1_dqn.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import copy\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "from plotly import tools\n",
    "from plotly.graph_objs import *\n",
    "from plotly.offline import init_notebook_mode, iplot, iplot_mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2014-03-27 00:00:00 2017-11-10 00:00:00\n"
     ]
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>OpenInt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-03-27</th>\n",
       "      <td>568.00</td>\n",
       "      <td>568.00</td>\n",
       "      <td>552.92</td>\n",
       "      <td>558.46</td>\n",
       "      <td>13052</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-28</th>\n",
       "      <td>561.20</td>\n",
       "      <td>566.43</td>\n",
       "      <td>558.67</td>\n",
       "      <td>559.99</td>\n",
       "      <td>41003</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2014-03-31</th>\n",
       "      <td>566.89</td>\n",
       "      <td>567.00</td>\n",
       "      <td>556.93</td>\n",
       "      <td>556.97</td>\n",
       "      <td>10772</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2014-04-01</th>\n",
       "      <td>558.71</td>\n",
       "      <td>568.45</td>\n",
       "      <td>558.71</td>\n",
       "      <td>567.16</td>\n",
       "      <td>7932</td>\n",
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       "      <th>2014-04-02</th>\n",
       "      <td>599.99</td>\n",
       "      <td>604.83</td>\n",
       "      <td>562.19</td>\n",
       "      <td>567.00</td>\n",
       "      <td>146697</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "              Open    High     Low   Close  Volume  OpenInt\n",
       "Date                                                       \n",
       "2014-03-27  568.00  568.00  552.92  558.46   13052        0\n",
       "2014-03-28  561.20  566.43  558.67  559.99   41003        0\n",
       "2014-03-31  566.89  567.00  556.93  556.97   10772        0\n",
       "2014-04-01  558.71  568.45  558.71  567.16    7932        0\n",
       "2014-04-02  599.99  604.83  562.19  567.00  146697        0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('./Data/Stocks/goog.us.txt')\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "data = data.set_index('Date')\n",
    "print(data.index.min(), data.index.max())\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(446, 470)"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_split = '2016-01-01'\n",
    "train = data[:date_split]\n",
    "test = data[date_split:]\n",
    "len(train),len(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_train_test(train, test, date_split):\n",
    "    \n",
    "    data = [\n",
    "        Candlestick(x=train.index, open=train['Open'], high=train['High'], low=train['Low'], close=train['Close'], name='train'),\n",
    "        Candlestick(x=test.index, open=test['Open'], high=test['High'], low=test['Low'], close=test['Close'], name='test')\n",
    "    ]\n",
    "    layout = {\n",
    "         'shapes': [\n",
    "             {'x0': date_split, 'x1': date_split, 'y0': 0, 'y1': 1, 'xref': 'x', 'yref': 'paper', 'line': {'color': 'rgb(0,0,0)', 'width': 1}}\n",
    "         ],\n",
    "        'annotations': [\n",
    "            {'x': date_split, 'y': 1.0, 'xref': 'x', 'yref': 'paper', 'showarrow': False, 'xanchor': 'left', 'text': ' test data'},\n",
    "            {'x': date_split, 'y': 1.0, 'xref': 'x', 'yref': 'paper', 'showarrow': False, 'xanchor': 'right', 'text': 'train data '}\n",
    "        ]\n",
    "    }\n",
    "    figure = Figure(data=data, layout=layout)\n",
    "    iplot(figure)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       ],
       "layout": {
        "annotations": [
         {
          "showarrow": false,
          "text": " test data",
          "x": "2016-01-01",
          "xanchor": "left",
          "xref": "x",
          "y": 1,
          "yref": "paper"
         },
         {
          "showarrow": false,
          "text": "train data ",
          "x": "2016-01-01",
          "xanchor": "right",
          "xref": "x",
          "y": 1,
          "yref": "paper"
         }
        ],
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          "y0": 0,
          "y1": 1,
          "yref": "paper"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_train_test(train, test, date_split)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Environment1:\n",
    "    \n",
    "    def __init__(self, data, history_t=90):\n",
    "        self.data = data\n",
    "        self.history_t = history_t\n",
    "        self.reset()\n",
    "        \n",
    "    def reset(self):\n",
    "        self.t = 0\n",
    "        self.done = False\n",
    "        self.profits = 0\n",
    "        self.positions = []\n",
    "        self.position_value = 0\n",
    "        self.history = [0 for _ in range(self.history_t)]\n",
    "        return [self.position_value] + self.history # obs\n",
    "    \n",
    "    def step(self, act):\n",
    "        reward = 0\n",
    "        \n",
    "        # act = 0: stay, 1: buy, 2: sell\n",
    "        if act == 1:\n",
    "            self.positions.append(self.data.iloc[self.t, :]['Close'])\n",
    "        elif act == 2: # sell\n",
    "            if len(self.positions) == 0:\n",
    "                reward = -1\n",
    "            else:\n",
    "                profits = 0\n",
    "                for p in self.positions:\n",
    "                    profits += (self.data.iloc[self.t, :]['Close'] - p)\n",
    "                reward += profits\n",
    "                self.profits += profits\n",
    "                self.positions = []\n",
    "        \n",
    "        # set next time\n",
    "        self.t += 1\n",
    "        \n",
    "        self.position_value = 0\n",
    "        for p in self.positions:\n",
    "            self.position_value += (self.data.iloc[self.t, :]['Close'] - p)\n",
    "        self.history.pop(0)\n",
    "        self.history.append(self.data.iloc[self.t, :]['Close'] - self.data.iloc[(self.t-1), :]['Close'])\n",
    "        if (self.t==len(self.data)-1):\n",
    "            self.done=True\n",
    "        # clipping reward\n",
    "        if reward > 0:\n",
    "            reward = 1\n",
    "        elif reward < 0:\n",
    "            reward = -1\n",
    "        #print (\"t={%d}, done={%str}\"%(self.t,self.done))\n",
    "        return [self.position_value] + self.history, reward, self.done # obs, reward, done\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
      "1\n",
      "t={1}, done={Falsetr}\n",
      "([1.5299999999999727, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5299999999999727], 0, False)\n",
      "0\n",
      "t={2}, done={Falsetr}\n",
      "([-1.490000000000009, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5299999999999727, -3.019999999999982], 0, False)\n",
      "1\n",
      "t={3}, done={Falsetr}\n",
      "([18.889999999999873, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.5299999999999727, -3.019999999999982, 10.18999999999994], 0, False)\n"
     ]
    }
   ],
   "source": [
    "env = Environment1(train)\n",
    "print(env.reset())\n",
    "for _ in range(3):\n",
    "    pact = np.random.randint(3)\n",
    "    print (pact)\n",
    "    print(env.step(pact))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "#def train_dqn(env):\n",
    "    \n",
    "class Q_Network(nn.Module):\n",
    "        \n",
    "    def __init__(self,obs_len,hidden_size,actions_n):\n",
    "            \n",
    "        super(Q_Network,self).__init__()\n",
    "            \n",
    "        self.fc_val = nn.Sequential(\n",
    "            nn.Linear(obs_len, hidden_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_size, hidden_size),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(hidden_size, actions_n)\n",
    "        )\n",
    "        \n",
    "        \n",
    "    def forward(self,x):\n",
    "        h =  self.fc_val(x)\n",
    "        return (h)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "hidden_size=100\n",
    "input_size=env.history_t+1\n",
    "output_size=3\n",
    "USE_CUDA = False\n",
    "LR = 0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = Q_Network(input_size, hidden_size, output_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q_ast = copy.deepcopy(Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "if USE_CUDA:\n",
    "    Q = Q.cuda()\n",
    "loss_function = nn.MSELoss()\n",
    "optimizer = optim.Adam(list(Q.parameters()), lr=LR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "epoch_num = 50\n",
    "step_max = len(env.data)-1\n",
    "memory_size = 200\n",
    "batch_size = 50\n",
    "gamma = 0.97"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t={4}, done={Falsetr}\n"
     ]
    }
   ],
   "source": [
    "obs, reward, done = env.step(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "memory = []\n",
    "total_step = 0\n",
    "total_rewards = []\n",
    "total_losses = []\n",
    "epsilon = 1.0\n",
    "epsilon_decrease = 1e-3\n",
    "epsilon_min = 0.1\n",
    "start_reduce_epsilon = 200\n",
    "train_freq = 10\n",
    "update_q_freq = 20\n",
    "gamma = 0.97\n",
    "show_log_freq = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "for epoch in range(epoch_num):\n",
    "\n",
    "    pobs = env.reset()\n",
    "    step = 0\n",
    "    done = False\n",
    "    total_reward = 0\n",
    "    total_loss = 0\n",
    "\n",
    "    while not done and step < step_max:\n",
    "\n",
    "        # select act\n",
    "        pact = np.random.randint(3)\n",
    "        if np.random.rand() > epsilon:\n",
    "            pact = Q(torch.from_numpy(np.array(pobs, dtype=np.float32).reshape(1, -1)))\n",
    "            pact = np.argmax(pact.data)\n",
    "            pact = pact.numpy()\n",
    "\n",
    "        # act\n",
    "        obs, reward, done = env.step(pact)\n",
    "\n",
    "        # add memory\n",
    "        memory.append((pobs, pact, reward, obs, done))\n",
    "        if len(memory) > memory_size:\n",
    "            memory.pop(0)\n",
    "\n",
    "        # train or update q\n",
    "        if len(memory) == memory_size:\n",
    "            if total_step % train_freq == 0:\n",
    "                shuffled_memory = np.random.permutation(memory)\n",
    "                memory_idx = range(len(shuffled_memory))\n",
    "                for i in memory_idx[::batch_size]:\n",
    "                    batch = np.array(shuffled_memory[i:i+batch_size])\n",
    "                    b_pobs = np.array(batch[:, 0].tolist(), dtype=np.float32).reshape(batch_size, -1)\n",
    "                    b_pact = np.array(batch[:, 1].tolist(), dtype=np.int32)\n",
    "                    b_reward = np.array(batch[:, 2].tolist(), dtype=np.int32)\n",
    "                    b_obs = np.array(batch[:, 3].tolist(), dtype=np.float32).reshape(batch_size, -1)\n",
    "                    b_done = np.array(batch[:, 4].tolist(), dtype=np.bool)\n",
    "\n",
    "                    q = Q(torch.from_numpy(b_pobs))\n",
    "                    q_ = Q_ast(torch.from_numpy(b_obs))\n",
    "                    maxq = np.max(q_.data.numpy(),axis=1)\n",
    "                    target = copy.deepcopy(q.data)\n",
    "                    for j in range(batch_size):\n",
    "                        target[j, b_pact[j]] = b_reward[j]+gamma*maxq[j]*(not b_done[j])\n",
    "                    Q.zero_grad()\n",
    "                    loss = loss_function(q, target)\n",
    "                    total_loss += loss.data.item()\n",
    "                    loss.backward()\n",
    "                    optimizer.step()\n",
    "                    \n",
    "            if total_step % update_q_freq == 0:\n",
    "                Q_ast = copy.deepcopy(Q)\n",
    "                \n",
    "            # epsilon\n",
    "            if epsilon > epsilon_min and total_step > start_reduce_epsilon:\n",
    "                epsilon -= epsilon_decrease\n",
    "\n",
    "            # next step\n",
    "            total_reward += reward\n",
    "            pobs = obs\n",
    "            step += 1\n",
    "            total_step += 1\n",
    "\n",
    "        total_rewards.append(total_reward)\n",
    "        total_losses.append(total_loss)\n",
    "\n",
    "        if (epoch+1) % show_log_freq == 0:\n",
    "            log_reward = sum(total_rewards[((epoch+1)-show_log_freq):])/show_log_freq\n",
    "            log_loss = sum(total_losses[((epoch+1)-show_log_freq):])/show_log_freq\n",
    "            elapsed_time = time.time()-start\n",
    "            print('\\t'.join(map(str, [epoch+1, epsilon, total_step, log_reward, log_loss, elapsed_time])))\n",
    "            start = time.time()\n",
    "            \n",
    "#return Q, total_losses, total_rewards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_env = Environment1(test)\n",
    "pobs = test_env.reset()\n",
    "test_acts = []\n",
    "test_rewards = []\n",
    "\n",
    "for _ in range(len(test_env.data)-1):\n",
    "    \n",
    "    pact = Q(torch.from_numpy(np.array(pobs, dtype=np.float32).reshape(1, -1)))\n",
    "    pact = np.argmax(pact.data)\n",
    "    test_acts.append(pact.item())\n",
    "            \n",
    "    obs, reward, done = test_env.step(pact.numpy())\n",
    "    test_rewards.append(reward)\n",
    "\n",
    "    pobs = obs\n",
    "        \n",
    "test_profits = test_env.profits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3841.4399999999973"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_profits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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