6-Reinforcement Learning (Long-Short) (Dummy data).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predict Long, Short or Stable position based on some indicators computed from historical price values.\n",
    "\n",
    "- State : Features + Closing price\n",
    "- Action : Pick Short, Long or Stable position\n",
    "- Reward : (p1-p2)*position\n",
    "\n",
    "shortfalls:\n",
    "- No temporal modeling\n",
    "- No portfolia management\n",
    "- Very basic reward function\n",
    "- Avg. Training reward flactuates due to intantenous rate of return differences used in the reward function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      " > There are 1014 rows\n",
      "(964, 8)\n",
      "(50, 8)\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "np.random.seed(1335)  # for reproducibility\n",
    "np.set_printoptions(precision=5, suppress=True, linewidth=150)\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import reinforcement_learning.backtest as twp\n",
    "from reinforcement_learning.utils import *\n",
    "\n",
    "DATA_PATH = \"../data/bitcoin-historical-data/coinbaseUSD_1-min_data_2014-12-01_to_2017-10-20.csv.csv\"\n",
    "TIME_GRAN = 24*60  # time granularity in minutes\n",
    "TRAIN_START = 0 / TIME_GRAN  # take only afterwards\n",
    "PRED_DAYS = 50  # num prediction days for the trained models\n",
    "TEST_START = ((24*60) / TIME_GRAN) * PRED_DAYS \n",
    "NUM_ACTIONS = 3\n",
    "\n",
    "df = read_dummy_data1(DATA_PATH, TIME_GRAN)\n",
    "df_train = df.iloc[TRAIN_START:df.shape[0]-TEST_START,] \n",
    "df_test = df.iloc[df.shape[0]-TEST_START:,]\n",
    "print(df_train.shape)\n",
    "print(df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:10: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(64, kernel_initializer=\"lecun_uniform\", input_shape=(7,))`\n",
      "  # Remove the CWD from sys.path while we load stuff.\n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:14: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(64, kernel_initializer=\"lecun_uniform\")`\n",
      "  \n",
      "/usr/local/lib/python2.7/dist-packages/ipykernel_launcher.py:18: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(3, kernel_initializer=\"lecun_uniform\")`\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.optimizers import RMSprop, Adam\n",
    "\n",
    "num_features = 7\n",
    "\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(64, init='lecun_uniform', input_shape=(num_features,)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(64, init='lecun_uniform'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.2))\n",
    "\n",
    "model.add(Dense(NUM_ACTIONS, init='lecun_uniform'))\n",
    "model.add(Activation('linear'))\n",
    "\n",
    "rms = RMSprop()\n",
    "adam = Adam()\n",
    "model.compile(loss='mse', optimizer=rms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #: 0 SimReward: 0.000047 Epsilon: 0.900000\n",
      "Epoch #: 1 SimReward: 0.000068 Epsilon: 0.880000\n",
      "Epoch #: 2 SimReward: 0.000132 Epsilon: 0.860000\n",
      "Epoch #: 3 SimReward: 0.000215 Epsilon: 0.840000\n",
      "Epoch #: 4 SimReward: 0.000253 Epsilon: 0.820000\n",
      "Epoch #: 5 SimReward: 0.000031 Epsilon: 0.800000\n",
      "Epoch #: 6 SimReward: 0.000170 Epsilon: 0.780000\n",
      "Epoch #: 7 SimReward: 0.000222 Epsilon: 0.760000\n",
      "Epoch #: 8 SimReward: 0.000200 Epsilon: 0.740000\n",
      "Epoch #: 9 SimReward: 0.000176 Epsilon: 0.720000\n",
      "Epoch #: 10 SimReward: 0.000133 Epsilon: 0.700000\n",
      "Epoch #: 11 SimReward: 0.000251 Epsilon: 0.680000\n",
      "Epoch #: 12 SimReward: 0.000231 Epsilon: 0.660000\n",
      "Epoch #: 13 SimReward: 0.000298 Epsilon: 0.640000\n",
      "Epoch #: 14 SimReward: 0.000275 Epsilon: 0.620000\n",
      "Epoch #: 15 SimReward: 0.000262 Epsilon: 0.600000\n",
      "Epoch #: 16 SimReward: 0.000248 Epsilon: 0.580000\n",
      "Epoch #: 17 SimReward: 0.000261 Epsilon: 0.560000\n",
      "Epoch #: 18 SimReward: 0.000294 Epsilon: 0.540000\n",
      "Epoch #: 19 SimReward: 0.000212 Epsilon: 0.520000\n",
      "Epoch #: 20 SimReward: 0.000244 Epsilon: 0.500000\n",
      "Epoch #: 21 SimReward: 0.000242 Epsilon: 0.480000\n",
      "Epoch #: 22 SimReward: 0.000344 Epsilon: 0.460000\n",
      "Epoch #: 23 SimReward: 0.000219 Epsilon: 0.440000\n",
      "Epoch #: 24 SimReward: 0.000352 Epsilon: 0.420000\n",
      "Epoch #: 25 SimReward: 0.000184 Epsilon: 0.400000\n",
      "Epoch #: 26 SimReward: 0.000260 Epsilon: 0.380000\n",
      "Epoch #: 27 SimReward: 0.000340 Epsilon: 0.360000\n",
      "Epoch #: 28 SimReward: 0.000277 Epsilon: 0.340000\n",
      "Epoch #: 29 SimReward: 0.000276 Epsilon: 0.320000\n",
      "Epoch #: 30 SimReward: 0.000334 Epsilon: 0.300000\n",
      "Epoch #: 31 SimReward: 0.000293 Epsilon: 0.280000\n",
      "Epoch #: 32 SimReward: 0.000283 Epsilon: 0.260000\n",
      "Epoch #: 33 SimReward: 0.000291 Epsilon: 0.240000\n",
      "Epoch #: 34 SimReward: 0.000260 Epsilon: 0.220000\n",
      "Epoch #: 35 SimReward: 0.000273 Epsilon: 0.200000\n",
      "Epoch #: 36 SimReward: 0.000313 Epsilon: 0.180000\n",
      "Epoch #: 37 SimReward: 0.000276 Epsilon: 0.160000\n",
      "Epoch #: 38 SimReward: 0.000295 Epsilon: 0.140000\n",
      "Epoch #: 39 SimReward: 0.000334 Epsilon: 0.120000\n",
      "Epoch #: 40 SimReward: 0.000390 Epsilon: 0.100000\n",
      "Epoch #: 41 SimReward: 0.000323 Epsilon: 0.100000\n",
      "Epoch #: 42 SimReward: 0.000266 Epsilon: 0.100000\n",
      "Epoch #: 43 SimReward: 0.000305 Epsilon: 0.100000\n",
      "Epoch #: 44 SimReward: 0.000255 Epsilon: 0.100000\n",
      "Epoch #: 45 SimReward: 0.000452 Epsilon: 0.100000\n",
      "Epoch #: 46 SimReward: 0.000315 Epsilon: 0.100000\n",
      "Epoch #: 47 SimReward: 0.000306 Epsilon: 0.100000\n",
      "Epoch #: 48 SimReward: 0.000364 Epsilon: 0.100000\n",
      "Epoch #: 49 SimReward: 0.000341 Epsilon: 0.100000\n",
      "Completed in 876.030000\n"
     ]
    }
   ],
   "source": [
    "import timeit\n",
    "\n",
    "start_time = timeit.default_timer()\n",
    "\n",
    "# set train and test data\n",
    "train_data = df_train\n",
    "test_data = df_test\n",
    "start_timestep = 14\n",
    "\n",
    "# training arguments\n",
    "epochs = 50\n",
    "batch_size = 10\n",
    "gamma = 0.85  \n",
    "epsilon = 0.9  # parameter setting initial random actions, that decays as learning \n",
    "\n",
    "# stores tuples of (S, A, R, S')\n",
    "signal = pd.Series(index=np.arange(len(train_data)))\n",
    "\n",
    "# replay memory\n",
    "replay_memory = ReplayMemory(900)\n",
    "\n",
    "# let the agent learn\n",
    "learning_progress = []\n",
    "for i in range(epochs):\n",
    "    avg_sim_reward = AverageMeter()\n",
    "    terminal_state = 0\n",
    "    state, xdata, price_data = create_states(train_data)\n",
    "    time_step = start_timestep\n",
    "    \n",
    "    #while game still in progress\n",
    "    while(terminal_state == 0):\n",
    "        \n",
    "        # We are in state S\n",
    "        # 1- Compute the next action based on state S with the network\n",
    "        qval = model.predict(state, batch_size=1)\n",
    "        if (random.random() < epsilon): #choose random action\n",
    "            action = np.random.randint(0,NUM_ACTIONS) #assumes 4 different actions\n",
    "        else: #choose best action from Q(s,a) values\n",
    "            action = (np.argmax(qval))\n",
    "            \n",
    "        # 2- Take action, observe new state S'\n",
    "        new_state, time_step, signal, terminal_state = take_action(state, xdata, action, signal, time_step)\n",
    "        \n",
    "        # 3- Compute the reward\n",
    "        reward = get_reward(new_state, time_step, action, price_data, signal, eval=False)\n",
    "#         print(\" Action: {}, Signal: {}, Reward: {}\".format(action, signal[time_step], reward))\n",
    "        \n",
    "        avg_sim_reward.update(reward)\n",
    "        \n",
    "        #Experience replay storage\n",
    "        replay_memory.push(state, action, new_state, reward)\n",
    "        state = new_state\n",
    "        optimize_DQN(model, replay_memory, batch_size, gamma)\n",
    "        \n",
    "    learning_progress.append(avg_sim_reward.avg)\n",
    "    print(\"Epoch #: %s SimReward: %f Epsilon: %f\" % (i, avg_sim_reward.avg, epsilon))\n",
    "    \n",
    "    # decay epsilon\n",
    "    if epsilon > 0.1: #decrement epsilon over time\n",
    "        epsilon -= (1.0/epochs)\n",
    "\n",
    "elapsed = np.round(timeit.default_timer() - start_time, decimals=2)\n",
    "print(\"Completed in %f\" % (elapsed,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " # Final test reward: 34.0\n"
     ]
    }
   ],
   "source": [
    "signal = pd.Series(index=np.arange(len(test_data)))\n",
    "state, xdata, price_data = create_states(test_data, test=True)\n",
    "\n",
    "time_step = 1\n",
    "terminal_state = 0\n",
    "avg_test_reward = AverageMeter()\n",
    "while(terminal_state == 0):\n",
    "    #We are in state S\n",
    "    # 1- Compute the next action based on state S with the network\n",
    "    qval = model.predict(state, batch_size=1)\n",
    "    action = (np.argmax(qval))\n",
    "    # 2- Take action, observe new state S'\n",
    "    new_state, time_step, signal, terminal_state = take_action(state, xdata, action, signal, time_step)\n",
    "    # 3- Compute the reward\n",
    "#     reward = get_reward(new_state, time_step, action, close_prices, signal, eval=False)\n",
    "#     avg_test_reward.update(reward)\n",
    "    state = new_state\n",
    "#     print(\" # Average test reward: {}\".format(avg_test_reward.avg))\n",
    "    \n",
    "final_reward = get_reward(new_state, time_step, action, price_data, signal, eval=True)\n",
    "print(\" # Final test reward: {}\".format(final_reward))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ -1.  34.]\n",
      " [  0.  16.]]\n"
     ]
    }
   ],
   "source": [
    "price_data = pd.Series(price_data)\n",
    "bt = twp.Backtest(price_data, signal, signalType='shares')\n",
    "bt.data['delta'] = bt.data['shares'].diff().fillna(0)\n",
    "\n",
    "unique, counts = np.unique(filter(lambda v: v==v, signal.values), return_counts=True)\n",
    "print(np.asarray((unique, counts)).T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mTBgwABo1gsmT4bjjkk4kSZIkSUpKbRmLk1SDTj8d3nwTLr8czjkHfvQjWLky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\n0PuUSwDApBseTgYGk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gQbTt6hQ/W333qr/X/eeWe06woT4RIAAAAA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\n/eUv4VWhrl9vAaTfSl9kp/Hjx2t8hrtyOG4d0+a2DdOeI6mPpO8kTZJ0p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U032dDZ5s1r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\n4tKhXz/piCOkcePyfw2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f38707f1a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[20, 40])\n",
    "plt.subplot(3,1,1)\n",
    "bt.plotTrades()\n",
    "\n",
    "plt.subplot(3,1,2)\n",
    "bt.pnl.plot(style='x-')\n",
    "\n",
    "plt.subplot(3,1,3)\n",
    "plt.plot(learning_progress)\n",
    "\n",
    "plt.savefig('reinforcement_learning/plt/summary'+'.png', bbox_inches='tight', pad_inches=1, dpi=72)\n",
    "# plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}