Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "\n",
    "# Importing DataLoaders for each model. These models include rule-based, vanilla DQN and encoder-decoder DQN.\n",
    "from DataLoader.DataLoader import YahooFinanceDataLoader\n",
    "from DataLoader.DataForPatternBasedAgent import DataForPatternBasedAgent\n",
    "from DataLoader.DataAutoPatternExtractionAgent import DataAutoPatternExtractionAgent\n",
    "from DataLoader.DataSequential import DataSequential \n",
    "\n",
    "from DeepRLAgent.MLPEncoder.Train import Train as SimpleMLP\n",
    "from DeepRLAgent.SimpleCNNEncoder.Train import Train as SimpleCNN\n",
    "from EncoderDecoderAgent.GRU.Train import Train as gru\n",
    "from EncoderDecoderAgent.CNN.Train import Train as cnn\n",
    "from EncoderDecoderAgent.CNN2D.Train import Train as cnn2d\n",
    "from EncoderDecoderAgent.CNNAttn.Train import Train as cnn_attn\n",
    "from EncoderDecoderAgent.CNN_GRU.Train import Train as cnn_gru\n",
    "\n",
    "\n",
    "# Imports for Deep RL Agent\n",
    "from DeepRLAgent.VanillaInput.Train import Train as DeepRL\n",
    "\n",
    "\n",
    "\n",
    "# Imports for RL Agent with n-step SARSA\n",
    "from RLAgent.Train import Train as RLTrain\n",
    "\n",
    "# Imports for Rule-Based\n",
    "from PatternDetectionInCandleStick.LabelPatterns import label_candles\n",
    "from PatternDetectionInCandleStick.Evaluation import Evaluation\n",
    "\n",
    "import torch\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from kaleido.scopes.plotly import PlotlyScope\n",
    "import plotly.graph_objs as go\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import random\n",
    "\n",
    "CURRENT_PATH = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`BATCH_SIZE` is set to 10 based on some analysis on the experiments. You can change it to whatever you want. `n_step` is the cumulative reward of n-steps in the future. `initial_investment` is the amount invested at the beginning of the process of trading. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 10\n",
    "GAMMA=0.7\n",
    "n_step = 10\n",
    "\n",
    "initial_investment = 1000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`train_portfolios` and `test_portfolios` are dictionaries for saving the portfolio result of different models. The `key` of the dictionary is the model name and the `value` is a list of values showing the value of portfolio at each time-step. The `window_size_experiment` is the dictionary for storing the results of the experiments done for evaluating the effect of different window-sizes on portfolio values.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_portfolios = {}\n",
    "test_portfolios = {}\n",
    "window_size_experiment = {}\n",
    "window_sizes = [3, 5, 8, 10, 12, 15, 20, 25, 30, 40, 50, 75]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following two helper functions are for storing the portfolio result of diffrent experiments given the `model_name` and a list of portfolio values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_train_portfo(model_name, portfo):\n",
    "    counter = 0\n",
    "    key = f'{model_name}'\n",
    "    while key in train_portfolios.keys():\n",
    "        counter += 1\n",
    "        key = f'{model_name}{counter}'\n",
    "        \n",
    "    train_portfolios[key] = portfo\n",
    "\n",
    "def add_test_portfo(model_name, portfo):\n",
    "    counter = 0\n",
    "    key = f'{model_name}'\n",
    "    while key in test_portfolios.keys():\n",
    "        counter += 1\n",
    "        key = f'{model_name}{counter}'\n",
    "    \n",
    "    test_portfolios[key] = portfo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following blocks are for choosing a specific data to run the experiments. For example if you want to run the experiments on `BTC-USD` you should only run the first block, for `GOOGL` run the second block, blah blah blah. As also discussed in the documentation of the YahooFinanceDataLoader, you can set the `begin_date` and `end_date` to select a specific period from the original data. Also you can set the `split_point` which is a date of splitting train and test data. If your origianl file has changed or you are running the data for the first time, set the `load_from_file` to `false`. Otherwise set it to `True` to load from the preprocessed file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 809/809 [00:14<00:00, 57.37it/s]\n",
      "100%|██████████| 808/808 [00:06<00:00, 118.60it/s]\n"
     ]
    }
   ],
   "source": [
    "# BTC-USD\n",
    "\n",
    "DATASET_NAME = 'BTC-USD'\n",
    "DATASET_FOLDER = r'BTC-USD'\n",
    "FILE = r'BTC-USD.csv'\n",
    "data_loader = YahooFinanceDataLoader(DATASET_FOLDER, FILE, '2021-01-10', load_from_file=False)\n",
    "transaction_cost = 0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block is for choosing the observation-space mode. `state_mode = 1` is when the observation space contains only the Open, High, Low, and Close data. `state_mode = 2` also has a `trend` showing the trend of the stock before the candle stick at a specific time-step. `state_mode = 3` has OHLC and trend plus the size of the body, upper and lower shadow of the candle. `state_mode = 4` contains only the trend of the stock before that time-step plus the size of the body of the candle, upper and lower shadows. `state_mode = 5` is a window of `window-size` OHLCs plus the trend of the stock in that window. Pay attention that the window_size here is only for `DataAutoPatternExtractionAgent` not for the `DataSequential` which is the data used for time-series models like CNN and GRU. `DataForPatternBasedAgent` uses the extracted patterns as observation space. This kind of observation space is mainly used by RL agent with SARSA-$\\lambda$ and one kind of DQN model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Agent with Auto pattern extraction\n",
    "# State Mode\n",
    "state_mode = 1  # OHLC\n",
    "# state_mode = 2  # OHLC + trend\n",
    "# state_mode = 3  # OHLC + trend + %body + %upper-shadow + %lower-shadow\n",
    "window_size = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataTrain_autoPatternExtractionAgent = DataAutoPatternExtractionAgent(data_loader.data_train, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "dataTest_autoPatternExtractionAgent = DataAutoPatternExtractionAgent(data_loader.data_test, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "dataTrain_patternBased = DataForPatternBasedAgent(data_loader.data_train, data_loader.patterns, 'action_deepRL', device, GAMMA, n_step, BATCH_SIZE, transaction_cost)\n",
    "dataTest_patternBased = DataForPatternBasedAgent(data_loader.data_test, data_loader.patterns, 'action_deepRL', device, GAMMA, n_step, BATCH_SIZE, transaction_cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As discussed before, when `state_mode` is set to 4, the observation space has the trend, length of the body, upper and lower shadow of the candle. Therefore, we call this `state_mode` as candle representation because it contains a representation of the candle stick, not its original data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_mode = 4  # trend + %body + %upper-shadow + %lower-shadow\n",
    "\n",
    "dataTrain_autoPatternExtractionAgent_candle_rep = DataAutoPatternExtractionAgent(data_loader.data_train, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "dataTest_autoPatternExtractionAgent_candle_rep = DataAutoPatternExtractionAgent(data_loader.data_test, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following blocks are for loading windowed input for non-timeseries models. As we discussed above, the `state_mode` is 5 in this case and you should provide the window-size in this case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_mode = 5  # window with k candles inside + the trend of those candles\n",
    "window_size = 20\n",
    "dataTrain_autoPatternExtractionAgent_windowed = DataAutoPatternExtractionAgent(data_loader.data_train, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "dataTest_autoPatternExtractionAgent_windowed = DataAutoPatternExtractionAgent(data_loader.data_test, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This block is for loading the data for encoder-decoder models with time-series encoder. The `window_size` here is the number of candles inside one single time-step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size = 15\n",
    "\n",
    "dataTrain_sequential = DataSequential(data_loader.data_train,\n",
    "                           'action_encoder_decoder', device, GAMMA,\n",
    "                           n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "dataTest_sequential = DataSequential(data_loader.data_test,\n",
    "                          'action_encoder_decoder', device, GAMMA,\n",
    "                          n_step, BATCH_SIZE, window_size, transaction_cost)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reinforcement Learning Agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section is for running the experiments for RL agent with n-step SARSA algorithm. The following block is for setting the HPs for this algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_iteration = 1000\n",
    "gamma = 0.9\n",
    "alpha = 0.3\n",
    "epsilon = 0.1\n",
    "n = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the following block, if you want the agent to learn a new policy, uncomment the two lines:\n",
    "```\n",
    "rlAgent.training()\n",
    "rlAgent.write_to_file()\n",
    "```\n",
    "but if you want to load a trained model, comment out the above two lines and instead use the function:\n",
    "```\n",
    "rlAgent.read_from_file(<file name inside the ./Objects/RLAgent directory>)\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\n",
      "Historical VAR is -3.0\n",
      "Variance-Covariance VAR with 95% confidence is -3.6885225250763134\n",
      "Variance-Covariance VAR with 99% confidence is -5.2086408990471575\n",
      "##################################################\n",
      "Arithmetic Return: 115.71050091792435\n",
      "##################################################\n",
      "Average daily return: 0.30856133578113154\n",
      "##################################################\n",
      "Daily return variance (return type: Arithmetic): 4.997318059869731\n",
      "##################################################\n",
      "Daily return variance (return type: Logarithmic): 4.898986949948898\n",
      "##################################################\n",
      "Time weighted return: 0.0027245192314691025\n",
      "##################################################\n",
      "Total Return: 177.40418858389376 %\n",
      "##################################################\n",
      "Sharp Ratio: 0.1330929178304503\n",
      "##################################################\n",
      "Value at Risk (Monte Carlo method): -3.3727005151038005\n",
      "##################################################\n",
      "Volatility: 43.28965549009543\n",
      "##################################################\n",
      "Initial Investment: 1000\n",
      "##################################################\n",
      "Final Portfolio Value: 2774.0418858389376\n",
      "##################################################\n",
      "116 & 0.31 & 5.00 & 0.003 & 177 \\% & 0.133 & -3.37 & 43.3 & 1000 & 2774.0 \\\\\n",
      "test\n",
      "Historical VAR is -6.0\n",
      "Variance-Covariance VAR with 95% confidence is -4.399449322136733\n",
      "Variance-Covariance VAR with 99% confidence is -6.212555709441569\n",
      "##################################################\n",
      "Arithmetic Return: -12.527225014608696\n",
      "##################################################\n",
      "Average daily return: -0.028864573766379496\n",
      "##################################################\n",
      "Daily return variance (return type: Arithmetic): 7.10933125364531\n",
      "##################################################\n",
      "Daily return variance (return type: Logarithmic): 7.205301041305145\n",
      "##################################################\n",
      "Time weighted return: -0.0007936272481505746\n",
      "##################################################\n",
      "Total Return: -29.147573657387248 %\n",
      "##################################################\n",
      "Sharp Ratio: -0.016434774384690588\n",
      "##################################################\n",
      "Value at Risk (Monte Carlo method): -4.419653797467393\n",
      "##################################################\n",
      "Volatility: 55.546824968508005\n",
      "##################################################\n",
      "Initial Investment: 1000\n",
      "##################################################\n",
      "Final Portfolio Value: 708.5242634261275\n",
      "##################################################\n",
      "-13 & -0.03 & 7.11 & -0.001 & -29 \\% & -0.016 & -4.42 & 55.5 & 1000 & 708.5 \\\\\n"
     ]
    }
   ],
   "source": [
    "# Read RL Agent experiment\n",
    "data_test_rlagent = dataTest_patternBased.data if dataTest_patternBased is not None else None\n",
    "\n",
    "rlAgent = RLTrain(dataTrain_patternBased.data, data_test_rlagent, data_loader.patterns, DATASET_NAME, n=n, num_iteration=num_iteration, gamma=gamma, alpha=alpha, epsilon=epsilon)\n",
    "\n",
    "#rlAgent.training()\n",
    "#rlAgent.write_to_file()\n",
    "\n",
    "# rlAgent.read_from_file('GOOGL-TRAIN_TEST_SPLIT(True)-NUM_ITERATIONS10000-N_STEP10-GAMMA1-ALPHA0.1-EPSILON0.1-EXPERIMENT(1).pkl')\n",
    "# rlAgent.read_from_file('AAPL-TRAIN_TEST_SPLIT(True)-NUM_ITERATIONS1000-N_STEP10-GAMMA1-ALPHA0.1-EPSILON0.1-EXPERIMENT.pkl')\n",
    "# rlAgent.read_from_file('BTC-USD-TRAIN_TEST_SPLIT(True)-NUM_ITERATIONS10000-N_STEP10-GAMMA0.9-ALPHA0.1-EPSILON0.1-EXPERIMENT.pkl')\n",
    "# rlAgent.read_from_file('KSS-TRAIN_TEST_SPLIT(True)-NUM_ITERATIONS10000-N_STEP10-GAMMA0.9-ALPHA0.1-EPSILON0.1-EXPERIMENT(1).pkl')\n",
    "\n",
    "ev_rlAgent = rlAgent.test(test_type= 'train')\n",
    "print('train')\n",
    "ev_rlAgent.evaluate()\n",
    "rlAgent_portfolio_train = ev_rlAgent.get_daily_portfolio_value()\n",
    "ev_rlAgent = rlAgent.test(test_type= 'test')\n",
    "print('test')\n",
    "ev_rlAgent.evaluate()\n",
    "rlAgent_portfolio_test = ev_rlAgent.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = f'RL'\n",
    "# model_kind = f'RL-{n}'\n",
    "\n",
    "add_train_portfo(model_kind, rlAgent_portfolio_train)\n",
    "add_test_portfo(model_kind, rlAgent_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deep Reinforcement Learning Agent "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section dedicates to the Deep Q-Learning agent without encoder part. `TARGET_UPDATE` is how frequent the policy network is hard-copied to the target network in terms of number of episodes. `n_actions` is set to 3 because we have `buy`, `sell` and `none` actions. `n_episodes` is the number of episodes to run the algorithm. `EPS` is the $\\epsilon$ in the $\\epsilon$-greedy method. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPS=0.1\n",
    "ReplayMemorySize=20\n",
    "TARGET_UPDATE=5\n",
    "n_actions=3\n",
    "n_episodes = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following bocks, each block is for running the DQN algorithm in a specific condition. For each block, if you want to train from scratch, use the following two lines:\n",
    "```\n",
    "deepRLAgent.train(n_episodes)\n",
    "file_name = None\n",
    "```\n",
    "pay attention that the `file_name` should be set to `None` so that for the evaluation process, it does not load from file and instead use the recently trained agent. If you want to load from a specific model, set the `file_name` to the name of the file inside the `./Objects/DeepRL` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Patterns as input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model uses the extracted patterns as observation space. These patterns are extracted in the preprocessing phase and used here instead of directly applying OHLC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.34s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    }
   ],
   "source": [
    "deepRLAgent = DeepRL(data_loader, dataTrain_patternBased, dataTest_patternBased , DATASET_NAME, state_mode, window_size, transaction_cost, \n",
    "                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,  ReplayMemorySize=ReplayMemorySize,\n",
    "                    TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "#EPS=EPS,\n",
    "#n_actions=n_actions,\n",
    "deepRLAgent.train(n_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'GOOGL; DATA_KIND(PatternBased); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TransactionCost(0.0) TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(PatternBased); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TransactionCost(0.0) TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT(1).pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(PatternBased); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TransactionCost(0.0) TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT(1).pkl'\n",
    "# file_name = 'KSS; DATA_KIND(PatternBased); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "ev_deepRLAgent = deepRLAgent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "#file_name=file_name, action_name=dataTrain_autoPatternExtractionAgent_windowed.action_name,\n",
    "deepRLAgent_portfolio_train = ev_deepRLAgent.get_daily_portfolio_value()\n",
    "ev_deepRLAgent = deepRLAgent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "#file_name=file_name, action_name=dataTrain_autoPatternExtractionAgent_windowed.action_name,\n",
    "deepRLAgent_portfolio_test = ev_deepRLAgent.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'DQN-pattern'\n",
    "\n",
    "add_train_portfo(model_kind, deepRLAgent_portfolio_train)\n",
    "add_test_portfo(model_kind, deepRLAgent_portfolio_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OHLC as input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model uses the OHLC representation of candles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.75s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    }
   ],
   "source": [
    "deepRLAgent = DeepRL(data_loader, dataTrain_autoPatternExtractionAgent, dataTest_autoPatternExtractionAgent, DATASET_NAME, \n",
    "                     state_mode, window_size, transaction_cost,\n",
    "                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,  ReplayMemorySize=ReplayMemorySize,\n",
    "                    TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "\n",
    "deepRLAgent.train(n_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TransactionCost(0.0) TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TransactionCost(0.0) TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT(1).pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TransactionCost(0.0) TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT(1).pkl'\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'GE; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); DeepRL; TC(0); StateMode(1); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'HSI; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); DeepRL; TC(0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'AAL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0); StateMode(1); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "ev_deepRLAgentAutoExtraction = deepRLAgent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "deepRLAgentAutoExtraction_portfolio_train = ev_deepRLAgentAutoExtraction.get_daily_portfolio_value()\n",
    "ev_deepRLAgentAutoExtraction = deepRLAgent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "deepRLAgentAutoExtraction_portfolio_test = ev_deepRLAgentAutoExtraction.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'DQN-vanilla'\n",
    "# model_kind = 'DQN'\n",
    "\n",
    "add_train_portfo(model_kind, deepRLAgentAutoExtraction_portfolio_train)\n",
    "add_test_portfo(model_kind, deepRLAgentAutoExtraction_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With candle representation as input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model uses the `state_mode = 4` as input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.71s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    }
   ],
   "source": [
    "deepRLAgent = DeepRL(data_loader, dataTrain_autoPatternExtractionAgent_candle_rep, \n",
    "                     dataTest_autoPatternExtractionAgent_candle_rep, \n",
    "                     DATASET_NAME,  state_mode, window_size, transaction_cost,\n",
    "                     BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, ReplayMemorySize=ReplayMemorySize,\n",
    "                     TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "\n",
    "deepRLAgent.train(n_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0); StateMode(4); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(4); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); DeepRL; TC(0); StateMode(4); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(4); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "ev_deepRLAgentAutoExtraction = deepRLAgent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "deepRLAgentAutoExtraction_portfolio_train = ev_deepRLAgentAutoExtraction.get_daily_portfolio_value()\n",
    "ev_deepRLAgentAutoExtraction = deepRLAgent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "deepRLAgentAutoExtraction_portfolio_test = ev_deepRLAgentAutoExtraction.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'DQN-candle-rep'\n",
    "\n",
    "add_train_portfo(model_kind, deepRLAgentAutoExtraction_portfolio_train)\n",
    "add_test_portfo(model_kind, deepRLAgentAutoExtraction_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Windowed Input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input is a window of OHLC candles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.60s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    }
   ],
   "source": [
    "deepRLAgent = DeepRL(data_loader, dataTrain_autoPatternExtractionAgent_windowed, \n",
    "                     dataTest_autoPatternExtractionAgent_windowed, \n",
    "                     DATASET_NAME,  state_mode, window_size, transaction_cost,\n",
    "                     BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, ReplayMemorySize=ReplayMemorySize,\n",
    "                     TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "\n",
    "deepRLAgent.train(n_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); DeepRL; TC(0); StateMode(5); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(5); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0); StateMode(5); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(5); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(5); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0); StateMode(5); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); DeepRL; TC(0); StateMode(5); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(5); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'GE; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); DeepRL; TC(0); StateMode(5); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'HSI; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); DeepRL; TC(0); StateMode(5); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'AAL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0); StateMode(5); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "ev_deepRLAgentAutoExtraction = deepRLAgent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "deepRLAgentAutoExtraction_portfolio_train = ev_deepRLAgentAutoExtraction.get_daily_portfolio_value()\n",
    "ev_deepRLAgentAutoExtraction = deepRLAgent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "deepRLAgentAutoExtraction_portfolio_test = ev_deepRLAgentAutoExtraction.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'DQN-windowed'\n",
    "\n",
    "add_train_portfo(model_kind, deepRLAgentAutoExtraction_portfolio_train)\n",
    "add_test_portfo(model_kind, deepRLAgentAutoExtraction_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Window Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block is for testing the effect of window-size in the input representation on the performance of the model. Here we assume that we have a set of pretrained models in the `./Objects/DeepRL` directory. If you do not have these pretrained models, you should train from scratch and set `file_name = None`. The set of window sizes that are being tested in the experiments are in `window_sizes` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/12 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.68s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 1/12 [00:32<05:57, 32.49s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.61s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 2/12 [01:03<05:18, 31.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.85s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 3/12 [01:34<04:43, 31.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.38s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 4/12 [02:09<04:20, 32.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.70s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████▏     | 5/12 [02:42<03:48, 32.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.63s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 6/12 [03:14<03:15, 32.54s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.59s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 7/12 [03:45<02:40, 32.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.56s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 8/12 [04:18<02:09, 32.29s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.55s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|███████▌  | 9/12 [04:47<01:34, 31.50s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.57s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████▎ | 10/12 [05:19<01:03, 31.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.51s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 11/12 [05:49<00:30, 30.89s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training DeepRL ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.45s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [06:17<00:00, 31.43s/it]\n"
     ]
    }
   ],
   "source": [
    "n_classes = 64\n",
    "state_mode = 5\n",
    "\n",
    "for window_size in tqdm(window_sizes):\n",
    "    \n",
    "    dataTrain_autoPatternExtractionAgent_windowed = DataAutoPatternExtractionAgent(data_loader.data_train, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "    dataTest_autoPatternExtractionAgent_windowed = DataAutoPatternExtractionAgent(data_loader.data_test, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "    \n",
    "    deepRLAgent = DeepRL(data_loader, dataTrain_autoPatternExtractionAgent_windowed, \n",
    "                         dataTest_autoPatternExtractionAgent_windowed, \n",
    "                         DATASET_NAME,  state_mode, window_size, transaction_cost,\n",
    "                         BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, ReplayMemorySize=ReplayMemorySize,\n",
    "                         TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "\n",
    "  \n",
    "    file_name = None\n",
    "    deepRLAgent.train(n_episodes)\n",
    "    \n",
    "#     file_name = f'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); DeepRL; TC(0.0); StateMode(4); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "\n",
    "    ev_deepRLAgentAutoExtraction = deepRLAgent.test(\n",
    "                                      initial_investment=initial_investment, test_type='train')\n",
    "    #file_name=file_name, action_name=dataTrain_autoPatternExtractionAgent_windowed.action_name,\n",
    "    deepRLAgentAutoExtraction_portfolio_train = ev_deepRLAgentAutoExtraction.get_daily_portfolio_value()\n",
    "    ev_deepRLAgentAutoExtraction = deepRLAgent.test(\n",
    "                                      initial_investment=initial_investment, test_type='test')\n",
    "    #file_name=file_name, action_name=dataTrain_autoPatternExtractionAgent_windowed.action_name,\n",
    "    deepRLAgentAutoExtraction_portfolio_test = ev_deepRLAgentAutoExtraction.get_daily_portfolio_value()\n",
    "\n",
    "    model_kind = 'DQN-windowed'\n",
    "\n",
    "    add_train_portfo(model_kind, deepRLAgentAutoExtraction_portfolio_train)\n",
    "    add_test_portfo(model_kind, deepRLAgentAutoExtraction_portfolio_test)\n",
    "    \n",
    "    if model_kind not in window_size_experiment.keys():\n",
    "        window_size_experiment[model_kind] = {}\n",
    "\n",
    "    window_size_experiment[model_kind][window_size] = \\\n",
    "        ((test_portfolios[model_kind][-1] - test_portfolios[model_kind][0])/test_portfolios[model_kind][0] * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoder Decoder models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section dedicates to the Deep Q-Learning agent with encoder. `TARGET_UPDATE` is how frequent the policy network is hard-copied to the target network in terms of number of episodes. `n_actions` is set to 3 because we have `buy`, `sell` and `none` actions. `n_episodes` is the number of episodes to run the algorithm. `EPS` is the $\\epsilon$ in the $\\epsilon$-greedy method. Decoder models include: MLP, 1-layerd 1d CNN, 1-layerd 2d CNN, two layered 1d CNN, GRU, CNN-GRU, CNN-Attn (CNN with an attention layer). For details about these models, please refer to the paper and README file.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 10\n",
    "EPS = 0.1\n",
    "# EPS_START = 0.9\n",
    "# EPS_END = 0.05\n",
    "# EPS_DECAY = 200\n",
    "\n",
    "ReplayMemorySize = 20\n",
    "\n",
    "TARGET_UPDATE = 5\n",
    "n_actions = 3\n",
    "# window_size = 20\n",
    "\n",
    "num_episodes = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following bocks, each block is for running the DQN algorithm in a specific condition. For each block, if you want to train from scratch, use the following two lines:\n",
    "```\n",
    "<name of the agent>.train(n_episodes)\n",
    "file_name = None\n",
    "```\n",
    "pay attention that the `file_name` should be set to `None` so that for the evaluation process, it does not load from file and instead use the recently trained agent. If you want to load from a specific model, set the `file_name` to the name of the file inside the `./Objects/<agent name>` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple MLP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model uses MLP as encoder with alternative input representation as below: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pattern Input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model uses the extracted patterns as observation space. These patterns are extracted in the preprocessing phase and used here instead of directly applying OHLC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.44s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    }
   ],
   "source": [
    "\n",
    "n_classes = 64\n",
    "\n",
    "simpleMLP = SimpleMLP(data_loader, dataTrain_patternBased, dataTest_patternBased, DATASET_NAME, \n",
    "                    state_mode, window_size, transaction_cost, n_classes, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                    ReplayMemorySize=ReplayMemorySize, TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "#EPS=EPS,\n",
    "#n_actions=n_actions, \n",
    "simpleMLP.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'BTC-USD; DATA_KIND(PatternBased); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TransactionCost(0.0); StateMode(1); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT(1).pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(PatternBased); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(1); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(PatternBased); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(1); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'KSS; DATA_KIND(PatternBased); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(1); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "ev_simpleMLP = simpleMLP.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "#file_name=file_name, action_name=dataTrain_patternBased.action_name,\n",
    "simpleMLP_portfolio_train = ev_simpleMLP.get_daily_portfolio_value()\n",
    "ev_simpleMLP = simpleMLP.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "#file_name=file_name, action_name=dataTrain_patternBased.action_name,\n",
    "simpleMLP_portfolio_test = ev_simpleMLP.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'MLP-pattern'\n",
    "\n",
    "add_train_portfo(model_kind, simpleMLP_portfolio_train)\n",
    "add_test_portfo(model_kind, simpleMLP_portfolio_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### OHLC input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model uses the OHLC representation of candles. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.37s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    }
   ],
   "source": [
    "n_classes = 64\n",
    "\n",
    "simpleMLP = SimpleMLP(data_loader, dataTrain_autoPatternExtractionAgent, dataTest_autoPatternExtractionAgent, DATASET_NAME, \n",
    "                    state_mode, window_size, transaction_cost, n_classes, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                    ReplayMemorySize=ReplayMemorySize, TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "#EPS=EPS,\n",
    "#n_actions=n_actions,\n",
    "simpleMLP.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'GOOGL; MLP; StateMode(1); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'AAPL; MLP; StateMode(1); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(1); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'BTC-USD; MLP; StateMode(1); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'GE; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); MLP; TC(0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'HSI; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); MLP; TC(0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'AAL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0); StateMode(1); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "ev_simpleMLP = simpleMLP.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "#file_name=file_name, action_name=dataTrain_autoPatternExtractionAgent.action_name,\n",
    "simpleMLP_portfolio_train = ev_simpleMLP.get_daily_portfolio_value()\n",
    "ev_simpleMLP = simpleMLP.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "#file_name=file_name, action_name=dataTrain_autoPatternExtractionAgent.action_name,\n",
    "simpleMLP_portfolio_test = ev_simpleMLP.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'MLP-vanilla'\n",
    "# model_kind = 'MLP'\n",
    "\n",
    "add_train_portfo(model_kind, simpleMLP_portfolio_train)\n",
    "add_test_portfo(model_kind, simpleMLP_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Candle Representation Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.60s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    }
   ],
   "source": [
    "n_classes = 64\n",
    "\n",
    "simpleMLP = SimpleMLP(data_loader, dataTrain_autoPatternExtractionAgent_candle_rep, \n",
    "                      dataTest_autoPatternExtractionAgent_candle_rep, DATASET_NAME, \n",
    "                    state_mode, window_size, transaction_cost, n_classes, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                    ReplayMemorySize=ReplayMemorySize, TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "\n",
    "simpleMLP.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "\n",
    "# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TransactionCost(0.0); StateMode(4); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT(1).pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(4); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(4); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(4); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "ev_simpleMLP = simpleMLP.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "simpleMLP_portfolio_train = ev_simpleMLP.get_daily_portfolio_value()\n",
    "ev_simpleMLP = simpleMLP.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "simpleMLP_portfolio_test = ev_simpleMLP.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'MLP-candle-rep'\n",
    "\n",
    "add_train_portfo(model_kind, simpleMLP_portfolio_train)\n",
    "add_test_portfo(model_kind, simpleMLP_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Windowed Input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input is a window of OHLC candles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.65s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    }
   ],
   "source": [
    "n_classes = 64\n",
    "\n",
    "simpleMLP = SimpleMLP(data_loader, dataTrain_autoPatternExtractionAgent_windowed, \n",
    "                      dataTest_autoPatternExtractionAgent_windowed, DATASET_NAME, \n",
    "                    state_mode, window_size, transaction_cost, n_classes, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,\n",
    "                    ReplayMemorySize=ReplayMemorySize, TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "\n",
    "simpleMLP.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "\n",
    "# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TransactionCost(0.0); StateMode(5); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(5); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(5); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(5); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(5); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(5); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(5); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0.0); StateMode(5); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'GE; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); MLP; TC(0); StateMode(5); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'HSI; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); MLP; TC(0); StateMode(5); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'AAL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); MLP; TC(0); StateMode(5); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "ev_simpleMLP = simpleMLP.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "simpleMLP_portfolio_train = ev_simpleMLP.get_daily_portfolio_value()\n",
    "ev_simpleMLP = simpleMLP.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "simpleMLP_portfolio_test = ev_simpleMLP.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'MLP-windowed'\n",
    "\n",
    "add_train_portfo(model_kind, simpleMLP_portfolio_train)\n",
    "add_test_portfo(model_kind, simpleMLP_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Window Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block is for testing the effect of window-size in the input representation on the performance of the model. Here we assume that we have a set of pretrained models in the `./Objects/<model name>` directory. If you do not have these pretrained models, you should train from scratch and set `file_name = None`. The set of window sizes that are being tested in the experiments are in `window_sizes` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/12 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.87s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  8%|▊         | 1/12 [00:49<09:05, 49.60s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:03<00:00,  3.10s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 17%|█▋        | 2/12 [01:38<08:14, 49.46s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 25%|██▌       | 3/12 [02:12<06:17, 41.99s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.20s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 4/12 [02:46<05:11, 38.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.34s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 42%|████▏     | 5/12 [03:21<04:23, 37.59s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.38s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 6/12 [04:02<03:51, 38.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.16s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 58%|█████▊    | 7/12 [04:34<03:02, 36.56s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.17s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 67%|██████▋   | 8/12 [05:06<02:20, 35.03s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.14s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|███████▌  | 9/12 [05:36<01:40, 33.61s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.10s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 83%|████████▎ | 10/12 [06:11<01:07, 33.87s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:02<00:00,  2.05s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 11/12 [06:45<00:34, 34.02s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MLP ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1/1 [00:01<00:00,  1.94s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [07:15<00:00, 36.28s/it]\n"
     ]
    }
   ],
   "source": [
    "n_classes = 64\n",
    "state_mode = 5\n",
    "\n",
    "for window_size in tqdm(window_sizes):\n",
    "    \n",
    "    dataTrain_autoPatternExtractionAgent_windowed = DataAutoPatternExtractionAgent(data_loader.data_train, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "    dataTest_autoPatternExtractionAgent_windowed = DataAutoPatternExtractionAgent(data_loader.data_test, state_mode, 'action_encoder_decoder', device, GAMMA, n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "    \n",
    "    simpleMLP = SimpleMLP(data_loader, dataTrain_autoPatternExtractionAgent_windowed, \n",
    "                          dataTest_autoPatternExtractionAgent_windowed, DATASET_NAME, \n",
    "                        state_mode, window_size, transaction_cost, n_classes, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                        ReplayMemorySize=ReplayMemorySize, TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "\n",
    "    \n",
    "    simpleMLP.train(num_episodes)\n",
    "    file_name = None\n",
    "    \n",
    "#     file_name = f'GE; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2015-01-01); MLP; TC(0); StateMode(5); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "    ev_simpleMLP = simpleMLP.test(\n",
    "                                      initial_investment=initial_investment, test_type='train')\n",
    "    simpleMLP_portfolio_train = ev_simpleMLP.get_daily_portfolio_value()\n",
    "    ev_simpleMLP = simpleMLP.test(\n",
    "                                      initial_investment=initial_investment, test_type='test')\n",
    "    simpleMLP_portfolio_test = ev_simpleMLP.get_daily_portfolio_value()\n",
    "\n",
    "    model_kind = 'MLP-windowed'\n",
    "\n",
    "    add_train_portfo(model_kind, simpleMLP_portfolio_train)\n",
    "    add_test_portfo(model_kind, simpleMLP_portfolio_test)\n",
    "    \n",
    "    if model_kind not in window_size_experiment.keys():\n",
    "        window_size_experiment[model_kind] = {}\n",
    "\n",
    "    window_size_experiment[model_kind][window_size] = \\\n",
    "        ((test_portfolios[model_kind][-1] - test_portfolios[model_kind][0])/test_portfolios[model_kind][0] * 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple CNN "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1-layerd 1d CNN as encoder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"n_classes = 128\\n\\nsimpleCNN = SimpleCNN(data_loader, dataTrain_autoPatternExtractionAgent, dataTest_autoPatternExtractionAgent, DATASET_NAME, \\n                    state_mode, window_size, transaction_cost, n_classes, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \\n                    ReplayMemorySize=ReplayMemorySize, TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\\n\\nsimpleCNN.train(num_episodes)\\nfile_name = None\\n\\n# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); SimpleCNNEncoder; TC(0.0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); SimpleCNNEncoder; TC(0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); SimpleCNNEncoder; TC(0.0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); SimpleCNNEncoder; TC(0.0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n\\nev_simpleCNN = simpleCNN.test(\\n                                  initial_investment=initial_investment, test_type='train')\\nsimpleCNN_portfolio_train = ev_simpleCNN.get_daily_portfolio_value()\\nev_simpleCNN = simpleCNN.test(\\n                                  initial_investment=initial_investment, test_type='test')\\nsimpleCNN_portfolio_test = ev_simpleCNN.get_daily_portfolio_value()\\n\\n# model_kind = 'CNN1d-vanilla'\\nmodel_kind = 'CNN1D'\\n\\nadd_train_portfo(model_kind, simpleCNN_portfolio_train)\\nadd_test_portfo(model_kind, simpleCNN_portfolio_test)\""
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"n_classes = 128\n",
    "\n",
    "simpleCNN = SimpleCNN(data_loader, dataTrain_autoPatternExtractionAgent, dataTest_autoPatternExtractionAgent, DATASET_NAME, \n",
    "                    state_mode, window_size, transaction_cost, n_classes, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                    ReplayMemorySize=ReplayMemorySize, TARGET_UPDATE=TARGET_UPDATE, n_step=n_step)\n",
    "\n",
    "simpleCNN.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'KSS; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); SimpleCNNEncoder; TC(0.0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); SimpleCNNEncoder; TC(0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(None); END_DATE(None); SPLIT_POINT(2018-01-01); SimpleCNNEncoder; TC(0.0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(AutoPatternExtraction); BEGIN_DATE(2010-01-01); END_DATE(2020-08-24); SPLIT_POINT(2018-01-01); SimpleCNNEncoder; TC(0.0); StateMode(1); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "ev_simpleCNN = simpleCNN.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "simpleCNN_portfolio_train = ev_simpleCNN.get_daily_portfolio_value()\n",
    "ev_simpleCNN = simpleCNN.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "simpleCNN_portfolio_test = ev_simpleCNN.get_daily_portfolio_value()\n",
    "\n",
    "# model_kind = 'CNN1d-vanilla'\n",
    "model_kind = 'CNN1D'\n",
    "\n",
    "add_train_portfo(model_kind, simpleCNN_portfolio_train)\n",
    "add_test_portfo(model_kind, simpleCNN_portfolio_test)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1-layerd 2d CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"decoder_input_size = 128\\n\\ncnn2d_agent = cnn2d(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, decoder_input_size, \\n                    transaction_cost, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,  ReplayMemorySize=ReplayMemorySize,\\n                    TARGET_UPDATE=TARGET_UPDATE, n_step=n_step, window_size=window_size)\\n\\ncnn2d_agent.train(num_episodes)\\nfile_name = None\\n\\n# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN2D; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN2D; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'GOOGL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN2D; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n# file_name = 'AAPL; DATA_KIND(LSTMSequential); Dates(2010-01-01, 2018-01-01, 2020-08-24); CNN2D; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n\\nev_cnn2d_agent = cnn2d_agent.test(\\n                                  initial_investment=initial_investment, test_type='train')\\ncnn2d_agent_portfolio_train = ev_cnn2d_agent.get_daily_portfolio_value()\\nev_cnn2d_agent = cnn2d_agent.test(\\n                                  initial_investment=initial_investment, test_type='test')\\ncnn2d_agent_portfolio_test = ev_cnn2d_agent.get_daily_portfolio_value()\\n\\nmodel_kind = 'CNN2D'\\n\\nadd_train_portfo(model_kind, cnn2d_agent_portfolio_train)\\nadd_test_portfo(model_kind, cnn2d_agent_portfolio_test)\""
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"decoder_input_size = 128\n",
    "\n",
    "cnn2d_agent = cnn2d(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, decoder_input_size, \n",
    "                    transaction_cost, BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,  ReplayMemorySize=ReplayMemorySize,\n",
    "                    TARGET_UPDATE=TARGET_UPDATE, n_step=n_step, window_size=window_size)\n",
    "\n",
    "cnn2d_agent.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN2D; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN2D; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN2D; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(LSTMSequential); Dates(2010-01-01, 2018-01-01, 2020-08-24); CNN2D; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "\n",
    "ev_cnn2d_agent = cnn2d_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "cnn2d_agent_portfolio_train = ev_cnn2d_agent.get_daily_portfolio_value()\n",
    "ev_cnn2d_agent = cnn2d_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "cnn2d_agent_portfolio_test = ev_cnn2d_agent.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'CNN2D'\n",
    "\n",
    "add_train_portfo(model_kind, cnn2d_agent_portfolio_train)\n",
    "add_test_portfo(model_kind, cnn2d_agent_portfolio_test)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GRU as Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"hidden_size = 64\\n\\ngru_agent = gru(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, hidden_size,\\n                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,\\n                    ReplayMemorySize=ReplayMemorySize,\\n                    TARGET_UPDATE=TARGET_UPDATE,\\n                 \\n                    n_step=n_step,\\n                    window_size=window_size)\\n\\ngru_agent.train(num_episodes)\\nfile_name = None\\n\\n# file_name = 'GOOGL; DATA_KIND(LSTMSequential); GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'AAPL; DATA_KIND(LSTMSequential); GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.7; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0.0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); GRU; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'HSI; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); GRU; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'AAL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n\\n# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n# file_name = 'GOOGL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n# file_name = 'AAPL; DATA_KIND(LSTMSequential); Dates(2010-01-01, 2018-01-01, 2020-08-24); GRU; TC(0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n\\nmodel_kind = 'GRU'\\n\\nev_gru_agent = gru_agent.test(\\n                                  initial_investment=initial_investment, test_type='train')\\ngru_agent_portfolio_train = ev_gru_agent.get_daily_portfolio_value()\\nev_gru_agent = gru_agent.test(\\n                                  initial_investment=initial_investment, test_type='test')\\ngru_agent_portfolio_test = ev_gru_agent.get_daily_portfolio_value()\\n\\nmodel_kind = 'GRU'\\n\\nadd_train_portfo('GRU', gru_agent_portfolio_train)\\nadd_test_portfo('GRU', gru_agent_portfolio_test)\""
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"hidden_size = 64\n",
    "\n",
    "gru_agent = gru(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, hidden_size,\n",
    "                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,\n",
    "                    ReplayMemorySize=ReplayMemorySize,\n",
    "                    TARGET_UPDATE=TARGET_UPDATE,\n",
    "                 \n",
    "                    n_step=n_step,\n",
    "                    window_size=window_size)\n",
    "\n",
    "gru_agent.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'GOOGL; DATA_KIND(LSTMSequential); GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(LSTMSequential); GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.7; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0.0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); GRU; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'HSI; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); GRU; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'AAL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'GOOGL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(LSTMSequential); Dates(2010-01-01, 2018-01-01, 2020-08-24); GRU; TC(0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "model_kind = 'GRU'\n",
    "\n",
    "ev_gru_agent = gru_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "gru_agent_portfolio_train = ev_gru_agent.get_daily_portfolio_value()\n",
    "ev_gru_agent = gru_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "gru_agent_portfolio_test = ev_gru_agent.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'GRU'\n",
    "\n",
    "add_train_portfo('GRU', gru_agent_portfolio_train)\n",
    "add_test_portfo('GRU', gru_agent_portfolio_test)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### window size test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block is for testing the effect of window-size in the input representation on the performance of the model. Here we assume that we have a set of pretrained models in the `./Objects/GRU` directory. If you do not have these pretrained models, you should train from scratch and set `file_name = None`. The set of window sizes that are being tested in the experiments are in `window_sizes` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"hidden_size = 64\\n\\nfor window_size in tqdm(window_sizes):\\n    dataTrain_sequential = DataSequential(data_loader.data_train,\\n                               'action_encoder_decoder', device, GAMMA,\\n                               n_step, BATCH_SIZE, window_size, transaction_cost)\\n    dataTest_sequential = DataSequential(data_loader.data_test,\\n                              'action_encoder_decoder', device, GAMMA,\\n                              n_step, BATCH_SIZE, window_size, transaction_cost)  \\n    \\n    gru_agent = gru(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, hidden_size,\\n                        BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \\n                        ReplayMemorySize=ReplayMemorySize,\\n                        TARGET_UPDATE=TARGET_UPDATE,\\n                       \\n                        n_step=n_step,\\n                        window_size=window_size)\\n\\n    gru_agent.train(num_episodes)\\n    file_name = None\\n    \\n#     file_name = f'GOOGL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n\\n\\n    ev_gru_agent = gru_agent.test(\\n                                      initial_investment=initial_investment, test_type='train')\\n    gru_agent_portfolio_train = ev_gru_agent.get_daily_portfolio_value()\\n    ev_gru_agent = gru_agent.test(\\n                                      initial_investment=initial_investment, test_type='test')\\n    gru_agent_portfolio_test = ev_gru_agent.get_daily_portfolio_value()\\n\\n    add_train_portfo('GRU', gru_agent_portfolio_train)\\n    add_test_portfo('GRU', gru_agent_portfolio_test)\\n\\n    if 'GRU' not in window_size_experiment.keys():\\n        window_size_experiment['GRU'] = {}\\n\\n    window_size_experiment['GRU'][window_size] =         ((test_portfolios['GRU'][-1] - test_portfolios['GRU'][0])/test_portfolios['GRU'][0] * 100)\""
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"hidden_size = 64\n",
    "\n",
    "for window_size in tqdm(window_sizes):\n",
    "    dataTrain_sequential = DataSequential(data_loader.data_train,\n",
    "                               'action_encoder_decoder', device, GAMMA,\n",
    "                               n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "    dataTest_sequential = DataSequential(data_loader.data_test,\n",
    "                              'action_encoder_decoder', device, GAMMA,\n",
    "                              n_step, BATCH_SIZE, window_size, transaction_cost)  \n",
    "    \n",
    "    gru_agent = gru(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, hidden_size,\n",
    "                        BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                        ReplayMemorySize=ReplayMemorySize,\n",
    "                        TARGET_UPDATE=TARGET_UPDATE,\n",
    "                       \n",
    "                        n_step=n_step,\n",
    "                        window_size=window_size)\n",
    "\n",
    "    gru_agent.train(num_episodes)\n",
    "    file_name = None\n",
    "    \n",
    "#     file_name = f'GOOGL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); GRU; TC(0); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "\n",
    "\n",
    "    ev_gru_agent = gru_agent.test(\n",
    "                                      initial_investment=initial_investment, test_type='train')\n",
    "    gru_agent_portfolio_train = ev_gru_agent.get_daily_portfolio_value()\n",
    "    ev_gru_agent = gru_agent.test(\n",
    "                                      initial_investment=initial_investment, test_type='test')\n",
    "    gru_agent_portfolio_test = ev_gru_agent.get_daily_portfolio_value()\n",
    "\n",
    "    add_train_portfo('GRU', gru_agent_portfolio_train)\n",
    "    add_test_portfo('GRU', gru_agent_portfolio_test)\n",
    "\n",
    "    if 'GRU' not in window_size_experiment.keys():\n",
    "        window_size_experiment['GRU'] = {}\n",
    "\n",
    "    window_size_experiment['GRU'][window_size] = \\\n",
    "        ((test_portfolios['GRU'][-1] - test_portfolios['GRU'][0])/test_portfolios['GRU'][0] * 100)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convolutional"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two-layered 1d CNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"cnn_agent = cnn(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost,\\n                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \\n                    ReplayMemorySize=ReplayMemorySize,\\n                    TARGET_UPDATE=TARGET_UPDATE,\\n               \\n                    n_step=n_step,\\n                    window_size=window_size)\\n\\ncnn_agent.train(num_episodes)\\nfile_name = None\\n\\n# file_name = 'GOOGL; DATA_KIND(LSTMSequential); Convolutional; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'AAPL; DATA_KIND(LSTMSequential); Convolutional; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); Convolutional; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN; TC(0.0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'HSI; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'AAL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN; TC(0); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n\\n# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n\\nmodel_kind = 'CNN'\\n\\nev_cnn = cnn_agent.test(\\n                                  initial_investment=initial_investment, test_type='train')\\ncnn_portfolio_train = ev_cnn.get_daily_portfolio_value()\\nev_cnn = cnn_agent.test(\\n                                  initial_investment=initial_investment, test_type='test')\\ncnn_portfolio_test = ev_cnn.get_daily_portfolio_value()\\n\\nmodel_kind = 'CNN'\\n\\nadd_train_portfo('CNN', cnn_portfolio_train)\\nadd_test_portfo('CNN', cnn_portfolio_test)\""
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"cnn_agent = cnn(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost,\n",
    "                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                    ReplayMemorySize=ReplayMemorySize,\n",
    "                    TARGET_UPDATE=TARGET_UPDATE,\n",
    "               \n",
    "                    n_step=n_step,\n",
    "                    window_size=window_size)\n",
    "\n",
    "cnn_agent.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'GOOGL; DATA_KIND(LSTMSequential); Convolutional; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(LSTMSequential); Convolutional; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); Convolutional; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN; TC(0.0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'HSI; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'AAL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN; TC(0); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN; TC(0.0); WindowSize(3); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "model_kind = 'CNN'\n",
    "\n",
    "ev_cnn = cnn_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "cnn_portfolio_train = ev_cnn.get_daily_portfolio_value()\n",
    "ev_cnn = cnn_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "cnn_portfolio_test = ev_cnn.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'CNN'\n",
    "\n",
    "add_train_portfo('CNN', cnn_portfolio_train)\n",
    "add_test_portfo('CNN', cnn_portfolio_test)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Window Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block is for testing the effect of window-size in the input representation on the performance of the model. Here we assume that we have a set of pretrained models in the `./Objects/CNN` directory. If you do not have these pretrained models, you should train from scratch and set `file_name = None`. The set of window sizes that are being tested in the experiments are in `window_sizes` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"for window_size in tqdm(window_sizes):\\n    dataTrain_sequential = DataSequential(data_loader.data_train,\\n                               'action_encoder_decoder', device, GAMMA,\\n                               n_step, BATCH_SIZE, window_size, transaction_cost)\\n    dataTest_sequential = DataSequential(data_loader.data_test,\\n                              'action_encoder_decoder', device, GAMMA,\\n                              n_step, BATCH_SIZE, window_size, transaction_cost) \\n    \\n    cnn_agent = cnn(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost,\\n                        BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \\n                        ReplayMemorySize=ReplayMemorySize,\\n                        TARGET_UPDATE=TARGET_UPDATE,\\n                    \\n                        n_step=n_step,\\n                        window_size=window_size)\\n\\n    cnn_agent.train(num_episodes)\\n    file_name = None\\n    \\n#     file_name = f'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN; TC(0); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n    \\n    ev_cnn = cnn_agent.test(\\n                                      initial_investment=initial_investment, test_type='train')\\n    cnn_portfolio_train = ev_cnn.get_daily_portfolio_value()\\n    ev_cnn = cnn_agent.test(\\n                                      initial_investment=initial_investment, test_type='test')\\n    cnn_portfolio_test = ev_cnn.get_daily_portfolio_value()\\n\\n    add_train_portfo('CNN', cnn_portfolio_train)\\n    add_test_portfo('CNN', cnn_portfolio_test)\\n    \\n    if 'CNN' not in window_size_experiment.keys():\\n        window_size_experiment['CNN'] = {}\\n\\n    window_size_experiment['CNN'][window_size] =         ((test_portfolios['CNN'][-1] - test_portfolios['CNN'][0])/test_portfolios['CNN'][0] * 100)\""
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"for window_size in tqdm(window_sizes):\n",
    "    dataTrain_sequential = DataSequential(data_loader.data_train,\n",
    "                               'action_encoder_decoder', device, GAMMA,\n",
    "                               n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "    dataTest_sequential = DataSequential(data_loader.data_test,\n",
    "                              'action_encoder_decoder', device, GAMMA,\n",
    "                              n_step, BATCH_SIZE, window_size, transaction_cost) \n",
    "    \n",
    "    cnn_agent = cnn(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost,\n",
    "                        BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                        ReplayMemorySize=ReplayMemorySize,\n",
    "                        TARGET_UPDATE=TARGET_UPDATE,\n",
    "                    \n",
    "                        n_step=n_step,\n",
    "                        window_size=window_size)\n",
    "\n",
    "    cnn_agent.train(num_episodes)\n",
    "    file_name = None\n",
    "    \n",
    "#     file_name = f'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN; TC(0); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "    \n",
    "    ev_cnn = cnn_agent.test(\n",
    "                                      initial_investment=initial_investment, test_type='train')\n",
    "    cnn_portfolio_train = ev_cnn.get_daily_portfolio_value()\n",
    "    ev_cnn = cnn_agent.test(\n",
    "                                      initial_investment=initial_investment, test_type='test')\n",
    "    cnn_portfolio_test = ev_cnn.get_daily_portfolio_value()\n",
    "\n",
    "    add_train_portfo('CNN', cnn_portfolio_train)\n",
    "    add_test_portfo('CNN', cnn_portfolio_test)\n",
    "    \n",
    "    if 'CNN' not in window_size_experiment.keys():\n",
    "        window_size_experiment['CNN'] = {}\n",
    "\n",
    "    window_size_experiment['CNN'][window_size] = \\\n",
    "        ((test_portfolios['CNN'][-1] - test_portfolios['CNN'][0])/test_portfolios['CNN'][0] * 100)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN-GRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\ncnn_gru_agent = cnn_gru(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, hidden_size,\\n                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \\n                    ReplayMemorySize=ReplayMemorySize,\\n                    TARGET_UPDATE=TARGET_UPDATE,\\n    \\n                    n_step=n_step,\\n                    window_size=window_size)\\n\\ncnn_gru_agent.train(num_episodes)\\nfile_name = None\\n\\n# file_name = 'GOOGL; DATA_KIND(LSTMSequential); CNN_GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'AAPL; DATA_KIND(LSTMSequential); CNN_GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); CNN_GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-GRU; TC(0.0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN-GRU; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'HSI; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN-GRU; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'AAL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-GRU; TC(0); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n\\nev_cnn_gru = cnn_gru_agent.test(\\n                                  initial_investment=initial_investment, test_type='train')\\ncnn_gru_portfolio_train = ev_cnn_gru.get_daily_portfolio_value()\\nev_cnn_gru = cnn_gru_agent.test(\\n                                  initial_investment=initial_investment, test_type='test')\\ncnn_gru_portfolio_test = ev_cnn_gru.get_daily_portfolio_value()\\n\\nmodel_kind = 'CNN-GRU'\\n\\nadd_train_portfo('CNN-GRU', cnn_gru_portfolio_train)\\nadd_test_portfo('CNN-GRU', cnn_gru_portfolio_test)\""
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "cnn_gru_agent = cnn_gru(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, hidden_size,\n",
    "                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                    ReplayMemorySize=ReplayMemorySize,\n",
    "                    TARGET_UPDATE=TARGET_UPDATE,\n",
    "    \n",
    "                    n_step=n_step,\n",
    "                    window_size=window_size)\n",
    "\n",
    "cnn_gru_agent.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'GOOGL; DATA_KIND(LSTMSequential); CNN_GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(LSTMSequential); CNN_GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); CNN_GRU; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-GRU; TC(0.0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN-GRU; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'HSI; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN-GRU; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'AAL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-GRU; TC(0); WindowSize(10); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "ev_cnn_gru = cnn_gru_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "cnn_gru_portfolio_train = ev_cnn_gru.get_daily_portfolio_value()\n",
    "ev_cnn_gru = cnn_gru_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "cnn_gru_portfolio_test = ev_cnn_gru.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'CNN-GRU'\n",
    "\n",
    "add_train_portfo('CNN-GRU', cnn_gru_portfolio_train)\n",
    "add_test_portfo('CNN-GRU', cnn_gru_portfolio_test)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Window Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block is for testing the effect of window-size in the input representation on the performance of the model. Here we assume that we have a set of pretrained models in the `./Objects/CNN-GRU` directory. If you do not have these pretrained models, you should train from scratch and set `file_name = None`. The set of window sizes that are being tested in the experiments are in `window_sizes` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nhidden_size = 64\\n\\nfor window_size in tqdm(window_sizes):\\n    dataTrain_sequential = DataSequential(data_loader.data_train,\\n                               'action_encoder_decoder', device, GAMMA,\\n                               n_step, BATCH_SIZE, window_size, transaction_cost)\\n    dataTest_sequential = DataSequential(data_loader.data_test,\\n                              'action_encoder_decoder', device, GAMMA,\\n                              n_step, BATCH_SIZE, window_size, transaction_cost) \\n    \\n    cnn_gru_agent = cnn_gru(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, hidden_size,\\n                        BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \\n                        ReplayMemorySize=ReplayMemorySize,\\n                        TARGET_UPDATE=TARGET_UPDATE,\\n                     \\n                        n_step=n_step,\\n                        window_size=window_size)\\n    \\n    cnn_gru_agent.train(num_episodes)\\n    file_name = None\\n\\n\\n#     file_name = f'GOOGL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-GRU; TC(0); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n    \\n    ev_cnn_gru = cnn_gru_agent.test(\\n                                      initial_investment=initial_investment, test_type='train')\\n    cnn_gru_portfolio_train = ev_cnn_gru.get_daily_portfolio_value()\\n    ev_cnn_gru = cnn_gru_agent.test(\\n                                      initial_investment=initial_investment, test_type='test')\\n    cnn_gru_portfolio_test = ev_cnn_gru.get_daily_portfolio_value()\\n\\n    add_train_portfo('CNN-GRU', cnn_gru_portfolio_train)\\n    add_test_portfo('CNN-GRU', cnn_gru_portfolio_test)\\n    \\n    if 'CNN-GRU' not in window_size_experiment.keys():\\n        window_size_experiment['CNN-GRU'] = {}\\n\\n    window_size_experiment['CNN-GRU'][window_size] =         ((test_portfolios['CNN-GRU'][-1] - test_portfolios['CNN-GRU'][0])/test_portfolios['CNN-GRU'][0] * 100)\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "hidden_size = 64\n",
    "\n",
    "for window_size in tqdm(window_sizes):\n",
    "    dataTrain_sequential = DataSequential(data_loader.data_train,\n",
    "                               'action_encoder_decoder', device, GAMMA,\n",
    "                               n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "    dataTest_sequential = DataSequential(data_loader.data_test,\n",
    "                              'action_encoder_decoder', device, GAMMA,\n",
    "                              n_step, BATCH_SIZE, window_size, transaction_cost) \n",
    "    \n",
    "    cnn_gru_agent = cnn_gru(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, hidden_size,\n",
    "                        BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                        ReplayMemorySize=ReplayMemorySize,\n",
    "                        TARGET_UPDATE=TARGET_UPDATE,\n",
    "                     \n",
    "                        n_step=n_step,\n",
    "                        window_size=window_size)\n",
    "    \n",
    "    cnn_gru_agent.train(num_episodes)\n",
    "    file_name = None\n",
    "\n",
    "\n",
    "#     file_name = f'GOOGL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-GRU; TC(0); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "    \n",
    "    ev_cnn_gru = cnn_gru_agent.test(\n",
    "                                      initial_investment=initial_investment, test_type='train')\n",
    "    cnn_gru_portfolio_train = ev_cnn_gru.get_daily_portfolio_value()\n",
    "    ev_cnn_gru = cnn_gru_agent.test(\n",
    "                                      initial_investment=initial_investment, test_type='test')\n",
    "    cnn_gru_portfolio_test = ev_cnn_gru.get_daily_portfolio_value()\n",
    "\n",
    "    add_train_portfo('CNN-GRU', cnn_gru_portfolio_train)\n",
    "    add_test_portfo('CNN-GRU', cnn_gru_portfolio_test)\n",
    "    \n",
    "    if 'CNN-GRU' not in window_size_experiment.keys():\n",
    "        window_size_experiment['CNN-GRU'] = {}\n",
    "\n",
    "    window_size_experiment['CNN-GRU'][window_size] = \\\n",
    "        ((test_portfolios['CNN-GRU'][-1] - test_portfolios['CNN-GRU'][0])/test_portfolios['CNN-GRU'][0] * 100)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CNN with Attention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" \\nattn_output_size = hidden_size\\n\\ncnn_attn_agent = cnn_attn(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, attn_output_size, \\n                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,\\n                    ReplayMemorySize=ReplayMemorySize,\\n                    TARGET_UPDATE=TARGET_UPDATE,\\n             \\n                    n_step=n_step,\\n                    window_size=window_size)\\n\\ncnn_attn_agent.train(num_episodes)\\nfile_name = None\\n\\n# file_name = 'GOOGL; DATA_KIND(LSTMSequential); CNNAttn; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'AAPL; DATA_KIND(LSTMSequential); Dates(2010-01-01, 2018-01-01, 2020-08-24); CNN-ATTN; TC(0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); CNNAttn; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\\n# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-ATTN; TC(0.0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN-ATTN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'HSI; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN-ATTN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n# file_name = 'AAL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-ATTN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\\n\\nev_cnn_attn = cnn_attn_agent.test(\\n                                  initial_investment=initial_investment, test_type='train')\\ncnn_attn_portfolio_train = ev_cnn_attn.get_daily_portfolio_value()\\nev_cnn_attn = cnn_attn_agent.test(\\n                                  initial_investment=initial_investment, test_type='test')\\ncnn_attn_portfolio_test = ev_cnn_attn.get_daily_portfolio_value()\\n\\nmodel_kind = 'CNN-ATTN'\\n\\nadd_train_portfo('CNN-ATTN', cnn_attn_portfolio_train)\\nadd_test_portfo('CNN-ATTN', cnn_attn_portfolio_test)\""
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" \n",
    "attn_output_size = hidden_size\n",
    "\n",
    "cnn_attn_agent = cnn_attn(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, attn_output_size, \n",
    "                    BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA,\n",
    "                    ReplayMemorySize=ReplayMemorySize,\n",
    "                    TARGET_UPDATE=TARGET_UPDATE,\n",
    "             \n",
    "                    n_step=n_step,\n",
    "                    window_size=window_size)\n",
    "\n",
    "cnn_attn_agent.train(num_episodes)\n",
    "file_name = None\n",
    "\n",
    "# file_name = 'GOOGL; DATA_KIND(LSTMSequential); CNNAttn; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'AAPL; DATA_KIND(LSTMSequential); Dates(2010-01-01, 2018-01-01, 2020-08-24); CNN-ATTN; TC(0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'BTC-USD; DATA_KIND(LSTMSequential); CNNAttn; PredictionStep(None); WindowSize(20); TRAIN_TEST_SPLIT(True); BATCH_SIZE10; GAMMA0.7; EPSILON0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10; EXPERIMENT.pkl'\n",
    "# file_name = 'KSS; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-ATTN; TC(0.0); WindowSize(20); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'GE; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN-ATTN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'HSI; DATA_KIND(LSTMSequential); Dates(None, 2015-01-01, None); CNN-ATTN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "# file_name = 'AAL; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-ATTN; TC(0); WindowSize(15); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10.pkl'\n",
    "\n",
    "ev_cnn_attn = cnn_attn_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='train')\n",
    "cnn_attn_portfolio_train = ev_cnn_attn.get_daily_portfolio_value()\n",
    "ev_cnn_attn = cnn_attn_agent.test(\n",
    "                                  initial_investment=initial_investment, test_type='test')\n",
    "cnn_attn_portfolio_test = ev_cnn_attn.get_daily_portfolio_value()\n",
    "\n",
    "model_kind = 'CNN-ATTN'\n",
    "\n",
    "add_train_portfo('CNN-ATTN', cnn_attn_portfolio_train)\n",
    "add_test_portfo('CNN-ATTN', cnn_attn_portfolio_test)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test Window Size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block is for testing the effect of window-size in the input representation on the performance of the model. Here we assume that we have a set of pretrained models in the `./Objects/CNNAttn` directory. If you do not have these pretrained models, you should train from scratch and set `file_name = None`. The set of window sizes that are being tested in the experiments are in `window_sizes` list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nattn_output_size = hidden_size\\n\\nfor window_size in tqdm(window_sizes):\\n    dataTrain_sequential = DataSequential(data_loader.data_train,\\n                               'action_encoder_decoder', device, GAMMA,\\n                               n_step, BATCH_SIZE, window_size, transaction_cost)\\n    dataTest_sequential = DataSequential(data_loader.data_test,\\n                              'action_encoder_decoder', device, GAMMA,\\n                              n_step, BATCH_SIZE, window_size, transaction_cost) \\n    \\n    cnn_attn_agent = cnn_attn(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, attn_output_size, \\n                        BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \\n                        ReplayMemorySize=ReplayMemorySize,\\n                        TARGET_UPDATE=TARGET_UPDATE,\\n                        \\n                        n_step=n_step,\\n                        window_size=window_size)\\n    \\n    cnn_attn_agent.train(num_episodes)\\n    file_name = None\\n\\n#     file_name = f'BTC-USD; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-ATTN; TC(0); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\\n    \\n    ev_cnn_attn = cnn_attn_agent.test(\\n                                      initial_investment=initial_investment, test_type='train')\\n    cnn_attn_portfolio_train = ev_cnn_attn.get_daily_portfolio_value()\\n    ev_cnn_attn = cnn_attn_agent.test(\\n                                      initial_investment=initial_investment, test_type='test')\\n    cnn_attn_portfolio_test = ev_cnn_attn.get_daily_portfolio_value()\\n\\n    add_train_portfo('CNN-ATTN', cnn_attn_portfolio_train)\\n    add_test_portfo('CNN-ATTN', cnn_attn_portfolio_test)\\n\\n    if 'CNN-ATTN' not in window_size_experiment.keys():\\n        window_size_experiment['CNN-ATTN'] = {}\\n\\n    window_size_experiment['CNN-ATTN'][window_size] =         ((test_portfolios['CNN-ATTN'][-1] - test_portfolios['CNN-ATTN'][0])/test_portfolios['CNN-ATTN'][0] * 100)\\n    \""
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "attn_output_size = hidden_size\n",
    "\n",
    "for window_size in tqdm(window_sizes):\n",
    "    dataTrain_sequential = DataSequential(data_loader.data_train,\n",
    "                               'action_encoder_decoder', device, GAMMA,\n",
    "                               n_step, BATCH_SIZE, window_size, transaction_cost)\n",
    "    dataTest_sequential = DataSequential(data_loader.data_test,\n",
    "                              'action_encoder_decoder', device, GAMMA,\n",
    "                              n_step, BATCH_SIZE, window_size, transaction_cost) \n",
    "    \n",
    "    cnn_attn_agent = cnn_attn(data_loader, dataTrain_sequential, dataTest_sequential, DATASET_NAME, transaction_cost, attn_output_size, \n",
    "                        BATCH_SIZE=BATCH_SIZE, GAMMA=GAMMA, \n",
    "                        ReplayMemorySize=ReplayMemorySize,\n",
    "                        TARGET_UPDATE=TARGET_UPDATE,\n",
    "                        \n",
    "                        n_step=n_step,\n",
    "                        window_size=window_size)\n",
    "    \n",
    "    cnn_attn_agent.train(num_episodes)\n",
    "    file_name = None\n",
    "\n",
    "#     file_name = f'BTC-USD; DATA_KIND(LSTMSequential); Dates(None, 2018-01-01, None); CNN-ATTN; TC(0); WindowSize({window_size}); BATCH_SIZE10; GAMMA0.7; EPS0.1; REPLAY_MEMORY_SIZE20; C5; N_SARSA10(1).pkl'\n",
    "    \n",
    "    ev_cnn_attn = cnn_attn_agent.test(\n",
    "                                      initial_investment=initial_investment, test_type='train')\n",
    "    cnn_attn_portfolio_train = ev_cnn_attn.get_daily_portfolio_value()\n",
    "    ev_cnn_attn = cnn_attn_agent.test(\n",
    "                                      initial_investment=initial_investment, test_type='test')\n",
    "    cnn_attn_portfolio_test = ev_cnn_attn.get_daily_portfolio_value()\n",
    "\n",
    "    add_train_portfo('CNN-ATTN', cnn_attn_portfolio_train)\n",
    "    add_test_portfo('CNN-ATTN', cnn_attn_portfolio_test)\n",
    "\n",
    "    if 'CNN-ATTN' not in window_size_experiment.keys():\n",
    "        window_size_experiment['CNN-ATTN'] = {}\n",
    "\n",
    "    window_size_experiment['CNN-ATTN'][window_size] = \\\n",
    "        ((test_portfolios['CNN-ATTN'][-1] - test_portfolios['CNN-ATTN'][0])/test_portfolios['CNN-ATTN'][0] * 100)\n",
    "    \"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rule Based"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rule-base agent that uses a set of rules for generating trading strategies. You can find the explanation about this agent in both the paper and the README file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\n",
      "Historical VAR is -3.0\n",
      "Variance-Covariance VAR with 95% confidence is -3.44067030602553\n",
      "Variance-Covariance VAR with 99% confidence is -4.858643523054234\n",
      "##################################################\n",
      "Arithmetic Return: 118.63128517803483\n",
      "##################################################\n",
      "Average daily return: 0.31635009380809287\n",
      "##################################################\n",
      "Daily return variance (return type: Arithmetic): 4.348287292843275\n",
      "##################################################\n",
      "Daily return variance (return type: Logarithmic): 4.220988997792534\n",
      "##################################################\n",
      "Time weighted return: 0.0029213854856051658\n",
      "##################################################\n",
      "Total Return: 198.5963422541493 %\n",
      "##################################################\n",
      "Sharp Ratio: 0.15047006965785234\n",
      "##################################################\n",
      "Value at Risk (Monte Carlo method): -3.1175474228628453\n",
      "##################################################\n",
      "Volatility: 40.38078422735532\n",
      "##################################################\n",
      "Initial Investment: 1000\n",
      "##################################################\n",
      "Final Portfolio Value: 2985.963422541493\n",
      "##################################################\n",
      "119 & 0.32 & 4.35 & 0.003 & 199 \\% & 0.150 & -3.12 & 40.4 & 1000 & 2986.0 \\\\\n",
      "test\n",
      "Historical VAR is -3.0\n",
      "Variance-Covariance VAR with 95% confidence is -3.557473211864702\n",
      "Variance-Covariance VAR with 99% confidence is -5.023583384027125\n",
      "##################################################\n",
      "Arithmetic Return: 23.897049164192836\n",
      "##################################################\n",
      "Average daily return: 0.055062325263117134\n",
      "##################################################\n",
      "Daily return variance (return type: Arithmetic): 4.6485273289751925\n",
      "##################################################\n",
      "Daily return variance (return type: Logarithmic): 4.604644639902502\n",
      "##################################################\n",
      "Time weighted return: 0.00027650805166956616\n",
      "##################################################\n",
      "Total Return: 12.748321614093495 %\n",
      "##################################################\n",
      "Sharp Ratio: 0.02353425844481722\n",
      "##################################################\n",
      "Value at Risk (Monte Carlo method): -3.495408176502808\n",
      "##################################################\n",
      "Volatility: 44.91615367298533\n",
      "##################################################\n",
      "Initial Investment: 1000\n",
      "##################################################\n",
      "Final Portfolio Value: 1127.483216140935\n",
      "##################################################\n",
      "24 & 0.06 & 4.65 & 0.000 & 13 \\% & 0.024 & -3.50 & 44.9 & 1000 & 1127.5 \\\\\n"
     ]
    }
   ],
   "source": [
    "ev_RuleBased = Evaluation(dataTrain_patternBased.data, 'action', 1000)\n",
    "print('train')\n",
    "ev_RuleBased.evaluate()\n",
    "ruleBased_portfolio_train = ev_RuleBased.get_daily_portfolio_value()\n",
    "ev_RuleBased = Evaluation(dataTest_patternBased.data, 'action', 1000)\n",
    "print('test')\n",
    "ev_RuleBased.evaluate()\n",
    "ruleBased_portfolio_test = ev_RuleBased.get_daily_portfolio_value()\n",
    "\n",
    "add_train_portfo('Rule-Based', ruleBased_portfolio_train)\n",
    "add_test_portfo('Rule-Based', ruleBased_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Buy and Hold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buy the stock in the beginning of the process and hold it until the end without selling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train\n",
      "Historical VAR is -5.0\n",
      "Variance-Covariance VAR with 95% confidence is -6.287155043845873\n",
      "Variance-Covariance VAR with 99% confidence is -8.878225001309627\n",
      "##################################################\n",
      "Arithmetic Return: 201.68680823859154\n",
      "##################################################\n",
      "Average daily return: 0.5378314886362445\n",
      "##################################################\n",
      "Daily return variance (return type: Arithmetic): 14.519125269185164\n",
      "##################################################\n",
      "Daily return variance (return type: Logarithmic): 16.27896117645391\n",
      "##################################################\n",
      "Time weighted return: 0.004597515564389809\n",
      "##################################################\n",
      "Total Return: 458.51831585191024 %\n",
      "##################################################\n",
      "Sharp Ratio: 0.14125999515917692\n",
      "##################################################\n",
      "Value at Risk (Monte Carlo method): -5.73694760140869\n",
      "##################################################\n",
      "Volatility: 73.78802054496676\n",
      "##################################################\n",
      "Initial Investment: 1000\n",
      "##################################################\n",
      "Final Portfolio Value: 5585.183158519102\n",
      "##################################################\n",
      "202 & 0.54 & 14.52 & 0.005 & 459 \\% & 0.141 & -5.74 & 73.8 & 1000 & 5585.2 \\\\\n",
      "test\n",
      "Historical VAR is -7.0\n",
      "Variance-Covariance VAR with 95% confidence is -6.7377272919305735\n",
      "Variance-Covariance VAR with 99% confidence is -9.514487630423174\n",
      "##################################################\n",
      "Arithmetic Return: 45.674622623710874\n",
      "##################################################\n",
      "Average daily return: 0.10524106595325085\n",
      "##################################################\n",
      "Daily return variance (return type: Arithmetic): 16.67473611034935\n",
      "##################################################\n",
      "Daily return variance (return type: Logarithmic): 16.619698724131364\n",
      "##################################################\n",
      "Time weighted return: 0.00022068347575654457\n",
      "##################################################\n",
      "Total Return: 10.050158459882526 %\n",
      "##################################################\n",
      "Sharp Ratio: 0.025747358459468236\n",
      "##################################################\n",
      "Value at Risk (Monte Carlo method): -6.619223342987046\n",
      "##################################################\n",
      "Volatility: 85.06959193443694\n",
      "##################################################\n",
      "Initial Investment: 1000\n",
      "##################################################\n",
      "Final Portfolio Value: 1100.5015845988253\n",
      "##################################################\n",
      "46 & 0.11 & 16.67 & 0.000 & 10 \\% & 0.026 & -6.62 & 85.1 & 1000 & 1100.5 \\\\\n"
     ]
    }
   ],
   "source": [
    "dataTrain_patternBased.data[dataTrain_patternBased.action_name] = 'buy'\n",
    "ev_BandH = Evaluation(dataTrain_patternBased.data, dataTrain_patternBased.action_name, initial_investment)\n",
    "print('train')\n",
    "ev_BandH.evaluate()\n",
    "BandH_portfolio_train = ev_BandH.get_daily_portfolio_value()\n",
    "\n",
    "dataTest_patternBased.data[dataTest_patternBased.action_name] = 'buy'\n",
    "ev_BandH = Evaluation(dataTest_patternBased.data, dataTest_patternBased.action_name, initial_investment)\n",
    "print('test')\n",
    "ev_BandH.evaluate()\n",
    "BandH_portfolio_test = ev_BandH.get_daily_portfolio_value()\n",
    "\n",
    "add_train_portfo('B&H', BandH_portfolio_train)\n",
    "add_test_portfo('B&H', BandH_portfolio_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Diagrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the blocks related to `Action List`, you can represent the strategies generated by each model on a diagram. In order to do so, you should first run the block related to that model in the above, then run one of the blocks for related to `Action List` for plotting the strategy devised by that specific model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Action List on candlestick chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, in order to plot a better representation, we limit the number of canldlestick in each plot to 100. You can increase or decrease this amount by changing the values of `begin` and `end` in the following block. The `begin` and `end` are indices to the original data to select the interval of data you want to see it's trading strategy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "begin = 0\n",
    "end =100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_test = dataTest_autoPatternExtractionAgent\n",
    "# data_test = dataTest_autoPatternExtractionAgent_windowed\n",
    "data_test = dataTest_sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data_test.data[data_test.data[data_test.action_name] == 'None']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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           "bgcolor": "#E5ECF6",
           "caxis": {
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
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           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
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           "zerolinewidth": 2
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        },
        "width": 900
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment_num = 1\n",
    "RESULTS_PATH = f'Results/WindowSize/{model_kind}/'\n",
    "\n",
    "import os\n",
    "if not os.path.exists(RESULTS_PATH):\n",
    "    os.mkdir(RESULTS_PATH)\n",
    "\n",
    "while os.path.exists(f'{RESULTS_PATH}{DATASET_NAME};{model_kind};actions({experiment_num}).svg'):\n",
    "    experiment_num += 1\n",
    "\n",
    "fig_file = f'{RESULTS_PATH}{DATASET_NAME};{model_kind};actions({experiment_num}).svg'\n",
    "\n",
    "scope = PlotlyScope()\n",
    "\n",
    "df1 = data_loader.data_test_with_date[begin:end]\n",
    "actionlist = list(data_test.data[data_test.action_name][begin:end])\n",
    "df1[data_test.action_name] = actionlist\n",
    "\n",
    "buy = df1.copy()\n",
    "sell = df1.copy()\n",
    "none = df1.copy()\n",
    "\n",
    "# buy['action'] = [(c + o)/2 if a == 0 else None for a, o, c in zip(df2.action, df1.open, df1.close)]\n",
    "# sell['action'] = [(c + o)/2 if a == 2 else None for a, o, c in zip(df2.action, df1.open, df1.close)]\n",
    "# none['action'] = [(c + o)/2 if a == 1 else None for a, o, c in zip(df2.action, df1.open, df1.close)]\n",
    "\n",
    "buy['action'] = [(c + o)/2 if a == 'buy' else None for a, o, c in zip(df1[data_test.action_name], df1.open, df1.close)]\n",
    "sell['action'] = [(c + o)/2 if a == 'sell' else None for a, o, c in zip(df1[data_test.action_name], df1.open, df1.close)]\n",
    "none['action'] = [(c + o)/2 if a == 'None' else None for a, o, c in zip(df1[data_test.action_name], df1.open, df1.close)]\n",
    "\n",
    "\n",
    "data=[go.Candlestick(x=df1.index,\n",
    "                open=df1['open'],\n",
    "                high=df1['high'],\n",
    "                low=df1['low'],\n",
    "                close=df1['close'], increasing_line_color= 'lightgreen', decreasing_line_color= '#ff6961'),\n",
    "     go.Scatter(x=df1.index, y=buy.action, mode = 'markers', \n",
    "                marker=dict(color='green', colorscale='Viridis'), name=\"buy\"),\n",
    "     go.Scatter(x=df1.index, y=sell.action, mode = 'markers', \n",
    "                marker=dict(color='red', colorscale='Viridis'), name=\"sell\")]\n",
    "\n",
    "layout = go.Layout(\n",
    "    autosize=False,\n",
    "    width=900,\n",
    "    height=600)\n",
    "\n",
    "\n",
    "figSignal = go.Figure(data=data, layout=layout)\n",
    "figSignal.show()\n",
    "# with open(fig_file, \"wb\") as f:\n",
    "#     f.write(scope.transform(figSignal, format=\"svg\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Data (Plotted using seaborn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block plots the result portfolio of different models stored in the `train_portfolios` dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13ccecd90>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment_num = 1\n",
    "RESULTS_PATH = 'Results/Train/'\n",
    "\n",
    "import os\n",
    "\n",
    "if not os.path.exists(RESULTS_PATH):\n",
    "    os.mkdir(RESULTS_PATH)\n",
    "\n",
    "while os.path.exists(f'{RESULTS_PATH}{DATASET_NAME};train;EXPERIMENT({experiment_num}).jpg'):\n",
    "    experiment_num += 1\n",
    "\n",
    "fig_file = f'{RESULTS_PATH}{DATASET_NAME};train;EXPERIMENT({experiment_num}).jpg'\n",
    "\n",
    "sns.set(rc={'figure.figsize': (15, 7)})\n",
    "\n",
    "items = list(test_portfolios.keys())\n",
    "random.shuffle(items)\n",
    "\n",
    "first = True\n",
    "for k in items:\n",
    "    profit_percentage = [(train_portfolios[k][i] - train_portfolios[k][0])/train_portfolios[k][0] * 100 \n",
    "              for i in range(len(train_portfolios[k]))]\n",
    "    difference = len(train_portfolios[k]) - len(data_loader.data_train_with_date)\n",
    "    df = pd.DataFrame({'date': data_loader.data_train_with_date.index, \n",
    "                       'portfolio':profit_percentage[difference:]})\n",
    "    if not first:\n",
    "        df.plot(ax=ax, x='date', y='portfolio', label=k)\n",
    "    else:\n",
    "        ax = df.plot(x='date', y='portfolio', label=k)\n",
    "        first = False\n",
    "\n",
    "ax.set(xlabel='Time', ylabel='%Rate of Return')\n",
    "ax.set_title(f'%Rate of Return at each point of time for training data of {DATASET_NAME}')\n",
    "        \n",
    "plt.legend()\n",
    "# plt.savefig(fig_file, dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Data (Plotted using Seaborn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shuffle = False\n",
    "shuffle = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block plots the results of test portfolios."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "experiment_num = 1\n",
    "RESULTS_PATH = 'Results/Test/'\n",
    "\n",
    "import os\n",
    "\n",
    "if not os.path.exists(RESULTS_PATH):\n",
    "    os.mkdir(RESULTS_PATH)\n",
    "\n",
    "while os.path.exists(f'{RESULTS_PATH}{DATASET_NAME};test;EXPERIMENT({experiment_num}).jpg'):\n",
    "    experiment_num += 1\n",
    "\n",
    "fig_file = f'{RESULTS_PATH}{DATASET_NAME};test;EXPERIMENT({experiment_num}).jpg'\n",
    "\n",
    "sns.set(rc={'figure.figsize': (15, 7)})\n",
    "sns.set_palette(sns.color_palette(\"Paired\", 15))\n",
    "\n",
    "items = list(test_portfolios.keys())\n",
    "\n",
    "if shuffle:\n",
    "    random.shuffle(items)\n",
    "\n",
    "first = True\n",
    "for k in items:\n",
    "    profit_percentage = [(test_portfolios[k][i] - test_portfolios[k][0])/test_portfolios[k][0] * 100 \n",
    "                  for i in range(len(test_portfolios[k]))]\n",
    "    difference = len(test_portfolios[k]) - len(data_loader.data_test_with_date)\n",
    "    df = pd.DataFrame({'date': data_loader.data_test_with_date.index, \n",
    "                       'portfolio':profit_percentage[difference:]})\n",
    "    if not first:\n",
    "        df.plot(ax=ax, x='date', y='portfolio', label=k)\n",
    "    else:\n",
    "        ax = df.plot(x='date', y='portfolio', label=k)\n",
    "        first = False\n",
    "        \n",
    "ax.set(xlabel='Time', ylabel='%Rate of Return')\n",
    "ax.set_title(f'Comparing the %Rate of Return for different models '\n",
    "             f'at each point of time for test data of {DATASET_NAME}')\n",
    "plt.legend()\n",
    "plt.savefig(fig_file, dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test window size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This block plots a line showing the relation between the window-size and the profit in each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x13c9e2970>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EMG7cuOvuA8D48eMZOHAg8+bNo2zZsjzwwAPXva+XlxdTpkxh1KhRzJgxg/T0dPr06UOtWrXYvHmz07bly5fn3Xff5Y033sAYg4eHB1OnTnUKddfSuHFjx3vv2bNnjt7blfMOGzaM1157jYyMDPLly8e0adMoWLAg//rXvxg2bBgtWrSgaNGiVKxY0bHfuHHjGDFiBGFhYaSmptKqVStat27tdFnxlVdeYfDgwXz11Ve4u7vTtGlT6tSpk+Ma5e5gM9czHikieWLAgAGObxuJiMjNoctSIiIiYikauRERERFL0ciNiIiIWIrCjYiIiFiKwo2IiIhYisKNiIiIWMpdd5+bM2eSsds1hxqgWDEf4uOTbnUZdwX12rXUb9dRr11HvXbm5majSJFr37vprgs3drtRuLmKeuE66rVrqd+uo167jnp9fXRZSkRERCxF4UZEREQs5a67LCUiItZmjCEp6RwXLyZht2fc6nJumpgYN8dz0e4mHh5eFCnih7v79UcWhRsREbGUM2disdlsFC1aAnd3j5v2kNtbzcPDjfT0uyvcGGNITj7PmTOxFC9+/Q/s1WUpERGxlNTUFHx9i+Hh4WmZYHO3stls3HNPIdLTU3O0n8KNiIhYjMFm0683q8hNQNWnLyIiIpaicCMiIiKWonAjIiKSh06dOsmjj9anW7eOdOvWkWefjeC994aTkBAPwMWLF5k0aTzPPhtB167P8uqrPdixY5tj/1Gj3uHVV3tgzF838FuyZBGjRr2Tq3q6deuYo+1fe+0lp3ryyo28p79TuBEREcljxYv7ERn5JZGRX/Lll99StGgxhgzpjzGGgQP/j/T0dGbPns+sWXPp0+f/ePfdt/nll52O/X/7bS/z58+9KbVERn55U45zO9NXwUVExNJOJqdyMjln37a5XqXv8aL0PV452sdms9G9e0/Cwp7gl192cuzYUcaN+xAPj8u/kitWrEyXLi/w2WfT+eCDKQB06PAcn302g3r1grn33vuyPPaECWMpV+4B2rRpS1TUAubP/5I5c74hPT2d9u3DmT8/ikcfrc+6dduYOfNj4uJiOX78GNHRp2nVKpyuXbuTmprK+++PYP/+fZQsWZpz5846jv/555+yfPlS3NzcqFOnPq+80puBA9+kTZu2NGgQzMcff8TBgwf4z38+JC4ujr59X2H27PksXfo9X389F7vdUKlSZd54oz/e3t4sW7aYWbNmcs89PpQsWZL8+Qvk/EO4Bo3ciIiIuJinpyf33Xcfv/66h4oVKzmCzRU1atTkt99+dby+776ydOvWndGj33W6PPV3DRo0ZPv2LQDs2LGV8+fPk5AQz+7du6hatXqm8xw69DsTJnzEJ59E8sUXs0hMTOSbb74CYM6cb3j99Tc5ceIEABs3rmfdurXMmDGbTz+dw4kTx1m48FuCgoLZvn0rAL/8spOjR4+QkZHB5s0baNCgIX/8cZhFixYydeqnREZ+SZEiRZk7dzZxcbFMnfohH300nWnTPuXChQs33tj/TyM3IiJiabkZXXENG5cuXbrmV51TUy9hjPMN+9q3f5afflrJ11/Pw8fH55pHDAysxdixo8jIyODo0aM8/vgT7Nq1k/37fyUoqGGm7WvWrI2npydFihSlUKFCJCcnsWvXdlq3jgAuh6pq1aoDsH37Vpo2DSVfvnwAtGzZmqVLF9Onz/8xYMAbXLiQDED58hU4eHA/mzZtoG3bZ9i5cxt//nmcnj2fByA9PY2KFSuzZ88vVK1anaJFiwHwxBMtHCHpRinciIiIuFhaWhrHjx/llVd68/33UaSnp+Ph4cGZM2fw9fXl11/3UKnSQ077uLm5MXDgUHr1esERPuLiYnnzzT4AFC9enHHjPqR8+YosX76UgIAAAgNrsX37Fnbv/oWOHbtmqsPL66/QZ7PZ/v+okA34a3TI3d0dIFPYMgYyMtIpUaIkdrth9eqfqFbtEYoWLcq2bVs5cGAfVatW58CB/Tz2WFNef70fABcuXCAjI4Pt27dw9SDUlfPcDLosJSIi4kJ2u52ZMz/m4YerUaNGTQICyjF58gTS09NZuvR7evXqTmTkTLp1ezHTvvfdV5YuXV5g7tzZgPNE5XHjPgQgKCiYyMgZBAbWIjCwFuvWrSV//vz4+vpeV321a9dl+fJl2O12Tp8+xZ49uwGoWbMOK1b8wKVLKaSnp7NkyX+pWbM2APXrBzFr1kwCA2tRs2Ydvv32K6pUqYa7uzuBgbVYu3Y1Z84kYIzhP/8Zzfz5X1K9eg1+/XU3sbEx2O12fvrpx5vQ3cs0ciMiIpLH4uJiHV/BttszqFChEu+8Mwqbzcbo0f9h2rTJPPdcOzw8PClYsCD33nsvmzdvpHr1GpmO1bZtB1av/inLczVo0JBx48YQGFibQoUK4etb5JqXpLISEdGOI0cO06lTW0qWLMUDDzwIQHBwI37//QDdu3chIyOdunXr8/TTzwAQFNSQefO+oHr1GuTPn5/09DTHOStUqMjzz/egd++XMcZQvnxFnnuuG97e3rz+ej9ef/0V8uXLT7ly9193jdmxmX+amWRB8fFJ2O131VvOkp9fQWJjE291GXcF9dq11G/XuR17ffr0UUqWDLjVZdwQu93Oxo3rCQpq6JiTczc+OPOKa32mbm42ihW79twjjdyIiIjcZtzc3AgObnSry7hjac6NiIiIWIrCjYiIiFiKwo2IiIhYisKNiIiIWIrCjYiIiFiKwo2IiEgeOnXqJA0b1mbs2FFOy3///QANG9ZmyZJFNGxY+5r7tm0bxnPPtaNbt44899wzvPDCc+zYsS1Xdbz5Zm/i4mKve/tRo95hyZJFuTpXTuzYsY3XXnvpph5TXwUXERHJY4ULF2bz5o1kZGQ4HjOwcuWP+PoWyXbff/97IqVKlcbDw43Vq1czfPhgoqJ+yHENV+5gfDdQuBEREUtb/Fs0/917Ok+O3bpqSVo+XCLb7fLnL0CFChX55ZedjkcWbNmyidq16+bofDVr1iY+Pp5z585SuLCvY/ncuV9w5kwCr7zSmy1bNjFkSH+WLFmJh4cHnTq1ZdKkj3nppW5MmvQxO3duZ/PmDZw/f56TJ09Qp0593nxzAMYYJk+ewPr16yhevDh2u53AwFoALF78X+bN+wKbzUalSg/Rt+9bfPzxZMqVe4A2bdoSFbWA+fO/ZM6cb0hPT6d9+3Dmz49i27YtzJw5jfT0dEqVKkP//oMpXNiXLVs28eGH4/Hy8iIgoFyOenA9dFlKRETEBUJCmrFq1UoA9u37lfLlK+Dp6ZmjY/z44zLuvbesU7CBy48/uPJE7e3bt+Lt7c3Bg/s5efIE99zj43jy9hV79uxm1KixzJo1jw0bfubw4UOsXr2SgwcP8MUX8xkx4n1OnDgOwOHDh/j880+ZPPkTPv/8K/Lly89nn02nQYOGbN++BYAdO7Zy/vx5EhLi2b17F1WrVicxMZFp0ybzn/9M5rPPvqRu3fpMnTqJ1NRURo0axsiR7/Ppp1/g7e2dm3b+I43ciIiIpbV8uMR1ja7ktYYNGzN9+lTsdjsrV/7IY481Y+XK5dnu169fHzw8PElPT8PfvyQjRozOtE1AQDmSk5M4f/48u3fvJCKiHbt27SBfvvzXfK5UtWrVKVDgHgBKly7D+fPn2LlzO02ahODh4UGRIkWoXz8YgF27thMc3MgRqFq3bsPo0cN58cWXGTt2FBkZGRw9epTHH3+CXbt2sn//rwQFNeS33/YSHX2a3r1fBi4/U6tQocL88cchihXzczxLqkWLVkyfPjVXPc2Kwo2IiIgLFChQgPLlK7B79y527NjKyy+/dl3h5uo5N1c/W+rKgzgBIiO/pF69BqxduwqwERzciBkzpgE2XnyxZ6Zjenk5j5YYY7DZbFz9tMkrc4MyP4/RkJGRgbe3N+XLV2T58qUEBAQQGFiL7du3sHv3L3Ts2JU9e3ZRvfojvP/+BAAuXbrExYsXOX36FGAynedm0mUpERERF3nssaZMmzaZSpUexsPjxsYXIiO/dPyBy08Dnz37M6pXr0GFCpX43/+OcPz4USpWrHxdx6tduy4//fQjqampnD9/ns2bNwIQGFiLdevWcv78OQD++9+FBAZenjcUFBRMZOQMAgNrObbLnz8/vr6+PPxwVX79dQ/Hjh39//XO4KOPPqB8+QokJCTw++8HAVixIueTo7NzS0ZuFi1axNSpU0lPT6dr16506tTJaf2+ffsYPHgwycnJ1K5dm+HDhzv9EPz222+0b9+evXv3urp0ERGRXAsObsyYMSN48cWXM61r1uyvB2WWKFGKL76Yn6NjBwbWIj4+jsDAWthsNipUqJhpbs4/adToUfbt+40uXZ6haNFilCv3AADly1egc+fnee21l0hPT6dSpYfo128gcDlQjRs3hsDA2hQqVAhf3yKOy2DFihVnwIChDB06ELs9Az+/Egwd+i4eHh68884oRo4ciru7+3WHr5ywGWP+Pt6Up6Kjo3n22WdZsGABXl5edOjQgfHjx1O+fHnHNq1atWLkyJHUqFGDQYMGUbVqVTp2vDz8dvHiRV544QV27NjBgQMHcnz++Pikawyx3Z38/AoSG5t4q8u4K6jXrqV+u87t2OvTp49SsmTArS7jpvv7Zam7ybU+Uzc3G8WK+Vxze5dfltqwYQP169fH19eXAgUKEBoayrJlyxzrT5w4QUpKCjVq1AAgIiLCaf2YMWPo2rWrq8sWERGRO4TLw01MTAx+fn6O1/7+/kRHR2e53s/Pz7F+5cqVpKSk0Lx5c9cVLCIiIncUl8+5sdvt2Gw2x+srM7SzWx8bG8vUqVOJjIy8ofNnNYR1t/LzK3irS7hrqNeupX67zu3W69hYN9zdwWaz3ndmPDys956yY4zBzc0tRz9nLg83JUuWZNu2v56LERsbi7+/v9P62Ni/nn0RFxeHv78/q1ev5uzZs06Tj8PDw5kzZw4+PtcfWDTn5i+347Vyq1KvXUv9dp3bsdfu7t7ExcVQsGAR3N09nP7BfCe7G+fcGGNITj6Pm5tHpp+zf5pz4/JwExQUxKRJk0hISCB//vwsX76cESNGONaXKVMGb29vtm/fTq1atYiKiqJx48a0a9eOdu3aObarVKkSUVFRri5fRERuc0WK+JGUdI6EhGjs9oxbXc5N4+bmht1+d4UbAA8PL4oU8ct+w6v3yaNaslSiRAn69u1Lly5dSEtLo23btlSvXp0ePXrQu3dvqlWrxrhx4xgyZAhJSUlUqVKFLl26uLpMERG5Q9lsNgoW9KVgQd9bXcpNdTuOkt2uXP5V8FtNl6X+ov9QXEe9di3123XUa9dRr53dVl8FFxEREclLCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKQo3IiIiYikKNyIiImIpCjciIiJiKbck3CxatIgnn3ySJ554gjlz5mRav2/fPiIiIggNDWXw4MGkp6cDsH37dtq2bUt4eDhdu3blxIkTri5dREREbnMuDzfR0dFMmDCBL7/8koULF/LVV19x6NAhp2369evH0KFD+eGHHzDGMH/+fMfykSNHEhUVRVhYGCNHjnR1+SIiInKbc3m42bBhA/Xr18fX15cCBQoQGhrKsmXLHOtPnDhBSkoKNWrUACAiIoJly5aRmppKnz59qFy5MgCVKlXi1KlTri5fREREbnMerj5hTEwMfn5+jtf+/v7s3r07y/V+fn5ER0fj5eVFeHg4AHa7ncmTJ9O0adMcn79YMZ8bqN56/PwK3uoS7hrqtWup366jXruOen19XB5u7HY7NpvN8doY4/Q6u/WpqakMGDCA9PR0evbsmePzx8cnYbebXFZvLX5+BYmNTbzVZdwV1GvXUr9dR712HfXamZubLcsBC5dflipZsiSxsbGO17Gxsfj7+2e5Pi4uzrE+OTmZF198kfT0dKZOnYqnp6frChcREZE7gsvDTVBQEBs3biQhIYGLFy+yfPlyGjdu7FhfpkwZvL292b59OwBRUVGO9f369SMgIIAPPvgALy8vV5cuIiIidwCXX5YqUaIEffv2pUuXLqSlpdG2bVuqV69Ojx496N27N9WqVWPcuHEMGTKEpKQkqlSpQpcuXfjtt99YuXIl5cuXp02bNsDl+TrTp0939VsQERGR25jNGHNXTUDRnJu/6Pqt66jXrqV+u4567TrqtbPbas6NiIiISF5SuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLyfYOxX/88QfTp0/n7NmzXH2/v2nTpuVpYSIiIiK5kW24GTBgANWrV6dOnTpOT+cWERERuR1lG24uXrzIkCFDXFGLiIiIyA3Lds5NQEAAMTExrqhFRERE5IZlO3Jjt9tp1aoVVapUwdvb27Fcc25ERETkdpRtuGnWrBnNmjVzRS0iIiIiNyzbcLNw4UJmzZrlilpEREREbli2c24SExO5cOGCK2oRERERuWHZjtzkz5+fkJAQKlWqRIECBRzLNedGREREbkfZhpu2bdu6og4RERGRmyLbcNOmTRtX1CEiIiJyU2QbbgIDA695Z+IdO3bkSUEiIiIiNyLbcPP99987/p6amsrixYvJnz9/nhYlIiIiklvZfluqTJkyjj/3338/r732GsuWLXNFbSIiIiI5lm24+bvDhw8THx+fF7WIiIiI3LAczbkxxpCWlsabb76Z54WJiIiI5EaO5tzYbDYKFSqEj49PnhYlIiIiklvZXpYaNmyYY85N6dKl8fHxoX379q6oTURERCTHshy56d27N0eOHOH48eOEhYU5lqenp+Pl5eWS4kRERERyKstw89Zbb3HixAnefvtt3n77bcdyd3d3ypcv75LiRERERHIqy3Bz7733cu+997Js2TJSU1M5evQoFStWJCUlRfe5ERERkdtWtnNu9uzZQ9OmTenZsyfR0dE8+uijujuxiIiI3LayDTfvv/8+kZGR+Pr6UrJkScaOHcuoUaNcUZuIiIhIjmUbblJSUpzm2DRp0oSMjIw8LUpEREQkt7INNx4eHpw7d85xI78//vgjz4sSERERya1sb+LXq1cvnnvuOeLi4njjjTdYv3497777ritqExEREcmxbMNNSEgIDzzwAOvXr8dut/Pqq6/y4IMPuqI2ERERkRz7x3Bz4cIFvLy8CAgIICAgALg8B2fChAn07dvXJQWKiIiI5ESWc24WLlxIvXr1CA4OZv/+/QCsWLGC5s2b88MPP7isQBEREZGcyHLkZtq0acydO5djx44xffp0SpYsyZw5c3j55Zd54YUXXFmjiIiIyHXLMtx4enpStWpVqlatyqhRoyhdujSLFi3ivvvuc2V9IiIiIjmSZbjx8Phrlbe3N5988glFihRxSVEiIiIiuZXtfW4AChcufFODzaJFi3jyySd54oknmDNnTqb1+/btIyIigtDQUAYPHkx6ejoAJ0+epFOnTjRv3pxevXqRnJx802oSERERa8gy3Jw/f54ff/yR5cuXk5iYyPLly53+5FZ0dDQTJkzgyy+/ZOHChXz11VccOnTIaZt+/foxdOhQfvjhB4wxzJ8/H4Dhw4fTsWNHli1bRtWqVZkyZUqu6xARERFryjLclC5dms8//5zZs2dTqlQpZs+e7fjzxRdf5PqEGzZsoH79+vj6+lKgQAFCQ0NZtmyZY/2JEydISUmhRo0aAERERLBs2TLS0tLYunUroaGhTstFRERErpblnJvZs2fnyQljYmLw8/NzvPb392f37t1Zrvfz8yM6OpozZ87g4+PjmAt0ZXlOFSvmcwPVW4+fX8FbXcJdQ712LfXbddRr11Gvr0+2dyi+2ex2u+M5VQDGGKfXWa3/+3ZAptfXIz4+Cbvd5KJy6/HzK0hsbOKtLuOuoF67lvrtOuq166jXztzcbFkOWFzXhOKbqWTJksTGxjpex8bG4u/vn+X6uLg4/P39KVq0KImJiY4nkv99PxERERG4BeEmKCiIjRs3kpCQwMWLF1m+fDmNGzd2rC9Tpgze3t5s374dgKioKBo3boynpye1a9dmyZIlwOU7KF+9n4iIiAj8w2Wps2fP/uOOvr6+uTphiRIl6Nu3L126dCEtLY22bdtSvXp1evToQe/evalWrRrjxo1jyJAhJCUlUaVKFbp06QLAsGHDGDBgAFOnTqVUqVKMHz8+VzWIiIiIddmMMdecgFK5cmXHXJdMO9ls7Nu3L8+Lywuac/MXXb91HfXatdRv11GvXUe9dvZPc26yHLm58rBMERERkTtJtt+WSk1NZc2aNY67AWdkZHDs2DH69u2b58WJiIiI5FS24aZv374cP36c2NhYHn74YX755Rfq1q3ritpEREREcizbb0vt27ePBQsW8PjjjzNo0CDmzp3LuXPnXFGbiIiISI5lG278/f3x8PCgXLlyHDx4kAoVKpCYqAlNIiIicnvKNtwUKFCARYsWUblyZZYuXcqBAwe4cOGCK2oTERERybFsw83QoUPZt28fwcHBuLm50blzZ7p37+6K2kRERERyLNtwc+jQId566y1sNhsffPABW7ZsIX/+/K6oTURERCTHsvy21E8//UR6ejpjx47FGOO4mV96ejqTJk3iqaeeclWNIiIiItcty3Czb98+Nm3aRHx8PJ9//vlfO3h40K1bN1fUJiIiIpJjWYabV199lVdffZU5c+bQqVMnV9YkIiIikmvZ3sSvQ4cOTJ8+nbVr15Kenk5wcDAvv/wyHh7Z7ioiIiLictlOKJ4wYQKbNm2ia9euPP/88+zcuZOxY8e6ojYRERGRHMt2+GXt2rV8++23eHp6AvDoo4/SunVrBg0alOfFiYiIiORUtiM3xhhHsAHw8vJyei0iIiJyO8k23FSuXJn33nuPY8eOcfz4cUaPHk3FihVdUZuIiIhIjmUZbvr16wfAsGHDOH/+PB06dKB9+/YkJCTw9ttvu6xAERERkZzIcs7NoUOHAPDx8WHMmDEuK0hERETkRmR7WUpERETkTpLlyM2BAweoWbNmpuXGGGw2Gzt27MjTwkRERERyI8twc//99/PJJ5+4shYRERGRG5ZluPHy8qJMmTKurEVERETkhmU558bb29uVdYiIiIjcFFmGm3nz5rmyDhEREZGb4rq/LbVhwwZatWpF06ZNWbBgQV7WJCIiIpJrWc65SUtLc3rMQmRkJN9++y0ArVu3JiIiIu+rExEREcmhLEduOnfuzJo1axyvfXx8+OGHH1i5ciX58uVzSXEiIiIiOZVluPnkk09Ys2YNr776KsePH2fIkCEcPnyYPXv2MHHiRFfWKCIiInLdsrwsVahQIYYOHcr+/fsZOnQojzzyCL169dKojYiIiNzWshy5ycjIYM2aNSQkJPDpp59Srlw5unbtyg8//ODK+kRERERyJMuRm969e1O8eHGSk5NZvnw577zzDk2bNuWjjz5i/vz5zJw505V1ioiIiFyXLMPN77//zkcffURaWhpPPfUUcHlScf/+/Tl8+LCr6hMRERHJkSzDTa1atXjxxRe5dOkSzZo1c1r34IMP5nlhIiIiIrmRZbgZPXo0Bw4cwMvLi/vvv9+VNYmIiIjkWpbhBqBSpUquqkNERETkprjuxy+IiIiI3AkUbkRERMRSFG5ERETEUhRuRERExFIUbkRERMRSFG5ERETEUlwebk6ePEmnTp1o3rw5vXr1Ijk5OdM2qamp9OvXjxYtWtCmTRvHHZGTk5Pp06cPYWFhhIWFsXjxYleXLyIiIrc5l4eb4cOH07FjR5YtW0bVqlWZMmVKpm1mz55N/vz5Wbp0KYMGDWLgwIEAfPLJJ5QuXZpFixYRGRnJ6NGjiYuLc/VbEBERkduYS8NNWloaW7duJTQ0FICIiAiWLVuWabvVq1fTunVrAOrUqUNCQgInT56kbt26dO7cGYBixYrh6+urcCMiIiJO/vEOxTfbmTNn8PHxwcPj8mn9/PyIjo7OtF1MTAx+fn6O135+fpw+fZrg4GDHsiVLlpCamkr58uVzVEOxYj65rN6a/PwK3uoS7hrqtWup366jXruOen198izcLF26lNGjRzstCwgIwGazOS37+2sAY4zTcmMMbm5/DTItXbqU9957jxkzZjiC0vWKj0/Cbjc52seq/PwKEhubeKvLuCuo166lfruOeu066rUzNzdblgMWeRZuWrRoQYsWLZyWpaWlUa9ePTIyMnB3dyc2NhZ/f/9M+5YoUYKYmBjKli0LQFxcnGO72bNnM3PmTGbOnKlnX4mIiEgmLp1z4+npSe3atVmyZAkACxcupHHjxpm2a9KkCVFRUQBs27YNb29vSpcuzYoVK4iMjGTu3LkKNiIiInJNNmOMS6/RnDhxggEDBhAfH0+pUqUYP348hQsXZu7cucTExNCnTx8uXbrE0KFD2bt3L15eXowcOZIqVarQunVrEhISKFasmON4I0eOpFq1atd9fl2W+ouGOF1HvXYt9dt11GvXUa+d/dNlKZeHm1tN4eYv+g/FddRr11K/XUe9dh312tk/hRvdoVhEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsReFGRERELEXhRkRERCxF4UZEREQsxeXh5uTJk3Tq1InmzZvTq1cvkpOTM22TmppKv379aNGiBW3atOHw4cNO69PT03nmmWdYsGCBq8oWERGRO4TLw83w4cPp2LEjy5Yto2rVqkyZMiXTNrNnzyZ//vwsXbqUQYMGMXDgQKf1H330Ef/73/9cVLGIiIjcSVwabtLS0ti6dSuhoaEAREREsGzZskzbrV69mtatWwNQp04dEhISOHnyJAA7duxg//79hISEuK5wERERuWN4uPJkZ86cwcfHBw+Py6f18/MjOjo603YxMTH4+fk5Xvv5+XH69GkKFSrE6NGjmTp1KuPGjctVDcWK+eSueIvy8yt4q0u4a6jXrqV+u4567Trq9fXJs3CzdOlSRo8e7bQsICAAm83mtOzvrwGMMU7LjTG4ubkxfPhwevbsSfHixXNdV3x8Ena7yfX+VuLnV5DY2MRbXcZdQb12LfXbddRr11Gvnbm52bIcsMizcNOiRQtatGjhtCwtLY169eqRkZGBu7s7sbGx+Pv7Z9q3RIkSxMTEULZsWQDi4uLw8/Nj48aNHDx4kEmTJnHq1Ck2bdqEh4eH4xKWiIiIiEsvS3l6elK7dm2WLFlCWFgYCxcupHHjxpm2a9KkCVFRUdSuXZtt27bh7e1NmTJlWLdunWObAQMGULduXQUbERERceLyb0sNGzaM+fPn8+STT7Jt2zZef/11AObOncvEiRMB6Ny5M6mpqbRs2ZJRo0YxduxYV5cpIiIidyibMeaumoCiOTd/0fVb11GvXUv9dh312nXUa2f/NOdGdygWERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFLUbgRERERS1G4EREREUtRuBERERFL8bjVBbiam5vtVpdwW1E/XEe9di3123XUa9dRr//yT72wGWOMC2sRERERyVO6LCUiIiKWonAjIiIilqJwIyIiIpaicCMiIiKWonAjIiIilqJwIyIiIpaicCMiIiKWonAjIiIilqJwIyIiIpaicCMiIiKWonBjcSdPnqRTp040b96cXr16kZycnGmb1NRU+vXrR4sWLWjTpg2HDx92Wp+ens4zzzzDggULXFX2HelGep2cnEyfPn0ICwsjLCyMxYsXu7r8O8KiRYt48skneeKJJ5gzZ06m9fv27SMiIoLQ0FAGDx5Meno6cH2fjWSW235v376dtm3bEh4eTteuXTlx4oSrS7/j5LbXV/z2229UrVrVVeXe/oxY2ksvvWS+//57Y4wxkydPNmPHjs20zYwZM8zbb79tjDFmy5Ytpl27dk7rP/jgA1O3bl3z7bff5n3Bd7Ab6fX48ePNmDFjjDHGxMXFmeDgYBMbG+uiyu8Mp0+fNiEhIebMmTMmOTnZhIWFmd9//91pm5YtW5qdO3caY4wZOHCgmTNnjjHm+j4bcXYj/Q4JCTH79u0zxhjz9ddfm5dfftmltd9pbqTXxhhz4cIF06FDB1OxYkVXln1b08iNhaWlpbF161ZCQ0MBiIiIYNmyZZm2W716Na1btwagTp06JCQkcPLkSQB27NjB/v37CQkJcV3hd6Ab7XXdunXp3LkzAMWKFcPX15e4uDjXvYE7wIYNG6hfvz6+vr4UKFCA0NBQpx6fOHGClJQUatSoAfz1GVzvZyPOctvv1NRU+vTpQ+XKlQGoVKkSp06duhVv4Y6R215fMWbMGLp27erqsm9rCjcWdubMGXx8fPDw8ADAz8+P6OjoTNvFxMTg5+fneO3n58fp06dJSkpi9OjRjBgxwmU136lutNfBwcGULl0agCVLlpCamkr58uVdU/wd4u+98/f3d+rxtXobHR193Z+NOMttv728vAgPDwfAbrczefJkmjZt6rrC70C57TXAypUrSUlJoXnz5q4r+A7gcasLkJtj6dKljB492mlZQEAANpvNadnfXwMYY5yWG2Nwc3Nj+PDh9OzZk+LFi+dN0XeovOj11cd+7733mDFjhuOXsVxmt9sz9e7q11mt//t2cO3PRpzltt9XpKamMmDAANLT0+nZs6drir5D5bbXsbGxTJ06lcjISFeWe0fQ/z0tokWLFrRo0cJpWVpaGvXq1SMjIwN3d3diY2Px9/fPtG+JEiWIiYmhbNmyAMTFxeHn58fGjRs5ePAgkyZN4tSpU2zatAkPDw/HZZW71c3u9ZXtZs+ezcyZM5k5cyaVKlXK+zdyhylZsiTbtm1zvP57j0uWLElsbKzj9ZXeFi1alMTExGw/G3GW237D5QnyvXr1wtfXl6lTp+Lp6em6wu9Aue316tWrOXv2LJ06dXKsCw8PZ86cOfj4+Lim+NuULktZmKenJ7Vr12bJkiUALFy4kMaNG2farkmTJkRFRQGwbds2vL29KVOmDOvWrSMqKoqoqCgee+wxevfufdcHm6zcSK9Lly7NihUriIyMZO7cuQo2WQgKCmLjxo0kJCRw8eJFli9f7tTjMmXK4O3tzfbt2wGIioqicePG1/3ZiLPc9hugX79+BAQE8MEHH+Dl5XVL6r+T5LbX7dq1Y8WKFY7/T19Zd7cHGwCbMcbc6iIk75w4cYIBAwYQHx9PqVKlGD9+PIULF2bu3LnExMTQp08fLl26xNChQ9m7dy9eXl6MHDmSKlWqOB1nwIAB1K1bl4iIiFv0Tm5/N9Lr1q1bk5CQQLFixRzHGzlyJNWqVbuF7+j2s2jRIj7++GPS0tJo27YtPXr0oEePHvTu3Ztq1aqxf/9+hgwZQlJSElWqVGH06NF4eXll+dnIP8tNvw8dOkSbNm0oX76849Kqv78/06dPv8Xv5vaW25/tq1WqVIkDBw7condwe1G4EREREUvRZSkRERGxFIUbERERsRSFGxEREbEUhRsRERGxFIUbERERsRSFGxG5qbp3786sWbMcr48cOUKlSpUYP368Y1l8fDxVq1alc+fOHDp0KEfH79mz5015Qn1aWhpjx44lLCyM1q1bExYWxrRp07jyBdLBgwezYcOGGz6PiLiewo2I3FSNGzdm8+bNjterVq0iJCSElStXOpZt2rSJmjVrMnv27Fv2DK1Zs2bx559/8t133/Hf//6XL7/8kuXLlzN//nwARo0aRVBQ0C2pTURujMKNiNxUjRs3Ztu2bdjtduByuHnppZdITk7m2LFjAGzcuJFHH32Uxx57jD179rB582Y6dOhAv379eOqpp2jVqpXjbqzR0dE8//zztGzZkh49ejjdhn7btm20b9+esLAwIiIiWLt2LRkZGdSvX5+jR48C8PHHHzs91b5bt26sWbOG2NhY0tLSSE1NBaBgwYKMHTuWwMBAADp37syyZcv48ccfCQ8Pd/wJDAykX79+AOzYsYOOHTvSpk0bnn76aVatWpXH3RWR66FwIyI31f3330+hQoU4cOAA586d48iRI9SoUYPGjRvz008/AZfDTZMmTZz22717Ny+88AILFy4kIiKCCRMmAPDuu+/yyCOPsHjxYoYMGcKRI0eAy09i7927N4MHD2bRokW8//779OvXj5MnTxISEsLPP/8MwM8//0xaWhpHjhwhMTGR/fv306BBA55//nmio6OpX78+nTt3ZsKECaSmplKxYkWnupo1a+a4vX2fPn0oXrw4/fv359y5cwwcOJCxY8fy3XffMWXKFN555x1OnjyZ1y0WkWwo3IjITXfl0tTatWsJCgrCzc2NkJAQ1q1bx59//onNZuPBBx902qd06dI89NBDADz88MOcO3cOgA0bNjge+xEQEEC9evWAy2GobNmyPPLIIwBUqFCBmjVrsmXLFpo1a8batWtJSkoiNjaWVq1asWHDBtasWUOjRo3w8vKiZMmSLFiwgAULFtC8eXOOHDnCM888w5w5c675nnbt2sU777zDtGnTKF68OLt27SI2NpZXX32V8PBwXnrpJWw2m25/L3Ib0FPBReSma9y4Md988w3e3t48/vjjADRo0IAhQ4Y4Lkn9Xb58+Rx/t9lsjom9V/8dcDyvKCMjA5vN5nQMYwzp6ekEBwczZMgQ1qxZQ7169QgKCmLu3Lnkz5+fJ598EoCxY8fSrl07ypcvT/ny5enUqRNRUVFMnz7d6SnLcHlS9L/+9S/GjRvnCGUZGRk8+OCDfP31147toqOjKVq0aG7bJiI3iUZuROSmq1evHvv27WPLli00atQIuBxeqlSpwhdffJHpktQ/adSoEV999RUAJ0+edExWrlGjBn/88Qe7d+8G4Pfff2fr1q3UrVsXb29v6tSpw+TJkwkODqZu3brs2rWLbdu2OepJSEhg4sSJXLx4EbgcjH7//Xcefvhhp/PHxsbSo0cP3nrrLceo0ZXzHz16lK1btwKwb98+QkNDiY6Ozk3LROQm0siNiNx0+fPnp1y5cqSlpVGwYEHH8iZNmvDvf//bKSRkZ9iwYQwcOJAWLVpQsmRJKleuDEDRokWZOHEiI0aMICUlBZvNxujRo7n//vuBy3Nlli9fTv369cmXLx+VK1emcOHCeHt7O447YcIEWrdujZeXF+np6dSvX5+hQ4c6nX/SpEnEx8cza9YsZsyYAfz1lOsPP/yQsWPHcunSJYwxjB07lnvvvfeGeiciN05PBRcRERFL0WUpERERsRSFGxEREbEUhRsRERGxFIUbERERsRSFGxEREbEUhRsRERGxFIUbERERsZT/B4P6SvZ+QrSkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.interpolate import interp1d\n",
    "import numpy as np\n",
    "\n",
    "# 300 represents number of points to make between T.min and T.max\n",
    "# xnew = np.linspace(3, 75, 300)\n",
    "\n",
    "experiment_num = 1\n",
    "RESULTS_PATH = 'Results/WindowSize/'\n",
    "\n",
    "import os\n",
    "\n",
    "if not os.path.exists(RESULTS_PATH):\n",
    "    os.mkdir(RESULTS_PATH)\n",
    "\n",
    "while os.path.exists(f'{RESULTS_PATH}{DATASET_NAME};WindowSize;test({experiment_num}).jpg'):\n",
    "    experiment_num += 1\n",
    "\n",
    "fig_file = f'{RESULTS_PATH}{DATASET_NAME};WindowSize;test({experiment_num}).jpg'\n",
    "\n",
    "sns.set(rc={'figure.figsize': (9, 5)})\n",
    "sns.set_palette(sns.color_palette(\"Paired\", 15))\n",
    "\n",
    "first = True\n",
    "for key, val in window_size_experiment.items():\n",
    "    total_returns = list(val.values())\n",
    "    \n",
    "    # Normalize returns:\n",
    "    total_returns = [(r - min(total_returns))/(max(total_returns) - min(total_returns)) for r in total_returns]\n",
    "#     f1 = interp1d(window_sizes, total_returns, kind='previous')\n",
    "    if first:\n",
    "#         ax = sns.lineplot(xnew, f1(xnew), label=key)\n",
    "        ax = sns.lineplot(window_sizes, total_returns, label=key)\n",
    "        first = False\n",
    "    else:\n",
    "        sns.lineplot(ax=ax, x=window_sizes, y=total_returns, label=key)\n",
    "#         sns.lineplot(ax=ax, x=xnew, y=f1(xnew), label=key)\n",
    "        \n",
    "ax.set(xlabel='WindowSize', ylabel='%Total Return')\n",
    "ax.set_title(f'The impact of window size on different models')\n",
    "plt.legend()\n",
    "# plt.savefig(fig_file, dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following block plots the relation between the window-size and the profit in each model in a heatmap diagram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i7qPOpOaeqqoq2NmZXNipFZMaIiJqTOo9qfnkdVHnPfXJtscciXWYnKUMGjQIPXr0QGBgIPz9/UWPdxEREZGF8C3dpjlw4AByc3Nx+PBhrF+/Hk2bNkVgYCBiYmIsGR8RERGZysJzaho6k9dM2dnZ4bnnnoO3tzd69OiBvLw8ZGVlWTI2IiIiModOELdJhMmVmpCQEGg0GoSEhKBPnz6Ii4szafkXERER1Q+BL7Q0TXR0NI4fP46TJ0+iqKgIRUVF8PPzQ9u2bS0YHhEREZlMQlUXMUxe/XSPTqfDnj17kJycjGvXrnFJNxER0UPU9+qnW/E1nwpsCqd/ffuYI7EOkys127dvx7Fjx3DmzBl4eXlh3LhxCAwMtGBoREREZJZGPlHY5KTmjz/+QEREBP71r38ZvPuJiIiIGohGPvxk8uqnmTNnol+/fpDL5Xj77bctGRMRERGJIOgEUZtUiHpEsEqletxxEBER0aOSUIIihqikxsy5xURERFQfuKTbfGvWrHnccRAREdGjYqXGuGnTphndv3DhwscWDBERET0CJjXG+fr61ui7evUq1q5di65du1okKCIiIiJz1ZnUhIUZPshn48aN2LFjBz788EOMHTvWYoERERGReRr7nFeT59Rcu3ZNPxS1fft2tGnTxmJBERERkQiNfPjJpOfUbNy4Ea+99hqGDBmCTZs2MaEhIiJqiPiWbuPeeOMNnDlzBuPGjYOzszPS0tIM9o8aNcpSsREREZEZpPQgPTHqTGpat26N1q1bo6CgAAUFBTX2M6khIiJqIJjUGGdsyXZlZeVjDYaIiIgeQeN+9p5pE4Vzc3ORnJyMn3/+GTqdDp07d8Z7772HH3/8Eb6+vggICLB0nERERFQHDj/V4cSJE4iPj8eECRMwbdo03LlzB6dPn8YHH3yANm3aID4+vj7iJCIiorowqTFu5cqVWL16NTp16qTv69KlC/bu3QuZTGbR4IiIiMgMjXz4qc4l3aWlpQYJDQAUFxdj8ODB0Gg0FguMiIiIzCPoBFGbVNSZ1Ny5cwfV1dUGfa6uroiOjuZEYSIiooZEJ3KTiDqTmsDAQCxcuNAgsamursaiRYvg7+9v0eCIiIjIdPVRqUlPT0dISAiGDBmCLVu21Nh//vx5hIeHIygoCDNmzEBVVRWAu4uOIiIiMHLkSERHRyMvLw8AcPLkSfj5+WHkyJEYOXJknS/SNkYm1PGiiPLycrz77ru4evUqOnXqBJlMhnPnzqF9+/ZITk6Gg4ODyRdTq0tFB0pERPSkUSia1ev1ikeKW43smvYfk44rKCjA66+/jt27d0MulyMyMhJLly5Fhw4d9McMGzYM8+bNQ7du3TB9+nR06dIFUVFRGDBgAJKTk+Hl5YWdO3fi4MGDWLVqFdatWwetVou3335bVOz3q3OisKOjI9avX4+cnBycPn0aFRUVePPNN+Hj4/PIFyciIqLHRxA5lKTRaGqdJ+vs7AxnZ2d9++jRo+jduzdcXFwAAEFBQcjKysLEiRMBAHl5ebhz5w66desGAAgPD8eKFSsQERGBuLg4eHl5AQA6duyIzZs3AwDOnj2LwsJC7N27F56enpgzZw48PDxE3UedSU1RURHi4uLw+++/45lnnoFMJsPXX3+N7t27Y8mSJWjWrH6zUCIiInoIkUnNhg0bsHLlyhr9EydORGxsrL6tUqmgUCj0baVSiTNnzjx0v0KhQEFBAeRyOUaOHHk3RJ0OK1euxKBBgwAAzZo1Q3BwMIYMGYJt27Zh8uTJ2L59u6j7qDOpWbJkCXr06IH169fD3t4ewN0nCX/xxReYP38+PvvsM1EXJiIiosdLbKUmOjoaYWFhNfrvr9IAdxOS+x/nIgiCQbuu/ZWVlZg6dSqqqqr0w01z587V73/99dexZMkSlJaWiiqa1JnUnDp1CpmZmQZ9crkcU6ZM0WddRERE9OR6cJjpYdzd3ZGTk6Nvq9VqKJVKg/1qtVrfLiws1O8vKyvDO++8AxcXF6xatQr29vbQ6XRYvXo1xo8fD1tbW/159//bHHWufnrYRGCZTAYbmzpPJyIiovpi4SXdffv2xbFjx1BcXIzy8nLs37/fYCW0p6cnHBwckJubCwBIS0vT74+Pj0ebNm2wfPlyyOVyAICNjQ2+++477Nu3DwCQmpqKrl27omnTpqJuv85KjbGnBvOJwkRERA2H2OEnU7m5uWHy5MkYO3YstFotIiIi8OKLLyImJgaTJk2Ct7c3EhMTMXPmTNy6dQudO3fG2LFj8euvv+LgwYPo0KGDfphLqVTif//3f7Fo0SLMmjULSUlJcHV1xeLFi0XHV+eS7i5dusDNza1GvyAIUKvVOHv2rMkX45JuIiJqTOp7SbdqoLgl3cqDpi3pbujqrNTcKwkRERFRw2bpSk1DV2dS4+npWR9xEBER0aMSGve0kDqTGiIiInoysFJDREREkiDoWKkhIiIiCWClhoiIiCRB4JwaIiIikgJWaoiIiEgSOKeGiIiIJMH443Slj0kNERGRRLBSQ0RERJLApIaIiIgkgcNPREREJAmNvVJjY+0AiIiIiB4HVmqIiIgkgg/fIyIiIkngw/eIiIhIEnSs1BAREZEUcPiJiIiIJKGxr35iUkNERCQRfE4NERERSQIrNURERCQJnChMREREksCJwkRERCQJnFNDREREktDYh5/47iciIiKJEASZqM0c6enpCAkJwZAhQ7Bly5Ya+8+fP4/w8HAEBQVhxowZqKqqAgDk5+dj9OjRGDp0KN555x2UlZUBADQaDcaPH4/g4GCMHj0aarVa9P0zqSEiIpIIQRC3maqgoADLli3D1q1bkZqaih07duCPP/4wOCY+Ph6zZ8/Gvn37IAgCUlJSAAAJCQmIiopCVlYWunTpguTkZADA8uXL4ePjg8zMTPzjH//A/PnzRd8/kxoiIiKJ0AkyUZtGo8H169drbBqNxuDrHz16FL1794aLiwuaNm2KoKAgZGVl6ffn5eXhzp076NatGwAgPDwcWVlZ0Gq1yM7ORlBQkEE/ABw6dAjDhw8HAAwbNgyHDx+GVqsVdf+cU0NERCQRYlc/bdiwAStXrqzRP3HiRMTGxurbKpUKCoVC31YqlThz5sxD9ysUChQUFKCkpAROTk6ws7Mz6H/wHDs7Ozg5OaG4uBhubm5m3weTGiIiIokQO1E4OjoaYWFhNfqdnZ0Nv75OB5ns/19DEASD9sP2P3gcgBrt+8+xsRE3kMSkhoiIqJFzdnaukcDUxt3dHTk5Ofq2Wq2GUqk02H//RN/CwkIolUq4urqitLQU1dXVsLW1NThPqVSisLAQ7u7uqKqqQllZGVxcXETdB+fUEBERSYQgcjNV3759cezYMRQXF6O8vBz79++Hv7+/fr+npyccHByQm5sLAEhLS4O/vz/s7e3h4+ODjIwMAEBqaqr+vICAAKSmpgIAMjIy4OPjA3t7e1H3LxOE+ntUj1pdWl+XIiIisjqFolm9Xu+oxyuizuv71y6Tj01PT8fq1auh1WoRERGBmJgYxMTEYNKkSfD29saFCxcwc+ZM3Lp1C507d8bChQshl8uRl5eHqVOnoqioCB4eHli6dCmaN2+OGzduYOrUqbh27RqaNWuGxMREtGrVStR9MKkhIiKykPpOao64R4g676W/dz7mSKyDc2qIiIgkQmftAKyMSQ0REZFECGjcr0lgUkNERCQROr7QkoiIiKRAx0oNERERSQGHn4iIiEgSOFGYiIiIJIGVGiIiIpIEVmqIiIhIEpjUEBERkSRw+ImIiIgkQde4cxomNURERFLB59QQERGRJDTyBwrDxtoBEBERET0OrNQQERFJBFc/ERERkSToZJxTQ0RERBLQ2OfUMKkhIiKSCA4/ERERkSTwOTVEREQkCXxODREREUkC59QQERGRJHD4iYiIiCSBE4WJiIhIEjj8RERERJLA4SciIiKSBGsNP+Xn5yM+Ph5FRUVo164dEhMT8dRTTxkcU1lZiRkzZuDcuXNo0qQJEhMT0b59e5SVlWH69Om4fPkyAGDChAkIDQ0FAAwcOBBOTk76r/Hll1/Cw8PjoXEwqSEiIpIIayU1CQkJiIqKQmhoKJKSkpCcnIz4+HiDYzZt2gRHR0dkZmYiOzsb06ZNQ0pKCtasWYOWLVvi888/R1FREUaOHAk/Pz/Y2trC3t4eaWlpJsfBt3QTERFJhCATt2k0Gly/fr3GptFo6rymVqtFdnY2goKCAADh4eHIysqqcdyhQ4cwYsQIAECvXr1QXFyM/Px8+Pr6YsyYMQCAFi1awMXFBYWFhTh79iwEQUBkZCTCwsKQmZlZZyxGKzVjxoyBzMjLsTZu3FjnBYiIiKh+iK3UbNiwAStXrqzRP3HiRMTGxho9t6SkBE5OTrCzu5tSKBQKFBQU1DhOpVJBoVDo2wqFAn///TdeeuklfV9GRgYqKyvRoUMHXL9+Hf369cOHH36IwsJCjB49Gs8//zzat2//0FiMJjX3biQlJQVNmjTBqFGjYGdnh71796KiosLoTRIREVH9EpvUREdHIywsrEa/s7OzQTszMxMLFy406GvTpk2NAkhtBRFBEAz6BUGAjc3/HzDKzMzEggUL8NVXX8HOzg6DBg3CoEGDAACtWrXC4MGD8dNPP4lPanx9fQEAixYtwq5du/T93bp1Q3h4uLFTiYiIqJ6JXdLt7OxcI4GpTXBwMIKDgw36tFot/Pz8UF1dDVtbW6jVaiiVyhrnurm5QaVS4ZlnngEAFBYW6o/btGkT1q5di7Vr16Jjx44AgB9++AFPP/00vL299V/jXjXoYUyaU1NRUYH//ve/+vbFixdRVVVlyqlEREQkYfb29vDx8UFGRgYAIDU1Ff7+/jWOCwgI0E/6zcnJgYODA1q2bIkDBw7g66+/xrZt2/QJDQDk5eUhKSkJOp0OhYWF+P777xEYGGg0FpkgCHUmdj/99BOmTp0KNzc3CIKAoqIiLFmyBD4+PubcN9TqUrOOJyIiepIpFM3q9XqfP/OGqPPi/tz8SNfNy8vD1KlTUVRUBA8PDyxduhTNmzfHtm3boFKpEBcXh4qKCsyePRvnzp2DXC7HvHnz0LlzZ4wYMQLFxcVo0aKF/uvNmzcPnTp1QkJCAnJzc6HT6TBp0iSEhIQYjcOkpAa4u778t99+g0wmQ8eOHessAdWGSQ0RETUm9Z3ULBOZ1Ex+xKSmoTBp+OnmzZuYO3cuFi9eDE9PT8yaNQs3b960dGxERERkBp3ITSpMSmpmzZoFb29v3LhxA02bNoVSqazxUB0iIiKyLkHkJhUmJTXXr1/Ha6+9BhsbG8jlckyePBl///23pWMjIiIiM+hk4japMGlijK2tLUpLS/Xry69cuWKwtpyIiIisT0pDSWKYlNTExsZizJgx+Ouvv/Duu+/i9OnTWLBggaVjIyIiIjNIaShJDJOSmpdeegldunTBmTNnUF1djblz5+Lpp5+2dGxERERkBl0jT2tMSmoGDhyIHj16IDAwEP7+/nBxcbFwWERERGQuDj+Z4MCBA8jNzcXhw4exfv16NG3aFIGBgYiJibF0fERERGSixl2nMXH1k52dHZ577jl4e3ujR48eyMvLq/W14kRERGQ9jf05NSZVakJCQqDRaBASEoI+ffogLi7OpBdfERERUf2R0vJsMUxKaqKjo3H8+HGcPHkSRUVFKCoqgp+fH9q2bWvh8IiIiMhUjX2isMnvfgIAnU6HPXv2IDk5GdeuXcP58+fNuhjf/URERI1Jfb/7aUbbKFHnzb+y9TFHYh0mVWq2b9+Oo0eP4uzZs/Dy8sK4cePqfP03ERER1S8pzY8Rw6Sk5o8//sCrr76KxMREyOVyS8dEREREIjT24SeTVj9NmTIFR48eRWRkJEaOHIkFCxbg9u3blo6NiIiIyGQmJTWffvop7ty5gwULFmDRokWoqqrCnDlzLB0bERERmaGxv6XbpOGnX375BXv27NG3Z8+ejZCQEIsFRUREROZr7HNqTKrUCIIAjUajb2s0Gtja2losKCIiIjKfDoKoTSpMqtS8+eabiIiIwIABAyAIAn744QeMHz/e0rERERGRGaSTnohjUlLzyiuvwNvbG9nZ2dDpdPjiiy/QsWNHS8dGREREZmjsw09Gk5rU1FSD9lNPPQUAOH/+PM6fP49Ro0ZZKi4iIiIyk9DIazVGk5oTJ04AAP78809cvXoVgYGBsLGxwU8//YQOHTowqSEiImpAWKkxYuHChQCAMWPGYM+ePXB1dQUA3Lx5E++9957loyMiIiKTSWnSrxgmzalRqVRwcXHRtx0dHaFWqy0VExEREYnQuFMaE5OawMBAvPXWWxgyZAgEQUBmZiaCg4MtHRsRERGZgZUaE0ybNg379u3DyZMnIZPJMG7cOAwcONDSsREREZEZrDWnJj8/H/Hx8SgqKkK7du2QmJioX1x0T2VlJWbMmIFz586hSZMmSExMRPv27aHVauHn54fWrVvrj929ezdsbW2xbt06pKSkQBAEfPDBBxgyZIjROExKagDg2WefRYsWLSAId7PA7Oxs9OrVy5x7JiIiIguy1uqnhIQEREVFITQ0FElJSUhOTkZ8fLzBMZs2bYKjoyMyMzORnZ2NadOmISUlBRcvXkT37t2xdu1ag+PPnDmDPXv2IC0tDbdu3cJrr70GX19fg+kwDzIpqUlISMAPP/xgkEXJZDJs3LjRjFsmIiIiSxJbqdFoNAZvDrjH2dkZzs7ORs/VarXIzs5GUlISACA8PBxvvPFGjaTm0KFDiIuLAwD06tULxcXFyM/Px9mzZ1FcXIzw8HDY2dnhww8/hK+vLw4fPozBgwfDwcEBDg4O8PX1xaFDh4yuvDYpqTly5AiysrLQpEkTUw4nIiIiKxBbqdmwYQNWrlxZo3/ixImIjY01em5JSQmcnJxgZ3c3pVAoFCgoKKhxnEqlgkKh0LcVCgX+/vtvyGQyDBw4EG+//TZ+//13xMTEID09HSqVCt7e3jWON8akpKZ169b6YSciIiJqmMRWaqKjoxEWFlaj/8EqTWZmpv5xL/e0adMGMpnMoO/BNnD3PZL39wuCABsbG0RGRur7XnjhBbz44ov4v//7P+h0Ne/Gxsb4KytNSmqaN2+O0NBQdO/eHXK5XN//4I0RERGR9ehEFiBMGWYCgODg4Bqrn+9N9K2uroatrS3UajWUSmWNc93c3KBSqfDMM88AAAoLC6FUKpGamooePXro+wVBgL29Pdzd3Q0eH6NWq9GuXTuj8Zn0lu5+/fohNjYWL7/8Mnx9ffUbERERNW729vbw8fFBRkYGgLuvWPL3969xXEBAANLS0gAAOTk5cHBwQMuWLXHx4kWsW7cOAHD58mWcP38ePXv2hL+/P/bv34/y8nIUFxfj+PHj6NOnj9FYZIKRcSW1Wg2FQoH8/Pxa97ds2dK0O9Z/vVKzjiciInqSKRTN6vV6b7QJF3Xe5qu7H+m6eXl5mDp1KoqKiuDh4YGlS5eiefPm2LZtG1QqFeLi4lBRUYHZs2fj3LlzkMvlmDdvHjp37oxbt25h+vTpuHz5MmQyGWbMmIHevXsDANatW4ddu3ahqqoK77zzTp2vZzKa1Lz99ttYvXo1BgwYUPNEmQwHDx4066aZ1BARUWNS30lNVJua82JMsfXqt485EuswOqdm9erVAIBvvvkGLVq0qJeAiIiISBy+pdsEY8aMgbOzMwICAtC/f394eXlZOi4iIiIyE9/SbYKMjAxcv34dhw8fxueff44rV67Az88Pn3zyiYXDIyIiIlPx3U8m0Ol0KCkpQXl5OQRBQFVVFYqLiy0dGxEREZmBw08m6NWrFxwdHREVFYX333+fw09EREQNEIefTPD555/j+PHj+PHHH3HkyBH4+PjA19cXL730kqXjIyIiIhM19qf/G13S/SCNRoPvvvsOq1evhlqtxqlTp8y6GJd0ExFRY1LfS7pHPjNM1Hlpf+59zJFYh0mVmsTERBw/fhylpaXo168fZs2aBT8/P0vHRkRERGbg8JMJWrRogcWLF+PZZ5+1dDxEREQkUmOfKGzSu5/eeustfUJT21s8iYiIyPp0EERtUmFSpeZ+jX0SEhERUUPV2P8fbXZSQ0RERA1TY59TY9Lw082bN/X/Tk1NtVQsRERE9AgEkf9JhdGk5sSJE3j55ZfRu3dvDB06FBcuXKivuIiIiMhMjX1OjdGkZvHixfj000+Rm5uLt956C0uWLKmvuIiIiIjMYjSpqaqqQv/+/dG0aVO89tpryM/Pr6+4iIiIyEyCIIjapMLoRGEbG8OcRy6XWzQYIiIiEk9KQ0liGE1qtFot/vrrL30W92C7ZcuWlo+QiIiITCKlSb9iGE1qbt++jTfeeMOgNDV69GgAgEwmw8GDBy0bHREREZlMJ6GhJDGMJjXff/99fcVBREREj6hxpzQmPqemNsOHD3+ccRAREdEjauxLukU/Ufj69euPMw4iIiJ6RFJKUMQQndTIZLLHGQcRERE9IiktzxaD734iIiKSCFZqjPDy8mJFhoiI6AnBJd1GLFy48KH7mOwQERE1LNYafsrPz0d8fDyKiorQrl07JCYm4qmnnjI4prKyEjNmzMC5c+fQpEkTJCYmon379pg9ezZ+/vln/XG//fYbli1bhqFDh2LgwIFwcnLS7/vyyy/h4eHx0DiMJjXTpk1DixYt0KdPH9jb29fYP2rUKFPvl4iIiCzMWsNPCQkJiIqKQmhoKJKSkpCcnIz4+HiDYzZt2gRHR0dkZmYiOzsb06ZNQ0pKCubOnas/ZufOncjMzERQUBBKSkpgb2+PtLQ0k+MwmtR8++23yMjIwJEjR+Dl5YWQkBD07du3xusTiIiIyPrEVmo0Gg00Gk2NfmdnZzg7Oxs9V6vVIjs7G0lJSQCA8PBwvPHGGzWSmkOHDiEuLg4A0KtXLxQXFyM/P1//doKSkhKsWLEC27Ztg0wmw9mzZyEIAiIjI1FRUYHx48cjODjYaCxGk5pOnTqhU6dO+OCDD3D27FlkZGRg6dKl6NKlC0JDQ+Hn52f0ixMREVH9EVup2bBhA1auXFmjf+LEiYiNjTV6bklJCZycnGBndzelUCgUKCgoqHGcSqWCQqHQtxUKBf7++299UvP1118jNDQUnp6eAO4OV/Xr1w8ffvghCgsLMXr0aDz//PNo3779Q2MxefWTt7c3vL29kZOTg8TERKSnp+PUqVOmnk5EREQWJnaicHR0NMLCwmr0P1ilyczMrDHftk2bNjXm2dY271YQBIN+QRD0Iz86nQ67du3Czp079fsHDRqEQYMGAQBatWqFwYMH46effnq0pEYQBGRnZyMrKwuHDx9Gp06dMGbMGPTv37+uU4mIiKgeiX33kynDTAAQHBxcYwhIq9XCz88P1dXVsLW1hVqthlKprHGum5sbVCoVnnnmGQBAYWGh/rhTp06hbdu2cHd31x//ww8/4Omnn4a3t7e+71416GGMTo6ZM2cOBg4ciI0bN6Jnz55IT0/HF198gdDQUDRt2rSOWyciIiKps7e3h4+PDzIyMgAAqamp8Pf3r3FcQECAftJvTk4OHBwc9ENPp0+fRs+ePQ2Oz8vLQ1JSEnQ6HQoLC/H9998jMDDQaCwywcisIi8vL7i4uOgTmAfLSea+pVutLjXreCIioieZQtGsXq/X2U3cXNdfCk480nXz8vIwdepUFBUVwcPDA0uXLkXz5s2xbds2qFQqxMXFoaKiArNnz8a5c+cgl8sxb948dO7cGQDwySefoGPHjnj99df1X7OqqgoJCQnIzc2FTqfDpEmTEBISYjQOo0lNXl6e0ZPvTeYxFZMaIiJqTOo7qemk9BV13nnVyccciXUYHZwyN2khIiIi6+EThYmIiEgSxE4UlgomNURERBLBSg0RERFJAis1REREJAms1BAREZEkCILO2iFYFZMaIiIiibDWW7obCiY1REREEiH2Ld1SwaSGiIhIIlipISIiIklgpYaIiIgkgUu6iYiISBK4pJuIiIgkgcNPREREJAmcKExERESS0NgrNTbWDoCIiIjocWClhoiISCK4+omIiIgkobEPPzGpISIikghOFCYiIiJJYKWGiIiIJIFzaoiIiEgS+ERhIiIikgRWaoiIiEgSOKeGiIiIJKGxDz/xicJEREQSIQiCqO1R5efnY/To0Rg6dCjeeecdlJWVPfTYI0eOIDo62iDmRYsWYejQoQgJCUFubq5+37p16zB06FAEBQVh//79dcbBpIaIiEgirJXUJCQkICoqCllZWejSpQuSk5NrHKPT6bBu3TpMmTIFOp1O379v3z5cunQJGRkZSEpKwrRp01BVVYUzZ85gz549SEtLw9atW7F48WLcuHHDaBxMaoiIiCRCELlpNBpcv369xqbRaOq8plarRXZ2NoKCggAA4eHhyMrKqnHcpUuXcOnSJXz66acG/f/5z38QEhICGxsbtGvXDh4eHjh16hQOHz6MwYMHw8HBAS1atICvry8OHTpkNJZ6nVOjUDSrz8sRERE1KlWVeaLO++KLL7By5coa/RMnTkRsbKzRc0tKSuDk5AQ7u7sphUKhQEFBQY3jnnvuOcyfPx8nTpww6FepVFAqlfq2QqHA33//DZVKBW9v7xr9xnCiMBERUSMXHR2NsLCwGv3Ozs4G7czMTCxcuNCgr02bNpDJZAZ9D7aN0el0BscLggAbGxuDIap7bGyMDzAxqSEiImrknJ2dayQwtQkODkZwcLBBn1arhZ+fH6qrq2Frawu1Wm1QeamLu7s7VCqVvl1YWAilUgl3d3eo1Wp9v1qtRrt27Yx+Lc6pISIiItHs7e3h4+ODjIwMAEBqair8/f1NPt/f3x/p6emorq7G1atXceXKFXh7e8Pf3x/79+9HeXk5iouLcfz4cfTp08fo12KlhoiIiB7JnDlzMHXqVKxatQoeHh5YunQpAGDbtm1QqVSIi4t76LlDhw7FmTNnMGLECADA/Pnz0aRJE7z44osYMWIEIiIiUFVVhUmTJsHNzc1oHDKhsT9+kIiIiCSBw09EREQkCUxqiIiISBKY1BAREZEkMKkhIiIiSXjiVj99/vnn2LdvH2QyGSIiIvDWW29ZOySMGTMGxcXF+qcpzp07F127drVqTGlpaVizZg2Au8vlPv74Y6vFcuvWLURGRuLLL79Eq1atcPToUSxcuBAVFRUIDg7G5MmTrR7Tjh07sGnTJshkMnTp0gUJCQmQy+VWjWnatGnIzc2Fo6MjgLtP9hw8eHC9xrRy5UpkZmYCAAICAvDRRx9Z/edXW0zW/l7V9rlk7e/TPYsWLUJJSQk+++yzBhFTbZ+XZWVlVovrm2++webNm/Xt69evY+TIkSgvL7f63x+JIDxBTpw4IURGRgparVYoLy8X+vfvL1y6dMmqMel0OuHll18WtFqtVeO43+3bt4VevXoJRUVFglarFSIiIoQjR45YJZbTp08Lw4YNEzp37ixcu3ZNKC8vFwICAoQ///xT0Gq1wrhx44RDhw5ZNabLly8LgwcPFkpLSwWdTid89NFHwvr1660akyAIwrBhw4SCgoJ6jeN+R44cEV577TWhoqJCqKysFMaOHSukp6db9edXW0z79++36veqts+l8+fPW/33XBAE4ejRo4Kfn5/w8ccfN4i/vdo+LxtCXPf89ttvwuDBg4WioiKr//2ROE/U8JOvry82btwIOzs7FBUVobq6Gk2bNrVqTJcvXwYAjBs3DiNGjDDI+K2luroaOp0O5eXlqKqqQlVVFRwcHKwSS0pKCubMmaN/uuSZM2fQpk0btG7dGnZ2dhg+fHitLz6rz5jkcjnmzJkDJycnyGQyPP/888jPz7dqTOXl5cjPz8f06dMxfPhwrFixotZHhluSQqHA1KlTIZfLYW9vj/bt2+PKlStW/fnVFlN+fr5Vv1e1fS5pNBqr/57fuHEDy5Ytw4QJEwA0jL+92j4vG0Jc93zyySeYPHkyHB0drf73R+I8UUkNcPfJhStWrEBoaCj69OlT54N4LE2j0aBPnz5ISkrC119/je3bt+PIkSNWjcnJyQlxcXEIDg5GQEAAPD090aNHD6vEMn/+fPj4+OjbKpUKCoVC31YqlbW++Kw+Y/L09MRLL70EACguLsaWLVswcOBAq8ZUWFiI3r17Y8GCBUhJSUFOTg527txZrzE999xz6NatGwDgypUryMzMhEwms+rPr7aY+vXrZ/Xv1YOfSw3h93z27NmYPHmy/tH3DSGm2j4v8/PzrR4XABw9ehR37txBcHBwg/j7I3GeuKQGACZNmoRjx47hr7/+QkpKilVj6d69OxYvXoxmzZrB1dUVERER+M9//mPVmC5cuIBdu3bhhx9+wI8//ggbGxusXbvWqjHdU9uLy8x58ZklFRQUIDo6Gq+88gr8/PysGkvr1q2RlJQEpVIJR0dHjBkzxmq/V7///jvGjRuHjz76CK1bt24QP7/7Y3r22WcbxPfq/s+lK1euWPX79M0338DDw8PgkfIN4W+vts/LFStWWD0uANi+fbt+jmZD+vsj8zxRSc2lS5dw/vx5AICjoyOGDBmCixcvWjWmnJwcHDt2TN8WBEE/Ac5afvrpJ/Tp0wctWrSAXC5HeHg4Tp48adWY7qntBWXmvPjMUi5duoTIyEiEhYXhvffes3Y4uHjxIvbt26dvW+v3Kjc3F2+++SY++OADhIWFNYif34MxWft7Vdvn0okTJ6z6fcrIyMCRI0cwcuRIrFixAt9//z2++eYbq//savu89PT0tHpclZWVyM7OxoABAwA0nL8/Mt8TldRcv34dM2fORGVlJSorK3Hw4EH07NnTqjGVlpZi8eLFqKiowK1bt/Dtt99afYa8l5cXjh49itu3b0MQBHz//ffw9va2akz3dO3aFf/9739x9epVVFdXY+/evWa9+MwSbt26hf/5n/9BXFwcxo0bZ9VY7hEEAQsWLMDNmzeh1WqxY8eOev+9+uuvv/Dee+8hMTERoaGhAKz/86stJmt/r2r7XIqMjLTq92n9+vXYu3cv0tLSMGnSJAwYMABfffWV1f/2avu8nDJlitXjunjxItq2baufo2nt3ykS74lKPQMCAnDmzBmMGjUKtra2GDJkiP6DzVr69++Pn3/+GaNGjYJOp0NUVBS6d+9u1Zhefvll/PrrrwgPD4e9vT28vb0xfvx4q8Z0j4ODAz777DPExsaioqICAQEBGDp0qFVj2rlzJwoLC7F+/XqsX78eADBgwACjL2CzNC8vL4wfPx6vv/46qqqqMGTIEAwbNqxeY1i7di0qKirw2Wef6fsiIyOt+vN7WEzW/F497HPJ1dW1Qf2eN4S/vYd9Xlo7rmvXrsHd3V3fbgh/fyQOX2hJREREkvBEDT8RERERPQyTGiIiIpIEJjVEREQkCUxqiIiISBKY1BAREZEkMKkhIiIiSWBSQ0RERJLApIaIiIgk4f8BZxUQRUaFfZIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "experiment_num = 1\n",
    "RESULTS_PATH = 'Results/WindowSize/'\n",
    "\n",
    "import os\n",
    "\n",
    "if not os.path.exists(RESULTS_PATH):\n",
    "    os.mkdir(RESULTS_PATH)\n",
    "\n",
    "while os.path.exists(f'{RESULTS_PATH}{DATASET_NAME};WindowSize;heatmap;test({experiment_num}).jpg'):\n",
    "    experiment_num += 1\n",
    "\n",
    "fig_file = f'{RESULTS_PATH}{DATASET_NAME};WindowSize;heatmap;test({experiment_num}).jpg'\n",
    "\n",
    "sns.set(rc={'figure.figsize': (10, 5)})\n",
    "\n",
    "normalized = {}\n",
    "for k1 in window_size_experiment.keys():\n",
    "    normalized[k1] = {}\n",
    "    total_returns = list(window_size_experiment[k1].values())\n",
    "    max_return = max(total_returns)\n",
    "    min_return = min(total_returns)\n",
    "    for k2 in window_size_experiment[k1].keys():\n",
    "        return_val = window_size_experiment[k1][k2]\n",
    "        normalized[k1][k2] = (return_val - min_return)/(max_return - min_return)\n",
    "        \n",
    "\n",
    "df = pd.DataFrame.transpose(pd.DataFrame.from_dict(normalized))\n",
    "sns.heatmap(df)\n",
    "\n",
    "plt.savefig(fig_file, dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}