17_convolutional_neural_networks.ipynb

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   "source": [
    "# Artificial Intelligence in Finance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convolutional Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dr Yves J Hilpisch | The AI Machine\n",
    "\n",
    "http://aimachine.io | http://twitter.com/dyjh"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pylab import plt, mpl\n",
    "plt.style.use('seaborn')\n",
    "mpl.rcParams['savefig.dpi'] = 300\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "os.environ['PYTHONHASHSEED'] = '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'http://hilpisch.com/aiif_eikon_eod_data.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbol = 'EUR='"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(pd.read_csv(url, index_col=0,\n",
    "                                parse_dates=True).dropna()[symbol])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lags = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = [symbol, 'r', 'd', 'sma', 'min', 'max', 'mom', 'vol']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_lags(data, symbol, lags, window=20, features=features):\n",
    "    cols = []\n",
    "    df = data.copy()\n",
    "    df.dropna(inplace=True)\n",
    "    df['r'] = np.log(df / df.shift(1))\n",
    "    df['sma'] = df[symbol].rolling(window).mean()\n",
    "    df['min'] = df[symbol].rolling(window).min()\n",
    "    df['max'] = df[symbol].rolling(window).max()\n",
    "    df['mom'] = df['r'].rolling(window).mean()\n",
    "    df['vol'] = df['r'].rolling(window).std()\n",
    "    df.dropna(inplace=True)\n",
    "    df['d'] = np.where(df['r'] > 0, 1, 0)\n",
    "    for f in features:\n",
    "        for lag in range(1, lags + 1):\n",
    "            col = f'{f}_lag_{lag}'\n",
    "            df[col] = df[f].shift(lag)\n",
    "            cols.append(col)\n",
    "    df.dropna(inplace=True)\n",
    "    return df, cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data, cols = add_lags(data, symbol, lags, window=20, features=features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "split = int(len(data) * 0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data.iloc[:split].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu, std = train[cols].mean(), train[cols].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train[cols] = (train[cols] - mu) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = data.iloc[split:].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[cols] = (test[cols] - mu) / std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv1D, Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def set_seeds(seed=1000):\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    tf.random.set_seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_seeds()\n",
    "model = Sequential()\n",
    "model.add(Conv1D(filters=96, kernel_size=5, activation='relu',\n",
    "                 input_shape=(len(cols), 1)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(10, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "h = model.fit(np.atleast_3d(train[cols]), train['d'],\n",
    "          epochs=60, batch_size=48, verbose=False,\n",
    "          validation_split=0.15, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = pd.DataFrame(h.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "res.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.evaluate(np.atleast_3d(test[cols]), test['d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['p'] = np.where(model.predict(np.atleast_3d(test[cols])) > 0.5, 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['p'] = np.where(test['p'] > 0, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['p'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(test['p'].diff() != 0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['s'] = test['p'] * test['r']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ptc = 0.00012 / test[symbol]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test['s_'] = np.where(test['p'] != 0, test['s'] - ptc, test['s'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[['r', 's', 's_']].sum().apply(np.exp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test[['r', 's', 's_']].cumsum().apply(np.exp).plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src='http://hilpisch.com/taim_logo.png' width=\"350px\" align=\"right\">"
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