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fintechexplained/notebook.ipynb

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notebook.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create a variable and Print Hello in Python\n",
"\n",
"message = 'Hello Finance And Technology Explained'\n",
"print(message)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3, 6, 9, 12]\n"
]
},
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a collection to store a list of integers in Python\n",
"\n",
"myList = [3,6,9,12]\n",
"print(myList)\n",
"len(myList)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mandatory Input:Hello Function Argument 1\n",
"Optional Input:Some Default Value\n",
"Hello Function Argument 1--Some Default Value\n",
"Mandatory Input:Hello Function Argument 1\n",
"Optional Input:Hello Function Argument 2\n",
"Hello Function Argument 1--Hello Function Argument 2\n"
]
}
],
"source": [
"#How to create a function in Python\n",
"def MyFunction(mandatoryInput, optionalInput=\"Some Default Value\"):\n",
" print(\"Mandatory Input:\"+mandatoryInput)\n",
" print(\"Optional Input:\"+optionalInput)\n",
" return mandatoryInput + \"--\" + optionalInput\n",
"\n",
"#Call the new function\n",
"result = MyFunction(\"Hello Function Argument 1\")\n",
"print(result)\n",
"result = MyFunction(\"Hello Function Argument 1\", \"Hello Function Argument 2\")\n",
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2\n",
"4\n",
"6\n",
"8\n"
]
}
],
"source": [
"#Implement a for loop in Python\n",
"for myVariable in range(1,5):\n",
" print(2 * myVariable)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Let's start looking into Python Packages\n",
"Let's start looking into Python Packages\n"
]
}
],
"source": [
"# Implement a if then else condition in Python\n",
"message = \"We Are Ready To Start\"\n",
"if (\"We Are Ready To Start\" == message):\n",
" print(\"Let's start looking into Python Packages\")\n",
" \n",
"message = 10\n",
"if (12 > message):\n",
" print(\"Let's start looking into Python Packages\")"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GBP/USD Close</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Date</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1998-12-31</th>\n",
" <td>88.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-01</th>\n",
" <td>88.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-04</th>\n",
" <td>85.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-05</th>\n",
" <td>83.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-06</th>\n",
" <td>86.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-07</th>\n",
" <td>83.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-08</th>\n",
" <td>82.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-11</th>\n",
" <td>77.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-12</th>\n",
" <td>83.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1999-01-13</th>\n",
" <td>86.46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GBP/USD Close\n",
"Date \n",
"1998-12-31 88.67\n",
"1999-01-01 88.67\n",
"1999-01-04 85.60\n",
"1999-01-05 83.95\n",
"1999-01-06 86.96\n",
"1999-01-07 83.12\n",
"1999-01-08 82.37\n",
"1999-01-11 77.03\n",
"1999-01-12 83.44\n",
"1999-01-13 86.46"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#import pandas\n",
"import pandas as pa\n",
"import matplotlib as plot\n",
"\n",
"#get data\n",
"def GetData(fileName):\n",
" return read_csv(fileName, header=0, parse_dates=[0], index_col=0)\n",
"\n",
"#read time series from the exchange.csv file \n",
"exchangeRatesSeries = GetData('exchange.csv')\n",
"\n",
"#view top 10 records\n",
"exchangeRatesSeries.head(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GBP/USD Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>5100.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>89.732223</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>63.037321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>17.040000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>45.965000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>73.315500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>94.187500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>250.280000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GBP/USD Close\n",
"count 5100.000000\n",
"mean 89.732223\n",
"std 63.037321\n",
"min 17.040000\n",
"25% 45.965000\n",
"50% 73.315500\n",
"75% 94.187500\n",
"max 250.280000"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#view basic stats information on data\n",
"exchangeRatesSeries.describe()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x50d6daa4a8>"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#plot the time series data\n",
"exchangeRatesSeries.plot(color='green')"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000050DA8C2080>]],\n",
" dtype=object)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0+lQ0k2I4aBkwJ5XNAW5J08uAM9OngaYD2yNiY6PrNzOzcsqOOXwEuFbSvsBDwNkUoXK9pHOAR4BTU93bgROBTuDZVNfMzCpSKgAi4j6g3omGmXXqBnBumfWZmdng8TeBzcwy5QAwM8uUA8DMLFMOADOzTDkAzMwy5QAwM8uUA8DMLFMOADOzTDkAzMwy5QAwM8uUA8DMLFMOADOzTDkAzMwy5QAwM8uUA8DMLFMOADOzTDkAzMwyVfaWkGZmQ651/m2VrXvDgpMqW/dQ8xGAmVmmHABmZplyAJiZZcoBYGaWKQeAmVmmHABmZplyAJiZZap0AEgaIennkm5Nj6dIWiVpvaRvS9o3lY9KjzvT/Nay6zYzs8YNxhHA+cC6mseXAJdFxFRgK3BOKj8H2BoRrwMuS/XMzKwipQJA0iTgJOA/02MB7wBuSFWWACen6dnpMWn+zFTfzMwqoIhofGHpBuCLwAHAJ4CzgJXpXT6SJgN3RMThktYCsyKiK837DXBMRDzR6znnAnMBWlpajlq6dGnD7Vvz2PaGl222ltGw6bmqW1GtRvpg2sSxQ9OYinR3dzNmzJiqm1Gpen1Q5f9yVftYmX1hxowZqyOirb96DV8LSNK7gc0RsVpSe09xnaoxgHk7CyIWAgsB2traor29vXeVATurwuuH7Kl503Zw6Zq8L83USB9sOKN9aBpTkY6ODsrs83uDen1Q5f9yVftYM/aFMq84bwPeI+lE4OXAK4CvAeMkjYyIHcAk4PFUvwuYDHRJGgmMBbaUWL+ZmZXQ8DmAiLgoIiZFRCtwGnBXRJwB/BA4JVWbA9ySppelx6T5d0WZ8SczMytlKL4HcCFwgaRO4CBgUSpfBByUyi8A5g/Bus3MbIAGZdA5IjqAjjT9EHB0nTrPA6cOxvrMzKw8fxPYzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTDgAzs0w5AMzMMuUAMDPLlAPAzCxTI6tugJnZcNY6/7ZK1nv1rP2HfB0+AjAzy5QDwMwsUw4AM7NMOQDMzDLVcABImizph5LWSXpA0vmp/EBJyyWtT7/Hp3JJukJSp6T7JR05WBthZmZ7rswRwA5gXkT8FTAdOFfSYcB8YEVETAVWpMcAJwBT089c4KoS6zYzs5IaDoCI2BgRP0vTTwPrgInAbGBJqrYEODlNzwauicJKYJykQxpuuZmZlaKIKP8kUitwN3A48EhEjKuZtzUixku6FVgQEfek8hXAhRFxb6/nmktxhEBLS8tRS5cubbhdax7b3vCyzdYyGjY9V3UrqtVIH0ybOHZoGlOR7u5uxowZU3UzKlWvD15K/8uDZcrYEQ3vCzNmzFgdEW391Sv9RTBJY4AbgY9FxFOS+qxap2yX9ImIhcBCgLa2tmhvb2+4bWdV9AWORsybtoNL1+T9vbxG+mDDGe1D05iKdHR0UGafH2rN+FLUvGkvcuk9z/Qqze9/4+pZ+w/5vlDqU0CS9qF48b82Im5KxZt6hnbS782pvAuYXLP4JODxMus3M7PGlfkUkIBFwLqI+GrNrGXAnDQ9B7ilpvzM9Gmg6cD2iNjY6PrNzKycMsdVbwPeD6yRdF8q+xSwALhe0jnAI8Cpad7twIlAJ/AscHaJdZuZWUkNB0A6mdvXgP/MOvUDOLfR9ZmZ2eDyN4HNzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NMOQDMzDLlADAzy1TTA0DSLEkPSuqUNL/Z6zczs0JTA0DSCOBK4ATgMOB0SYc1sw1mZlZo9hHA0UBnRDwUEX8AlgKzm9wGMzMDFBHNW5l0CjArIj6QHr8fOCYizqupMxeYmx6+HniwaQ2s1gTgiaobUTH3gfsA3Ac9yvTDoRHxyv4qjWzwyRulOmV/lkARsRBY2JzmDB+S7o2ItqrbUSX3gfsA3Ac9mtEPzR4C6gIm1zyeBDze5DaYmRnND4CfAlMlTZG0L3AasKzJbTAzM5o8BBQROySdB3wfGAEsjogHmtmGYSy7Ya863AfuA3Af9BjyfmjqSWAzMxs+/E1gM7NMOQDMzDLlAKiApA2S1ki6T9K9qexAScslrU+/x1fdzsEmabGkzZLW1pTV3W4VrkiXDLlf0pHVtXzw9NEHn5X0WNof7pN0Ys28i1IfPCjp+GpaPbgkTZb0Q0nrJD0g6fxUns2+sJs+aO6+EBH+afIPsAGY0KvsS8D8ND0fuKTqdg7Bdh8LHAms7W+7gROBOyi+OzIdWFV1+4ewDz4LfKJO3cOAXwCjgCnAb4ARVW/DIPTBIcCRafoA4NdpW7PZF3bTB03dF3wEMHzMBpak6SXAyRW2ZUhExN3All7FfW33bOCaKKwExkk6pDktHTp99EFfZgNLI+KFiHgY6KS4nMpLWkRsjIifpemngXXARDLaF3bTB30Zkn3BAVCNAO6UtDpd+gKgJSI2QrFzAAdX1rrm6mu7JwKP1tTrYvf/IC9156XhjcU1w397fR9IagX+BlhFpvtCrz6AJu4LDoBqvC0ijqS4Kuq5ko6tukHDUL+XDdmLXAW8FjgC2Ahcmsr36j6QNAa4EfhYRDy1u6p1yvaKfqjTB03dFxwAFYiIx9PvzcDNFIdym3oOa9PvzdW1sKn62u5sLhsSEZsi4sWI+BPwDXYe2u+1fSBpH4oXvmsj4qZUnNW+UK8Pmr0vOACaTNL+kg7omQaOA9ZSXBJjTqo2B7ilmhY2XV/bvQw4M30CZDqwvWd4YG/Tazz7vRT7AxR9cJqkUZKmAFOBnzS7fYNNkoBFwLqI+GrNrGz2hb76oOn7QtVnw3P7AV5DcTb/F8ADwKdT+UHACmB9+n1g1W0dgm2/juKw9o8U72jO6Wu7KQ55r6T4tMMaoK3q9g9hH3wzbeP96R/9kJr6n0598CBwQtXtH6Q+eDvF8MX9wH3p58Sc9oXd9EFT9wVfCsLMLFMeAjIzy5QDwMwsUw4AM7NMOQDMzDLlADAzy5QDwMwsUw4AM7NM/R9cieLEXaYrGwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"exchangeRatesSeries.hist()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted=15.219305\n"
]
}
],
"source": [
"from statsmodels.tsa.arima_model import ARIMA\n",
"\n",
"#Function that calls ARIMA model to fit and forecast the data\n",
"def StartARIMAForecasting(Actual, P, D, Q):\n",
"\tmodel = ARIMA(Actual, order=(P, D, Q))\n",
"\tmodel_fit = model.fit(disp=0)\n",
"\tprediction = model_fit.forecast()[0]\n",
"\treturn prediction\n",
"\n",
"#creating data\n",
"ExchangeRates = [[1],[1.5],[2],[4],[6],[8],[16],[1]]\n",
"\n",
"#predict next value\n",
"predicted = StartARIMAForecasting(ExchangeRates, 1,1,0)\n",
"#display the value\n",
"print('Predicted=%f' % (predicted))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actual=25.703000, Predicted=24.576407\n",
"Actual=25.211000, Predicted=25.332257\n",
"Actual=25.110000, Predicted=25.111075\n",
"Actual=24.872000, Predicted=25.226216\n",
"Actual=25.363000, Predicted=24.936624\n",
"Actual=25.170000, Predicted=25.239772\n",
"Actual=27.179000, Predicted=25.092933\n",
"Actual=27.836000, Predicted=26.591637\n",
"Actual=27.985000, Predicted=27.171861\n",
"Actual=27.105000, Predicted=27.780563\n",
"Actual=26.920000, Predicted=27.315171\n",
"Actual=26.985000, Predicted=27.143033\n",
"Actual=26.249000, Predicted=26.975665\n",
"Actual=25.962000, Predicted=26.429684\n",
"Actual=25.566000, Predicted=26.183522\n",
"Actual=25.989000, Predicted=25.718159\n",
"Actual=25.758000, Predicted=25.920294\n",
"Actual=25.783000, Predicted=25.706840\n",
"Actual=26.084000, Predicted=25.801640\n",
"Actual=26.210000, Predicted=25.960352\n",
"Actual=27.010000, Predicted=26.080079\n",
"Actual=26.853000, Predicted=26.716710\n",
"Actual=25.614000, Predicted=26.700089\n",
"Actual=25.077000, Predicted=25.999553\n",
"Actual=25.090000, Predicted=25.482576\n",
"Actual=25.618000, Predicted=25.170893\n",
"Actual=25.980000, Predicted=25.426873\n",
"Actual=26.420000, Predicted=25.729131\n",
"Actual=27.080000, Predicted=26.184598\n",
"Actual=27.629000, Predicted=26.760655\n",
"Actual=27.264000, Predicted=27.295644\n",
"Actual=26.970000, Predicted=27.231556\n",
"Actual=27.910000, Predicted=27.115386\n",
"Actual=27.352000, Predicted=27.663582\n",
"Actual=27.880000, Predicted=27.287700\n",
"Actual=29.473000, Predicted=27.821930\n",
"Actual=28.115000, Predicted=28.849559\n",
"Actual=27.909000, Predicted=28.153537\n",
"Actual=27.951000, Predicted=28.250526\n",
"Actual=28.690000, Predicted=27.951473\n",
"Actual=29.292000, Predicted=28.431873\n",
"Actual=28.809000, Predicted=28.924221\n",
"Actual=28.281000, Predicted=28.801670\n",
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]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\farhadm\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actual=51.895000, Predicted=52.741261\n",
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"Actual=70.705000, Predicted=70.610954\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actual=70.220000, Predicted=70.623381\n",
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"Actual=72.450000, Predicted=71.477591\n",
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"Actual=72.470000, Predicted=72.420228\n",
"Actual=71.170000, Predicted=72.465444\n",
"Actual=71.300000, Predicted=71.566146\n",
"Actual=70.250000, Predicted=71.538109\n",
"Actual=71.210000, Predicted=70.524587\n",
"Actual=70.288000, Predicted=71.144334\n",
"Actual=70.091000, Predicted=70.344004\n",
"Actual=70.227000, Predicted=70.349601\n",
"Actual=70.264000, Predicted=70.217633\n",
"Actual=71.750000, Predicted=70.215341\n",
"Actual=72.030000, Predicted=71.288704\n",
"Actual=71.890000, Predicted=71.615108\n",
"Actual=72.358000, Predicted=71.872193\n",
"Actual=72.624000, Predicted=72.242969\n",
"Actual=71.866000, Predicted=72.435031\n",
"Actual=71.896000, Predicted=72.032341\n",
"Actual=71.616000, Predicted=72.047651\n",
"Actual=72.322000, Predicted=71.683719\n",
"Actual=72.618000, Predicted=72.164608\n",
"Actual=72.420000, Predicted=72.367320\n",
"Actual=72.756000, Predicted=72.412508\n",
"Actual=72.480000, Predicted=72.693641\n",
"Actual=72.210000, Predicted=72.482797\n",
"Actual=72.560000, Predicted=72.347216\n",
"Actual=72.643000, Predicted=72.506237\n",
"Actual=72.014000, Predicted=72.534307\n",
"Actual=71.534000, Predicted=72.180944\n",
"Actual=72.337000, Predicted=71.810069\n",
"Actual=72.050000, Predicted=72.190818\n",
"Actual=70.655000, Predicted=71.952552\n",
"Actual=70.514000, Predicted=71.135244\n",
"Actual=70.775000, Predicted=70.853050\n",
"Actual=70.560000, Predicted=70.713644\n",
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"Actual=71.399000, Predicted=71.639343\n",
"Actual=70.206000, Predicted=71.456432\n",
"Actual=70.053000, Predicted=70.612068\n",
"Actual=70.636000, Predicted=70.351599\n",
"Actual=71.510000, Predicted=70.481244\n",
"Actual=71.671000, Predicted=71.112330\n",
"Actual=71.954000, Predicted=71.428259\n",
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"Actual=72.330000, Predicted=72.111285\n",
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"Actual=72.910000, Predicted=73.115269\n",
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"Actual=71.862000, Predicted=71.597254\n",
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"Actual=70.849000, Predicted=71.132542\n",
"Actual=71.526000, Predicted=70.873469\n",
"Actual=70.994000, Predicted=71.333313\n",
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"Actual=72.258000, Predicted=71.883401\n",
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"Actual=72.716000, Predicted=72.022430\n",
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"Actual=72.481000, Predicted=73.060717\n",
"Actual=71.784000, Predicted=72.702113\n",
"Actual=71.540000, Predicted=72.132994\n",
"Actual=70.758000, Predicted=71.756228\n",
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"Actual=73.010000, Predicted=71.137856\n",
"Actual=73.704000, Predicted=72.347839\n",
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"Actual=73.958000, Predicted=74.095071\n",
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"Actual=78.089000, Predicted=75.532679\n",
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"Actual=78.571000, Predicted=77.975711\n",
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"Actual=81.870000, Predicted=78.008719\n",
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"Actual=84.887000, Predicted=82.618953\n",
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"Actual=86.843000, Predicted=86.431195\n",
"Actual=87.230000, Predicted=86.695602\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actual=87.399000, Predicted=87.140994\n",
"Actual=87.080000, Predicted=87.262787\n",
"Actual=87.257000, Predicted=87.140668\n",
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"Actual=80.751000, Predicted=83.124310\n",
"Actual=82.420000, Predicted=81.644518\n",
"Actual=84.600000, Predicted=82.398867\n",
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"Actual=87.230000, Predicted=86.304398\n",
"Actual=86.244000, Predicted=86.657722\n",
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"Actual=85.750000, Predicted=84.966195\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actual=85.953000, Predicted=85.559560\n",
"Actual=85.788000, Predicted=85.727932\n",
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"Actual=30.961000, Predicted=31.331811\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actual=31.670000, Predicted=30.955056\n",
"Actual=32.233000, Predicted=31.584441\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actual=40.767000, Predicted=41.877670\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Actual=52.340000, Predicted=52.907712\n",
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"Actual=48.560000, Predicted=48.426822\n",
"Actual=48.080000, Predicted=48.558153\n",
"Actual=47.400000, Predicted=48.211725\n",
"Test Mean Squared Error (smaller the better fit): 1.551\n"
]
},
{
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
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],
"source": [
"from pandas import read_csv\n",
"from matplotlib import pyplot\n",
"from statsmodels.tsa.arima_model import ARIMA\n",
"from sklearn.metrics import MeanSquaredError\n",
"\n",
"#get data\n",
"def GetData(fileName):\n",
" return read_csv(fileName, header=0, parse_dates=[0], index_col=0).values\n",
"\n",
"#Function that calls ARIMA model to fit and forecast the data\n",
"def StartARIMAForecasting(Actual, P, D, Q):\n",
"\tmodel = ARIMA(Actual, order=(P, D, Q))\n",
"\tmodel_fit = model.fit(disp=0)\n",
"\tprediction = model_fit.forecast()[0]\n",
"\treturn prediction\n",
" \n",
"#Get exchange rates\n",
"ActualData = GetData('exchange.csv')\n",
"#Size of exchange rates\n",
"NumberOfElements = len(ActualData)\n",
"\n",
"#Use 70% of data as training, rest 30% to Test model\n",
"TrainingSize = int(NumberOfElements * 0.7)\n",
"TrainingData = ActualData[0:TrainingSize]\n",
"TestData = ActualData[TrainingSize:NumberOfElements]\n",
"\n",
"#new arrays to store actual and predictions\n",
"Actual = [x for x in TrainingData]\n",
"Predictions = list()\n",
"\n",
"\n",
"#in a for loop, predict values using ARIMA model\n",
"for timepoint in range(len(TestData)):\n",
"\tActualValue = TestData[timepoint]\n",
"\t#forcast value\n",
"\tPrediction = StartARIMAForecasting(Actual, 3,1,0) \n",
"\tprint('Actual=%f, Predicted=%f' % (ActualValue, Prediction))\n",
"\t#add it in the list\n",
"\tPredictions.append(Prediction)\n",
"\tActual.append(ActualValue)\n",
"\n",
"#Print MSE to see how good the model is\n",
"Error = MeanSquaredError(TestData, Predictions)\n",
"print('Test Mean Squared Error (smaller the better fit): %.3f' % Error)\n",
"# plot\n",
"pyplot.plot(TestData)\n",
"pyplot.plot(Predictions, color='red')\n",
"pyplot.show()"
]
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@peymandarabi990
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how can we predict the next future?

@fintechexplained
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fintechexplained commented Oct 31, 2019 via email

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