{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code Execution\n",
    "\n",
    "\n",
    "AgentChat offers a `CodeExecutorAgent` agent that can execute code in messages it receives. \n",
    "\n",
    ":::{note}\n",
    "See [here](pkg-info-autogen-agentchat) for installation instructions.\n",
    ":::\n",
    "\n",
    ":::{warning}\n",
    "🚧 Under construction 🚧\n",
    ":::"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------------------------------------\n",
      "user:\n",
      "Create a plot of NVDIA and TSLA stock returns YTD from 2024-01-01 and save it to 'nvidia_tesla_2024_ytd.png'.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "coding_assistant:\n",
      "To create a plot of NVIDIA and TSLA stock returns from January 1, 2024, year-to-date, we will perform the following steps:\n",
      "\n",
      "1. **Install Required Libraries:**\n",
      "   We'll need `pandas`, `matplotlib`, and `yfinance` to fetch and plot the stock data. Make sure these libraries are installed.\n",
      "\n",
      "2. **Fetch the Stock Data:**\n",
      "   Use the `yfinance` library to download the stock data for NVIDIA (ticker: NVDA) and Tesla (ticker: TSLA) from January 1, 2024.\n",
      "\n",
      "3. **Calculate Stock Returns:**\n",
      "   Calculate the percentage returns of both stocks over the date range.\n",
      "\n",
      "4. **Plot the Data:**\n",
      "   Plot NVIDIA and Tesla returns using `matplotlib` and save the plot as 'nvidia_tesla_2024_ytd.png'.\n",
      "\n",
      "Here's the complete code to perform these steps:\n",
      "\n",
      "```python\n",
      "# filename: plot_nvidia_tesla_2024_ytd.py\n",
      "\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import yfinance as yf\n",
      "\n",
      "# Step 1: Fetch the Stock Data\n",
      "start_date = \"2024-01-01\"\n",
      "end_date = pd.Timestamp.now().strftime('%Y-%m-%d')\n",
      "\n",
      "nvda = yf.download('NVDA', start=start_date, end=end_date)\n",
      "tsla = yf.download('TSLA', start=start_date, end=end_date)\n",
      "\n",
      "# Step 2: Calculate the returns\n",
      "nvda['Returns'] = nvda['Adj Close'].pct_change()\n",
      "tsla['Returns'] = tsla['Adj Close'].pct_change()\n",
      "\n",
      "# Step 3: Drop the first NaN values from the dataset\n",
      "nvda = nvda.dropna(subset=['Returns'])\n",
      "tsla = tsla.dropna(subset=['Returns'])\n",
      "\n",
      "# Step 4: Plot the Data\n",
      "plt.figure(figsize=(10, 6))\n",
      "plt.plot(nvda['Returns'].index, (nvda['Returns'] + 1).cumprod() - 1, label='NVIDIA (NVDA)')\n",
      "plt.plot(tsla['Returns'].index, (tsla['Returns'] + 1).cumprod() - 1, label='Tesla (TSLA)')\n",
      "\n",
      "# Adding titles and labels\n",
      "plt.title('NVIDIA and Tesla Stock Returns YTD (2024)')\n",
      "plt.xlabel('Date')\n",
      "plt.ylabel('Cumulative Return')\n",
      "plt.legend()\n",
      "\n",
      "# Save the plot\n",
      "plt.savefig('nvidia_tesla_2024_ytd.png')\n",
      "\n",
      "# Display a confirmation message\n",
      "print(\"The plot has been saved as 'nvidia_tesla_2024_ytd.png'.\")\n",
      "```\n",
      "\n",
      "Please save the code above in a file named `plot_nvidia_tesla_2024_ytd.py` and execute it. This will generate the plot of NVIDIA and Tesla stock returns for the year 2024 up to today's date and save it as `'nvidia_tesla_2024_ytd.png'`.\n",
      "\n",
      "After running the code, check that the file `nvidia_tesla_2024_ytd.png` has been created in the directory. If there are any errors or issues, let me know so I can help you resolve them.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "code_executor:\n",
      "Traceback (most recent call last):\n",
      "  File \"/workspace/plot_nvidia_tesla_2024_ytd.py\", line 3, in <module>\n",
      "    import pandas as pd\n",
      "ModuleNotFoundError: No module named 'pandas'\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "coding_assistant:\n",
      "It seems that the `pandas` library is not installed in your environment. Let's update the script to include the installation of the required libraries within the code itself.\n",
      "\n",
      "Here's the updated script that installs the necessary libraries before executing the main tasks:\n",
      "\n",
      "```python\n",
      "# filename: plot_nvidia_tesla_2024_ytd.py\n",
      "\n",
      "import subprocess\n",
      "import sys\n",
      "\n",
      "# Function to install required libraries\n",
      "def install(package):\n",
      "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
      "\n",
      "# Install the required libraries\n",
      "try:\n",
      "    import pandas as pd\n",
      "except ImportError:\n",
      "    install(\"pandas\")\n",
      "    import pandas as pd\n",
      "    \n",
      "try:\n",
      "    import matplotlib.pyplot as plt\n",
      "except ImportError:\n",
      "    install(\"matplotlib\")\n",
      "    import matplotlib.pyplot as plt\n",
      "\n",
      "try:\n",
      "    import yfinance as yf\n",
      "except ImportError:\n",
      "    install(\"yfinance\")\n",
      "    import yfinance as yf\n",
      "\n",
      "# Step 1: Fetch the Stock Data\n",
      "start_date = \"2024-01-01\"\n",
      "end_date = pd.Timestamp.now().strftime('%Y-%m-%d')\n",
      "\n",
      "nvda = yf.download('NVDA', start=start_date, end=end_date)\n",
      "tsla = yf.download('TSLA', start=start_date, end=end_date)\n",
      "\n",
      "# Step 2: Calculate the returns\n",
      "nvda['Returns'] = nvda['Adj Close'].pct_change()\n",
      "tsla['Returns'] = tsla['Adj Close'].pct_change()\n",
      "\n",
      "# Step 3: Drop the first NaN values from the dataset\n",
      "nvda = nvda.dropna(subset=['Returns'])\n",
      "tsla = tsla.dropna(subset=['Returns'])\n",
      "\n",
      "# Step 4: Plot the Data\n",
      "plt.figure(figsize=(10, 6))\n",
      "plt.plot(nvda['Returns'].index, (nvda['Returns'] + 1).cumprod() - 1, label='NVIDIA (NVDA)')\n",
      "plt.plot(tsla['Returns'].index, (tsla['Returns'] + 1).cumprod() - 1, label='Tesla (TSLA)')\n",
      "\n",
      "# Adding titles and labels\n",
      "plt.title('NVIDIA and Tesla Stock Returns YTD (2024)')\n",
      "plt.xlabel('Date')\n",
      "plt.ylabel('Cumulative Return')\n",
      "plt.legend()\n",
      "\n",
      "# Save the plot\n",
      "plt.savefig('nvidia_tesla_2024_ytd.png')\n",
      "\n",
      "# Display a confirmation message\n",
      "print(\"The plot has been saved as 'nvidia_tesla_2024_ytd.png'.\")\n",
      "```\n",
      "\n",
      "Please save the code above in a file named `plot_nvidia_tesla_2024_ytd.py` and execute it again. This script will handle the installation of any missing libraries automatically before proceeding with the data fetching, processing, and plotting tasks.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "code_executor:\n",
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      "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\n",
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      "Successfully built peewee\n",
      "Installing collected packages: webencodings, peewee, multitasking, urllib3, soupsieve, platformdirs, lxml, idna, html5lib, frozendict, charset-normalizer, certifi, requests, beautifulsoup4, yfinance\n",
      "Successfully installed beautifulsoup4-4.12.3 certifi-2024.8.30 charset-normalizer-3.3.2 frozendict-2.4.4 html5lib-1.1 idna-3.10 lxml-5.3.0 multitasking-0.0.11 peewee-3.17.6 platformdirs-4.3.6 requests-2.32.3 soupsieve-2.6 urllib3-2.2.3 webencodings-0.5.1 yfinance-0.2.43\n",
      "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "The plot has been saved as 'nvidia_tesla_2024_ytd.png'.\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "coding_assistant:\n",
      "The plot has been successfully saved as `nvidia_tesla_2024_ytd.png`. You should now see the file in your directory. This file contains the year-to-date cumulative return plot of NVIDIA and Tesla stocks for the year 2024.\n",
      "\n",
      "If you need any further assistance, feel free to ask. TERMINATE\n",
      "\n",
      "TeamRunResult(result='The plot has been successfully saved as `nvidia_tesla_2024_ytd.png`. You should now see the file in your directory. This file contains the year-to-date cumulative return plot of NVIDIA and Tesla stocks for the year 2024.\\n\\nIf you need any further assistance, feel free to ask. TERMINATE')\n"
     ]
    }
   ],
   "source": [
    "from autogen_agentchat.agents import CodeExecutorAgent, CodingAssistantAgent\n",
    "from autogen_agentchat.teams import RoundRobinGroupChat, StopMessageTermination\n",
    "from autogen_core.components.models import OpenAIChatCompletionClient\n",
    "from autogen_ext.code_executor.docker_executor import DockerCommandLineCodeExecutor\n",
    "\n",
    "async with DockerCommandLineCodeExecutor(work_dir=\"coding\") as code_executor:  # type: ignore[syntax]\n",
    "    code_executor_agent = CodeExecutorAgent(\"code_executor\", code_executor=code_executor)\n",
    "    coding_assistant_agent = CodingAssistantAgent(\n",
    "        \"coding_assistant\", model_client=OpenAIChatCompletionClient(model=\"gpt-4o\")\n",
    "    )\n",
    "    group_chat = RoundRobinGroupChat([coding_assistant_agent, code_executor_agent])\n",
    "    result = await group_chat.run(\n",
    "        task=\"Create a plot of NVDIA and TSLA stock returns YTD from 2024-01-01 and save it to 'nvidia_tesla_2024_ytd.png'.\",\n",
    "        termination_condition=StopMessageTermination(),\n",
    "    )\n",
    "    print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(filename=\"coding/nvidia_tesla_2024_ytd.png\")  # type: ignore"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}