free-agent/evolution-strategy-bayesian-agent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: bayesian-optimization==0.6 in /home/husein/.local/lib/python3.6/site-packages (0.6.0)\n",
      "Requirement already satisfied: scikit-learn>=0.18.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization==0.6) (0.19.1)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization==0.6) (1.2.0)\n",
      "Requirement already satisfied: numpy>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from bayesian-optimization==0.6) (1.14.5)\n",
      "\u001b[33mYou are using pip version 18.1, however version 19.0.3 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install bayesian-optimization==0.6 --user"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I use `bayesian-optimization==0.6`, my backend pretty much stick with this version, so migrating will break the code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random\n",
    "from bayes_opt import BayesianOptimization\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seaborn==0.9.0\n",
      "pandas==0.23.4\n",
      "numpy==1.14.5\n",
      "matplotlib==3.0.2\n"
     ]
    }
   ],
   "source": [
    "import pkg_resources\n",
    "import types\n",
    "\n",
    "\n",
    "def get_imports():\n",
    "    for name, val in globals().items():\n",
    "        if isinstance(val, types.ModuleType):\n",
    "            name = val.__name__.split('.')[0]\n",
    "        elif isinstance(val, type):\n",
    "            name = val.__module__.split('.')[0]\n",
    "        poorly_named_packages = {'PIL': 'Pillow', 'sklearn': 'scikit-learn'}\n",
    "        if name in poorly_named_packages.keys():\n",
    "            name = poorly_named_packages[name]\n",
    "        yield name\n",
    "\n",
    "\n",
    "imports = list(set(get_imports()))\n",
    "requirements = []\n",
    "for m in pkg_resources.working_set:\n",
    "    if m.project_name in imports and m.project_name != 'pip':\n",
    "        requirements.append((m.project_name, m.version))\n",
    "\n",
    "for r in requirements:\n",
    "    print('{}=={}'.format(*r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_state(data, t, n):\n",
    "    d = t - n + 1\n",
    "    block = data[d : t + 1] if d >= 0 else -d * [data[0]] + data[0 : t + 1]\n",
    "    res = []\n",
    "    for i in range(n - 1):\n",
    "        res.append(block[i + 1] - block[i])\n",
    "    return np.array([res])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TSLA Time Period: **Mar 23, 2018 - Mar 23, 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>311.250000</td>\n",
       "      <td>311.250000</td>\n",
       "      <td>300.450012</td>\n",
       "      <td>301.540009</td>\n",
       "      <td>301.540009</td>\n",
       "      <td>6654900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-26</td>\n",
       "      <td>307.339996</td>\n",
       "      <td>307.589996</td>\n",
       "      <td>291.359985</td>\n",
       "      <td>304.179993</td>\n",
       "      <td>304.179993</td>\n",
       "      <td>8375200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>304.269989</td>\n",
       "      <td>277.179993</td>\n",
       "      <td>279.179993</td>\n",
       "      <td>279.179993</td>\n",
       "      <td>13872000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-28</td>\n",
       "      <td>264.579987</td>\n",
       "      <td>268.679993</td>\n",
       "      <td>252.100006</td>\n",
       "      <td>257.779999</td>\n",
       "      <td>257.779999</td>\n",
       "      <td>21001400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>256.489990</td>\n",
       "      <td>270.959991</td>\n",
       "      <td>248.210007</td>\n",
       "      <td>266.130005</td>\n",
       "      <td>266.130005</td>\n",
       "      <td>15170700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2018-03-23  311.250000  311.250000  300.450012  301.540009  301.540009   \n",
       "1  2018-03-26  307.339996  307.589996  291.359985  304.179993  304.179993   \n",
       "2  2018-03-27  304.000000  304.269989  277.179993  279.179993  279.179993   \n",
       "3  2018-03-28  264.579987  268.679993  252.100006  257.779999  257.779999   \n",
       "4  2018-03-29  256.489990  270.959991  248.210007  266.130005  266.130005   \n",
       "\n",
       "     Volume  \n",
       "0   6654900  \n",
       "1   8375200  \n",
       "2  13872000  \n",
       "3  21001400  \n",
       "4  15170700  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../dataset/TSLA.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "close = df.Close.values.tolist()\n",
    "window_size = 30\n",
    "skip = 5\n",
    "l = len(close) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Deep_Evolution_Strategy:\n",
    "\n",
    "    inputs = None\n",
    "\n",
    "    def __init__(\n",
    "        self, weights, reward_function, population_size, sigma, learning_rate\n",
    "    ):\n",
    "        self.weights = weights\n",
    "        self.reward_function = reward_function\n",
    "        self.population_size = population_size\n",
    "        self.sigma = sigma\n",
    "        self.learning_rate = learning_rate\n",
    "\n",
    "    def _get_weight_from_population(self, weights, population):\n",
    "        weights_population = []\n",
    "        for index, i in enumerate(population):\n",
    "            jittered = self.sigma * i\n",
    "            weights_population.append(weights[index] + jittered)\n",
    "        return weights_population\n",
    "\n",
    "    def get_weights(self):\n",
    "        return self.weights\n",
    "\n",
    "    def train(self, epoch = 100, print_every = 1):\n",
    "        lasttime = time.time()\n",
    "        for i in range(epoch):\n",
    "            population = []\n",
    "            rewards = np.zeros(self.population_size)\n",
    "            for k in range(self.population_size):\n",
    "                x = []\n",
    "                for w in self.weights:\n",
    "                    x.append(np.random.randn(*w.shape))\n",
    "                population.append(x)\n",
    "            for k in range(self.population_size):\n",
    "                weights_population = self._get_weight_from_population(\n",
    "                    self.weights, population[k]\n",
    "                )\n",
    "                rewards[k] = self.reward_function(weights_population)\n",
    "            rewards = (rewards - np.mean(rewards)) / np.std(rewards)\n",
    "            for index, w in enumerate(self.weights):\n",
    "                A = np.array([p[index] for p in population])\n",
    "                self.weights[index] = (\n",
    "                    w\n",
    "                    + self.learning_rate\n",
    "                    / (self.population_size * self.sigma)\n",
    "                    * np.dot(A.T, rewards).T\n",
    "                )\n",
    "            if (i + 1) % print_every == 0:\n",
    "                print(\n",
    "                    'iter %d. reward: %f'\n",
    "                    % (i + 1, self.reward_function(self.weights))\n",
    "                )\n",
    "        print('time taken to train:', time.time() - lasttime, 'seconds')\n",
    "\n",
    "\n",
    "class Model:\n",
    "    def __init__(self, input_size, layer_size, output_size):\n",
    "        self.weights = [\n",
    "            np.random.randn(input_size, layer_size),\n",
    "            np.random.randn(layer_size, output_size),\n",
    "            np.random.randn(layer_size, 1),\n",
    "            np.random.randn(1, layer_size),\n",
    "        ]\n",
    "\n",
    "    def predict(self, inputs):\n",
    "        feed = np.dot(inputs, self.weights[0]) + self.weights[-1]\n",
    "        decision = np.dot(feed, self.weights[1])\n",
    "        buy = np.dot(feed, self.weights[2])\n",
    "        return decision, buy\n",
    "\n",
    "    def get_weights(self):\n",
    "        return self.weights\n",
    "\n",
    "    def set_weights(self, weights):\n",
    "        self.weights = weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent:\n",
    "    def __init__(\n",
    "        self,\n",
    "        population_size,\n",
    "        sigma,\n",
    "        learning_rate,\n",
    "        model,\n",
    "        money,\n",
    "        max_buy,\n",
    "        max_sell,\n",
    "        skip,\n",
    "        window_size,\n",
    "    ):\n",
    "        self.window_size = window_size\n",
    "        self.skip = skip\n",
    "        self.POPULATION_SIZE = population_size\n",
    "        self.SIGMA = sigma\n",
    "        self.LEARNING_RATE = learning_rate\n",
    "        self.model = model\n",
    "        self.initial_money = money\n",
    "        self.max_buy = max_buy\n",
    "        self.max_sell = max_sell\n",
    "        self.es = Deep_Evolution_Strategy(\n",
    "            self.model.get_weights(),\n",
    "            self.get_reward,\n",
    "            self.POPULATION_SIZE,\n",
    "            self.SIGMA,\n",
    "            self.LEARNING_RATE,\n",
    "        )\n",
    "\n",
    "    def act(self, sequence):\n",
    "        decision, buy = self.model.predict(np.array(sequence))\n",
    "        return np.argmax(decision[0]), int(buy[0])\n",
    "\n",
    "    def get_reward(self, weights):\n",
    "        initial_money = self.initial_money\n",
    "        starting_money = initial_money\n",
    "        self.model.weights = weights\n",
    "        state = get_state(close, 0, self.window_size + 1)\n",
    "        inventory = []\n",
    "        quantity = 0\n",
    "        for t in range(0, l, self.skip):\n",
    "            action, buy = self.act(state)\n",
    "            next_state = get_state(close, t + 1, self.window_size + 1)\n",
    "            if action == 1 and initial_money >= close[t]:\n",
    "                if buy < 0:\n",
    "                    buy = 1\n",
    "                if buy > self.max_buy:\n",
    "                    buy_units = self.max_buy\n",
    "                else:\n",
    "                    buy_units = buy\n",
    "                total_buy = buy_units * close[t]\n",
    "                initial_money -= total_buy\n",
    "                inventory.append(total_buy)\n",
    "                quantity += buy_units\n",
    "            elif action == 2 and len(inventory) > 0:\n",
    "                if quantity > self.max_sell:\n",
    "                    sell_units = self.max_sell\n",
    "                else:\n",
    "                    sell_units = quantity\n",
    "                quantity -= sell_units\n",
    "                total_sell = sell_units * close[t]\n",
    "                initial_money += total_sell\n",
    "\n",
    "            state = next_state\n",
    "        return ((initial_money - starting_money) / starting_money) * 100\n",
    "\n",
    "    def fit(self, iterations, checkpoint):\n",
    "        self.es.train(iterations, print_every = checkpoint)\n",
    "\n",
    "    def buy(self):\n",
    "        initial_money = self.initial_money\n",
    "        state = get_state(close, 0, self.window_size + 1)\n",
    "        starting_money = initial_money\n",
    "        states_sell = []\n",
    "        states_buy = []\n",
    "        inventory = []\n",
    "        quantity = 0\n",
    "        for t in range(0, l, self.skip):\n",
    "            action, buy = self.act(state)\n",
    "            next_state = get_state(close, t + 1, self.window_size + 1)\n",
    "            if action == 1 and initial_money >= close[t]:\n",
    "                if buy < 0:\n",
    "                    buy = 1\n",
    "                if buy > self.max_buy:\n",
    "                    buy_units = self.max_buy\n",
    "                else:\n",
    "                    buy_units = buy\n",
    "                total_buy = buy_units * close[t]\n",
    "                initial_money -= total_buy\n",
    "                inventory.append(total_buy)\n",
    "                quantity += buy_units\n",
    "                states_buy.append(t)\n",
    "                print(\n",
    "                    'day %d: buy %d units at price %f, total balance %f'\n",
    "                    % (t, buy_units, total_buy, initial_money)\n",
    "                )\n",
    "            elif action == 2 and len(inventory) > 0:\n",
    "                bought_price = inventory.pop(0)\n",
    "                if quantity > self.max_sell:\n",
    "                    sell_units = self.max_sell\n",
    "                else:\n",
    "                    sell_units = quantity\n",
    "                if sell_units < 1:\n",
    "                    continue\n",
    "                quantity -= sell_units\n",
    "                total_sell = sell_units * close[t]\n",
    "                initial_money += total_sell\n",
    "                states_sell.append(t)\n",
    "                try:\n",
    "                    invest = ((total_sell - bought_price) / bought_price) * 100\n",
    "                except:\n",
    "                    invest = 0\n",
    "                print(\n",
    "                    'day %d, sell %d units at price %f, investment %f %%, total balance %f,'\n",
    "                    % (t, sell_units, total_sell, invest, initial_money)\n",
    "                )\n",
    "            state = next_state\n",
    "\n",
    "        invest = ((initial_money - starting_money) / starting_money) * 100\n",
    "        print(\n",
    "            '\\ntotal gained %f, total investment %f %%'\n",
    "            % (initial_money - starting_money, invest)\n",
    "        )\n",
    "        plt.figure(figsize = (20, 10))\n",
    "        plt.plot(close, label = 'true close', c = 'g')\n",
    "        plt.plot(\n",
    "            close, 'X', label = 'predict buy', markevery = states_buy, c = 'b'\n",
    "        )\n",
    "        plt.plot(\n",
    "            close, 'o', label = 'predict sell', markevery = states_sell, c = 'r'\n",
    "        )\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def best_agent(\n",
    "    window_size, skip, population_size, sigma, learning_rate, size_network\n",
    "):\n",
    "    model = Model(window_size, size_network, 3)\n",
    "    agent = Agent(\n",
    "        population_size,\n",
    "        sigma,\n",
    "        learning_rate,\n",
    "        model,\n",
    "        10000,\n",
    "        5,\n",
    "        5,\n",
    "        skip,\n",
    "        window_size,\n",
    "    )\n",
    "    try:\n",
    "        agent.fit(100, 1000)\n",
    "        return agent.es.reward_function(agent.es.weights)\n",
    "    except:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_agent(\n",
    "    window_size, skip, population_size, sigma, learning_rate, size_network\n",
    "):\n",
    "    global accbest\n",
    "    param = {\n",
    "        'window_size': int(np.around(window_size)),\n",
    "        'skip': int(np.around(skip)),\n",
    "        'population_size': int(np.around(population_size)),\n",
    "        'sigma': max(min(sigma, 1), 0.0001),\n",
    "        'learning_rate': max(min(learning_rate, 0.5), 0.000001),\n",
    "        'size_network': int(np.around(size_network)),\n",
    "    }\n",
    "    print('\\nSearch parameters %s' % (param))\n",
    "    investment = best_agent(**param)\n",
    "    print('stop after 100 iteration with investment %f' % (investment))\n",
    "    if investment > accbest:\n",
    "        costbest = investment\n",
    "    return investment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mInitialization\u001b[0m\n",
      "\u001b[94m----------------------------------------------------------------------------------------------------------------------------\u001b[0m\n",
      " Step |   Time |      Value |   learning_rate |   population_size |     sigma |   size_network |      skip |   window_size | \n",
      "\n",
      "Search parameters {'window_size': 32, 'skip': 4, 'population_size': 14, 'sigma': 0.6924932742559208, 'learning_rate': 0.4506746405913942, 'size_network': 903}\n",
      "time taken to train: 3.6314964294433594 seconds\n",
      "stop after 100 iteration with investment 45.469898\n",
      "    1 | 00m03s | \u001b[35m  45.46990\u001b[0m | \u001b[32m         0.4507\u001b[0m | \u001b[32m          14.1205\u001b[0m | \u001b[32m   0.6925\u001b[0m | \u001b[32m      903.2810\u001b[0m | \u001b[32m   3.8596\u001b[0m | \u001b[32m      32.0389\u001b[0m | \n",
      "\n",
      "Search parameters {'window_size': 9, 'skip': 2, 'population_size': 40, 'sigma': 0.6314318303690627, 'learning_rate': 0.2665435889829382, 'size_network': 418}\n",
      "time taken to train: 6.387232542037964 seconds\n",
      "stop after 100 iteration with investment 46.435302\n",
      "    2 | 00m06s | \u001b[35m  46.43530\u001b[0m | \u001b[32m         0.2665\u001b[0m | \u001b[32m          40.1437\u001b[0m | \u001b[32m   0.6314\u001b[0m | \u001b[32m      417.6211\u001b[0m | \u001b[32m   2.4864\u001b[0m | \u001b[32m       8.5411\u001b[0m | \n",
      "\n",
      "Search parameters {'window_size': 15, 'skip': 14, 'population_size': 19, 'sigma': 0.08278555353887103, 'learning_rate': 0.28395843327770764, 'size_network': 121}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "    3 | 00m00s |    0.00000 |          0.2840 |           19.2441 |    0.0828 |       121.0415 |   13.7214 |       15.4565 | \n",
      "\n",
      "Search parameters {'window_size': 20, 'skip': 3, 'population_size': 17, 'sigma': 0.9721007155778852, 'learning_rate': 0.2755723397763024, 'size_network': 777}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:39: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken to train: 3.6136224269866943 seconds\n",
      "stop after 100 iteration with investment 20.886098\n",
      "    4 | 00m03s |   20.88610 |          0.2756 |           16.9868 |    0.9721 |       776.5696 |    2.8687 |       19.7415 | \n",
      "\n",
      "Search parameters {'window_size': 10, 'skip': 10, 'population_size': 12, 'sigma': 0.9587684522335657, 'learning_rate': 0.3107466445720601, 'size_network': 196}\n",
      "time taken to train: 0.47393083572387695 seconds\n",
      "stop after 100 iteration with investment 15.384199\n",
      "    5 | 00m00s |   15.38420 |          0.3107 |           12.0360 |    0.9588 |       196.2702 |   10.3654 |        9.5100 | \n",
      "\n",
      "Search parameters {'window_size': 16, 'skip': 15, 'population_size': 16, 'sigma': 0.9424607807175174, 'learning_rate': 0.4304065851197905, 'size_network': 101}\n",
      "time taken to train: 0.4492471218109131 seconds\n",
      "stop after 100 iteration with investment 10.937500\n",
      "    6 | 00m00s |   10.93750 |          0.4304 |           15.7218 |    0.9425 |       101.4025 |   14.7170 |       16.4290 | \n",
      "\n",
      "Search parameters {'window_size': 44, 'skip': 14, 'population_size': 9, 'sigma': 0.5515595926157524, 'learning_rate': 0.1058658719049542, 'size_network': 448}\n",
      "time taken to train: 1.143993616104126 seconds\n",
      "stop after 100 iteration with investment 14.495498\n",
      "    7 | 00m01s |   14.49550 |          0.1059 |            8.6050 |    0.5516 |       447.5149 |   14.1363 |       43.8864 | \n",
      "\n",
      "Search parameters {'window_size': 11, 'skip': 9, 'population_size': 13, 'sigma': 0.3095428607448544, 'learning_rate': 0.04079037201287228, 'size_network': 900}\n",
      "time taken to train: 1.3032267093658447 seconds\n",
      "stop after 100 iteration with investment 6.128101\n",
      "    8 | 00m01s |    6.12810 |          0.0408 |           12.8935 |    0.3095 |       900.3144 |    8.7717 |       10.8557 | \n",
      "\n",
      "Search parameters {'window_size': 17, 'skip': 6, 'population_size': 19, 'sigma': 0.6364998429932328, 'learning_rate': 0.29209145932218566, 'size_network': 535}\n",
      "time taken to train: 1.9795026779174805 seconds\n",
      "stop after 100 iteration with investment 23.538000\n",
      "    9 | 00m01s |   23.53800 |          0.2921 |           18.8682 |    0.6365 |       535.3654 |    6.1929 |       16.9913 | \n",
      "\n",
      "Search parameters {'window_size': 2, 'skip': 8, 'population_size': 45, 'sigma': 0.23630620909949168, 'learning_rate': 0.2469324378001854, 'size_network': 483}\n",
      "time taken to train: 2.0241739749908447 seconds\n",
      "stop after 100 iteration with investment 7.828999\n",
      "   10 | 00m02s |    7.82900 |          0.2469 |           44.5681 |    0.2363 |       482.7787 |    8.1918 |        2.4514 | \n",
      "\n",
      "Search parameters {'window_size': 3, 'skip': 8, 'population_size': 6, 'sigma': 0.3690419820520124, 'learning_rate': 0.2500034872048501, 'size_network': 66}\n",
      "time taken to train: 0.1856238842010498 seconds\n",
      "stop after 100 iteration with investment 6.033901\n",
      "   11 | 00m00s |    6.03390 |          0.2500 |            5.7217 |    0.3690 |        66.4484 |    8.4349 |        2.6576 | \n",
      "\n",
      "Search parameters {'window_size': 5, 'skip': 13, 'population_size': 21, 'sigma': 0.7845492963667585, 'learning_rate': 0.18249610602293675, 'size_network': 682}\n",
      "time taken to train: 1.0442640781402588 seconds\n",
      "stop after 100 iteration with investment 3.329900\n",
      "   12 | 00m01s |    3.32990 |          0.1825 |           20.7766 |    0.7845 |       681.5072 |   13.4947 |        5.0838 | \n",
      "\n",
      "Search parameters {'window_size': 9, 'skip': 8, 'population_size': 31, 'sigma': 0.584901850128559, 'learning_rate': 0.262432628184034, 'size_network': 455}\n",
      "time taken to train: 1.8967516422271729 seconds\n",
      "stop after 100 iteration with investment 16.867999\n",
      "   13 | 00m01s |   16.86800 |          0.2624 |           31.0438 |    0.5849 |       454.5904 |    7.7382 |        9.0253 | \n",
      "\n",
      "Search parameters {'window_size': 5, 'skip': 5, 'population_size': 21, 'sigma': 0.26583128202542755, 'learning_rate': 0.17776810195709006, 'size_network': 367}\n",
      "time taken to train: 1.3621792793273926 seconds\n",
      "stop after 100 iteration with investment 29.612498\n",
      "   14 | 00m01s |   29.61250 |          0.1778 |           20.7062 |    0.2658 |       367.4264 |    4.5233 |        5.0714 | \n",
      "\n",
      "Search parameters {'window_size': 21, 'skip': 3, 'population_size': 23, 'sigma': 0.5312612811941403, 'learning_rate': 0.25789044017589463, 'size_network': 155}\n",
      "time taken to train: 2.652163505554199 seconds\n",
      "stop after 100 iteration with investment 37.225500\n",
      "   15 | 00m02s |   37.22550 |          0.2579 |           22.7385 |    0.5313 |       155.3037 |    2.5507 |       21.2792 | \n",
      "\n",
      "Search parameters {'window_size': 4, 'skip': 1, 'population_size': 14, 'sigma': 0.7564163325071124, 'learning_rate': 0.40307249149418684, 'size_network': 379}\n",
      "time taken to train: 3.518620729446411 seconds\n",
      "stop after 100 iteration with investment 62.153502\n",
      "   16 | 00m03s | \u001b[35m  62.15350\u001b[0m | \u001b[32m         0.4031\u001b[0m | \u001b[32m          13.5794\u001b[0m | \u001b[32m   0.7564\u001b[0m | \u001b[32m      379.4240\u001b[0m | \u001b[32m   1.4926\u001b[0m | \u001b[32m       3.5441\u001b[0m | \n",
      "\n",
      "Search parameters {'window_size': 50, 'skip': 2, 'population_size': 43, 'sigma': 0.2197222083454031, 'learning_rate': 0.4674263070099041, 'size_network': 760}\n",
      "time taken to train: 19.268061637878418 seconds\n",
      "stop after 100 iteration with investment 72.960099\n",
      "   17 | 00m19s | \u001b[35m  72.96010\u001b[0m | \u001b[32m         0.4674\u001b[0m | \u001b[32m          42.8796\u001b[0m | \u001b[32m   0.2197\u001b[0m | \u001b[32m      759.9597\u001b[0m | \u001b[32m   1.9957\u001b[0m | \u001b[32m      49.5908\u001b[0m | \n",
      "\n",
      "Search parameters {'window_size': 26, 'skip': 12, 'population_size': 48, 'sigma': 0.4952569660825247, 'learning_rate': 0.3424567460907903, 'size_network': 900}\n",
      "time taken to train: 7.226728439331055 seconds\n",
      "stop after 100 iteration with investment 14.478001\n",
      "   18 | 00m07s |   14.47800 |          0.3425 |           47.5428 |    0.4953 |       899.7087 |   11.8071 |       25.6620 | \n",
      "\n",
      "Search parameters {'window_size': 38, 'skip': 11, 'population_size': 6, 'sigma': 0.8568025149071787, 'learning_rate': 0.3517351727084189, 'size_network': 980}\n",
      "time taken to train: 1.3971071243286133 seconds\n",
      "stop after 100 iteration with investment 14.713399\n",
      "   19 | 00m01s |   14.71340 |          0.3517 |            5.6559 |    0.8568 |       980.1978 |   11.1964 |       37.5201 | \n",
      "\n",
      "Search parameters {'window_size': 16, 'skip': 9, 'population_size': 31, 'sigma': 0.04116018942639576, 'learning_rate': 0.4462154885816546, 'size_network': 872}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   20 | 00m00s |    0.00000 |          0.4462 |           31.2642 |    0.0412 |       871.7114 |    9.4960 |       15.9637 | \n",
      "\n",
      "Search parameters {'window_size': 19, 'skip': 11, 'population_size': 32, 'sigma': 0.47272286939193925, 'learning_rate': 0.2110219583380834, 'size_network': 281}\n",
      "time taken to train: 1.7874493598937988 seconds\n",
      "stop after 100 iteration with investment 15.373998\n",
      "   21 | 00m01s |   15.37400 |          0.2110 |           31.8644 |    0.4727 |       280.6654 |   11.2746 |       19.3018 | \n",
      "\n",
      "Search parameters {'window_size': 26, 'skip': 7, 'population_size': 3, 'sigma': 0.6844687524887009, 'learning_rate': 0.0944715663501871, 'size_network': 914}\n",
      "time taken to train: 0.5886580944061279 seconds\n",
      "stop after 100 iteration with investment 12.319299\n",
      "   22 | 00m00s |   12.31930 |          0.0945 |            3.0635 |    0.6845 |       914.2091 |    6.7413 |       25.7872 | \n",
      "\n",
      "Search parameters {'window_size': 28, 'skip': 3, 'population_size': 12, 'sigma': 0.5125345048920551, 'learning_rate': 0.21801331961507173, 'size_network': 558}\n",
      "time taken to train: 2.5648751258850098 seconds\n",
      "stop after 100 iteration with investment 33.169600\n",
      "   23 | 00m02s |   33.16960 |          0.2180 |           11.8223 |    0.5125 |       557.5803 |    3.0897 |       27.7219 | \n",
      "\n",
      "Search parameters {'window_size': 5, 'skip': 4, 'population_size': 45, 'sigma': 0.1265470327238655, 'learning_rate': 0.48218855938970684, 'size_network': 525}\n",
      "time taken to train: 3.909914493560791 seconds\n",
      "stop after 100 iteration with investment 21.085901\n",
      "   24 | 00m03s |   21.08590 |          0.4822 |           45.2744 |    0.1265 |       524.8536 |    4.4636 |        4.5786 | \n",
      "\n",
      "Search parameters {'window_size': 9, 'skip': 2, 'population_size': 29, 'sigma': 0.9049065403007066, 'learning_rate': 0.38962121170124975, 'size_network': 204}\n",
      "time taken to train: 3.9239423274993896 seconds\n",
      "stop after 100 iteration with investment 40.901604\n",
      "   25 | 00m03s |   40.90160 |          0.3896 |           28.8579 |    0.9049 |       204.2013 |    1.9670 |        8.7195 | \n",
      "\n",
      "Search parameters {'window_size': 38, 'skip': 3, 'population_size': 41, 'sigma': 0.3113494369250888, 'learning_rate': 0.42379002609601546, 'size_network': 614}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken to train: 10.965286254882812 seconds\n",
      "stop after 100 iteration with investment 54.670205\n",
      "   26 | 00m10s |   54.67021 |          0.4238 |           40.6576 |    0.3113 |       614.1548 |    2.5591 |       37.7406 | \n",
      "\n",
      "Search parameters {'window_size': 38, 'skip': 1, 'population_size': 34, 'sigma': 0.33251817501018216, 'learning_rate': 0.28025378213533453, 'size_network': 601}\n",
      "time taken to train: 19.4577956199646 seconds\n",
      "stop after 100 iteration with investment 90.161499\n",
      "   27 | 00m19s | \u001b[35m  90.16150\u001b[0m | \u001b[32m         0.2803\u001b[0m | \u001b[32m          34.1475\u001b[0m | \u001b[32m   0.3325\u001b[0m | \u001b[32m      600.7703\u001b[0m | \u001b[32m   1.2320\u001b[0m | \u001b[32m      37.7156\u001b[0m | \n",
      "\n",
      "Search parameters {'window_size': 23, 'skip': 2, 'population_size': 15, 'sigma': 0.25337476478163296, 'learning_rate': 0.4721917822578962, 'size_network': 777}\n",
      "time taken to train: 4.5063018798828125 seconds\n",
      "stop after 100 iteration with investment 35.491501\n",
      "   28 | 00m04s |   35.49150 |          0.4722 |           15.0837 |    0.2534 |       777.1621 |    2.1678 |       22.5169 | \n",
      "\n",
      "Search parameters {'window_size': 37, 'skip': 1, 'population_size': 29, 'sigma': 0.8732198087436757, 'learning_rate': 0.021617376925168078, 'size_network': 834}\n",
      "time taken to train: 18.457762718200684 seconds\n",
      "stop after 100 iteration with investment 0.367701\n",
      "   29 | 00m18s |    0.36770 |          0.0216 |           29.2968 |    0.8732 |       834.4437 |    1.2878 |       37.4394 | \n",
      "\n",
      "Search parameters {'window_size': 22, 'skip': 10, 'population_size': 34, 'sigma': 0.931415927507302, 'learning_rate': 0.1502122497456846, 'size_network': 377}\n",
      "time taken to train: 2.5566844940185547 seconds\n",
      "stop after 100 iteration with investment 11.250799\n",
      "   30 | 00m02s |   11.25080 |          0.1502 |           34.2506 |    0.9314 |       376.8366 |   10.3104 |       22.3349 | \n",
      "\u001b[31mBayesian Optimization\u001b[0m\n",
      "\u001b[94m----------------------------------------------------------------------------------------------------------------------------\u001b[0m\n",
      " Step |   Time |      Value |   learning_rate |   population_size |     sigma |   size_network |      skip |   window_size | \n",
      "\n",
      "Search parameters {'window_size': 39, 'skip': 2, 'population_size': 32, 'sigma': 0.934251078387382, 'learning_rate': 0.23986973695035896, 'size_network': 602}\n",
      "time taken to train: 11.161960363388062 seconds\n",
      "stop after 100 iteration with investment 45.801800\n",
      "   31 | 00m12s |   45.80180 |          0.2399 |           31.7850 |    0.9343 |       602.4628 |    1.6398 |       38.7034 | \n",
      "\n",
      "Search parameters {'window_size': 48, 'skip': 11, 'population_size': 5, 'sigma': 0.82819865732244, 'learning_rate': 0.258973848167657, 'size_network': 513}\n",
      "time taken to train: 0.8257131576538086 seconds\n",
      "stop after 100 iteration with investment 11.031597\n",
      "   32 | 00m01s |   11.03160 |          0.2590 |            5.0252 |    0.8282 |       513.0593 |   11.1859 |       47.8048 | \n",
      "\n",
      "Search parameters {'window_size': 10, 'skip': 10, 'population_size': 39, 'sigma': 0.952082073395817, 'learning_rate': 0.05084755823239097, 'size_network': 760}\n",
      "time taken to train: 2.986116886138916 seconds\n",
      "stop after 100 iteration with investment 1.604099\n",
      "   33 | 00m03s |    1.60410 |          0.0508 |           38.5205 |    0.9521 |       760.2650 |    9.5442 |        9.7626 | \n",
      "\n",
      "Search parameters {'window_size': 49, 'skip': 4, 'population_size': 31, 'sigma': 0.7123005097916996, 'learning_rate': 0.08936228401608749, 'size_network': 689}\n",
      "time taken to train: 9.168430089950562 seconds\n",
      "stop after 100 iteration with investment 23.429304\n",
      "   34 | 00m10s |   23.42930 |          0.0894 |           30.9345 |    0.7123 |       688.9225 |    3.6114 |       49.4008 | \n",
      "\n",
      "Search parameters {'window_size': 11, 'skip': 7, 'population_size': 44, 'sigma': 0.6914780569033353, 'learning_rate': 0.4260041398991975, 'size_network': 406}\n",
      "time taken to train: 2.953139543533325 seconds\n",
      "stop after 100 iteration with investment 22.639500\n",
      "   35 | 00m03s |   22.63950 |          0.4260 |           43.6345 |    0.6915 |       406.0963 |    7.1675 |       11.4653 | \n",
      "\n",
      "Search parameters {'window_size': 9, 'skip': 4, 'population_size': 11, 'sigma': 0.31584827212577704, 'learning_rate': 0.30732453983094393, 'size_network': 179}\n",
      "time taken to train: 0.8408491611480713 seconds\n",
      "stop after 100 iteration with investment 34.574998\n",
      "   36 | 00m01s |   34.57500 |          0.3073 |           11.2473 |    0.3158 |       178.8690 |    3.7974 |        8.7130 | \n",
      "\n",
      "Search parameters {'window_size': 32, 'skip': 5, 'population_size': 7, 'sigma': 0.7215504733844512, 'learning_rate': 0.2536350848590793, 'size_network': 788}\n",
      "time taken to train: 1.5779805183410645 seconds\n",
      "stop after 100 iteration with investment 35.942901\n",
      "   37 | 00m02s |   35.94290 |          0.2536 |            7.2798 |    0.7216 |       788.0273 |    5.3914 |       32.2254 | \n",
      "\n",
      "Search parameters {'window_size': 30, 'skip': 7, 'population_size': 3, 'sigma': 0.13212271540650566, 'learning_rate': 0.22355084099626585, 'size_network': 715}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   38 | 00m01s |    0.00000 |          0.2236 |            2.6159 |    0.1321 |       715.3900 |    7.2454 |       30.0190 | \n",
      "\n",
      "Search parameters {'window_size': 38, 'skip': 3, 'population_size': 36, 'sigma': 0.3234773477207441, 'learning_rate': 0.14336537507276517, 'size_network': 600}\n",
      "time taken to train: 9.481030225753784 seconds\n",
      "stop after 100 iteration with investment 46.123005\n",
      "   39 | 00m10s |   46.12301 |          0.1434 |           35.5993 |    0.3235 |       600.2903 |    2.9762 |       37.5038 | \n",
      "\n",
      "Search parameters {'window_size': 45, 'skip': 9, 'population_size': 28, 'sigma': 0.4850038120889745, 'learning_rate': 0.0558956221483831, 'size_network': 304}\n",
      "time taken to train: 3.2149434089660645 seconds\n",
      "stop after 100 iteration with investment 14.373000\n",
      "   40 | 00m04s |   14.37300 |          0.0559 |           27.6985 |    0.4850 |       303.9253 |    9.4846 |       45.4310 | \n",
      "\n",
      "Search parameters {'window_size': 22, 'skip': 15, 'population_size': 27, 'sigma': 0.892154361425812, 'learning_rate': 0.11103204478310967, 'size_network': 720}\n",
      "time taken to train: 2.9948313236236572 seconds\n",
      "stop after 100 iteration with investment 2.085701\n",
      "   41 | 00m04s |    2.08570 |          0.1110 |           26.7859 |    0.8922 |       719.9819 |   14.6931 |       21.9758 | \n",
      "\n",
      "Search parameters {'window_size': 28, 'skip': 11, 'population_size': 29, 'sigma': 0.182839958629257, 'learning_rate': 0.4152058364378557, 'size_network': 414}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   42 | 00m03s |    0.00000 |          0.4152 |           29.1302 |    0.1828 |       414.2307 |   10.6240 |       27.6846 | \n",
      "\n",
      "Search parameters {'window_size': 6, 'skip': 11, 'population_size': 8, 'sigma': 0.3107794826020311, 'learning_rate': 0.02994227973133117, 'size_network': 706}\n",
      "time taken to train: 0.47510623931884766 seconds\n",
      "stop after 100 iteration with investment 10.840799\n",
      "   43 | 00m01s |   10.84080 |          0.0299 |            7.7921 |    0.3108 |       705.7487 |   11.4479 |        6.4711 | \n",
      "\n",
      "Search parameters {'window_size': 33, 'skip': 6, 'population_size': 23, 'sigma': 0.18897818948672943, 'learning_rate': 0.48092906089670234, 'size_network': 420}\n",
      "time taken to train: 3.241325616836548 seconds\n",
      "stop after 100 iteration with investment 15.043197\n",
      "   44 | 00m04s |   15.04320 |          0.4809 |           23.2099 |    0.1890 |       420.3809 |    6.2120 |       32.9792 | \n",
      "\n",
      "Search parameters {'window_size': 17, 'skip': 3, 'population_size': 32, 'sigma': 0.06697003222307954, 'learning_rate': 0.4646900168899678, 'size_network': 546}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   45 | 00m03s |    0.00000 |          0.4647 |           32.4220 |    0.0670 |       546.1121 |    3.0166 |       16.6439 | \n",
      "\n",
      "Search parameters {'window_size': 13, 'skip': 10, 'population_size': 31, 'sigma': 0.7861817735301436, 'learning_rate': 0.24402019776924927, 'size_network': 482}\n",
      "time taken to train: 2.0156164169311523 seconds\n",
      "stop after 100 iteration with investment 17.416799\n",
      "   46 | 00m03s |   17.41680 |          0.2440 |           30.8409 |    0.7862 |       481.8969 |   10.3213 |       12.9426 | \n",
      "\n",
      "Search parameters {'window_size': 45, 'skip': 8, 'population_size': 40, 'sigma': 0.6965006558869755, 'learning_rate': 0.45589740049481137, 'size_network': 900}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken to train: 10.297287225723267 seconds\n",
      "stop after 100 iteration with investment 24.141198\n",
      "   47 | 00m11s |   24.14120 |          0.4559 |           40.3615 |    0.6965 |       900.4400 |    8.2389 |       45.1515 | \n",
      "\n",
      "Search parameters {'window_size': 18, 'skip': 14, 'population_size': 2, 'sigma': 0.04039851162238903, 'learning_rate': 0.22501631948214842, 'size_network': 723}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   48 | 00m01s |    0.00000 |          0.2250 |            2.2811 |    0.0404 |       723.0895 |   13.6784 |       18.3855 | \n",
      "\n",
      "Search parameters {'window_size': 31, 'skip': 8, 'population_size': 14, 'sigma': 0.454631410056508, 'learning_rate': 0.24625843061415703, 'size_network': 74}\n",
      "time taken to train: 0.7722418308258057 seconds\n",
      "stop after 100 iteration with investment 18.272197\n",
      "   49 | 00m02s |   18.27220 |          0.2463 |           14.1262 |    0.4546 |        74.3288 |    7.5932 |       30.7744 | \n",
      "\n",
      "Search parameters {'window_size': 15, 'skip': 6, 'population_size': 27, 'sigma': 0.15621515707814737, 'learning_rate': 0.1193213493313695, 'size_network': 634}\n",
      "time taken to train: 3.032594919204712 seconds\n",
      "stop after 100 iteration with investment 26.547500\n",
      "   50 | 00m04s |   26.54750 |          0.1193 |           26.8286 |    0.1562 |       633.9069 |    6.2033 |       14.6754 | \n",
      "\n",
      "Search parameters {'window_size': 29, 'skip': 12, 'population_size': 42, 'sigma': 0.6697896535290164, 'learning_rate': 0.4745180059332752, 'size_network': 349}\n",
      "time taken to train: 3.507105827331543 seconds\n",
      "stop after 100 iteration with investment 17.257600\n",
      "   51 | 00m04s |   17.25760 |          0.4745 |           41.9340 |    0.6698 |       349.1451 |   12.2459 |       28.8649 | \n",
      "\n",
      "Search parameters {'window_size': 30, 'skip': 10, 'population_size': 6, 'sigma': 0.36634648134375375, 'learning_rate': 0.012531352066896569, 'size_network': 627}\n",
      "time taken to train: 0.8845171928405762 seconds\n",
      "stop after 100 iteration with investment 9.192099\n",
      "   52 | 00m01s |    9.19210 |          0.0125 |            6.3290 |    0.3663 |       626.6453 |    9.9106 |       30.4519 | \n",
      "\n",
      "Search parameters {'window_size': 41, 'skip': 10, 'population_size': 20, 'sigma': 0.7232212850756303, 'learning_rate': 0.05422750825215879, 'size_network': 77}\n",
      "time taken to train: 1.018505334854126 seconds\n",
      "stop after 100 iteration with investment 10.100899\n",
      "   53 | 00m02s |   10.10090 |          0.0542 |           20.3173 |    0.7232 |        77.1594 |    9.8805 |       40.7962 | \n",
      "\n",
      "Search parameters {'window_size': 46, 'skip': 2, 'population_size': 13, 'sigma': 0.7461784153297715, 'learning_rate': 0.18026891305088624, 'size_network': 748}\n",
      "time taken to train: 5.579480409622192 seconds\n",
      "stop after 100 iteration with investment 45.839198\n",
      "   54 | 00m06s |   45.83920 |          0.1803 |           13.2481 |    0.7462 |       748.2901 |    2.1922 |       45.9253 | \n",
      "\n",
      "Search parameters {'window_size': 12, 'skip': 7, 'population_size': 21, 'sigma': 0.12757225010664022, 'learning_rate': 0.4720147812316741, 'size_network': 478}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   55 | 00m02s |    0.00000 |          0.4720 |           21.3350 |    0.1276 |       477.9011 |    6.7735 |       12.1196 | \n",
      "\n",
      "Search parameters {'window_size': 29, 'skip': 4, 'population_size': 37, 'sigma': 0.6847804984410423, 'learning_rate': 0.3799327602962851, 'size_network': 450}\n",
      "time taken to train: 6.137412786483765 seconds\n",
      "stop after 100 iteration with investment 39.676200\n",
      "   56 | 00m07s |   39.67620 |          0.3799 |           36.7378 |    0.6848 |       449.9529 |    4.2075 |       28.9233 | \n",
      "\n",
      "Search parameters {'window_size': 34, 'skip': 11, 'population_size': 14, 'sigma': 0.8010607500809694, 'learning_rate': 0.23083060941714112, 'size_network': 757}\n",
      "time taken to train: 2.378453493118286 seconds\n",
      "stop after 100 iteration with investment 13.318699\n",
      "   57 | 00m03s |   13.31870 |          0.2308 |           14.2680 |    0.8011 |       757.3544 |   10.8331 |       34.4581 | \n",
      "\n",
      "Search parameters {'window_size': 10, 'skip': 7, 'population_size': 28, 'sigma': 0.5713362277179944, 'learning_rate': 0.04831456297103356, 'size_network': 593}\n",
      "time taken to train: 2.1748082637786865 seconds\n",
      "stop after 100 iteration with investment 12.414400\n",
      "   58 | 00m03s |   12.41440 |          0.0483 |           27.5151 |    0.5713 |       592.7145 |    7.0072 |       10.1533 | \n",
      "\n",
      "Search parameters {'window_size': 16, 'skip': 13, 'population_size': 45, 'sigma': 0.4706884846581156, 'learning_rate': 0.034235641925639194, 'size_network': 443}\n",
      "time taken to train: 2.8119874000549316 seconds\n",
      "stop after 100 iteration with investment 0.959402\n",
      "   59 | 00m04s |    0.95940 |          0.0342 |           45.2785 |    0.4707 |       443.1858 |   13.4948 |       16.3061 | \n",
      "\n",
      "Search parameters {'window_size': 15, 'skip': 2, 'population_size': 30, 'sigma': 0.6549664938594614, 'learning_rate': 0.20784803825055315, 'size_network': 872}\n",
      "time taken to train: 7.9712584018707275 seconds\n",
      "stop after 100 iteration with investment 44.488004\n",
      "   60 | 00m09s |   44.48800 |          0.2078 |           30.0239 |    0.6550 |       871.5196 |    2.1500 |       14.9509 | \n",
      "\n",
      "Search parameters {'window_size': 10, 'skip': 15, 'population_size': 24, 'sigma': 0.8533447765395747, 'learning_rate': 0.3191278111563541, 'size_network': 641}\n",
      "time taken to train: 1.436133623123169 seconds\n",
      "stop after 100 iteration with investment 11.594501\n",
      "   61 | 00m02s |   11.59450 |          0.3191 |           23.6787 |    0.8533 |       640.9846 |   14.7180 |        9.6982 | \n",
      "\n",
      "Search parameters {'window_size': 44, 'skip': 9, 'population_size': 36, 'sigma': 0.463919314907331, 'learning_rate': 0.1936992237562697, 'size_network': 941}\n",
      "time taken to train: 9.152456760406494 seconds\n",
      "stop after 100 iteration with investment 20.351601\n",
      "   62 | 00m10s |   20.35160 |          0.1937 |           36.1552 |    0.4639 |       940.5797 |    9.1233 |       44.4202 | \n",
      "\n",
      "Search parameters {'window_size': 37, 'skip': 1, 'population_size': 33, 'sigma': 0.36295005827770077, 'learning_rate': 0.47908981350049923, 'size_network': 602}\n",
      "time taken to train: 18.611863613128662 seconds\n",
      "stop after 100 iteration with investment 118.454998\n",
      "   63 | 00m19s | \u001b[35m 118.45500\u001b[0m | \u001b[32m         0.4791\u001b[0m | \u001b[32m          33.3752\u001b[0m | \u001b[32m   0.3630\u001b[0m | \u001b[32m      601.8918\u001b[0m | \u001b[32m   1.3983\u001b[0m | \u001b[32m      37.0267\u001b[0m | \n",
      "\n",
      "Search parameters {'window_size': 16, 'skip': 5, 'population_size': 16, 'sigma': 0.09646575043468991, 'learning_rate': 0.2729165143295437, 'size_network': 58}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   64 | 00m01s |    0.00000 |          0.2729 |           16.4097 |    0.0965 |        57.8640 |    4.9026 |       16.2834 | \n",
      "\n",
      "Search parameters {'window_size': 30, 'skip': 6, 'population_size': 7, 'sigma': 0.7289102584243544, 'learning_rate': 0.12484596802107553, 'size_network': 185}\n",
      "time taken to train: 0.5961647033691406 seconds\n",
      "stop after 100 iteration with investment 15.445500\n",
      "   65 | 00m02s |   15.44550 |          0.1248 |            7.3242 |    0.7289 |       185.1033 |    6.4609 |       30.1676 | \n",
      "\n",
      "Search parameters {'window_size': 49, 'skip': 7, 'population_size': 33, 'sigma': 0.5581064171753295, 'learning_rate': 0.48357752208712235, 'size_network': 124}\n",
      "time taken to train: 2.7921245098114014 seconds\n",
      "stop after 100 iteration with investment 30.849402\n",
      "   66 | 00m04s |   30.84940 |          0.4836 |           33.2937 |    0.5581 |       124.2780 |    6.8382 |       49.4273 | \n",
      "\n",
      "Search parameters {'window_size': 26, 'skip': 10, 'population_size': 24, 'sigma': 0.24577665028491352, 'learning_rate': 0.4231196805573851, 'size_network': 454}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   67 | 00m03s |    0.00000 |          0.4231 |           23.8407 |    0.2458 |       453.9105 |   10.4039 |       25.7967 | \n",
      "\n",
      "Search parameters {'window_size': 25, 'skip': 14, 'population_size': 15, 'sigma': 0.8651996621858432, 'learning_rate': 0.46975452925924466, 'size_network': 375}\n",
      "time taken to train: 1.1424753665924072 seconds\n",
      "stop after 100 iteration with investment 11.169699\n",
      "   68 | 00m02s |   11.16970 |          0.4698 |           15.4829 |    0.8652 |       375.0137 |   13.9859 |       25.0451 | \n",
      "\n",
      "Search parameters {'window_size': 9, 'skip': 13, 'population_size': 12, 'sigma': 0.3524741428994743, 'learning_rate': 0.3886982495816039, 'size_network': 175}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop after 100 iteration with investment 0.000000\n",
      "   69 | 00m02s |    0.00000 |          0.3887 |           11.7058 |    0.3525 |       174.7174 |   12.9577 |        8.5275 | \n",
      "\n",
      "Search parameters {'window_size': 5, 'skip': 12, 'population_size': 6, 'sigma': 0.8007659832230785, 'learning_rate': 0.27630041062284755, 'size_network': 154}\n",
      "time taken to train: 0.17244505882263184 seconds\n",
      "stop after 100 iteration with investment 6.806999\n",
      "   70 | 00m01s |    6.80700 |          0.2763 |            5.7301 |    0.8008 |       154.3186 |   12.0720 |        4.5087 | \n",
      "\n",
      "Search parameters {'window_size': 11, 'skip': 10, 'population_size': 37, 'sigma': 0.05355815832861388, 'learning_rate': 0.2366377480881774, 'size_network': 303}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   71 | 00m02s |    0.00000 |          0.2366 |           37.4679 |    0.0536 |       302.7305 |    9.5728 |       10.6437 | \n",
      "\n",
      "Search parameters {'window_size': 10, 'skip': 4, 'population_size': 7, 'sigma': 0.08748548532444651, 'learning_rate': 0.18131358696548933, 'size_network': 756}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   72 | 00m02s |    0.00000 |          0.1813 |            7.2251 |    0.0875 |       756.4472 |    4.3724 |       10.1733 | \n",
      "\n",
      "Search parameters {'window_size': 23, 'skip': 3, 'population_size': 47, 'sigma': 0.2069924002582705, 'learning_rate': 0.2339565390615853, 'size_network': 748}\n",
      "time taken to train: 10.470397710800171 seconds\n",
      "stop after 100 iteration with investment 41.974299\n",
      "   73 | 00m12s |   41.97430 |          0.2340 |           46.5060 |    0.2070 |       747.5559 |    3.2126 |       22.9940 | \n",
      "\n",
      "Search parameters {'window_size': 18, 'skip': 4, 'population_size': 15, 'sigma': 0.096468529225561, 'learning_rate': 0.24318988867713998, 'size_network': 719}\n",
      "time taken to train: 2.4836621284484863 seconds\n",
      "stop after 100 iteration with investment 4.511399\n",
      "   74 | 00m04s |    4.51140 |          0.2432 |           15.1338 |    0.0965 |       719.0122 |    4.1038 |       17.6563 | \n",
      "\n",
      "Search parameters {'window_size': 39, 'skip': 13, 'population_size': 23, 'sigma': 0.20501017884787212, 'learning_rate': 0.4093471252831627, 'size_network': 470}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   75 | 00m02s |    0.00000 |          0.4093 |           22.8299 |    0.2050 |       470.4530 |   12.6440 |       38.6439 | \n",
      "\n",
      "Search parameters {'window_size': 9, 'skip': 11, 'population_size': 32, 'sigma': 0.873932761251215, 'learning_rate': 0.1969100795960079, 'size_network': 607}\n",
      "time taken to train: 1.9335334300994873 seconds\n",
      "stop after 100 iteration with investment 7.525198\n",
      "   76 | 00m03s |    7.52520 |          0.1969 |           32.1473 |    0.8739 |       606.6092 |   10.5312 |        8.5077 | \n",
      "\n",
      "Search parameters {'window_size': 27, 'skip': 2, 'population_size': 29, 'sigma': 0.22134396177855, 'learning_rate': 0.40153464213790957, 'size_network': 915}\n",
      "time taken to train: 10.16161847114563 seconds\n",
      "stop after 100 iteration with investment 36.609398\n",
      "   77 | 00m12s |   36.60940 |          0.4015 |           29.4131 |    0.2213 |       914.7459 |    2.1501 |       26.9411 | \n",
      "\n",
      "Search parameters {'window_size': 8, 'skip': 2, 'population_size': 43, 'sigma': 0.2028727427873136, 'learning_rate': 0.4726119214341495, 'size_network': 915}\n",
      "time taken to train: 8.813605308532715 seconds\n",
      "stop after 100 iteration with investment 54.560602\n",
      "   78 | 00m11s |   54.56060 |          0.4726 |           42.8794 |    0.2029 |       915.4439 |    1.9333 |        8.1425 | \n",
      "\n",
      "Search parameters {'window_size': 29, 'skip': 8, 'population_size': 13, 'sigma': 0.19122254845668435, 'learning_rate': 0.185498852537461, 'size_network': 737}\n",
      "time taken to train: 2.1560752391815186 seconds\n",
      "stop after 100 iteration with investment 10.917298\n",
      "   79 | 00m03s |   10.91730 |          0.1855 |           12.5186 |    0.1912 |       736.6909 |    8.3923 |       28.8096 | \n",
      "\n",
      "Search parameters {'window_size': 38, 'skip': 11, 'population_size': 2, 'sigma': 0.5976383670876801, 'learning_rate': 0.3717027673547307, 'size_network': 603}\n",
      "stop after 100 iteration with investment 0.000000\n",
      "   80 | 00m02s |    0.00000 |          0.3717 |            1.9506 |    0.5976 |       603.2334 |   11.1509 |       38.3818 | \n"
     ]
    }
   ],
   "source": [
    "accbest = 0.0\n",
    "NN_BAYESIAN = BayesianOptimization(\n",
    "    find_best_agent,\n",
    "    {\n",
    "        'window_size': (2, 50),\n",
    "        'skip': (1, 15),\n",
    "        'population_size': (1, 50),\n",
    "        'sigma': (0.01, 0.99),\n",
    "        'learning_rate': (0.000001, 0.49),\n",
    "        'size_network': (10, 1000),\n",
    "    },\n",
    ")\n",
    "NN_BAYESIAN.maximize(init_points = 30, n_iter = 50, acq = 'ei', xi = 0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best AGENT accuracy value: 118.454998\n",
      "Best AGENT parameters:  {'window_size': 37.026745406700485, 'skip': 1.398295139557024, 'population_size': 33.375200286661, 'sigma': 0.36295005827770077, 'learning_rate': 0.47908981350049923, 'size_network': 601.8917542486957}\n"
     ]
    }
   ],
   "source": [
    "print('Best AGENT accuracy value: %f' % NN_BAYESIAN.res['max']['max_val'])\n",
    "print('Best AGENT parameters: ', NN_BAYESIAN.res['max']['max_params'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### My selected parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken to train: 7.432262897491455 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "60.71330993000004"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_agent(\n",
    "    window_size = 30, \n",
    "    skip = 1, \n",
    "    population_size = 15, \n",
    "    sigma = 0.1, \n",
    "    learning_rate = 0.03, \n",
    "    size_network = 500\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bayesian parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken to train: 18.46750020980835 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "105.43940030999998"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_agent(\n",
    "    window_size = int(np.around(NN_BAYESIAN.res['max']['max_params']['window_size'])), \n",
    "    skip = int(np.around(NN_BAYESIAN.res['max']['max_params']['skip'])), \n",
    "    population_size = int(np.around(NN_BAYESIAN.res['max']['max_params']['population_size'])), \n",
    "    sigma = NN_BAYESIAN.res['max']['max_params']['sigma'], \n",
    "    learning_rate = NN_BAYESIAN.res['max']['max_params']['learning_rate'], \n",
    "    size_network = int(np.around(NN_BAYESIAN.res['max']['max_params']['size_network']))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### My selected parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 100. reward: 78.018904\n",
      "iter 200. reward: 104.486503\n",
      "iter 300. reward: 111.254201\n",
      "iter 400. reward: 112.303196\n",
      "iter 500. reward: 117.427600\n",
      "time taken to train: 35.953824043273926 seconds\n",
      "day 2: buy 5 units at price 1395.899965, total balance 8604.100035\n",
      "day 3: buy 5 units at price 1288.899995, total balance 7315.200040\n",
      "day 4: buy 5 units at price 1330.650025, total balance 5984.550015\n",
      "day 5: buy 5 units at price 1262.399980, total balance 4722.150035\n",
      "day 6: buy 1 units at price 267.529999, total balance 4454.620036\n",
      "day 8, sell 5 units at price 1528.600005, investment 9.506415 %, total balance 5983.220041,\n",
      "day 11, sell 5 units at price 1523.500060, investment 18.201572 %, total balance 7506.720101,\n",
      "day 12, sell 5 units at price 1504.649965, investment 13.076311 %, total balance 9011.370066,\n",
      "day 14, sell 5 units at price 1501.699980, investment 18.955957 %, total balance 10513.070046,\n",
      "day 15, sell 1 units at price 291.209991, investment 8.851341 %, total balance 10804.280037,\n",
      "day 16: buy 5 units at price 1438.450010, total balance 9365.830027\n",
      "day 18, sell 5 units at price 1500.399935, investment 4.306714 %, total balance 10866.229962,\n",
      "day 23: buy 5 units at price 1427.400055, total balance 9438.829907\n",
      "day 24: buy 5 units at price 1470.399935, total balance 7968.429972\n",
      "day 25: buy 5 units at price 1469.499970, total balance 6498.930002\n",
      "day 28: buy 1 units at price 284.450012, total balance 6214.479990\n",
      "day 31: buy 5 units at price 1509.850005, total balance 4704.629985\n",
      "day 32: buy 1 units at price 306.850006, total balance 4397.779979\n",
      "day 34, sell 5 units at price 1505.299990, investment 5.457470 %, total balance 5903.079969,\n",
      "day 35: buy 5 units at price 1459.850005, total balance 4443.229964\n",
      "day 37, sell 5 units at price 1432.400055, investment -2.584323 %, total balance 5875.630019,\n",
      "day 38: buy 5 units at price 1422.700045, total balance 4452.929974\n",
      "day 41: buy 5 units at price 1375.050050, total balance 3077.879924\n",
      "day 42: buy 1 units at price 279.070007, total balance 2798.809917\n",
      "day 44, sell 5 units at price 1394.250030, investment -5.120785 %, total balance 4193.059947,\n",
      "day 47: buy 5 units at price 1423.650055, total balance 2769.409892\n",
      "day 48: buy 1 units at price 291.820007, total balance 2477.589885\n",
      "day 49: buy 5 units at price 1483.699950, total balance 993.889935\n",
      "day 50: buy 5 units at price 1455.650025, total balance -461.760090\n",
      "day 56, sell 5 units at price 1723.899995, investment 506.046730 %, total balance 1262.139905,\n",
      "day 57, sell 5 units at price 1788.600005, investment 18.462099 %, total balance 3050.739910,\n",
      "day 58, sell 5 units at price 1790.850065, investment 483.623930 %, total balance 4841.589975,\n",
      "day 59, sell 5 units at price 1854.149935, investment 27.009619 %, total balance 6695.739910,\n",
      "day 60, sell 5 units at price 1762.749940, investment 23.901728 %, total balance 8458.489850,\n",
      "day 62, sell 5 units at price 1737.550050, investment 26.362677 %, total balance 10196.039900,\n",
      "day 63: buy 5 units at price 1668.150025, total balance 8527.889875\n",
      "day 64: buy 1 units at price 333.010010, total balance 8194.879865\n",
      "day 65, sell 5 units at price 1710.000000, investment 512.749474 %, total balance 9904.879865,\n",
      "day 66, sell 5 units at price 1722.500000, investment 20.991812 %, total balance 11627.379865,\n",
      "day 68, sell 5 units at price 1714.750060, investment 487.605380 %, total balance 13342.129925,\n",
      "day 75: buy 5 units at price 1594.799955, total balance 11747.329970\n",
      "day 76: buy 1 units at price 316.709991, total balance 11430.619979\n",
      "day 78: buy 5 units at price 1550.500030, total balance 9880.119949\n",
      "day 79, sell 5 units at price 1613.450010, investment 1.169429 %, total balance 11493.569959,\n",
      "day 81, sell 5 units at price 1601.150055, investment 405.557166 %, total balance 13094.720014,\n",
      "day 82: buy 5 units at price 1567.899935, total balance 11526.820079\n",
      "day 83: buy 5 units at price 1516.000060, total balance 10010.820019\n",
      "day 84: buy 5 units at price 1487.149965, total balance 8523.670054\n",
      "day 85: buy 5 units at price 1543.699950, total balance 6979.970104\n",
      "day 88: buy 5 units at price 1450.850065, total balance 5529.120039\n",
      "day 89: buy 5 units at price 1490.700075, total balance 4038.419964\n",
      "day 90: buy 5 units at price 1504.199980, total balance 2534.219984\n",
      "day 91, sell 5 units at price 1747.700045, investment 12.718479 %, total balance 4281.920029,\n",
      "day 92, sell 5 units at price 1740.850065, investment 11.030687 %, total balance 6022.770094,\n",
      "day 93, sell 5 units at price 1709.949950, investment 12.793528 %, total balance 7732.720044,\n",
      "day 94, sell 5 units at price 1897.850035, investment 27.616587 %, total balance 9630.570079,\n",
      "day 95, sell 5 units at price 1851.699980, investment 19.952066 %, total balance 11482.270059,\n",
      "day 96, sell 5 units at price 1762.250060, investment 21.463279 %, total balance 13244.520119,\n",
      "day 98, sell 5 units at price 1782.050020, investment 19.544505 %, total balance 15026.570139,\n",
      "day 99, sell 1 units at price 347.640015, investment -76.888710 %, total balance 15374.210154,\n",
      "day 103: buy 5 units at price 1542.200010, total balance 13832.010144\n",
      "day 105, sell 5 units at price 1608.200075, investment 4.279605 %, total balance 15440.210219,\n",
      "day 106: buy 1 units at price 320.100006, total balance 15120.110213\n",
      "day 108, sell 1 units at price 319.269989, investment -0.259299 %, total balance 15439.380202,\n",
      "day 109: buy 5 units at price 1559.299925, total balance 13880.080277\n",
      "day 111: buy 1 units at price 303.149994, total balance 13576.930283\n",
      "day 112: buy 5 units at price 1508.300020, total balance 12068.630263\n",
      "day 114: buy 5 units at price 1403.699950, total balance 10664.930313\n",
      "day 115: buy 5 units at price 1404.750060, total balance 9260.180253\n",
      "day 118: buy 5 units at price 1397.200010, total balance 7862.980243\n",
      "day 121, sell 5 units at price 1476.000060, investment -5.342132 %, total balance 9338.980303,\n",
      "day 122, sell 5 units at price 1474.199980, investment 386.293917 %, total balance 10813.180283,\n",
      "day 124: buy 5 units at price 1495.099945, total balance 9318.080338\n",
      "day 125: buy 1 units at price 298.329987, total balance 9019.750351\n",
      "day 127: buy 1 units at price 299.679993, total balance 8720.070358\n",
      "day 128: buy 5 units at price 1504.949950, total balance 7215.120408\n",
      "day 129, sell 5 units at price 1547.899935, investment 2.625467 %, total balance 8763.020343,\n",
      "day 131: buy 5 units at price 1323.849945, total balance 7439.170398\n",
      "day 132, sell 5 units at price 1553.500060, investment 10.671804 %, total balance 8992.670458,\n",
      "day 134, sell 5 units at price 1473.999940, investment 4.929694 %, total balance 10466.670398,\n",
      "day 135: buy 5 units at price 1409.149935, total balance 9057.520463\n",
      "day 136: buy 5 units at price 1309.750060, total balance 7747.770403\n",
      "day 137: buy 1 units at price 250.559998, total balance 7497.210405\n",
      "day 138: buy 5 units at price 1313.999940, total balance 6183.210465\n",
      "day 139: buy 5 units at price 1284.400025, total balance 4898.810440\n",
      "day 141: buy 5 units at price 1293.899995, total balance 3604.910445\n",
      "day 142: buy 5 units at price 1297.949980, total balance 2306.960465\n",
      "day 143: buy 1 units at price 276.589996, total balance 2030.370469\n",
      "day 144: buy 5 units at price 1358.899995, total balance 671.470474\n",
      "day 146, sell 5 units at price 1300.000000, investment -6.956771 %, total balance 1971.470474,\n",
      "day 147: buy 5 units at price 1304.750060, total balance 666.720414\n",
      "day 149: buy 5 units at price 1442.500000, total balance -775.779586\n",
      "day 152, sell 5 units at price 1674.250030, investment 11.982482 %, total balance 898.470444,\n",
      "day 153: buy 1 units at price 329.899994, total balance 568.570450\n",
      "day 155, sell 5 units at price 1721.399995, investment 477.012057 %, total balance 2289.970445,\n",
      "day 156: buy 5 units at price 1732.050020, total balance 557.920425\n",
      "day 158, sell 5 units at price 1705.299990, investment 469.040320 %, total balance 2263.220415,\n",
      "day 159, sell 5 units at price 1740.800020, investment 15.671622 %, total balance 4004.020435,\n",
      "day 160, sell 5 units at price 1756.999970, investment 32.718967 %, total balance 5761.020405,\n",
      "day 162, sell 5 units at price 1656.399995, investment 17.546043 %, total balance 7417.420400,\n",
      "day 164, sell 5 units at price 1720.000000, investment 31.322766 %, total balance 9137.420400,\n",
      "day 165, sell 5 units at price 1742.200010, investment 595.322487 %, total balance 10879.620410,\n",
      "day 167: buy 1 units at price 353.470001, total balance 10526.150409\n",
      "day 168: buy 1 units at price 347.489990, total balance 10178.660419\n",
      "day 169: buy 1 units at price 338.190002, total balance 9840.470417\n",
      "day 171, sell 5 units at price 1730.000000, investment 31.659062 %, total balance 11570.470417,\n",
      "day 174: buy 1 units at price 341.170013, total balance 11229.300404\n",
      "day 175: buy 1 units at price 350.480011, total balance 10878.820393\n",
      "day 178, sell 5 units at price 1815.299990, investment 41.334472 %, total balance 12694.120383,\n",
      "day 180, sell 5 units at price 1825.749970, investment 41.104411 %, total balance 14519.870353,\n",
      "day 181, sell 5 units at price 1833.800050, investment 41.284339 %, total balance 16353.670403,\n",
      "day 182, sell 5 units at price 1833.000030, investment 562.713784 %, total balance 18186.670433,\n",
      "day 183, sell 5 units at price 1883.950045, investment 38.637873 %, total balance 20070.620478,\n",
      "day 185: buy 1 units at price 348.420013, total balance 19722.200465\n",
      "day 186: buy 1 units at price 337.029999, total balance 19385.170466\n",
      "day 187, sell 3 units at price 998.910003, investment -23.440509 %, total balance 20384.080469,\n",
      "day 188: buy 5 units at price 1576.900025, total balance 18807.180444\n",
      "day 190: buy 5 units at price 1476.950075, total balance 17330.230369\n",
      "day 191: buy 1 units at price 326.089996, total balance 17004.140373\n",
      "day 192: buy 5 units at price 1580.650025, total balance 15423.490348\n",
      "day 194, sell 5 units at price 1663.999940, investment 15.355282 %, total balance 17087.490288,\n",
      "day 195: buy 5 units at price 1550.599975, total balance 15536.890313\n",
      "day 196: buy 1 units at price 300.359985, total balance 15236.530328\n",
      "day 197: buy 5 units at price 1588.450010, total balance 13648.080318\n",
      "day 198, sell 5 units at price 1674.799955, investment 407.668986 %, total balance 15322.880273,\n",
      "day 201, sell 5 units at price 1724.850005, investment -0.415693 %, total balance 17047.730278,\n",
      "day 203, sell 5 units at price 1671.999970, investment 373.024575 %, total balance 18719.730248,\n",
      "day 205, sell 5 units at price 1730.249940, investment 397.927995 %, total balance 20449.980188,\n",
      "day 206, sell 2 units at price 694.619996, investment 105.393416 %, total balance 21144.600184,\n",
      "day 207: buy 5 units at price 1511.300050, total balance 19633.300134\n",
      "day 208: buy 1 units at price 298.920013, total balance 19334.380121\n",
      "day 209: buy 5 units at price 1437.949980, total balance 17896.430141\n",
      "day 210: buy 5 units at price 1457.550050, total balance 16438.880091\n",
      "day 211: buy 5 units at price 1485.200045, total balance 14953.680046\n",
      "day 213, sell 5 units at price 1487.299955, investment 335.940997 %, total balance 16440.980001,\n",
      "day 215: buy 5 units at price 1535.099945, total balance 14905.880056\n",
      "day 216, sell 5 units at price 1561.049955, investment 345.403420 %, total balance 16466.930011,\n",
      "day 217: buy 1 units at price 312.890015, total balance 16154.039996\n",
      "day 218, sell 5 units at price 1606.750030, investment 361.153197 %, total balance 17760.790026,\n",
      "day 219, sell 5 units at price 1586.100005, investment 370.610928 %, total balance 19346.890031,\n",
      "day 221: buy 5 units at price 1528.999940, total balance 17817.890091\n",
      "day 223: buy 5 units at price 1559.049990, total balance 16258.840101\n",
      "day 225, sell 5 units at price 1518.849945, investment -3.681278 %, total balance 17777.690046,\n",
      "day 228, sell 5 units at price 1512.799990, investment 2.427294 %, total balance 19290.490036,\n",
      "day 229: buy 5 units at price 1456.150055, total balance 17834.339981\n",
      "day 230: buy 1 units at price 294.709991, total balance 17539.629990\n",
      "day 231, sell 5 units at price 1493.849945, investment 358.109713 %, total balance 19033.479935,\n",
      "day 232, sell 5 units at price 1489.299925, investment -5.779274 %, total balance 20522.779860,\n",
      "day 233, sell 3 units at price 944.219970, investment -39.106153 %, total balance 21466.999830,\n",
      "day 235: buy 5 units at price 1473.950045, total balance 19993.049785\n",
      "day 236: buy 1 units at price 285.359985, total balance 19707.689800\n",
      "day 237: buy 5 units at price 1382.700045, total balance 18324.989755\n",
      "day 238: buy 1 units at price 276.239990, total balance 18048.749765\n",
      "day 241, sell 5 units at price 1454.600065, investment 384.285570 %, total balance 19503.349830,\n",
      "day 242: buy 1 units at price 283.359985, total balance 19219.989845\n",
      "day 244, sell 5 units at price 1449.799955, investment -8.728638 %, total balance 20669.789800,\n",
      "day 245, sell 3 units at price 826.289979, investment -45.325882 %, total balance 21496.079779,\n",
      "\n",
      "total gained 11496.079779, total investment 114.960798 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Model(input_size = 30, \n",
    "              layer_size = 500, \n",
    "              output_size = 3)\n",
    "agent = Agent(population_size = 15, \n",
    "              sigma = 0.1, \n",
    "              learning_rate = 0.03, \n",
    "              model = model, \n",
    "              money = 10000, \n",
    "              max_buy = 5, \n",
    "              max_sell = 5, \n",
    "              skip = 1, \n",
    "              window_size = 30)\n",
    "agent.fit(500, 100)\n",
    "agent.buy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### bayesian parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 100. reward: 115.522302\n",
      "iter 200. reward: 122.923396\n",
      "iter 300. reward: 128.013799\n",
      "iter 400. reward: 129.764900\n",
      "iter 500. reward: 133.173699\n",
      "time taken to train: 93.03121662139893 seconds\n",
      "day 2: buy 5 units at price 1395.899965, total balance 8604.100035\n",
      "day 3: buy 5 units at price 1288.899995, total balance 7315.200040\n",
      "day 4: buy 5 units at price 1330.650025, total balance 5984.550015\n",
      "day 5: buy 5 units at price 1262.399980, total balance 4722.150035\n",
      "day 6: buy 5 units at price 1337.649995, total balance 3384.500040\n",
      "day 7: buy 5 units at price 1434.700010, total balance 1949.800030\n",
      "day 11, sell 5 units at price 1523.500060, investment 9.141063 %, total balance 3473.300090,\n",
      "day 12: buy 1 units at price 300.929993, total balance 3172.370097\n",
      "day 13, sell 5 units at price 1470.399935, investment 14.081771 %, total balance 4642.770032,\n",
      "day 14, sell 5 units at price 1501.699980, investment 12.854616 %, total balance 6144.470012,\n",
      "day 15, sell 5 units at price 1456.049955, investment 15.339827 %, total balance 7600.519967,\n",
      "day 18, sell 5 units at price 1500.399935, investment 12.166855 %, total balance 9100.919902,\n",
      "day 19: buy 1 units at price 290.239990, total balance 8810.679912\n",
      "day 20: buy 5 units at price 1416.849975, total balance 7393.829937\n",
      "day 21, sell 5 units at price 1417.299955, investment -1.212801 %, total balance 8811.129892,\n",
      "day 22: buy 5 units at price 1403.450010, total balance 7407.679882\n",
      "day 23: buy 1 units at price 285.480011, total balance 7122.199871\n",
      "day 25: buy 5 units at price 1469.499970, total balance 5652.699901\n",
      "day 28: buy 1 units at price 284.450012, total balance 5368.249889\n",
      "day 29: buy 1 units at price 294.089996, total balance 5074.159893\n",
      "day 30: buy 1 units at price 302.769989, total balance 4771.389904\n",
      "day 32, sell 5 units at price 1534.250030, investment 409.836196 %, total balance 6305.639934,\n",
      "day 33, sell 5 units at price 1525.099945, investment 425.461686 %, total balance 7830.739879,\n",
      "day 34, sell 5 units at price 1505.299990, investment 6.242723 %, total balance 9336.039869,\n",
      "day 35: buy 1 units at price 291.970001, total balance 9044.069868\n",
      "day 36: buy 5 units at price 1420.899965, total balance 7623.169903\n",
      "day 37: buy 1 units at price 286.480011, total balance 7336.689892\n",
      "day 38: buy 5 units at price 1422.700045, total balance 5913.989847\n",
      "day 39: buy 5 units at price 1384.100035, total balance 4529.889812\n",
      "day 40: buy 5 units at price 1422.449950, total balance 3107.439862\n",
      "day 41: buy 5 units at price 1375.050050, total balance 1732.389812\n",
      "day 43: buy 5 units at price 1389.250030, total balance 343.139782\n",
      "day 45: buy 5 units at price 1418.800050, total balance -1075.660268\n",
      "day 52, sell 5 units at price 1580.449980, investment 12.611776 %, total balance 504.789712,\n",
      "day 54: buy 5 units at price 1660.500030, total balance -1155.710318\n",
      "day 55, sell 5 units at price 1713.849945, investment 500.339736 %, total balance 558.139627,\n",
      "day 56, sell 5 units at price 1723.899995, investment 17.312013 %, total balance 2282.039622,\n",
      "day 57, sell 5 units at price 1788.600005, investment 528.792382 %, total balance 4070.639627,\n",
      "day 58, sell 5 units at price 1790.850065, investment 508.946271 %, total balance 5861.489692,\n",
      "day 59, sell 5 units at price 1854.149935, investment 512.395549 %, total balance 7715.639627,\n",
      "day 60, sell 5 units at price 1762.749940, investment 503.743513 %, total balance 9478.389567,\n",
      "day 61, sell 5 units at price 1811.100005, investment 27.461472 %, total balance 11289.489572,\n",
      "day 62: buy 5 units at price 1737.550050, total balance 9551.939522\n",
      "day 63, sell 5 units at price 1668.150025, investment 482.291944 %, total balance 11220.089547,\n",
      "day 64: buy 5 units at price 1665.050050, total balance 9555.039497\n",
      "day 65, sell 5 units at price 1710.000000, investment 20.193994 %, total balance 11265.039497,\n",
      "day 66, sell 5 units at price 1722.500000, investment 24.449097 %, total balance 12987.539497,\n",
      "day 68, sell 3 units at price 1028.850036, investment -27.670563 %, total balance 14016.389533,\n",
      "day 70: buy 5 units at price 1554.299925, total balance 12462.089608\n",
      "day 72: buy 5 units at price 1544.499970, total balance 10917.589638\n",
      "day 73: buy 5 units at price 1592.550050, total balance 9325.039588\n",
      "day 76: buy 5 units at price 1583.549955, total balance 7741.489633\n",
      "day 78: buy 5 units at price 1550.500030, total balance 6190.989603\n",
      "day 80, sell 5 units at price 1619.250030, investment 17.759352 %, total balance 7810.239633,\n",
      "day 82, sell 5 units at price 1567.899935, investment 12.859449 %, total balance 9378.139568,\n",
      "day 83: buy 5 units at price 1516.000060, total balance 7862.139508\n",
      "day 85: buy 1 units at price 308.739990, total balance 7553.399518\n",
      "day 86: buy 5 units at price 1533.249970, total balance 6020.149548\n",
      "day 87: buy 5 units at price 1485.899965, total balance 4534.249583\n",
      "day 88: buy 5 units at price 1450.850065, total balance 3083.399518\n",
      "day 90: buy 5 units at price 1504.199980, total balance 1579.199538\n",
      "day 92, sell 5 units at price 1740.850065, investment 22.698760 %, total balance 3320.049603,\n",
      "day 93, sell 5 units at price 1709.949950, investment 2.978014 %, total balance 5029.999553,\n",
      "day 94, sell 5 units at price 1897.850035, investment 9.225633 %, total balance 6927.849588,\n",
      "day 95, sell 5 units at price 1851.699980, investment 11.209869 %, total balance 8779.549568,\n",
      "day 96, sell 5 units at price 1762.250060, investment 13.379022 %, total balance 10541.799628,\n",
      "day 98, sell 5 units at price 1782.050020, investment 15.380386 %, total balance 12323.849648,\n",
      "day 99, sell 5 units at price 1738.200075, investment 9.145711 %, total balance 14062.049723,\n",
      "day 100, sell 5 units at price 1693.450010, investment 6.940107 %, total balance 15755.499733,\n",
      "day 101, sell 1 units at price 335.450012, investment -78.365043 %, total balance 16090.949745,\n",
      "day 102: buy 1 units at price 305.500000, total balance 15785.449745\n",
      "day 105: buy 5 units at price 1608.200075, total balance 14177.249670\n",
      "day 106: buy 1 units at price 320.100006, total balance 13857.149664\n",
      "day 108, sell 5 units at price 1596.349945, investment 5.300124 %, total balance 15453.499609,\n",
      "day 110: buy 5 units at price 1525.050050, total balance 13928.449559\n",
      "day 111, sell 5 units at price 1515.749970, investment 390.947081 %, total balance 15444.199529,\n",
      "day 112: buy 5 units at price 1508.300020, total balance 13935.899509\n",
      "day 113: buy 5 units at price 1444.750060, total balance 12491.149449\n",
      "day 115: buy 5 units at price 1404.750060, total balance 11086.399389\n",
      "day 116: buy 5 units at price 1316.199950, total balance 9770.199439\n",
      "day 118: buy 5 units at price 1397.200010, total balance 8372.999429\n",
      "day 119: buy 5 units at price 1452.700045, total balance 6920.299384\n",
      "day 120, sell 5 units at price 1447.299955, investment -5.605741 %, total balance 8367.599339,\n",
      "day 122: buy 1 units at price 294.839996, total balance 8072.759343\n",
      "day 123: buy 5 units at price 1424.799955, total balance 6647.959388\n",
      "day 124, sell 5 units at price 1495.099945, investment 0.619152 %, total balance 8143.059333,\n",
      "day 125, sell 5 units at price 1491.649935, investment 2.812136 %, total balance 9634.709268,\n",
      "day 126, sell 5 units at price 1495.500030, investment -0.578377 %, total balance 11130.209298,\n",
      "day 128, sell 5 units at price 1504.949950, investment 392.618642 %, total balance 12635.159248,\n",
      "day 129, sell 5 units at price 1547.899935, investment -3.749542 %, total balance 14183.059183,\n",
      "day 131: buy 5 units at price 1323.849945, total balance 12859.209238\n",
      "day 132, sell 5 units at price 1553.500060, investment 385.317098 %, total balance 14412.709298,\n",
      "day 133: buy 5 units at price 1505.099945, total balance 12907.609353\n",
      "day 135: buy 5 units at price 1409.149935, total balance 11498.459418\n",
      "day 136: buy 5 units at price 1309.750060, total balance 10188.709358\n",
      "day 137: buy 1 units at price 250.559998, total balance 9938.149360\n",
      "day 138: buy 5 units at price 1313.999940, total balance 8624.149420\n",
      "day 139: buy 5 units at price 1284.400025, total balance 7339.749395\n",
      "day 140: buy 5 units at price 1261.149980, total balance 6078.599415\n",
      "day 141: buy 5 units at price 1293.899995, total balance 4784.699420\n",
      "day 143: buy 5 units at price 1382.949980, total balance 3401.749440\n",
      "day 144: buy 5 units at price 1358.899995, total balance 2042.849445\n",
      "day 146: buy 5 units at price 1300.000000, total balance 742.849445\n",
      "day 149: buy 5 units at price 1442.500000, total balance -699.650555\n",
      "day 153, sell 5 units at price 1649.499970, investment 8.160383 %, total balance 949.849415,\n",
      "day 154: buy 5 units at price 1686.600035, total balance -736.750620\n",
      "day 155, sell 5 units at price 1721.399995, investment 14.128487 %, total balance 984.649375,\n",
      "day 157: buy 5 units at price 1706.999970, total balance -722.350595\n",
      "day 158, sell 5 units at price 1705.299990, investment 18.034256 %, total balance 982.949395,\n",
      "day 159, sell 5 units at price 1740.800020, investment 23.922402 %, total balance 2723.749415,\n",
      "day 162: buy 5 units at price 1656.399995, total balance 1067.349420\n",
      "day 163, sell 5 units at price 1693.650055, investment 28.677262 %, total balance 2760.999475,\n",
      "day 164, sell 5 units at price 1720.000000, investment 23.103349 %, total balance 4480.999475,\n",
      "day 167, sell 5 units at price 1767.350005, investment 21.659665 %, total balance 6248.349480,\n",
      "day 169, sell 5 units at price 1690.950010, investment 473.514460 %, total balance 7939.299490,\n",
      "day 170: buy 5 units at price 1629.149935, total balance 6310.149555\n",
      "day 172: buy 1 units at price 343.920013, total balance 5966.229542\n",
      "day 175, sell 5 units at price 1752.400055, investment 22.992708 %, total balance 7718.629597,\n",
      "day 176, sell 5 units at price 1792.449950, investment 35.396761 %, total balance 9511.079547,\n",
      "day 177, sell 5 units at price 1798.500060, investment 19.493730 %, total balance 11309.579607,\n",
      "day 178, sell 5 units at price 1815.299990, investment 28.822345 %, total balance 13124.879597,\n",
      "day 179: buy 1 units at price 357.970001, total balance 12766.909596\n",
      "day 180, sell 5 units at price 1825.749970, investment 39.396823 %, total balance 14592.659566,\n",
      "day 181, sell 5 units at price 1833.800050, investment 631.880613 %, total balance 16426.459616,\n",
      "day 182, sell 5 units at price 1833.000030, investment 39.497726 %, total balance 18259.459646,\n",
      "day 184, sell 5 units at price 1828.549955, investment 42.366079 %, total balance 20088.009601,\n",
      "day 185, sell 5 units at price 1742.100065, investment 38.135836 %, total balance 21830.109666,\n",
      "day 186, sell 1 units at price 337.029999, investment -73.952392 %, total balance 22167.139665,\n",
      "day 187: buy 1 units at price 332.970001, total balance 21834.169664\n",
      "day 188: buy 5 units at price 1576.900025, total balance 20257.269639\n",
      "day 189: buy 5 units at price 1598.849945, total balance 18658.419694\n",
      "day 190: buy 5 units at price 1476.950075, total balance 17181.469619\n",
      "day 192: buy 5 units at price 1580.650025, total balance 15600.819594\n",
      "day 194: buy 1 units at price 332.799988, total balance 15268.019606\n",
      "day 196: buy 5 units at price 1501.799925, total balance 13766.219681\n",
      "day 197: buy 5 units at price 1588.450010, total balance 12177.769671\n",
      "day 198, sell 5 units at price 1674.799955, investment 21.103437 %, total balance 13852.569626,\n",
      "day 200, sell 5 units at price 1692.649995, investment 24.560306 %, total balance 15545.219621,\n",
      "day 201, sell 5 units at price 1724.850005, investment 32.680770 %, total balance 17270.069626,\n",
      "day 202, sell 5 units at price 1736.300050, investment 20.367421 %, total balance 19006.369676,\n",
      "day 203, sell 5 units at price 1671.999970, investment -0.865651 %, total balance 20678.369646,\n",
      "day 204, sell 5 units at price 1722.149965, investment 0.887522 %, total balance 22400.519611,\n",
      "day 205: buy 5 units at price 1730.249940, total balance 20670.269671\n",
      "day 206, sell 5 units at price 1736.549990, investment 4.838807 %, total balance 22406.819661,\n",
      "day 207: buy 5 units at price 1511.300050, total balance 20895.519611\n",
      "day 208, sell 5 units at price 1494.600065, investment -8.258900 %, total balance 22390.119676,\n",
      "day 209: buy 1 units at price 287.589996, total balance 22102.529680\n",
      "day 211: buy 5 units at price 1485.200045, total balance 20617.329635\n",
      "day 212: buy 5 units at price 1481.900025, total balance 19135.429610\n",
      "day 213: buy 5 units at price 1487.299955, total balance 17648.129655\n",
      "day 215: buy 1 units at price 307.019989, total balance 17341.109666\n",
      "day 217, sell 5 units at price 1564.450075, investment 354.887769 %, total balance 18905.559741,\n",
      "day 218, sell 5 units at price 1606.750030, investment 348.850469 %, total balance 20512.309771,\n",
      "day 219, sell 5 units at price 1586.100005, investment 376.349221 %, total balance 22098.409776,\n",
      "day 220: buy 5 units at price 1537.550050, total balance 20560.859726\n",
      "day 222: buy 1 units at price 312.839996, total balance 20248.019730\n",
      "day 223, sell 5 units at price 1559.049990, investment -1.131970 %, total balance 21807.069720,\n",
      "day 225, sell 5 units at price 1518.849945, investment -5.003597 %, total balance 23325.919665,\n",
      "day 244: buy 1 units at price 289.959991, total balance 23035.959674\n",
      "day 245: buy 1 units at price 275.429993, total balance 22760.529681\n",
      "day 247, sell 2 units at price 534.940002, investment 84.487522 %, total balance 23295.469683,\n",
      "\n",
      "total gained 13295.469683, total investment 132.954697 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Model(input_size = int(np.around(NN_BAYESIAN.res['max']['max_params']['window_size'])), \n",
    "              layer_size = int(np.around(NN_BAYESIAN.res['max']['max_params']['size_network'])), \n",
    "              output_size = 3)\n",
    "agent = Agent(population_size = int(np.around(NN_BAYESIAN.res['max']['max_params']['population_size'])), \n",
    "              sigma = NN_BAYESIAN.res['max']['max_params']['sigma'], \n",
    "              learning_rate = NN_BAYESIAN.res['max']['max_params']['learning_rate'], \n",
    "              model = model, \n",
    "              money = 10000, \n",
    "              max_buy = 5, \n",
    "              max_sell = 5, \n",
    "              skip = int(np.around(NN_BAYESIAN.res['max']['max_params']['skip'])), \n",
    "              window_size = int(np.around(NN_BAYESIAN.res['max']['max_params']['window_size'])))\n",
    "agent.fit(500, 100)\n",
    "agent.buy()"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}