testing_models.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "C:\\Users\\ikdem\\AppData\\Local\\Programs\\Python\\Python35\\lib\\site-packages\\empyrical\\utils.py:32: UserWarning: Unable to import pandas_datareader. Suppressing import error and continuing. All data reading functionality will raise errors; but has been deprecated and will be removed in a later version.\n",
      "  warnings.warn(msg)\n"
     ]
    }
   ],
   "source": [
    "# REPRODUCIBILATY\n",
    "from numpy.random import seed\n",
    "seed(12)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(12)\n",
    "\n",
    "# IMPORTING IMPORTANT LIBRARIES\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import concatenate as conc\n",
    "import math\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, classification_report, precision_recall_fscore_support\n",
    "from keras.optimizers import SGD, RMSprop, Adagrad, Adadelta, Adam, Nadam\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, LSTM, Bidirectional\n",
    "from keras.utils import plot_model\n",
    "from keras import backend as K\n",
    "from empyrical import max_drawdown\n",
    "import os\n",
    "import time\n",
    "from datetime import timedelta\n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def percentage_change(inp):\n",
    "    arr =  ((np.diff(inp) / inp[:-1]))\n",
    "    return arr\n",
    "\n",
    "def binary(inp):\n",
    "    l = []\n",
    "    for i in range(len(inp)):\n",
    "        if i == 0:\n",
    "            continue\n",
    "        else:\n",
    "            if (inp[i] - inp[i-1]) > 0:\n",
    "                l.append(1)\n",
    "            else:\n",
    "                l.append(0)\n",
    "    return np.array(l)\n",
    "\n",
    "def strategy_profit(yhat, yt, plot = False):\n",
    "    signal = np.array([1 if p == 1 else -1 for p in binary(yhat)]) # Creates a trading signal to buy if price rises, sell if price drops\n",
    "    signal = np.hstack((0,signal))\n",
    "    \n",
    "    df = pd.DataFrame() # Create dataframe for easier computation\n",
    "    \n",
    "    returns = yt.reshape((yt.shape[0], )) # create returns series from original data \n",
    "    returns[0] = 0\n",
    "    \n",
    "    df[\"return_strat\"] = (returns * signal) # daily returns strategy\n",
    "    np_return_strat = df[\"return_strat\"].values # numpy array of daily returns\n",
    "    \n",
    "    df[\"cumulative_return_strat\"] = ((1 + df['return_strat']).cumprod()) # cumulative return of strategy when reinvesting entire portfolio value\n",
    "    df[\"return\"] = returns \n",
    "    df[\"cumulative_return\"] = ((1 + df['return']).cumprod()) # Returns of the buy and hold strategy (buy at t=0 and hold untill t=end)\n",
    "    df[\"signal\"] = signal\n",
    "\n",
    "\n",
    "    if plot:\n",
    "        plt.plot(df[\"cumulative_return\"], \"r\", label = \"Buy and hold\")\n",
    "        plt.plot(df[\"cumulative_return_strat\"], \"g\", label = \"Strategy\")\n",
    "        plt.plot()\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "    return float(df.iloc[-1:][\"cumulative_return_strat\"] - df.iloc[-1:][\"cumulative_return\"]), \\\n",
    "            (math.sqrt(365.25) * np.mean(np_return_strat) / np.std(np_return_strat)), \\\n",
    "              max_drawdown(df[\"return_strat\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 920 - 1009\n",
    "# prediction period vdataset[\"EtherPrice\"].iloc[-1023:-934]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTING DATASET\n",
    "dataset = pd.read_csv('ethereum_trainval_dataset.csv')\n",
    "dataset = dataset.sort_values(by=[\"UnixTimeStamp\"])\n",
    "dataset = dataset.reindex(index = dataset.index[::-1])\n",
    "tdataset = pd.read_csv('ethereum_test_dataset.csv')\n",
    "tdataset = tdataset.sort_values(by=[\"UnixTimeStamp\"])\n",
    "tdataset = tdataset.reindex(index = tdataset.index[::-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deleting unwanted columns\n",
    "del dataset[\"eth_supply\"]\n",
    "del dataset[\"eth_ethersupply\"]\n",
    "del dataset[\"eth_marketcap\"]\n",
    "del dataset[\"Unnamed: 0\"]\n",
    "del dataset[\"UnixTimeStamp\"]\n",
    "del dataset[\"eth_ens_register\"]\n",
    "del tdataset[\"Date(UTC)\"]\n",
    "del tdataset[\"UnixTimeStamp\"]\n",
    "del tdataset[\"Unnamed: 0\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ikdem\\AppData\\Local\\Programs\\Python\\Python35\\lib\\site-packages\\ipykernel_launcher.py:4: DeprecationWarning: \n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "# Make price column the last one for easier use later on\n",
    "cols = list(dataset)\n",
    "cols[0], cols[11] = cols[11], cols[0]\n",
    "dataset = dataset.ix[:,cols]\n",
    "dataset = dataset[::-1]\n",
    "tdataset = tdataset[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accounting for blocktime being halved at index 809\n",
    "dataset[\"eth_blocktime\"].loc[809:] = dataset[809:][\"eth_blocktime\"] * 2\n",
    "dataset[\"eth_uncles\"].loc[809:] = dataset[809:][\"eth_uncles\"] / 2\n",
    "dataset[\"eth_blocks\"].loc[809:] = dataset[809:][\"eth_blocks\"] / 2\n",
    "dataset[\"eth_difficulty\"].loc[809:] = dataset[809:][\"eth_difficulty\"] * 2\n",
    "\n",
    "dataset[\"eth_gasprice\"][497] = dataset[\"eth_gasprice\"][497] / 41\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "tdataset = tdataset[[\"GasUsed\", \"TxGrowth\", \"AddressCount\", \"NetworkHash\", \"BlockDifficulty\", \"BlockCountRewards\", \\\n",
    "                     \"Uncles\", \"BlockSize\", \"BlockTime\", \"AvgGasPrice\", \"GasLimit\", \"EtherPrice\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset[13:]\n",
    "tdataset = tdataset[13:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to numpy array and normalize data.\n",
    "dataset = dataset.values.astype(\"float32\")\n",
    "tdataset = tdataset.values.astype(\"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(90, 11)\n",
      "(90,)\n"
     ]
    }
   ],
   "source": [
    "dataset_y = percentage_change(dataset[:, -1])\n",
    "tdataset_y = percentage_change(tdataset[:, -1])\n",
    "\n",
    "scaler_z = StandardScaler()\n",
    "\n",
    "dataset_X = scaler_z.fit_transform(dataset[:, :-1])\n",
    "tdataset_X = scaler_z.transform(tdataset[:, :-1])\n",
    "\n",
    "tdataset_X = tdataset_X[0:-1,: ]\n",
    "dataset_X = dataset_X[0:-1,: ]\n",
    "\n",
    "tdataset_X = tdataset_X[921:1011]\n",
    "yt = tdataset_y[921:1011]\n",
    "\n",
    "print(tdataset_X.shape)\n",
    "print(yt.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(828, 1, 11)\n",
      "(828,)\n",
      "(92, 1, 11)\n",
      "(92,)\n"
     ]
    }
   ],
   "source": [
    "## Train/test split\n",
    "split = int(len(dataset)*0.9)\n",
    "\n",
    "train_X = dataset_X[:split, :]\n",
    "val_X = dataset_X[split:, :]\n",
    "\n",
    "train_y = dataset_y[:split]\n",
    "test_y = dataset_y[split:]\n",
    "\n",
    "yv = test_y\n",
    "\n",
    "# Reshape for LSTM\n",
    "train_X = train_X.reshape((train_X.shape[0], 1, train_X.shape[1]))\n",
    "val_X = val_X.reshape((val_X.shape[0], 1, val_X.shape[1]))\n",
    "test_X = tdataset_X.reshape((tdataset_X.shape[0], 1, tdataset_X.shape[1]))\n",
    "\n",
    "\n",
    "\n",
    "print(train_X.shape)\n",
    "print(train_y.shape)\n",
    "print(val_X.shape)\n",
    "print(test_y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL 1 (uncomment below to test this model)\n",
    "\n",
    "# rms = RMSprop(lr = 0.0005)\n",
    "\n",
    "# model = Sequential()\n",
    "\n",
    "# model.add((LSTM(256, input_shape=(1, 11)))) \n",
    "# model.add(Dense(1))\n",
    "\n",
    "# model.add(Activation('linear'))\n",
    "\n",
    "# # Compile and Run\n",
    "# model.compile(loss = \"mean_squared_error\", optimizer = rms) # Try SGD, adam, adagrad and compare!!!\n",
    "# model.fit(train_X, train_y, epochs = 10000, batch_size = 128, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL 2 (uncomment below to test this model)\n",
    "# rms = RMSprop(lr = 0.0005)\n",
    "\n",
    "# model = Sequential()\n",
    "\n",
    "# model.add((LSTM(64, input_shape=(1, 11), return_sequences = True))) \n",
    "# model.add((LSTM(64, input_shape=(1, 11))))\n",
    "\n",
    "# model.add(Dense(1))\n",
    "# model.add(Activation('linear'))\n",
    "\n",
    "# # Compile and Run\n",
    "# model.compile(loss = \"mean_squared_error\", optimizer = rms) # Try SGD, adam, adagrad and compare!!!\n",
    "# model.fit(train_X, train_y, epochs = 2500, batch_size = 64, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/7500\n",
      " - 2s - loss: 0.0064\n",
      "Epoch 2/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 3/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 4/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 5/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 6/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 7/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 8/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 9/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 10/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 11/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 12/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 13/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 14/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 15/7500\n",
      " - 0s - loss: 0.0064\n",
      "Epoch 16/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 17/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 18/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 19/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 20/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 21/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 22/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 23/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 24/7500\n",
      " - 0s - loss: 0.0062\n",
      "Epoch 25/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 26/7500\n",
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      "Epoch 27/7500\n",
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      "Epoch 28/7500\n",
      " - 0s - loss: 0.0062\n",
      "Epoch 29/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 30/7500\n",
      " - 0s - loss: 0.0062\n",
      "Epoch 31/7500\n",
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      "Epoch 32/7500\n",
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      "Epoch 33/7500\n",
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      "Epoch 34/7500\n",
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      "Epoch 35/7500\n",
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      "Epoch 36/7500\n",
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      "Epoch 37/7500\n",
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      "Epoch 38/7500\n",
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      "Epoch 39/7500\n",
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      "Epoch 40/7500\n",
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      "Epoch 41/7500\n",
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      "Epoch 47/7500\n",
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      "Epoch 48/7500\n",
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      "Epoch 49/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 50/7500\n",
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      "Epoch 56/7500\n",
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      "Epoch 57/7500\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 58/7500\n",
      " - 0s - loss: 0.0063\n",
      "Epoch 59/7500\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 60/7500\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 61/7500\n",
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      "Epoch 62/7500\n",
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      "Epoch 63/7500\n",
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      "Epoch 64/7500\n",
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      "Epoch 65/7500\n",
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      "Epoch 66/7500\n",
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      "Epoch 67/7500\n",
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      "Epoch 68/7500\n",
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      "Epoch 69/7500\n",
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      "Epoch 70/7500\n",
      " - 0s - loss: 0.0062\n",
      "Epoch 71/7500\n",
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      "Epoch 72/7500\n",
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      "Epoch 73/7500\n",
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      "Epoch 74/7500\n",
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      "Epoch 75/7500\n",
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      "Epoch 76/7500\n",
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      "Epoch 77/7500\n",
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      "Epoch 78/7500\n",
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      "Epoch 79/7500\n",
      " - 0s - loss: 0.0062\n",
      "Epoch 80/7500\n",
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      "Epoch 81/7500\n",
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      "Epoch 82/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 83/7500\n",
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      "Epoch 84/7500\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 85/7500\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 86/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 87/7500\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 88/7500\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 89/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 90/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 91/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 92/7500\n",
      " - 0s - loss: 0.0061\n",
      "Epoch 93/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 94/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 95/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 96/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 97/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 98/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 99/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 100/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 101/7500\n",
      " - 0s - loss: 0.0060\n",
      "Epoch 102/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 103/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 104/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 105/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 106/7500\n",
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      "Epoch 107/7500\n",
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      "Epoch 108/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 109/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 110/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 111/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 112/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 113/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 114/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 115/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 116/7500\n",
      " - 0s - loss: 0.0059\n",
      "Epoch 117/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 118/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 119/7500\n",
      " - 0s - loss: 0.0057\n",
      "Epoch 120/7500\n",
      " - 0s - loss: 0.0057\n",
      "Epoch 121/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 122/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 123/7500\n",
      " - 0s - loss: 0.0058\n",
      "Epoch 124/7500\n",
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     "text": [
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "text": [
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    {
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2828884d160>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MODEL 3 (uncomment to test this model)\n",
    "rms = RMSprop(lr = 0.0005)\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add((LSTM(64, input_shape=(1, 11), return_sequences = True))) \n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add((LSTM(128, input_shape=(1, 11), return_sequences = True)))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add((LSTM(128, input_shape=(1, 11))))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Dense(64))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Dense(32))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.add(Activation('linear'))\n",
    "\n",
    "# Compile and Run\n",
    "model.compile(loss = \"mean_squared_error\", optimizer = rms) # Try SGD, adam, adagrad and compare!!!\n",
    "model.fit(train_X, train_y, epochs = 7500, batch_size = 128, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat = model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "90\n",
      "92\n"
     ]
    }
   ],
   "source": [
    "print(len(yhat))\n",
    "print(len(yv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2828624ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(yhat, \"g\", label = \"predicted\")\n",
    "plt.plot(yt, \"r\", label = \"real\")\n",
    "plt.title(\"Model 1\") \n",
    "plt.legend()\n",
    "plt.savefig(\"test.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x282f17f0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['rmse: 0.1073330034751382', 'mae: 0.088136464', (0.5109798162631058, 0.5056179775280899, 0.5059925093632959, None), 'strategy outperformance in %: 31.090473748308934', 'Sharpe ratio: 0.9174664621478757', 'Max drawdown in %: -28.638110820713113']\n"
     ]
    }
   ],
   "source": [
    "def error_performance(yhat, yt, plot = False):\n",
    "    rmse_normalized = math.sqrt(mean_squared_error(yhat, yt))\n",
    "    mae_normalized = mean_absolute_error(yhat, yt)\n",
    "    excess_r, sharpe, drawdown = strategy_profit(yhat, yt, plot)\n",
    "\n",
    "    error_performance = [\"rmse: \" + str(rmse_normalized), \"mae: \" + str(mae_normalized),\n",
    "                             precision_recall_fscore_support(binary(yhat), binary(yt), average = 'weighted'),\n",
    "                               \"strategy outperformance in %: \" + str(excess_r*100), \"Sharpe ratio: \" + str(sharpe),\n",
    "                                 \"Max drawdown in %: \" + str(drawdown*100)]\n",
    "    return error_performance\n",
    "\n",
    "print(error_performance(yhat, yt, plot = True)[0:6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}