Basics.ipynb

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    "# Analyzing Stock Prices\n",
    "### Stock Price Data\n",
    "- we'll work with stock market data that was downloaded from [Yahoo Finance](https://finance.yahoo.com/) using the [yahoo_finance]https://pypi.python.org/pypi/yahoo-finance() Python package. This data consists of the daily stock prices from 2007-1-1 to 2017-04-17 for several hundred stock symbols traded on the [NASDAQ](http://www.nasdaq.com/) stock exchange, stored in the prices folder. The download_data.py script in the same folder as the Jupyter notebook was used to download all of the stock price data. Each file in the prices folder is named for a specific stock symbol, and contains the:\n",
    "    - date -- date that the data is from.\n",
    "    - close -- the closing price on that day, which is the price when the trading day ends.\n",
    "    - open -- the opening price on that day, which is the price when the trading day starts.\n",
    "    - high -- the highest price the stock reached during trading.\n",
    "    - low -- the lowest price the stock reached during trading.\n",
    "    - volume -- the number of shares that were traded during the day.\n",
    "- the prices are sorted in ascending order by day. Stock trading doesn't happen on certain days, like weekends and holidays, so there are gaps between days -- we only have data for days on which trading happening.\n",
    "- To read in and store all of the data, we'll need several layers of indices:\n",
    "    - Layer 1 -- the stock symbol, or an numeric index representing the stock symbol.\n",
    "    - Layer 2 -- the rows in a stock symbol csv file.\n",
    "    - Layer 3 -- The column names in a stock symbol csv file.\n",
    "- Here's an example of a data structure that stores all three layers:\n",
    "```\n",
    "{\n",
    "    'aapl': [\n",
    "        [\"2007-01-03\", 83.800002, 86.289999, 86.579999, 81.899999, 309579900],\n",
    "        [\"2007-01-04\", 85.659998, 84.050001, 85.949998, 83.820003, 211815100],\n",
    "        ...\n",
    "    ],\n",
    "    'goog': [\n",
    "        ...\n",
    "    ],\n",
    "    ...\n",
    "}\n",
    "```\n",
    "- The layer 1 data structure is a hash table, the layer 2 data structure is a list, and the layer 3 data structure is a list.\n",
    "\n",
    "#### Goals\n",
    "- Think of an appropriate data structure for each layer. Some suggestions are:\n",
    "    - Hash table, hash table, array.\n",
    "    - List, list, list.\n",
    "    - Hash table, list, list.\n",
    "- Write out a quick justification for why you chose this data structure. Remember that you may need to change this, depending on the types of analysis you do later.\n",
    "- Use multiple processes to read the data into the data structure you chose. You should use [concurrent.futures](https://docs.python.org/3/library/concurrent.futures.html) for this.\n",
    "    - Remember to strip leading and trailing newlines from the files when you open time to avoid issues with blank lines.\n",
    "\n"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "import concurrent.futures\n",
    "import os\n",
    "\n",
    "def read_file(filename):\n",
    "    with open(filename, 'r') as f:\n",
    "        data = f.read().strip()\n",
    "    key = filename.replace(\".csv\", \"\").replace(\"prices/\", \"\")\n",
    "    data = data.split(\"\\n\")\n",
    "    data = [d.split(\",\") for d in data]\n",
    "    return key, data\n",
    "\n",
    "results = []\n",
    "pool = concurrent.futures.ProcessPoolExecutor(max_workers=2)\n",
    "filenames = [\"prices/{}\".format(f) for f in os.listdir(\"prices\")]\n",
    "prices = pool.map(read_file, filenames)\n",
    "prices = list(prices)\n",
    "prices = dict(prices)"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I chose to use a hash table, then a list, then another list. This is because it closely mirrors the existing structure of the data. It allows stock symbols to be easily looked up, and enables me to index values efficiently.\n",
    "\n",
    "\n",
    "# Computing Aggregates\n",
    "- Now that we've read in the data, we can use it to compute aggregates. For example, we can find:\n",
    "    - The average closing price of all stocks over the time period.\n",
    "    - The average volume for each stock.\n",
    "    - The difference between the average opening price and the average closing price for each stock.\n",
    "    - The difference between the average high and the average low for each stock.\n",
    "- In order to do this, you may need to transform the data structure into:\n",
    "    - Layer 1 -- hash table (each stock symbol is a key)\n",
    "    - Layer 2 -- hash table (each column header is a key)\n",
    "    - Layer 3 -- array.\n",
    "- This data structure will look like this:\n",
    "```\n",
    "{\n",
    "    'aapl': {\n",
    "        \"date\": [\n",
    "                \"2007-01-03\",\n",
    "                \"2007-01-04\",\n",
    "                ...\n",
    "            ],\n",
    "        \"close\": [\n",
    "                83.800002,\n",
    "                85.659998,\n",
    "                ...\n",
    "            ],\n",
    "        ...\n",
    "\n",
    "    },\n",
    "    'goog': {\n",
    "        ...\n",
    "    },\n",
    "    ...\n",
    "}\n",
    "```\n",
    "- This will make it much easier to compute column-wise aggregates, since you can work on a single list at a time. It may also be a good idea to convert the columns to the appropriate type, to make computations easier later.\n",
    "\n",
    "#### Goals\n",
    "- Transform the data to a different format if you want.\n",
    "- Compute whatever aggregates you want to with the data.\n",
    "- Write up your findings.\n",
    "- Remember that you can extract a single column from a list of lists using a list comprehension.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from dateutil.parser import parse\n",
    "\n",
    "prices_columns = {}\n",
    "\n",
    "for k,v in prices.items():\n",
    "    price = v\n",
    "    headers = price[0]\n",
    "    price_columns = {}\n",
    "    for i, header in enumerate(headers):\n",
    "        values = [p[i] for p in price[1:]]\n",
    "        if i > 0:\n",
    "            values = [float(v) for v in values]\n",
    "        else:\n",
    "            values = [parse(v) for v in values]\n",
    "        price_columns[header] = values\n",
    "    prices_columns[k] = price_columns\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from statistics import mean\n",
    "\n",
    "average_closing = {}\n",
    "for k,v in prices_columns.items():\n",
    "    average_closing[k] = mean(v[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       " ('cost', 96.17006946409266),\n",
       " ('alxn', 97.1099267011583),\n",
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       " ('csgp', 103.10355984362934),\n",
       " ('dhil', 104.54806553783784),\n",
       " ('djco', 110.25166789845561),\n",
       " ('bbh', 113.28309655096525),\n",
       " ('esgr', 114.26885330617759),\n",
       " ('biib', 164.53822006138998),\n",
       " ('eqix', 165.3847721150579),\n",
       " ('bidu', 193.53191124478764),\n",
       " ('fcnca', 200.25248278146717),\n",
       " ('atri', 228.38977615984555),\n",
       " ('cme', 230.2946601100386),\n",
       " ('aapl', 257.17654040231656),\n",
       " ('amzn', 275.13407757104244)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "closing_tuples = [(k,v) for k,v in average_closing.items()]\n",
    "sorted(closing_tuples, key=lambda x:x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It appears the AMZN and AAPL have the highest average closing prices, while BLFS, and APDN have the lowest average closing prices.\n",
    "\n",
    "# Finding the Most Traded Stock Each Day\n",
    "- Now that we've computed some aggregates, we can work on finding the most traded stock each day. You'll need to create a data structure that stores the dates and the stock symbols that were the most traded on that day. Here's an example:\n",
    "```\n",
    "[\n",
    "    [\"2007-01-03\", \"AAPL\"],\n",
    "    [\"2007-01-04\", \"GOOG\"],\n",
    "    ...\n",
    "]\n",
    "```\n",
    "- In order to find this, we'll need to combine the volume for each stock on each day, and the stock symbol, then sort the volume in descending order. You can use any kind of sort you want for this, including the built-in sorts. It might be useful to profile the different types of sorts. After sorting, we can then grab the symbol of the most traded stock each day.\n",
    "- Finding the most traded stock can help us find trends in the broader market, and see what companies are \"hot\" at what times.\n",
    "\n",
    "#### Goals\n",
    "- Create a data structure that will allow you to effectively compute the most traded stock each day.\n",
    "- Find the most traded stock each day, and create a list of lists.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trades = {}\n",
    "for k, v in prices_columns.items():\n",
    "    for i,date in enumerate(v[\"date\"]):\n",
    "        if date not in trades:\n",
    "            trades[date] = []\n",
    "        trades[date].append([k,v[\"volume\"][i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       " [datetime.datetime(2009, 10, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 10, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 10, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 10, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 10, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 10, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 3, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 6, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 12, 0, 0), 'amd'],\n",
       " [datetime.datetime(2009, 11, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 20, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 24, 0, 0), 'brcd'],\n",
       " [datetime.datetime(2009, 11, 25, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 11, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 3, 0, 0), 'cmcsa'],\n",
       " [datetime.datetime(2009, 12, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 14, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 21, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 24, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2009, 12, 31, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 6, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 12, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 14, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 20, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 21, 0, 0), 'ebay'],\n",
       " [datetime.datetime(2010, 1, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 25, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 1, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 3, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 12, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 23, 0, 0), 'brcd'],\n",
       " [datetime.datetime(2010, 2, 24, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 25, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 2, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 3, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 8, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 3, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 12, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 24, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 25, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 3, 31, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 6, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 12, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 14, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 20, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 21, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 4, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 3, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 6, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 12, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 14, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 20, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 21, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 24, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 25, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 5, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 3, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 14, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 21, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 24, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 25, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 6, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 6, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 12, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 14, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 20, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 21, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 7, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 3, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 6, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 12, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 8, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 16, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 8, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 20, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 24, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 25, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 8, 31, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 3, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 14, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 9, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 20, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 21, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 24, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 9, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 6, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 11, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 12, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 14, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 19, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 20, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 21, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 25, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 27, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 28, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 10, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 3, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 4, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 5, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 9, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 10, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 11, 11, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 11, 12, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 11, 15, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 11, 16, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 11, 17, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 18, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 19, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 11, 22, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 23, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 24, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 26, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 29, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 11, 30, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 1, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 2, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 3, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 12, 6, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 7, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 8, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 9, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 12, 10, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 13, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 14, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 15, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 16, 0, 0), 'aapl'],\n",
       " [datetime.datetime(2010, 12, 17, 0, 0), 'csco'],\n",
       " [datetime.datetime(2010, 12, 20, 0, 0), 'aapl'],\n",
       " ...]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_traded = []\n",
    "for k, v in trades.items():\n",
    "    ordered = sorted(v, key=lambda x: x[1])\n",
    "    symbol = ordered[-1][0]\n",
    "    most_traded.append([k, symbol])\n",
    "most_traded = sorted(most_traded, key=lambda x: x[0])\n",
    "\n",
    "most_traded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Searching For High Volume Days\n",
    "- Let's say we want to search for transactions in a list on a specific date. We can use a binary or a linear search for this, but binary search will be faster if we want to do repeated searches.\n",
    "- Let's search for all transactions on days with unusually high volume. In order to do this, we'll need to:\n",
    "    - Compute total volume of trading for each day\n",
    "    - Sort and find the 10 highest volume days overall\n",
    "    - Find all prices for all stocks on each of the high volume days\n",
    "    \n",
    "#### Goals\n",
    "- Find the prices and volume for each stock on each high transaction day. This should be a truncated version of the original data structure used to read the data in -- it should only have data for the 10 highest volume days.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "daily_volumes = {}\n",
    "\n",
    "most_traded = []\n",
    "for k, v in trades.items():\n",
    "    volume = sum([item[1] for item in v])\n",
    "    daily_volumes[k] = volume\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[datetime.datetime(2008, 1, 24, 0, 0), 1533363200.0],\n",
       " [datetime.datetime(2008, 1, 16, 0, 0), 1536176400.0],\n",
       " [datetime.datetime(2007, 11, 8, 0, 0), 1553880500.0],\n",
       " [datetime.datetime(2008, 9, 29, 0, 0), 1555072400.0],\n",
       " [datetime.datetime(2008, 2, 7, 0, 0), 1559032100.0],\n",
       " [datetime.datetime(2008, 1, 22, 0, 0), 1578877700.0],\n",
       " [datetime.datetime(2008, 10, 8, 0, 0), 1599183500.0],\n",
       " [datetime.datetime(2007, 7, 26, 0, 0), 1611272800.0],\n",
       " [datetime.datetime(2008, 10, 10, 0, 0), 1770266900.0],\n",
       " [datetime.datetime(2008, 1, 23, 0, 0), 1964583900.0]]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume_tuples = [[k,v] for k,v in daily_volumes.items()]\n",
    "volume_tuples = sorted(volume_tuples, key=lambda x: x[1])\n",
    "\n",
    "volume_tuples[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "high_volume_days = [v[0] for v in volume_tuples[-10:]]\n",
    "\n",
    "def binary_search(array, search):\n",
    "    m = 0\n",
    "    i = 0\n",
    "    z = len(array) - 1\n",
    "    while i<= z:\n",
    "        m = math.floor(i + ((z - i) / 2))\n",
    "        if array[m] == search:\n",
    "            return m\n",
    "        elif array[m] < search:\n",
    "            i = m + 1\n",
    "        elif array[m] > search:\n",
    "            z = m - 1\n",
    "\n",
    "high_volume_transactions = {}\n",
    "for k,v in prices_columns.items():\n",
    "    for day in high_volume_days:\n",
    "        ind = binary_search(v[\"date\"], day)\n",
    "        if ind is None:\n",
    "            continue\n",
    "        if k not in high_volume_transactions:\n",
    "            high_volume_transactions[k] = []\n",
    "        high_volume_transactions[k].append(prices[k][ind])\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding Profitable Stocks\n",
    "- Now that we've done some basic analysis, let's see which stocks would have been the most profitable to buy on 2007-01-03. We can do this by:\n",
    "    - Subtracting the initial price from the final price, then computing a percentage relative to the initial price. This will tell us how much our initial investment would have grown or shrunk.\n",
    "    - Sorting all of the percentages.\n",
    "    - Finding the stock that grew the most in the time period.\n",
    "    \n",
    "#### Goals\n",
    "- Find the most profitable stocks from 2007 to present.\n",
    "- Write up your findings\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['achc', 1330.0000666666667],\n",
       " ['bcli', 1339.2137535980346],\n",
       " ['cui', 1525.1625162516252],\n",
       " ['apdn', 1549.6700659868025],\n",
       " ['anip', 1707.3554472785033],\n",
       " ['amzn', 2230.7234281466817],\n",
       " ['blfs', 2437.4365640858978],\n",
       " ['arcw', 3898.60048982856],\n",
       " ['adxs', 4005.0000000000005],\n",
       " ['admp', 7483.8389225948395]]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profits = []\n",
    "for k,v in prices_columns.items():\n",
    "    percentage = (v[\"close\"][-1] - v[\"close\"][0]) / v[\"close\"][0]\n",
    "    profits.append([k,percentage * 100])\n",
    "\n",
    "profits = sorted(profits, key=lambda x: x[1])\n",
    "\n",
    "profits[-10:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The most profitable stock to buy in 2007 would have been ADMP, which appreciated from around 7 cents to its current price of 4.43.\n",
    "\n",
    "# Next Steps\n",
    "- We've done some basic analysis of the data, but there's still quite a bit more depth to go into:\n",
    "    - What stocks would have been best to short at the start of the period?\n",
    "    - Which stocks have the most [after-hours trading](http://www.nasdaq.com/quotes/after-hours.aspx), and show the biggest changes between the closing price and the next day open?\n",
    "    - Can [technical indicators](http://www.investopedia.com/terms/t/technicalindicator.asp) like [Bollinger Bands help us forecast the market?](https://en.wikipedia.org/wiki/Bollinger_Bands)\n",
    "    - What time periods have resulted in steady increases in prices, and what periods have resulted in steady declines?\n",
    "    - Based on price, what was the optimal day to buy each stock if we wanted to hold them until now?\n",
    "    - On days with high trading volume, do stocks move in one direction (up or down) more than the other one?\n",
    "- Most problems in data engineering are also fundamentally scale problems. The more data you have, the harder it is to process, and the more tradeoffs you have to make. It would be useful to download more stock data, so that scale becomes more of a problem. Can you work with 1 gigabyte of stock data, and have a reasonably fast processing methods? How about 10 gigabytes?\n",
    "\n"
   ]
  }
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