Reinforcement_Learning_for_Stock_Market_Trading.ipynb

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      "name": "Reinforcement Learning for Stock Market Trading.ipynb",
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      "machine_shape": "hm",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/sergejhorvat/Tensorflow2.0_Udemy/blob/master/Reinforcement_Learning_for_Stock_Market_Trading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFYKMfEEEzni",
        "colab_type": "text"
      },
      "source": [
        "## Stage 1: Installing dependencies and environment setup\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEPWe6QNErug",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 570
        }
      },
      "source": [
        "!pip install tensorflow-gpu==2.0.0rc0"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/21/28/f2a27a62943d5f041e4a6fd404b2d21cb7c59b2242a4e73b03d9ba166552/tf_estimator_nightly-1.14.0.dev2019080601-py2.py3-none-any.whl (501kB)\n",
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            "Installing collected packages: tb-nightly, tf-estimator-nightly, tensorflow-gpu\n",
            "Successfully installed tb-nightly-1.15.0a20190806 tensorflow-gpu-2.0.0rc0 tf-estimator-nightly-1.14.0.dev2019080601\n"
          ],
          "name": "stdout"
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
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      },
      "source": [
        "!pip install pandas-datareader"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pandas-datareader in /usr/local/lib/python3.6/dist-packages (0.7.4)\n",
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          ],
          "name": "stdout"
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J0BVbvetGrlR",
        "colab_type": "text"
      },
      "source": [
        "## Stage 2: Importing project dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qALiMginGiMW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import math\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas_datareader as data_reader\n",
        "\n",
        "from tqdm import tqdm_notebook, tqdm\n",
        "from collections import deque"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drGd9adHGuje",
        "colab_type": "code",
        "outputId": "767a664a-02ec-4b31-c575-16b77464c81b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.0-rc0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "__E4SMMaGwqb",
        "colab_type": "text"
      },
      "source": [
        "## Stage 3: Building the AI Trader network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCggfrCtGyiG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AI_Trader():\n",
        "  \n",
        "  def __init__(self, state_size, action_space=3, model_name=\"AITrader\"): #Stay, Buy, Sell\n",
        "    \n",
        "    self.state_size = state_size\n",
        "    self.action_space = action_space\n",
        "    self.memory = deque(maxlen=2000)\n",
        "    self.inventory = []\n",
        "    self.model_name = model_name\n",
        "    \n",
        "    # Define hyperparamaters\n",
        "    self.gamma = 0.95\n",
        "    self.epsilon = 1.0\n",
        "    self.epsilon_final = 0.01\n",
        "    self.epsilon_decay = 0.995\n",
        "        \n",
        "    # Call a function  to build a model trought this class constructor\n",
        "    # More parameters could be ustilized to programaticaly define network size (layers and neurons)\n",
        "    self.model = self.model_builder()\n",
        "    \n",
        "    \n",
        "  def model_builder(self):\n",
        "    \n",
        "    model = tf.keras.models.Sequential()    \n",
        "    model.add(tf.keras.layers.Dense(units=32, activation='relu', input_dim=self.state_size))    \n",
        "    model.add(tf.keras.layers.Dense(units=64, activation='relu'))    \n",
        "    model.add(tf.keras.layers.Dense(units=128, activation='relu'))    \n",
        "    model.add(tf.keras.layers.Dense(units=self.action_space, activation='linear'))    \n",
        "    model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=0.001))    \n",
        "    return model\n",
        "  \n",
        "  \n",
        "  \n",
        "  \n",
        "  # Trade function that takes state as an input and returns an action \n",
        "  # to perform in perticular state \n",
        "  def trade(self, state):\n",
        "    \n",
        "    # Should we perform a renadom generated action or action defined in model?\n",
        "    \n",
        "    # If value from our random generator is smaller or equal to our epsilon \n",
        "    #     then we will retun a random action from action_space [0-3)\n",
        "    if random.random() <= self.epsilon:\n",
        "      return random.randrange(self.action_space)\n",
        "    \n",
        "    # If our random is greater than epsilon then we will use model to perform action\n",
        "    actions = self.model.predict(state)\n",
        "    # return only a one number defining an action (#Stay - 0 , Buy - 1, Sell - 2) \n",
        "    #    that has maximum probability\n",
        "    return np.argmax(actions[0])\n",
        "  \n",
        "  \n",
        "  \n",
        "  def batch_train(self, batch_size):\n",
        "    \n",
        "    batch = []\n",
        "    \n",
        "    # Iterrate in momory, we do not want to randolmy select data as we are dealing with \n",
        "    #    time constraint data. We will always sample from the end of memory size of bath\n",
        "    for i in range(len(self.memory) - batch_size + 1, len(self.memory)):\n",
        "      # insert data from memory to batch      \n",
        "      batch.append(self.memory[i])\n",
        "    \n",
        "    \n",
        "    # Iterate trought batch of data and train the model for each sample from batch\n",
        "    # Order of variables in for loop is important\n",
        "    for state, action, reward, next_state, done in batch:\n",
        "      # Reward if agent is in terminal state\n",
        "      reward = reward\n",
        "      # Check that agent is not in terminal state\n",
        "      # If not in terminal state calculate reward for actions that could be played\n",
        "      if not done:\n",
        "        # Discounted total reward:\n",
        "        reward = reward + self.gamma * np.amax(self.model.predict(next_state)[0])        \n",
        "      # Target variable that is predicted by the model (action)\n",
        "      target = self.model.predict(state)\n",
        "      target[0][action] = reward\n",
        "      \n",
        "      self.model.fit(state, target, epochs=1, verbose=0)\n",
        "      \n",
        "    # We will decrease epsilon parameter that is 1 as defined in __init__  so\n",
        "    #    so we can stop performing random actions at some point\n",
        "    if self.epsilon > self.epsilon_final:\n",
        "      self.epsilon *= self.epsilon_decay"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7tcUyJMnS479",
        "colab_type": "text"
      },
      "source": [
        "## Stage 4: Dataset preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ya_4XPUMS8Pe",
        "colab_type": "text"
      },
      "source": [
        "### Defining helper functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kK6UDGe0TACz",
        "colab_type": "text"
      },
      "source": [
        "#### Sigmoid"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IzXG-49iSZX3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Usually used at the end of a network for binary classifictation\n",
        "# It changes range of input to scale of [0,1]\n",
        "# So we can normalize input data for comparision day by day if they are on different scale\n",
        "def sigmoid(x):\n",
        "  return 1 / (1 + math.exp(-x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0VRbmx_OTDtZ",
        "colab_type": "text"
      },
      "source": [
        "#### Price format function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N8vVBL8YTGaE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def stocks_price_format(n):\n",
        "  if n < 0:\n",
        "    return \"- $ {0:2f}\".format(abs(n))\n",
        "  else:\n",
        "    return \"$ {0:2f}\".format(abs(n))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8_bXTiFTHnH",
        "colab_type": "text"
      },
      "source": [
        "#### Dataset loader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pXYjiBwNRsV",
        "colab_type": "code",
        "outputId": "8f17f57f-8b5d-458a-a0ad-511fed3c6105",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        }
      },
      "source": [
        "# Check the data gathered with pandas data_reader:\n",
        "dataset = data_reader.DataReader(name=\"AAPL\", data_source=\"yahoo\")\n",
        "print(\"Data set top rows:\", \"\\n\" ,dataset.head())\n",
        "\n",
        "print(\"Test some cutting with pandas\")\n",
        "print(\"Start date: \", str(dataset.index[0]).split()[0])\n",
        "print(\"End date: \", str(dataset.index[-1]).split()[0])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data set top rows: \n",
            "                  High        Low       Open      Close       Volume  Adj Close\n",
            "Date                                                                          \n",
            "2010-01-04  30.642857  30.340000  30.490000  30.572857  123432400.0  26.681330\n",
            "2010-01-05  30.798571  30.464285  30.657143  30.625713  150476200.0  26.727465\n",
            "2010-01-06  30.747143  30.107143  30.625713  30.138571  138040000.0  26.302330\n",
            "2010-01-07  30.285715  29.864286  30.250000  30.082857  119282800.0  26.253704\n",
            "2010-01-08  30.285715  29.865715  30.042856  30.282858  111902700.0  26.428249\n",
            "Test some cutting with pandas\n",
            "Start date:  2010-01-04\n",
            "End date:  2019-09-23\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAcu8ZZzTNwm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def dataset_loader(stock_name, web_data_source=\"yahoo\"):\n",
        "  \n",
        "  #Use pandas data reader for reading stock data from warious sources like \"yahoo\", \"google\"\n",
        "  dataset = data_reader.DataReader(name=stock_name, data_source=\"yahoo\")\n",
        "   \n",
        "  # Get start and end time to variables from dataset\n",
        "  start_date = str(dataset.index[0]).split()[0]\n",
        "  end_date = str(dataset.index[-1]).split()[0]\n",
        "  \n",
        "  # Model will use \"Close\" column for training \n",
        "  close = dataset['Close']\n",
        "\n",
        "  return close"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eG5ueRzH1Kh8",
        "colab_type": "text"
      },
      "source": [
        "### State creator"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zwTS0EyZ0O1k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data -> dataset to predict from, gathered by data:loader()\n",
        "# Timestep -> Day in the dataset that we want to predict for [0:datalength]\n",
        "# window_suze -> how many days in past we want to use to predict current status[1:datalength]\n",
        "#         Try different setup to see what creates best fit\n",
        "def state_creator(data, timestep, window_size):\n",
        "  \n",
        "  # starting day of our state\n",
        "  starting_id = timestep - window_size + 1\n",
        "  \n",
        "  if starting_id >= 0:\n",
        "    windowed_data = data[starting_id:timestep+1]\n",
        "  else:\n",
        "    # Replicate member (data[0]) needed times\n",
        "    windowed_data = - starting_id * [data[0]] + list(data[0:timestep+1])\n",
        "    \n",
        "  state = []\n",
        "  # Iterate trough whole windowed_data minus current state (-1)\n",
        "  for i in range(window_size - 1):\n",
        "    # Normalize the difference from current day and the next day\n",
        "    # Because the prices can be very different and we want them on same scale\n",
        "    state.append(sigmoid(windowed_data[i+1] - windowed_data[i]))\n",
        "    \n",
        "  return np.array([state])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sV2a68QC34N-",
        "colab_type": "text"
      },
      "source": [
        "### Loading a dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "acuYlqD83xcX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Tage data for Apple\n",
        "stock_name = \"AAPL\"\n",
        "data = dataset_loader(stock_name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_KUeBY14o8Z",
        "colab_type": "text"
      },
      "source": [
        "## Stage 5: Training the AI Trader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hkyW12-L4qo_",
        "colab_type": "text"
      },
      "source": [
        "### Setting hyper parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dKfuoXO84c5X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "window_size = 10\n",
        "episodes = 1000 # same as epoch\n",
        "\n",
        "batch_size = 32\n",
        "data_samples = len(data) - 1 # discard last value, that we will predict on"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2kvNZx24sJ7",
        "colab_type": "text"
      },
      "source": [
        "### Defining the Trader model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M4LmF2V94trY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "trader = AI_Trader(window_size)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y66jkzwW4wNV",
        "colab_type": "code",
        "outputId": "0d36a199-9a32-4ee9-dc99-1f85a1ee0a54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        }
      },
      "source": [
        "trader.model.summary()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 32)                352       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                2112      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               8320      \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 3)                 387       \n",
            "=================================================================\n",
            "Total params: 11,171\n",
            "Trainable params: 11,171\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRip_rac5zHS",
        "colab_type": "text"
      },
      "source": [
        "### Training loop"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b0tytPl-5hdg",
        "colab_type": "code",
        "outputId": "853d5cff-6991-411c-e8b2-856b16d3a6c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for episode in range(1, episodes + 1):\n",
        "  \n",
        "  # To keep track of training process\n",
        "  # .format populates {} with variables in .format(x,y)\n",
        "  print(\"Episode: {}/{}\".format(episode, episodes))\n",
        "  \n",
        "  # Create state\n",
        "  # second parameter is timestep = 0\n",
        "  state = state_creator(data, 0, window_size + 1)\n",
        "  \n",
        "  total_profit = 0\n",
        "  # Empty inventory before starting episode\n",
        "  trader.inventory = []\n",
        "  \n",
        "  # One timestep is one day so number of timesteps we have represent data we have\n",
        "  # tqdm is used for visualization\n",
        "  for t in tqdm(range(data_samples)):\n",
        "    \n",
        "    # First we will access action that is going to be taken by model \n",
        "    action = trader.trade(state)\n",
        "    \n",
        "    # Use action to get to next state(t+)\n",
        "    next_state = state_creator(data, t+1, window_size + 1)\n",
        "    # As we did not calculate anything up to this point reward is 0\n",
        "    reward = 0\n",
        "    \n",
        "    if action == 1: #Buying\n",
        "      # Put buyed stock to inventory to trade with\n",
        "      trader.inventory.append(data[t])\n",
        "      print(\"AI Trader bought: \", stocks_price_format(data[t]))\n",
        "      \n",
        "    # To sell we need to have something in inventory  \n",
        "    elif action == 2 and len(trader.inventory) > 0: #Selling\n",
        "      # Check buy price, pop removes first value from list\n",
        "      buy_price = trader.inventory.pop(0)\n",
        "      \n",
        "      # If we gain money (current price - buy price) we have reward \n",
        "      #    if we lost money then reward is 0\n",
        "      reward = max(data[t] - buy_price, 0)\n",
        "      total_profit += data[t] - buy_price\n",
        "      print(\"AI Trader sold: \", stocks_price_format(data[t]), \" Profit: \" + stocks_price_format(data[t] - buy_price) )\n",
        "      \n",
        "    # if t is last sample in our dateset we are done\n",
        "    #     we do not have any steps to perform in current episode\n",
        "    if t == data_samples - 1:\n",
        "      done = True\n",
        "    else:\n",
        "      done = False\n",
        "    \n",
        "    # Append all data to trader-agent memory, experience buffer\n",
        "    trader.memory.append((state, action, reward, next_state, done))\n",
        "    \n",
        "    # change state to next state, so we are done with an episode\n",
        "    state = next_state\n",
        "    \n",
        "    if done:\n",
        "      print(\"########################\")\n",
        "      print(\"TOTAL PROFIT: {}\".format(total_profit))\n",
        "      print(\"########################\")\n",
        "    \n",
        "    # Chekc if we have more information in our memory than batch size\n",
        "    if len(trader.memory) > batch_size:\n",
        "      trader.batch_train(batch_size)\n",
        "  \n",
        "  # Save the model every 10 episodes\n",
        "  if episode % 10 == 0:\n",
        "    trader.model.save(\"ai_trader_{}.h5\".format(episode))\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": [
        {
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          "name": "stderr"
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        {
          "output_type": "stream",
          "text": [
            "Episode: 1/1000\n",
            "AI Trader bought:  $ 30.092857\n",
            "AI Trader bought:  $ 29.918571\n",
            "AI Trader sold:  $ 29.418571  Profit: - $ 0.674286\n",
            "AI Trader bought:  $ 30.719999\n",
            "AI Trader bought:  $ 30.247143\n",
            "AI Trader sold:  $ 29.724285  Profit: - $ 0.194286\n",
            "AI Trader bought:  $ 29.010000\n",
            "AI Trader bought:  $ 29.420000\n",
            "AI Trader bought:  $ 29.697144\n",
            "AI Trader sold:  $ 27.922857  Profit: - $ 2.797142\n",
            "AI Trader sold:  $ 27.731428  Profit: - $ 2.515715\n",
            "AI Trader bought:  $ 28.027143\n",
            "AI Trader bought:  $ 28.381428\n",
            "AI Trader sold:  $ 28.625713  Profit: - $ 0.384287\n",
            "AI Trader bought:  $ 28.990000\n",
            "AI Trader sold:  $ 28.809999  Profit: - $ 0.610001\n"
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        },
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        },
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            "AI Trader bought:  $ 33.965714\n"
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        },
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        },
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        },
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        },
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        },
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        },
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            "AI Trader sold:  $ 38.838570  Profit: $ 1.739998\n"
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        },
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            "AI Trader bought:  $ 36.952858\n"
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        },
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        },
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            "AI Trader sold:  $ 36.755714  Profit: - $ 0.114285\n"
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        },
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          ],
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        },
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        },
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        },
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        },
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            "AI Trader bought:  $ 37.418571\n"
          ],
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        },
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        },
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            "AI Trader sold:  $ 37.155716  Profit: - $ 0.262856\n"
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        },
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        },
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          ],
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        },
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        },
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          ],
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        },
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        },
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          ],
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        },
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          ],
          "name": "stderr"
        },
        {
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          ],
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        },
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        },
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            "AI Trader sold:  $ 34.325714  Profit: - $ 1.670002\n"
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        },
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        },
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          ],
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        },
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          "name": "stderr"
        },
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            "AI Trader sold:  $ 34.728573  Profit: - $ 0.934284\n"
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        },
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        },
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          ],
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        },
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        },
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            "AI Trader bought:  $ 36.024284\n"
          ],
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        },
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        },
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            "AI Trader bought:  $ 36.967144\n"
          ],
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        },
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          "name": "stderr"
        },
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            "AI Trader sold:  $ 36.830002  Profit: $ 1.715717\n"
          ],
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        },
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          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "AI Trader sold:  $ 37.560001  Profit: $ 2.861431\n"
          ],
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        },
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          "name": "stderr"
        },
        {
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            "AI Trader sold:  $ 37.581429  Profit: $ 3.064285\n"
          ],
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        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader sold:  $ 37.630001  Profit: $ 1.868572\n"
          ],
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        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader sold:  $ 38.148571  Profit: $ 2.124287\n"
          ],
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        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "AI Trader sold:  $ 38.294285  Profit: $ 1.327141\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader bought:  $ 40.535713\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 39.805714  Profit: - $ 0.730000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader bought:  $ 45.428570\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader bought:  $ 44.212856\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 44.361427  Profit: - $ 1.067142\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader sold:  $ 44.217144  Profit: $ 0.004288\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "AI Trader bought:  $ 44.119999\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader sold:  $ 44.007141  Profit: - $ 0.112858\n"
          ],
          "name": "stdout"
        },
        {
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          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader bought:  $ 42.928570\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader bought:  $ 44.061428\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader sold:  $ 43.818573  Profit: $ 0.890003\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader bought:  $ 44.765713\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader bought:  $ 44.971428\n"
          ],
          "name": "stdout"
        },
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          ],
          "name": "stderr"
        },
        {
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            "AI Trader sold:  $ 45.200001  Profit: $ 1.138573\n"
          ],
          "name": "stdout"
        },
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          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 45.450001  Profit: $ 0.684288\n"
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        },
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        },
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        },
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        },
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        },
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          ],
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        },
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        },
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          ],
          "name": "stdout"
        },
        {
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        },
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        },
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          ],
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        },
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          ],
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        },
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          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 21%|██        | 506/2446 [25:48<1:38:10,  3.04s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader bought:  $ 59.718571\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 21%|██        | 507/2446 [25:51<1:37:37,  3.02s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 60.342857  Profit: $ 2.468571\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 21%|██        | 508/2446 [25:54<1:36:56,  3.00s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 60.247143  Profit: $ 2.389999\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 21%|██        | 509/2446 [25:57<1:36:39,  2.99s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 60.462856  Profit: $ 1.715714\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 21%|██        | 510/2446 [26:00<1:37:19,  3.02s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 60.364285  Profit: $ 1.301426\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 21%|██        | 511/2446 [26:03<1:36:57,  3.01s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader bought:  $ 60.198570\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 21%|██        | 512/2446 [26:06<1:36:22,  2.99s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 59.972858  Profit: $ 0.254288\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r 21%|██        | 513/2446 [26:09<1:36:42,  3.00s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "AI Trader sold:  $ 60.671429  Profit: $ 0.472858\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " 22%|██▏       | 536/2446 [27:45<1:35:49,  3.01s/it]"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}