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Dataquest Stock Price Prediction Project
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting the Stock Market"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load Packages"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" div#notebook-container { width: 85%; }\n",
" div#menubar-container { width: 65%; }\n",
" div#maintoolbar-container { width: 99%; }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import display, HTML\n",
"display(HTML(data=\"\"\"\n",
"<style>\n",
" div#notebook-container { width: 85%; }\n",
" div#menubar-container { width: 65%; }\n",
" div#maintoolbar-container { width: 99%; }\n",
"</style>\n",
"\"\"\"))\n",
"from datetime import datetime\n",
"from itertools import permutations \n",
"from collections import Counter\n",
"import sympy\n",
"\n",
"import numpy as np\n",
"from numpy import arange\n",
"from numpy.random import randint, seed, random\n",
"import pandas as pd\n",
"import pandas_profiling\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import matplotlib.style as style\n",
"\n",
"import seaborn as sns\n",
"\n",
"from scipy.stats import percentileofscore, chisquare, chi2_contingency\n",
"from scipy import stats\n",
"from scipy.spatial import distance\n",
"\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
"from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
"from sklearn.linear_model import LinearRegression, LogisticRegression\n",
"from sklearn.metrics.pairwise import euclidean_distances\n",
"from sklearn.cluster import KMeans"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The Dataset\n",
"\n",
"In this project, you'll be working with data from the [S&P500 Index](https://en.wikipedia.org/wiki/S%26P_500_Index). The S&P500 is a stock market index. Before we get into what an index is, we'll need to get into the basics of the stock market.\n",
"\n",
"Some companies are publicly traded, which means that anyone can buy and sell their shares on the open market. A share entitles the owner to some control over the direction of the company, and to some percentage (or share) of the earnings of the company. When you buy or sell shares, it's common to say that you're trading a stock.\n",
"\n",
"The price of a share is based mainly on supply and demand for a given stock. For example, Apple stock has a price of 120 dollars per share as of December 2015 -- http://www.nasdaq.com/symbol/aapl. A stock that is in less demand, like Ford Motor Company, has a lower price -- http://finance.yahoo.com/q?s=F. Stock price is also influenced by other factors, including the number of shares a company has issued.\n",
"\n",
"Stocks are traded daily, and the price can rise or fall from the beginning of a trading day to the end based on demand. Stocks that are in more in demand, such as Apple, are traded more often than stocks of smaller companies.\n",
"\n",
"Indexes aggregate the prices of multiple stocks together, and allow you to see how the market as a whole is performing. For example, the [Dow Jones Industrial Average](https://en.wikipedia.org/wiki/Dow_Jones_Industrial_Average) aggregates the stock prices of 30 large American companies together. The S&P500 Index aggregates the stock prices of 500 large companies. When an index fund goes up or down, you can say that the underlying market or sector it represents is also going up or down. For example, if the Dow Jones Industrial Average price goes down one day, you can say that American stocks overall went down (ie, most American stocks went down in price).\n",
"\n",
"You'll be using historical data on the price of the S&P500 Index to make predictions about future prices. Predicting whether an index will go up or down will help us forecast how the stock market as a whole will perform. Since stocks tend to correlate with how well the economy as a whole is performing, it can also help us make economic forecasts.\n",
"\n",
"There are also thousands of traders who make money by buying and selling [Exchange Traded Funds](https://en.wikipedia.org/wiki/Exchange-traded_fund). ETFs allow you to buy and sell indexes like stocks. This means that you could \"buy\" the S&P500 Index ETF when the price is low, and sell when it's high to make a profit. Creating a predictive model could allow traders to make money on the stock market.\n",
"\n",
"In this mission, you'll be working with a csv file containing index prices. Each row in the file contains a daily record of the price of the S&P500 Index from 1950 to 2015. The dataset is stored in sphist.csv.\n",
"\n",
"The columns of the dataset are:\n",
"\n",
"* Date -- The date of the record.\n",
"* Open -- The opening price of the day (when trading starts).\n",
"* High -- The highest trade price during the day.\n",
"* Low -- The lowest trade price during the day.\n",
"* Close -- The closing price for the day (when trading is finished).\n",
"* Volume -- The number of shares traded.\n",
"* Adj Close -- The daily closing price, adjusted retroactively to include any corporate actions. Read more [here](https://www.investopedia.com/terms/a/adjusted_closing_price.asp).\n",
"\n",
"You'll be using this dataset to develop a predictive model. You'll train the model with data from 1950-2012, and try to make predictions from 2013-2015."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading in the Data\n",
"\n",
"### Instructions\n",
"\n",
"Here are the steps you'll need to take, at a high level:\n",
"\n",
"* Read the data into a Pandas DataFrame. You can use the read_csv Pandas function for this.\n",
"* Convert the Date column to a Pandas date type. This will allow you to do date comparisons with the column.\n",
" * You can perform this conversion with the [to_datetime](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html) function in Pandas.\n",
" * Once you convert the column, you can perform comparisons with df[\"Date\"] > datetime(year=2015, month=4, day=1). This will generate a Boolean series that tells you if each item in the Date column is after 2015-04-01. You'll have to import the datetime module from the [datetime](https://docs.python.org/3/library/datetime.html) library first with from datetime import datetime.\n",
"* Sort the dataframe on the Date column. It's currently in descending order, but we'll want it to be in ascending order for some of the next steps. You can use the [DataFrame.sort_values()](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.sort_values.html) method on data frames for this.\n",
"\n",
"Make sure to run the predict.py script using python predict.py as you work through the steps."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"stocks = pd.read_csv('sphist.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2015-12-07</td>\n",
" <td>2090.419922</td>\n",
" <td>2090.419922</td>\n",
" <td>2066.780029</td>\n",
" <td>2077.070068</td>\n",
" <td>4.043820e+09</td>\n",
" <td>2077.070068</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2015-12-04</td>\n",
" <td>2051.239990</td>\n",
" <td>2093.840088</td>\n",
" <td>2051.239990</td>\n",
" <td>2091.689941</td>\n",
" <td>4.214910e+09</td>\n",
" <td>2091.689941</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2015-12-03</td>\n",
" <td>2080.709961</td>\n",
" <td>2085.000000</td>\n",
" <td>2042.349976</td>\n",
" <td>2049.620117</td>\n",
" <td>4.306490e+09</td>\n",
" <td>2049.620117</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2015-12-02</td>\n",
" <td>2101.709961</td>\n",
" <td>2104.270020</td>\n",
" <td>2077.110107</td>\n",
" <td>2079.510010</td>\n",
" <td>3.950640e+09</td>\n",
" <td>2079.510010</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2015-12-01</td>\n",
" <td>2082.929932</td>\n",
" <td>2103.370117</td>\n",
" <td>2082.929932</td>\n",
" <td>2102.629883</td>\n",
" <td>3.712120e+09</td>\n",
" <td>2102.629883</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"0 2015-12-07 2090.419922 2090.419922 2066.780029 2077.070068 \n",
"1 2015-12-04 2051.239990 2093.840088 2051.239990 2091.689941 \n",
"2 2015-12-03 2080.709961 2085.000000 2042.349976 2049.620117 \n",
"3 2015-12-02 2101.709961 2104.270020 2077.110107 2079.510010 \n",
"4 2015-12-01 2082.929932 2103.370117 2082.929932 2102.629883 \n",
"\n",
" Volume Adj Close \n",
"0 4.043820e+09 2077.070068 \n",
"1 4.214910e+09 2091.689941 \n",
"2 4.306490e+09 2049.620117 \n",
"3 3.950640e+09 2079.510010 \n",
"4 3.712120e+09 2102.629883 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stocks.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Volume')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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ytZHxse3HZvW8kbETqmrnHNYGAACA/yIkBAAAAJuku1+ZoWvFy5Ost3PNiUlekOTsqhrrmrKV7jplfO96Fp1sQ/TykalKctIKpz94bMkM/4YHlaq6VpLXZrxryYVJ7t3dr9ncqmayZ8r4rFuOrXWrsY26/z6Q5P0jU7erqmuuZ+0F9vp1nDstJLSeNT80MnajVZ77kJGxc7r7X9dRT5Kku/8zyUdGpu623rUBAADgQEJCAAAAsIm6+5Pd/ZgM3StekOSidS55iySvqqrnVNXSLam2yl2mjL92Dmv/w4zXTFVdNcmdRqY+0N0fnUNNc1NVN0pyRpIfGpn+eJK7zSOUsMFOT/LpkfE7VdVYh6krqaoTknz/yNQFWTkkMnYvnNvdZ6/m2isYu/+ukvH761D3se6+cB3nTzv3XXNe8ypVtWO5k6qqkvzAyNSb1lHLUmeNjE39XAMAAIC1EBICAACALdDdH+jun8vQxeLkJH+S5N+SXL7GJZ+S5JVVdTD8t/6JI2Nf6u5z5rD2+6aMLxfSuE2SsW3ZDqqwzSRA884M3aaWek+Su3b3xza3qtl19+VJ/nLK9LTuQEs9LkOHqKX2TjpKjaqq6ye56cjUtPtmVmu5/w5V56zz/K+MjH2ruz8z5zWT5NornHeLJN8xMj7WmWitPj8yNnYvAwAAwJqN7c8OAAAAbJLuvjTJayaPVNWRSb7vgMedM3xBvRonJ/mdJL85/0pnMrZ9z9g2TTPr7i9U1WeSHLtk6obLnHbClPEz51HTPFTVDyR5dZLrjEy/Nsmjunvf5la1LnuSPH1k/LFV9RuTINFypm1NtmeF86ZtHfXvK5y3WtPWWe7+O1R9cZ3nf2OT1kySq61w3rTP4IdX1R3WUc+BxtYZ+zwAAACANRMSAgAAgINId1+c5C2TR5L/2oLq/kl+PMl9snxn4N+oqr/t7nmFImYy2ZbnyJGp9Ww7tNQFuXJI6Khljr/xlPHz51POXJye8aDCC5P8XHdftsn1rEt3f7Sq3pnkrkumjs1wD58+7dxJYOpmI1P/1t0rhc2m3Qfzuv8umPG6h7KNCLVtVVBu6efNfvfa4OsKCQEAADBXB0MLcgAAAGAZ3f257t7T3fdLcrtMug5NUdnaTkLXzvjfGy6e4zW+PDK23JfpY9sEJcmX5lDLvIwFhD6Q5EnbLSB0gD1Txlfacmza/LT1DjTtPpjX/XdJxrcEFOZYbCttR7ZRxgKXAAAAsGZCQgAAALCNdPeHuvvkJL+6zGE/UlVHb1ZNS0wL5Hx1jtcYW+tayxx/1Snjl8yhlnnpkbHbJXlJVV1ls4uZk79O8rWR8R+uqtHXq6qukeSRI1PfTPKyVVxzQ++/7u6M/07L3X9sf1ffouvWFl0XAACABSUkBAAAANtQdz87yZ9MmT4syT03sZwDfWXK+I45XuOaM1w3SS6dMj4tULIVnpjxDjW7kry8qo7Y5HrWrbu/nORVI1M7kjxiymkPz3j3lNd09+dXcdkNvf8m2+ldY4brshi2azcvAAAAuAIhIQAAANi+npXxrbeS5K6bWMeBvpzxsMs8t80ZW+uLyxw/LcBx1BxqmYvu/oskP5HkWyPTj0jy91U1tiXZwW7PlPFpW4qtZ6uxZPp9MK/77zsy/ve05e4/tr9pnaju0N21gY+dm/lLAgAAsPiEhAAAAGCbmnRqeeOU6RtuZi37TbZjunhk6pg5Xub6I2PLhTQ+N2X8BnOoZW66+2VJfizD1lpLnZzk1ZPtuLaTf0ry6ZHxe1TVzgMHqurGSe47cuwFSV63yut9acr4vO6/sXsv2d4hoYOpo9bB6j+njN9sU6sAAACAdRISAgAAgO3tvVPGj97UKq5oLJRzu3ksXFXXSXKTkalpX+InySemjN9x/RXNV3f/fZIfSfKNken7J3ltVY1tt3ZQ6u7Lk/zlyFRl6Jx0oMcmucrIsXu7e6zD0phpgbDvWeX5K/neKePL3X/zMnZPJMl6O0xdd53nHwo+OWX8FptaBQAAAKyTkBAAAABsbxdNGR8LW2yWM0fGrltVx81h7TtMGX/PMuecnfHuPCetv5z56+7XZOgctG9k+t5J3lhV89y+baPtmTL+uBWer3T+lXT3BUk+NTJ1+9WusYJp6yx3/83LWIeuJLnWOtc9fp3nHwo+kOSykfH7bHYhAAAAsB5CQgAAALC9TesC8uVVnHvplPHD11jLfu+aMv7gda6bDOGZWa6Z7r40yb+OTH1PVd18DjXNXXefnuRBSb4yMn33JKdPuiod9Lr7o0neMTJ186q6e5JU1R2T3GbkmH/r7vfPeMmxe+GEqrr1jOuMGbv/LsvWhoRutM51v3+d5y+87v5akrNGpu5ZVesNaQEAAMCmERICAACA7W3adjfnrOLcS6aMX2NtpfyXsUBIkuxaz6JVdZUkPzYydXmSd69w+mvHlkzys+upaSN191uT3C/jga87J/nnqrre5la1ZnumjO/vHvT4KfMvXsO1Nur+u03GOwl9oLu/up61V+nCjHezWfNWfpP31APWXNGh5dUjY1dNcspmFwIAAABrJSQEAAAA29TkC/5pnXX+bRVLTAsJHbu2igbd/b4kHxqZuntV3XUdSz8+yQ1Hxl/f3V9c4dyXZggTLXVKVd10HTVtqO5+Z4Ytjca2lbtDkrdU1Q02t6o1+eskXxsZf/SkE8tjRua+mWTvGq71N0m+NTL+M1V17TWst9/TpoyvpcaZdffXk3xkZOre61j2R5PMYxvAQ8FLMx7SeppuQgAAAGwXQkIAAACwgarq16pqo76Ef2qS64+MX5bkX1Zx/mcyvuXYbddT1MTzpow/p6pm/nvEJNzxB1Omn7vS+d39qSSvGpk6MslpVVWz1rRZuvusDEGQC0amb5PkrVW1rmDXRuvui5O8cmTq2kl2JxnriPTa7v78Gq712Yy/1tdP8sxZ10uSSbjtsSNTX0ty2lrWXKP3jozdu6puPOtCk2DL76+/pENDd5+X5O9Hpo5Jsudg/gwBAACA/YSEAAAAYGM9Lsl/VNXz59mxpqp+MMnvTZl+fXefv9Ia3f3NJB8emXrUHL7wfkmSi0fGT0zyJ7MsVFVHZOhEMxaI+miSN65yqd/OeCeQ+yb5s7WElw5UVevdpm2q7n5/knsm+ezI9C2TnFFVx2/U9edkz5TxsS3kljt+Nf50yvhTq+qRsyxUVTfJcP+NvSf2dvdYl6eN8oaRsatkeoBu1KQL2YuSfOc8ijqE/EbGg5U/kuSP1/sZsl9V3aqqHjGPtQAAAOBAQkIAAACw8a6a5ElJPl5Vr6yqh0+CLzOrqsOr6qlJXpdkLJTSSX53hiXfPjJ2pyTPq6qx7i6r0t2XJPnVKdNPqapnV9VVV1pn0kHor5Pcf+wySX6uu3uVNZ2d5I+mTP9MkleusSPL3arqDUlOmfXcWXT3RzIEhc4bmb5ZhqDQwRz6+Ockn1rlsRdmuMfXpLvPSPKykanDkry0qn58NetU1S2TvCnJWMDvwiTPWGuNa/TKJF8eGX9cVT15NQtU1ZGTdR41z8IOBd398SS/OWX6qUneOAmVzayqrlJV962qV2XYrvEhaywTAAAAphISAgAAgM1zRJKHZ/iC/rNVtbuqHrmaraKq6jur6teSfDBDJ56rTTn0ud39rzPUtGfK+JMmNb63ql5RVS+pqj0jj1tNW7i7X5jktVOmfznJmVX1I1V1pd+lqq5VVT+Z4ff94SlrPKe7V7Ot2oF+O8n/nTL30Axdn/6gqr5vWjelyZf5t6uqp1fVBybr3T/jnWbmqrs/luQeST4xMn1chqDQ1NdkK3X35Un+cpWH7510ulqPJ2fYUm+pqybZOwnsff/Y61xVN62q307yviS3nrL+Kd09tgXchunuryX58ynTz528T0eDYlV1w0nA8GNJTj5g6p/nXOaie3aSV0yZu2+GMOifV9U9xj7bDlRVJ1TVj1bVC5P8Z5LTkzwsm/BZAgAAwKGpVvk/2wEAAABrUFUfTHKbVRz6hQzBj88k+WqGL4mPTHJ0ku9Ocu1VrHF6kgfPGq6oqtcleeAs5xzg3t39lmXWvkGSdyY5YZk1Lk5ydobf/SpJjk1yu4x3StrvXUl+cBKamElVHZPkzVn5dTk/yceTXJBkX5KjkhwzOW/HyPG/1N3LbqVWVU9I8hdLx7t7plDAJFj2zxm2GlvqgiT36e4PzrLmZqiqW2TYIm4lt+/uf5/D9e6RYTu6qy9z2GcnNf1nkmtlCFvdNssHNf53d//ijLU8IfN57a+VITx33DKHfTjDvXtxkusmuUmG+3bptd6f5DEZ3n9LLfveXlLTWzJ0uTrQi7v7Cas5f8qaz0ryzCXD53b3znWsea8M7/2lTujuc2ZY5+pJXpXxDmcH+kaG1+rzSS5KcniGz5Gjktw8yXWWOXdd/34AAAAw5vCtLgAAAABIMoSBjs6w1dda7E3yxDV2X3lChiDPzdZ47am6+/yquk+SNyT5rimHHZnkrjMs+3+TPGQtAaFJTRdOwgKvSnL3ZQ69weRx0Onuz1TVPZP8U4ZAy4Gun+QtVfVD3f1vm1/ddN39H1X1jiR3W+aw980jIDS53hlV9fAkf5shADTmxpPHaj03yS+tt7a16u5LJoGj12V6+OnWmd4Bab+PJXlApnclY4ru/npVnZzk+UmeuMyhV0tyx82pCgAAAFZmuzEAAADYWM9O8pYkl23Q+p9I8qDufmx3f2MtC0y2TLpDktMydL6Yq+7+ZJKTkvzNOpe6PMn/ztBB6EvrrOnzSe6d5PeSXLrOuvab+7/dcrr7/CT3SjIWBDo6yb9U1UmbWdMq7Vnn/Ey6+40ZQmjvW+dSF2cI4j2lt7g1d3e/OcmDM3QdW4u3Jblbd39uflUdWrr7m93900kekeRTc17+nAyd4QAAAGCuhIQAAABgA3X3X3T3vZPcMMlPJnlZkvPWuexlGbZQ+okkt+nu169zvXT3xd39xAwdVX42yYuSvCfDFmBfzjpDTt39pe5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"text/plain": [
"<Figure size 2880x1080 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(40,15))\n",
"plt.scatter(stocks.Date, stocks.Volume, c='b')\n",
"plt.title('Stock Volume', fontsize=60)\n",
"plt.xlabel('Date', fontsize=40)\n",
"plt.ylabel('Volume', fontsize=40)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 16590 entries, 0 to 16589\n",
"Data columns (total 7 columns):\n",
"Date 16590 non-null object\n",
"Open 16590 non-null float64\n",
"High 16590 non-null float64\n",
"Low 16590 non-null float64\n",
"Close 16590 non-null float64\n",
"Volume 16590 non-null float64\n",
"Adj Close 16590 non-null float64\n",
"dtypes: float64(6), object(1)\n",
"memory usage: 907.4+ KB\n"
]
}
],
"source": [
"stocks.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"stocks['Date'] = pd.to_datetime(stocks.Date)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 16590 entries, 0 to 16589\n",
"Data columns (total 7 columns):\n",
"Date 16590 non-null datetime64[ns]\n",
"Open 16590 non-null float64\n",
"High 16590 non-null float64\n",
"Low 16590 non-null float64\n",
"Close 16590 non-null float64\n",
"Volume 16590 non-null float64\n",
"Adj Close 16590 non-null float64\n",
"dtypes: datetime64[ns](1), float64(6)\n",
"memory usage: 907.4 KB\n"
]
}
],
"source": [
"stocks.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"stocks.sort_values('Date', inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>16589</td>\n",
" <td>1950-01-03</td>\n",
" <td>16.66</td>\n",
" <td>16.66</td>\n",
" <td>16.66</td>\n",
" <td>16.66</td>\n",
" <td>1260000.0</td>\n",
" <td>16.66</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16588</td>\n",
" <td>1950-01-04</td>\n",
" <td>16.85</td>\n",
" <td>16.85</td>\n",
" <td>16.85</td>\n",
" <td>16.85</td>\n",
" <td>1890000.0</td>\n",
" <td>16.85</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16587</td>\n",
" <td>1950-01-05</td>\n",
" <td>16.93</td>\n",
" <td>16.93</td>\n",
" <td>16.93</td>\n",
" <td>16.93</td>\n",
" <td>2550000.0</td>\n",
" <td>16.93</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16586</td>\n",
" <td>1950-01-06</td>\n",
" <td>16.98</td>\n",
" <td>16.98</td>\n",
" <td>16.98</td>\n",
" <td>16.98</td>\n",
" <td>2010000.0</td>\n",
" <td>16.98</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16585</td>\n",
" <td>1950-01-09</td>\n",
" <td>17.08</td>\n",
" <td>17.08</td>\n",
" <td>17.08</td>\n",
" <td>17.08</td>\n",
" <td>2520000.0</td>\n",
" <td>17.08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume Adj Close\n",
"16589 1950-01-03 16.66 16.66 16.66 16.66 1260000.0 16.66\n",
"16588 1950-01-04 16.85 16.85 16.85 16.85 1890000.0 16.85\n",
"16587 1950-01-05 16.93 16.93 16.93 16.93 2550000.0 16.93\n",
"16586 1950-01-06 16.98 16.98 16.98 16.98 2010000.0 16.98\n",
"16585 1950-01-09 17.08 17.08 17.08 17.08 2520000.0 17.08"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stocks.head()"
]
},
{
"attachments": {
"image.png": {
"image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating Indicators\n",
"\n",
"Datasets taken from the stock market need to be handled differently than datasets from other sectors when it comes time to make predictions. In a normal machine learning exercise, we treat each row as independent. Stock market data is sequential, and each observation comes a day after the previous observation. Thus, the observations are not all independent, and you can't treat them as such.\n",
"\n",
"This means you have to be extra careful to not inject \"future\" knowledge\" into past rows when you do training and prediction. Injecting future knowledge will make our model look good when you're training and testing it, but will make it fail in the real world. This is how many algorithmic traders lose money.\n",
"\n",
"The time series nature of the data means that can generate indicators to make our model more accurate. For instance, you can create a new column that contains the average price of the last 10 trades for each row. This will incorporate information from multiple prior rows into one, and will make predictions much more accurate.\n",
"\n",
"When you do this, you have to be careful not to use the current row in the values you average. You want to teach the model how to predict the current price from historical prices. If you include the current price in the prices you average, it will be equivalent to handing the answers to the model upfront, and will make it impossible to use in the \"real world\", where you don't know the price upfront.\n",
"\n",
"Here are some indicators that are interesting to generate for each row:\n",
"\n",
"* The average price from the past 5 days.\n",
"* The average price for the past 30 days.\n",
"* The average price for the past 365 days.\n",
"* The ratio between the average price for the past 5 days, and the average price for the past 365 days.\n",
"* The standard deviation of the price over the past 5 days.\n",
"* The standard deviation of the price over the past 365 days.\n",
"* The ratio between the standard deviation for the past 5 days, and the standard deviation for the past 365 days.\n",
"\n",
"\"Days\" means \"trading days\" -- so if you're computing the average of the past 5 days, it should be the 5 most recent dates before the current one. Assume that \"price\" means the Close column. Always be careful not to include the current price in these indicators! You're predicting the next day price, so our indicators are designed to predict the current price from the previous prices.\n",
"\n",
"Some of these indicators require a year of historical data to compute. Our first day of data falls on 1950-01-03, so the first day you can start computing indicators on is 1951-01-03.\n",
"\n",
"To compute indicators, you'll need to loop through each day from 1951-01-03 to 2015-12-07 (the last day you have prices for). For instance, if we were computing the average price from the past 5 days, we'd start at 1951-01-03, get the prices for each day from 1950-12-26 to 1951-01-02, and find the average. The reason why we start on the 26th, and take more than 5 calendar days into account is because the stock market is shutdown on certain holidays. Since we're looking at the past 5 trading days, we need to look at more than 5 calendar days to find them. Here's a diagram showing how we average 5 days to get the average closing price for 1951-01-03:\n",
"\n",
"![image.png](attachment:image.png)"
]
},
{
"attachments": {
"image.png": {
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"You'd then move to 1951-01-04, and find the average price from 1950-12-30 to 1951-01-03. Here's a diagram showing how we might compute the average here:\n",
"\n",
"![image.png](attachment:image.png)\n",
"\n",
"We'd keep repeating this process to compute all of the averages. Note how when we compute the average of the past 5 days for 1951-01-04, we don't include 1951-01-04 in that average. It's critical not to do this, or our model won't work in the \"real world\"."
]
},
{
"attachments": {
"image.png": {
"image/png": 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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"Here's a table of how the first 10 dates would look if we computed the 5 day average closing price. Close is the closing price for that day, and day_5 is the average of the past 5 trading closing prices at each row:\n",
"\n",
"![image.png](attachment:image.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Instructions\n",
"\n",
"Pick 3 indicators to compute, and generate a different column for each one.\n",
"\n",
"There are a few different ways to do this:\n",
"\n",
"* You can use a for loop along with the [iterrows](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iterrows.html) method to loop over the rows in the DataFrame and compute the indicators. This is the recommended way, as it's a bit simpler to understand what's happening. Since you'll be looping over all of the rows, for any date that comes before there is enough historical data to compute an indicator, just fill in 0.\n",
"* Pandas has some [time series tools](https://pandas.pydata.org/pandas-docs/stable/user_guide/computation.html) that can help, including the [rolling](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rolling.html) function, which will do most of the hard computation for you. Set the window equal to the number of trading days in the past you want to use to compute the indicators. This will add in NaN values for any row where there aren't enough historical trading days to do the computation. Note: There is a giant caveat here, which is that the rolling mean will use the current day's price. You'll need to reindex the resulting series to shift all the values \"forward\" one day. For example, the rolling mean calculated for 1950-01-03 will need to be assigned to 1950-01-04, and so on. You can use the [shift](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.DataFrame.shift.html) method on Dataframes to do this."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"def avg_ndays_stock_price(df, n):\n",
" col_name = 'Avg Price ' + str(n) + ' Day'\n",
" for index, row in df.iterrows():\n",
" start_index = index + (n - 1)\n",
" if start_index not in df.index:\n",
" df.loc[index, col_name] = 0\n",
" else:\n",
" # assign mean nday to column\n",
" df.loc[index, col_name] = round(df.loc[start_index:index, 'Close'].mean(),2)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"def std_ndays_stock_price(df, n):\n",
" col_name = 'Std Price ' + str(n) + ' Day'\n",
" for index, row in df.iterrows():\n",
" start_index = index + (n - 1)\n",
" if start_index not in df.index:\n",
" df.loc[index, col_name] = 0\n",
" else:\n",
" # assign standard deviation nday to column\n",
" df.loc[index, col_name] = round(df.loc[start_index:index, 'Close'].std(), 4)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def avg_ndays_stock_volume(df, n):\n",
" col_name = 'Avg Volume ' + str(n) + ' Day'\n",
" for index, row in df.iterrows():\n",
" start_index = index + (n - 1)\n",
" # checks if row exist\n",
" if start_index not in df.index:\n",
" # adds 0 to rows with no historical data\n",
" df.loc[index, col_name] = 0 \n",
" else:\n",
" # assign mean nday to column\n",
" df.loc[index, col_name] = round(df.loc[start_index:index, 'Volume'].mean(),2)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"def std_ndays_stock_volume(df, n):\n",
" col_name = 'Std Volume ' + str(n) + ' Day'\n",
" for index, row in df.iterrows():\n",
" start_index = index + (n - 1)\n",
" # checks if row exist\n",
" if start_index not in df.index:\n",
" # adds 0 to rows with no historical data\n",
" df.loc[index, col_name] = 0\n",
" else:\n",
" # assign standard deviation nday to column\n",
" df.loc[index, col_name] = round(df.loc[start_index:index, 'Volume'].std(), 4)\n",
" return df"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(16590, 7)\n",
"(16590, 19)\n"
]
}
],
"source": [
"print(stocks.shape)\n",
"df = avg_ndays_stock_price(stocks, 5)\n",
"df = std_ndays_stock_price(stocks, 5)\n",
"df = avg_ndays_stock_price(stocks, 30)\n",
"df = std_ndays_stock_price(stocks, 30)\n",
"df = avg_ndays_stock_price(stocks, 365)\n",
"df = std_ndays_stock_price(stocks, 365)\n",
"\n",
"df = avg_ndays_stock_volume(stocks, 5)\n",
"df = std_ndays_stock_volume(stocks, 5)\n",
"df = avg_ndays_stock_volume(stocks, 30)\n",
"df = std_ndays_stock_volume(stocks, 30)\n",
"df = avg_ndays_stock_volume(stocks, 365)\n",
"df = std_ndays_stock_volume(stocks, 365)\n",
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
" <th>Adj Close</th>\n",
" <th>Avg Price 5 Day</th>\n",
" <th>Std Price 5 Day</th>\n",
" <th>Avg Price 30 Day</th>\n",
" <th>Std Price 30 Day</th>\n",
" <th>Avg Price 365 Day</th>\n",
" <th>Std Price 365 Day</th>\n",
" <th>Avg Volume 5 Day</th>\n",
" <th>Std Volume 5 Day</th>\n",
" <th>Avg Volume 30 Day</th>\n",
" <th>Std Volume 30 Day</th>\n",
" <th>Avg Volume 365 Day</th>\n",
" <th>Std Volume 365 Day</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>16589</td>\n",
" <td>1950-01-03</td>\n",
" <td>16.660000</td>\n",
" <td>16.660000</td>\n",
" <td>16.660000</td>\n",
" <td>16.660000</td>\n",
" <td>1260000.0</td>\n",
" <td>16.660000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.0</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16588</td>\n",
" <td>1950-01-04</td>\n",
" <td>16.850000</td>\n",
" <td>16.850000</td>\n",
" <td>16.850000</td>\n",
" <td>16.850000</td>\n",
" <td>1890000.0</td>\n",
" <td>16.850000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.0</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16587</td>\n",
" <td>1950-01-05</td>\n",
" <td>16.930000</td>\n",
" <td>16.930000</td>\n",
" <td>16.930000</td>\n",
" <td>16.930000</td>\n",
" <td>2550000.0</td>\n",
" <td>16.930000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.0</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16586</td>\n",
" <td>1950-01-06</td>\n",
" <td>16.980000</td>\n",
" <td>16.980000</td>\n",
" <td>16.980000</td>\n",
" <td>16.980000</td>\n",
" <td>2010000.0</td>\n",
" <td>16.980000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.0</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16585</td>\n",
" <td>1950-01-09</td>\n",
" <td>17.080000</td>\n",
" <td>17.080000</td>\n",
" <td>17.080000</td>\n",
" <td>17.080000</td>\n",
" <td>2520000.0</td>\n",
" <td>17.080000</td>\n",
" <td>16.90</td>\n",
" <td>0.1580</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>2046000.0</td>\n",
" <td>529650.8284</td>\n",
" <td>0.00</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16229</td>\n",
" <td>1951-06-12</td>\n",
" <td>21.520000</td>\n",
" <td>21.520000</td>\n",
" <td>21.520000</td>\n",
" <td>21.520000</td>\n",
" <td>1200000.0</td>\n",
" <td>21.520000</td>\n",
" <td>21.53</td>\n",
" <td>0.0536</td>\n",
" <td>21.81</td>\n",
" <td>0.5802</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>1192000.0</td>\n",
" <td>122147.4519</td>\n",
" <td>1807333.33</td>\n",
" <td>1.554093e+06</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16228</td>\n",
" <td>1951-06-13</td>\n",
" <td>21.549999</td>\n",
" <td>21.549999</td>\n",
" <td>21.549999</td>\n",
" <td>21.549999</td>\n",
" <td>1060000.0</td>\n",
" <td>21.549999</td>\n",
" <td>21.55</td>\n",
" <td>0.0451</td>\n",
" <td>21.78</td>\n",
" <td>0.5658</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>1164000.0</td>\n",
" <td>135203.5502</td>\n",
" <td>1784000.00</td>\n",
" <td>1.560072e+06</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16227</td>\n",
" <td>1951-06-14</td>\n",
" <td>21.840000</td>\n",
" <td>21.840000</td>\n",
" <td>21.840000</td>\n",
" <td>21.840000</td>\n",
" <td>1300000.0</td>\n",
" <td>21.840000</td>\n",
" <td>21.60</td>\n",
" <td>0.1402</td>\n",
" <td>21.75</td>\n",
" <td>0.5433</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>1156000.0</td>\n",
" <td>122800.6515</td>\n",
" <td>1764000.00</td>\n",
" <td>1.562378e+06</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16226</td>\n",
" <td>1951-06-15</td>\n",
" <td>22.040001</td>\n",
" <td>22.040001</td>\n",
" <td>22.040001</td>\n",
" <td>22.040001</td>\n",
" <td>1370000.0</td>\n",
" <td>22.040001</td>\n",
" <td>21.71</td>\n",
" <td>0.2222</td>\n",
" <td>21.73</td>\n",
" <td>0.5087</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>1230000.0</td>\n",
" <td>116619.0379</td>\n",
" <td>1741000.00</td>\n",
" <td>1.562949e+06</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16225</td>\n",
" <td>1951-06-18</td>\n",
" <td>22.049999</td>\n",
" <td>22.049999</td>\n",
" <td>22.049999</td>\n",
" <td>22.049999</td>\n",
" <td>1050000.0</td>\n",
" <td>22.049999</td>\n",
" <td>21.80</td>\n",
" <td>0.2562</td>\n",
" <td>21.70</td>\n",
" <td>0.4736</td>\n",
" <td>19.45</td>\n",
" <td>1.7903</td>\n",
" <td>1196000.0</td>\n",
" <td>142232.2045</td>\n",
" <td>1707666.67</td>\n",
" <td>1.566790e+06</td>\n",
" <td>1989479.45</td>\n",
" <td>772310.6495</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>365 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volume \\\n",
"16589 1950-01-03 16.660000 16.660000 16.660000 16.660000 1260000.0 \n",
"16588 1950-01-04 16.850000 16.850000 16.850000 16.850000 1890000.0 \n",
"16587 1950-01-05 16.930000 16.930000 16.930000 16.930000 2550000.0 \n",
"16586 1950-01-06 16.980000 16.980000 16.980000 16.980000 2010000.0 \n",
"16585 1950-01-09 17.080000 17.080000 17.080000 17.080000 2520000.0 \n",
"... ... ... ... ... ... ... \n",
"16229 1951-06-12 21.520000 21.520000 21.520000 21.520000 1200000.0 \n",
"16228 1951-06-13 21.549999 21.549999 21.549999 21.549999 1060000.0 \n",
"16227 1951-06-14 21.840000 21.840000 21.840000 21.840000 1300000.0 \n",
"16226 1951-06-15 22.040001 22.040001 22.040001 22.040001 1370000.0 \n",
"16225 1951-06-18 22.049999 22.049999 22.049999 22.049999 1050000.0 \n",
"\n",
" Adj Close Avg Price 5 Day Std Price 5 Day Avg Price 30 Day \\\n",
"16589 16.660000 0.00 0.0000 0.00 \n",
"16588 16.850000 0.00 0.0000 0.00 \n",
"16587 16.930000 0.00 0.0000 0.00 \n",
"16586 16.980000 0.00 0.0000 0.00 \n",
"16585 17.080000 16.90 0.1580 0.00 \n",
"... ... ... ... ... \n",
"16229 21.520000 21.53 0.0536 21.81 \n",
"16228 21.549999 21.55 0.0451 21.78 \n",
"16227 21.840000 21.60 0.1402 21.75 \n",
"16226 22.040001 21.71 0.2222 21.73 \n",
"16225 22.049999 21.80 0.2562 21.70 \n",
"\n",
" Std Price 30 Day Avg Price 365 Day Std Price 365 Day \\\n",
"16589 0.0000 0.00 0.0000 \n",
"16588 0.0000 0.00 0.0000 \n",
"16587 0.0000 0.00 0.0000 \n",
"16586 0.0000 0.00 0.0000 \n",
"16585 0.0000 0.00 0.0000 \n",
"... ... ... ... \n",
"16229 0.5802 0.00 0.0000 \n",
"16228 0.5658 0.00 0.0000 \n",
"16227 0.5433 0.00 0.0000 \n",
"16226 0.5087 0.00 0.0000 \n",
"16225 0.4736 19.45 1.7903 \n",
"\n",
" Avg Volume 5 Day Std Volume 5 Day Avg Volume 30 Day \\\n",
"16589 0.0 0.0000 0.00 \n",
"16588 0.0 0.0000 0.00 \n",
"16587 0.0 0.0000 0.00 \n",
"16586 0.0 0.0000 0.00 \n",
"16585 2046000.0 529650.8284 0.00 \n",
"... ... ... ... \n",
"16229 1192000.0 122147.4519 1807333.33 \n",
"16228 1164000.0 135203.5502 1784000.00 \n",
"16227 1156000.0 122800.6515 1764000.00 \n",
"16226 1230000.0 116619.0379 1741000.00 \n",
"16225 1196000.0 142232.2045 1707666.67 \n",
"\n",
" Std Volume 30 Day Avg Volume 365 Day Std Volume 365 Day \n",
"16589 0.000000e+00 0.00 0.0000 \n",
"16588 0.000000e+00 0.00 0.0000 \n",
"16587 0.000000e+00 0.00 0.0000 \n",
"16586 0.000000e+00 0.00 0.0000 \n",
"16585 0.000000e+00 0.00 0.0000 \n",
"... ... ... ... \n",
"16229 1.554093e+06 0.00 0.0000 \n",
"16228 1.560072e+06 0.00 0.0000 \n",
"16227 1.562378e+06 0.00 0.0000 \n",
"16226 1.562949e+06 0.00 0.0000 \n",
"16225 1.566790e+06 1989479.45 772310.6495 \n",
"\n",
"[365 rows x 19 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head(365)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Splitting up the Data\n",
"\n",
"Since you're computing indicators that use historical data, there are some rows where there isn't enough historical data to generate them. Some of the indicators use 365 days of historical data, and the dataset starts on 1950-01-03. Thus, any rows that fall before 1951-01-03 don't have enough historical data to compute all the indicators. You'll need to remove these rows before you split the data.\n",
"\n",
"If you have a Dataframe df, you can select any rows with the Date column greater than 1951-01-02 using df[df[\"Date\"] > datetime(year=1951, month=1, day=2)]\n",
"\n",
"### Instructions\n",
"\n",
"* Remove any rows from the DataFrame that fall before 1951-01-03.\n",
"* Use the [dropna](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html) method to remove any rows with NaN values. Pass in the axis=0 argument to drop rows.\n",
"* Generate two new dataframes to use in making our algorithm. train should contain any rows in the data with a date less than 2013-01-01. test should contain any rows with a date greater than or equal to 2013-01-01."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(16590, 19)\n"
]
}
],
"source": [
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Date 0\n",
"Open 0\n",
"High 0\n",
"Low 0\n",
"Close 0\n",
"Volume 0\n",
"Adj Close 0\n",
"Avg Price 5 Day 0\n",
"Std Price 5 Day 0\n",
"Avg Price 30 Day 0\n",
"Std Price 30 Day 0\n",
"Avg Price 365 Day 0\n",
"Std Price 365 Day 0\n",
"Avg Volume 5 Day 0\n",
"Std Volume 5 Day 0\n",
"Avg Volume 30 Day 0\n",
"Std Volume 30 Day 0\n",
"Avg Volume 365 Day 0\n",
"Std Volume 365 Day 0\n",
"dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Leo\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" \n"
]
}
],
"source": [
"df = df[df[\"Date\"] > datetime(year=1951, month=1, day=2)]\n",
"df.dropna(axis=0, inplace=True)\n",
"train = df[df[\"Date\"] < datetime(year=2013, month=1, day=1)]\n",
"test = df[df[\"Date\"] >= datetime(year=2013, month=1, day=1)]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(16340, 19)\n"
]
}
],
"source": [
"print(df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Date 0\n",
"Open 0\n",
"High 0\n",
"Low 0\n",
"Close 0\n",
"Volume 0\n",
"Adj Close 0\n",
"Avg Price 5 Day 0\n",
"Std Price 5 Day 0\n",
"Avg Price 30 Day 0\n",
"Std Price 30 Day 0\n",
"Avg Price 365 Day 0\n",
"Std Price 365 Day 0\n",
"Avg Volume 5 Day 0\n",
"Std Volume 5 Day 0\n",
"Avg Volume 30 Day 0\n",
"Std Volume 30 Day 0\n",
"Avg Volume 365 Day 0\n",
"Std Volume 365 Day 0\n",
"dtype: int64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
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" <td>16339</td>\n",
" <td>1951-01-03</td>\n",
" <td>20.690001</td>\n",
" <td>20.690001</td>\n",
" <td>20.690001</td>\n",
" <td>20.690001</td>\n",
" <td>3.370000e+06</td>\n",
" <td>20.690001</td>\n",
" <td>20.51</td>\n",
" <td>0.2045</td>\n",
" <td>19.84</td>\n",
" <td>0.4343</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>3.268000e+06</td>\n",
" <td>2.690167e+05</td>\n",
" <td>2.694667e+06</td>\n",
" <td>6.950948e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16338</td>\n",
" <td>1951-01-04</td>\n",
" <td>20.870001</td>\n",
" <td>20.870001</td>\n",
" <td>20.870001</td>\n",
" <td>20.870001</td>\n",
" <td>3.390000e+06</td>\n",
" <td>20.870001</td>\n",
" <td>20.63</td>\n",
" <td>0.2141</td>\n",
" <td>19.87</td>\n",
" <td>0.4730</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>3.358000e+06</td>\n",
" <td>1.976613e+05</td>\n",
" <td>2.732667e+06</td>\n",
" <td>7.010826e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16337</td>\n",
" <td>1951-01-05</td>\n",
" <td>20.870001</td>\n",
" <td>20.870001</td>\n",
" <td>20.870001</td>\n",
" <td>20.870001</td>\n",
" <td>3.390000e+06</td>\n",
" <td>20.870001</td>\n",
" <td>20.73</td>\n",
" <td>0.1819</td>\n",
" <td>19.91</td>\n",
" <td>0.5068</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>3.324000e+06</td>\n",
" <td>1.663731e+05</td>\n",
" <td>2.778667e+06</td>\n",
" <td>6.972939e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16336</td>\n",
" <td>1951-01-08</td>\n",
" <td>21.000000</td>\n",
" <td>21.000000</td>\n",
" <td>21.000000</td>\n",
" <td>21.000000</td>\n",
" <td>2.780000e+06</td>\n",
" <td>21.000000</td>\n",
" <td>20.84</td>\n",
" <td>0.1170</td>\n",
" <td>19.93</td>\n",
" <td>0.5431</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>3.192000e+06</td>\n",
" <td>2.766225e+05</td>\n",
" <td>2.780333e+06</td>\n",
" <td>6.972334e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16335</td>\n",
" <td>1951-01-09</td>\n",
" <td>21.120001</td>\n",
" <td>21.120001</td>\n",
" <td>21.120001</td>\n",
" <td>21.120001</td>\n",
" <td>3.800000e+06</td>\n",
" <td>21.120001</td>\n",
" <td>20.91</td>\n",
" <td>0.1611</td>\n",
" <td>19.96</td>\n",
" <td>0.5810</td>\n",
" <td>0.00</td>\n",
" <td>0.0000</td>\n",
" <td>3.346000e+06</td>\n",
" <td>3.643213e+05</td>\n",
" <td>2.819667e+06</td>\n",
" <td>7.207634e+05</td>\n",
" <td>0.000000e+00</td>\n",
" <td>0.000000e+00</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>743</td>\n",
" <td>2012-12-24</td>\n",
" <td>1430.150024</td>\n",
" <td>1430.150024</td>\n",
" <td>1424.660034</td>\n",
" <td>1426.660034</td>\n",
" <td>1.248960e+09</td>\n",
" <td>1426.660034</td>\n",
" <td>1436.62</td>\n",
" <td>8.5897</td>\n",
" <td>1407.49</td>\n",
" <td>24.5204</td>\n",
" <td>1326.41</td>\n",
" <td>89.9835</td>\n",
" <td>3.667348e+09</td>\n",
" <td>1.477523e+09</td>\n",
" <td>3.381918e+09</td>\n",
" <td>7.656156e+08</td>\n",
" <td>3.878488e+09</td>\n",
" <td>7.749962e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>742</td>\n",
" <td>2012-12-26</td>\n",
" <td>1426.660034</td>\n",
" <td>1429.420044</td>\n",
" <td>1416.430054</td>\n",
" <td>1419.829956</td>\n",
" <td>2.285030e+09</td>\n",
" <td>1419.829956</td>\n",
" <td>1431.23</td>\n",
" <td>9.0587</td>\n",
" <td>1408.81</td>\n",
" <td>24.0560</td>\n",
" <td>1326.72</td>\n",
" <td>90.1114</td>\n",
" <td>3.263906e+09</td>\n",
" <td>1.535105e+09</td>\n",
" <td>3.372501e+09</td>\n",
" <td>7.776211e+08</td>\n",
" <td>3.872807e+09</td>\n",
" <td>7.790566e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>741</td>\n",
" <td>2012-12-27</td>\n",
" <td>1419.829956</td>\n",
" <td>1422.800049</td>\n",
" <td>1401.800049</td>\n",
" <td>1418.099976</td>\n",
" <td>2.830180e+09</td>\n",
" <td>1418.099976</td>\n",
" <td>1427.69</td>\n",
" <td>10.2086</td>\n",
" <td>1410.27</td>\n",
" <td>23.2154</td>\n",
" <td>1327.00</td>\n",
" <td>90.2365</td>\n",
" <td>3.055982e+09</td>\n",
" <td>1.502584e+09</td>\n",
" <td>3.351655e+09</td>\n",
" <td>7.836766e+08</td>\n",
" <td>3.868936e+09</td>\n",
" <td>7.807206e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>740</td>\n",
" <td>2012-12-28</td>\n",
" <td>1418.099976</td>\n",
" <td>1418.099976</td>\n",
" <td>1401.579956</td>\n",
" <td>1402.430054</td>\n",
" <td>2.426680e+09</td>\n",
" <td>1402.430054</td>\n",
" <td>1419.43</td>\n",
" <td>10.7019</td>\n",
" <td>1411.83</td>\n",
" <td>20.8585</td>\n",
" <td>1327.26</td>\n",
" <td>90.3156</td>\n",
" <td>2.804002e+09</td>\n",
" <td>1.475799e+09</td>\n",
" <td>3.295561e+09</td>\n",
" <td>7.877764e+08</td>\n",
" <td>3.864302e+09</td>\n",
" <td>7.842493e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>739</td>\n",
" <td>2012-12-31</td>\n",
" <td>1402.430054</td>\n",
" <td>1426.739990</td>\n",
" <td>1398.109985</td>\n",
" <td>1426.189941</td>\n",
" <td>3.204330e+09</td>\n",
" <td>1426.189941</td>\n",
" <td>1418.64</td>\n",
" <td>9.8208</td>\n",
" <td>1414.26</td>\n",
" <td>17.8347</td>\n",
" <td>1327.53</td>\n",
" <td>90.4639</td>\n",
" <td>2.399036e+09</td>\n",
" <td>7.366851e+08</td>\n",
" <td>3.271409e+09</td>\n",
" <td>7.787457e+08</td>\n",
" <td>3.861288e+09</td>\n",
" <td>7.846668e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>15601 rows × 19 columns</p>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close \\\n",
"16339 1951-01-03 20.690001 20.690001 20.690001 20.690001 \n",
"16338 1951-01-04 20.870001 20.870001 20.870001 20.870001 \n",
"16337 1951-01-05 20.870001 20.870001 20.870001 20.870001 \n",
"16336 1951-01-08 21.000000 21.000000 21.000000 21.000000 \n",
"16335 1951-01-09 21.120001 21.120001 21.120001 21.120001 \n",
"... ... ... ... ... ... \n",
"743 2012-12-24 1430.150024 1430.150024 1424.660034 1426.660034 \n",
"742 2012-12-26 1426.660034 1429.420044 1416.430054 1419.829956 \n",
"741 2012-12-27 1419.829956 1422.800049 1401.800049 1418.099976 \n",
"740 2012-12-28 1418.099976 1418.099976 1401.579956 1402.430054 \n",
"739 2012-12-31 1402.430054 1426.739990 1398.109985 1426.189941 \n",
"\n",
" Volume Adj Close Avg Price 5 Day Std Price 5 Day \\\n",
"16339 3.370000e+06 20.690001 20.51 0.2045 \n",
"16338 3.390000e+06 20.870001 20.63 0.2141 \n",
"16337 3.390000e+06 20.870001 20.73 0.1819 \n",
"16336 2.780000e+06 21.000000 20.84 0.1170 \n",
"16335 3.800000e+06 21.120001 20.91 0.1611 \n",
"... ... ... ... ... \n",
"743 1.248960e+09 1426.660034 1436.62 8.5897 \n",
"742 2.285030e+09 1419.829956 1431.23 9.0587 \n",
"741 2.830180e+09 1418.099976 1427.69 10.2086 \n",
"740 2.426680e+09 1402.430054 1419.43 10.7019 \n",
"739 3.204330e+09 1426.189941 1418.64 9.8208 \n",
"\n",
" Avg Price 30 Day Std Price 30 Day Avg Price 365 Day \\\n",
"16339 19.84 0.4343 0.00 \n",
"16338 19.87 0.4730 0.00 \n",
"16337 19.91 0.5068 0.00 \n",
"16336 19.93 0.5431 0.00 \n",
"16335 19.96 0.5810 0.00 \n",
"... ... ... ... \n",
"743 1407.49 24.5204 1326.41 \n",
"742 1408.81 24.0560 1326.72 \n",
"741 1410.27 23.2154 1327.00 \n",
"740 1411.83 20.8585 1327.26 \n",
"739 1414.26 17.8347 1327.53 \n",
"\n",
" Std Price 365 Day Avg Volume 5 Day Std Volume 5 Day \\\n",
"16339 0.0000 3.268000e+06 2.690167e+05 \n",
"16338 0.0000 3.358000e+06 1.976613e+05 \n",
"16337 0.0000 3.324000e+06 1.663731e+05 \n",
"16336 0.0000 3.192000e+06 2.766225e+05 \n",
"16335 0.0000 3.346000e+06 3.643213e+05 \n",
"... ... ... ... \n",
"743 89.9835 3.667348e+09 1.477523e+09 \n",
"742 90.1114 3.263906e+09 1.535105e+09 \n",
"741 90.2365 3.055982e+09 1.502584e+09 \n",
"740 90.3156 2.804002e+09 1.475799e+09 \n",
"739 90.4639 2.399036e+09 7.366851e+08 \n",
"\n",
" Avg Volume 30 Day Std Volume 30 Day Avg Volume 365 Day \\\n",
"16339 2.694667e+06 6.950948e+05 0.000000e+00 \n",
"16338 2.732667e+06 7.010826e+05 0.000000e+00 \n",
"16337 2.778667e+06 6.972939e+05 0.000000e+00 \n",
"16336 2.780333e+06 6.972334e+05 0.000000e+00 \n",
"16335 2.819667e+06 7.207634e+05 0.000000e+00 \n",
"... ... ... ... \n",
"743 3.381918e+09 7.656156e+08 3.878488e+09 \n",
"742 3.372501e+09 7.776211e+08 3.872807e+09 \n",
"741 3.351655e+09 7.836766e+08 3.868936e+09 \n",
"740 3.295561e+09 7.877764e+08 3.864302e+09 \n",
"739 3.271409e+09 7.787457e+08 3.861288e+09 \n",
"\n",
" Std Volume 365 Day \n",
"16339 0.000000e+00 \n",
"16338 0.000000e+00 \n",
"16337 0.000000e+00 \n",
"16336 0.000000e+00 \n",
"16335 0.000000e+00 \n",
"... ... \n",
"743 7.749962e+08 \n",
"742 7.790566e+08 \n",
"741 7.807206e+08 \n",
"740 7.842493e+08 \n",
"739 7.846668e+08 \n",
"\n",
"[15601 rows x 19 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volume</th>\n",
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" <tr>\n",
" <td>738</td>\n",
" <td>2013-01-02</td>\n",
" <td>1426.189941</td>\n",
" <td>1462.430054</td>\n",
" <td>1426.189941</td>\n",
" <td>1462.420044</td>\n",
" <td>4.202600e+09</td>\n",
" <td>1462.420044</td>\n",
" <td>1425.79</td>\n",
" <td>22.2613</td>\n",
" <td>1417.68</td>\n",
" <td>16.8526</td>\n",
" <td>1327.91</td>\n",
" <td>90.7390</td>\n",
" <td>2.989764e+09</td>\n",
" <td>7.674944e+08</td>\n",
" <td>3.276632e+09</td>\n",
" <td>7.846227e+08</td>\n",
" <td>3.862480e+09</td>\n",
" <td>7.848543e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>737</td>\n",
" <td>2013-01-03</td>\n",
" <td>1462.420044</td>\n",
" <td>1465.469971</td>\n",
" <td>1455.530029</td>\n",
" <td>1459.369995</td>\n",
" <td>3.829730e+09</td>\n",
" <td>1459.369995</td>\n",
" <td>1433.70</td>\n",
" <td>26.2743</td>\n",
" <td>1420.09</td>\n",
" <td>17.4708</td>\n",
" <td>1328.22</td>\n",
" <td>90.9959</td>\n",
" <td>3.298704e+09</td>\n",
" <td>7.224721e+08</td>\n",
" <td>3.291797e+09</td>\n",
" <td>7.909560e+08</td>\n",
" <td>3.859719e+09</td>\n",
" <td>7.831860e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>736</td>\n",
" <td>2013-01-04</td>\n",
" <td>1459.369995</td>\n",
" <td>1467.939941</td>\n",
" <td>1458.989990</td>\n",
" <td>1466.469971</td>\n",
" <td>3.424290e+09</td>\n",
" <td>1466.469971</td>\n",
" <td>1443.38</td>\n",
" <td>27.9452</td>\n",
" <td>1422.71</td>\n",
" <td>18.3398</td>\n",
" <td>1328.56</td>\n",
" <td>91.2790</td>\n",
" <td>3.417526e+09</td>\n",
" <td>6.733364e+08</td>\n",
" <td>3.299034e+09</td>\n",
" <td>7.911482e+08</td>\n",
" <td>3.859449e+09</td>\n",
" <td>7.833194e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>735</td>\n",
" <td>2013-01-07</td>\n",
" <td>1466.469971</td>\n",
" <td>1466.469971</td>\n",
" <td>1456.619995</td>\n",
" <td>1461.890015</td>\n",
" <td>3.304970e+09</td>\n",
" <td>1461.890015</td>\n",
" <td>1455.27</td>\n",
" <td>16.4533</td>\n",
" <td>1425.08</td>\n",
" <td>18.6783</td>\n",
" <td>1328.90</td>\n",
" <td>91.5444</td>\n",
" <td>3.593184e+09</td>\n",
" <td>4.153739e+08</td>\n",
" <td>3.320297e+09</td>\n",
" <td>7.820986e+08</td>\n",
" <td>3.858814e+09</td>\n",
" <td>7.836758e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>734</td>\n",
" <td>2013-01-08</td>\n",
" <td>1461.890015</td>\n",
" <td>1461.890015</td>\n",
" <td>1451.640015</td>\n",
" <td>1457.150024</td>\n",
" <td>3.601600e+09</td>\n",
" <td>1457.150024</td>\n",
" <td>1461.46</td>\n",
" <td>3.5054</td>\n",
" <td>1426.68</td>\n",
" <td>19.3121</td>\n",
" <td>1329.24</td>\n",
" <td>91.7901</td>\n",
" <td>3.672638e+09</td>\n",
" <td>3.561743e+08</td>\n",
" <td>3.390185e+09</td>\n",
" <td>7.040724e+08</td>\n",
" <td>3.857703e+09</td>\n",
" <td>7.837524e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>2015-12-01</td>\n",
" <td>2082.929932</td>\n",
" <td>2103.370117</td>\n",
" <td>2082.929932</td>\n",
" <td>2102.629883</td>\n",
" <td>3.712120e+09</td>\n",
" <td>2102.629883</td>\n",
" <td>2090.23</td>\n",
" <td>7.9568</td>\n",
" <td>2076.28</td>\n",
" <td>23.9705</td>\n",
" <td>2035.91</td>\n",
" <td>64.3525</td>\n",
" <td>3.232372e+09</td>\n",
" <td>1.111591e+09</td>\n",
" <td>3.856341e+09</td>\n",
" <td>5.899408e+08</td>\n",
" <td>3.526090e+09</td>\n",
" <td>6.925928e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>2015-12-02</td>\n",
" <td>2101.709961</td>\n",
" <td>2104.270020</td>\n",
" <td>2077.110107</td>\n",
" <td>2079.510010</td>\n",
" <td>3.950640e+09</td>\n",
" <td>2079.510010</td>\n",
" <td>2088.31</td>\n",
" <td>9.3336</td>\n",
" <td>2077.91</td>\n",
" <td>22.3781</td>\n",
" <td>2036.23</td>\n",
" <td>64.2776</td>\n",
" <td>3.245514e+09</td>\n",
" <td>1.121578e+09</td>\n",
" <td>3.876979e+09</td>\n",
" <td>5.817195e+08</td>\n",
" <td>3.529468e+09</td>\n",
" <td>6.916451e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>2015-12-03</td>\n",
" <td>2080.709961</td>\n",
" <td>2085.000000</td>\n",
" <td>2042.349976</td>\n",
" <td>2049.620117</td>\n",
" <td>4.306490e+09</td>\n",
" <td>2049.620117</td>\n",
" <td>2080.46</td>\n",
" <td>19.5999</td>\n",
" <td>2078.93</td>\n",
" <td>20.1838</td>\n",
" <td>2036.51</td>\n",
" <td>64.1216</td>\n",
" <td>3.536224e+09</td>\n",
" <td>1.181180e+09</td>\n",
" <td>3.899603e+09</td>\n",
" <td>5.848831e+08</td>\n",
" <td>3.532802e+09</td>\n",
" <td>6.924515e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2015-12-04</td>\n",
" <td>2051.239990</td>\n",
" <td>2093.840088</td>\n",
" <td>2051.239990</td>\n",
" <td>2091.689941</td>\n",
" <td>4.214910e+09</td>\n",
" <td>2091.689941</td>\n",
" <td>2080.77</td>\n",
" <td>19.8061</td>\n",
" <td>2080.24</td>\n",
" <td>19.6764</td>\n",
" <td>2036.87</td>\n",
" <td>64.0589</td>\n",
" <td>4.085838e+09</td>\n",
" <td>2.493154e+08</td>\n",
" <td>3.892405e+09</td>\n",
" <td>5.794230e+08</td>\n",
" <td>3.535838e+09</td>\n",
" <td>6.930075e+08</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2015-12-07</td>\n",
" <td>2090.419922</td>\n",
" <td>2090.419922</td>\n",
" <td>2066.780029</td>\n",
" <td>2077.070068</td>\n",
" <td>4.043820e+09</td>\n",
" <td>2077.070068</td>\n",
" <td>2080.10</td>\n",
" <td>19.8776</td>\n",
" <td>2080.30</td>\n",
" <td>19.6624</td>\n",
" <td>2037.20</td>\n",
" <td>63.9566</td>\n",
" <td>4.045596e+09</td>\n",
" <td>2.328943e+08</td>\n",
" <td>3.890250e+09</td>\n",
" <td>5.787116e+08</td>\n",
" <td>3.539304e+09</td>\n",
" <td>6.923741e+08</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
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],
"text/plain": [
" Date Open High Low Close \\\n",
"738 2013-01-02 1426.189941 1462.430054 1426.189941 1462.420044 \n",
"737 2013-01-03 1462.420044 1465.469971 1455.530029 1459.369995 \n",
"736 2013-01-04 1459.369995 1467.939941 1458.989990 1466.469971 \n",
"735 2013-01-07 1466.469971 1466.469971 1456.619995 1461.890015 \n",
"734 2013-01-08 1461.890015 1461.890015 1451.640015 1457.150024 \n",
".. ... ... ... ... ... \n",
"4 2015-12-01 2082.929932 2103.370117 2082.929932 2102.629883 \n",
"3 2015-12-02 2101.709961 2104.270020 2077.110107 2079.510010 \n",
"2 2015-12-03 2080.709961 2085.000000 2042.349976 2049.620117 \n",
"1 2015-12-04 2051.239990 2093.840088 2051.239990 2091.689941 \n",
"0 2015-12-07 2090.419922 2090.419922 2066.780029 2077.070068 \n",
"\n",
" Volume Adj Close Avg Price 5 Day Std Price 5 Day \\\n",
"738 4.202600e+09 1462.420044 1425.79 22.2613 \n",
"737 3.829730e+09 1459.369995 1433.70 26.2743 \n",
"736 3.424290e+09 1466.469971 1443.38 27.9452 \n",
"735 3.304970e+09 1461.890015 1455.27 16.4533 \n",
"734 3.601600e+09 1457.150024 1461.46 3.5054 \n",
".. ... ... ... ... \n",
"4 3.712120e+09 2102.629883 2090.23 7.9568 \n",
"3 3.950640e+09 2079.510010 2088.31 9.3336 \n",
"2 4.306490e+09 2049.620117 2080.46 19.5999 \n",
"1 4.214910e+09 2091.689941 2080.77 19.8061 \n",
"0 4.043820e+09 2077.070068 2080.10 19.8776 \n",
"\n",
" Avg Price 30 Day Std Price 30 Day Avg Price 365 Day Std Price 365 Day \\\n",
"738 1417.68 16.8526 1327.91 90.7390 \n",
"737 1420.09 17.4708 1328.22 90.9959 \n",
"736 1422.71 18.3398 1328.56 91.2790 \n",
"735 1425.08 18.6783 1328.90 91.5444 \n",
"734 1426.68 19.3121 1329.24 91.7901 \n",
".. ... ... ... ... \n",
"4 2076.28 23.9705 2035.91 64.3525 \n",
"3 2077.91 22.3781 2036.23 64.2776 \n",
"2 2078.93 20.1838 2036.51 64.1216 \n",
"1 2080.24 19.6764 2036.87 64.0589 \n",
"0 2080.30 19.6624 2037.20 63.9566 \n",
"\n",
" Avg Volume 5 Day Std Volume 5 Day Avg Volume 30 Day Std Volume 30 Day \\\n",
"738 2.989764e+09 7.674944e+08 3.276632e+09 7.846227e+08 \n",
"737 3.298704e+09 7.224721e+08 3.291797e+09 7.909560e+08 \n",
"736 3.417526e+09 6.733364e+08 3.299034e+09 7.911482e+08 \n",
"735 3.593184e+09 4.153739e+08 3.320297e+09 7.820986e+08 \n",
"734 3.672638e+09 3.561743e+08 3.390185e+09 7.040724e+08 \n",
".. ... ... ... ... \n",
"4 3.232372e+09 1.111591e+09 3.856341e+09 5.899408e+08 \n",
"3 3.245514e+09 1.121578e+09 3.876979e+09 5.817195e+08 \n",
"2 3.536224e+09 1.181180e+09 3.899603e+09 5.848831e+08 \n",
"1 4.085838e+09 2.493154e+08 3.892405e+09 5.794230e+08 \n",
"0 4.045596e+09 2.328943e+08 3.890250e+09 5.787116e+08 \n",
"\n",
" Avg Volume 365 Day Std Volume 365 Day \n",
"738 3.862480e+09 7.848543e+08 \n",
"737 3.859719e+09 7.831860e+08 \n",
"736 3.859449e+09 7.833194e+08 \n",
"735 3.858814e+09 7.836758e+08 \n",
"734 3.857703e+09 7.837524e+08 \n",
".. ... ... \n",
"4 3.526090e+09 6.925928e+08 \n",
"3 3.529468e+09 6.916451e+08 \n",
"2 3.532802e+09 6.924515e+08 \n",
"1 3.535838e+09 6.930075e+08 \n",
"0 3.539304e+09 6.923741e+08 \n",
"\n",
"[739 rows x 19 columns]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Making Predictions\n",
"\n",
"Now, you can define an error metric, train a model using the train data, and make predictions on the test data.\n",
"\n",
"It's recommended to use [Mean Absolute Error](https://en.wikipedia.org/wiki/Mean_absolute_error), also called MAE, as an error metric, because it will show you how \"close\" you were to the price in intuitive terms. [Mean Squared Error](https://en.wikipedia.org/wiki/Mean_squared_error), or MSE, is an alternative that is more commonly used, but makes it harder to intuitively tell how far off you are from the true price because it squares the error.\n",
"\n",
"### Instructions\n",
"\n",
"* Pick an error metric.\n",
"* Initialize an instance of the [LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) class.\n",
"* Train a linear regression model, using the train Dataframe. Leave out all of the original columns (Close, High, Low, Open, Volume, Adj Close, Date) when training your model. These all contain knowledge of the future that you don't want to feed the model. Use the Close column as the target.\n",
"* Make predictions for the Close column of the test data, using the same columns for training as you did with train.\n",
"* Compute the error between the predictions and the Close column of test."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close',\n",
" 'Avg Price 5 Day', 'Std Price 5 Day', 'Avg Price 30 Day',\n",
" 'Std Price 30 Day', 'Avg Price 365 Day', 'Std Price 365 Day',\n",
" 'Avg Volume 5 Day', 'Std Volume 5 Day', 'Avg Volume 30 Day',\n",
" 'Std Volume 30 Day', 'Avg Volume 365 Day', 'Std Volume 365 Day'],\n",
" dtype='object')"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11.93595588485392"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = ['Avg Price 5 Day', 'Std Price 5 Day', 'Avg Price 30 Day', \\\n",
" 'Std Price 30 Day', 'Avg Price 365 Day', 'Std Price 365 Day']\n",
"y_train = train['Close']\n",
"y_test = test['Close']\n",
"\n",
"# instantiate model\n",
"lr = LinearRegression()\n",
"# fit model\n",
"lr.fit(train[features], y_train)\n",
"# predict model\n",
"predictions = lr.predict(test[features])\n",
"# test model\n",
"mae = mean_absolute_error(test['Close'], predictions)\n",
"mae"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.07953948e+00, -2.06551454e-01, -7.91936105e-02, 9.14256546e-02,\n",
" -6.47158132e-04, 5.24102042e-03])"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr.coef_"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.05487448493005331"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr.intercept_"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Leo\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
"\n",
"To register the converters:\n",
"\t>>> from pandas.plotting import register_matplotlib_converters\n",
"\t>>> register_matplotlib_converters()\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Stock Price')"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 2880x1080 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(40,15))\n",
"plt.scatter(test['Date'], test['Close'], c='b')\n",
"plt.scatter(test['Date'], predictions, c='r')\n",
"plt.title('Stock Predictions', fontsize=60)\n",
"plt.xlabel('Date', fontsize=40)\n",
"plt.ylabel('Stock Price', fontsize=40)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Improving Error\n",
"\n",
"Congratulations! You can now predict the S&P500 (with some error). You can improve the error of this model significantly, though. Think about some indicators that might be helpful to compute.\n",
"\n",
"Here are some ideas that might be helpful:\n",
"\n",
"* The average volume over the past five days.\n",
"* The average volume over the past year.\n",
"* The ratio between the average volume for the past five days, and the average volume for the past year.\n",
"* The standard deviation of the average volume over the past five days.\n",
"* The standard deviation of the average volume over the past year.\n",
"* The ratio between the standard deviation of the average volume for the past five days, and the standard deviation of the average volume for the past year.\n",
"* The year component of the date.\n",
"* The ratio between the lowest price in the past year and the current price.\n",
"* The ratio between the highest price in the past year and the current price.\n",
"* The month component of the date.\n",
"* The day of week.\n",
"* The day component of the date.\n",
"* The number of holidays in the prior month.\n",
"\n",
"### Instructions\n",
"\n",
"Add 2 additional indicators to your dataframe, and see if the error is reduced. You'll need to insert these indicators at the same point where you insert the others, before you clean out rows with NaN values and split the dataframe into train and `test."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11.960202192277311"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = ['Avg Price 5 Day', 'Std Price 5 Day', 'Avg Price 30 Day', \\\n",
" 'Std Price 30 Day', 'Avg Price 365 Day', 'Std Price 365 Day', \\\n",
" 'Avg Volume 5 Day', 'Std Volume 5 Day', 'Avg Volume 30 Day', \\\n",
" 'Std Volume 30 Day', 'Avg Volume 365 Day', 'Std Volume 365 Day']\n",
"y_train = train['Close']\n",
"y_test = test['Close']\n",
"\n",
"# instantiate model\n",
"lr = LinearRegression()\n",
"# fit model\n",
"lr.fit(train[features], y_train)\n",
"# predict model\n",
"predictions = lr.predict(test[features])\n",
"# test model\n",
"mae = mean_absolute_error(test['Close'], predictions)\n",
"mae"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.07760237e+00, -2.04476309e-01, -7.84381668e-02, 9.75785299e-02,\n",
" -1.97650884e-05, 7.91658560e-03, -5.39331940e-10, 2.16775615e-09,\n",
" 6.96329036e-10, -2.33016836e-09, 9.26969237e-10, -3.77309004e-09])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr.coef_"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.0876194354404447"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr.intercept_"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Stock Price')"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 2880x1080 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(40,15))\n",
"plt.scatter(test['Date'], test['Close'], c='b')\n",
"plt.scatter(test['Date'], predictions, c='r')\n",
"plt.title('Stock Predictions', fontsize=60)\n",
"plt.xlabel('Date', fontsize=40)\n",
"plt.ylabel('Stock Price', fontsize=40)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"features = ['Avg Price 5 Day', 'Std Price 5 Day', 'Avg Price 30 Day', \\\n",
" 'Std Price 30 Day', 'Avg Price 365 Day', 'Std Price 365 Day', \\\n",
" 'Avg Volume 5 Day', 'Std Volume 5 Day', 'Avg Volume 30 Day', \\\n",
" 'Std Volume 30 Day', 'Avg Volume 365 Day', 'Std Volume 365 Day']\n",
"y_train = train['Close']\n",
"y_test = test['Close']\n",
"\n",
"results = {}\n",
"feature_list = []\n",
"\n",
"for feature in features:\n",
" feature_list.append(feature) \n",
" # instantiate model\n",
" lr = LinearRegression()\n",
" # fit model\n",
" lr.fit(train[feature_list], y_train)\n",
" # predict model\n",
" predictions = lr.predict(test[feature_list])\n",
" # test model\n",
" mae = mean_absolute_error(test['Close'], predictions)\n",
" results[tuple(feature_list)] = mae"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day'): 11.805347782941194,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day',\n",
" 'Avg Price 365 Day',\n",
" 'Std Price 365 Day'): 11.93595588485392,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day',\n",
" 'Avg Price 365 Day',\n",
" 'Std Price 365 Day',\n",
" 'Avg Volume 5 Day'): 11.937468284690883,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day',\n",
" 'Avg Price 365 Day'): 11.938430456423463,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day'): 11.938433123358328,\n",
" ('Avg Price 5 Day', 'Std Price 5 Day'): 11.957965690278314,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day',\n",
" 'Avg Price 365 Day',\n",
" 'Std Price 365 Day',\n",
" 'Avg Volume 5 Day',\n",
" 'Std Volume 5 Day',\n",
" 'Avg Volume 30 Day',\n",
" 'Std Volume 30 Day',\n",
" 'Avg Volume 365 Day',\n",
" 'Std Volume 365 Day'): 11.960202192277311,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day',\n",
" 'Avg Price 365 Day',\n",
" 'Std Price 365 Day',\n",
" 'Avg Volume 5 Day',\n",
" 'Std Volume 5 Day',\n",
" 'Avg Volume 30 Day'): 11.963932665089686,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day',\n",
" 'Avg Price 365 Day',\n",
" 'Std Price 365 Day',\n",
" 'Avg Volume 5 Day',\n",
" 'Std Volume 5 Day'): 11.970832672173376,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day',\n",
" 'Avg Price 365 Day',\n",
" 'Std Price 365 Day',\n",
" 'Avg Volume 5 Day',\n",
" 'Std Volume 5 Day',\n",
" 'Avg Volume 30 Day',\n",
" 'Std Volume 30 Day',\n",
" 'Avg Volume 365 Day'): 11.985133357957164,\n",
" ('Avg Price 5 Day',\n",
" 'Std Price 5 Day',\n",
" 'Avg Price 30 Day',\n",
" 'Std Price 30 Day',\n",
" 'Avg Price 365 Day',\n",
" 'Std Price 365 Day',\n",
" 'Avg Volume 5 Day',\n",
" 'Std Volume 5 Day',\n",
" 'Avg Volume 30 Day',\n",
" 'Std Volume 30 Day'): 11.985789542369043,\n",
" ('Avg Price 5 Day',): 12.05338738870169}"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"{k: v for k, v in sorted(results.items(), key=lambda item: item[1])}"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"11.805347782941194"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"features = ['Avg Price 5 Day', 'Std Price 5 Day', 'Avg Price 30 Day']\n",
"y_train = train['Close']\n",
"y_test = test['Close']\n",
"\n",
"# instantiate model\n",
"lr = LinearRegression()\n",
"# fit model\n",
"lr.fit(train[features], y_train)\n",
"# predict model\n",
"predictions = lr.predict(test[features])\n",
"# test model\n",
"mae = mean_absolute_error(test['Close'], predictions)\n",
"mae"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1.0796239, -0.1003418, -0.0786633])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr.coef_"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.07180909809756031"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lr.intercept_"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x1a24ab24358>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 2880x1080 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use('fivethirtyeight')\n",
"# fandango_2015.Fandango_Stars.plot.kde(label = '2015', legend = True, figsize=(20,10))\n",
"# fandango_2016.fandango.plot.kde(label = '2016', legend = True)\n",
"plt.figure(figsize=(40,15))\n",
"plt.scatter(test['Date'], test['Close'], c='b', label = 'Close Price')\n",
"plt.scatter(test['Date'], predictions, c='r', label = 'Prediction Price')\n",
"plt.title('Stock Predictions', fontsize=60)\n",
"plt.xlabel('Date', fontsize=40)\n",
"plt.ylabel('Stock Price', fontsize=40)\n",
"plt.legend(fontsize=20, markerscale=2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Next Steps\n",
"\n",
"There's a lot of improvement still to be made on the indicator side, and we urge you to think of better indicators that you could use for prediction. We can also make significant structural improvements to the algorithm, and pull in data from other sources.\n",
"\n",
"* Accuracy would improve greatly by making predictions only one day ahead. For example, train a model using data from 1951-01-03 to 2013-01-02, make predictions for 2013-01-03, and then train another model using data from 1951-01-03 to 2013-01-03, make predictions for 2013-01-04, and so on. This more closely simulates what you'd do if you were trading using the algorithm.\n",
"\n",
"* You can also improve the algorithm used significantly. Try other techniques, like a random forest, and see if they perform better.\n",
"\n",
"* You can also incorporate outside data, such as the weather in New York City (where most trading happens) the day before, and the amount of Twitter activity around certain stocks.\n",
"\n",
"* You can also make the system real-time by writing an automated script to download the latest data when the market closes, and make predictions for the next day.\n",
"\n",
"* Finally, you can make the system \"higher-resolution\". You're currently making daily predictions, but you could make hourly, minute-by-minute, or second by second predictions. This will require obtaining more data, though. You could also make predictions for individual stocks instead of the S&P500."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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