agent/evolution-strategy-bayesian-agent.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>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>2017-07-31</td>\n",
       "      <td>335.500000</td>\n",
       "      <td>341.489990</td>\n",
       "      <td>321.040009</td>\n",
       "      <td>323.470001</td>\n",
       "      <td>323.470001</td>\n",
       "      <td>8535100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>323.000000</td>\n",
       "      <td>324.450012</td>\n",
       "      <td>316.130005</td>\n",
       "      <td>319.570007</td>\n",
       "      <td>319.570007</td>\n",
       "      <td>8303100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-02</td>\n",
       "      <td>318.940002</td>\n",
       "      <td>327.119995</td>\n",
       "      <td>311.220001</td>\n",
       "      <td>325.890015</td>\n",
       "      <td>325.890015</td>\n",
       "      <td>13091500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-03</td>\n",
       "      <td>345.329987</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>343.149994</td>\n",
       "      <td>347.089996</td>\n",
       "      <td>347.089996</td>\n",
       "      <td>13535000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-04</td>\n",
       "      <td>347.000000</td>\n",
       "      <td>357.269989</td>\n",
       "      <td>343.299988</td>\n",
       "      <td>356.910004</td>\n",
       "      <td>356.910004</td>\n",
       "      <td>9268900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2017-07-31  335.500000  341.489990  321.040009  323.470001  323.470001   \n",
       "1  2017-08-01  323.000000  324.450012  316.130005  319.570007  319.570007   \n",
       "2  2017-08-02  318.940002  327.119995  311.220001  325.890015  325.890015   \n",
       "3  2017-08-03  345.329987  350.000000  343.149994  347.089996  347.089996   \n",
       "4  2017-08-04  347.000000  357.269989  343.299988  356.910004  356.910004   \n",
       "\n",
       "     Volume  \n",
       "0   8535100  \n",
       "1   8303100  \n",
       "2  13091500  \n",
       "3  13535000  \n",
       "4   9268900  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('TSLA.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Deep_Evolution_Strategy:\n",
    "\n",
    "    inputs = None\n",
    "\n",
    "    def __init__(self, weights, reward_function, population_size, sigma, learning_rate):\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(self.weights, population[k])\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] = w + self.learning_rate/(self.population_size * self.sigma) * np.dot(A.T, rewards).T\n",
    "            if (i+1) % print_every == 0:\n",
    "                print('iter %d. reward: %f' %  (i+1, self.reward_function(self.weights)))\n",
    "        print('time taken to train:', time.time()-lasttime, 'seconds')\n",
    "        \n",
    "class Model:\n",
    "    def __init__(self, input_size, layer_size, output_size):\n",
    "        self.weights = [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",
    "    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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent:\n",
    "    \n",
    "    def __init__(self, population_size, sigma, learning_rate, model, money, max_buy, max_sell, skip, window_size):\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(self.model.get_weights(), self.get_reward, self.POPULATION_SIZE, self.SIGMA, self.LEARNING_RATE)\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('day %d: buy %d units at price %f, total balance %f'%(t,buy_units, total_buy,initial_money))\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('day %d, sell %d units at price %f, investment %f %%, total balance %f,'%(t, sell_units, total_sell, invest, initial_money))\n",
    "            state = next_state\n",
    "        \n",
    "        invest = ((initial_money - starting_money) / starting_money) * 100\n",
    "        print('\\ntotal gained %f, total investment %f %%'%(initial_money - starting_money,invest))\n",
    "        plt.figure(figsize=(20,10))\n",
    "        plt.plot(close, label='true close',c='g')\n",
    "        plt.plot(close, 'X', label='predict buy',markevery=states_buy, c='b')\n",
    "        plt.plot(close, 'o', label='predict sell',markevery=states_sell,c='r')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def best_agent(window_size, skip, population_size, sigma, learning_rate, size_network):\n",
    "    model = Model(window_size, size_network, 3)\n",
    "    agent = Agent(population_size, sigma, learning_rate,model,10000,5,5,skip,window_size)\n",
    "    try:\n",
    "        agent.fit(10, 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(window_size, skip, population_size, sigma, learning_rate, size_network):\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 {'size_network': 965, 'window_size': 45, 'population_size': 35, 'skip': 12, 'learning_rate': 0.20812911817036672, 'sigma': 0.9085617370780164}\n",
      "time taken to train: 9.127995014190674 seconds\n",
      "stop after 500 iteration with investment 4.781100\n",
      "    1 | 00m09s | \u001b[35m   4.78110\u001b[0m | \u001b[32m         0.2081\u001b[0m | \u001b[32m          35.3435\u001b[0m | \u001b[32m   0.9086\u001b[0m | \u001b[32m      964.6260\u001b[0m | \u001b[32m  12.0911\u001b[0m | \u001b[32m      44.6417\u001b[0m | \n",
      "\n",
      "Search parameters {'size_network': 171, 'window_size': 33, 'population_size': 44, 'skip': 12, 'learning_rate': 0.2543689544854069, 'sigma': 0.32876073755283014}\n",
      "time taken to train: 2.5704901218414307 seconds\n",
      "stop after 500 iteration with investment 8.530600\n",
      "    2 | 00m02s | \u001b[35m   8.53060\u001b[0m | \u001b[32m         0.2544\u001b[0m | \u001b[32m          43.8516\u001b[0m | \u001b[32m   0.3288\u001b[0m | \u001b[32m      170.8788\u001b[0m | \u001b[32m  12.1451\u001b[0m | \u001b[32m      33.2896\u001b[0m | \n",
      "\n",
      "Search parameters {'size_network': 646, 'window_size': 9, 'population_size': 15, 'skip': 4, 'learning_rate': 0.3631012918436726, 'sigma': 0.06666132121569619}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/ipykernel_launcher.py:35: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop after 500 iteration with investment 0.000000\n",
      "    3 | 00m00s |    0.00000 |          0.3631 |           14.5608 |    0.0667 |       646.0338 |    3.5228 |        9.4381 | \n",
      "\n",
      "Search parameters {'size_network': 871, 'window_size': 5, 'population_size': 19, 'skip': 14, 'learning_rate': 0.2565872257604924, 'sigma': 0.3936407767103818}\n",
      "time taken to train: 1.0878596305847168 seconds\n",
      "stop after 500 iteration with investment 7.360502\n",
      "    4 | 00m01s |    7.36050 |          0.2566 |           18.8311 |    0.3936 |       870.7867 |   14.1011 |        4.9752 | \n",
      "\n",
      "Search parameters {'size_network': 317, 'window_size': 4, 'population_size': 48, 'skip': 3, 'learning_rate': 0.1573589486643058, 'sigma': 0.5051669694485229}\n",
      "time taken to train: 4.5775306224823 seconds\n",
      "stop after 500 iteration with investment 14.285701\n",
      "    5 | 00m04s | \u001b[35m  14.28570\u001b[0m | \u001b[32m         0.1574\u001b[0m | \u001b[32m          48.0568\u001b[0m | \u001b[32m   0.5052\u001b[0m | \u001b[32m      317.1404\u001b[0m | \u001b[32m   3.3509\u001b[0m | \u001b[32m       3.5796\u001b[0m | \n",
      "\n",
      "Search parameters {'size_network': 232, 'window_size': 44, 'population_size': 15, 'skip': 2, 'learning_rate': 0.40899880850228576, 'sigma': 0.9089280759815139}\n",
      "time taken to train: 4.484399795532227 seconds\n",
      "stop after 500 iteration with investment 44.338299\n",
      "    6 | 00m04s | \u001b[35m  44.33830\u001b[0m | \u001b[32m         0.4090\u001b[0m | \u001b[32m          14.6696\u001b[0m | \u001b[32m   0.9089\u001b[0m | \u001b[32m      231.7271\u001b[0m | \u001b[32m   2.2459\u001b[0m | \u001b[32m      44.4285\u001b[0m | \n",
      "\n",
      "Search parameters {'size_network': 790, 'window_size': 22, 'population_size': 47, 'skip': 4, 'learning_rate': 0.3621626969219844, 'sigma': 0.6591562686361769}\n",
      "time taken to train: 10.18967056274414 seconds\n",
      "stop after 500 iteration with investment 29.876401\n",
      "    7 | 00m10s |   29.87640 |          0.3622 |           46.7509 |    0.6592 |       790.3085 |    3.5175 |       21.7895 | \n",
      "\n",
      "Search parameters {'size_network': 126, 'window_size': 38, 'population_size': 16, 'skip': 3, 'learning_rate': 0.26821031693916486, 'sigma': 0.3311922640653769}\n",
      "time taken to train: 2.3747472763061523 seconds\n",
      "stop after 500 iteration with investment 36.288303\n",
      "    8 | 00m02s |   36.28830 |          0.2682 |           15.6297 |    0.3312 |       126.1011 |    2.6425 |       37.7457 | \n",
      "\n",
      "Search parameters {'size_network': 727, 'window_size': 47, 'population_size': 9, 'skip': 8, 'learning_rate': 0.3333335581407005, 'sigma': 0.6766789397265714}\n",
      "time taken to train: 2.278529405593872 seconds\n",
      "stop after 500 iteration with investment 15.978700\n",
      "    9 | 00m02s |   15.97870 |          0.3333 |            9.0188 |    0.6767 |       726.6212 |    7.6235 |       47.4471 | \n",
      "\n",
      "Search parameters {'size_network': 34, 'window_size': 18, 'population_size': 11, 'skip': 11, 'learning_rate': 0.20738441586805925, 'sigma': 0.3868037154879664}\n",
      "time taken to train: 0.3504476547241211 seconds\n",
      "stop after 500 iteration with investment 13.023001\n",
      "   10 | 00m00s |   13.02300 |          0.2074 |           10.7830 |    0.3868 |        33.6579 |   10.7904 |       17.9637 | \n",
      "\n",
      "Search parameters {'size_network': 115, 'window_size': 31, 'population_size': 46, 'skip': 14, 'learning_rate': 0.3916502682014877, 'sigma': 0.808901729761732}\n",
      "time taken to train: 1.9679555892944336 seconds\n",
      "stop after 500 iteration with investment 13.193999\n",
      "   11 | 00m01s |   13.19400 |          0.3917 |           46.0772 |    0.8089 |       114.6862 |   14.3917 |       30.8412 | \n",
      "\n",
      "Search parameters {'size_network': 135, 'window_size': 37, 'population_size': 10, 'skip': 8, 'learning_rate': 0.3884817275366324, 'sigma': 0.9817920112790528}\n",
      "time taken to train: 0.743511438369751 seconds\n",
      "stop after 500 iteration with investment 9.065101\n",
      "   12 | 00m00s |    9.06510 |          0.3885 |            9.5629 |    0.9818 |       134.5590 |    8.0695 |       36.8869 | \n",
      "\n",
      "Search parameters {'size_network': 748, 'window_size': 5, 'population_size': 23, 'skip': 5, 'learning_rate': 0.04849734711154209, 'sigma': 0.9333245771783646}\n",
      "time taken to train: 2.0988779067993164 seconds\n",
      "stop after 500 iteration with investment 3.377500\n",
      "   13 | 00m02s |    3.37750 |          0.0485 |           22.7552 |    0.9333 |       748.2111 |    5.3385 |        5.0191 | \n",
      "\n",
      "Search parameters {'size_network': 120, 'window_size': 24, 'population_size': 40, 'skip': 2, 'learning_rate': 0.11875116033765766, 'sigma': 0.27843710379229164}\n",
      "time taken to train: 6.827124357223511 seconds\n",
      "stop after 500 iteration with investment 43.952801\n",
      "   14 | 00m06s |   43.95280 |          0.1188 |           39.7945 |    0.2784 |       119.5262 |    2.1656 |       24.4661 | \n",
      "\n",
      "Search parameters {'size_network': 863, 'window_size': 19, 'population_size': 48, 'skip': 7, 'learning_rate': 0.3273208211166093, 'sigma': 0.6752902358976147}\n",
      "time taken to train: 7.411391973495483 seconds\n",
      "stop after 500 iteration with investment 13.109499\n",
      "   15 | 00m07s |   13.10950 |          0.3273 |           47.9520 |    0.6753 |       863.4400 |    7.1851 |       18.7052 | \n",
      "\n",
      "Search parameters {'size_network': 661, 'window_size': 42, 'population_size': 18, 'skip': 10, 'learning_rate': 0.04848982575540207, 'sigma': 0.5277478763180736}\n",
      "time taken to train: 3.954888343811035 seconds\n",
      "stop after 500 iteration with investment 8.690199\n",
      "   16 | 00m03s |    8.69020 |          0.0485 |           17.9476 |    0.5277 |       661.0300 |   10.4699 |       42.4952 | \n",
      "\n",
      "Search parameters {'size_network': 864, 'window_size': 26, 'population_size': 12, 'skip': 5, 'learning_rate': 0.4694753799634952, 'sigma': 0.9235405682540894}\n",
      "time taken to train: 2.547067642211914 seconds\n",
      "stop after 500 iteration with investment 20.335395\n",
      "   17 | 00m02s |   20.33539 |          0.4695 |           11.6334 |    0.9235 |       864.1832 |    5.1017 |       25.8312 | \n",
      "\n",
      "Search parameters {'size_network': 167, 'window_size': 44, 'population_size': 34, 'skip': 14, 'learning_rate': 0.4654714722245062, 'sigma': 0.4370378468430398}\n",
      "time taken to train: 2.176396608352661 seconds\n",
      "stop after 500 iteration with investment 7.047001\n",
      "   18 | 00m02s |    7.04700 |          0.4655 |           34.2006 |    0.4370 |       166.5370 |   14.2006 |       43.6633 | \n",
      "\n",
      "Search parameters {'size_network': 496, 'window_size': 41, 'population_size': 47, 'skip': 12, 'learning_rate': 0.1907773746111234, 'sigma': 0.6188638396411351}\n",
      "time taken to train: 7.1664392948150635 seconds\n",
      "stop after 500 iteration with investment 8.457100\n",
      "   19 | 00m07s |    8.45710 |          0.1908 |           47.4556 |    0.6189 |       495.7854 |   12.3376 |       41.4013 | \n",
      "\n",
      "Search parameters {'size_network': 18, 'window_size': 34, 'population_size': 43, 'skip': 2, 'learning_rate': 0.367600007488567, 'sigma': 0.10612174408155739}\n",
      "time taken to train: 7.238054275512695 seconds\n",
      "stop after 500 iteration with investment 38.673301\n",
      "   20 | 00m07s |   38.67330 |          0.3676 |           43.2848 |    0.1061 |        18.4978 |    1.9336 |       34.1157 | \n",
      "\n",
      "Search parameters {'size_network': 571, 'window_size': 5, 'population_size': 46, 'skip': 8, 'learning_rate': 0.23936675525271184, 'sigma': 0.2784753319847558}\n",
      "time taken to train: 2.729438066482544 seconds\n",
      "stop after 500 iteration with investment 12.087002\n",
      "   21 | 00m02s |   12.08700 |          0.2394 |           46.1439 |    0.2785 |       571.1950 |    7.7026 |        4.7205 | \n",
      "\n",
      "Search parameters {'size_network': 679, 'window_size': 12, 'population_size': 9, 'skip': 10, 'learning_rate': 0.20250442982278602, 'sigma': 0.9211519358968071}\n",
      "time taken to train: 0.7510397434234619 seconds\n",
      "stop after 500 iteration with investment 6.240801\n",
      "   22 | 00m00s |    6.24080 |          0.2025 |            9.3642 |    0.9212 |       679.0109 |   10.1417 |       11.9077 | \n",
      "\n",
      "Search parameters {'size_network': 256, 'window_size': 15, 'population_size': 31, 'skip': 9, 'learning_rate': 0.317592499368132, 'sigma': 0.2807870967565507}\n",
      "time taken to train: 1.7370507717132568 seconds\n",
      "stop after 500 iteration with investment 9.625102\n",
      "   23 | 00m01s |    9.62510 |          0.3176 |           31.2573 |    0.2808 |       256.3173 |    8.8223 |       15.4547 | \n",
      "\n",
      "Search parameters {'size_network': 432, 'window_size': 12, 'population_size': 22, 'skip': 4, 'learning_rate': 0.05162100468965195, 'sigma': 0.05818932412422159}\n",
      "time taken to train: 2.3786113262176514 seconds\n",
      "stop after 500 iteration with investment 7.946002\n",
      "   24 | 00m02s |    7.94600 |          0.0516 |           22.2628 |    0.0582 |       432.3956 |    4.3683 |       11.8802 | \n",
      "\n",
      "Search parameters {'size_network': 872, 'window_size': 8, 'population_size': 50, 'skip': 14, 'learning_rate': 0.3707774139300084, 'sigma': 0.20011081949413312}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop after 500 iteration with investment 0.000000\n",
      "   25 | 00m01s |    0.00000 |          0.3708 |           49.5414 |    0.2001 |       871.5092 |   13.6096 |        8.2158 | \n",
      "\n",
      "Search parameters {'size_network': 421, 'window_size': 21, 'population_size': 9, 'skip': 14, 'learning_rate': 0.25976543883201275, 'sigma': 0.9725216158425024}\n",
      "time taken to train: 0.646111011505127 seconds\n",
      "stop after 500 iteration with investment 4.305499\n",
      "   26 | 00m00s |    4.30550 |          0.2598 |            9.1358 |    0.9725 |       421.0735 |   14.3967 |       21.4845 | \n",
      "\n",
      "Search parameters {'size_network': 284, 'window_size': 12, 'population_size': 10, 'skip': 6, 'learning_rate': 0.020358001425177755, 'sigma': 0.9197175163542975}\n",
      "time taken to train: 0.706169843673706 seconds\n",
      "stop after 500 iteration with investment -1.747999\n",
      "   27 | 00m00s |   -1.74800 |          0.0204 |           10.0361 |    0.9197 |       284.2565 |    6.0083 |       12.1130 | \n",
      "\n",
      "Search parameters {'size_network': 692, 'window_size': 38, 'population_size': 15, 'skip': 9, 'learning_rate': 0.2027825704666622, 'sigma': 0.091266245978315}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   28 | 00m00s |    0.00000 |          0.2028 |           14.7814 |    0.0913 |       691.6259 |    8.9534 |       37.6406 | \n",
      "\n",
      "Search parameters {'size_network': 827, 'window_size': 20, 'population_size': 48, 'skip': 4, 'learning_rate': 0.18158596455218518, 'sigma': 0.6783173655935514}\n",
      "time taken to train: 9.79844331741333 seconds\n",
      "stop after 500 iteration with investment 21.988201\n",
      "   29 | 00m09s |   21.98820 |          0.1816 |           48.2035 |    0.6783 |       826.9275 |    3.9517 |       19.5230 | \n",
      "\n",
      "Search parameters {'size_network': 143, 'window_size': 30, 'population_size': 28, 'skip': 7, 'learning_rate': 0.13105756987390643, 'sigma': 0.7361080219833085}\n",
      "time taken to train: 2.001174211502075 seconds\n",
      "stop after 500 iteration with investment 8.103600\n",
      "   30 | 00m02s |    8.10360 |          0.1311 |           27.5658 |    0.7361 |       142.5016 |    7.1759 |       29.6394 | \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 {'size_network': 121, 'window_size': 26, 'population_size': 36, 'skip': 4, 'learning_rate': 0.0018799573065171626, 'sigma': 0.0663883344293885}\n",
      "time taken to train: 3.4686832427978516 seconds\n",
      "stop after 500 iteration with investment 6.976802\n",
      "   31 | 00m05s |    6.97680 |          0.0019 |           36.0901 |    0.0664 |       120.5553 |    3.9436 |       26.4852 | \n",
      "\n",
      "Search parameters {'size_network': 231, 'window_size': 45, 'population_size': 16, 'skip': 3, 'learning_rate': 0.32666295162363906, 'sigma': 0.029920505891804068}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   32 | 00m00s |    0.00000 |          0.3267 |           16.1675 |    0.0299 |       230.9626 |    3.0566 |       44.8258 | \n",
      "\n",
      "Search parameters {'size_network': 163, 'window_size': 34, 'population_size': 35, 'skip': 7, 'learning_rate': 0.12072748809615562, 'sigma': 0.4342356653798713}\n",
      "time taken to train: 2.8217649459838867 seconds\n",
      "stop after 500 iteration with investment 14.197500\n",
      "   33 | 00m03s |   14.19750 |          0.1207 |           35.0433 |    0.4342 |       162.5797 |    7.1430 |       33.6904 | \n",
      "\n",
      "Search parameters {'size_network': 702, 'window_size': 16, 'population_size': 18, 'skip': 12, 'learning_rate': 0.050834735394798325, 'sigma': 0.5679136436319719}\n",
      "time taken to train: 1.7587316036224365 seconds\n",
      "stop after 500 iteration with investment 4.705101\n",
      "   34 | 00m02s |    4.70510 |          0.0508 |           18.0889 |    0.5679 |       702.1747 |   11.8551 |       15.8383 | \n",
      "\n",
      "Search parameters {'size_network': 984, 'window_size': 41, 'population_size': 43, 'skip': 5, 'learning_rate': 0.15461322584052392, 'sigma': 0.30364618015052375}\n",
      "time taken to train: 13.707158327102661 seconds\n",
      "stop after 500 iteration with investment 25.189199\n",
      "   35 | 00m14s |   25.18920 |          0.1546 |           42.8820 |    0.3036 |       983.6130 |    4.8628 |       41.0657 | \n",
      "\n",
      "Search parameters {'size_network': 210, 'window_size': 12, 'population_size': 31, 'skip': 4, 'learning_rate': 0.39525931310934465, 'sigma': 0.881977673994732}\n",
      "time taken to train: 2.6515161991119385 seconds\n",
      "stop after 500 iteration with investment 23.119700\n",
      "   36 | 00m03s |   23.11970 |          0.3953 |           30.9447 |    0.8820 |       209.6313 |    4.3923 |       12.1952 | \n",
      "\n",
      "Search parameters {'size_network': 873, 'window_size': 43, 'population_size': 46, 'skip': 7, 'learning_rate': 0.13515718218819645, 'sigma': 0.9858118767273032}\n",
      "time taken to train: 12.831010341644287 seconds\n",
      "stop after 500 iteration with investment 2.475400\n",
      "   37 | 00m13s |    2.47540 |          0.1352 |           45.7650 |    0.9858 |       873.2782 |    6.6460 |       42.7295 | \n",
      "\n",
      "Search parameters {'size_network': 226, 'window_size': 45, 'population_size': 36, 'skip': 11, 'learning_rate': 0.44099531987620944, 'sigma': 0.737678549004284}\n",
      "time taken to train: 3.4412660598754883 seconds\n",
      "stop after 500 iteration with investment 16.076503\n",
      "   38 | 00m04s |   16.07650 |          0.4410 |           36.3216 |    0.7377 |       225.5655 |   11.0154 |       44.7666 | \n",
      "\n",
      "Search parameters {'size_network': 849, 'window_size': 5, 'population_size': 38, 'skip': 9, 'learning_rate': 0.3228199351258138, 'sigma': 0.791194073667455}\n",
      "time taken to train: 2.6037259101867676 seconds\n",
      "stop after 500 iteration with investment 4.732402\n",
      "   39 | 00m03s |    4.73240 |          0.3228 |           37.7023 |    0.7912 |       848.5415 |    9.3157 |        4.8611 | \n",
      "\n",
      "Search parameters {'size_network': 355, 'window_size': 36, 'population_size': 23, 'skip': 11, 'learning_rate': 0.2769678179512155, 'sigma': 0.049214230306831315}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   40 | 00m01s |    0.00000 |          0.2770 |           22.5428 |    0.0492 |       355.2136 |   11.1971 |       35.5959 | \n",
      "\n",
      "Search parameters {'size_network': 211, 'window_size': 30, 'population_size': 13, 'skip': 2, 'learning_rate': 0.29964520064286665, 'sigma': 0.5130656860331229}\n",
      "time taken to train: 2.77719783782959 seconds\n",
      "stop after 500 iteration with investment 52.051298\n",
      "   41 | 00m03s | \u001b[35m  52.05130\u001b[0m | \u001b[32m         0.2996\u001b[0m | \u001b[32m          13.4159\u001b[0m | \u001b[32m   0.5131\u001b[0m | \u001b[32m      210.7991\u001b[0m | \u001b[32m   1.7566\u001b[0m | \u001b[32m      30.2065\u001b[0m | \n",
      "\n",
      "Search parameters {'size_network': 661, 'window_size': 8, 'population_size': 10, 'skip': 10, 'learning_rate': 0.26402932489965697, 'sigma': 0.6747607410121118}\n",
      "time taken to train: 0.681563138961792 seconds\n",
      "stop after 500 iteration with investment 5.086000\n",
      "   42 | 00m01s |    5.08600 |          0.2640 |            9.7585 |    0.6748 |       661.2186 |   10.1702 |        8.2211 | \n",
      "\n",
      "Search parameters {'size_network': 797, 'window_size': 18, 'population_size': 47, 'skip': 6, 'learning_rate': 0.07162267971730223, 'sigma': 0.8982667697391342}\n",
      "time taken to train: 7.345368385314941 seconds\n",
      "stop after 500 iteration with investment 3.092999\n",
      "   43 | 00m08s |    3.09300 |          0.0716 |           47.1247 |    0.8983 |       797.3038 |    5.8167 |       18.0957 | \n",
      "\n",
      "Search parameters {'size_network': 397, 'window_size': 42, 'population_size': 43, 'skip': 14, 'learning_rate': 0.2612377086661758, 'sigma': 0.42360085426318567}\n",
      "time taken to train: 5.139040231704712 seconds\n",
      "stop after 500 iteration with investment 10.797500\n",
      "   44 | 00m05s |   10.79750 |          0.2612 |           42.9462 |    0.4236 |       396.8724 |   13.8560 |       42.2076 | \n",
      "\n",
      "Search parameters {'size_network': 814, 'window_size': 27, 'population_size': 29, 'skip': 4, 'learning_rate': 0.4732208983823781, 'sigma': 0.4351061716091205}\n",
      "time taken to train: 6.950222015380859 seconds\n",
      "stop after 500 iteration with investment 35.565803\n",
      "   45 | 00m07s |   35.56580 |          0.4732 |           29.1474 |    0.4351 |       813.7243 |    3.8957 |       27.4798 | \n",
      "\n",
      "Search parameters {'size_network': 110, 'window_size': 28, 'population_size': 11, 'skip': 6, 'learning_rate': 0.37214003019831404, 'sigma': 0.19445322043820285}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stop after 500 iteration with investment 0.000000\n",
      "   46 | 00m01s |    0.00000 |          0.3721 |           10.5887 |    0.1945 |       109.5451 |    6.0076 |       28.4630 | \n",
      "\n",
      "Search parameters {'size_network': 461, 'window_size': 36, 'population_size': 28, 'skip': 9, 'learning_rate': 0.061676407561564, 'sigma': 0.3563517422123184}\n",
      "time taken to train: 3.9091644287109375 seconds\n",
      "stop after 500 iteration with investment 7.832001\n",
      "   47 | 00m04s |    7.83200 |          0.0617 |           27.7358 |    0.3564 |       461.4185 |    9.4074 |       36.1291 | \n",
      "\n",
      "Search parameters {'size_network': 607, 'window_size': 8, 'population_size': 9, 'skip': 2, 'learning_rate': 0.306617584841014, 'sigma': 0.5687996807759338}\n",
      "time taken to train: 1.670348882675171 seconds\n",
      "stop after 500 iteration with investment 33.201499\n",
      "   48 | 00m02s |   33.20150 |          0.3066 |            9.0855 |    0.5688 |       606.9883 |    2.2248 |        8.3144 | \n",
      "\n",
      "Search parameters {'size_network': 390, 'window_size': 44, 'population_size': 22, 'skip': 11, 'learning_rate': 0.21980759345901107, 'sigma': 0.3491632454068447}\n",
      "time taken to train: 3.0743813514709473 seconds\n",
      "stop after 500 iteration with investment 8.593000\n",
      "   49 | 00m04s |    8.59300 |          0.2198 |           21.7404 |    0.3492 |       389.7823 |   10.8024 |       43.9694 | \n",
      "\n",
      "Search parameters {'size_network': 914, 'window_size': 42, 'population_size': 45, 'skip': 14, 'learning_rate': 0.1161932378532287, 'sigma': 0.4339065419432963}\n",
      "time taken to train: 10.43766188621521 seconds\n",
      "stop after 500 iteration with investment 13.247999\n",
      "   50 | 00m11s |   13.24800 |          0.1162 |           45.1497 |    0.4339 |       913.7450 |   14.3133 |       42.3764 | \n",
      "\n",
      "Search parameters {'size_network': 750, 'window_size': 11, 'population_size': 16, 'skip': 6, 'learning_rate': 0.06679793838252655, 'sigma': 0.8788673038673723}\n",
      "time taken to train: 1.695613145828247 seconds\n",
      "stop after 500 iteration with investment -2.249900\n",
      "   51 | 00m02s |   -2.24990 |          0.0668 |           15.5706 |    0.8789 |       750.3760 |    5.6875 |       10.7365 | \n",
      "\n",
      "Search parameters {'size_network': 375, 'window_size': 39, 'population_size': 45, 'skip': 14, 'learning_rate': 0.2500615913878533, 'sigma': 0.10060689594571869}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   52 | 00m03s |    0.00000 |          0.2501 |           45.0259 |    0.1006 |       375.4423 |   13.7034 |       38.6680 | \n",
      "\n",
      "Search parameters {'size_network': 363, 'window_size': 30, 'population_size': 38, 'skip': 10, 'learning_rate': 0.23889028004012916, 'sigma': 0.45132125988955274}\n",
      "time taken to train: 3.8545708656311035 seconds\n",
      "stop after 500 iteration with investment 10.828999\n",
      "   53 | 00m04s |   10.82900 |          0.2389 |           37.5437 |    0.4513 |       362.8067 |   10.2624 |       30.2990 | \n",
      "\n",
      "Search parameters {'size_network': 158, 'window_size': 26, 'population_size': 20, 'skip': 6, 'learning_rate': 0.4827739840604225, 'sigma': 0.6777510159615946}\n",
      "time taken to train: 1.5623507499694824 seconds\n",
      "stop after 500 iteration with investment 14.884299\n",
      "   54 | 00m02s |   14.88430 |          0.4828 |           20.2433 |    0.6778 |       158.2983 |    6.0984 |       25.8878 | \n",
      "\n",
      "Search parameters {'size_network': 689, 'window_size': 12, 'population_size': 41, 'skip': 10, 'learning_rate': 0.29878782677111315, 'sigma': 0.8055413117706315}\n",
      "time taken to train: 3.399224042892456 seconds\n",
      "stop after 500 iteration with investment 7.930200\n",
      "   55 | 00m04s |    7.93020 |          0.2988 |           40.9321 |    0.8055 |       689.2962 |   10.4055 |       11.7676 | \n",
      "\n",
      "Search parameters {'size_network': 39, 'window_size': 21, 'population_size': 2, 'skip': 11, 'learning_rate': 0.2887581316134336, 'sigma': 0.3750150166594899}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   56 | 00m00s |    0.00000 |          0.2888 |            2.4015 |    0.3750 |        38.8847 |   10.7697 |       21.0883 | \n",
      "\n",
      "Search parameters {'size_network': 882, 'window_size': 36, 'population_size': 36, 'skip': 11, 'learning_rate': 0.3220939769910967, 'sigma': 0.37155175926338757}\n",
      "time taken to train: 7.647109270095825 seconds\n",
      "stop after 500 iteration with investment 12.319902\n",
      "   57 | 00m08s |   12.31990 |          0.3221 |           35.6945 |    0.3716 |       881.5178 |   11.2613 |       36.1553 | \n",
      "\n",
      "Search parameters {'size_network': 791, 'window_size': 26, 'population_size': 19, 'skip': 12, 'learning_rate': 0.21785258011829986, 'sigma': 0.6493472347876398}\n",
      "time taken to train: 2.991887331008911 seconds\n",
      "stop after 500 iteration with investment 6.800400\n",
      "   58 | 00m03s |    6.80040 |          0.2179 |           18.7206 |    0.6493 |       791.2737 |   11.8460 |       26.0150 | \n",
      "\n",
      "Search parameters {'size_network': 63, 'window_size': 27, 'population_size': 5, 'skip': 14, 'learning_rate': 0.17030435484661208, 'sigma': 0.5920612087229635}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   59 | 00m00s |    0.00000 |          0.1703 |            5.3920 |    0.5921 |        62.7552 |   14.1611 |       26.6267 | \n",
      "\n",
      "Search parameters {'size_network': 842, 'window_size': 11, 'population_size': 21, 'skip': 2, 'learning_rate': 0.3847583100076275, 'sigma': 0.02890521047885678}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   60 | 00m01s |    0.00000 |          0.3848 |           20.9076 |    0.0289 |       842.3612 |    1.7051 |       11.1076 | \n",
      "\n",
      "Search parameters {'size_network': 101, 'window_size': 15, 'population_size': 17, 'skip': 13, 'learning_rate': 0.12529167687228343, 'sigma': 0.42281039030172146}\n",
      "time taken to train: 0.5478827953338623 seconds\n",
      "stop after 500 iteration with investment 8.359300\n",
      "   61 | 00m01s |    8.35930 |          0.1253 |           16.9834 |    0.4228 |       101.2163 |   13.3329 |       15.2881 | \n",
      "\n",
      "Search parameters {'size_network': 457, 'window_size': 26, 'population_size': 13, 'skip': 2, 'learning_rate': 0.3121802381972277, 'sigma': 0.08642633980634913}\n",
      "time taken to train: 3.607534646987915 seconds\n",
      "stop after 500 iteration with investment 10.468799\n",
      "   62 | 00m04s |   10.46880 |          0.3122 |           12.8283 |    0.0864 |       457.4692 |    2.3026 |       25.9891 | \n",
      "\n",
      "Search parameters {'size_network': 89, 'window_size': 25, 'population_size': 26, 'skip': 6, 'learning_rate': 0.09096135331043995, 'sigma': 0.7191453389652688}\n",
      "time taken to train: 1.6653099060058594 seconds\n",
      "stop after 500 iteration with investment 4.472799\n",
      "   63 | 00m02s |    4.47280 |          0.0910 |           25.7670 |    0.7191 |        89.2539 |    5.9551 |       24.5134 | \n",
      "\n",
      "Search parameters {'size_network': 605, 'window_size': 35, 'population_size': 23, 'skip': 6, 'learning_rate': 0.02762097650936996, 'sigma': 0.6783283261762227}\n",
      "time taken to train: 4.80453085899353 seconds\n",
      "stop after 500 iteration with investment 2.425600\n",
      "   64 | 00m05s |    2.42560 |          0.0276 |           22.9007 |    0.6783 |       604.5249 |    6.3742 |       35.1381 | \n",
      "\n",
      "Search parameters {'size_network': 672, 'window_size': 19, 'population_size': 1, 'skip': 13, 'learning_rate': 0.015290993415723951, 'sigma': 0.7986456660464525}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   65 | 00m01s |    0.00000 |          0.0153 |            1.2253 |    0.7986 |       672.4942 |   12.7345 |       18.7508 | \n",
      "\n",
      "Search parameters {'size_network': 468, 'window_size': 36, 'population_size': 28, 'skip': 3, 'learning_rate': 0.43628148364892455, 'sigma': 0.8394476145576959}\n",
      "time taken to train: 6.947840213775635 seconds\n",
      "stop after 500 iteration with investment 39.695004\n",
      "   66 | 00m07s |   39.69500 |          0.4363 |           28.2402 |    0.8394 |       468.1019 |    3.3734 |       35.8988 | \n",
      "\n",
      "Search parameters {'size_network': 58, 'window_size': 20, 'population_size': 7, 'skip': 4, 'learning_rate': 0.029470131991004858, 'sigma': 0.7454463713438167}\n",
      "time taken to train: 0.5600194931030273 seconds\n",
      "stop after 500 iteration with investment 7.189500\n",
      "   67 | 00m01s |    7.18950 |          0.0295 |            6.7877 |    0.7454 |        57.6036 |    3.6257 |       20.4999 | \n",
      "\n",
      "Search parameters {'size_network': 650, 'window_size': 4, 'population_size': 43, 'skip': 10, 'learning_rate': 0.053913713942890004, 'sigma': 0.6711610012895268}\n",
      "time taken to train: 2.2549538612365723 seconds\n",
      "stop after 500 iteration with investment -0.768200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   68 | 00m03s |   -0.76820 |          0.0539 |           43.2069 |    0.6712 |       649.5645 |    9.6053 |        4.4240 | \n",
      "\n",
      "Search parameters {'size_network': 441, 'window_size': 27, 'population_size': 3, 'skip': 3, 'learning_rate': 0.16304674397378732, 'sigma': 0.10811409306536884}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   69 | 00m01s |    0.00000 |          0.1630 |            2.8793 |    0.1081 |       440.9811 |    2.8290 |       27.2369 | \n",
      "\n",
      "Search parameters {'size_network': 894, 'window_size': 30, 'population_size': 30, 'skip': 5, 'learning_rate': 0.46175970281455464, 'sigma': 0.7752735597676016}\n",
      "time taken to train: 7.832126140594482 seconds\n",
      "stop after 500 iteration with investment 22.597998\n",
      "   70 | 00m08s |   22.59800 |          0.4618 |           29.7216 |    0.7753 |       893.9025 |    4.9908 |       30.1173 | \n",
      "\n",
      "Search parameters {'size_network': 642, 'window_size': 42, 'population_size': 28, 'skip': 11, 'learning_rate': 0.4490886103156396, 'sigma': 0.21567685686805282}\n",
      "stop after 500 iteration with investment 0.000000\n",
      "   71 | 00m05s |    0.00000 |          0.4491 |           27.8437 |    0.2157 |       641.7183 |   11.1992 |       42.3714 | \n",
      "\n",
      "Search parameters {'size_network': 775, 'window_size': 10, 'population_size': 17, 'skip': 12, 'learning_rate': 0.22381895313079783, 'sigma': 0.8732087547025723}\n",
      "time taken to train: 1.278925895690918 seconds\n",
      "stop after 500 iteration with investment 7.615800\n",
      "   72 | 00m02s |    7.61580 |          0.2238 |           17.1439 |    0.8732 |       775.3309 |   11.8142 |        9.9468 | \n",
      "\n",
      "Search parameters {'size_network': 452, 'window_size': 25, 'population_size': 19, 'skip': 14, 'learning_rate': 0.11814509172839692, 'sigma': 0.621310236995694}\n",
      "time taken to train: 1.6872444152832031 seconds\n",
      "stop after 500 iteration with investment 8.748200\n",
      "   73 | 00m02s |    8.74820 |          0.1181 |           18.6027 |    0.6213 |       451.7529 |   13.5067 |       25.4414 | \n",
      "\n",
      "Search parameters {'size_network': 176, 'window_size': 20, 'population_size': 34, 'skip': 1, 'learning_rate': 0.28208006704411104, 'sigma': 0.7152490670931403}\n",
      "time taken to train: 11.095934867858887 seconds\n",
      "stop after 500 iteration with investment 58.273906\n",
      "   74 | 00m12s | \u001b[35m  58.27391\u001b[0m | \u001b[32m         0.2821\u001b[0m | \u001b[32m          33.5484\u001b[0m | \u001b[32m   0.7152\u001b[0m | \u001b[32m      175.6885\u001b[0m | \u001b[32m   1.2806\u001b[0m | \u001b[32m      20.4978\u001b[0m | \n",
      "\n",
      "Search parameters {'size_network': 195, 'window_size': 35, 'population_size': 27, 'skip': 4, 'learning_rate': 0.037420300687942575, 'sigma': 0.6230256188954031}\n",
      "time taken to train: 3.4751625061035156 seconds\n",
      "stop after 500 iteration with investment 6.026797\n",
      "   75 | 00m04s |    6.02680 |          0.0374 |           26.7061 |    0.6230 |       194.9641 |    3.9514 |       34.9279 | \n",
      "\n",
      "Search parameters {'size_network': 332, 'window_size': 12, 'population_size': 48, 'skip': 2, 'learning_rate': 0.17401523836338986, 'sigma': 0.22188122308709138}\n",
      "time taken to train: 8.18549919128418 seconds\n",
      "stop after 500 iteration with investment 48.462397\n",
      "   76 | 00m09s |   48.46240 |          0.1740 |           47.6947 |    0.2219 |       331.8716 |    1.8929 |       11.8061 | \n",
      "\n",
      "Search parameters {'size_network': 695, 'window_size': 42, 'population_size': 35, 'skip': 12, 'learning_rate': 0.31039297780641983, 'sigma': 0.341927805862142}\n",
      "time taken to train: 6.3662707805633545 seconds\n",
      "stop after 500 iteration with investment 12.363202\n",
      "   77 | 00m07s |   12.36320 |          0.3104 |           35.0419 |    0.3419 |       694.9643 |   11.9958 |       41.8521 | \n",
      "\n",
      "Search parameters {'size_network': 274, 'window_size': 13, 'population_size': 14, 'skip': 3, 'learning_rate': 0.026985194765702575, 'sigma': 0.075951875571834}\n",
      "time taken to train: 1.6851987838745117 seconds\n",
      "stop after 500 iteration with investment 21.029603\n",
      "   78 | 00m02s |   21.02960 |          0.0270 |           14.3401 |    0.0760 |       274.3952 |    2.6560 |       13.2169 | \n",
      "\n",
      "Search parameters {'size_network': 887, 'window_size': 9, 'population_size': 47, 'skip': 2, 'learning_rate': 0.11922250238670969, 'sigma': 0.592385381300321}\n",
      "time taken to train: 10.488324642181396 seconds\n",
      "stop after 500 iteration with investment 42.002592\n",
      "   79 | 00m11s |   42.00259 |          0.1192 |           46.7709 |    0.5924 |       886.9777 |    2.4768 |        9.2298 | \n",
      "\n",
      "Search parameters {'size_network': 854, 'window_size': 3, 'population_size': 14, 'skip': 7, 'learning_rate': 0.06436875677323921, 'sigma': 0.6325806099181369}\n",
      "time taken to train: 0.9816009998321533 seconds\n",
      "stop after 500 iteration with investment 7.856398\n",
      "   80 | 00m01s |    7.85640 |          0.0644 |           13.6935 |    0.6326 |       853.9054 |    7.2485 |        3.1386 | \n"
     ]
    }
   ],
   "source": [
    "accbest = 0.0\n",
    "NN_BAYESIAN = BayesianOptimization(find_best_agent, \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",
    "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: 58.273906\n",
      "Best AGENT parameters:  {'size_network': 175.6884650775251, 'window_size': 20.49784121062309, 'sigma': 0.7152490670931403, 'population_size': 33.54835524216057, 'learning_rate': 0.28208006704411104, 'skip': 1.2806149560030797}\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": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken to train: 8.902231216430664 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "44.664403540000016"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is my parameter\n",
    "best_agent(30,1,15,0.1,0.03,500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time taken to train: 11.191874265670776 seconds\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "54.70499670999992"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is from bayesian\n",
    "best_agent(20,1,34,0.7152490670931403,0.28208006704411104,176)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 100. reward: 63.307998\n",
      "iter 200. reward: 68.621898\n",
      "iter 300. reward: 81.728899\n",
      "iter 400. reward: 85.140902\n",
      "iter 500. reward: 87.653898\n",
      "time taken to train: 41.50784707069397 seconds\n",
      "day 0: buy 5 units at price 1617.350005, total balance 8382.649995\n",
      "day 1: buy 5 units at price 1597.850035, total balance 6784.799960\n",
      "day 2: buy 5 units at price 1629.450075, total balance 5155.349885\n",
      "day 3: buy 5 units at price 1735.449980, total balance 3419.899905\n",
      "day 4, sell 5 units at price 1784.550020, investment 10.337899 %, total balance 5204.449925,\n",
      "day 5, sell 5 units at price 1775.850065, investment 11.139971 %, total balance 6980.299990,\n",
      "day 6, sell 5 units at price 1826.100005, investment 12.068485 %, total balance 8806.399995,\n",
      "day 7, sell 5 units at price 1817.649995, investment 4.736525 %, total balance 10624.049990,\n",
      "day 14: buy 5 units at price 1737.299955, total balance 8886.750035\n",
      "day 15: buy 5 units at price 1689.299925, total balance 7197.450110\n",
      "day 19: buy 5 units at price 1740.249940, total balance 5457.200170\n",
      "day 20: buy 5 units at price 1728.300020, total balance 3728.900150\n",
      "day 21: buy 5 units at price 1736.799925, total balance 1992.100225\n",
      "day 27: buy 1 units at price 350.609985, total balance 1641.490240\n",
      "day 28: buy 5 units at price 1716.999970, total balance -75.509730\n",
      "day 29, sell 5 units at price 1818.450010, investment 4.671045 %, total balance 1742.940280,\n",
      "day 30, sell 5 units at price 1813.750000, investment 7.366961 %, total balance 3556.690280,\n",
      "day 32, sell 5 units at price 1888.200075, investment 8.501660 %, total balance 5444.890355,\n",
      "day 33, sell 5 units at price 1899.049990, investment 9.879649 %, total balance 7343.940345,\n",
      "day 34, sell 5 units at price 1925.000000, investment 10.836025 %, total balance 9268.940345,\n",
      "day 35, sell 5 units at price 1875.500030, investment 434.924877 %, total balance 11144.440375,\n",
      "day 36, sell 1 units at price 373.910004, investment -78.223063 %, total balance 11518.350379,\n",
      "day 41: buy 5 units at price 1704.850005, total balance 9813.500374\n",
      "day 42: buy 5 units at price 1698.000030, total balance 8115.500344\n",
      "day 43: buy 5 units at price 1705.500030, total balance 6410.000314\n",
      "day 44: buy 5 units at price 1707.649995, total balance 4702.350319\n",
      "day 45: buy 5 units at price 1740.700075, total balance 2961.650244\n",
      "day 46, sell 5 units at price 1775.050050, investment 4.117667 %, total balance 4736.700294,\n",
      "day 47, sell 5 units at price 1776.649935, investment 4.631914 %, total balance 6513.350229,\n",
      "day 48: buy 5 units at price 1784.400025, total balance 4728.950204\n",
      "day 51, sell 5 units at price 1773.000030, investment 3.957784 %, total balance 6501.950234,\n",
      "day 53, sell 5 units at price 1777.850035, investment 4.110915 %, total balance 8279.800269,\n",
      "day 54, sell 5 units at price 1753.000030, investment 0.706610 %, total balance 10032.800299,\n",
      "day 55: buy 1 units at price 355.750000, total balance 9677.050299\n",
      "day 56, sell 5 units at price 1798.249970, investment 0.776168 %, total balance 11475.300269,\n",
      "day 57, sell 1 units at price 351.809998, investment -1.107520 %, total balance 11827.110267,\n",
      "day 63: buy 5 units at price 1604.349975, total balance 10222.760292\n",
      "day 67: buy 5 units at price 1496.300050, total balance 8726.460242\n",
      "day 68: buy 5 units at price 1530.449980, total balance 7196.010262\n",
      "day 69: buy 5 units at price 1513.899995, total balance 5682.110267\n",
      "day 71: buy 5 units at price 1521.950075, total balance 4160.160192\n",
      "day 72: buy 5 units at price 1514.949950, total balance 2645.210242\n",
      "day 73: buy 5 units at price 1514.949950, total balance 1130.260292\n",
      "day 75, sell 5 units at price 1543.500060, investment -3.792808 %, total balance 2673.760352,\n",
      "day 76: buy 5 units at price 1556.499940, total balance 1117.260412\n",
      "day 77: buy 5 units at price 1562.500000, total balance -445.239588\n",
      "day 79, sell 5 units at price 1543.699950, investment 3.167807 %, total balance 1098.460362,\n",
      "day 80, sell 5 units at price 1589.049990, investment 3.828940 %, total balance 2687.510352,\n",
      "day 81: buy 5 units at price 1563.000030, total balance 1124.510322\n",
      "day 83, sell 5 units at price 1584.049990, investment 4.633727 %, total balance 2708.560312,\n",
      "day 85: buy 5 units at price 1537.700045, total balance 1170.860267\n",
      "day 86: buy 5 units at price 1544.250030, total balance -373.389763\n",
      "day 87, sell 5 units at price 1532.649995, investment 0.703040 %, total balance 1159.260232,\n",
      "day 89: buy 5 units at price 1518.500060, total balance -359.239828\n",
      "day 93, sell 5 units at price 1644.550020, investment 8.554743 %, total balance 1285.310192,\n",
      "day 95, sell 5 units at price 1695.149995, investment 11.894785 %, total balance 2980.460187,\n",
      "day 97, sell 5 units at price 1717.250060, investment 10.327666 %, total balance 4697.710247,\n",
      "day 98: buy 1 units at price 338.869995, total balance 4358.840252\n",
      "day 99, sell 5 units at price 1655.500030, investment 5.952002 %, total balance 6014.340282,\n",
      "day 101, sell 5 units at price 1658.300020, investment 6.097248 %, total balance 7672.640302,\n",
      "day 104: buy 5 units at price 1558.200075, total balance 6114.440227\n",
      "day 105: buy 5 units at price 1576.799925, total balance 4537.640302\n",
      "day 106: buy 5 units at price 1556.750030, total balance 2980.890272\n",
      "day 108: buy 5 units at price 1586.250000, total balance 1394.640272\n",
      "day 110: buy 5 units at price 1582.899935, total balance -188.259663\n",
      "day 112, sell 5 units at price 1668.450010, investment 8.502956 %, total balance 1480.190347,\n",
      "day 114: buy 1 units at price 337.950012, total balance 1142.240335\n",
      "day 116, sell 5 units at price 1700.299990, investment 10.105226 %, total balance 2842.540325,\n",
      "day 117, sell 5 units at price 1735.800020, investment 14.310171 %, total balance 4578.340345,\n",
      "day 118, sell 5 units at price 1722.850035, investment 408.410323 %, total balance 6301.190380,\n",
      "day 119, sell 5 units at price 1750.099945, investment 12.315483 %, total balance 8051.290325,\n",
      "day 120, sell 5 units at price 1757.799990, investment 11.478949 %, total balance 9809.090315,\n",
      "day 121, sell 5 units at price 1763.950045, investment 13.309781 %, total balance 11573.040360,\n",
      "day 122, sell 5 units at price 1729.450075, investment 9.027586 %, total balance 13302.490435,\n",
      "day 124, sell 2 units at price 685.700012, investment -56.680773 %, total balance 13988.190447,\n",
      "day 128: buy 5 units at price 1746.250000, total balance 12241.940447\n",
      "day 132: buy 5 units at price 1725.000000, total balance 10516.940447\n",
      "day 135: buy 5 units at price 1578.650055, total balance 8938.290392\n",
      "day 136: buy 5 units at price 1618.300020, total balance 7319.990372\n",
      "day 137: buy 5 units at price 1611.549990, total balance 5708.440382\n",
      "day 139, sell 5 units at price 1677.449950, investment -3.939874 %, total balance 7385.890332,\n",
      "day 141: buy 1 units at price 333.299988, total balance 7052.590344\n",
      "day 142: buy 1 units at price 346.170013, total balance 6706.420331\n",
      "day 143, sell 5 units at price 1760.249940, investment 2.043475 %, total balance 8466.670271,\n",
      "day 144, sell 5 units at price 1787.100065, investment 13.204320 %, total balance 10253.770336,\n",
      "day 145, sell 5 units at price 1754.949950, investment 8.444042 %, total balance 12008.720286,\n",
      "day 147, sell 5 units at price 1654.649965, investment 2.674442 %, total balance 13663.370251,\n",
      "day 148: buy 1 units at price 335.119995, total balance 13328.250256\n",
      "day 149, sell 3 units at price 1000.050018, investment 200.045021 %, total balance 14328.300274,\n",
      "day 153: buy 5 units at price 1635.850065, total balance 12692.450209\n",
      "day 154, sell 5 units at price 1727.550050, investment 5.605647 %, total balance 14420.000259,\n",
      "day 159: buy 5 units at price 1567.799990, total balance 12852.200269\n",
      "day 161, sell 5 units at price 1582.649995, investment 0.947187 %, total balance 14434.850264,\n",
      "day 162: buy 5 units at price 1545.500030, total balance 12889.350234\n",
      "day 164: buy 1 units at price 304.179993, total balance 12585.170241\n",
      "day 165: buy 5 units at price 1395.899965, total balance 11189.270276\n",
      "day 166: buy 5 units at price 1288.899995, total balance 9900.370281\n",
      "day 167: buy 5 units at price 1330.650025, total balance 8569.720256\n",
      "day 168: buy 5 units at price 1262.399980, total balance 7307.320276\n",
      "day 169: buy 5 units at price 1337.649995, total balance 5969.670281\n",
      "day 170: buy 5 units at price 1434.700010, total balance 4534.970271\n",
      "day 171, sell 5 units at price 1528.600005, investment -1.093499 %, total balance 6063.570276,\n",
      "day 172, sell 5 units at price 1496.499940, investment 391.978425 %, total balance 7560.070216,\n",
      "day 174: buy 1 units at price 304.700012, total balance 7255.370204\n",
      "day 175: buy 5 units at price 1504.649965, total balance 5750.720239\n",
      "day 176, sell 5 units at price 1470.399935, investment 5.337057 %, total balance 7221.120174,\n",
      "day 177, sell 5 units at price 1501.699980, investment 16.510201 %, total balance 8722.820154,\n",
      "day 178, sell 5 units at price 1456.049955, investment 9.423960 %, total balance 10178.870109,\n",
      "day 179: buy 1 units at price 287.690002, total balance 9891.180107\n",
      "day 181, sell 5 units at price 1500.399935, investment 18.852975 %, total balance 11391.580042,\n",
      "day 182, sell 5 units at price 1451.199950, investment 8.488764 %, total balance 12842.779992,\n",
      "day 184: buy 5 units at price 1417.299955, total balance 11425.480037\n",
      "day 185: buy 5 units at price 1403.450010, total balance 10022.030027\n",
      "day 186: buy 5 units at price 1427.400055, total balance 8594.629972\n",
      "day 187: buy 5 units at price 1470.399935, total balance 7124.230037\n",
      "day 188: buy 5 units at price 1469.499970, total balance 5654.730067\n",
      "day 193, sell 5 units at price 1513.849945, investment 5.516828 %, total balance 7168.580012,\n",
      "day 194, sell 5 units at price 1509.850005, investment 395.520166 %, total balance 8678.430017,\n",
      "day 195: buy 1 units at price 306.850006, total balance 8371.580011\n",
      "day 196, sell 5 units at price 1525.099945, investment 1.359119 %, total balance 9896.679956,\n",
      "day 197: buy 5 units at price 1505.299990, total balance 8391.379966\n",
      "day 198, sell 5 units at price 1459.850005, investment 407.438561 %, total balance 9851.229971,\n",
      "day 200: buy 5 units at price 1432.400055, total balance 8418.829916\n",
      "day 201: buy 5 units at price 1422.700045, total balance 6996.129871\n",
      "day 203: buy 5 units at price 1422.449950, total balance 5573.679921\n",
      "day 204: buy 5 units at price 1375.050050, total balance 4198.629871\n",
      "day 205: buy 5 units at price 1395.350035, total balance 2803.279836\n",
      "day 206: buy 5 units at price 1389.250030, total balance 1414.029806\n",
      "day 210: buy 1 units at price 284.730011, total balance 1129.299795\n",
      "day 211: buy 5 units at price 1459.100035, total balance -329.800240\n",
      "day 212, sell 5 units at price 1483.699950, investment 4.684964 %, total balance 1153.899710,\n",
      "day 213: buy 5 units at price 1455.650025, total balance -301.750315\n",
      "day 215, sell 5 units at price 1580.449980, investment 12.611776 %, total balance 1278.699665,\n",
      "day 216, sell 5 units at price 1588.300020, investment 11.272240 %, total balance 2866.999685,\n",
      "day 217, sell 5 units at price 1660.500030, investment 12.928462 %, total balance 4527.499715,\n",
      "day 218, sell 5 units at price 1713.849945, investment 16.628103 %, total balance 6241.349660,\n",
      "day 219, sell 5 units at price 1723.899995, investment 461.805430 %, total balance 7965.249655,\n",
      "day 220, sell 5 units at price 1788.600005, investment 18.820170 %, total balance 9753.849660,\n",
      "day 221, sell 5 units at price 1790.850065, investment 25.024434 %, total balance 11544.699725,\n",
      "day 222, sell 5 units at price 1854.149935, investment 30.326132 %, total balance 13398.849660,\n",
      "day 223, sell 5 units at price 1762.749940, investment 23.923512 %, total balance 15161.599600,\n",
      "day 225, sell 5 units at price 1737.550050, investment 26.362677 %, total balance 16899.149650,\n",
      "day 227: buy 1 units at price 333.010010, total balance 16566.139640\n",
      "day 229, sell 5 units at price 1722.500000, investment 23.445727 %, total balance 18288.639640,\n",
      "day 231, sell 1 units at price 342.950012, investment -75.314018 %, total balance 18631.589652,\n",
      "day 233: buy 5 units at price 1554.299925, total balance 17077.289727\n",
      "day 235: buy 5 units at price 1544.499970, total balance 15532.789757\n",
      "day 237, sell 5 units at price 1612.350005, investment 466.273291 %, total balance 17145.139762,\n",
      "day 238: buy 5 units at price 1594.799955, total balance 15550.339807\n",
      "day 239: buy 1 units at price 316.709991, total balance 15233.629816\n",
      "day 241, sell 5 units at price 1550.500030, investment 6.264135 %, total balance 16784.129846,\n",
      "day 243, sell 5 units at price 1619.250030, investment 11.238966 %, total balance 18403.379876,\n",
      "day 244, sell 1 units at price 320.230011, investment -3.837722 %, total balance 18723.609887,\n",
      "day 245: buy 1 units at price 313.579987, total balance 18410.029900\n",
      "day 246: buy 1 units at price 303.200012, total balance 18106.829888\n",
      "day 247, sell 2 units at price 594.859986, investment -61.728108 %, total balance 18701.689874,\n",
      "\n",
      "total gained 8701.689874, total investment 87.016899 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc23d16198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is my parameter\n",
    "model = Model(30, 500, 3)\n",
    "agent = Agent(15, 0.1, 0.03,model,10000,5,5,1,30)\n",
    "agent.fit(500, 100)\n",
    "agent.buy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 100. reward: 64.774399\n",
      "iter 200. reward: 77.959298\n",
      "iter 300. reward: 88.457299\n",
      "iter 400. reward: 90.379699\n",
      "iter 500. reward: 91.761199\n",
      "time taken to train: 55.998340368270874 seconds\n",
      "day 0: buy 5 units at price 1617.350005, total balance 8382.649995\n",
      "day 1: buy 5 units at price 1597.850035, total balance 6784.799960\n",
      "day 3, sell 5 units at price 1735.449980, investment 7.302067 %, total balance 8520.249940,\n",
      "day 4, sell 5 units at price 1784.550020, investment 11.684450 %, total balance 10304.799960,\n",
      "day 14: buy 5 units at price 1737.299955, total balance 8567.500005\n",
      "day 15: buy 5 units at price 1689.299925, total balance 6878.200080\n",
      "day 16: buy 5 units at price 1706.750030, total balance 5171.450050\n",
      "day 17: buy 5 units at price 1763.849945, total balance 3407.600105\n",
      "day 18: buy 5 units at price 1764.649965, total balance 1642.950140\n",
      "day 19: buy 5 units at price 1740.249940, total balance -97.299800\n",
      "day 24, sell 5 units at price 1776.999970, investment 2.285156 %, total balance 1679.700170,\n",
      "day 27: buy 5 units at price 1753.049925, total balance -73.349755\n",
      "day 30, sell 5 units at price 1813.750000, investment 7.366961 %, total balance 1740.400245,\n",
      "day 32, sell 5 units at price 1888.200075, investment 10.631319 %, total balance 3628.600320,\n",
      "day 33, sell 5 units at price 1899.049990, investment 7.665054 %, total balance 5527.650310,\n",
      "day 34, sell 5 units at price 1925.000000, investment 9.086790 %, total balance 7452.650310,\n",
      "day 35, sell 5 units at price 1875.500030, investment 7.771877 %, total balance 9328.150340,\n",
      "day 37, sell 5 units at price 1832.400055, investment 4.526404 %, total balance 11160.550395,\n",
      "day 38: buy 5 units at price 1755.449980, total balance 9405.100415\n",
      "day 39: buy 1 units at price 344.989990, total balance 9060.110425\n",
      "day 40: buy 5 units at price 1726.250000, total balance 7333.860425\n",
      "day 41: buy 5 units at price 1704.850005, total balance 5629.010420\n",
      "day 42: buy 5 units at price 1698.000030, total balance 3931.010390\n",
      "day 43: buy 5 units at price 1705.500030, total balance 2225.510360\n",
      "day 44: buy 5 units at price 1707.649995, total balance 517.860365\n",
      "day 45: buy 5 units at price 1740.700075, total balance -1222.839710\n",
      "day 48, sell 5 units at price 1784.400025, investment 1.649152 %, total balance 561.560315,\n",
      "day 49: buy 5 units at price 1714.700010, total balance -1153.139695\n",
      "day 50, sell 5 units at price 1777.949980, investment 415.362773 %, total balance 624.810285,\n",
      "day 51, sell 5 units at price 1773.000030, investment 2.708184 %, total balance 2397.810315,\n",
      "day 52, sell 5 units at price 1778.399965, investment 4.314160 %, total balance 4176.210280,\n",
      "day 53, sell 5 units at price 1777.850035, investment 4.702591 %, total balance 5954.060315,\n",
      "day 54, sell 5 units at price 1753.000030, investment 2.785107 %, total balance 7707.060345,\n",
      "day 56, sell 5 units at price 1798.249970, investment 5.305535 %, total balance 9505.310315,\n",
      "day 57: buy 1 units at price 351.809998, total balance 9153.500317\n",
      "day 58, sell 5 units at price 1725.500030, investment -0.873214 %, total balance 10879.000347,\n",
      "day 60, sell 2 units at price 674.679992, investment -60.653176 %, total balance 11553.680339,\n",
      "day 61: buy 5 units at price 1629.199980, total balance 9924.480359\n",
      "day 62: buy 1 units at price 326.170013, total balance 9598.310346\n",
      "day 63: buy 5 units at price 1604.349975, total balance 7993.960371\n",
      "day 64, sell 5 units at price 1600.399935, investment 354.904620 %, total balance 9594.360306,\n",
      "day 66: buy 5 units at price 1605.399935, total balance 7988.960371\n",
      "day 67: buy 5 units at price 1496.300050, total balance 6492.660321\n",
      "day 68: buy 5 units at price 1530.449980, total balance 4962.210341\n",
      "day 69: buy 5 units at price 1513.899995, total balance 3448.310346\n",
      "day 70: buy 5 units at price 1530.249940, total balance 1918.060406\n",
      "day 71: buy 5 units at price 1521.950075, total balance 396.110331\n",
      "day 75: buy 5 units at price 1543.500060, total balance -1147.389729\n",
      "day 76, sell 5 units at price 1556.499940, investment -4.462315 %, total balance 409.110211,\n",
      "day 78, sell 5 units at price 1575.249940, investment 382.953637 %, total balance 1984.360151,\n",
      "day 79: buy 5 units at price 1543.699950, total balance 440.660201\n",
      "day 81: buy 1 units at price 312.600006, total balance 128.060195\n",
      "day 82, sell 5 units at price 1577.749940, investment -1.657995 %, total balance 1705.810135,\n",
      "day 84, sell 5 units at price 1587.749940, investment -1.099414 %, total balance 3293.560075,\n",
      "day 85: buy 5 units at price 1537.700045, total balance 1755.860030\n",
      "day 86, sell 5 units at price 1544.250030, investment 3.204570 %, total balance 3300.110060,\n",
      "day 87: buy 5 units at price 1532.649995, total balance 1767.460065\n",
      "day 88, sell 5 units at price 1526.000060, investment -0.290759 %, total balance 3293.460125,\n",
      "day 89: buy 5 units at price 1518.500060, total balance 1774.960065\n",
      "day 90, sell 5 units at price 1566.300050, investment 3.461263 %, total balance 3341.260115,\n",
      "day 91: buy 5 units at price 1556.199950, total balance 1785.060165\n",
      "day 92, sell 5 units at price 1575.650025, investment 2.966841 %, total balance 3360.710190,\n",
      "day 94, sell 5 units at price 1705.149995, investment 12.037183 %, total balance 5065.860185,\n",
      "day 96, sell 5 units at price 1689.450075, investment 9.455783 %, total balance 6755.310260,\n",
      "day 97, sell 5 units at price 1717.250060, investment 11.242477 %, total balance 8472.560320,\n",
      "day 98, sell 5 units at price 1694.349975, investment 442.018536 %, total balance 10166.910295,\n",
      "day 99: buy 1 units at price 331.100006, total balance 9835.810289\n",
      "day 100, sell 5 units at price 1644.900055, investment 6.971451 %, total balance 11480.710344,\n",
      "day 102, sell 3 units at price 975.600036, investment -36.345543 %, total balance 12456.310380,\n",
      "day 103: buy 5 units at price 1586.450045, total balance 10869.860335\n",
      "day 104: buy 5 units at price 1558.200075, total balance 9311.660260\n",
      "day 105: buy 5 units at price 1576.799925, total balance 7734.860335\n",
      "day 106: buy 5 units at price 1556.750030, total balance 6178.110305\n",
      "day 107: buy 5 units at price 1602.649995, total balance 4575.460310\n",
      "day 108: buy 5 units at price 1586.250000, total balance 2989.210310\n",
      "day 109: buy 5 units at price 1573.099975, total balance 1416.110335\n",
      "day 110: buy 5 units at price 1582.899935, total balance -166.789600\n",
      "day 111, sell 5 units at price 1682.050020, investment 10.770494 %, total balance 1515.260420,\n",
      "day 112: buy 5 units at price 1668.450010, total balance -153.189590\n",
      "day 113, sell 5 units at price 1673.999940, investment 7.569721 %, total balance 1520.810350,\n",
      "day 115, sell 5 units at price 1681.100005, investment 407.731795 %, total balance 3201.910355,\n",
      "day 117, sell 5 units at price 1735.800020, investment 9.414099 %, total balance 4937.710375,\n",
      "day 118, sell 5 units at price 1722.850035, investment 10.566676 %, total balance 6660.560410,\n",
      "day 119, sell 5 units at price 1750.099945, investment 10.990616 %, total balance 8410.660355,\n",
      "day 121, sell 5 units at price 1763.950045, investment 13.309781 %, total balance 10174.610400,\n",
      "day 122: buy 5 units at price 1729.450075, total balance 8445.160325\n",
      "day 123, sell 5 units at price 1688.200075, investment 5.338039 %, total balance 10133.360400,\n",
      "day 124: buy 1 units at price 342.850006, total balance 9790.510394\n",
      "day 125, sell 5 units at price 1747.649995, investment 10.174941 %, total balance 11538.160389,\n",
      "day 126: buy 5 units at price 1729.100035, total balance 9809.060354\n",
      "day 127, sell 5 units at price 1771.549990, investment 12.615220 %, total balance 11580.610344,\n",
      "day 128: buy 1 units at price 349.250000, total balance 11231.360344\n",
      "day 129, sell 5 units at price 1718.750000, investment 8.582353 %, total balance 12950.110344,\n",
      "day 130: buy 5 units at price 1665.650025, total balance 11284.460319\n",
      "day 131: buy 5 units at price 1669.850005, total balance 9614.610314\n",
      "day 132: buy 5 units at price 1725.000000, total balance 7889.610314\n",
      "day 133: buy 5 units at price 1576.150055, total balance 6313.460259\n",
      "day 134: buy 5 units at price 1552.100065, total balance 4761.360194\n",
      "day 135: buy 5 units at price 1578.650055, total balance 3182.710139\n",
      "day 136: buy 5 units at price 1618.300020, total balance 1564.410119\n",
      "day 137: buy 5 units at price 1611.549990, total balance -47.139871\n",
      "day 139, sell 5 units at price 1677.449950, investment 0.539419 %, total balance 1630.310079,\n",
      "day 142, sell 5 units at price 1730.850065, investment 0.080950 %, total balance 3361.160144,\n",
      "day 143, sell 5 units at price 1760.249940, investment 413.416920 %, total balance 5121.410084,\n",
      "day 144, sell 5 units at price 1787.100065, investment 3.354348 %, total balance 6908.510149,\n",
      "day 145, sell 5 units at price 1754.949950, investment 402.491038 %, total balance 8663.460099,\n",
      "day 146, sell 5 units at price 1715.299990, investment 2.980816 %, total balance 10378.760089,\n",
      "day 147: buy 5 units at price 1654.649965, total balance 8724.110124\n",
      "day 148, sell 5 units at price 1675.599975, investment 0.344341 %, total balance 10399.710099,\n",
      "day 149: buy 1 units at price 333.350006, total balance 10066.360093\n",
      "day 150, sell 5 units at price 1641.000060, investment -4.869562 %, total balance 11707.360153,\n",
      "day 152, sell 5 units at price 1645.500030, investment 4.399960 %, total balance 13352.860183,\n",
      "day 153: buy 5 units at price 1635.850065, total balance 11717.010118\n",
      "day 154, sell 5 units at price 1727.550050, investment 11.304038 %, total balance 13444.560168,\n",
      "day 155: buy 1 units at price 341.839996, total balance 13102.720172\n",
      "day 156, sell 4 units at price 1306.520020, investment -17.238148 %, total balance 14409.240192,\n",
      "day 165: buy 5 units at price 1395.899965, total balance 13013.340227\n",
      "day 166: buy 5 units at price 1288.899995, total balance 11724.440232\n",
      "day 167: buy 5 units at price 1330.650025, total balance 10393.790207\n",
      "day 168: buy 5 units at price 1262.399980, total balance 9131.390227\n",
      "day 169: buy 5 units at price 1337.649995, total balance 7793.740232\n",
      "day 171, sell 5 units at price 1528.600005, investment 9.506415 %, total balance 9322.340237,\n",
      "day 172, sell 5 units at price 1496.499940, investment 16.106753 %, total balance 10818.840177,\n",
      "day 174, sell 5 units at price 1523.500060, investment 14.492919 %, total balance 12342.340237,\n",
      "day 175, sell 5 units at price 1504.649965, investment 19.189638 %, total balance 13846.990202,\n",
      "day 177, sell 5 units at price 1501.699980, investment 12.264044 %, total balance 15348.690182,\n",
      "day 182: buy 5 units at price 1451.199950, total balance 13897.490232\n",
      "day 184: buy 5 units at price 1417.299955, total balance 12480.190277\n",
      "day 185: buy 5 units at price 1403.450010, total balance 11076.740267\n",
      "day 186: buy 5 units at price 1427.400055, total balance 9649.340212\n",
      "day 187: buy 1 units at price 294.079987, total balance 9355.260225\n",
      "day 188, sell 5 units at price 1469.499970, investment 1.261027 %, total balance 10824.760195,\n",
      "day 189, sell 5 units at price 1499.600065, investment 5.806824 %, total balance 12324.360260,\n",
      "day 191: buy 5 units at price 1422.250060, total balance 10902.110200\n",
      "day 192: buy 5 units at price 1470.449980, total balance 9431.660220\n",
      "day 193, sell 5 units at price 1513.849945, investment 7.866325 %, total balance 10945.510165,\n",
      "day 194, sell 5 units at price 1509.850005, investment 5.776233 %, total balance 12455.360170,\n",
      "day 195, sell 5 units at price 1534.250030, investment 421.711812 %, total balance 13989.610200,\n",
      "day 197, sell 5 units at price 1505.299990, investment 5.839334 %, total balance 15494.910190,\n",
      "day 198: buy 5 units at price 1459.850005, total balance 14035.060185\n",
      "day 199: buy 5 units at price 1420.899965, total balance 12614.160220\n",
      "day 200: buy 5 units at price 1432.400055, total balance 11181.760165\n",
      "day 201: buy 5 units at price 1422.700045, total balance 9759.060120\n",
      "day 202: buy 5 units at price 1384.100035, total balance 8374.960085\n",
      "day 203: buy 5 units at price 1422.449950, total balance 6952.510135\n",
      "day 204: buy 5 units at price 1375.050050, total balance 5577.460085\n",
      "day 205: buy 5 units at price 1395.350035, total balance 4182.110050\n",
      "day 206: buy 5 units at price 1389.250030, total balance 2792.860020\n",
      "day 207: buy 5 units at price 1394.250030, total balance 1398.609990\n",
      "day 208: buy 5 units at price 1418.800050, total balance -20.190060\n",
      "day 211, sell 5 units at price 1459.100035, investment -0.771869 %, total balance 1438.909975,\n",
      "day 213, sell 5 units at price 1455.650025, investment -0.287699 %, total balance 2894.560000,\n",
      "day 215, sell 5 units at price 1580.449980, investment 11.228800 %, total balance 4475.009980,\n",
      "day 217, sell 5 units at price 1660.500030, investment 15.924320 %, total balance 6135.510010,\n",
      "day 218, sell 5 units at price 1713.849945, investment 20.464602 %, total balance 7849.359955,\n",
      "day 219, sell 5 units at price 1723.899995, investment 24.550246 %, total balance 9573.259950,\n",
      "day 220, sell 5 units at price 1788.600005, investment 25.740804 %, total balance 11361.859955,\n",
      "day 221, sell 5 units at price 1790.850065, investment 30.238900 %, total balance 13152.710020,\n",
      "day 222, sell 5 units at price 1854.149935, investment 32.880631 %, total balance 15006.859955,\n",
      "day 223, sell 5 units at price 1762.749940, investment 26.885003 %, total balance 16769.609895,\n",
      "day 224, sell 5 units at price 1811.100005, investment 29.897792 %, total balance 18580.709900,\n",
      "day 225: buy 1 units at price 347.510010, total balance 18233.199890\n",
      "day 226, sell 2 units at price 667.260010, investment -52.970117 %, total balance 18900.459900,\n",
      "day 227: buy 5 units at price 1665.050050, total balance 17235.409850\n",
      "day 228, sell 5 units at price 1710.000000, investment 392.072156 %, total balance 18945.409850,\n",
      "day 229: buy 5 units at price 1722.500000, total balance 17222.909850\n",
      "day 230, sell 5 units at price 1749.649965, investment 5.080923 %, total balance 18972.559815,\n",
      "day 231: buy 1 units at price 342.950012, total balance 18629.609803\n",
      "day 232, sell 1 units at price 335.070007, investment -80.547460 %, total balance 18964.679810,\n",
      "day 233: buy 5 units at price 1554.299925, total balance 17410.379885\n",
      "day 234: buy 5 units at price 1545.800020, total balance 15864.579865\n",
      "day 235: buy 5 units at price 1544.499970, total balance 14320.079895\n",
      "day 236, sell 5 units at price 1592.550050, investment 364.367982 %, total balance 15912.629945,\n",
      "day 238, sell 5 units at price 1594.799955, investment 2.605677 %, total balance 17507.429900,\n",
      "day 240, sell 5 units at price 1594.349975, investment 3.140766 %, total balance 19101.779875,\n",
      "day 241: buy 5 units at price 1550.500030, total balance 17551.279845\n",
      "day 242, sell 5 units at price 1613.450010, investment 4.464231 %, total balance 19164.729855,\n",
      "day 247: buy 5 units at price 1487.149965, total balance 17677.579890\n",
      "day 248, sell 5 units at price 1543.699950, investment 3.802574 %, total balance 19221.279840,\n",
      "\n",
      "total gained 9221.279840, total investment 92.212798 %\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fdc21c60a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is from bayesian\n",
    "model = Model(20, 176, 3)\n",
    "agent = Agent(34, 0.7152490670931403, 0.28208006704411104,model,10000,5,5,1,20)\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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}