[k][1],X[k][2], c = 'red') 77 | elif(int(y[k]==0)): 78 | s=ax.scatter(X[k][0], X[k][1],X[k][2], c = 'grey') 79 | elif(int(y[k]==-1)): 80 | d=ax.scatter(X[k][0], X[k][1],X[k][2], c = 'green') 81 | 82 | 83 | ax.set_xlabel('diff_average_bid') 84 | ax.set_ylabel('diff_average_ask') 85 | ax.set_zlabel('diff_weighted_price') 86 | plt.legend((u, s, d), 87 | ('go up', 'same', 'go down'), 88 | scatterpoints=1, 89 | loc='upper left', 90 | ncol=3, 91 | fontsize=8) 92 | #plt.xlim(-12,12) 93 | 94 | #ax.plot(x, y, z, label='parametric curve') 95 | 96 | plt.show() 97 | 98 | #sguess() 99 | # import numpy as np 100 | # x,y= np.loadtxt('testSet-cp.txt', delimiter='\t') 101 | # print x 102 | # print y 103 | import numpy as np 104 | X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]]) 105 | y = np.array([1, 1, 2, 2]) 106 | from sklearn.svm import SVC 107 | clf = SVC() 108 | clf.fit(X, y) 109 | 110 | 111 | print(clf.predict([[-0.8, -1]])) -------------------------------------------------------------------------------- /Visualization/PriceAndPredictionPlot.R: -------------------------------------------------------------------------------- 1 | multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) { 2 | library(grid) 3 | 4 | # Make a list from the ... arguments and plotlist 5 | plots <- c(list(...), plotlist) 6 | 7 | numPlots = length(plots) 8 | 9 | # If layout is NULL, then use 'cols' to determine layout 10 | if (is.null(layout)) { 11 | # Make the panel 12 | # ncol: Number of columns of plots 13 | # nrow: Number of rows needed, calculated from # of cols 14 | layout <- matrix(seq(1, cols * ceiling(numPlots/cols)), 15 | ncol = cols, nrow = ceiling(numPlots/cols)) 16 | } 17 | 18 | if (numPlots==1) { 19 | print(plots[[1]]) 20 | 21 | } else { 22 | # Set up the page 23 | grid.newpage() 24 | pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout)))) 25 | 26 | # Make each plot, in the correct location 27 | for (i in 1:numPlots) { 28 | # Get the i,j matrix positions of the regions that contain this subplot 29 | matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE)) 30 | 31 | print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row, 32 | layout.pos.col = matchidx$col)) 33 | } 34 | } 35 | } 36 | 37 | 38 | 39 | 40 | setwd('~/Dropbox/CME practicum/Data/Apr 15th/Data') 41 | tdata=read.csv("tradeDataForPlotting.txt") 42 | p=tdata$trade.price 43 | n=length(p) 44 | pchange=p[-1]-p[-n] 45 | p=ifelse(pchange>0,'up',ifelse(pchange<0,'down','stay')) 46 | direction=tdata$buy.sell 47 | direction[direction==0]=-1 48 | nd=length(direction) 49 | for(i in 2:nd){ 50 | if(direction[i]*direction[i-1]>0) 51 | direction[i]=direction[i-1]+direction[i] 52 | } 53 | tdata$buy.sell=direction 54 | fu=tdata[tdata$p!='stay',] 55 | tdata=cbind(tdata[1:(n-1),],p) 56 | fu=tdata[tdata$p!='stay',] 57 | percent80=round(0.8*nrow(fu)); 58 | test=fu[percent80:nrow(fu),] 59 | 60 | 61 | tdata=cbind(tdata[1:(n-1),],p) 62 | library(ggplot2) 63 | test=tdata[tdata$p!='stay',] 64 | n=length(tdata[,1]) 65 | test=tdata[(n+1-11397):n,] 66 | qplot(test$trade.volume,data=test,geom="density",colour=p,xlim=c(0,15)) 67 | 68 | data=read.csv("~/Dropbox/CME practicum/Apr 15th/Data/esOrderWholeDay.txt") 69 | trade=read.csv("~/Desktop/python/esTradeWholeDay.txt") 70 | data=cbind(data,0) 71 | colnames(data)[14]='movement' 72 | type=data$type 73 | data$movement[type=='trade']=trade$next.price.move 74 | 75 | # svm 76 | n <-nrow(df)*.8 77 | > x <-df[1:n,3:12] 78 | > y <-df[1:n,14] 79 | > g=y[y!=0] 80 | > g=as.factor(g) 81 | > f=x[y!=0,] 82 | > library('e1071') 83 | > model <-svm(f,g, kernel = "radial") 84 | training <-df[(n+1):nrow(df),3:12] 85 | > z <-df[(n+1):nrow(df),14] 86 | > t=training[z!=0,] 87 | > z=z[z!=0] 88 | > pred <-predict(model,t) 89 | 90 | training <-df[(n+1):nrow(df),1:13] 91 | z <-df[(n+1):nrow(df),14] 92 | t=training[z!=0,] 93 | fu1=data.frame(t,z,pred) 94 | accr=ifelse(fu1$z==fu1$pred,1,0) 95 | fu1=data.frame(fu1,accr) 96 | fu1=fu1[1:(length(fu1[,1])-1),] 97 | 98 | accr_rate=vector() 99 | accr_rate[1]=0 100 | for(i in 101:length(accr)){ 101 | accr_rate[i-100]=sum(accr[i-100:i]/i) 102 | } 103 | 104 | 105 | 106 | vol=data$trade.volume[type=='trade'] 107 | n=length(vol) 108 | vchange=vol[-1]-vol[-n] 109 | vchange=ifelse(vchange<0,'decrease',ifelse(vchange>0,'increase','stay')) 110 | data=cbind(data,0) 111 | colnames(data)[15]='vol.movement' 112 | vchange1=c(0,vchange) 113 | data$vol.movement[data$type=='trade']=vchange1 114 | 115 | 116 | fu=data[data$time>=1.437187e+08,] 117 | n=length(fu[,1]) 118 | time=fu$time 119 | time=(time-time[1])/1000 120 | fu$time=time 121 | 122 | n=length(fu1[,1]) 123 | time=fu1$time 124 | time=(time-time[1])/1000 125 | fu1$time=time 126 | 127 | 128 | #fu1=fu[fu$type=='trade',] 129 | #fu1$trade.price=ifelse(fu1$buy.sell==1,fu1$trade.price+200,fu1$trade.price-200) 130 | #fu$trade.price[fu$type=='trade']=fu1$trade.price 131 | 132 | fu=fu[fu$time>=25000,] 133 | fu1=fu1[fu1$time>=25000,] 134 | wrong=fu1[fu1$accr==0,] 135 | wrong$z[wrong$z==-1]='dark blue' 136 | wrong$z[wrong$z==1]='grey' 137 | 138 | 139 | right=fu1[fu1$accr==1,] 140 | right$z[right$z==-1]='dark blue' 141 | right$z[right$z==1]='grey' 142 | 143 | p=ggplot()+geom_line(data=fu,aes(x=time,y=best.ask+200,colour='ask'))+xlab('time')+ylab('price') 144 | p=p+geom_line(data=fu,aes(x=time,y=best.bid-200,colour='bid'))+xlab('time')+ylab('price') 145 | p=p+geom_line(data=fu,aes(x=time,y=trade.volume+197000,color='dark blue',alpha=0.6)) 146 | p1=p+geom_point(x=wrong$time,y=wrong$trade.price,data=wrong,color=wrong$z)+labs(title="plot of wrong prediction") 147 | p2=p+geom_point(x=right$time,y=right$trade.price,data=right,color=right$z)+labs(title="plot of right prediction") 148 | p3=qplot(x=time,y=accr_rate*100,data=fu2,geom="line",ylim=c(60,100),main="Accuracy of prediction") 149 | 150 | multiplot(p1,p2,p3) 151 | 152 | 153 | 154 | #fu1$trade.price=ifelse(fu1$z==1,fu1$trade.price+200,fu1$trade.price-200) 155 | 156 | 157 | 158 | 159 | 160 | 161 | 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | 171 | 172 | 173 | 174 | 175 | 176 | 177 | -------------------------------------------------------------------------------- /Visualization/Rplot.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fushuyue/Ml_HFT/23145c9173c2dc358f0d2da8a15e1b84ca50f153/Visualization/Rplot.pdf -------------------------------------------------------------------------------- /Visualization/iceberg.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fushuyue/Ml_HFT/23145c9173c2dc358f0d2da8a15e1b84ca50f153/Visualization/iceberg.png -------------------------------------------------------------------------------- /Visualization/presentation.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/fushuyue/Ml_HFT/23145c9173c2dc358f0d2da8a15e1b84ca50f153/Visualization/presentation.png -------------------------------------------------------------------------------- /Visualization/visualizeIceberg.r: -------------------------------------------------------------------------------- 1 | iceberg = read.csv('~/Desktop/python/iceberg.txt') 2 | data=read.csv('~/Dropbox/CME practicum/Apr 22nd/Data/with1BookRec.txt') 3 | fu = iceberg[iceberg$type!='in the spread',] 4 | library('ggplot2') 5 | p = ggplot() + 6 | geom_line(data = data, aes(x = time.5.n./10000000, y = trade.price),colour = 'tomato1')+xlab('time') 7 | 8 | q = ggplot() + geom_bar(data = data, aes(x = time.5.n./10000000,y=vol), stat ='identity', 9 | fill = 'grey28', alpha=0.8, width = 0.5) 10 | f = q + geom_bar(data = fu,aes(x = time/10000000, y=volume), stat = 'identity', 11 | fill = 'grey60', width = 0.5) + xlab('time') + ylab('order size') 12 | 13 | multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) { 14 | library(grid) 15 | 16 | # Make a list from the ... arguments and plotlist 17 | plots <- c(list(...), plotlist) 18 | 19 | numPlots = length(plots) 20 | 21 | # If layout is NULL, then use 'cols' to determine layout 22 | if (is.null(layout)) { 23 | # Make the panel 24 | # ncol: Number of columns of plots 25 | # nrow: Number of rows needed, calculated from # of cols 26 | layout <- matrix(seq(1, cols * ceiling(numPlots/cols)), 27 | ncol = cols, nrow = ceiling(numPlots/cols)) 28 | } 29 | 30 | if (numPlots==1) { 31 | print(plots[[1]]) 32 | 33 | } else { 34 | # Set up the page 35 | grid.newpage() 36 | pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout)))) 37 | 38 | # Make each plot, in the correct location 39 | for (i in 1:numPlots) { 40 | # Get the i,j matrix positions of the regions that contain this subplot 41 | matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE)) 42 | 43 | print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row, 44 | layout.pos.col = matchidx$col)) 45 | } 46 | } 47 | } 48 | 49 | multiplot(p,q) -------------------------------------------------------------------------------- /Visualization/visualizeSpread.R: -------------------------------------------------------------------------------- 1 | setwd('~/Dropbox/CME practicum/Apr 22nd/SVM/testing set/') 2 | tdata=read.csv("with1_80_20_testing_data.csv") 3 | data=read.csv('~/Desktop/python/esOrderWholeDay.txt') 4 | time=tdata$time.5.n. 5 | Data=data[data$time>=time[1],] 6 | 7 | fu = tdata[30:100,] 8 | time = fu$time.5.n 9 | df = Data[Data$time <= time[71],] 10 | df = df[df$time>=time[1],] 11 | accr = ifelse(fu$real==fu$pred,'right','wrong') 12 | fu = cbind(fu,accr) 13 | 14 | fu$time.5.n = fu$time.5.n/1000 15 | df$time = df$time/1000 16 | fu$real1 = ifelse(fu$real1 == 1, 'firebrick1', 'green') 17 | 18 | 19 | p = ggplot()+geom_line(data = df,aes(x = time/10000,y = best.ask+30,colour = best.ask.size),size = 3)+ 20 | xlab('time')+ylab('price') 21 | 22 | p = p+geom_line(data = df,aes(x = time/10000,y = best.bid-30,colour = best.bid.size), size = 3) 23 | 24 | p = p + scale_color_gradient(name = 'liquidity of \nask and bid',low = 'pink', high = 'red') 25 | 26 | fuup = fu[fu$real1 == 'firebrick1',] 27 | fudown = fu[fu$real1 == 'green',] 28 | 29 | p1 = p + geom_point(data = fuup,aes(x = time.5.n/10000, y = trade_price1, alpha = 0.8, shape = factor(accr)),colour = 'blue',size = 5) 30 | p1 = p1 + geom_point(data = fudown,aes(x = time.5.n/10000, y = trade_price1, alpha = 0.8, shape = factor(accr)),colour = 'black',size = 5) 31 | p1 = p1 + guides(alpha = F) + scale_shape_discrete(name = 'prediction outcome', solid = F) + guides(alpha=F) 32 | 33 | multiplot <- function(..., plotlist=NULL, file, cols=1, layout=NULL) { 34 | library(grid) 35 | 36 | # Make a list from the ... arguments and plotlist 37 | plots <- c(list(...), plotlist) 38 | 39 | numPlots = length(plots) 40 | 41 | # If layout is NULL, then use 'cols' to determine layout 42 | if (is.null(layout)) { 43 | # Make the panel 44 | # ncol: Number of columns of plots 45 | # nrow: Number of rows needed, calculated from # of cols 46 | layout <- matrix(seq(1, cols * ceiling(numPlots/cols)), 47 | ncol = cols, nrow = ceiling(numPlots/cols)) 48 | } 49 | 50 | if (numPlots==1) { 51 | print(plots[[1]]) 52 | 53 | } else { 54 | # Set up the page 55 | grid.newpage() 56 | pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout)))) 57 | 58 | # Make each plot, in the correct location 59 | for (i in 1:numPlots) { 60 | # Get the i,j matrix positions of the regions that contain this subplot 61 | matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE)) 62 | 63 | print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row, 64 | layout.pos.col = matchidx$col)) 65 | } 66 | } 67 | } 68 | --------------------------------------------------------------------------------