test_assignment.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1NDJLPTn7ATlQTV-W2CMwZvVC2YW-BU93",
      "authorship_tag": "ABX9TyNTT/bAEysXXaByQkm1OzbT",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/shelan-de-livera/finalprojecttestrepo/blob/main/test_assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Install PyTorch"
      ],
      "metadata": {
        "id": "98AGf6jsMXO-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NgNweD1sPZq0",
        "outputId": "7addf70f-25b8-492e-e75e-01a7b35c7be3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gym in /usr/local/lib/python3.10/dist-packages (0.25.2)\n",
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          ]
        }
      ],
      "source": [
        "!pip install gym torch numpy pandas"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install stable-baselines3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsoWiEHOU3GP",
        "outputId": "2aa5af2e-037b-4945-dfb7-f9211a5310f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting stable-baselines3\n",
            "  Downloading stable_baselines3-2.1.0-py3-none-any.whl (178 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting gymnasium<0.30,>=0.28.1 (from stable-baselines3)\n",
            "  Downloading gymnasium-0.29.1-py3-none-any.whl (953 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m953.9/953.9 kB\u001b[0m \u001b[31m25.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.20 in /usr/local/lib/python3.10/dist-packages (from stable-baselines3) (1.23.5)\n",
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            "Requirement already satisfied: typing-extensions>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium<0.30,>=0.28.1->stable-baselines3) (4.5.0)\n",
            "Collecting farama-notifications>=0.0.1 (from gymnasium<0.30,>=0.28.1->stable-baselines3)\n",
            "  Downloading Farama_Notifications-0.0.4-py3-none-any.whl (2.5 kB)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable-baselines3) (3.13.1)\n",
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            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable-baselines3) (3.1.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable-baselines3) (2023.6.0)\n",
            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13->stable-baselines3) (2.1.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable-baselines3) (1.2.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable-baselines3) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable-baselines3) (4.44.0)\n",
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            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->stable-baselines3) (2023.3.post1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->stable-baselines3) (1.16.0)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13->stable-baselines3) (1.3.0)\n",
            "Installing collected packages: farama-notifications, gymnasium, stable-baselines3\n",
            "Successfully installed farama-notifications-0.0.4 gymnasium-0.29.1 stable-baselines3-2.1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import libraries for the Environment"
      ],
      "metadata": {
        "id": "MoiRYGJiMiK8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gym\n",
        "from gym import spaces\n",
        "import stable_baselines3\n",
        "from stable_baselines3 import PPO\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IyCVg7_YPeII",
        "outputId": "9ca4385b-1b4e-400e-9771-099342d2b9d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create a Gym environment to simulate crypto trading"
      ],
      "metadata": {
        "id": "btIGfbFzMoQr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "your_observation_size = 10\n",
        "number_of_actions = 3"
      ],
      "metadata": {
        "id": "I8LEwlGxKhsX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Round-Trip Strategy Reward Function"
      ],
      "metadata": {
        "id": "0gWIvANHM7h1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def round_trip_strategy_reward(action, current_portfolio_value, previous_portfolio_value):\n",
        "    # Implement your Round-Trip Strategy reward logic\n",
        "    if action == \"hold\":\n",
        "        reward = 0  # No reward for holding\n",
        "    elif action == \"buy\":\n",
        "        # Negative reward for buying\n",
        "        reward = -0.01 * current_portfolio_value  # Assuming a 1% cost for buying\n",
        "    elif action == \"sell\":\n",
        "        # Positive reward for selling\n",
        "        reward = 0.01 * current_portfolio_value  # Assuming a 1% gain for selling\n",
        "    else:\n",
        "        raise ValueError(\"Invalid action\")\n",
        "\n",
        "    # Additional reward/penalty based on the change in portfolio value\n",
        "    reward += 0.01 * (current_portfolio_value - previous_portfolio_value)\n",
        "\n",
        "    return reward"
      ],
      "metadata": {
        "id": "aPwUXCQJQtbP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Create a Gym environment"
      ],
      "metadata": {
        "id": "OANaC0liNCD3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CryptoTradingEnv(gym.Env):\n",
        "    def __init__(self, data):\n",
        "        super(CryptoTradingEnv, self).__init__()\n",
        "        self.data = data\n",
        "        self.current_step = 0\n",
        "\n",
        "        # Define your observation space and action space here\n",
        "        self.observation_space = spaces.Box(low=0, high=1, shape=(your_observation_size,))   ##########################################\n",
        "        self.action_space = spaces.Discrete(number_of_actions)   ##########################################\n",
        "\n",
        "    def reset(self):\n",
        "        # Reset the environment to the initial state\n",
        "        self.current_step = 0\n",
        "        self.done = False\n",
        "        # You may initialize other variables or states here if needed     ##########################################\n",
        "\n",
        "        # Return the initial observation\n",
        "        return self._get_observation()\n",
        "\n",
        "    def step(self, action):\n",
        "        # Implement the logic for taking an action and calculating the next state and reward\n",
        "        if self.done:\n",
        "            # If the episode is done, no need to take further actions\n",
        "            return self._get_observation(), 0, True, {}\n",
        "\n",
        "        # Execute the action and update the environment state\n",
        "        # Replace the following line with your logic      ##########################################\n",
        "        # Example: self.current_portfolio_value = self.execute_trade(action)\n",
        "\n",
        "        # Increment the time step\n",
        "        self.current_step += 1\n",
        "\n",
        "        # Check if the episode is done (end of data or any other condition)\n",
        "        if self.current_step == len(self.data) - 1:\n",
        "            self.done = True\n",
        "\n",
        "        # Calculate the reward based on your Round-Trip Strategy\n",
        "        reward = self.calculate_reward(action)\n",
        "\n",
        "        # Return the next observation, reward, done flag, and additional information (if any)\n",
        "        return self._get_observation(), reward, self.done, {}\n",
        "\n",
        "    def _get_observation(self):\n",
        "        # Replace this with your logic to get the current observation    ##########################################\n",
        "        # Example: observation = self.data[self.current_step]\n",
        "\n",
        "        # Placeholder: Replace with actual observation logic    ##########################################\n",
        "        observation = np.random.rand(your_observation_size)     ########################################## Replace this line with the actual logic to obtain observations from your data.\n",
        "\n",
        "        return observation\n",
        "\n",
        "    def calculate_reward(self, action):\n",
        "        # Replace this with your logic to calculate the reward based on the Round-Trip Strategy    ##########################################\n",
        "        # Example: reward = self.round_trip_strategy_reward(action)\n",
        "\n",
        "        # Replace this with your logic to calculate the reward based on the Round-Trip Strategy\n",
        "        reward = self.round_trip_strategy_reward(action, current_portfolio_value, previous_portfolio_value)\n",
        "\n",
        "        return reward"
      ],
      "metadata": {
        "id": "sgc7WwdgPeS6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Deep Q-Network (DQN)"
      ],
      "metadata": {
        "id": "HCkOfQg5Mwt1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class DQN(nn.Module):\n",
        "    def __init__(self, state_size, action_size):\n",
        "        super(DQN, self).__init__()\n",
        "        self.fc1 = nn.Linear(state_size, 64)\n",
        "        self.fc2 = nn.Linear(64, 32)\n",
        "        self.fc3 = nn.Linear(32, action_size)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.fc1(x))\n",
        "        x = torch.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x"
      ],
      "metadata": {
        "id": "VNYxWaMdQtYo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training Loop"
      ],
      "metadata": {
        "id": "qddWieoHNHVN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_episodes = 1000  # Replace 1000 with the desired number of episodes"
      ],
      "metadata": {
        "id": "emsEUu59NhGP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GgDIK38yR1sd",
        "outputId": "cbdc7312-54af-4d96-ddc4-2053b81a198d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load your financial data    ###########################  BTCUSDT\n",
        "data = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Sample Tradernet/Binance_BTCUSDT_d_EDIT.csv')\n",
        "# data = pd.read_csv('crypto_data.csv')"
      ],
      "metadata": {
        "id": "Jt1zZAFUOQlO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "env = CryptoTradingEnv(data)"
      ],
      "metadata": {
        "id": "pr6BybbVNykb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_steps = 1000  # Replace 1000 with your desired maximum number of steps"
      ],
      "metadata": {
        "id": "eV4Ar9HdT0B1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for episode in range(num_episodes):\n",
        "    state = env.reset()\n",
        "    total_reward = 0\n",
        "\n",
        "    for step in range(max_steps):\n",
        "        action = agent.select_action(state)\n",
        "        next_state, reward, done, _ = env.step(action)\n",
        "        agent.store_experience(state, action, reward, next_state, done)\n",
        "\n",
        "        # Convert state and next_state to PyTorch tensors if not already\n",
        "        state = torch.tensor(state, dtype=torch.float32)\n",
        "        next_state = torch.tensor(next_state, dtype=torch.float32)\n",
        "\n",
        "        # Update the DQN\n",
        "        optimizer.zero_grad()\n",
        "        loss = agent.compute_loss()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        state = next_state\n",
        "        total_reward += reward\n",
        "\n",
        "        if done:\n",
        "            break\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "IUHxWN74Qtdm",
        "outputId": "368d0690-aaed-44a5-897a-9508ccb28c0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-d45a0e276edd>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m         \u001b[0maction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_action\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m         \u001b[0mnext_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstore_experience\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnext_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'agent' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LifKfiixQtgH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DRyF-MpSQti3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_0O9Eu5uQtlw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}