Model_RAC_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "data = pd.read_csv(\"C:/Users/kkeaa/Downloads/Project-main/Project-main/CENTEL_1522_years.csv\")\n",
    "dt=data\n",
    "data.drop(data.columns[[0]],axis=1,inplace=True)\n",
    "window_size = int(len(data)/(8))\n",
    "splittrain = []\n",
    "splittest = []\n",
    "#15-19 18-22\n",
    "#slicing window\n",
    "for i in range(0, len(data)-4*window_size,window_size):\n",
    "   splittrain.append(data[i:i+4*window_size])\n",
    "   splittest.append(data[i+4*window_size:i+5*window_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weight\n",
    "from scipy.optimize import NonlinearConstraint, Bounds, LinearConstraint\n",
    "import sympy as sp\n",
    "w1=sp.symbols('w1')\n",
    "w2=sp.symbols('w2')\n",
    "w3=sp.symbols('w3')\n",
    "w4=sp.symbols('w4')\n",
    "w5=sp.symbols('w5')\n",
    "w6=sp.symbols('w6')\n",
    "w7=sp.symbols('w7')\n",
    "w8=sp.symbols('w8')\n",
    "w9=sp.symbols('w9')\n",
    "w10=sp.symbols('w10')\n",
    "td=sp.symbols('td')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def const(p):\n",
    "    w1,w2,w3,w4,w5,w6,w7,w8,w9,w10 = p\n",
    "    return w1+w2+w3+w4+w5+w6+w7+w8+w9+w10\n",
    "nlc = NonlinearConstraint(const, 1 ,1)\n",
    "import numpy as np\n",
    "from scipy.optimize import differential_evolution\n",
    "from scipy.optimize import NonlinearConstraint, Bounds\n",
    "\n",
    "bounds = [[0, 1], [0, 1],[0, 1], [0, 1],[0, 1],[0, 1],[0, 1], [0, 1],[0, 1],[0,1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import matplotlib.pyplot as plt\n",
    "distortions = []\n",
    "inertias = []\n",
    "labels = {}\n",
    "centers = {}\n",
    "K = range(1, 10)\n",
    "\n",
    "for k in K:\n",
    "    kmeans = KMeans(n_clusters=k, n_init='auto').fit(data)\n",
    "    inertias.append(kmeans.inertia_)\n",
    "    labels[k] = kmeans.labels_\n",
    "    centers[k] = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(K, inertias, 'bx-')\n",
    "plt.xlabel('Values of K')\n",
    "plt.ylabel('Inertia')\n",
    "plt.title('The Elbow Method using Inertia')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "coll,buy_idx,sell_idx=[],[],[]\n",
    "decision_d_l=[]\n",
    "count1,count2,count3=0,0,0\n",
    "def func(p):\n",
    "    global S,B,resl,resfl\n",
    "    S=0\n",
    "    B=0\n",
    "    resl=[]\n",
    "    resfl=[]\n",
    "    td=0.2\n",
    "    w1,w2,w3,w4,w5,w6,w7,w8,w9,w10 = p\n",
    "    mon=100000\n",
    "    amount=0\n",
    "    for i in range(0,len(dfo)):\n",
    "        r = ((w1*sl1[i]+w2*sl2[i]+w3*sl3[i]+w4*sl4[i]+w5*sl5[i]+w6*sl6[i]+w7*sl7[i]+w8*sl8[i]+w9*sl9[i]+w10*sl10[i])/(w1+w2+w3+w4+w5+w6+w7+w8+w9+w10))#w1=sp.symbols('w1')\n",
    "        if i!=0:\n",
    "            if dfo['adj_close_price'][i]<dfo['adj_close_price'][i-1]*0.8:\n",
    "                mon+=amount*dfo['adj_close_price'][i]*0.998\n",
    "                amount=0\n",
    "        if r>td:\n",
    "            sumall=mon//dfo['adj_close_price'][i]\n",
    "            amount+=sumall\n",
    "            mon-=sumall*dfo['adj_close_price'][i]*1.002\n",
    "            B+=1\n",
    "        elif r<-td:\n",
    "            mon+=amount*dfo['adj_close_price'][i]*0.998\n",
    "            amount=0\n",
    "            S+=1\n",
    "        else:\n",
    "            pass\n",
    "    ret=mon+amount*dfo['adj_close_price'][len(dfo)-1]\n",
    "    return -((ret-100000)/1000)\n",
    "def fobj():\n",
    "    global sl1,sl2,sl3,sl4,sl5,sl6,sl7,sl8,sl9,sl10,dfo,decision_d_l,bh_test_l,bh_train_l,train_l,test_l\n",
    "    bh_test_l,bh_train_l,train_l,test_l=[],[],[],[]\n",
    "    for j in range(len(splittrain)):\n",
    "        dfo=splittrain[j]\n",
    "        dfo=dfo.reset_index()\n",
    "        sl1=dfo['volume_prof_signal']\n",
    "        sl2=dfo['stoch_signal_2']\n",
    "        sl3=dfo['bb_signal']\n",
    "        sl4=dfo['cci_signal']\n",
    "        sl5=dfo['bb_rsi_signal']\n",
    "        sl6=dfo['renko_adx_signal']\n",
    "        sl7=dfo['ma50_100_signal']\n",
    "        sl8=dfo['rsiandmacd']\n",
    "        sl9=dfo['fake_obv_macd_crossover_signal']\n",
    "        sl10=dfo['wpr_signal']\n",
    "        result = differential_evolution(func, bounds,constraints=(nlc),maxiter=1500,popsize=80,recombination=0.8)\n",
    "        coll.append([result.x,-result.fun,j])\n",
    "        print([result.x,-result.fun,j])\n",
    "        train_l.append(-result.fun)\n",
    "        a=result.x\n",
    "        b=j\n",
    "        td=0.2\n",
    "        w=a[0:10]\n",
    "        df=splittest[b]\n",
    "        il=[]\n",
    "        global count1,count2,count3,sell_idx,buy_idx\n",
    "        mon=100000\n",
    "        amount=0\n",
    "        sumall=0\n",
    "        df=df.reset_index()\n",
    "        sl1=dfo['volume_prof_signal']\n",
    "        sl2=dfo['stoch_signal_2']\n",
    "        sl3=dfo['bb_signal']\n",
    "        sl4=dfo['cci_signal']\n",
    "        sl5=dfo['bb_rsi_signal']\n",
    "        sl6=dfo['renko_adx_signal']\n",
    "        sl7=dfo['ma50_100_signal']\n",
    "        sl8=dfo['rsiandmacd']\n",
    "        sl9=dfo['fake_obv_macd_crossover_signal']\n",
    "        sl10=dfo['wpr_signal']\n",
    "\n",
    "        for i in range(0,len(df)):\n",
    "            decision_d= ((w[0]*sl1[i]+w[1]*sl2[i]+w[2]*sl3[i]+w[3]*sl4[i]+w[4]*sl5[i]+w[5]*sl6[i]+w[6]*sl7[i]+w[7]*sl8[i]+w[8]*sl9[i]+w[9]*sl10[i])/sum(w))\n",
    "            if i!=0:\n",
    "                if df['adj_close_price'][i]<df['adj_close_price'][i-1]*0.8:\n",
    "                        mon+=amount*df['adj_close_price'][i]*0.998\n",
    "                        amount=0\n",
    "                        sell_idx.append(i)\n",
    "            if decision_d>td:\n",
    "                sumall=mon//df['adj_close_price'][i]\n",
    "                amount+=sumall\n",
    "                mon-=sumall*df['adj_close_price'][i]*1.002\n",
    "                count1+=1\n",
    "                buy_idx.append(i)\n",
    "            elif decision_d<-td:\n",
    "                mon+=amount*df['adj_close_price'][i]*0.998\n",
    "                amount=0\n",
    "                count2+=1\n",
    "                sell_idx.append(i)\n",
    "            else:\n",
    "                count3+=1\n",
    "            decision_d_l.append(decision_d)\n",
    "        ret=mon+amount*df['adj_close_price'][len(df)-1]\n",
    "        stocknum=100000/dfo['adj_close_price'][0]\n",
    "        bh_test_l.append(((stocknum*(0.998*df['adj_close_price'][len(df)-1])+(100000-stocknum*df['adj_close_price'][0]*1.002))-100000)/1000)\n",
    "        bh_train_l.append(((stocknum*(0.998*dfo['adj_close_price'][len(dfo)-1])+(100000-stocknum*dfo['adj_close_price'][0]*1.002))-100000)/1000)\n",
    "        test_l.append((ret-100000)/1000)\n",
    "        print(\"B&H_train :\",bh_train_l)\n",
    "        print(\"ats_train :\",train_l)\n",
    "        print(\"B&H_test :\",bh_test_l)\n",
    "        print (\"ATS test:\",b,(ret-100000)/1000)\n",
    "\n",
    "    print(\"B&H_train :\",bh_train_l)\n",
    "    print(\"ats_train :\",train_l)\n",
    "    print(\"B&H_test :\",bh_test_l)\n",
    "    print(\"ats_test :\",test_l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kkeaa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\scipy\\optimize\\_hessian_update_strategy.py:182: UserWarning:\n",
      "\n",
      "delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.11784119, 0.06558635, 0.02068441, 0.08493635, 0.58508089,\n",
      "       0.02780968, 0.02308711, 0.00173262, 0.03384693, 0.03939448]), 98.3370962988357, 0]\n",
      "B&H_train : [-17.707726366917093]\n",
      "ats_train : [98.3370962988357]\n",
      "B&H_test : [3.561435637576156]\n",
      "ATS test: 0 0.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kkeaa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\scipy\\optimize\\_hessian_update_strategy.py:182: UserWarning:\n",
      "\n",
      "delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.02288324, 0.02260935, 0.14357972, 0.16948536, 0.13376254,\n",
      "       0.05374061, 0.0057    , 0.21866667, 0.0460953 , 0.18347722]), 87.30782365115732, 1]\n",
      "B&H_train : [-17.707726366917093, 2.147097781812059]\n",
      "ats_train : [98.3370962988357, 87.30782365115732]\n",
      "B&H_test : [3.561435637576156, 34.481848604705824]\n",
      "ATS test: 1 16.698543999999995\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kkeaa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\scipy\\optimize\\_hessian_update_strategy.py:182: UserWarning:\n",
      "\n",
      "delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.00853425, 0.17194009, 0.1361946 , 0.01890062, 0.02157899,\n",
      "       0.02664405, 0.19319567, 0.05840129, 0.15875537, 0.20585507]), 265.9124709743462, 2]\n",
      "B&H_train : [-17.707726366917093, 2.147097781812059, 148.56031071288314]\n",
      "ats_train : [98.3370962988357, 87.30782365115732, 265.9124709743462]\n",
      "B&H_test : [3.561435637576156, 34.481848604705824, 44.484867015086024]\n",
      "ATS test: 2 22.857967000000006\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kkeaa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\scipy\\optimize\\_hessian_update_strategy.py:182: UserWarning:\n",
      "\n",
      "delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.03471557, 0.18017898, 0.00120999, 0.01492995, 0.00625652,\n",
      "       0.29457147, 0.22074907, 0.08875521, 0.13261321, 0.02602002]), 208.6136796965599, 3]\n",
      "B&H_train : [-17.707726366917093, 2.147097781812059, 148.56031071288314, 130.4488888888889]\n",
      "ats_train : [98.3370962988357, 87.30782365115732, 265.9124709743462, 208.6136796965599]\n",
      "B&H_test : [3.561435637576156, 34.481848604705824, 44.484867015086024, -26.428888888888874]\n",
      "ATS test: 3 -3.7660895000000165\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\kkeaa\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\scipy\\optimize\\_hessian_update_strategy.py:182: UserWarning:\n",
      "\n",
      "delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([0.07709772, 0.15829359, 0.02046319, 0.00614489, 0.0828181 ,\n",
      "       0.33224506, 0.00764355, 0.20488989, 0.04761052, 0.0627935 ]), 94.87947849999999, 4]\n",
      "B&H_train : [-17.707726366917093, 2.147097781812059, 148.56031071288314, 130.4488888888889, 42.924031007751935]\n",
      "ats_train : [98.3370962988357, 87.30782365115732, 265.9124709743462, 208.6136796965599, 94.87947849999999]\n",
      "B&H_test : [3.561435637576156, 34.481848604705824, 44.484867015086024, -26.428888888888874, -0.5860465116279083]\n",
      "ATS test: 4 0.0\n",
      "B&H_train : [-17.707726366917093, 2.147097781812059, 148.56031071288314, 130.4488888888889, 42.924031007751935]\n",
      "ats_train : [98.3370962988357, 87.30782365115732, 265.9124709743462, 208.6136796965599, 94.87947849999999]\n",
      "B&H_test : [3.561435637576156, 34.481848604705824, 44.484867015086024, -26.428888888888874, -0.5860465116279083]\n",
      "ats_test : [0.0, 16.698543999999995, 22.857967000000006, -3.7660895000000165, 0.0]\n"
     ]
    }
   ],
   "source": [
    "fobj()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal = ['volume_prof_signal','stoch_signal_2','bb_signal','cci_signal','bb_rsi_signal','renko_adx_signal','ma50_100_signal','rsiandmacd','fake_obv_macd_crossover_signal','wpr_signal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DE prediction\n",
    "action_list = []\n",
    "for i in range(0,4*window_size):\n",
    "    td = 0.2\n",
    "    value = 0\n",
    "    for j in range(10):\n",
    "        value += data[signal[j]][i]*coll[0][0][j]\n",
    "    if(value>td):\n",
    "        action_list.append(1)\n",
    "    elif (value<-td):\n",
    "        action_list.append(-1)\n",
    "    else:\n",
    "        action_list.append(0)\n",
    "for i in range(4*window_size,5*window_size):\n",
    "    td = 0.2\n",
    "    value = 0\n",
    "    for j in range(10):\n",
    "        value += data[signal[j]][i]*coll[1][0][j]\n",
    "    if(value>td):\n",
    "        action_list.append(1)\n",
    "    elif (value<-td):\n",
    "        action_list.append(-1)\n",
    "    else:\n",
    "        action_list.append(0)\n",
    "for i in range(5*window_size,6*window_size):\n",
    "    td = 0.2\n",
    "    value = 0\n",
    "    for j in range(10):\n",
    "        value += data[signal[j]][i]*coll[2][0][j]\n",
    "    if(value>td):\n",
    "        action_list.append(1)\n",
    "    elif (value<-td):\n",
    "        action_list.append(-1)\n",
    "    else:\n",
    "        action_list.append(0)\n",
    "for i in range(6*window_size,7*window_size):\n",
    "    td = 0.2\n",
    "    value = 0\n",
    "    for j in range(10):\n",
    "        value += data[signal[j]][i]*coll[3][0][j]\n",
    "    if(value>td):\n",
    "        action_list.append(1)\n",
    "    elif (value<-td):\n",
    "        action_list.append(-1)\n",
    "    else:\n",
    "        action_list.append(0)\n",
    "for i in range(7*window_size,8*window_size+2):\n",
    "    td = 0.2\n",
    "    value = 0\n",
    "    for j in range(10):\n",
    "        value += data[signal[j]][i]*coll[4][0][j]\n",
    "    if(value>td):\n",
    "        action_list.append(1)\n",
    "    elif (value<-td):\n",
    "        action_list.append(-1)\n",
    "    else:\n",
    "        action_list.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy poly:96.02%\n"
     ]
    }
   ],
   "source": [
    "#SVM\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Split the dataset into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(data[signal], action_list, test_size=0.2, random_state=0)\n",
    "svm_classifier = SVC(kernel='poly', C=1.0)\n",
    "\n",
    "# Train the classifier on the training data\n",
    "svm_classifier.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the testing data\n",
    "svm_pred = svm_classifier.predict(X_test)\n",
    "\n",
    "# Evaluate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, svm_pred)\n",
    "print(f\"Accuracy poly:{accuracy * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy : 95.58%\n"
     ]
    }
   ],
   "source": [
    "#LOGISTIC\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import datasets\n",
    "\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train,y_train)\n",
    "lo_pred = model.predict(X_test)\n",
    "accuracy = accuracy_score(y_test, lo_pred)\n",
    "print(f\"Accuracy : {accuracy * 100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9646017699115044\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for i in range(10):\n",
    "    knn_classifier = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "    # Train the classifier on the training set\n",
    "    knn_classifier.fit(X_train, y_train)\n",
    "\n",
    "    # Make predictions on the test set\n",
    "    knn_pred = knn_classifier.predict(X_test)\n",
    "\n",
    "    # Evaluate the accuracy of the classifier\n",
    "accuracy = accuracy_score(y_test, knn_pred)\n",
    "print(f'Accuracy: {accuracy}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "#divide data to 4:1\n",
    "import math\n",
    "num = math.ceil(len(action_list)/5)\n",
    "de_pred = action_list[-num:]\n",
    "stock = data['adj_close_price'][-num:]\n",
    "stock = stock.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate(prc,lst,ren,pred,stk,budget):\n",
    "    for i in range(ren):\n",
    "        if i == 0 :\n",
    "            if pred[i] == 1 :\n",
    "                stk += 1\n",
    "                prc.append(stock[i])\n",
    "                lst.append(stock[i]/prc[i]*100)\n",
    "            elif pred[i] == -1 :\n",
    "                prc.append(0)\n",
    "                lst.append(0)\n",
    "            elif pred[i] == 0 :\n",
    "                prc.append(0)\n",
    "                lst.append(0)\n",
    "        else :\n",
    "            if pred[i] == 1 :\n",
    "                stk += 1\n",
    "                prc.append(prc[i-1]+stock[i])\n",
    "                lst.append((stk*stock[i]+budget)/prc[i]*100)\n",
    "            elif pred[i] == -1 :\n",
    "                if stk > 0 :\n",
    "                    stk -= 1\n",
    "                    prc.append(prc[i-1])\n",
    "                    budget += stock[i]\n",
    "                    lst.append((stk*stock[i]+budget)/prc[i]*100)\n",
    "                else :\n",
    "                    prc.append(prc[i-1])\n",
    "                    if prc[i] <= 0 :\n",
    "                        lst.append(lst[i-1])\n",
    "                    else :\n",
    "                        lst.append(budget/prc[i]*100)\n",
    "            elif pred[i] == 0 :\n",
    "                prc.append(prc[i-1])\n",
    "                if prc[i] != 0 :\n",
    "                    lst.append((stk*stock[i]+budget)/prc[i]*100)\n",
    "                else:\n",
    "                    lst.append(lst[i-1])\n",
    "    return prc,lst,budget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ALL ML AND DE\n",
    "de_prc , de_lst ,de_bud= calculate([],[],num,de_pred,0,0)\n",
    "svm_prc , svm_lst,svm_d=bud = calculate([],[],num,svm_pred,0,0)\n",
    "lo_prc , lo_lst,lo_bud = calculate([],[],num,lo_pred,0,0)\n",
    "knn_prc , knn_lst , knn_bud = calculate([],[],num,knn_pred,0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#B&H\n",
    "bh_lst = []\n",
    "for i in range(num):\n",
    "    bh_lst.append(stock[i]/stock[0]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "fig = make_subplots(rows=3, cols=2, subplot_titles=['DE', 'SVM', 'LO', 'KNN'])\n",
    "\n",
    "DE = go.Scatter(y=de_lst, mode='lines', name='DE')\n",
    "SVM = go.Scatter(y=svm_lst, mode='lines', name='SVM')\n",
    "LO = go.Scatter(y=lo_lst, mode='lines', name='LO')\n",
    "KNN = go.Scatter(y=knn_lst , mode='lines' , name='KNN')\n",
    "BH = go.Scatter(y=bh_lst , mode='lines' , name='BH')\n",
    "\n",
    "fig.add_trace(DE, row=1, col=1)\n",
    "fig.add_trace(SVM, row=1, col=2)\n",
    "fig.add_trace(LO, row=2 , col=1)\n",
    "fig.add_trace(KNN, row=2 , col=2)\n",
    "fig.add_trace(BH, row=3 , col=1)\n",
    "fig.update_yaxes(range=[80, 110])\n",
    "\n",
    "fig.update_layout(title_text='Graph', showlegend=False)\n",
    "\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "fig = go.Figure([DE , SVM , LO , KNN , BH])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DE Growth -2.9036827195467367 %\n",
      "SVM Growth -6.469002695417785 %\n",
      "LOGISTIC Growth -100 %\n",
      "KNN Growth -100 %\n",
      "BH Growth -1.0471204188481664 %\n"
     ]
    }
   ],
   "source": [
    "print(f'DE Growth {de_lst[-1]-100} %')\n",
    "print(f'SVM Growth {svm_lst[-1]-100} %')\n",
    "print(f'LOGISTIC Growth {lo_lst[-1]-100} %')\n",
    "print(f'KNN Growth {knn_lst[-1]-100} %')\n",
    "print(f'BH Growth {bh_lst[-1]-100} %')"
   ]
  }
 ],
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