2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb

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    "If you were to pick the three most ridiculous fads of 2017, they would definitely be fidget spinners (are they still cool? Do kids still use that word \"cool\"?), artificial intelligence and, yes, cryptocurrencies. Joking aside, I'm actually impressed by the underlying concept and I'm quite bullish on the long term prospects of this disruptive technology. But enough about fidget spinners!!! I'm actually not a hodler of any cryptos. So, while I may not have a ticket to the moon, I can at least get on board the hype train by successfully predicting the price of cryptos by harnessing deep learning, machine learning and artificial intelligence (yes, all of them!).\n",
    "\n",
    "I thought this was a completely unique concept to combine deep learning and cryptos (blog-wise at least), but in researching this post (i.e. looking for code to copy+paste), I came across [something quite similar](http://www.jakob-aungiers.com/articles/a/Multidimensional-LSTM-Networks-to-Predict-Bitcoin-Price). That post only touched on Bitcoin (the most famous crypto of them all), but I'll also discuss Ethereum (commonly known as ether, eth or lambo-money). And since Ether is clearly superior to Bitcoin ([have you not heard of Metropolis?](https://ethereumprice.org/ground-breaking-metropolis-release-flying-radar/)), this post will definitely be better than that other one (though [this project](https://github.com/llens/CryptoCurrencyTrader) includes Ether).\n",
    "\n",
    "We're going to employ a Long Short Term Memory (LSTM) model; it's a particular type of deep learning model that is well suited to time series data (or any data with temporal/spatial/structural order e.g. movies, sentences, etc.). If you wish to truly understand the underlying theory (what kind of crypto enthusiast are you?), then I'd recommend [this blog](http://colah.github.io/posts/2015-08-Understanding-LSTMs/) or [this blog](http://blog.echen.me/2017/05/30/exploring-lstms/) or the [original (white)paper](http://www.bioinf.jku.at/publications/older/2604.pdf). As I'm shamelessly trying to appeal to a wider non-machine learning audience, I'll keep the code to a minimum. There's a Jupyter (Python) notebook available [here](https://github.com/dashee87/blogScripts/blob/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb), if you want to play around with the data or build your own models. Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data\n",
    "\n",
    "Before we build the model, we need to obtain some data for it. There's a [dataset on Kaggle](https://www.kaggle.com/mczielinski/bitcoin-historical-data) that details minute by minute Bitcoin prices (plus some other factors) for the last few years (featured on that [other blog post](http://www.jakob-aungiers.com/articles/a/Multidimensional-LSTM-Networks-to-Predict-Bitcoin-Price)). Over this timescale, noise could overwhelm the signal, so we'll opt for daily prices. The issue here is that we may have not sufficient data (we'll have hundreds of rows rather than thousands or millions). In deep learning, no model can overcome a severe lack of data. I also don't want to rely on static files, as that'll complicate the process of updating the model in the future with new data. Instead, we'll aim to pull data from websites and APIs.\n",
    "\n",
    "As we'll be combining multiple cryptos in one model, it's probably a good idea to pull the data from one source. We'll use [coinmarketcap.com](https://coinmarketcap.com). For now, we'll only consider Bitcoin and Ether, but it wouldn't be hard to add the [latest overhyped altcoin](https://coinmarketcap.com/currencies/iota/) using this approach. Before we import the data, we must load some python packages that will make our lives so much easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
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     "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>Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>19106.4</td>\n",
       "      <td>19371.0</td>\n",
       "      <td>18355.9</td>\n",
       "      <td>19114.2</td>\n",
       "      <td>14839500000</td>\n",
       "      <td>320000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>19475.8</td>\n",
       "      <td>20089.0</td>\n",
       "      <td>18974.1</td>\n",
       "      <td>19140.8</td>\n",
       "      <td>13314600000</td>\n",
       "      <td>326141000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-16</td>\n",
       "      <td>17760.3</td>\n",
       "      <td>19716.7</td>\n",
       "      <td>17515.3</td>\n",
       "      <td>19497.4</td>\n",
       "      <td>12740600000</td>\n",
       "      <td>297376000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>16601.3</td>\n",
       "      <td>18154.1</td>\n",
       "      <td>16601.3</td>\n",
       "      <td>17706.9</td>\n",
       "      <td>14310000000</td>\n",
       "      <td>277936000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-14</td>\n",
       "      <td>16384.6</td>\n",
       "      <td>17085.8</td>\n",
       "      <td>16185.9</td>\n",
       "      <td>16564.0</td>\n",
       "      <td>13777400000</td>\n",
       "      <td>274269000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        Date     Open     High      Low    Close       Volume    Market Cap\n",
       "0 2017-12-18  19106.4  19371.0  18355.9  19114.2  14839500000  320000000000\n",
       "1 2017-12-17  19475.8  20089.0  18974.1  19140.8  13314600000  326141000000\n",
       "2 2017-12-16  17760.3  19716.7  17515.3  19497.4  12740600000  297376000000\n",
       "3 2017-12-15  16601.3  18154.1  16601.3  17706.9  14310000000  277936000000\n",
       "4 2017-12-14  16384.6  17085.8  16185.9  16564.0  13777400000  274269000000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import numpy as np\n",
    "\n",
    "# get market info for bitcoin from the start of 2016 to the current day\n",
    "bitcoin_market_info = pd.read_html(\"https://coinmarketcap.com/currencies/bitcoin/historical-data/?start=20130428&end=\"+time.strftime(\"%Y%m%d\"))[0]\n",
    "# convert the date string to the correct date format\n",
    "bitcoin_market_info = bitcoin_market_info.assign(Date=pd.to_datetime(bitcoin_market_info['Date']))\n",
    "# when Volume is equal to '-' convert it to 0\n",
    "bitcoin_market_info.loc[bitcoin_market_info['Volume']==\"-\",'Volume']=0\n",
    "# convert to int\n",
    "bitcoin_market_info['Volume'] = bitcoin_market_info['Volume'].astype('int64')\n",
    "# look at the first few rows\n",
    "bitcoin_market_info.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "      <th>Market Cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>721.73</td>\n",
       "      <td>803.93</td>\n",
       "      <td>689.23</td>\n",
       "      <td>794.64</td>\n",
       "      <td>3249230000</td>\n",
       "      <td>69578400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>696.24</td>\n",
       "      <td>735.83</td>\n",
       "      <td>696.24</td>\n",
       "      <td>719.98</td>\n",
       "      <td>2147390000</td>\n",
       "      <td>67106400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-16</td>\n",
       "      <td>686.19</td>\n",
       "      <td>718.38</td>\n",
       "      <td>680.79</td>\n",
       "      <td>696.21</td>\n",
       "      <td>2165690000</td>\n",
       "      <td>66124200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>696.38</td>\n",
       "      <td>697.13</td>\n",
       "      <td>621.06</td>\n",
       "      <td>684.45</td>\n",
       "      <td>2758710000</td>\n",
       "      <td>67090900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-14</td>\n",
       "      <td>700.59</td>\n",
       "      <td>753.12</td>\n",
       "      <td>664.99</td>\n",
       "      <td>695.82</td>\n",
       "      <td>3821580000</td>\n",
       "      <td>67482900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date    Open    High     Low   Close      Volume   Market Cap\n",
       "0 2017-12-18  721.73  803.93  689.23  794.64  3249230000  69578400000\n",
       "1 2017-12-17  696.24  735.83  696.24  719.98  2147390000  67106400000\n",
       "2 2017-12-16  686.19  718.38  680.79  696.21  2165690000  66124200000\n",
       "3 2017-12-15  696.38  697.13  621.06  684.45  2758710000  67090900000\n",
       "4 2017-12-14  700.59  753.12  664.99  695.82  3821580000  67482900000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get market info for ethereum from the start of 2016 to the current day\n",
    "eth_market_info = pd.read_html(\"https://coinmarketcap.com/currencies/ethereum/historical-data/?start=20130428&end=\"+time.strftime(\"%Y%m%d\"))[0]\n",
    "# convert the date string to the correct date format\n",
    "eth_market_info = eth_market_info.assign(Date=pd.to_datetime(eth_market_info['Date']))\n",
    "# look at the first few rows\n",
    "eth_market_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To explain what's just happened, we've loaded some python packages and then imported the table that you see on [this site](https://coinmarketcap.com/currencies/bitcoin/historical-data/). With a little bit of data cleaning, we arrive at the above table. We also do the same thing for ether by simply replacing 'bitcoin' with 'ethereum' in the url (code omitted).\n",
    "\n",
    "To prove that the data is accurate, we can plot the price and volume of both cryptos over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# getting the Bitcoin and Eth logos\n",
    "import sys\n",
    "from PIL import Image\n",
    "import io\n",
    "\n",
    "if sys.version_info[0] < 3:\n",
    "    import urllib2 as urllib\n",
    "    bt_img = urllib.urlopen(\"http://logok.org/wp-content/uploads/2016/10/Bitcoin-Logo-640x480.png\")\n",
    "    eth_img = urllib.urlopen(\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/05/Ethereum_logo_2014.svg/256px-Ethereum_logo_2014.svg.png\")\n",
    "else:\n",
    "    import urllib\n",
    "    bt_img = urllib.request.urlopen(\"http://logok.org/wp-content/uploads/2016/10/Bitcoin-Logo-640x480.png\")\n",
    "    eth_img = urllib.request.urlopen(\"https://upload.wikimedia.org/wikipedia/commons/thumb/0/05/Ethereum_logo_2014.svg/256px-Ethereum_logo_2014.svg.png\")\n",
    "\n",
    "image_file = io.BytesIO(bt_img.read())\n",
    "bitcoin_im = Image.open(image_file)\n",
    "\n",
    "image_file = io.BytesIO(eth_img.read())\n",
    "eth_im = Image.open(image_file)\n",
    "width_eth_im , height_eth_im  = eth_im.size\n",
    "eth_im = eth_im.resize((int(eth_im.size[0]*0.8), int(eth_im.size[1]*0.8)), Image.ANTIALIAS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bitcoin_market_info.columns =[bitcoin_market_info.columns[0]]+['bt_'+i for i in bitcoin_market_info.columns[1:]]\n",
    "eth_market_info.columns =[eth_market_info.columns[0]]+['eth_'+i for i in eth_market_info.columns[1:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CuMzb7DKgOxGuAC4VZzHQ6A0BPgycLiIVXnHE6V6bMcYcEFa8tPf/5BcvW9nr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ad17b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2,1, gridspec_kw = {'height_ratios':[3, 1]})\n",
    "ax1.set_ylabel('Closing Price ($)',fontsize=12)\n",
    "ax2.set_ylabel('Volume ($ bn)',fontsize=12)\n",
    "ax2.set_yticks([int('%d000000000'%i) for i in range(10)])\n",
    "ax2.set_yticklabels(range(10))\n",
    "ax1.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,7]])\n",
    "ax1.set_xticklabels('')\n",
    "ax2.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,7]])\n",
    "ax2.set_xticklabels([datetime.date(i,j,1).strftime('%b %Y')  for i in range(2013,2019) for j in [1,7]])\n",
    "ax1.plot(bitcoin_market_info['Date'].astype(datetime.datetime),bitcoin_market_info['bt_Open'])\n",
    "ax2.bar(bitcoin_market_info['Date'].astype(datetime.datetime).values, bitcoin_market_info['bt_Volume'].values)\n",
    "fig.tight_layout()\n",
    "fig.figimage(bitcoin_im, 100, 120, zorder=3,alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kHQaMMWYMvbq3mWvWvMaM0vx+7y1t/Od3EQ05OI6wr6WL/37qddbvbmLulKEH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\nyQKUMcaYnGQByhhjTE6yAGWMMSYn/X9ggi5ZtbbPtAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a119d3c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2,1, gridspec_kw = {'height_ratios':[3, 1]})\n",
    "#ax1.set_yscale('log')\n",
    "ax1.set_ylabel('Closing Price ($)',fontsize=12)\n",
    "ax2.set_ylabel('Volume ($ bn)',fontsize=12)\n",
    "ax2.set_yticks([int('%d000000000'%i) for i in range(10)])\n",
    "ax2.set_yticklabels(range(10))\n",
    "ax1.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,7]])\n",
    "ax1.set_xticklabels('')\n",
    "ax2.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,7]])\n",
    "ax2.set_xticklabels([datetime.date(i,j,1).strftime('%b %Y')  for i in range(2013,2019) for j in [1,7]])\n",
    "ax1.plot(eth_market_info['Date'].astype(datetime.datetime),eth_market_info['eth_Open'])\n",
    "ax2.bar(eth_market_info['Date'].astype(datetime.datetime).values, eth_market_info['eth_Volume'].values)\n",
    "fig.tight_layout()\n",
    "fig.figimage(eth_im, 300, 180, zorder=3, alpha=.6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>bt_Open</th>\n",
       "      <th>bt_High</th>\n",
       "      <th>bt_Low</th>\n",
       "      <th>bt_Close</th>\n",
       "      <th>bt_Volume</th>\n",
       "      <th>bt_Market Cap</th>\n",
       "      <th>eth_Open</th>\n",
       "      <th>eth_High</th>\n",
       "      <th>eth_Low</th>\n",
       "      <th>eth_Close</th>\n",
       "      <th>eth_Volume</th>\n",
       "      <th>eth_Market Cap</th>\n",
       "      <th>bt_day_diff</th>\n",
       "      <th>eth_day_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-12-18</td>\n",
       "      <td>19106.4</td>\n",
       "      <td>19371.0</td>\n",
       "      <td>18355.9</td>\n",
       "      <td>19114.2</td>\n",
       "      <td>14839500000</td>\n",
       "      <td>320000000000</td>\n",
       "      <td>721.73</td>\n",
       "      <td>803.93</td>\n",
       "      <td>689.23</td>\n",
       "      <td>794.64</td>\n",
       "      <td>3249230000</td>\n",
       "      <td>69578400000</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.101021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-12-17</td>\n",
       "      <td>19475.8</td>\n",
       "      <td>20089.0</td>\n",
       "      <td>18974.1</td>\n",
       "      <td>19140.8</td>\n",
       "      <td>13314600000</td>\n",
       "      <td>326141000000</td>\n",
       "      <td>696.24</td>\n",
       "      <td>735.83</td>\n",
       "      <td>696.24</td>\n",
       "      <td>719.98</td>\n",
       "      <td>2147390000</td>\n",
       "      <td>67106400000</td>\n",
       "      <td>-0.017201</td>\n",
       "      <td>0.034097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-12-16</td>\n",
       "      <td>17760.3</td>\n",
       "      <td>19716.7</td>\n",
       "      <td>17515.3</td>\n",
       "      <td>19497.4</td>\n",
       "      <td>12740600000</td>\n",
       "      <td>297376000000</td>\n",
       "      <td>686.19</td>\n",
       "      <td>718.38</td>\n",
       "      <td>680.79</td>\n",
       "      <td>696.21</td>\n",
       "      <td>2165690000</td>\n",
       "      <td>66124200000</td>\n",
       "      <td>0.097808</td>\n",
       "      <td>0.014602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-12-15</td>\n",
       "      <td>16601.3</td>\n",
       "      <td>18154.1</td>\n",
       "      <td>16601.3</td>\n",
       "      <td>17706.9</td>\n",
       "      <td>14310000000</td>\n",
       "      <td>277936000000</td>\n",
       "      <td>696.38</td>\n",
       "      <td>697.13</td>\n",
       "      <td>621.06</td>\n",
       "      <td>684.45</td>\n",
       "      <td>2758710000</td>\n",
       "      <td>67090900000</td>\n",
       "      <td>0.066597</td>\n",
       "      <td>-0.017131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-12-14</td>\n",
       "      <td>16384.6</td>\n",
       "      <td>17085.8</td>\n",
       "      <td>16185.9</td>\n",
       "      <td>16564.0</td>\n",
       "      <td>13777400000</td>\n",
       "      <td>274269000000</td>\n",
       "      <td>700.59</td>\n",
       "      <td>753.12</td>\n",
       "      <td>664.99</td>\n",
       "      <td>695.82</td>\n",
       "      <td>3821580000</td>\n",
       "      <td>67482900000</td>\n",
       "      <td>0.010949</td>\n",
       "      <td>-0.006809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  bt_Open  bt_High   bt_Low  bt_Close    bt_Volume  bt_Market Cap  \\\n",
       "0 2017-12-18  19106.4  19371.0  18355.9   19114.2  14839500000   320000000000   \n",
       "1 2017-12-17  19475.8  20089.0  18974.1   19140.8  13314600000   326141000000   \n",
       "2 2017-12-16  17760.3  19716.7  17515.3   19497.4  12740600000   297376000000   \n",
       "3 2017-12-15  16601.3  18154.1  16601.3   17706.9  14310000000   277936000000   \n",
       "4 2017-12-14  16384.6  17085.8  16185.9   16564.0  13777400000   274269000000   \n",
       "\n",
       "   eth_Open  eth_High  eth_Low  eth_Close  eth_Volume eth_Market Cap  \\\n",
       "0    721.73    803.93   689.23     794.64  3249230000    69578400000   \n",
       "1    696.24    735.83   696.24     719.98  2147390000    67106400000   \n",
       "2    686.19    718.38   680.79     696.21  2165690000    66124200000   \n",
       "3    696.38    697.13   621.06     684.45  2758710000    67090900000   \n",
       "4    700.59    753.12   664.99     695.82  3821580000    67482900000   \n",
       "\n",
       "   bt_day_diff  eth_day_diff  \n",
       "0     0.000408      0.101021  \n",
       "1    -0.017201      0.034097  \n",
       "2     0.097808      0.014602  \n",
       "3     0.066597     -0.017131  \n",
       "4     0.010949     -0.006809  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "market_info = pd.merge(bitcoin_market_info,eth_market_info, on=['Date'])\n",
    "market_info = market_info[market_info['Date']>='2016-01-01']\n",
    "for coins in ['bt_', 'eth_']: \n",
    "    kwargs = { coins+'day_diff': lambda x: (x[coins+'Close']-x[coins+'Open'])/x[coins+'Open']}\n",
    "    market_info = market_info.assign(**kwargs)\n",
    "market_info.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training, Test & Random Walks\n",
    "\n",
    "We have some data, so now we need to build a model. In deep learning, the data is typically split into training and test sets. The model is built on the training set and subsequently evaluated on the unseen test set. In time series models, we generally train on one period of time and then test on another separate period. Rather arbitrarily, I'll set the cut-off date to June 1st 2017 (i.e. model will be trained on data before that date and assessed on data after it)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+60/82/NbGApPnqTQ3jtAz+AQnf2D7GhuZXFVGW85/+yU65OcGNHZ2XnaJkYk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\nI6AMBoPBUJQYAWUwGAyGouT/A30cOnuxQIunAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11a501d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "split_date = '2017-06-01'\n",
    "fig, (ax1, ax2) = plt.subplots(2,1)\n",
    "ax1.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,7]])\n",
    "ax1.set_xticklabels('')\n",
    "ax2.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,7]])\n",
    "ax2.set_xticklabels([datetime.date(i,j,1).strftime('%b %Y')  for i in range(2013,2019) for j in [1,7]])\n",
    "ax1.plot(market_info[market_info['Date'] < split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date'] < split_date]['bt_Close'], \n",
    "         color='#B08FC7', label='Training')\n",
    "ax1.plot(market_info[market_info['Date'] >= split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date'] >= split_date]['bt_Close'], \n",
    "         color='#8FBAC8', label='Test')\n",
    "ax2.plot(market_info[market_info['Date'] < split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date'] < split_date]['eth_Close'], \n",
    "         color='#B08FC7')\n",
    "ax2.plot(market_info[market_info['Date'] >= split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date'] >= split_date]['eth_Close'], color='#8FBAC8')\n",
    "ax1.set_xticklabels('')\n",
    "ax1.set_ylabel('Bitcoin Price ($)',fontsize=12)\n",
    "ax2.set_ylabel('Ethereum Price ($)',fontsize=12)\n",
    "plt.tight_layout()\n",
    "ax1.legend(bbox_to_anchor=(0.03, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "fig.figimage(bitcoin_im.resize((int(bitcoin_im.size[0]*0.65), int(bitcoin_im.size[1]*0.65)), Image.ANTIALIAS), \n",
    "             200, 260, zorder=3,alpha=.5)\n",
    "fig.figimage(eth_im.resize((int(eth_im.size[0]*0.65), int(eth_im.size[1]*0.65)), Image.ANTIALIAS), \n",
    "             350, 40, zorder=3,alpha=.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that the training period mostly consists of periods when cryptos were relatively cheaper. As such, the training data may not be representative of the test data, undermining the model's ability to generalise to unseen data (you could try to make your data stationary- discussed [here](https://dashee87.github.io/data%20science/general/A-Road-Incident-Model-Analysis/)). But why let negative realities get in the way of baseless optimism? Before we take our deep artificially intelligent machine learning model to the moon, it's worth discussing a simpler model. The most basic model is to set tomorrow's price equal to today's price (which we'll crudely call a lag model). This is how we'd define such a model in mathematical terms:\n",
    "\n",
    "\\begin{align}\n",
    "PredPrice_{t} & = ActualPrice_{t-1}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FqX8CAgJo2bJl6c/UmNpQUOTl/aW76SZ7aHJgKbQ9o7ZDOm6+TpJYCXTD2QPK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\nGv8Jv+dz2koyHlGKVfCTX/8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qTVCqGgcgIvNwktIGoAvwoapmiUg7IEVVd1dlv8YYY6rf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11b1ae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# trivial lag model: P_t = P_(t-1)\n",
    "fig, (ax1, ax2) = plt.subplots(2,1)\n",
    "ax1.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax1.set_xticklabels('')\n",
    "ax2.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax2.set_xticklabels([datetime.date(2017,i+1,1).strftime('%b %d %Y')  for i in range(12)])\n",
    "ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date']>= split_date]['bt_Close'].values, label='Actual')\n",
    "ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "          market_info[market_info['Date']>= datetime.datetime.strptime(split_date, '%Y-%m-%d') - \n",
    "                      datetime.timedelta(days=1)]['bt_Close'][1:].values, label='Predicted')\n",
    "ax1.set_ylabel('Bitcoin Price ($)',fontsize=12)\n",
    "ax1.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "ax1.set_title('Simple Lag Model (Test Set)')\n",
    "ax2.set_ylabel('Etherum Price ($)',fontsize=12)\n",
    "ax2.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date']>= split_date]['eth_Close'].values, label='Actual')\n",
    "ax2.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "          market_info[market_info['Date']>= datetime.datetime.strptime(split_date, '%Y-%m-%d') - \n",
    "                      datetime.timedelta(days=1)]['eth_Close'][1:].values, label='Predicted')\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extending this trivial lag model, [stock prices are commonly treated as random walks](https://www.tcd.ie/Economics/assets/pdf/SER/2007/Samuel_Dupernex.pdf), which can be defined in these mathematical terms:\n",
    "\n",
    "\\begin{align}\n",
    "PredPrice_{t} & = ActualPrice_{t-1} * \\epsilon, \\epsilon \\sim N(\\mu, \\sigma)\n",
    "\\end{align}\n",
    "\n",
    "We'll determine &mu; and &sigma; from the training sets and apply the random walk model to the Bitcoin and Ethereum test sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we may want to make sure the daily change in price follows a normal distribution. We'll plot the histogram of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y39XAJaSfh31E0k4A+e/qTgVpZmbt1zIB5N+U2XLkPenx9p+RHkeel2ebR/rFPzMzGxB1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1297e510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "ax1.hist(market_info[market_info['Date']< split_date]['bt_day_diff'].values, bins=100)\n",
    "ax2.hist(market_info[market_info['Date']< split_date]['eth_day_diff'].values, bins=100)\n",
    "ax1.set_title('Bitcoin Daily Price Changes')\n",
    "ax2.set_title('Ethereum Daily Price Changes')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LZmYmHTt25LrrruOmm25iypQpbNq0idjYWF5++WWmTnWmObvjjjvYsmUL999/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\nGAwGQ73k/wGZ7rjk0+dDhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a11a2e890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(202)\n",
    "bt_r_walk_mean, bt_r_walk_sd = np.mean(market_info[market_info['Date']< split_date]['bt_day_diff'].values), \\\n",
    "                         np.std(market_info[market_info['Date']< split_date]['bt_day_diff'].values)\n",
    "bt_random_steps = np.random.normal(bt_r_walk_mean, bt_r_walk_sd, \n",
    "                (max(market_info['Date']).to_pydatetime() - datetime.datetime.strptime(split_date, '%Y-%m-%d')).days + 1)\n",
    "eth_r_walk_mean, eth_r_walk_sd = np.mean(market_info[market_info['Date']< split_date]['eth_day_diff'].values), \\\n",
    "                         np.std(market_info[market_info['Date']< split_date]['eth_day_diff'].values)\n",
    "eth_random_steps = np.random.normal(eth_r_walk_mean, eth_r_walk_sd, \n",
    "                (max(market_info['Date']).to_pydatetime() - datetime.datetime.strptime(split_date, '%Y-%m-%d')).days + 1)\n",
    "fig, (ax1, ax2) = plt.subplots(2,1)\n",
    "ax1.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax1.set_xticklabels('')\n",
    "ax2.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax2.set_xticklabels([datetime.date(2017,i+1,1).strftime('%b %d %Y')  for i in range(12)])\n",
    "ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "     market_info[market_info['Date']>= split_date]['bt_Close'].values, label='Actual')\n",
    "ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "      market_info[(market_info['Date']+ datetime.timedelta(days=1))>= split_date]['bt_Close'].values[1:] * \n",
    "     (1+bt_random_steps), label='Predicted')\n",
    "ax2.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "     market_info[market_info['Date']>= split_date]['eth_Close'].values, label='Actual')\n",
    "ax2.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "      market_info[(market_info['Date']+ datetime.timedelta(days=1))>= split_date]['eth_Close'].values[1:] * \n",
    "     (1+eth_random_steps), label='Predicted')\n",
    "ax1.set_title('Single Point Random Walk (Test Set)')\n",
    "ax1.set_ylabel('Bitcoin Price ($)',fontsize=12)\n",
    "ax2.set_ylabel('Ethereum Price ($)',fontsize=12)\n",
    "ax1.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow! Look at those prediction lines. Apart from a few kinks, it broadly tracks the actual closing price for each coin. It even captures the eth rises (and subsequent falls) in mid-June and late August. At this stage, if I was to announce the launch of sheehanCoin, I'm sure that ICO would stupidly over-subscribed. As pointed out on that other blog, models that only make predictions one point into the future are often misleadingly accurate, as errors aren't carried over to subsequent predictions. No matter how large the error, it's essentially reset at each time point, as the model is fed the true price. The Bitcoin random walk is particularly deceptive, as the scale of the y-axis is quite wide, making the prediction line appear quite smooth.\n",
    "\n",
    "Single point predictions are unfortunately quite common when evaluating time series models (e.g.[here](https://medium.com/@binsumi/neural-networks-and-bitcoin-d452bfd7757e) and [here](https://blog.statsbot.co/time-series-prediction-using-recurrent-neural-networks-lstms-807fa6ca7f)). A better idea could be to measure its accuracy on multi-point predictions. That way, errors from previous predictions aren't reset but rather are compounded by subsequent predictions. Thus, poor models are penalised more heavily. In mathematical terms:\n",
    "\n",
    "\\begin{align}\n",
    "PredPrice_{t} & = PredPrice_{t-1} * \\epsilon, \\epsilon \\sim N(\\mu, \\sigma)\\ \\&  \\ PredPrice_0 = Price_0\n",
    "\\end{align}\n",
    "\n",
    "Let's get our random walk model to predict the closing prices over the total test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KVVPacN09fZoP00WkdlyYRNzFwLV2e2kNpe+uI71OInIdrrZ1wBTjxhjTmVVW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\ntEuMgjIYDAZDu8QoKIPBYDC0S/4fFtda2H3tJhkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12ef6110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bt_random_walk = []\n",
    "eth_random_walk = []\n",
    "for n_step, (bt_step, eth_step) in enumerate(zip(bt_random_steps, eth_random_steps)):\n",
    "    if n_step==0:\n",
    "        bt_random_walk.append(market_info[market_info['Date']< split_date]['bt_Close'].values[0] * (bt_step+1))\n",
    "        eth_random_walk.append(market_info[market_info['Date']< split_date]['eth_Close'].values[0] * (eth_step+1))\n",
    "    else:\n",
    "        bt_random_walk.append(bt_random_walk[n_step-1] * (bt_step+1))\n",
    "        eth_random_walk.append(eth_random_walk[n_step-1] * (eth_step+1))\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1)\n",
    "ax1.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax1.set_xticklabels('')\n",
    "ax2.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax2.set_xticklabels([datetime.date(2017,i+1,1).strftime('%b %d %Y')  for i in range(12)])\n",
    "ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date']>= split_date]['bt_Close'].values, label='Actual')\n",
    "ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "         bt_random_walk[::-1], label='Predicted')\n",
    "ax2.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date']>= split_date]['eth_Close'].values, label='Actual')\n",
    "ax2.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "         eth_random_walk[::-1], label='Predicted')\n",
    "\n",
    "ax1.set_title('Full Interval Random Walk')\n",
    "ax1.set_ylabel('Bitcoin Price ($)',fontsize=12)\n",
    "ax2.set_ylabel('Ethereum Price ($)',fontsize=12)\n",
    "ax1.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model predictions are extremely sensitive to the random seed. I've selected one where the full interval random walk looks almost decent for Ethereum. In the [accompanying Jupyter notebook](https://github.com/dashee87/blogScripts/blob/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb), you can interactively play around with the seed value below to see how badly it can perform."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1bd766cb3d5440593001d005b2fd1e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "A Jupyter Widget"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.plot_func>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets\n",
    "\n",
    "def plot_func(freq):\n",
    "    np.random.seed(freq)\n",
    "    random_steps = np.random.normal(eth_r_walk_mean, eth_r_walk_sd, \n",
    "                (max(market_info['Date']).to_pydatetime() - datetime.datetime.strptime(split_date, '%Y-%m-%d')).days + 1)\n",
    "    random_walk = []\n",
    "    for n_step,i in enumerate(random_steps):\n",
    "        if n_step==0:\n",
    "            random_walk.append(market_info[market_info['Date']< split_date]['eth_Close'].values[0] * (i+1))\n",
    "        else:\n",
    "            random_walk.append(random_walk[n_step-1] * (i+1))\n",
    "    fig, (ax1, ax2) = plt.subplots(2,1)\n",
    "    ax1.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "    ax1.set_xticklabels('')\n",
    "    ax2.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "    ax2.set_xticklabels([datetime.date(2017,i+1,1).strftime('%b %d %Y')  for i in range(12)])\n",
    "    ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "         market_info[market_info['Date']>= split_date]['eth_Close'].values, label='Actual')\n",
    "    ax1.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "          market_info[(market_info['Date']+ datetime.timedelta(days=1))>= split_date]['eth_Close'].values[1:] * \n",
    "         (1+random_steps), label='Predicted')\n",
    "    ax2.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "          market_info[(market_info['Date']+ datetime.timedelta(days=1))>= split_date]['eth_Close'].values[1:] * \n",
    "         (1+random_steps))\n",
    "    ax2.plot(market_info[market_info['Date']>= split_date]['Date'].astype(datetime.datetime),\n",
    "             random_walk[::-1])\n",
    "    ax1.set_title('Single Point Random Walk')\n",
    "    ax1.set_ylabel('')\n",
    "    # for static figures, you may wish to insert the random seed value\n",
    "#    ax1.annotate('Random Seed: %d'%freq, xy=(0.75, 0.2),  xycoords='axes fraction',\n",
    "#            xytext=(0.75, 0.2), textcoords='axes fraction')\n",
    "    ax1.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "    ax2.set_title('Full Interval Random Walk')\n",
    "    fig.text(0.0, 0.5, 'Ethereum Price ($)', va='center', rotation='vertical',fontsize=12)\n",
    "    plt.tight_layout()\n",
    "#    plt.savefig('image%d.png'%freq, bbox_inches='tight')\n",
    "    plt.show()\n",
    "    \n",
    "interact(plot_func, freq =widgets.IntSlider(min=200,max=210,step=1,value=205, description='Random Seed:'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the single point random walk always looks quite accurate, even though there's no real substance behind it. Hopefully, you'll be more suspicious of any blog that claims to accurately predict prices. I probably shouldn't worry; it's not like crypto fans to be seduced by [slick marketing claims](https://uetoken.com/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Long Short Term Memory (LSTM)\n",
    "\n",
    "Like I said, if you're interested in the theory behind LSTMs, then I'll refer you to [this](http://colah.github.io/posts/2015-08-Understanding-LSTMs/), [this](http://blog.echen.me/2017/05/30/exploring-lstms/) and [this](http://www.bioinf.jku.at/publications/older/2604.pdf). Luckily, we don't need to build the network from scratch (or even understand it), there exists packages that include standard implementations of various deep learning algorithms (e.g. [TensorFlow](https://www.tensorflow.org/get_started/get_started), [Keras](https://keras.io/#keras-the-python-deep-learning-library), [PyTorch](http://pytorch.org/), etc.). I'll opt for Keras, as I find it the most intuitive for non-experts. If you're not familiar with Keras, then check out my [previous tutorial](https://dashee87.github.io/data%20science/deep%20learning/python/another-keras-tutorial-for-neural-network-beginners/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for coins in ['bt_', 'eth_']: \n",
    "    kwargs = { coins+'close_off_high': lambda x: 2*(x[coins+'High']- x[coins+'Close'])/(x[coins+'High']-x[coins+'Low'])-1,\n",
    "            coins+'volatility': lambda x: (x[coins+'High']- x[coins+'Low'])/(x[coins+'Open'])}\n",
    "    market_info = market_info.assign(**kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>bt_Close</th>\n",
       "      <th>bt_Volume</th>\n",
       "      <th>bt_close_off_high</th>\n",
       "      <th>bt_volatility</th>\n",
       "      <th>eth_Close</th>\n",
       "      <th>eth_Volume</th>\n",
       "      <th>eth_close_off_high</th>\n",
       "      <th>eth_volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>434.33</td>\n",
       "      <td>36278900</td>\n",
       "      <td>-0.560641</td>\n",
       "      <td>0.020292</td>\n",
       "      <td>0.948024</td>\n",
       "      <td>206062</td>\n",
       "      <td>-0.418477</td>\n",
       "      <td>0.025040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>2016-01-02</td>\n",
       "      <td>433.44</td>\n",
       "      <td>30096600</td>\n",
       "      <td>0.250597</td>\n",
       "      <td>0.009641</td>\n",
       "      <td>0.937124</td>\n",
       "      <td>255504</td>\n",
       "      <td>0.965898</td>\n",
       "      <td>0.034913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>2016-01-03</td>\n",
       "      <td>430.01</td>\n",
       "      <td>39633800</td>\n",
       "      <td>-0.173865</td>\n",
       "      <td>0.020827</td>\n",
       "      <td>0.971905</td>\n",
       "      <td>407632</td>\n",
       "      <td>-0.317885</td>\n",
       "      <td>0.060792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>433.09</td>\n",
       "      <td>38477500</td>\n",
       "      <td>-0.474265</td>\n",
       "      <td>0.012649</td>\n",
       "      <td>0.954480</td>\n",
       "      <td>346245</td>\n",
       "      <td>-0.057657</td>\n",
       "      <td>0.047943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>431.96</td>\n",
       "      <td>34522600</td>\n",
       "      <td>-0.013333</td>\n",
       "      <td>0.010391</td>\n",
       "      <td>0.950176</td>\n",
       "      <td>219833</td>\n",
       "      <td>0.697930</td>\n",
       "      <td>0.025236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  bt_Close  bt_Volume  bt_close_off_high  bt_volatility  \\\n",
       "717 2016-01-01    434.33   36278900          -0.560641       0.020292   \n",
       "716 2016-01-02    433.44   30096600           0.250597       0.009641   \n",
       "715 2016-01-03    430.01   39633800          -0.173865       0.020827   \n",
       "714 2016-01-04    433.09   38477500          -0.474265       0.012649   \n",
       "713 2016-01-05    431.96   34522600          -0.013333       0.010391   \n",
       "\n",
       "     eth_Close  eth_Volume  eth_close_off_high  eth_volatility  \n",
       "717   0.948024      206062           -0.418477        0.025040  \n",
       "716   0.937124      255504            0.965898        0.034913  \n",
       "715   0.971905      407632           -0.317885        0.060792  \n",
       "714   0.954480      346245           -0.057657        0.047943  \n",
       "713   0.950176      219833            0.697930        0.025236  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_data = market_info[['Date']+[coin+metric for coin in ['bt_', 'eth_'] \n",
    "                                   for metric in ['Close','Volume','close_off_high','volatility']]]\n",
    "# need to reverse the data frame so that subsequent rows represent later timepoints\n",
    "model_data = model_data.sort_values(by='Date')\n",
    "model_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I've created a new data frame called `model_data`. I've removed some of the previous columns (open price, daily highs and lows) and reformulated some new ones. `close_off_high` represents the gap between the closing price and price high for that day, where values of -1 and 1 mean the closing price was equal to the daily low or daily high, respectively. The `volatility` columns are simply the difference between high and low price divided by the opening price. You may also notice that `model_data` is arranged in order of earliest to latest. We don't actually need the date column anymore, as that information won't be fed into the model.\n",
    "\n",
    "Our LSTM model will use previous data (both bitcoin and eth) to predict the next day's closing price of a specific coin. We must decide how many previous days it will have access to. Again, it's rather arbitrary, but I'll opt for 10 days, as it's a nice round number. We build little data frames consisting of 10 consecutive days of data (called windows), so the first window will consist of the 0-9th rows of the training set (Python is zero-indexed), the second will be the rows 1-10, etc.  Picking a small window size means we can feed more windows into our model; the downside is that the model may not have sufficient information to detect complex long term behaviours (if such things exist). \n",
    "\n",
    "Deep learning models don't like inputs that vary wildly. Looking at those columns, some values range between -1 and 1, while others are on the scale of millions. We need to normalise the data, so that our inputs are somewhat consistent. Typically, you want values between -1 and 1. The `off_high` and `volatility` columns are fine as they are. For the remaining columns, like that [other blog post](http://www.jakob-aungiers.com/articles/a/Multidimensional-LSTM-Networks-to-Predict-Bitcoin-Price), we'll normalise the inputs to the first value in the window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we don't need the date columns anymore\n",
    "training_set, test_set = model_data[model_data['Date']<split_date], model_data[model_data['Date']>=split_date]\n",
    "training_set = training_set.drop('Date', 1)\n",
    "test_set = test_set.drop('Date', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "window_len = 10\n",
    "norm_cols = [coin+metric for coin in ['bt_', 'eth_'] for metric in ['Close','Volume']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LSTM_training_inputs = []\n",
    "for i in range(len(training_set)-window_len):\n",
    "    temp_set = training_set[i:(i+window_len)].copy()\n",
    "    for col in norm_cols:\n",
    "        temp_set.loc[:, col] = temp_set[col]/temp_set[col].iloc[0] - 1\n",
    "    LSTM_training_inputs.append(temp_set)\n",
    "LSTM_training_outputs = (training_set['eth_Close'][window_len:].values/training_set['eth_Close'][:-window_len].values)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LSTM_test_inputs = []\n",
    "for i in range(len(test_set)-window_len):\n",
    "    temp_set = test_set[i:(i+window_len)].copy()\n",
    "    for col in norm_cols:\n",
    "        temp_set.loc[:, col] = temp_set[col]/temp_set[col].iloc[0] - 1\n",
    "    LSTM_test_inputs.append(temp_set)\n",
    "LSTM_test_outputs = (test_set['eth_Close'][window_len:].values/test_set['eth_Close'][:-window_len].values)-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bt_Close</th>\n",
       "      <th>bt_Volume</th>\n",
       "      <th>bt_close_off_high</th>\n",
       "      <th>bt_volatility</th>\n",
       "      <th>eth_Close</th>\n",
       "      <th>eth_Volume</th>\n",
       "      <th>eth_close_off_high</th>\n",
       "      <th>eth_volatility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.560641</td>\n",
       "      <td>0.020292</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.418477</td>\n",
       "      <td>0.025040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>716</th>\n",
       "      <td>-0.002049</td>\n",
       "      <td>-0.170410</td>\n",
       "      <td>0.250597</td>\n",
       "      <td>0.009641</td>\n",
       "      <td>-0.011498</td>\n",
       "      <td>0.239937</td>\n",
       "      <td>0.965898</td>\n",
       "      <td>0.034913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>715</th>\n",
       "      <td>-0.009946</td>\n",
       "      <td>0.092475</td>\n",
       "      <td>-0.173865</td>\n",
       "      <td>0.020827</td>\n",
       "      <td>0.025190</td>\n",
       "      <td>0.978201</td>\n",
       "      <td>-0.317885</td>\n",
       "      <td>0.060792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>714</th>\n",
       "      <td>-0.002855</td>\n",
       "      <td>0.060603</td>\n",
       "      <td>-0.474265</td>\n",
       "      <td>0.012649</td>\n",
       "      <td>0.006810</td>\n",
       "      <td>0.680295</td>\n",
       "      <td>-0.057657</td>\n",
       "      <td>0.047943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>713</th>\n",
       "      <td>-0.005457</td>\n",
       "      <td>-0.048411</td>\n",
       "      <td>-0.013333</td>\n",
       "      <td>0.010391</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.066829</td>\n",
       "      <td>0.697930</td>\n",
       "      <td>0.025236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>-0.012019</td>\n",
       "      <td>-0.061645</td>\n",
       "      <td>-0.003623</td>\n",
       "      <td>0.012782</td>\n",
       "      <td>0.002991</td>\n",
       "      <td>0.498534</td>\n",
       "      <td>-0.214540</td>\n",
       "      <td>0.026263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>0.054613</td>\n",
       "      <td>1.413585</td>\n",
       "      <td>-0.951499</td>\n",
       "      <td>0.069045</td>\n",
       "      <td>-0.006349</td>\n",
       "      <td>2.142074</td>\n",
       "      <td>0.681644</td>\n",
       "      <td>0.040587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>0.043515</td>\n",
       "      <td>0.570968</td>\n",
       "      <td>0.294196</td>\n",
       "      <td>0.032762</td>\n",
       "      <td>0.040890</td>\n",
       "      <td>1.647747</td>\n",
       "      <td>-0.806717</td>\n",
       "      <td>0.055274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>709</th>\n",
       "      <td>0.030576</td>\n",
       "      <td>-0.110282</td>\n",
       "      <td>0.814194</td>\n",
       "      <td>0.017094</td>\n",
       "      <td>0.040937</td>\n",
       "      <td>0.098121</td>\n",
       "      <td>-0.411897</td>\n",
       "      <td>0.019021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>708</th>\n",
       "      <td>0.031451</td>\n",
       "      <td>-0.007801</td>\n",
       "      <td>-0.919598</td>\n",
       "      <td>0.017758</td>\n",
       "      <td>0.054014</td>\n",
       "      <td>0.896944</td>\n",
       "      <td>-0.938235</td>\n",
       "      <td>0.025266</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     bt_Close  bt_Volume  bt_close_off_high  bt_volatility  eth_Close  \\\n",
       "717  0.000000   0.000000          -0.560641       0.020292   0.000000   \n",
       "716 -0.002049  -0.170410           0.250597       0.009641  -0.011498   \n",
       "715 -0.009946   0.092475          -0.173865       0.020827   0.025190   \n",
       "714 -0.002855   0.060603          -0.474265       0.012649   0.006810   \n",
       "713 -0.005457  -0.048411          -0.013333       0.010391   0.002270   \n",
       "712 -0.012019  -0.061645          -0.003623       0.012782   0.002991   \n",
       "711  0.054613   1.413585          -0.951499       0.069045  -0.006349   \n",
       "710  0.043515   0.570968           0.294196       0.032762   0.040890   \n",
       "709  0.030576  -0.110282           0.814194       0.017094   0.040937   \n",
       "708  0.031451  -0.007801          -0.919598       0.017758   0.054014   \n",
       "\n",
       "     eth_Volume  eth_close_off_high  eth_volatility  \n",
       "717    0.000000           -0.418477        0.025040  \n",
       "716    0.239937            0.965898        0.034913  \n",
       "715    0.978201           -0.317885        0.060792  \n",
       "714    0.680295           -0.057657        0.047943  \n",
       "713    0.066829            0.697930        0.025236  \n",
       "712    0.498534           -0.214540        0.026263  \n",
       "711    2.142074            0.681644        0.040587  \n",
       "710    1.647747           -0.806717        0.055274  \n",
       "709    0.098121           -0.411897        0.019021  \n",
       "708    0.896944           -0.938235        0.025266  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LSTM_training_inputs[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table represents an example of our LSTM model input (we'll actually have hundreds of similar tables). We've normalised some columns so that their values are equal to 0 in the first time point, so we're aiming to predict changes in price relative to this timepoint. We're now ready to build the LSTM model. This is actually quite straightforward with Keras, you simply stack componenets on top of each other (better explained [here](https://dashee87.github.io/data%20science/deep%20learning/python/another-keras-tutorial-for-neural-network-beginners/))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# I find it easier to work with numpy arrays rather than pandas dataframes\n",
    "# especially as we now only have numerical data\n",
    "LSTM_training_inputs = [np.array(LSTM_training_input) for LSTM_training_input in LSTM_training_inputs]\n",
    "LSTM_training_inputs = np.array(LSTM_training_inputs)\n",
    "\n",
    "LSTM_test_inputs = [np.array(LSTM_test_inputs) for LSTM_test_inputs in LSTM_test_inputs]\n",
    "LSTM_test_inputs = np.array(LSTM_test_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# import the relevant Keras modules\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation, Dense\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout\n",
    "\n",
    "def build_model(inputs, output_size, neurons, activ_func=\"linear\",\n",
    "                dropout=0.25, loss=\"mae\", optimizer=\"adam\"):\n",
    "    model = Sequential()\n",
    "\n",
    "    model.add(LSTM(neurons, input_shape=(inputs.shape[1], inputs.shape[2])))\n",
    "    model.add(Dropout(dropout))\n",
    "    model.add(Dense(units=output_size))\n",
    "    model.add(Activation(activ_func))\n",
    "\n",
    "    model.compile(loss=loss, optimizer=optimizer)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, the `build_model` functions constructs an empty model unimaginatively called model (`model = Sequential`), to which an LSTM layer is added. That layer has been shaped to fit our inputs (n x m tables, where n and m represent the number of timepoints/rows and columns, respectively). The function also includes more generic neural network features, like [dropout](https://machinelearningmastery.com/dropout-regularization-deep-learning-models-keras/) and [activation functions](https://dashee87.github.io/data%20science/deep%20learning/visualising-activation-functions-in-neural-networks/). Now, we just need to specify the number of neurons to place in the LSTM layer (I've opted for 20 to keep runtime reasonable), as well as the data on which the model will be trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "12s - loss: 0.1647\n",
      "Epoch 2/50\n",
      "12s - loss: 0.1116\n",
      "Epoch 3/50\n",
      "12s - loss: 0.1065\n",
      "Epoch 4/50\n",
      "11s - loss: 0.0953\n",
      "Epoch 5/50\n",
      "11s - loss: 0.0865\n",
      "Epoch 6/50\n",
      "11s - loss: 0.0834\n",
      "Epoch 7/50\n",
      "11s - loss: 0.0840\n",
      "Epoch 8/50\n",
      "11s - loss: 0.0814\n",
      "Epoch 9/50\n",
      "11s - loss: 0.0784\n",
      "Epoch 10/50\n",
      "11s - loss: 0.0776\n",
      "Epoch 11/50\n",
      "12s - loss: 0.0757\n",
      "Epoch 12/50\n",
      "12s - loss: 0.0744\n",
      "Epoch 13/50\n",
      "12s - loss: 0.0744\n",
      "Epoch 14/50\n",
      "11s - loss: 0.0708\n",
      "Epoch 15/50\n",
      "12s - loss: 0.0703\n",
      "Epoch 16/50\n",
      "12s - loss: 0.0676\n",
      "Epoch 17/50\n",
      "12s - loss: 0.0755\n",
      "Epoch 18/50\n",
      "11s - loss: 0.0687\n",
      "Epoch 19/50\n",
      "12s - loss: 0.0707\n",
      "Epoch 20/50\n",
      "12s - loss: 0.0694\n",
      "Epoch 21/50\n",
      "12s - loss: 0.0686\n",
      "Epoch 22/50\n",
      "12s - loss: 0.0708\n",
      "Epoch 23/50\n",
      "11s - loss: 0.0677\n",
      "Epoch 24/50\n",
      "11s - loss: 0.0672\n",
      "Epoch 25/50\n",
      "12s - loss: 0.0679\n",
      "Epoch 26/50\n",
      "12s - loss: 0.0652\n",
      "Epoch 27/50\n",
      "12s - loss: 0.0653\n",
      "Epoch 28/50\n",
      "11s - loss: 0.0647\n",
      "Epoch 29/50\n",
      "11s - loss: 0.0684\n",
      "Epoch 30/50\n",
      "12s - loss: 0.0676\n",
      "Epoch 31/50\n",
      "12s - loss: 0.0682\n",
      "Epoch 32/50\n",
      "12s - loss: 0.0688\n",
      "Epoch 33/50\n",
      "12s - loss: 0.0681\n",
      "Epoch 34/50\n",
      "12s - loss: 0.0676\n",
      "Epoch 35/50\n",
      "12s - loss: 0.0653\n",
      "Epoch 36/50\n",
      "12s - loss: 0.0661\n",
      "Epoch 37/50\n",
      "12s - loss: 0.0645\n",
      "Epoch 38/50\n",
      "12s - loss: 0.0665\n",
      "Epoch 39/50\n",
      "11s - loss: 0.0671\n",
      "Epoch 40/50\n",
      "11s - loss: 0.0644\n",
      "Epoch 41/50\n",
      "11s - loss: 0.0668\n",
      "Epoch 42/50\n",
      "12s - loss: 0.0659\n",
      "Epoch 43/50\n",
      "12s - loss: 0.0647\n",
      "Epoch 44/50\n",
      "12s - loss: 0.0657\n",
      "Epoch 45/50\n",
      "12s - loss: 0.0648\n",
      "Epoch 46/50\n",
      "11s - loss: 0.0657\n",
      "Epoch 47/50\n",
      "11s - loss: 0.0679\n",
      "Epoch 48/50\n",
      "11s - loss: 0.0632\n",
      "Epoch 49/50\n",
      "12s - loss: 0.0673\n",
      "Epoch 50/50\n",
      "12s - loss: 0.0615\n"
     ]
    }
   ],
   "source": [
    "# random seed for reproducibility\n",
    "np.random.seed(202)\n",
    "# initialise model architecture\n",
    "eth_model = build_model(LSTM_training_inputs, output_size=1, neurons = 20)\n",
    "# model output is next price normalised to 10th previous closing price\n",
    "LSTM_training_outputs = (training_set['eth_Close'][window_len:].values/training_set['eth_Close'][:-window_len].values)-1\n",
    "# train model on data\n",
    "# note: eth_history contains information on the training error per epoch\n",
    "eth_history = eth_model.fit(LSTM_training_inputs, LSTM_training_outputs, \n",
    "                            epochs=50, batch_size=1, verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If everything went to plan, then we'd expect the training error to have gradually decreased over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gH+7+29qulawDAGNVUlbBSzNX897CDXy0aBObi0sBGNg5h+MG5DN+bG96tG+V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\npF7+P0alfjggTVNdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c24622f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1)\n",
    "\n",
    "ax1.plot(eth_history.epoch, eth_history.history['loss'])\n",
    "ax1.set_title('Training Error')\n",
    "\n",
    "if eth_model.loss == 'mae':\n",
    "    ax1.set_ylabel('Mean Absolute Error (MAE)',fontsize=12)\n",
    "# just in case you decided to change the model loss calculation\n",
    "else:\n",
    "    ax1.set_ylabel('Model Loss',fontsize=12)\n",
    "ax1.set_xlabel('# Epochs',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We've just built an LSTM model to predict tomorrow's Ethereum closing price. Let's see how well it performs. We start by examining its performance on the training set (data before June 2017). That number below the code represents the model's mean absolute error (mae) on the training set after the 50th training iteration (or epoch). Instead of relative changes, we can view the model output as daily closing prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jvRKWBUDnzp0JCgpi06ZN1ShZA6Agqkw5sDuceOKoMTdUjUdDiUgM0APYBDRV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\na9b/B4g+yAAT3AtsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c246b8390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
    "\n",
    "fig, ax1 = plt.subplots(1,1)\n",
    "ax1.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,5,9]])\n",
    "ax1.set_xticklabels([datetime.date(i,j,1).strftime('%b %Y')  for i in range(2013,2019) for j in [1,5,9]])\n",
    "ax1.plot(model_data[model_data['Date']< split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         training_set['eth_Close'][window_len:], label='Actual')\n",
    "ax1.plot(model_data[model_data['Date']< split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         ((np.transpose(eth_model.predict(LSTM_training_inputs))+1) * training_set['eth_Close'].values[:-window_len])[0], \n",
    "         label='Predicted')\n",
    "ax1.set_title('Training Set: Single Timepoint Prediction')\n",
    "ax1.set_ylabel('Ethereum Price ($)',fontsize=12)\n",
    "ax1.legend(bbox_to_anchor=(0.15, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "ax1.annotate('MAE: %.4f'%np.mean(np.abs((np.transpose(eth_model.predict(LSTM_training_inputs))+1)-\\\n",
    "            (training_set['eth_Close'].values[window_len:])/(training_set['eth_Close'].values[:-window_len]))), \n",
    "             xy=(0.75, 0.9),  xycoords='axes fraction',\n",
    "            xytext=(0.75, 0.9), textcoords='axes fraction')\n",
    "# figure inset code taken from http://akuederle.com/matplotlib-zoomed-up-inset\n",
    "axins = zoomed_inset_axes(ax1, 3.35, loc=10) # zoom-factor: 3.35, location: centre\n",
    "axins.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,5,9]])\n",
    "axins.plot(model_data[model_data['Date']< split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         training_set['eth_Close'][window_len:], label='Actual')\n",
    "axins.plot(model_data[model_data['Date']< split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         ((np.transpose(eth_model.predict(LSTM_training_inputs))+1) * training_set['eth_Close'].values[:-window_len])[0], \n",
    "         label='Predicted')\n",
    "axins.set_xlim([datetime.date(2017, 3, 1), datetime.date(2017, 5, 1)])\n",
    "axins.set_ylim([10,60])\n",
    "axins.set_xticklabels('')\n",
    "mark_inset(ax1, axins, loc1=1, loc2=3, fc=\"none\", ec=\"0.5\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We shouldn't be too surprised by its apparent accuracy here. The model could access the source of its error and adjust itself accordingly. In fact, it's not hard to attain almost zero training errors. We could just cram in hundreds of neurons and train for thousands of epochs (a process known as overfitting, where you're essentially predicting noise- I included the `Dropout()` call in the `build_model` function to mitigate this risk for our relatively small model). We should be more interested in its performance on the test dataset, as this represents completely new data for the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1rgLubCTR6h3nufyapoteyKdxN6WmssAngJ4dAvjHuJ70jm6Fl1fj/7/oFdz1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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1123cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1)\n",
    "ax1.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax1.set_xticklabels([datetime.date(2017,i+1,1).strftime('%b %d %Y')  for i in range(12)])\n",
    "ax1.plot(model_data[model_data['Date']>= split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         test_set['eth_Close'][window_len:], label='Actual')\n",
    "ax1.plot(model_data[model_data['Date']>= split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         ((np.transpose(eth_model.predict(LSTM_test_inputs))+1) * test_set['eth_Close'].values[:-window_len])[0], \n",
    "         label='Predicted')\n",
    "ax1.annotate('MAE: %.4f'%np.mean(np.abs((np.transpose(eth_model.predict(LSTM_test_inputs))+1)-\\\n",
    "            (test_set['eth_Close'].values[window_len:])/(test_set['eth_Close'].values[:-window_len]))), \n",
    "             xy=(0.75, 0.9),  xycoords='axes fraction',\n",
    "            xytext=(0.75, 0.9), textcoords='axes fraction')\n",
    "ax1.set_title('Test Set: Single Timepoint Prediction',fontsize=13)\n",
    "ax1.set_ylabel('Ethereum Price ($)',fontsize=12)\n",
    "ax1.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Caveats aside about the misleading nature of single point predictions, our LSTM model *seems* to have performed well on the unseen test set. The most obvious flaw is that it fails to detect the inevitable downturn when the eth price suddenly shoots up (e.g mid-June and October). In fact, this is a persistent failure; it's just more apparent at these spikes. The predicted price regularly seems equivalent to the actual price just shifted one day later (e.g. the drop in mid-July). Furthermore, the model seems to be systemically overestimating the future value of Ether (join the club, right?), as the predicted line near always runs higher than the actual line. I suspect this is because the training data represents a period during which the price of Ether rose astronomically, so it expects that trend to continue (don't we all). We can also build a similar LSTM model for Bitcoin- test set predictions are plotted below (see [Jupyter notebook for full code](https://github.com/dashee87/blogScripts/blob/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "13s - loss: 0.1006\n",
      "Epoch 2/50\n",
      "12s - loss: 0.0630\n",
      "Epoch 3/50\n",
      "12s - loss: 0.0450\n",
      "Epoch 4/50\n",
      "12s - loss: 0.0427\n",
      "Epoch 5/50\n",
      "12s - loss: 0.0374\n",
      "Epoch 6/50\n",
      "12s - loss: 0.0333\n",
      "Epoch 7/50\n",
      "12s - loss: 0.0299\n",
      "Epoch 8/50\n",
      "12s - loss: 0.0318\n",
      "Epoch 9/50\n",
      "12s - loss: 0.0310\n",
      "Epoch 10/50\n",
      "13s - loss: 0.0298\n",
      "Epoch 11/50\n",
      "12s - loss: 0.0264\n",
      "Epoch 12/50\n",
      "13s - loss: 0.0291\n",
      "Epoch 13/50\n",
      "13s - loss: 0.0275\n",
      "Epoch 14/50\n",
      "13s - loss: 0.0277\n",
      "Epoch 15/50\n",
      "12s - loss: 0.0280\n",
      "Epoch 16/50\n",
      "13s - loss: 0.0265\n",
      "Epoch 17/50\n",
      "12s - loss: 0.0258\n",
      "Epoch 18/50\n",
      "12s - loss: 0.0262\n",
      "Epoch 19/50\n",
      "12s - loss: 0.0260\n",
      "Epoch 20/50\n",
      "12s - loss: 0.0256\n",
      "Epoch 21/50\n",
      "12s - loss: 0.0249\n",
      "Epoch 22/50\n",
      "12s - loss: 0.0246\n",
      "Epoch 23/50\n",
      "12s - loss: 0.0248\n",
      "Epoch 24/50\n",
      "12s - loss: 0.0256\n",
      "Epoch 25/50\n",
      "12s - loss: 0.0238\n",
      "Epoch 26/50\n",
      "12s - loss: 0.0253\n",
      "Epoch 27/50\n",
      "12s - loss: 0.0251\n",
      "Epoch 28/50\n",
      "12s - loss: 0.0241\n",
      "Epoch 29/50\n",
      "12s - loss: 0.0244\n",
      "Epoch 30/50\n",
      "12s - loss: 0.0246\n",
      "Epoch 31/50\n",
      "12s - loss: 0.0242\n",
      "Epoch 32/50\n",
      "12s - loss: 0.0229\n",
      "Epoch 33/50\n",
      "12s - loss: 0.0239\n",
      "Epoch 34/50\n",
      "12s - loss: 0.0232\n",
      "Epoch 35/50\n",
      "12s - loss: 0.0238\n",
      "Epoch 36/50\n",
      "12s - loss: 0.0243\n",
      "Epoch 37/50\n",
      "12s - loss: 0.0233\n",
      "Epoch 38/50\n",
      "12s - loss: 0.0231\n",
      "Epoch 39/50\n",
      "11s - loss: 0.0246\n",
      "Epoch 40/50\n",
      "11s - loss: 0.0245\n",
      "Epoch 41/50\n",
      "11s - loss: 0.0230\n",
      "Epoch 42/50\n",
      "12s - loss: 0.0231\n",
      "Epoch 43/50\n",
      "11s - loss: 0.0223\n",
      "Epoch 44/50\n",
      "12s - loss: 0.0222\n",
      "Epoch 45/50\n",
      "12s - loss: 0.0237\n",
      "Epoch 46/50\n",
      "12s - loss: 0.0236\n",
      "Epoch 47/50\n",
      "11s - loss: 0.0219\n",
      "Epoch 48/50\n",
      "12s - loss: 0.0234\n",
      "Epoch 49/50\n",
      "12s - loss: 0.0232\n",
      "Epoch 50/50\n",
      "12s - loss: 0.0230\n"
     ]
    }
   ],
   "source": [
    "# random seed for reproducibility\n",
    "np.random.seed(202)\n",
    "# initialise model architecture\n",
    "bt_model = build_model(LSTM_training_inputs, output_size=1, neurons = 20)\n",
    "# train model on data\n",
    "# note: eth_history contains information on the training error per epoch\n",
    "bt_history = bt_model.fit(LSTM_training_inputs, \n",
    "                            (training_set['bt_Close'][window_len:].values/training_set['bt_Close'][:-window_len].values)-1, \n",
    "                            epochs=50, batch_size=1, verbose=2, shuffle=True)\n",
    "# #eth_model.save('eth_model%d.h5'%j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AM8yfFKXUMaWUFVhj5u1Sx/IqGO9tBrtWun+D/YzZvFbvcCht3P2bWlDBOLcc8B4IPgPr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\nqpRSSgWIJlWllFIqQDSpKqWUUgHy/wFFPZBxfxGv/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c256b1350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset\n",
    "\n",
    "fig, ax1 = plt.subplots(1,1)\n",
    "ax1.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,5,9]])\n",
    "ax1.set_xticklabels([datetime.date(i,j,1).strftime('%b %Y')  for i in range(2013,2019) for j in [1,5,9]])\n",
    "ax1.plot(model_data[model_data['Date']< split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         training_set['bt_Close'][window_len:], label='Actual')\n",
    "ax1.plot(model_data[model_data['Date']< split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         ((np.transpose(bt_model.predict(LSTM_training_inputs))+1) * training_set['bt_Close'].values[:-window_len])[0], \n",
    "         label='Predicted')\n",
    "ax1.set_title('Training Set: Single Timepoint Prediction')\n",
    "ax1.set_ylabel('Bitcoin Price ($)',fontsize=12)\n",
    "ax1.annotate('MAE: %.4f'%np.mean(np.abs((np.transpose(bt_model.predict(LSTM_training_inputs))+1)-\\\n",
    "            (training_set['bt_Close'].values[window_len:])/(training_set['bt_Close'].values[:-window_len]))), \n",
    "             xy=(0.75, 0.9),  xycoords='axes fraction',\n",
    "            xytext=(0.75, 0.9), textcoords='axes fraction')\n",
    "ax1.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "# figure inset code taken from http://akuederle.com/matplotlib-zoomed-up-inset\n",
    "axins = zoomed_inset_axes(ax1, 2.52, loc=10, bbox_to_anchor=(400, 307)) # zoom-factor: 2.52, location: centre\n",
    "axins.set_xticks([datetime.date(i,j,1) for i in range(2013,2019) for j in [1,5,9]])\n",
    "axins.plot(model_data[model_data['Date']< split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         training_set['bt_Close'][window_len:], label='Actual')\n",
    "axins.plot(model_data[model_data['Date']< split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         ((np.transpose(bt_model.predict(LSTM_training_inputs))+1) * training_set['bt_Close'].values[:-window_len])[0], \n",
    "         label='Predicted')\n",
    "axins.set_xlim([datetime.date(2017, 2, 15), datetime.date(2017, 5, 1)])\n",
    "axins.set_ylim([920, 1400])\n",
    "axins.set_xticklabels('')\n",
    "mark_inset(ax1, axins, loc1=1, loc2=3, fc=\"none\", ec=\"0.5\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UOhdffDFfffUVixYt4swzz+TMM8/k2WefLdbZYvLkybRv354hQ4YwYsQIbrjh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\nGKoEY2AMBoPBUCX8f3ylOgjyna0DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c24b28150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1,1)\n",
    "ax1.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax1.set_xticklabels([datetime.date(2017,i+1,1).strftime('%b %d %Y')  for i in range(12)])\n",
    "ax1.plot(model_data[model_data['Date']>= split_date]['Date'][10:].astype(datetime.datetime),\n",
    "         test_set['bt_Close'][window_len:], label='Actual')\n",
    "ax1.plot(model_data[model_data['Date']>= split_date]['Date'][10:].astype(datetime.datetime),\n",
    "         ((np.transpose(bt_model.predict(LSTM_test_inputs))+1) * test_set['bt_Close'].values[:-window_len])[0], \n",
    "         label='Predicted')\n",
    "ax1.annotate('MAE: %.4f'%np.mean(np.abs((np.transpose(bt_model.predict(LSTM_test_inputs))+1)-\\\n",
    "            (test_set['bt_Close'].values[window_len:])/(test_set['bt_Close'].values[:-window_len]))), \n",
    "             xy=(0.75, 0.9),  xycoords='axes fraction',\n",
    "            xytext=(0.75, 0.9), textcoords='axes fraction')\n",
    "ax1.set_title('Test Set: Single Timepoint Prediction',fontsize=13)\n",
    "ax1.set_ylabel('Bitcoin Price ($)',fontsize=12)\n",
    "ax1.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0., prop={'size': 14})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As I've stated earlier, single point predictions can be deceptive. Looking more closely, you'll notice that, again, the predicted values regularly mirror the previous values (e.g. October). Our fancy deep learning LSTM model has partially reproducted a [autregressive (AR) model](https://dashee87.github.io/data%20science/general/A-Road-Incident-Model-Analysis/) of some order `p`, where future values are simply the weighted sum of the previous `p` values. We can define an AR model in these mathematical terms:  \n",
    "\n",
    "\\begin{align}\n",
    "PredPrice_{t} & = \\phi_0 + \\phi_1*Price_{t-1} + \\ldots + \\phi_p*Price_{t-p} + \\epsilon_t, \\ \\epsilon_t \\sim N(0, \\sigma)\\\n",
    "\\end{align}\n",
    "\n",
    "The good news is that AR models are commonly employed in time series tasks (e.g. [stock market prices](https://upcommons.upc.edu/bitstream/handle/2099/3572/04marcek.pdf)), so the LSTM model appears to have landed on a sensible solution. The bad news is that it's a waste of the LSTM capabilities, we could have a built a much simpler AR model in much less time and probably achieved similar results (though the title of this post would have been much less clickbaity). [More complex does not automatically equal more accurate](https://pdfs.semanticscholar.org/696c/2fa5697f58914921ff37d69ced44ddea143f.pdf)). \n",
    "\n",
    "We'll now build LSTM models to predict crypto prices for the next 5 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "12s - loss: 0.2213\n",
      "Epoch 2/50\n",
      "12s - loss: 0.1575\n",
      "Epoch 3/50\n",
      "12s - loss: 0.1441\n",
      "Epoch 4/50\n",
      "12s - loss: 0.1380\n",
      "Epoch 5/50\n",
      "12s - loss: 0.1286\n",
      "Epoch 6/50\n",
      "12s - loss: 0.1254\n",
      "Epoch 7/50\n",
      "12s - loss: 0.1246\n",
      "Epoch 8/50\n",
      "12s - loss: 0.1265\n",
      "Epoch 9/50\n",
      "12s - loss: 0.1202\n",
      "Epoch 10/50\n",
      "12s - loss: 0.1201\n",
      "Epoch 11/50\n",
      "12s - loss: 0.1171\n",
      "Epoch 12/50\n",
      "12s - loss: 0.1203\n",
      "Epoch 13/50\n",
      "12s - loss: 0.1166\n",
      "Epoch 14/50\n",
      "12s - loss: 0.1175\n",
      "Epoch 15/50\n",
      "12s - loss: 0.1174\n",
      "Epoch 16/50\n",
      "12s - loss: 0.1192\n",
      "Epoch 17/50\n",
      "12s - loss: 0.1148\n",
      "Epoch 18/50\n",
      "12s - loss: 0.1169\n",
      "Epoch 19/50\n",
      "12s - loss: 0.1160\n",
      "Epoch 20/50\n",
      "12s - loss: 0.1151\n",
      "Epoch 21/50\n",
      "11s - loss: 0.1133\n",
      "Epoch 22/50\n",
      "12s - loss: 0.1113\n",
      "Epoch 23/50\n",
      "11s - loss: 0.1124\n",
      "Epoch 24/50\n",
      "11s - loss: 0.1101\n",
      "Epoch 25/50\n",
      "12s - loss: 0.1146\n",
      "Epoch 26/50\n",
      "12s - loss: 0.1107\n",
      "Epoch 27/50\n",
      "12s - loss: 0.1125\n",
      "Epoch 28/50\n",
      "12s - loss: 0.1108\n",
      "Epoch 29/50\n",
      "12s - loss: 0.1123\n",
      "Epoch 30/50\n",
      "12s - loss: 0.1105\n",
      "Epoch 31/50\n",
      "12s - loss: 0.1109\n",
      "Epoch 32/50\n",
      "11s - loss: 0.1097\n",
      "Epoch 33/50\n",
      "11s - loss: 0.1091\n",
      "Epoch 34/50\n",
      "1090s - loss: 0.1081\n",
      "Epoch 35/50\n",
      "12s - loss: 0.1096\n",
      "Epoch 36/50\n",
      "11s - loss: 0.1088\n",
      "Epoch 37/50\n",
      "11s - loss: 0.1078\n",
      "Epoch 38/50\n",
      "11s - loss: 0.1066\n",
      "Epoch 39/50\n",
      "11s - loss: 0.1099\n",
      "Epoch 40/50\n",
      "11s - loss: 0.1061\n",
      "Epoch 41/50\n",
      "11s - loss: 0.1081\n",
      "Epoch 42/50\n",
      "11s - loss: 0.1065\n",
      "Epoch 43/50\n",
      "11s - loss: 0.1050\n",
      "Epoch 44/50\n",
      "11s - loss: 0.1054\n",
      "Epoch 45/50\n",
      "11s - loss: 0.1056\n",
      "Epoch 46/50\n",
      "11s - loss: 0.1042\n",
      "Epoch 47/50\n",
      "11s - loss: 0.1022\n",
      "Epoch 48/50\n",
      "11s - loss: 0.1026\n",
      "Epoch 49/50\n",
      "11s - loss: 0.0998\n",
      "Epoch 50/50\n",
      "11s - loss: 0.1012\n"
     ]
    }
   ],
   "source": [
    "# random seed for reproducibility\n",
    "np.random.seed(202)\n",
    "# we'll try to predict the closing price for the next 5 days \n",
    "# change this value if you want to make longer/shorter prediction\n",
    "pred_range = 5\n",
    "# initialise model architecture\n",
    "eth_model = build_model(LSTM_training_inputs, output_size=pred_range, neurons = 20)\n",
    "# model output is next 5 prices normalised to 10th previous closing price\n",
    "LSTM_training_outputs = []\n",
    "for i in range(window_len, len(training_set['eth_Close'])-pred_range):\n",
    "    LSTM_training_outputs.append((training_set['eth_Close'][i:i+pred_range].values/\n",
    "                                  training_set['eth_Close'].values[i-window_len])-1)\n",
    "LSTM_training_outputs = np.array(LSTM_training_outputs)\n",
    "# train model on data\n",
    "# note: eth_history contains information on the training error per epoch\n",
    "eth_history = eth_model.fit(LSTM_training_inputs[:-pred_range], LSTM_training_outputs, \n",
    "                            epochs=50, batch_size=1, verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "11s - loss: 0.1042\n",
      "Epoch 2/50\n",
      "11s - loss: 0.0647\n",
      "Epoch 3/50\n",
      "11s - loss: 0.0527\n",
      "Epoch 4/50\n",
      "11s - loss: 0.0482\n",
      "Epoch 5/50\n",
      "11s - loss: 0.0446\n",
      "Epoch 6/50\n",
      "11s - loss: 0.0435\n",
      "Epoch 7/50\n",
      "11s - loss: 0.0419\n",
      "Epoch 8/50\n",
      "11s - loss: 0.0407\n",
      "Epoch 9/50\n",
      "11s - loss: 0.0411\n",
      "Epoch 10/50\n",
      "11s - loss: 0.0413\n",
      "Epoch 11/50\n",
      "11s - loss: 0.0396\n",
      "Epoch 12/50\n",
      "11s - loss: 0.0394\n",
      "Epoch 13/50\n",
      "11s - loss: 0.0395\n",
      "Epoch 14/50\n",
      "11s - loss: 0.0391\n",
      "Epoch 15/50\n",
      "11s - loss: 0.0384\n",
      "Epoch 16/50\n",
      "11s - loss: 0.0395\n",
      "Epoch 17/50\n",
      "11s - loss: 0.0384\n",
      "Epoch 18/50\n",
      "11s - loss: 0.0381\n",
      "Epoch 19/50\n",
      "11s - loss: 0.0386\n",
      "Epoch 20/50\n",
      "11s - loss: 0.0383\n",
      "Epoch 21/50\n",
      "11s - loss: 0.0384\n",
      "Epoch 22/50\n",
      "11s - loss: 0.0379\n",
      "Epoch 23/50\n",
      "11s - loss: 0.0375\n",
      "Epoch 24/50\n",
      "11s - loss: 0.0368\n",
      "Epoch 25/50\n",
      "11s - loss: 0.0373\n",
      "Epoch 26/50\n",
      "11s - loss: 0.0377\n",
      "Epoch 27/50\n",
      "11s - loss: 0.0372\n",
      "Epoch 28/50\n",
      "11s - loss: 0.0366\n",
      "Epoch 29/50\n",
      "11s - loss: 0.0377\n",
      "Epoch 30/50\n",
      "11s - loss: 0.0358\n",
      "Epoch 31/50\n",
      "11s - loss: 0.0372\n",
      "Epoch 32/50\n",
      "11s - loss: 0.0357\n",
      "Epoch 33/50\n",
      "11s - loss: 0.0359\n",
      "Epoch 34/50\n",
      "11s - loss: 0.0362\n",
      "Epoch 35/50\n",
      "11s - loss: 0.0357\n",
      "Epoch 36/50\n",
      "11s - loss: 0.0360\n",
      "Epoch 37/50\n",
      "11s - loss: 0.0358\n",
      "Epoch 38/50\n",
      "11s - loss: 0.0345\n",
      "Epoch 39/50\n",
      "11s - loss: 0.0358\n",
      "Epoch 40/50\n",
      "11s - loss: 0.0355\n",
      "Epoch 41/50\n",
      "11s - loss: 0.0367\n",
      "Epoch 42/50\n",
      "11s - loss: 0.0351\n",
      "Epoch 43/50\n",
      "11s - loss: 0.0353\n",
      "Epoch 44/50\n",
      "11s - loss: 0.0349\n",
      "Epoch 45/50\n",
      "11s - loss: 0.0348\n",
      "Epoch 46/50\n",
      "11s - loss: 0.0346\n",
      "Epoch 47/50\n",
      "11s - loss: 0.0347\n",
      "Epoch 48/50\n",
      "11s - loss: 0.0347\n",
      "Epoch 49/50\n",
      "11s - loss: 0.0339\n",
      "Epoch 50/50\n",
      "11s - loss: 0.0343\n"
     ]
    }
   ],
   "source": [
    "# random seed for reproducibility\n",
    "np.random.seed(202)\n",
    "# we'll try to predict the closing price for the next 5 days \n",
    "# change this value if you want to make longer/shorter prediction\n",
    "pred_range = 5\n",
    "# initialise model architecture\n",
    "bt_model = build_model(LSTM_training_inputs, output_size=pred_range, neurons = 20)\n",
    "# model output is next 5 prices normalised to 10th previous closing price\n",
    "LSTM_training_outputs = []\n",
    "for i in range(window_len, len(training_set['bt_Close'])-pred_range):\n",
    "    LSTM_training_outputs.append((training_set['bt_Close'][i:i+pred_range].values/\n",
    "                                  training_set['bt_Close'].values[i-window_len])-1)\n",
    "LSTM_training_outputs = np.array(LSTM_training_outputs)\n",
    "# train model on data\n",
    "# note: eth_history contains information on the training error per epoch\n",
    "bt_history = bt_model.fit(LSTM_training_inputs[:-pred_range], LSTM_training_outputs, \n",
    "                            epochs=50, batch_size=1, verbose=2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X1wOvkZGRlJWV1bzfv39/zXJlZSUXXXQR8+bNY/r06bjdbmbMmIH5r2gYdavy+rnj7Q38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\n0fRI/h9OVLLfi4nb6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c24638bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# little bit of reformatting the predictions to closing prices\n",
    "eth_pred_prices = ((eth_model.predict(LSTM_test_inputs)[:-pred_range][::pred_range]+1)*\\\n",
    "                   test_set['eth_Close'].values[:-(window_len + pred_range)][::5].reshape(int(np.ceil((len(LSTM_test_inputs)-pred_range)/float(pred_range))),1))\n",
    "bt_pred_prices = ((bt_model.predict(LSTM_test_inputs)[:-pred_range][::pred_range]+1)*\\\n",
    "                   test_set['bt_Close'].values[:-(window_len + pred_range)][::5].reshape(int(np.ceil((len(LSTM_test_inputs)-pred_range)/float(pred_range))),1))\n",
    "\n",
    "pred_colors = [\"#FF69B4\", \"#5D6D7E\", \"#F4D03F\",\"#A569BD\",\"#45B39D\"]\n",
    "fig, (ax1, ax2) = plt.subplots(2,1)\n",
    "ax1.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax2.set_xticks([datetime.date(2017,i+1,1) for i in range(12)])\n",
    "ax2.set_xticklabels([datetime.date(2017,i+1,1).strftime('%b %d %Y')  for i in range(12)])\n",
    "ax1.plot(model_data[model_data['Date']>= split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         test_set['bt_Close'][window_len:], label='Actual')\n",
    "ax2.plot(model_data[model_data['Date']>= split_date]['Date'][window_len:].astype(datetime.datetime),\n",
    "         test_set['eth_Close'][window_len:], label='Actual')\n",
    "for i, (eth_pred, bt_pred) in enumerate(zip(eth_pred_prices, bt_pred_prices)):\n",
    "    # Only adding lines to the legend once\n",
    "    if i<5:\n",
    "        ax1.plot(model_data[model_data['Date']>= split_date]['Date'][window_len:].astype(datetime.datetime)[i*pred_range:i*pred_range+pred_range],\n",
    "                 bt_pred, color=pred_colors[i%5], label=\"Predicted\")\n",
    "    else: \n",
    "        ax1.plot(model_data[model_data['Date']>= split_date]['Date'][window_len:].astype(datetime.datetime)[i*pred_range:i*pred_range+pred_range],\n",
    "                 bt_pred, color=pred_colors[i%5])\n",
    "    ax2.plot(model_data[model_data['Date']>= split_date]['Date'][window_len:].astype(datetime.datetime)[i*pred_range:i*pred_range+pred_range],\n",
    "             eth_pred, color=pred_colors[i%5])\n",
    "ax1.set_title('Test Set: 5 Timepoint Predictions',fontsize=13)\n",
    "ax1.set_ylabel('Bitcoin Price ($)',fontsize=12)\n",
    "ax1.set_xticklabels('')\n",
    "ax2.set_ylabel('Ethereum Price ($)',fontsize=12)\n",
    "ax1.legend(bbox_to_anchor=(0.13, 1), loc=2, borderaxespad=0., prop={'size': 12})\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictions are visibly less impressive than their single point counterparts. Nevertheless, I'm pleased that the model returned somewhat nuanced behaviours (e.g. the second line on the eth graph); it didn't simply forecast prices to move uniformly in one direction. So there are some grounds for optimism.\n",
    "\n",
    "Moving back to the single point predictions, our deep machine artificial neural model looks okay, but so did that boring random walk model. Like the random walk model, LSTM models can be sensitive to the choice of random seed (the model weights are initially randomly assigned). So, if we want to compare the two models, we'll run each one multiple (say, 25) times to get an estimate for the model error. The error will be calculated as the absolute difference between the actual and predicted closing prices changes in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# warning: this cell may take some time to execute\n",
    "# this code builds 25 LSTM models for eth and bitcoin each (with 25 different initialisations)\n",
    "# the models are then saved so you hopefully only need to run this cell once\n",
    "\n",
    "# random seed for reproducibility\n",
    "# for rand_seed in range(775,800):\n",
    "#     print(rand_seed)\n",
    "#     np.random.seed(rand_seed)\n",
    "#     temp_model = build_model(LSTM_training_inputs, output_size=1, neurons = 20)\n",
    "#     temp_model.fit(LSTM_training_inputs,\n",
    "#                  (training_set['eth_Close'][window_len:].values/training_set['eth_Close'][:-window_len].values)-1,\n",
    "#                  epochs=50, batch_size=1, verbose=0, shuffle=True)\n",
    "#     temp_model.save('eth_model_randseed_%d.h5'%rand_seed)\n",
    "#     temp_model = build_model(LSTM_training_inputs, output_size=1, neurons = 20)\n",
    "#     temp_model.fit(LSTM_training_inputs,\n",
    "#                  (training_set['bt_Close'][window_len:].values/training_set['bt_Close'][:-window_len].values)-1,\n",
    "#                  epochs=50, batch_size=1, verbose=0, shuffle=True)\n",
    "#     temp_model.save('bt_model_randseed_%d.h5'%rand_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "775\n",
      "776\n",
      "777\n",
      "778\n",
      "779\n",
      "780\n",
      "781\n",
      "782\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-b00d55baea84>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     temp_model.fit(LSTM_training_inputs,\n\u001b[1;32m      6\u001b[0m                  \u001b[0;34m(\u001b[0m\u001b[0mtraining_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'eth_Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mwindow_len\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mtraining_set\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'eth_Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mwindow_len\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                   epochs=50, batch_size=1, verbose=0, shuffle=True)\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mtemp_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'eth_model_randseed_%d.h5'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mrand_seed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0mtemp_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mLSTM_training_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneurons\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/keras/models.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, **kwargs)\u001b[0m\n\u001b[1;32m    865\u001b[0m                               \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    866\u001b[0m                               \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 867\u001b[0;31m                               initial_epoch=initial_epoch)\n\u001b[0m\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m     def evaluate(self, x, y, batch_size=32, verbose=1,\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1596\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1597\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1598\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1599\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1600\u001b[0m     def evaluate(self, x, y,\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/keras/engine/training.pyc\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1181\u001b[0m                     \u001b[0mbatch_logs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'size'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1182\u001b[0m                     \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1183\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1184\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1185\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2271\u001b[0m         updated = session.run(self.outputs + [self.updates_op],\n\u001b[1;32m   2272\u001b[0m                               \u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2273\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2274\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    777\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    779\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    980\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    981\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    983\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1030\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1031\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1033\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1034\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1037\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1038\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1040\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1041\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/admin/anaconda3/envs/py27/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1019\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1020\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1023\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for rand_seed in range(775,800):\n",
    "    print(rand_seed)\n",
    "    np.random.seed(rand_seed)\n",
    "    temp_model = build_model(LSTM_training_inputs, output_size=1, neurons = 20)\n",
    "    temp_model.fit(LSTM_training_inputs,\n",
    "                 (training_set['eth_Close'][window_len:].values/training_set['eth_Close'][:-window_len].values)-1,\n",
    "                  epochs=50, batch_size=1, verbose=0, shuffle=True)\n",
    "    temp_model.save('eth_model_randseed_%d.h5'%rand_seed)\n",
    "    temp_model = build_model(LSTM_training_inputs, output_size=1, neurons = 20)\n",
    "    temp_model.fit(LSTM_training_inputs,\n",
    "                 (training_set['bt_Close'][window_len:].values/training_set['bt_Close'][:-window_len].values)-1,\n",
    "                  epochs=50, batch_size=1, verbose=0, shuffle=True)\n",
    "    temp_model.save('bt_model_randseed_%d.h5'%rand_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# assuming the previous cell was completed, this cell loads in the different initialisations\n",
    "# and calculates the average mean absolute error (mae)\n",
    "\n",
    "from keras.models import load_model\n",
    "\n",
    "eth_preds = []\n",
    "bt_preds = []\n",
    "for rand_seed in range(775,800):\n",
    "    temp_model = load_model('eth_model_randseed_%d.h5'%rand_seed)\n",
    "    eth_preds.append(np.mean(abs(np.transpose(temp_model.predict(LSTM_test_inputs))-\n",
    "                (test_set['eth_Close'].values[window_len:]/test_set['eth_Close'].values[:-window_len]-1))))\n",
    "    temp_model = load_model('bt_model_randseed_%d.h5'%rand_seed)\n",
    "    bt_preds.append(np.mean(abs(np.transpose(temp_model.predict(LSTM_test_inputs))-\n",
    "                (test_set['bt_Close'].values[window_len:]/test_set['bt_Close'].values[:-window_len]-1))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "eth_random_walk_preds = []\n",
    "bt_random_walk_preds = []\n",
    "for rand_seed in range(775,800):\n",
    "    np.random.seed(rand_seed)\n",
    "    eth_random_walk_preds.append(\n",
    "        np.mean(np.abs((np.random.normal(eth_r_walk_mean, eth_r_walk_sd, len(test_set)-window_len)+1)-\n",
    "                       np.array(test_set['eth_Close'][window_len:])/np.array(test_set['eth_Close'][:-window_len]))))\n",
    "    bt_random_walk_preds.append(\n",
    "    np.mean(np.abs((np.random.normal(bt_r_walk_mean, bt_r_walk_sd, len(test_set)-window_len)+1)-\n",
    "                       np.array(test_set['bt_Close'][window_len:])/np.array(test_set['bt_Close'][:-window_len]))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "ax1.boxplot([bt_preds, bt_random_walk_preds],widths=0.75)\n",
    "ax1.set_ylim([0, 0.2])\n",
    "ax2.boxplot([eth_preds, eth_random_walk_preds],widths=0.75)\n",
    "ax2.set_ylim([0, 0.2])\n",
    "ax1.set_xticklabels(['LSTM', 'Random Walk'])\n",
    "ax2.set_xticklabels(['LSTM', 'Random Walk'])\n",
    "ax1.set_title('Bitcoin Test Set (25 runs)')\n",
    "ax2.set_title('Ethereum Test Set (25 runs)')\n",
    "ax2.set_yticklabels('')\n",
    "ax1.set_ylabel('Mean Absolute Error (MAE)',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe AI is worth the hype after all! Those graphs show the error on the test set after 25 different initialisations of each model. The LSTM model returns an average error of about 0.04 and 0.05 on the bitcoin and eth prices, respectively, crushing the corresponding random walk models.\n",
    "\n",
    "Aiming to beat random walks is a pretty low bar. It would be more interesting to compare the LSTM model against more appropriate time series models (weighted average, autoregression, [ARIMA](https://dashee87.github.io/data%20science/general/A-Road-Incident-Model-Analysis/) or Facebook's [Prophet algorithm](https://github.com/facebook/prophet)). On the other hand, I'm sure it wouldn't be hard to improve our LSTM model (gratuitously adding more layers and/or neurons, changing the batch size, learning rate, etc.). That said, hopefully you've detected my scepticism when it comes to applying deep learning to predict changes in crypto prices. That's because we're overlooking the best framework of all: human intelligence. Clearly, the perfect model* for predicting cryptos is:\n",
    "\n",
    "\\begin{align}\n",
    "& Price^{Crypto}_{t} > Price^{Crypto}_{t-1} \\,  \\forall \\ Crypto \\neq OmiseGo \\textrm{, }  \\\\\n",
    "& \\textrm{  where } Price^{Crypto}_{t} \\to moon \\textrm{, as } t \\to    \\infty           \n",
    "\\end{align}\n",
    "\n",
    "&#42; This blog does not constitute financial advice and should not be taken as such. While cryptocurrency investments will definitely go up in value forever, they may also go down.\n",
    "\n",
    "## Summary\n",
    "\n",
    "We've collected some crypto data and fed it into a supercool deeply intelligent machine learning LSTM model. Unfortunately, its predictions were not that different from just spitting out the previous value. How can we make the model learn more sophisticated behaviours?\n",
    "\n",
    "* **Change Loss Function**: MAE doesn't really encourage risk taking. For example, under mean squared error (MSE), the LSTM model would be forced to place more importance on detecting spikes/troughs. [More bespoke trading focused loss functions](http://www.faculty.ucr.edu/~taelee/paper/lossfunctions.pdf) could also move the model towards less conservative behaviours.\n",
    "\n",
    "* **Penalise conservative AR-type models**: This would incentivise the deep learning algorithm to explore more risky/interesting models. Easier said than done!\n",
    "\n",
    "* **Get more and/or better data**: If past prices alone are sufficient to decently forecast future prices, we need to include other features that provide comparable predictive power. That way, the LSTM model wouldn't be so reliant on past prices, potentially unlocking more complex behaviours. This is probably the best and hardest solution.\n",
    "\n",
    "If that's the positive spin, then the negative reality is that it's entirely possible that there is no detectable pattern to changes in crypto prices; that no model (however deep) can separate the signal from the noise (similar to the merits of using [deep learning to predict earthquakes](https://www.scientificamerican.com/article/can-artificial-intelligence-predict-earthquakes/)). And any pattern that does appear [can disappear as quickly](http://site.iugaza.edu.ps/wdaya/files/2013/03/A-Random-Walk-Down-Wall-Street.pdf) (see [efficient market hypothesis](results_merge.click2)). Just think how different Bitcoin in 2016 is to craze-riding Bitcoin of late 2017. Any model built on 2016 data would surely struggle to replicate these unprecedented movements. All of this suggests you might as well save yourself some time and stick to autoregression (unless you're writing a blog, of course).\n",
    "\n",
    "But I'm sure they'll eventually find some use cases for deep learning. In the meantime, you can build your own LSTM model by downloading the Python code [here](https://github.com/dashee87/blogScripts/blob/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb). Thanks for reading!"
   ]
  }
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