realtime-agent/realtime-evolution-strategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import random\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(z):\n",
    "    assert len(z.shape) == 2\n",
    "    s = np.max(z, axis=1)\n",
    "    s = s[:, np.newaxis]\n",
    "    e_x = np.exp(z - s)\n",
    "    div = np.sum(e_x, axis=1)\n",
    "    div = div[:, np.newaxis]\n",
    "    return e_x / div"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>2018-05-23</td>\n",
       "      <td>182.500000</td>\n",
       "      <td>186.910004</td>\n",
       "      <td>182.179993</td>\n",
       "      <td>186.899994</td>\n",
       "      <td>186.899994</td>\n",
       "      <td>16628100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-05-24</td>\n",
       "      <td>185.880005</td>\n",
       "      <td>186.800003</td>\n",
       "      <td>185.029999</td>\n",
       "      <td>185.929993</td>\n",
       "      <td>185.929993</td>\n",
       "      <td>12354700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-05-25</td>\n",
       "      <td>186.020004</td>\n",
       "      <td>186.330002</td>\n",
       "      <td>184.449997</td>\n",
       "      <td>184.919998</td>\n",
       "      <td>184.919998</td>\n",
       "      <td>10965100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-05-29</td>\n",
       "      <td>184.339996</td>\n",
       "      <td>186.809998</td>\n",
       "      <td>183.710007</td>\n",
       "      <td>185.740005</td>\n",
       "      <td>185.740005</td>\n",
       "      <td>16398900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-05-30</td>\n",
       "      <td>186.539993</td>\n",
       "      <td>188.000000</td>\n",
       "      <td>185.250000</td>\n",
       "      <td>187.669998</td>\n",
       "      <td>187.669998</td>\n",
       "      <td>13736900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2018-05-23  182.500000  186.910004  182.179993  186.899994  186.899994   \n",
       "1  2018-05-24  185.880005  186.800003  185.029999  185.929993  185.929993   \n",
       "2  2018-05-25  186.020004  186.330002  184.449997  184.919998  184.919998   \n",
       "3  2018-05-29  184.339996  186.809998  183.710007  185.740005  185.740005   \n",
       "4  2018-05-30  186.539993  188.000000  185.250000  187.669998  187.669998   \n",
       "\n",
       "     Volume  \n",
       "0  16628100  \n",
       "1  12354700  \n",
       "2  10965100  \n",
       "3  16398900  \n",
       "4  13736900  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('FB.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = [df['Close'].tolist(), df['Volume'].tolist()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_state(parameters, t, window_size = 20):\n",
    "    outside = []\n",
    "    d = t - window_size + 1\n",
    "    for parameter in parameters:\n",
    "        block = (\n",
    "            parameter[d : t + 1]\n",
    "            if d >= 0\n",
    "            else -d * [parameter[0]] + parameter[0 : t + 1]\n",
    "        )\n",
    "        res = []\n",
    "        for i in range(window_size - 1):\n",
    "            res.append(block[i + 1] - block[i])\n",
    "        for i in range(1, window_size, 1):\n",
    "            res.append(block[i] - block[0])\n",
    "        outside.append(res)\n",
    "    return np.array(outside).reshape((1, -1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our output size from `get_state` is 38. In this notebook, I only use `Close` and `Volume` parameters, you can choose any parameters you want from your DataFrame.\n",
    "\n",
    "After that, I want to add another parameters, my `inventory` size, mean of `inventory` and `capital`.\n",
    "\n",
    "Let say for an example,\n",
    "```\n",
    "inventory_size = 1\n",
    "mean_inventory = 0.5\n",
    "capital = 2\n",
    "last_state = 0\n",
    "```\n",
    "\n",
    "We have 3 actions,\n",
    "\n",
    "1. `0` for do nothing.\n",
    "2. `1` for buy.\n",
    "3. `2` for sell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "inventory_size = 1\n",
    "mean_inventory = 0.5\n",
    "capital = 2\n",
    "concat_parameters = np.concatenate([get_state(parameters, 20), [[inventory_size, \n",
    "                                                                mean_inventory,\n",
    "                                                                capital]]], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_size = concat_parameters.shape[1]\n",
    "input_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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) + 1e-7)\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",
    "class Model:\n",
    "    \n",
    "    DROPOUT = 0.9\n",
    "    def __init__(self, input_size, layer_size, output_size):\n",
    "        self.weights = [\n",
    "            np.random.normal(scale = 0.05, size = (input_size, layer_size)),\n",
    "            np.random.normal(scale = 0.05, size = (layer_size, layer_size)),\n",
    "            np.random.normal(scale = 0.05, size = (layer_size, output_size)),\n",
    "            np.zeros((1, layer_size)),\n",
    "            np.zeros((1, layer_size)),\n",
    "        ]\n",
    "\n",
    "    def predict(self, inputs):\n",
    "        feed = np.dot(inputs, self.weights[0]) + self.weights[-2]\n",
    "        feed = np.dot(feed, self.weights[1]) + self.weights[-1]\n",
    "        decision = np.dot(feed, self.weights[2])\n",
    "        return decision\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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent:\n",
    "\n",
    "    POPULATION_SIZE = 15\n",
    "    SIGMA = 0.1\n",
    "    LEARNING_RATE = 0.03\n",
    "\n",
    "    def __init__(self, model, timeseries, skip, initial_money, real_trend, minmax):\n",
    "        self.model = model\n",
    "        self.timeseries = timeseries\n",
    "        self.skip = skip\n",
    "        self.real_trend = real_trend\n",
    "        self.initial_money = initial_money\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",
    "        self.minmax = minmax\n",
    "        self._initiate()\n",
    "\n",
    "    def _initiate(self):\n",
    "        # i assume first index is the close value\n",
    "        self.trend = self.timeseries[0]\n",
    "        self._mean = np.mean(self.trend)\n",
    "        self._std = np.std(self.trend)\n",
    "        self._inventory = []\n",
    "        self._capital = self.initial_money\n",
    "        self._queue = []\n",
    "        self._scaled_capital = self.minmax.transform([[self._capital, 2]])[0, 0]\n",
    "\n",
    "    def reset_capital(self, capital):\n",
    "        if capital:\n",
    "            self._capital = capital\n",
    "        self._scaled_capital = self.minmax.transform([[self._capital, 2]])[0, 0]\n",
    "        self._queue = []\n",
    "        self._inventory = []\n",
    "\n",
    "    def trade(self, data):\n",
    "        \"\"\"\n",
    "        you need to make sure the data is [close, volume]\n",
    "        \"\"\"\n",
    "        scaled_data = self.minmax.transform([data])[0]\n",
    "        real_close = data[0]\n",
    "        close = scaled_data[0]\n",
    "        if len(self._queue) >= window_size:\n",
    "            self._queue.pop(0)\n",
    "        self._queue.append(scaled_data)\n",
    "        if len(self._queue) < window_size:\n",
    "            return {\n",
    "                'status': 'data not enough to trade',\n",
    "                'action': 'fail',\n",
    "                'balance': self._capital,\n",
    "                'timestamp': str(datetime.now()),\n",
    "            }\n",
    "        state = self.get_state(\n",
    "            window_size - 1,\n",
    "            self._inventory,\n",
    "            self._scaled_capital,\n",
    "            timeseries = np.array(self._queue).T.tolist(),\n",
    "        )\n",
    "        action, prob = self.act_softmax(state)\n",
    "        print(prob)\n",
    "        if action == 1 and self._scaled_capital >= close:\n",
    "            self._inventory.append(close)\n",
    "            self._scaled_capital -= close\n",
    "            self._capital -= real_close\n",
    "            return {\n",
    "                'status': 'buy 1 unit, cost %f' % (real_close),\n",
    "                'action': 'buy',\n",
    "                'balance': self._capital,\n",
    "                'timestamp': str(datetime.now()),\n",
    "            }\n",
    "        elif action == 2 and len(self._inventory):\n",
    "            bought_price = self._inventory.pop(0)\n",
    "            self._scaled_capital += close\n",
    "            self._capital += real_close\n",
    "            scaled_bought_price = self.minmax.inverse_transform(\n",
    "                [[bought_price, 2]]\n",
    "            )[0, 0]\n",
    "            try:\n",
    "                invest = (\n",
    "                    (real_close - scaled_bought_price) / scaled_bought_price\n",
    "                ) * 100\n",
    "            except:\n",
    "                invest = 0\n",
    "            return {\n",
    "                'status': 'sell 1 unit, price %f' % (real_close),\n",
    "                'investment': invest,\n",
    "                'gain': real_close - scaled_bought_price,\n",
    "                'balance': self._capital,\n",
    "                'action': 'sell',\n",
    "                'timestamp': str(datetime.now()),\n",
    "            }\n",
    "        else:\n",
    "            return {\n",
    "                'status': 'do nothing',\n",
    "                'action': 'nothing',\n",
    "                'balance': self._capital,\n",
    "                'timestamp': str(datetime.now()),\n",
    "            }\n",
    "\n",
    "    def change_data(self, timeseries, skip, initial_money, real_trend, minmax):\n",
    "        self.timeseries = timeseries\n",
    "        self.skip = skip\n",
    "        self.initial_money = initial_money\n",
    "        self.real_trend = real_trend\n",
    "        self.minmax = minmax\n",
    "        self._initiate()\n",
    "\n",
    "    def act(self, sequence):\n",
    "        decision = self.model.predict(np.array(sequence))\n",
    "\n",
    "        return np.argmax(decision[0])\n",
    "\n",
    "    def act_softmax(self, sequence):\n",
    "        decision = self.model.predict(np.array(sequence))\n",
    "\n",
    "        return np.argmax(decision[0]), softmax(decision)[0]\n",
    "\n",
    "    def get_state(self, t, inventory, capital, timeseries):\n",
    "        state = get_state(timeseries, t)\n",
    "        len_inventory = len(inventory)\n",
    "        if len_inventory:\n",
    "            mean_inventory = np.mean(inventory)\n",
    "        else:\n",
    "            mean_inventory = 0\n",
    "        z_inventory = (mean_inventory - self._mean) / self._std\n",
    "        z_capital = (capital - self._mean) / self._std\n",
    "        concat_parameters = np.concatenate(\n",
    "            [state, [[len_inventory, z_inventory, z_capital]]], axis = 1\n",
    "        )\n",
    "        return concat_parameters\n",
    "\n",
    "    def get_reward(self, weights):\n",
    "        initial_money = self._scaled_capital\n",
    "        starting_money = initial_money\n",
    "        invests = []\n",
    "        self.model.weights = weights\n",
    "        inventory = []\n",
    "        state = self.get_state(0, inventory, starting_money, self.timeseries)\n",
    "\n",
    "        for t in range(0, len(self.trend) - 1, self.skip):\n",
    "            action = self.act(state)\n",
    "            if action == 1 and starting_money >= self.trend[t]:\n",
    "                inventory.append(self.trend[t])\n",
    "                starting_money -= self.trend[t]\n",
    "\n",
    "            elif action == 2 and len(inventory):\n",
    "                bought_price = inventory.pop(0)\n",
    "                starting_money += self.trend[t]\n",
    "                invest = ((self.trend[t] - bought_price) / bought_price) * 100\n",
    "                invests.append(invest)\n",
    "\n",
    "            state = self.get_state(\n",
    "                t + 1, inventory, starting_money, self.timeseries\n",
    "            )\n",
    "        invests = np.mean(invests)\n",
    "        if np.isnan(invests):\n",
    "            invests = 0\n",
    "        score = (starting_money - initial_money) / initial_money * 100\n",
    "        return invests * 0.7 + score * 0.3\n",
    "\n",
    "    def fit(self, iterations, checkpoint):\n",
    "        self.es.train(iterations, print_every = checkpoint)\n",
    "\n",
    "    def buy(self):\n",
    "        initial_money = self._scaled_capital\n",
    "        starting_money = initial_money\n",
    "\n",
    "        real_initial_money = self.initial_money\n",
    "        real_starting_money = self.initial_money\n",
    "        inventory = []\n",
    "        real_inventory = []\n",
    "        state = self.get_state(0, inventory, starting_money, self.timeseries)\n",
    "        states_sell = []\n",
    "        states_buy = []\n",
    "\n",
    "        for t in range(0, len(self.trend) - 1, self.skip):\n",
    "            action, prob = self.act_softmax(state)\n",
    "            print(t, prob)\n",
    "\n",
    "            if action == 1 and starting_money >= self.trend[t] and t < (len(self.trend) - 1 - window_size):\n",
    "                inventory.append(self.trend[t])\n",
    "                real_inventory.append(self.real_trend[t])\n",
    "                real_starting_money -= self.real_trend[t]\n",
    "                starting_money -= self.trend[t]\n",
    "                states_buy.append(t)\n",
    "                print(\n",
    "                    'day %d: buy 1 unit at price %f, total balance %f'\n",
    "                    % (t, self.real_trend[t], real_starting_money)\n",
    "                )\n",
    "\n",
    "            elif action == 2 and len(inventory):\n",
    "                bought_price = inventory.pop(0)\n",
    "                real_bought_price = real_inventory.pop(0)\n",
    "                starting_money += self.trend[t]\n",
    "                real_starting_money += self.real_trend[t]\n",
    "                states_sell.append(t)\n",
    "                try:\n",
    "                    invest = (\n",
    "                        (self.real_trend[t] - real_bought_price)\n",
    "                        / real_bought_price\n",
    "                    ) * 100\n",
    "                except:\n",
    "                    invest = 0\n",
    "                print(\n",
    "                    'day %d, sell 1 unit at price %f, investment %f %%, total balance %f,'\n",
    "                    % (t, self.real_trend[t], invest, real_starting_money)\n",
    "                )\n",
    "            state = self.get_state(\n",
    "                t + 1, inventory, starting_money, self.timeseries\n",
    "            )\n",
    "\n",
    "        invest = (\n",
    "            (real_starting_money - real_initial_money) / real_initial_money\n",
    "        ) * 100\n",
    "        total_gains = real_starting_money - real_initial_money\n",
    "        return states_buy, states_sell, total_gains, invest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['GOOG.csv',\n",
       " 'FB.csv',\n",
       " 'LB.csv',\n",
       " 'MTDR.csv',\n",
       " 'CPRT.csv',\n",
       " 'FSV.csv',\n",
       " 'AMD.csv',\n",
       " 'TSLA.csv',\n",
       " 'SINA.csv',\n",
       " 'GWR.csv']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks = [i for i in os.listdir(os.getcwd()) if '.csv' in i and not 'TWTR' in i]\n",
    "stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I want to train on all stocks I downloaded except for Twitter. I want to use Twitter for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "skip = 1\n",
    "layer_size = 500\n",
    "output_size = 3\n",
    "window_size = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training stock GOOG.csv\n",
      "iter 10. reward: 19.271996\n",
      "iter 20. reward: 19.229381\n",
      "iter 30. reward: 29.036015\n",
      "iter 40. reward: 31.304303\n",
      "iter 50. reward: 33.186425\n",
      "iter 60. reward: 36.076844\n",
      "iter 70. reward: 40.975040\n",
      "iter 80. reward: 41.912853\n",
      "iter 90. reward: 40.478481\n",
      "iter 100. reward: 42.682678\n",
      "time taken to train: 43.78233885765076 seconds\n",
      "\n",
      "training stock FB.csv\n",
      "iter 10. reward: -2.559648\n",
      "iter 20. reward: 2.701059\n",
      "iter 30. reward: 9.726213\n",
      "iter 40. reward: 6.204836\n",
      "iter 50. reward: 7.806048\n",
      "iter 60. reward: 14.814569\n",
      "iter 70. reward: 6.992718\n",
      "iter 80. reward: 16.406013\n",
      "iter 90. reward: 6.684022\n",
      "iter 100. reward: 17.293638\n",
      "time taken to train: 43.38179135322571 seconds\n",
      "\n",
      "training stock LB.csv\n",
      "iter 10. reward: 4.461521\n",
      "iter 20. reward: 11.091525\n",
      "iter 30. reward: 11.463277\n",
      "iter 40. reward: 15.249977\n",
      "iter 50. reward: 22.730853\n",
      "iter 60. reward: 25.364136\n",
      "iter 70. reward: 25.682252\n",
      "iter 80. reward: 26.848195\n",
      "iter 90. reward: 28.715457\n",
      "iter 100. reward: 28.801292\n",
      "time taken to train: 41.94964289665222 seconds\n",
      "\n",
      "training stock MTDR.csv\n",
      "iter 10. reward: 9.353078\n",
      "iter 20. reward: 12.808122\n",
      "iter 30. reward: 14.130995\n",
      "iter 40. reward: 19.703863\n",
      "iter 50. reward: 20.017128\n",
      "iter 60. reward: 21.995427\n",
      "iter 70. reward: 25.283923\n",
      "iter 80. reward: 24.013738\n",
      "iter 90. reward: 24.358604\n",
      "iter 100. reward: 24.196169\n",
      "time taken to train: 42.37464618682861 seconds\n",
      "\n",
      "training stock CPRT.csv\n",
      "iter 10. reward: 1.288128\n",
      "iter 20. reward: 3.159178\n",
      "iter 30. reward: 6.673193\n",
      "iter 40. reward: 6.535538\n",
      "iter 50. reward: 6.831841\n",
      "iter 60. reward: 7.911191\n",
      "iter 70. reward: 6.810701\n",
      "iter 80. reward: 7.436193\n",
      "iter 90. reward: -13.729816\n",
      "iter 100. reward: 6.270685\n",
      "time taken to train: 40.91932535171509 seconds\n",
      "\n",
      "training stock FSV.csv\n",
      "iter 10. reward: 24.165996\n",
      "iter 20. reward: 38.608682\n",
      "iter 30. reward: 39.070580\n",
      "iter 40. reward: 41.429340\n",
      "iter 50. reward: 50.462552\n",
      "iter 60. reward: 51.657158\n",
      "iter 70. reward: 50.099709\n",
      "iter 80. reward: 51.467011\n",
      "iter 90. reward: 51.052404\n",
      "iter 100. reward: 55.858561\n",
      "time taken to train: 42.83869242668152 seconds\n",
      "\n",
      "training stock AMD.csv\n",
      "iter 10. reward: 30.992474\n",
      "iter 20. reward: 33.280274\n",
      "iter 30. reward: 28.365857\n",
      "iter 40. reward: 36.506988\n",
      "iter 50. reward: 38.197619\n",
      "iter 60. reward: 39.861738\n",
      "iter 70. reward: 40.603653\n",
      "iter 80. reward: 43.056979\n",
      "iter 90. reward: 44.564774\n",
      "iter 100. reward: 45.783450\n",
      "time taken to train: 43.07733774185181 seconds\n",
      "\n",
      "training stock TSLA.csv\n",
      "iter 10. reward: -0.524143\n",
      "iter 20. reward: 7.511310\n",
      "iter 30. reward: 8.984430\n",
      "iter 40. reward: 15.348754\n",
      "iter 50. reward: 17.638238\n",
      "iter 60. reward: 11.575226\n",
      "iter 70. reward: 17.112915\n",
      "iter 80. reward: 20.458708\n",
      "iter 90. reward: 20.670305\n",
      "iter 100. reward: 22.493782\n",
      "time taken to train: 42.41837239265442 seconds\n",
      "\n",
      "training stock SINA.csv\n",
      "iter 10. reward: -2.044741\n",
      "iter 20. reward: 0.608208\n",
      "iter 30. reward: 1.598062\n",
      "iter 40. reward: 3.279054\n",
      "iter 50. reward: 6.060360\n",
      "iter 60. reward: 6.329568\n",
      "iter 70. reward: 5.505500\n",
      "iter 80. reward: 6.238097\n",
      "iter 90. reward: 8.285963\n",
      "iter 100. reward: 8.425030\n",
      "time taken to train: 42.05575108528137 seconds\n",
      "\n",
      "training stock GWR.csv\n",
      "iter 10. reward: 7.726154\n",
      "iter 20. reward: 17.982519\n",
      "iter 30. reward: 24.144625\n",
      "iter 40. reward: 20.648465\n",
      "iter 50. reward: 25.824515\n",
      "iter 60. reward: 26.236556\n",
      "iter 70. reward: 32.939100\n",
      "iter 80. reward: 30.483997\n",
      "iter 90. reward: 31.186325\n",
      "iter 100. reward: 34.522247\n",
      "time taken to train: 42.912970781326294 seconds\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = Model(input_size = input_size, layer_size = layer_size, output_size = output_size)\n",
    "agent = None\n",
    "\n",
    "for no, stock in enumerate(stocks):\n",
    "    print('training stock %s'%(stock))\n",
    "    df = pd.read_csv(stock)\n",
    "    real_trend = df['Close'].tolist()\n",
    "    parameters = [df['Close'].tolist(), df['Volume'].tolist()]\n",
    "    minmax = MinMaxScaler(feature_range = (100, 200)).fit(np.array(parameters).T)\n",
    "    scaled_parameters = minmax.transform(np.array(parameters).T).T.tolist()\n",
    "    initial_money = np.max(parameters[0]) * 2\n",
    "    \n",
    "    if no == 0:\n",
    "        agent = Agent(model = model,\n",
    "                      timeseries = scaled_parameters,\n",
    "                      skip = skip,\n",
    "                      initial_money = initial_money,\n",
    "                      real_trend = real_trend,\n",
    "                      minmax = minmax)\n",
    "    else:\n",
    "        agent.change_data(timeseries = scaled_parameters,\n",
    "                          skip = skip,\n",
    "                          initial_money = initial_money,\n",
    "                          real_trend = real_trend,\n",
    "                          minmax = minmax)\n",
    "    \n",
    "    agent.fit(iterations = 100, checkpoint = 10)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If you saw the whole training session on certain stocks are negatives (like FB), means that, that stock markets are losing very bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('GOOG.csv')\n",
    "real_trend = df['Close'].tolist()\n",
    "parameters = [df['Close'].tolist(), df['Volume'].tolist()]\n",
    "minmax = MinMaxScaler(feature_range = (100, 200)).fit(np.array(parameters).T)\n",
    "scaled_parameters = minmax.transform(np.array(parameters).T).T.tolist()\n",
    "initial_money = np.max(parameters[0]) * 2\n",
    "\n",
    "    \n",
    "agent.change_data(timeseries = scaled_parameters,\n",
    "                  skip = skip,\n",
    "                  initial_money = initial_money,\n",
    "                  real_trend = real_trend,\n",
    "                  minmax = minmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [1. 0. 0.]\n",
      "1 [1. 0. 0.]\n",
      "2 [1. 0. 0.]\n",
      "3 [1. 0. 0.]\n",
      "4 [0. 0. 1.]\n",
      "5 [1. 0. 0.]\n",
      "6 [0. 0. 1.]\n",
      "7 [1. 0. 0.]\n",
      "8 [1. 0. 0.]\n",
      "9 [1. 0. 0.]\n",
      "10 [1. 0. 0.]\n",
      "11 [0. 0. 1.]\n",
      "12 [0. 0. 1.]\n",
      "13 [0. 0. 1.]\n",
      "14 [0. 0. 1.]\n",
      "15 [0. 0. 1.]\n",
      "16 [0. 0. 1.]\n",
      "17 [0. 1. 0.]\n",
      "day 17: buy 1 unit at price 1173.459961, total balance 1401.699951\n",
      "18 [1. 0. 0.]\n",
      "19 [0. 1. 0.]\n",
      "day 19: buy 1 unit at price 1169.839966, total balance 231.859985\n",
      "20 [0. 1. 0.]\n",
      "day 20: buy 1 unit at price 1157.660034, total balance -925.800049\n",
      "21 [0. 1. 0.]\n",
      "22 [0. 1. 0.]\n",
      "23 [0. 1. 0.]\n",
      "24 [0. 0. 1.]\n",
      "day 24, sell 1 unit at price 1103.979980, investment -5.920950 %, total balance 178.179931,\n",
      "25 [0. 1. 0.]\n",
      "day 25: buy 1 unit at price 1114.219971, total balance -936.040040\n",
      "26 [0. 1. 0.]\n",
      "27 [0. 1. 0.]\n",
      "28 [0. 1. 0.]\n",
      "29 [0. 1. 0.]\n",
      "30 [0. 1. 0.]\n",
      "31 [0. 1. 0.]\n",
      "32 [1. 0. 0.]\n",
      "33 [0. 0. 1.]\n",
      "day 33, sell 1 unit at price 1153.900024, investment -1.362575 %, total balance 217.859984,\n",
      "34 [1. 0. 0.]\n",
      "35 [1. 0. 0.]\n",
      "36 [1. 0. 0.]\n",
      "37 [0. 0. 1.]\n",
      "day 37, sell 1 unit at price 1198.800049, investment 3.553722 %, total balance 1416.660033,\n",
      "38 [1. 0. 0.]\n",
      "39 [1. 0. 0.]\n",
      "40 [1. 0. 0.]\n",
      "41 [0. 1. 0.]\n",
      "day 41: buy 1 unit at price 1205.500000, total balance 211.160033\n",
      "42 [0. 1. 0.]\n",
      "day 42: buy 1 unit at price 1248.079956, total balance -1036.919923\n",
      "43 [0. 1. 0.]\n",
      "44 [0. 1. 0.]\n",
      "45 [0. 1. 0.]\n",
      "46 [0. 1. 0.]\n",
      "47 [0. 1. 0.]\n",
      "48 [0. 1. 0.]\n",
      "49 [0. 1. 0.]\n",
      "50 [0. 1. 0.]\n",
      "51 [0. 1. 0.]\n",
      "52 [0. 0. 1.]\n",
      "day 52, sell 1 unit at price 1242.219971, investment 11.487857 %, total balance 205.300048,\n",
      "53 [0. 1. 0.]\n",
      "day 53: buy 1 unit at price 1245.609985, total balance -1040.309937\n",
      "54 [0. 1. 0.]\n",
      "55 [0. 1. 0.]\n",
      "56 [0. 1. 0.]\n",
      "57 [0. 0. 1.]\n",
      "day 57, sell 1 unit at price 1242.099976, investment 3.036083 %, total balance 201.790039,\n",
      "58 [0. 1. 0.]\n",
      "day 58: buy 1 unit at price 1214.380005, total balance -1012.589966\n",
      "59 [0. 1. 0.]\n",
      "60 [0. 1. 0.]\n",
      "61 [0. 1. 0.]\n",
      "62 [1. 0. 0.]\n",
      "63 [1. 0. 0.]\n",
      "64 [0. 1. 0.]\n",
      "65 [0. 1. 0.]\n",
      "66 [0. 1. 0.]\n",
      "67 [0. 0. 1.]\n",
      "day 67, sell 1 unit at price 1231.150024, investment -1.356478 %, total balance 218.560058,\n",
      "68 [0. 0. 1.]\n",
      "day 68, sell 1 unit at price 1249.300049, investment 0.296246 %, total balance 1467.860107,\n",
      "69 [1. 0. 0.]\n",
      "70 [1. 0. 0.]\n",
      "71 [1. 0. 0.]\n",
      "72 [1. 0. 0.]\n",
      "73 [1. 0. 0.]\n",
      "74 [1. 0. 0.]\n",
      "75 [1. 0. 0.]\n",
      "76 [1. 0. 0.]\n",
      "77 [0. 1. 0.]\n",
      "day 77: buy 1 unit at price 1162.819946, total balance 305.040161\n",
      "78 [0. 0. 1.]\n",
      "day 78, sell 1 unit at price 1175.329956, investment -3.215637 %, total balance 1480.370117,\n",
      "79 [0. 0. 1.]\n",
      "day 79, sell 1 unit at price 1172.530029, investment 0.835046 %, total balance 2652.900146,\n",
      "80 [0. 0. 1.]\n",
      "81 [0. 0. 1.]\n",
      "82 [0. 0. 1.]\n",
      "83 [1. 0. 0.]\n",
      "84 [1. 0. 0.]\n",
      "85 [1. 0. 0.]\n",
      "86 [1. 0. 0.]\n",
      "87 [1. 0. 0.]\n",
      "88 [1. 0. 0.]\n",
      "89 [1. 0. 0.]\n",
      "90 [0. 0. 1.]\n",
      "91 [0. 0. 1.]\n",
      "92 [0. 0. 1.]\n",
      "93 [0. 1. 0.]\n",
      "day 93: buy 1 unit at price 1168.189941, total balance 1484.710205\n",
      "94 [0. 0. 1.]\n",
      "day 94, sell 1 unit at price 1157.349976, investment -0.927928 %, total balance 2642.060181,\n",
      "95 [1. 0. 0.]\n",
      "96 [0. 1. 0.]\n",
      "day 96: buy 1 unit at price 1138.819946, total balance 1503.240235\n",
      "97 [1. 0. 0.]\n",
      "98 [0. 1. 0.]\n",
      "day 98: buy 1 unit at price 1079.319946, total balance 423.920289\n",
      "99 [0. 0. 1.]\n",
      "day 99, sell 1 unit at price 1110.079956, investment -2.523664 %, total balance 1534.000245,\n",
      "100 [1. 0. 0.]\n",
      "101 [0. 1. 0.]\n",
      "day 101: buy 1 unit at price 1121.280029, total balance 412.720216\n",
      "102 [1. 0. 0.]\n",
      "103 [0. 0. 1.]\n",
      "day 103, sell 1 unit at price 1087.969971, investment 0.801433 %, total balance 1500.690187,\n",
      "104 [0. 0. 1.]\n",
      "day 104, sell 1 unit at price 1096.459961, investment -2.213548 %, total balance 2597.150148,\n",
      "105 [1. 0. 0.]\n",
      "106 [1. 0. 0.]\n",
      "107 [1. 0. 0.]\n",
      "108 [1. 0. 0.]\n",
      "109 [1. 0. 0.]\n",
      "110 [1. 0. 0.]\n",
      "111 [1. 0. 0.]\n",
      "112 [1. 0. 0.]\n",
      "113 [1. 0. 0.]\n",
      "114 [1. 0. 0.]\n",
      "115 [1. 0. 0.]\n",
      "116 [0. 0. 1.]\n",
      "117 [0. 0. 1.]\n",
      "118 [0. 0. 1.]\n",
      "119 [1. 0. 0.]\n",
      "120 [0. 0. 1.]\n",
      "121 [1. 0. 0.]\n",
      "122 [1. 0. 0.]\n",
      "123 [1. 0. 0.]\n",
      "124 [0. 1. 0.]\n",
      "day 124: buy 1 unit at price 1061.489990, total balance 1535.660158\n",
      "125 [1. 0. 0.]\n",
      "126 [1. 0. 0.]\n",
      "127 [1. 0. 0.]\n",
      "128 [1. 0. 0.]\n",
      "129 [0. 1. 0.]\n",
      "day 129: buy 1 unit at price 1048.619995, total balance 487.040163\n",
      "130 [0. 0. 1.]\n",
      "day 130, sell 1 unit at price 1044.410034, investment -1.609055 %, total balance 1531.450197,\n",
      "131 [1. 0. 0.]\n",
      "132 [0. 0. 1.]\n",
      "day 132, sell 1 unit at price 1088.300049, investment 3.784026 %, total balance 2619.750246,\n",
      "133 [1. 0. 0.]\n",
      "134 [0. 0. 1.]\n",
      "135 [1. 0. 0.]\n",
      "136 [0. 0. 1.]\n",
      "137 [1. 0. 0.]\n",
      "138 [1. 0. 0.]\n",
      "139 [1. 0. 0.]\n",
      "140 [0. 1. 0.]\n",
      "day 140: buy 1 unit at price 1063.680054, total balance 1556.070192\n",
      "141 [0. 0. 1.]\n",
      "day 141, sell 1 unit at price 1061.900024, investment -0.167346 %, total balance 2617.970216,\n",
      "142 [1. 0. 0.]\n",
      "143 [0. 1. 0.]\n",
      "day 143: buy 1 unit at price 1016.530029, total balance 1601.440187\n",
      "144 [0. 1. 0.]\n",
      "day 144: buy 1 unit at price 1028.709961, total balance 572.730226\n",
      "145 [0. 0. 1.]\n",
      "day 145, sell 1 unit at price 1023.010010, investment 0.637461 %, total balance 1595.740236,\n",
      "146 [0. 0. 1.]\n",
      "day 146, sell 1 unit at price 1009.409973, investment -1.876135 %, total balance 2605.150209,\n",
      "147 [1. 0. 0.]\n",
      "148 [0. 0. 1.]\n",
      "149 [1. 0. 0.]\n",
      "150 [0. 1. 0.]\n",
      "day 150: buy 1 unit at price 1043.880005, total balance 1561.270204\n",
      "151 [1. 0. 0.]\n",
      "152 [1. 0. 0.]\n",
      "153 [1. 0. 0.]\n",
      "154 [0. 0. 1.]\n",
      "day 154, sell 1 unit at price 1016.059998, investment -2.665058 %, total balance 2577.330202,\n",
      "155 [0. 0. 1.]\n",
      "156 [0. 0. 1.]\n",
      "157 [0. 0. 1.]\n",
      "158 [1. 0. 0.]\n",
      "159 [1. 0. 0.]\n",
      "160 [1. 0. 0.]\n",
      "161 [0. 1. 0.]\n",
      "day 161: buy 1 unit at price 1044.689941, total balance 1532.640261\n",
      "162 [0. 1. 0.]\n",
      "day 162: buy 1 unit at price 1077.150024, total balance 455.490237\n",
      "163 [0. 1. 0.]\n",
      "day 163: buy 1 unit at price 1080.969971, total balance -625.479734\n",
      "164 [0. 1. 0.]\n",
      "day 164: buy 1 unit at price 1089.900024, total balance -1715.379758\n",
      "165 [0. 0. 1.]\n",
      "day 165, sell 1 unit at price 1098.260010, investment 5.127844 %, total balance -617.119748,\n",
      "166 [0. 1. 0.]\n",
      "day 166: buy 1 unit at price 1070.520020, total balance -1687.639768\n",
      "167 [0. 1. 0.]\n",
      "168 [0. 1. 0.]\n",
      "169 [0. 1. 0.]\n",
      "170 [0. 0. 1.]\n",
      "day 170, sell 1 unit at price 1070.079956, investment -0.656368 %, total balance -617.559812,\n",
      "171 [0. 1. 0.]\n",
      "day 171: buy 1 unit at price 1060.619995, total balance -1678.179807\n",
      "172 [0. 0. 1.]\n",
      "day 172, sell 1 unit at price 1089.060059, investment 0.748410 %, total balance -589.119748,\n",
      "173 [0. 1. 0.]\n",
      "day 173: buy 1 unit at price 1116.369995, total balance -1705.489743\n",
      "174 [0. 1. 0.]\n",
      "175 [0. 1. 0.]\n",
      "176 [0. 1. 0.]\n",
      "177 [1. 0. 0.]\n",
      "178 [1. 0. 0.]\n",
      "179 [0. 1. 0.]\n",
      "180 [0. 1. 0.]\n",
      "181 [0. 0. 1.]\n",
      "day 181, sell 1 unit at price 1121.369995, investment 2.887418 %, total balance -584.119748,\n",
      "182 [0. 1. 0.]\n",
      "day 182: buy 1 unit at price 1120.160034, total balance -1704.279782\n",
      "183 [0. 0. 1.]\n",
      "day 183, sell 1 unit at price 1121.670044, investment 4.778054 %, total balance -582.609738,\n",
      "184 [0. 0. 1.]\n",
      "day 184, sell 1 unit at price 1113.650024, investment 4.999908 %, total balance 531.040286,\n",
      "185 [0. 1. 0.]\n",
      "day 185: buy 1 unit at price 1118.560059, total balance -587.519773\n",
      "186 [0. 0. 1.]\n",
      "day 186, sell 1 unit at price 1113.800049, investment -0.230206 %, total balance 526.280276,\n",
      "187 [0. 1. 0.]\n",
      "day 187: buy 1 unit at price 1096.969971, total balance -570.689695\n",
      "188 [1. 0. 0.]\n",
      "189 [1. 0. 0.]\n",
      "190 [0. 1. 0.]\n",
      "day 190: buy 1 unit at price 1115.130005, total balance -1685.819700\n",
      "191 [0. 0. 1.]\n",
      "day 191, sell 1 unit at price 1116.050049, investment -0.366911 %, total balance -569.769651,\n",
      "192 [1. 0. 0.]\n",
      "193 [0. 1. 0.]\n",
      "day 193: buy 1 unit at price 1140.989990, total balance -1710.759641\n",
      "194 [1. 0. 0.]\n",
      "195 [0. 0. 1.]\n",
      "day 195, sell 1 unit at price 1162.030029, investment 3.886244 %, total balance -548.729612,\n",
      "196 [0. 0. 1.]\n",
      "day 196, sell 1 unit at price 1157.859985, investment 5.550746 %, total balance 609.130373,\n",
      "197 [0. 0. 1.]\n",
      "day 197, sell 1 unit at price 1143.300049, investment 2.526167 %, total balance 1752.430422,\n",
      "198 [0. 0. 1.]\n",
      "day 198, sell 1 unit at price 1142.319946, investment 0.116562 %, total balance 2894.750368,\n",
      "199 [0. 0. 1.]\n",
      "200 [0. 0. 1.]\n",
      "201 [1. 0. 0.]\n",
      "202 [1. 0. 0.]\n",
      "203 [0. 1. 0.]\n",
      "day 203: buy 1 unit at price 1184.459961, total balance 1710.290407\n",
      "204 [0. 1. 0.]\n",
      "day 204: buy 1 unit at price 1184.260010, total balance 526.030397\n",
      "205 [0. 0. 1.]\n",
      "day 205, sell 1 unit at price 1198.849976, investment 1.214901 %, total balance 1724.880373,\n",
      "206 [0. 1. 0.]\n",
      "day 206: buy 1 unit at price 1223.969971, total balance 500.910402\n",
      "207 [0. 1. 0.]\n",
      "day 207: buy 1 unit at price 1231.540039, total balance -730.629637\n",
      "208 [0. 0. 1.]\n",
      "day 208, sell 1 unit at price 1205.500000, investment 1.793524 %, total balance 474.870363,\n",
      "209 [0. 1. 0.]\n",
      "day 209: buy 1 unit at price 1193.000000, total balance -718.129637\n",
      "210 [0. 1. 0.]\n",
      "day 210: buy 1 unit at price 1184.619995, total balance -1902.749632\n",
      "211 [0. 1. 0.]\n",
      "212 [0. 1. 0.]\n",
      "213 [0. 1. 0.]\n",
      "214 [0. 1. 0.]\n",
      "215 [0. 1. 0.]\n",
      "216 [1. 0. 0.]\n",
      "217 [0. 1. 0.]\n",
      "218 [0. 0. 1.]\n",
      "day 218, sell 1 unit at price 1207.150024, investment -1.374212 %, total balance -695.599608,\n",
      "219 [0. 0. 1.]\n",
      "day 219, sell 1 unit at price 1203.839966, investment -2.249222 %, total balance 508.240358,\n",
      "220 [0. 1. 0.]\n",
      "day 220: buy 1 unit at price 1197.250000, total balance -689.009642\n",
      "221 [0. 0. 1.]\n",
      "day 221, sell 1 unit at price 1202.160034, investment 0.767815 %, total balance 513.150392,\n",
      "222 [0. 0. 1.]\n",
      "day 222, sell 1 unit at price 1204.619995, investment 1.688305 %, total balance 1717.770387,\n",
      "223 [1. 0. 0.]\n",
      "224 [0. 0. 1.]\n",
      "day 224, sell 1 unit at price 1221.099976, investment 1.992063 %, total balance 2938.870363,\n",
      "225 [0. 0. 1.]\n",
      "226 [1. 0. 0.]\n",
      "227 [0. 1. 0.]\n",
      "day 227: buy 1 unit at price 1236.369995, total balance 1702.500368\n",
      "228 [0. 0. 1.]\n",
      "day 228, sell 1 unit at price 1248.839966, investment 1.008595 %, total balance 2951.340334,\n",
      "229 [0. 0. 1.]\n",
      "230 [0. 1. 0.]\n",
      "day 230: buy 1 unit at price 1256.000000, total balance 1695.340334\n",
      "231 [0. 0. 1.]\n",
      "day 231, sell 1 unit at price 1263.449951, investment 0.593149 %, total balance 2958.790285,\n",
      "232 [0. 0. 1.]\n",
      "233 [0. 1. 0.]\n",
      "234 [0. 1. 0.]\n",
      "235 [0. 0. 1.]\n",
      "236 [1. 0. 0.]\n",
      "237 [0. 1. 0.]\n",
      "238 [0. 1. 0.]\n",
      "239 [1. 0. 0.]\n",
      "240 [0. 1. 0.]\n",
      "241 [0. 1. 0.]\n",
      "242 [0. 0. 1.]\n",
      "243 [0. 1. 0.]\n",
      "244 [0. 0. 1.]\n",
      "245 [0. 0. 1.]\n",
      "246 [1. 0. 0.]\n",
      "247 [1. 0. 0.]\n",
      "248 [1. 0. 0.]\n",
      "249 [0. 0. 1.]\n",
      "250 [1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "states_buy, states_sell, total_gains, invest = agent.buy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15, 5))\n",
    "plt.plot(df['Close'], color='r', lw=2.)\n",
    "plt.plot(df['Close'], '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
    "plt.plot(df['Close'], 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
    "plt.title('GOOG total gains %f, total investment %f%%'%(total_gains, invest))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 [1. 0. 0.]\n",
      "1 [1. 0. 0.]\n",
      "2 [0. 0. 1.]\n",
      "3 [1. 0. 0.]\n",
      "4 [0. 0. 1.]\n",
      "5 [1. 0. 0.]\n",
      "6 [0. 0. 1.]\n",
      "7 [0. 0. 1.]\n",
      "8 [1. 0. 0.]\n",
      "9 [0. 1. 0.]\n",
      "day 9: buy 1 unit at price 40.099998, total balance 53.419998\n",
      "10 [0. 0. 1.]\n",
      "day 10, sell 1 unit at price 39.700001, investment -0.997499 %, total balance 93.119999,\n",
      "11 [1. 0. 0.]\n",
      "12 [0. 1. 0.]\n",
      "day 12: buy 1 unit at price 41.419998, total balance 51.700001\n",
      "13 [0. 1. 0.]\n",
      "day 13: buy 1 unit at price 43.490002, total balance 8.209999\n",
      "14 [0. 0. 1.]\n",
      "day 14, sell 1 unit at price 44.070000, investment 6.397881 %, total balance 52.279999,\n",
      "15 [0. 0. 1.]\n",
      "day 15, sell 1 unit at price 46.759998, investment 7.518960 %, total balance 99.039997,\n",
      "16 [0. 1. 0.]\n",
      "day 16: buy 1 unit at price 45.799999, total balance 53.239998\n",
      "17 [0. 0. 1.]\n",
      "day 17, sell 1 unit at price 46.000000, investment 0.436683 %, total balance 99.239998,\n",
      "18 [0. 1. 0.]\n",
      "day 18: buy 1 unit at price 44.950001, total balance 54.289997\n",
      "19 [0. 0. 1.]\n",
      "day 19, sell 1 unit at price 46.130001, investment 2.625139 %, total balance 100.419998,\n",
      "20 [0. 1. 0.]\n",
      "day 20: buy 1 unit at price 45.240002, total balance 55.179996\n",
      "21 [0. 1. 0.]\n",
      "day 21: buy 1 unit at price 45.880001, total balance 9.299995\n",
      "22 [0. 1. 0.]\n",
      "23 [0. 1. 0.]\n",
      "24 [0. 1. 0.]\n",
      "25 [0. 1. 0.]\n",
      "26 [0. 1. 0.]\n",
      "27 [0. 1. 0.]\n",
      "28 [0. 0. 1.]\n",
      "day 28, sell 1 unit at price 43.889999, investment -2.984091 %, total balance 53.189994,\n",
      "29 [0. 1. 0.]\n",
      "day 29: buy 1 unit at price 45.060001, total balance 8.129993\n",
      "30 [0. 1. 0.]\n",
      "31 [0. 1. 0.]\n",
      "32 [0. 0. 1.]\n",
      "day 32, sell 1 unit at price 43.750000, investment -4.642548 %, total balance 51.879993,\n",
      "33 [1. 0. 0.]\n",
      "34 [1. 0. 0.]\n",
      "35 [0. 1. 0.]\n",
      "day 35: buy 1 unit at price 44.490002, total balance 7.389991\n",
      "36 [1. 0. 0.]\n",
      "37 [0. 0. 1.]\n",
      "day 37, sell 1 unit at price 44.709999, investment -0.776747 %, total balance 52.099990,\n",
      "38 [0. 1. 0.]\n",
      "day 38: buy 1 unit at price 43.340000, total balance 8.759990\n",
      "39 [0. 0. 1.]\n",
      "day 39, sell 1 unit at price 43.439999, investment -2.360088 %, total balance 52.199989,\n",
      "40 [0. 0. 1.]\n",
      "day 40, sell 1 unit at price 43.419998, investment 0.184582 %, total balance 95.619987,\n",
      "41 [0. 0. 1.]\n",
      "42 [0. 0. 1.]\n",
      "43 [1. 0. 0.]\n",
      "44 [1. 0. 0.]\n",
      "45 [0. 1. 0.]\n",
      "day 45: buy 1 unit at price 34.119999, total balance 61.499988\n",
      "46 [0. 0. 1.]\n",
      "day 46, sell 1 unit at price 31.379999, investment -8.030481 %, total balance 92.879987,\n",
      "47 [1. 0. 0.]\n",
      "48 [1. 0. 0.]\n",
      "49 [1. 0. 0.]\n",
      "50 [1. 0. 0.]\n",
      "51 [0. 0. 1.]\n",
      "52 [0. 0. 1.]\n",
      "53 [1. 0. 0.]\n",
      "54 [0. 0. 1.]\n",
      "55 [0. 0. 1.]\n",
      "56 [1. 0. 0.]\n",
      "57 [1. 0. 0.]\n",
      "58 [1. 0. 0.]\n",
      "59 [1. 0. 0.]\n",
      "60 [1. 0. 0.]\n",
      "61 [1. 0. 0.]\n",
      "62 [1. 0. 0.]\n",
      "63 [1. 0. 0.]\n",
      "64 [0. 0. 1.]\n",
      "65 [0. 0. 1.]\n",
      "66 [0. 0. 1.]\n",
      "67 [0. 0. 1.]\n",
      "68 [0. 0. 1.]\n",
      "69 [1. 0. 0.]\n",
      "70 [0. 1. 0.]\n",
      "day 70: buy 1 unit at price 35.180000, total balance 57.699987\n",
      "71 [0. 0. 1.]\n",
      "day 71, sell 1 unit at price 34.840000, investment -0.966458 %, total balance 92.539987,\n",
      "72 [0. 1. 0.]\n",
      "day 72: buy 1 unit at price 32.730000, total balance 59.809987\n",
      "73 [0. 1. 0.]\n",
      "day 73: buy 1 unit at price 30.809999, total balance 28.999988\n",
      "74 [0. 0. 1.]\n",
      "day 74, sell 1 unit at price 30.490000, investment -6.843874 %, total balance 59.489988,\n",
      "75 [0. 1. 0.]\n",
      "day 75: buy 1 unit at price 30.540001, total balance 28.949987\n",
      "76 [0. 1. 0.]\n",
      "day 76: buy 1 unit at price 30.889999, total balance -1.940012\n",
      "77 [0. 1. 0.]\n",
      "78 [0. 1. 0.]\n",
      "79 [1. 0. 0.]\n",
      "80 [0. 1. 0.]\n",
      "81 [0. 0. 1.]\n",
      "day 81, sell 1 unit at price 29.219999, investment -5.160662 %, total balance 27.279987,\n",
      "82 [1. 0. 0.]\n",
      "83 [0. 0. 1.]\n",
      "day 83, sell 1 unit at price 29.850000, investment -2.259335 %, total balance 57.129987,\n",
      "84 [1. 0. 0.]\n",
      "85 [1. 0. 0.]\n",
      "86 [1. 0. 0.]\n",
      "87 [1. 0. 0.]\n",
      "88 [1. 0. 0.]\n",
      "89 [1. 0. 0.]\n",
      "90 [1. 0. 0.]\n",
      "91 [1. 0. 0.]\n",
      "92 [0. 0. 1.]\n",
      "day 92, sell 1 unit at price 29.010000, investment -6.086109 %, total balance 86.139987,\n",
      "93 [0. 0. 1.]\n",
      "94 [1. 0. 0.]\n",
      "95 [1. 0. 0.]\n",
      "96 [1. 0. 0.]\n",
      "97 [1. 0. 0.]\n",
      "98 [0. 1. 0.]\n",
      "day 98: buy 1 unit at price 27.000000, total balance 59.139987\n",
      "99 [0. 0. 1.]\n",
      "day 99, sell 1 unit at price 27.990000, investment 3.666667 %, total balance 87.129987,\n",
      "100 [1. 0. 0.]\n",
      "101 [1. 0. 0.]\n",
      "102 [1. 0. 0.]\n",
      "103 [0. 0. 1.]\n",
      "104 [0. 0. 1.]\n",
      "105 [1. 0. 0.]\n",
      "106 [0. 1. 0.]\n",
      "day 106: buy 1 unit at price 28.770000, total balance 58.359987\n",
      "107 [1. 0. 0.]\n",
      "108 [0. 1. 0.]\n",
      "day 108: buy 1 unit at price 31.799999, total balance 26.559988\n",
      "109 [0. 0. 1.]\n",
      "day 109, sell 1 unit at price 32.360001, investment 12.478279 %, total balance 58.919989,\n",
      "110 [1. 0. 0.]\n",
      "111 [0. 1. 0.]\n",
      "day 111: buy 1 unit at price 33.860001, total balance 25.059988\n",
      "112 [0. 1. 0.]\n",
      "day 112: buy 1 unit at price 34.750000, total balance -9.690012\n",
      "113 [0. 0. 1.]\n",
      "day 113, sell 1 unit at price 34.619999, investment 8.867925 %, total balance 24.929987,\n",
      "114 [0. 1. 0.]\n",
      "day 114: buy 1 unit at price 34.299999, total balance -9.370012\n",
      "115 [0. 1. 0.]\n",
      "116 [0. 0. 1.]\n",
      "day 116, sell 1 unit at price 34.419998, investment 1.653860 %, total balance 25.049986,\n",
      "117 [0. 0. 1.]\n",
      "day 117, sell 1 unit at price 34.990002, investment 0.690653 %, total balance 60.039988,\n",
      "118 [0. 0. 1.]\n",
      "day 118, sell 1 unit at price 34.180000, investment -0.349851 %, total balance 94.219988,\n",
      "119 [0. 0. 1.]\n",
      "120 [0. 1. 0.]\n",
      "day 120: buy 1 unit at price 32.009998, total balance 62.209990\n",
      "121 [0. 1. 0.]\n",
      "day 121: buy 1 unit at price 32.490002, total balance 29.719988\n",
      "122 [0. 1. 0.]\n",
      "day 122: buy 1 unit at price 32.910000, total balance -3.190012\n",
      "123 [0. 1. 0.]\n",
      "124 [0. 1. 0.]\n",
      "125 [0. 1. 0.]\n",
      "126 [0. 1. 0.]\n",
      "127 [0. 1. 0.]\n",
      "128 [0. 1. 0.]\n",
      "129 [0. 1. 0.]\n",
      "130 [0. 0. 1.]\n",
      "day 130, sell 1 unit at price 32.610001, investment 1.874424 %, total balance 29.419989,\n",
      "131 [1. 0. 0.]\n",
      "132 [1. 0. 0.]\n",
      "133 [0. 0. 1.]\n",
      "day 133, sell 1 unit at price 31.450001, investment -3.200988 %, total balance 60.869990,\n",
      "134 [1. 0. 0.]\n",
      "135 [1. 0. 0.]\n",
      "136 [1. 0. 0.]\n",
      "137 [1. 0. 0.]\n",
      "138 [1. 0. 0.]\n",
      "139 [0. 0. 1.]\n",
      "day 139, sell 1 unit at price 34.450001, investment 4.679432 %, total balance 95.319991,\n",
      "140 [0. 1. 0.]\n",
      "day 140: buy 1 unit at price 36.250000, total balance 59.069991\n",
      "141 [0. 0. 1.]\n",
      "day 141, sell 1 unit at price 35.889999, investment -0.993106 %, total balance 94.959990,\n",
      "142 [1. 0. 0.]\n",
      "143 [1. 0. 0.]\n",
      "144 [0. 0. 1.]\n",
      "145 [1. 0. 0.]\n",
      "146 [0. 1. 0.]\n",
      "day 146: buy 1 unit at price 29.290001, total balance 65.669989\n",
      "147 [0. 1. 0.]\n",
      "day 147: buy 1 unit at price 27.309999, total balance 38.359990\n",
      "148 [1. 0. 0.]\n",
      "149 [0. 1. 0.]\n",
      "day 149: buy 1 unit at price 28.660000, total balance 9.699990\n",
      "150 [0. 1. 0.]\n",
      "151 [0. 1. 0.]\n",
      "152 [0. 1. 0.]\n",
      "153 [0. 1. 0.]\n",
      "154 [0. 0. 1.]\n",
      "day 154, sell 1 unit at price 27.990000, investment -4.438378 %, total balance 37.689990,\n",
      "155 [0. 0. 1.]\n",
      "day 155, sell 1 unit at price 29.950001, investment 9.666796 %, total balance 67.639991,\n",
      "156 [0. 0. 1.]\n",
      "day 156, sell 1 unit at price 31.340000, investment 9.351012 %, total balance 98.979991,\n",
      "157 [1. 0. 0.]\n",
      "158 [1. 0. 0.]\n",
      "159 [1. 0. 0.]\n",
      "160 [1. 0. 0.]\n",
      "161 [1. 0. 0.]\n",
      "162 [1. 0. 0.]\n",
      "163 [1. 0. 0.]\n",
      "164 [1. 0. 0.]\n",
      "165 [0. 0. 1.]\n",
      "166 [0. 1. 0.]\n",
      "day 166: buy 1 unit at price 32.250000, total balance 66.729991\n",
      "167 [0. 1. 0.]\n",
      "day 167: buy 1 unit at price 30.969999, total balance 35.759992\n",
      "168 [0. 1. 0.]\n",
      "day 168: buy 1 unit at price 31.610001, total balance 4.149991\n",
      "169 [0. 1. 0.]\n",
      "170 [0. 1. 0.]\n",
      "171 [0. 1. 0.]\n",
      "172 [0. 1. 0.]\n",
      "173 [0. 1. 0.]\n",
      "174 [0. 1. 0.]\n",
      "175 [0. 0. 1.]\n",
      "day 175, sell 1 unit at price 33.939999, investment 5.240307 %, total balance 38.089990,\n",
      "176 [0. 1. 0.]\n",
      "day 176: buy 1 unit at price 34.369999, total balance 3.719991\n",
      "177 [0. 1. 0.]\n",
      "178 [1. 0. 0.]\n",
      "179 [0. 0. 1.]\n",
      "day 179, sell 1 unit at price 30.010000, investment -3.099771 %, total balance 33.729991,\n",
      "180 [1. 0. 0.]\n",
      "181 [0. 1. 0.]\n",
      "day 181: buy 1 unit at price 30.389999, total balance 3.339992\n",
      "182 [1. 0. 0.]\n",
      "183 [1. 0. 0.]\n",
      "184 [0. 1. 0.]\n",
      "185 [0. 1. 0.]\n",
      "186 [0. 1. 0.]\n",
      "187 [0. 0. 1.]\n",
      "day 187, sell 1 unit at price 30.760000, investment -2.689026 %, total balance 34.099992,\n",
      "188 [0. 0. 1.]\n",
      "day 188, sell 1 unit at price 31.709999, investment -7.739308 %, total balance 65.809991,\n",
      "189 [0. 0. 1.]\n",
      "day 189, sell 1 unit at price 31.990000, investment 5.264893 %, total balance 97.799991,\n",
      "190 [1. 0. 0.]\n",
      "191 [1. 0. 0.]\n",
      "192 [0. 0. 1.]\n",
      "193 [0. 0. 1.]\n",
      "194 [1. 0. 0.]\n",
      "195 [1. 0. 0.]\n",
      "196 [1. 0. 0.]\n",
      "197 [0. 0. 1.]\n",
      "198 [0. 0. 1.]\n",
      "199 [0. 0. 1.]\n",
      "200 [0. 0. 1.]\n",
      "201 [0. 0. 1.]\n",
      "202 [1. 0. 0.]\n",
      "203 [0. 0. 1.]\n",
      "204 [1. 0. 0.]\n",
      "205 [1. 0. 0.]\n",
      "206 [0. 0. 1.]\n",
      "207 [1. 0. 0.]\n",
      "208 [1. 0. 0.]\n",
      "209 [0. 0. 1.]\n",
      "210 [0. 0. 1.]\n",
      "211 [0. 1. 0.]\n",
      "day 211: buy 1 unit at price 32.279999, total balance 65.519992\n",
      "212 [0. 1. 0.]\n",
      "day 212: buy 1 unit at price 32.869999, total balance 32.649993\n",
      "213 [0. 0. 1.]\n",
      "day 213, sell 1 unit at price 32.880001, investment 1.858742 %, total balance 65.529994,\n",
      "214 [0. 0. 1.]\n",
      "day 214, sell 1 unit at price 33.439999, investment 1.734104 %, total balance 98.969993,\n",
      "215 [0. 0. 1.]\n",
      "216 [0. 1. 0.]\n",
      "day 216: buy 1 unit at price 34.380001, total balance 64.589992\n",
      "217 [0. 1. 0.]\n",
      "day 217: buy 1 unit at price 34.419998, total balance 30.169994\n",
      "218 [0. 0. 1.]\n",
      "day 218, sell 1 unit at price 34.720001, investment 0.988947 %, total balance 64.889995,\n",
      "219 [0. 1. 0.]\n",
      "day 219: buy 1 unit at price 34.860001, total balance 30.029994\n",
      "220 [0. 1. 0.]\n",
      "day 220: buy 1 unit at price 35.139999, total balance -5.110005\n",
      "221 [0. 1. 0.]\n",
      "222 [0. 1. 0.]\n",
      "223 [0. 1. 0.]\n",
      "224 [0. 1. 0.]\n",
      "225 [0. 1. 0.]\n",
      "226 [0. 1. 0.]\n",
      "227 [0. 1. 0.]\n",
      "228 [0. 1. 0.]\n",
      "229 [0. 1. 0.]\n",
      "230 [0. 0. 1.]\n",
      "day 230, sell 1 unit at price 39.290001, investment 14.148760 %, total balance 34.179996,\n",
      "231 [1. 0. 0.]\n",
      "232 [0. 1. 0.]\n",
      "233 [0. 1. 0.]\n",
      "234 [1. 0. 0.]\n",
      "235 [0. 0. 1.]\n",
      "day 235, sell 1 unit at price 39.290001, investment 12.707974 %, total balance 73.469997,\n",
      "236 [0. 1. 0.]\n",
      "237 [0. 0. 1.]\n",
      "day 237, sell 1 unit at price 40.799999, investment 16.107001 %, total balance 114.269996,\n",
      "238 [0. 1. 0.]\n",
      "239 [0. 0. 1.]\n",
      "240 [0. 1. 0.]\n",
      "241 [0. 1. 0.]\n",
      "242 [0. 1. 0.]\n",
      "243 [0. 1. 0.]\n",
      "244 [0. 0. 1.]\n",
      "245 [0. 1. 0.]\n",
      "246 [0. 1. 0.]\n",
      "247 [0. 1. 0.]\n",
      "248 [0. 0. 1.]\n",
      "249 [0. 0. 1.]\n",
      "250 [0. 0. 1.]\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('TWTR.csv')\n",
    "real_trend = df['Close'].tolist()\n",
    "parameters = [df['Close'].tolist(), df['Volume'].tolist()]\n",
    "minmax = MinMaxScaler(feature_range = (100, 200)).fit(np.array(parameters).T)\n",
    "scaled_parameters = minmax.transform(np.array(parameters).T).T.tolist()\n",
    "initial_money = np.max(parameters[0]) * 2\n",
    "\n",
    "agent.change_data(timeseries = scaled_parameters,\n",
    "                  skip = skip,\n",
    "                  initial_money = initial_money,\n",
    "                  real_trend = real_trend,\n",
    "                  minmax = minmax)\n",
    "\n",
    "states_buy, states_sell, total_gains, invest = agent.buy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (15, 5))\n",
    "plt.plot(df['Close'], color='r', lw=2.)\n",
    "plt.plot(df['Close'], '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)\n",
    "plt.plot(df['Close'], 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)\n",
    "plt.title('TWTR total gains %f, total investment %f%%'%(total_gains, invest))\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.813767'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.813846'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.813901'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.813952'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814002'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814050'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814097'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814143'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814190'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814236'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814281'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814327'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814373'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814418'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814463'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814509'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814554'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814599'}\n",
      "{'status': 'data not enough to trade', 'action': 'fail', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.814644'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 93.51999599999999, 'timestamp': '2019-06-07 11:51:21.821402'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 45.240002', 'action': 'buy', 'balance': 48.27999399999999, 'timestamp': '2019-06-07 11:51:21.821923'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 45.880001', 'action': 'buy', 'balance': 2.399992999999988, 'timestamp': '2019-06-07 11:51:21.822377'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 2.399992999999988, 'timestamp': '2019-06-07 11:51:21.822817'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 2.399992999999988, 'timestamp': '2019-06-07 11:51:21.823254'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 2.399992999999988, 'timestamp': '2019-06-07 11:51:21.823689'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 2.399992999999988, 'timestamp': '2019-06-07 11:51:21.824137'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 2.399992999999988, 'timestamp': '2019-06-07 11:51:21.824578'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 2.399992999999988, 'timestamp': '2019-06-07 11:51:21.825011'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 43.889999', 'investment': -2.9840913800136613, 'gain': -1.350003000000008, 'balance': 46.289991999999984, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.825491'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 45.060001', 'action': 'buy', 'balance': 1.229990999999984, 'timestamp': '2019-06-07 11:51:21.825929'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 1.229990999999984, 'timestamp': '2019-06-07 11:51:21.826360'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 1.229990999999984, 'timestamp': '2019-06-07 11:51:21.826790'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 43.750000', 'investment': -4.642547849988059, 'gain': -2.130001, 'balance': 44.979990999999984, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.827264'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 44.979990999999984, 'timestamp': '2019-06-07 11:51:21.827694'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 44.979990999999984, 'timestamp': '2019-06-07 11:51:21.828127'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 44.490002', 'action': 'buy', 'balance': 0.48998899999998, 'timestamp': '2019-06-07 11:51:21.828565'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 0.48998899999998, 'timestamp': '2019-06-07 11:51:21.828998'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 44.709999', 'investment': -0.7767465429039904, 'gain': -0.3500020000000035, 'balance': 45.199987999999976, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.829477'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 43.340000', 'action': 'buy', 'balance': 1.8599879999999729, 'timestamp': '2019-06-07 11:51:21.829914'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 43.439999', 'investment': -2.3600875540531643, 'gain': -1.0500030000000038, 'balance': 45.29998699999997, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.830392'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 43.419998', 'investment': 0.18458237194276933, 'gain': 0.07999799999999624, 'balance': 88.71998499999998, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.830868'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 88.71998499999998, 'timestamp': '2019-06-07 11:51:21.831283'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 88.71998499999998, 'timestamp': '2019-06-07 11:51:21.831697'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 88.71998499999998, 'timestamp': '2019-06-07 11:51:21.832122'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 88.71998499999998, 'timestamp': '2019-06-07 11:51:21.832539'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 34.119999', 'action': 'buy', 'balance': 54.59998599999998, 'timestamp': '2019-06-07 11:51:21.832954'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 31.379999', 'investment': -8.030480891866386, 'gain': -2.740000000000002, 'balance': 85.97998499999997, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.833430'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.833845'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.834256'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.834666'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.835077'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.835488'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.835919'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.836361'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.836773'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.837189'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.837605'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.838017'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.838428'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.838846'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.839263'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.839673'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.840081'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.840503'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.840921'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.841333'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.97998499999997, 'timestamp': '2019-06-07 11:51:21.841742'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 35.490002', 'action': 'buy', 'balance': 50.48998299999997, 'timestamp': '2019-06-07 11:51:21.842157'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 35.349998', 'investment': -0.39448856610378513, 'gain': -0.14000400000000468, 'balance': 85.83998099999997, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.842639'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 85.83998099999997, 'timestamp': '2019-06-07 11:51:21.843063'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 35.180000', 'action': 'buy', 'balance': 50.659980999999966, 'timestamp': '2019-06-07 11:51:21.843482'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 34.840000', 'investment': -0.9664582148948161, 'gain': -0.3399999999999963, 'balance': 85.49998099999996, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.843961'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 32.730000', 'action': 'buy', 'balance': 52.769980999999966, 'timestamp': '2019-06-07 11:51:21.844387'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 30.809999', 'action': 'buy', 'balance': 21.959981999999968, 'timestamp': '2019-06-07 11:51:21.844829'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 30.490000', 'investment': -6.843874121600973, 'gain': -2.2399999999999984, 'balance': 52.44998199999996, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.845305'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 30.540001', 'action': 'buy', 'balance': 21.909980999999963, 'timestamp': '2019-06-07 11:51:21.845736'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 30.889999', 'action': 'buy', 'balance': -8.980018000000037, 'timestamp': '2019-06-07 11:51:21.846168'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': -8.980018000000037, 'timestamp': '2019-06-07 11:51:21.846596'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': -8.980018000000037, 'timestamp': '2019-06-07 11:51:21.847026'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': -8.980018000000037, 'timestamp': '2019-06-07 11:51:21.847455'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': -8.980018000000037, 'timestamp': '2019-06-07 11:51:21.847886'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 29.219999', 'investment': -5.160662290187015, 'gain': -1.5899999999999963, 'balance': 20.23998099999996, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.848540'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 20.23998099999996, 'timestamp': '2019-06-07 11:51:21.848994'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 29.850000', 'investment': -2.259335223990329, 'gain': -0.6900009999999988, 'balance': 50.089980999999966, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.849478'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 50.089980999999966, 'timestamp': '2019-06-07 11:51:21.849915'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 50.089980999999966, 'timestamp': '2019-06-07 11:51:21.850342'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 50.089980999999966, 'timestamp': '2019-06-07 11:51:21.850845'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 50.089980999999966, 'timestamp': '2019-06-07 11:51:21.851211'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 50.089980999999966, 'timestamp': '2019-06-07 11:51:21.851649'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 50.089980999999966, 'timestamp': '2019-06-07 11:51:21.852075'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 50.089980999999966, 'timestamp': '2019-06-07 11:51:21.852520'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 50.089980999999966, 'timestamp': '2019-06-07 11:51:21.852948'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 29.010000', 'investment': -6.086108970090928, 'gain': -1.879998999999998, 'balance': 79.09998099999997, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.853429'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 79.09998099999997, 'timestamp': '2019-06-07 11:51:21.853842'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 79.09998099999997, 'timestamp': '2019-06-07 11:51:21.854263'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 79.09998099999997, 'timestamp': '2019-06-07 11:51:21.854681'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 79.09998099999997, 'timestamp': '2019-06-07 11:51:21.855097'}\n",
      "[1. 0. 0.]\n",
      "{'status': 'do nothing', 'action': 'nothing', 'balance': 79.09998099999997, 'timestamp': '2019-06-07 11:51:21.855507'}\n",
      "[0. 1. 0.]\n",
      "{'status': 'buy 1 unit, cost 27.000000', 'action': 'buy', 'balance': 52.09998099999997, 'timestamp': '2019-06-07 11:51:21.855922'}\n",
      "[0. 0. 1.]\n",
      "{'status': 'sell 1 unit, price 27.990000', 'investment': 3.666666666666661, 'gain': 0.9899999999999984, 'balance': 80.08998099999997, 'action': 'sell', 'timestamp': '2019-06-07 11:51:21.856409'}\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "volume = df['Volume'].tolist()\n",
    "\n",
    "for i in range(100):\n",
    "    print(agent.trade([real_trend[i], volume[i]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "import pickle\n",
    "\n",
    "copy_model = copy.deepcopy(agent.model)\n",
    "\n",
    "with open('model.pkl', 'wb') as fopen:\n",
    "    pickle.dump(copy_model, fopen)"
   ]
  }
 ],
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