diff --git a/algotrading-indicator-builder.ipynb b/algotrading-indicator-builder.ipynb
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+++ b/algotrading-indicator-builder.ipynb
@@ -0,0 +1,1535 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyOZWWVZy5X7yMtpCB4OQ2xP",
+      "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/tmdcpro/ADA-vscode-visual-scripting-interface/blob/master/algotrading-indicator-builder.ipynb/" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg/" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KYNcVCyj3SW0"
+      },
+      "outputs": [],
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b339fd3a"
+      },
+      "source": [
+        "# Task\n",
+        "Create a Python script that uses the `ccxt` library to fetch historical OHLCV data for a user-specified crypto asset, timeframe, and time interval, loads it into a pandas DataFrame, calculates basic technical indicators, and visualizes the data as a candlestick chart using `plotly`. The script should also conceptually outline how AI could be integrated for natural language analysis and how trading signals could be generated and connected to exchanges."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "80335260"
+      },
+      "source": [
+        "## Set up the environment\n",
+        "\n",
+        "### Subtask:\n",
+        "Install necessary libraries like `ccxt`, `pandas`, `plotly`, and any others needed for technical analysis.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "56dd7626"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Install the necessary libraries using pip commands.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "01089f20",
+        "outputId": "6f3aa820-d811-493f-ce70-781645475e8a"
+      },
+      "source": [
+        "%pip install ccxt pandas plotly pandas_ta"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting ccxt\n",
+            "  Downloading ccxt-4.4.95-py2.py3-none-any.whl.metadata (131 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/131.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m122.9/131.6 kB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m131.6/131.6 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.2)\n",
+            "Requirement already satisfied: plotly in /usr/local/lib/python3.11/dist-packages (5.24.1)\n",
+            "Collecting pandas_ta\n",
+            "  Downloading pandas_ta-0.3.14b.tar.gz (115 kB)\n",
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/115.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.1/115.1 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Requirement already satisfied: setuptools>=60.9.0 in /usr/local/lib/python3.11/dist-packages (from ccxt) (75.2.0)\n",
+            "Requirement already satisfied: certifi>=2018.1.18 in /usr/local/lib/python3.11/dist-packages (from ccxt) (2025.7.14)\n",
+            "Requirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.11/dist-packages (from ccxt) (2.32.3)\n",
+            "Requirement already satisfied: cryptography>=2.6.1 in /usr/local/lib/python3.11/dist-packages (from ccxt) (43.0.3)\n",
+            "Requirement already satisfied: typing-extensions>=4.4.0 in /usr/local/lib/python3.11/dist-packages (from ccxt) (4.14.1)\n",
+            "Requirement already satisfied: aiohttp>=3.10.11 in /usr/local/lib/python3.11/dist-packages (from ccxt) (3.11.15)\n",
+            "Collecting aiodns>=1.1.1 (from ccxt)\n",
+            "  Downloading aiodns-3.5.0-py3-none-any.whl.metadata (5.8 kB)\n",
+            "Requirement already satisfied: yarl>=1.7.2 in /usr/local/lib/python3.11/dist-packages (from ccxt) (1.20.1)\n",
+            "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.0.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas) (2025.2)\n",
+            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.11/dist-packages (from plotly) (8.5.0)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from plotly) (25.0)\n",
+            "Collecting pycares>=4.9.0 (from aiodns>=1.1.1->ccxt)\n",
+            "  Downloading pycares-4.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.3 kB)\n",
+            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp>=3.10.11->ccxt) (2.6.1)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.11/dist-packages (from aiohttp>=3.10.11->ccxt) (1.4.0)\n",
+            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp>=3.10.11->ccxt) (25.3.0)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.11/dist-packages (from aiohttp>=3.10.11->ccxt) (1.7.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.11/dist-packages (from aiohttp>=3.10.11->ccxt) (6.6.3)\n",
+            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.11/dist-packages (from aiohttp>=3.10.11->ccxt) (0.3.2)\n",
+            "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.11/dist-packages (from cryptography>=2.6.1->ccxt) (1.17.1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas) (1.17.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.18.4->ccxt) (3.4.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.18.4->ccxt) (3.10)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.18.4->ccxt) (2.4.0)\n",
+            "Requirement already satisfied: pycparser in /usr/local/lib/python3.11/dist-packages (from cffi>=1.12->cryptography>=2.6.1->ccxt) (2.22)\n",
+            "Downloading ccxt-4.4.95-py2.py3-none-any.whl (5.7 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m5.7/5.7 MB\u001b[0m \u001b[31m61.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading aiodns-3.5.0-py3-none-any.whl (8.1 kB)\n",
+            "Downloading pycares-4.9.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (627 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m628.0/628.0 kB\u001b[0m \u001b[31m29.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hBuilding wheels for collected packages: pandas_ta\n",
+            "  Building wheel for pandas_ta (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for pandas_ta: filename=pandas_ta-0.3.14b0-py3-none-any.whl size=218910 sha256=6d9ec48f8f71d8f9df49c31a71f72a170f576b6f9753ff4d6040186b0f021287\n",
+            "  Stored in directory: /root/.cache/pip/wheels/7f/33/8b/50b245c5c65433cd8f5cb24ac15d97e5a3db2d41a8b6ae957d\n",
+            "Successfully built pandas_ta\n",
+            "Installing collected packages: pycares, pandas_ta, aiodns, ccxt\n",
+            "Successfully installed aiodns-3.5.0 ccxt-4.4.95 pandas_ta-0.3.14b0 pycares-4.9.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f369bc25"
+      },
+      "source": [
+        "## Load historical market data\n",
+        "\n",
+        "### Subtask:\n",
+        "Write code to connect to an exchange using `ccxt`, fetch historical OHLCV data for a user-specified crypto asset, timeframe, and time interval, and load it into a pandas DataFrame.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ee32083e"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Import necessary libraries, define parameters, instantiate the exchange, fetch OHLCV data, and convert it to a pandas DataFrame.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "id": "ccd6a42c",
+        "outputId": "c216d247-847a-431e-d270-86b0f68847e5"
+      },
+      "source": [
+        "import ccxt\n",
+        "import pandas as pd\n",
+        "import datetime\n",
+        "\n",
+        "exchange_id = 'binance'\n",
+        "symbol = 'BTC/USDT'\n",
+        "timeframe = '1d'\n",
+        "since = exchange.parse8601('2023-01-01T00:00:00Z')\n",
+        "\n",
+        "exchange = ccxt.binance()\n",
+        "\n",
+        "ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since)\n",
+        "\n",
+        "df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])\n",
+        "df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n",
+        "df.set_index('timestamp', inplace=True)\n",
+        "\n",
+        "display(df.head())"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'exchange' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-2-4274126448.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0msymbol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'BTC/USDT'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtimeframe\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'1d'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0msince\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexchange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse8601\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2023-01-01T00:00:00Z'\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      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mexchange\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mccxt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbinance\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;31mNameError\u001b[0m: name 'exchange' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b3368d55"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "The previous code failed because the `exchange` variable was used before it was assigned. The code block will be regenerated with the correct order of operations.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 502
+        },
+        "id": "RST9yVta4vTq",
+        "outputId": "01671f15-b7ca-42d1-a024-4585e80ef8c4"
+      },
+      "source": [
+        "import ccxt\n",
+        "import pandas as pd\n",
+        "import datetime\n",
+        "\n",
+        "exchange_id = 'binance'\n",
+        "symbol = 'BTC/USDT'\n",
+        "timeframe = '1d'\n",
+        "\n",
+        "exchange = ccxt.binance()\n",
+        "since = exchange.parse8601('2023-01-01T00:00:00Z')\n",
+        "\n",
+        "ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since)\n",
+        "\n",
+        "df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])\n",
+        "df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n",
+        "df.set_index('timestamp', inplace=True)\n",
+        "\n",
+        "display(df.head())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ExchangeNotAvailable",
+          "evalue": "binance GET https://api.binance.com/api/v3/exchangeInfo 451  {\n  \"code\": 0,\n  \"msg\": \"Service unavailable from a restricted location according to 'b. Eligibility' in https://www.binance.com/en/terms. Please contact customer service if you believe you received this message in error.\"\n}",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ccxt/base/exchange.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, url, method, headers, body)\u001b[0m\n\u001b[1;32m    581\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"%s %s, Response: %s %s %s\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_status_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_response\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m             \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_status\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[0m\u001b[1;32m    583\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/requests/models.py\u001b[0m in \u001b[0;36mraise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1023\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1024\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_error_msg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\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   1025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mHTTPError\u001b[0m: 451 Client Error:  for url: https://api.binance.com/api/v3/exchangeInfo",
+            "\nDuring handling of the above exception, another exception occurred:\n",
+            "\u001b[0;31mExchangeNotAvailable\u001b[0m                      Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-3-3488515890.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0msince\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexchange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse8601\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'2023-01-01T00:00:00Z'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mohlcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexchange\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch_ohlcv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msymbol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeframe\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msince\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     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mohlcv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'open'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'high'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'low'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'close'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'volume'\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/usr/local/lib/python3.11/dist-packages/ccxt/binance.py\u001b[0m in \u001b[0;36mfetch_ohlcv\u001b[0;34m(self, symbol, timeframe, since, limit, params)\u001b[0m\n\u001b[1;32m   4445\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mreturns\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mA\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mcandles\u001b[0m \u001b[0mordered\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvolume\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4446\u001b[0m         \"\"\"\n\u001b[0;32m-> 4447\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_markets\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[0m\u001b[1;32m   4448\u001b[0m         \u001b[0mpaginate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4449\u001b[0m         \u001b[0mpaginate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_option_and_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fetchOHLCV'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'paginate'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ccxt/base/exchange.py\u001b[0m in \u001b[0;36mload_markets\u001b[0;34m(self, reload, params)\u001b[0m\n\u001b[1;32m   1551\u001b[0m             \u001b[0mcurrencies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch_currencies\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[1;32m   1552\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cachedCurrencies'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcurrencies\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1553\u001b[0;31m         \u001b[0mmarkets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch_markets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\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   1554\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;34m'cachedCurrencies'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1555\u001b[0m             \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'cachedCurrencies'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ccxt/binance.py\u001b[0m in \u001b[0;36mfetch_markets\u001b[0;34m(self, params)\u001b[0m\n\u001b[1;32m   3039\u001b[0m             \u001b[0mmarketType\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfetchMarkets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3040\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmarketType\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'spot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3041\u001b[0;31m                 \u001b[0mpromisesRaw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpublicGetExchangeInfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\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[0m\u001b[1;32m   3042\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mfetchMargins\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_required_credentials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msandboxMode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3043\u001b[0m                     \u001b[0mpromisesRaw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msapiGetMarginAllPairs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\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/usr/local/lib/python3.11/dist-packages/ccxt/base/types.py\u001b[0m in \u001b[0;36munbound_method\u001b[0;34m(_self, params)\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0munbound_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_self\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\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---> 35\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_self\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\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     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munbound_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munbound_method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ccxt/binance.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, path, api, method, params, headers, body, config)\u001b[0m\n\u001b[1;32m  11336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11337\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'public'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'GET'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m}\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> 11338\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\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  11339\u001b[0m         \u001b[0;31m# a workaround for {\"code\":-2015,\"msg\":\"Invalid API-key, IP, or permissions for action.\"}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11340\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mapi\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'private'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ccxt/base/exchange.py\u001b[0m in \u001b[0;36mfetch2\u001b[0;34m(self, path, api, method, params, headers, body, config)\u001b[0m\n\u001b[1;32m   4523\u001b[0m                             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretryDelay\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4524\u001b[0m                     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4525\u001b[0;31m                         \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4526\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4527\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ccxt/base/exchange.py\u001b[0m in \u001b[0;36mfetch2\u001b[0;34m(self, path, api, method, params, headers, body, config)\u001b[0m\n\u001b[1;32m   4514\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretries\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\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[1;32m   4515\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4516\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'url'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'method'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'headers'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'body'\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[0m\u001b[1;32m   4517\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4518\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOperationFailed\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/usr/local/lib/python3.11/dist-packages/ccxt/base/exchange.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, url, method, headers, body)\u001b[0m\n\u001b[1;32m    598\u001b[0m             \u001b[0mskip_further_error_handling\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_errors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_status_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_status_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_response\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mjson_response\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_headers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_body\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    599\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mskip_further_error_handling\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 600\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandle_http_status_code\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_status_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_status_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_response\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    601\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mExchangeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdetails\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/ccxt/base/exchange.py\u001b[0m in \u001b[0;36mhandle_http_status_code\u001b[0;34m(self, code, reason, url, method, body)\u001b[0m\n\u001b[1;32m   1731\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcodeAsString\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhttpExceptions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1732\u001b[0m             \u001b[0mErrorClass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhttpExceptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcodeAsString\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1733\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mErrorClass\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcodeAsString\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mbody\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   1734\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mExchangeNotAvailable\u001b[0m: binance GET https://api.binance.com/api/v3/exchangeInfo 451  {\n  \"code\": 0,\n  \"msg\": \"Service unavailable from a restricted location according to 'b. Eligibility' in https://www.binance.com/en/terms. Please contact customer service if you believe you received this message in error.\"\n}"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4c6ab979"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "The previous attempt to fetch data from Binance failed due to geographical restrictions. I will try fetching data from a different exchange that might not have the same restrictions, like Kraken.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 238
+        },
+        "id": "b_jTznw644K5",
+        "outputId": "14e56bb2-e039-4703-e2f4-4409e01bbffb"
+      },
+      "source": [
+        "import ccxt\n",
+        "import pandas as pd\n",
+        "import datetime\n",
+        "\n",
+        "exchange_id = 'kraken'\n",
+        "symbol = 'BTC/USDT'\n",
+        "timeframe = '1d'\n",
+        "\n",
+        "exchange = ccxt.kraken()\n",
+        "since = exchange.parse8601('2023-01-01T00:00:00Z')\n",
+        "\n",
+        "ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since)\n",
+        "\n",
+        "df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])\n",
+        "df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n",
+        "df.set_index('timestamp', inplace=True)\n",
+        "\n",
+        "display(df.head())"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "               open     high      low    close      volume\n",
+              "timestamp                                                 \n",
+              "2023-08-01  29229.9  29726.3  28723.0  29726.3  404.129843\n",
+              "2023-08-02  29719.1  30032.9  28938.5  29164.5  346.822895\n",
+              "2023-08-03  29180.4  29424.2  28978.5  29194.5  163.807235\n",
+              "2023-08-04  29192.2  29326.0  28825.0  29115.6  204.277551\n",
+              "2023-08-05  29113.1  29143.7  28990.0  29070.3  134.234632"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-67666125-9365-4207-8518-1c7feb085be7\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>open</th>\n",
+              "      <th>high</th>\n",
+              "      <th>low</th>\n",
+              "      <th>close</th>\n",
+              "      <th>volume</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>timestamp</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>2023-08-01</th>\n",
+              "      <td>29229.9</td>\n",
+              "      <td>29726.3</td>\n",
+              "      <td>28723.0</td>\n",
+              "      <td>29726.3</td>\n",
+              "      <td>404.129843</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-08-02</th>\n",
+              "      <td>29719.1</td>\n",
+              "      <td>30032.9</td>\n",
+              "      <td>28938.5</td>\n",
+              "      <td>29164.5</td>\n",
+              "      <td>346.822895</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-08-03</th>\n",
+              "      <td>29180.4</td>\n",
+              "      <td>29424.2</td>\n",
+              "      <td>28978.5</td>\n",
+              "      <td>29194.5</td>\n",
+              "      <td>163.807235</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-08-04</th>\n",
+              "      <td>29192.2</td>\n",
+              "      <td>29326.0</td>\n",
+              "      <td>28825.0</td>\n",
+              "      <td>29115.6</td>\n",
+              "      <td>204.277551</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2023-08-05</th>\n",
+              "      <td>29113.1</td>\n",
+              "      <td>29143.7</td>\n",
+              "      <td>28990.0</td>\n",
+              "      <td>29070.3</td>\n",
+              "      <td>134.234632</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67666125-9365-4207-8518-1c7feb085be7')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg/" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
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+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
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+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
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+              "    width: 32px;\n",
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+              "  .colab-df-quickchart:hover {\n",
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+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
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+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
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+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
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+              "    60% {\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-8f21d4d6-5136-427c-a25f-3f243416e269 button');\n",
+              "          quickchartButtonEl.style.display =\n",
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+            ],
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+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5beffa2d"
+      },
+      "source": [
+        "## Perform basic data analysis\n",
+        "\n",
+        "### Subtask:\n",
+        "Calculate basic technical indicators (e.g., Moving Averages, RSI) using a library like `pandas_ta` and add them to the DataFrame.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9302e160"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Calculate the RSI and SMA indicators and add them to the DataFrame.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "393d3719",
+        "outputId": "a56cb1fc-7f93-469b-edc2-ebfb884b8c12"
+      },
+      "source": [
+        "import pandas_ta as ta\n",
+        "\n",
+        "df['RSI'] = df.ta.rsi(length=14)\n",
+        "df['SMA_20'] = df.ta.sma(length=20)\n",
+        "\n",
+        "display(df.head())"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ImportError",
+          "evalue": "cannot import name 'NaN' from 'numpy' (/usr/local/lib/python3.11/dist-packages/numpy/__init__.py)",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-5-3926056589.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas_ta\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RSI'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrsi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SMA_20'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    114\u001b[0m }\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcandles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcycles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mimport\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     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverlap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperformance\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/momentum/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msmi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msmi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqueeze_pro\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqueeze_pro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstc\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstoch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/momentum/squeeze_pro.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# -*- coding: utf-8 -*-\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNaN\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnpNaN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverlap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mema\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mImportError\u001b[0m: cannot import name 'NaN' from 'numpy' (/usr/local/lib/python3.11/dist-packages/numpy/__init__.py)",
+            "",
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
+          ],
+          "errorDetails": {
+            "actions": [
+              {
+                "action": "open_url",
+                "actionText": "Open Examples",
+                "url": "/notebooks/snippets/importing_libraries.ipynb"
+              }
+            ]
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "114de886"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "There was an ImportError when trying to import `NaN` from `numpy` within `pandas_ta`. This might be due to an older version of numpy or pandas_ta that has a compatibility issue. I will try installing a specific version of `pandas_ta` that is known to be compatible with common numpy versions. If that doesn't work, I will try upgrading numpy as well in a subsequent step.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NrbCNC1N5Noa",
+        "outputId": "a65fee33-9301-4f27-c45a-8816ce8eb7f0"
+      },
+      "source": [
+        "%pip install pandas_ta==0.3.1c0"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[31mERROR: Could not find a version that satisfies the requirement pandas_ta==0.3.1c0 (from versions: 0.3.14b0)\u001b[0m\u001b[31m\n",
+            "\u001b[0m\u001b[31mERROR: No matching distribution found for pandas_ta==0.3.1c0\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "791eb723"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "The specified version of `pandas_ta` was not found. I will try installing the latest version of `pandas_ta` again, and if the error persists, I will then try upgrading `numpy` to a recent version to see if that resolves the compatibility issue.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "34da0841",
+        "outputId": "52c67380-c659-46b3-a349-4a68156b667e"
+      },
+      "source": [
+        "%pip install --upgrade pandas_ta"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: pandas_ta in /usr/local/lib/python3.11/dist-packages (0.3.14b0)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from pandas_ta) (2.2.2)\n",
+            "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas->pandas_ta) (2.0.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->pandas_ta) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->pandas_ta) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->pandas_ta) (2025.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->pandas_ta) (1.17.0)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c87d656e"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Upgrading `pandas_ta` did not resolve the issue as the latest version is already installed. The error message points to an `ImportError` from `numpy`. I will try upgrading `numpy` to see if it resolves the compatibility issue with `pandas_ta`.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 453
+        },
+        "id": "f2f4dbbd",
+        "outputId": "1a8ef436-1415-44d0-cc1c-88015a070ca1"
+      },
+      "source": [
+        "%pip install --upgrade numpy"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.11/dist-packages (2.0.2)\n",
+            "Collecting numpy\n",
+            "  Downloading numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl.metadata (62 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.1/62.1 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading numpy-2.3.1-cp311-cp311-manylinux_2_28_x86_64.whl (16.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m16.9/16.9 MB\u001b[0m \u001b[31m91.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: numpy\n",
+            "  Attempting uninstall: numpy\n",
+            "    Found existing installation: numpy 2.0.2\n",
+            "    Uninstalling numpy-2.0.2:\n",
+            "      Successfully uninstalled numpy-2.0.2\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "opencv-python-headless 4.12.0.88 requires numpy<2.3.0,>=2; python_version >= \"3.9\", but you have numpy 2.3.1 which is incompatible.\n",
+            "cupy-cuda12x 13.3.0 requires numpy<2.3,>=1.22, but you have numpy 2.3.1 which is incompatible.\n",
+            "numba 0.60.0 requires numpy<2.1,>=1.22, but you have numpy 2.3.1 which is incompatible.\n",
+            "tensorflow 2.18.0 requires numpy<2.1.0,>=1.26.0, but you have numpy 2.3.1 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed numpy-2.3.1\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "numpy"
+                ]
+              },
+              "id": "1306129f7e234d7db219108e58ac9699"
+            }
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dda446b6"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Numpy has been upgraded. I will now attempt to calculate the technical indicators again using the updated numpy version and pandas_ta.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 202
+        },
+        "id": "8a12614e",
+        "outputId": "d15fd1b1-8f54-4205-9d3a-b711ed45f0a6"
+      },
+      "source": [
+        "df['RSI'] = df.ta.rsi(length=14)\n",
+        "df['SMA_20'] = df.ta.sma(length=20)\n",
+        "\n",
+        "display(df.head())"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "NameError",
+          "evalue": "name 'df' is not defined",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-1-3419467016.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RSI'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrsi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\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      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SMA_20'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\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;31mNameError\u001b[0m: name 'df' is not defined"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e8174116"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "The DataFrame `df` is not defined in the current session. I need to re-run the code that loads the historical data into the DataFrame before calculating the indicators.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "9yfRx04d5_lD",
+        "outputId": "211af9cb-c2d6-4414-9bc8-220dc6ee2cee"
+      },
+      "source": [
+        "import ccxt\n",
+        "import pandas as pd\n",
+        "import datetime\n",
+        "\n",
+        "exchange_id = 'kraken'\n",
+        "symbol = 'BTC/USDT'\n",
+        "timeframe = '1d'\n",
+        "\n",
+        "exchange = ccxt.kraken()\n",
+        "since = exchange.parse8601('2023-01-01T00:00:00Z')\n",
+        "\n",
+        "ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since)\n",
+        "\n",
+        "df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])\n",
+        "df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')\n",
+        "df.set_index('timestamp', inplace=True)\n",
+        "\n",
+        "import pandas_ta as ta\n",
+        "\n",
+        "df['RSI'] = df.ta.rsi(length=14)\n",
+        "df['SMA_20'] = df.ta.sma(length=20)\n",
+        "\n",
+        "display(df.head())"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ImportError",
+          "evalue": "cannot import name 'NaN' from 'numpy' (/usr/local/lib/python3.11/dist-packages/numpy/__init__.py)",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-2-2436672758.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timestamp'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas_ta\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RSI'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrsi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    114\u001b[0m }\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcandles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcycles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mimport\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     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverlap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperformance\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/momentum/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msmi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msmi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqueeze_pro\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqueeze_pro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstc\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstoch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/momentum/squeeze_pro.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# -*- coding: utf-8 -*-\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNaN\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnpNaN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverlap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mema\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mImportError\u001b[0m: cannot import name 'NaN' from 'numpy' (/usr/local/lib/python3.11/dist-packages/numpy/__init__.py)",
+            "",
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
+          ],
+          "errorDetails": {
+            "actions": [
+              {
+                "action": "open_url",
+                "actionText": "Open Examples",
+                "url": "/notebooks/snippets/importing_libraries.ipynb"
+              }
+            ]
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "72952a46"
+      },
+      "source": [
+        "## Perform basic data analysis\n",
+        "\n",
+        "### Subtask:\n",
+        "Retry calculating basic technical indicators (e.g., Moving Averages, RSI) using a library like `pandas_ta` and add them to the DataFrame, considering the previous import error.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "34a7cab9"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Calculate the 14-period RSI and 20-period SMA and add them to the DataFrame using pandas_ta. Display the head of the DataFrame to verify the new columns.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "f1456277",
+        "outputId": "326c4a53-02a9-40fc-f88f-4e5e7a764204"
+      },
+      "source": [
+        "import pandas_ta as ta\n",
+        "\n",
+        "df['RSI'] = df.ta.rsi(length=14)\n",
+        "df['SMA_20'] = df.ta.sma(length=20)\n",
+        "\n",
+        "display(df.head())"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ImportError",
+          "evalue": "cannot import name 'NaN' from 'numpy' (/usr/local/lib/python3.11/dist-packages/numpy/__init__.py)",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-3-3926056589.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas_ta\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RSI'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrsi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SMA_20'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    114\u001b[0m }\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcandles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcycles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mimport\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     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverlap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperformance\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/momentum/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msmi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msmi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqueeze_pro\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqueeze_pro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstc\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstoch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/momentum/squeeze_pro.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# -*- coding: utf-8 -*-\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNaN\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnpNaN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverlap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mema\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mImportError\u001b[0m: cannot import name 'NaN' from 'numpy' (/usr/local/lib/python3.11/dist-packages/numpy/__init__.py)",
+            "",
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
+          ],
+          "errorDetails": {
+            "actions": [
+              {
+                "action": "open_url",
+                "actionText": "Open Examples",
+                "url": "/notebooks/snippets/importing_libraries.ipynb"
+              }
+            ]
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ebe2d886"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "The import error from `numpy` persists, indicating a potential conflict or issue with the installed numpy version or its interaction with pandas_ta. Since the subtask is to calculate technical indicators using pandas_ta, and this import error prevents it, I will try to uninstall and reinstall pandas_ta to see if that resolves the dependency issue.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "0LU8rbuy6Xjb",
+        "outputId": "f82d5ab4-9951-43f8-dced-be97c7643777"
+      },
+      "source": [
+        "%pip uninstall pandas_ta -y\n",
+        "%pip install pandas_ta"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Found existing installation: pandas_ta 0.3.14b0\n",
+            "Uninstalling pandas_ta-0.3.14b0:\n",
+            "  Successfully uninstalled pandas_ta-0.3.14b0\n",
+            "Collecting pandas_ta\n",
+            "  Using cached pandas_ta-0.3.14b0-py3-none-any.whl\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (from pandas_ta) (2.2.2)\n",
+            "Requirement already satisfied: numpy>=1.23.2 in /usr/local/lib/python3.11/dist-packages (from pandas->pandas_ta) (2.3.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas->pandas_ta) (2.9.0.post0)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.11/dist-packages (from pandas->pandas_ta) (2025.2)\n",
+            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas->pandas_ta) (2025.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas->pandas_ta) (1.17.0)\n",
+            "Installing collected packages: pandas_ta\n",
+            "Successfully installed pandas_ta-0.3.14b0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dd5cc063"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Now that pandas_ta has been reinstalled, I will attempt to import it and calculate the technical indicators again.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "id": "fa4eb565",
+        "outputId": "7d0a8374-5f1c-4fe7-81b1-678d10a1f19e"
+      },
+      "source": [
+        "import pandas_ta as ta\n",
+        "\n",
+        "df['RSI'] = df.ta.rsi(length=14)\n",
+        "df['SMA_20'] = df.ta.sma(length=20)\n",
+        "\n",
+        "display(df.head())"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "ImportError",
+          "evalue": "cannot import name 'NaN' from 'numpy' (/usr/local/lib/python3.11/dist-packages/numpy/__init__.py)",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-5-3926056589.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas_ta\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'RSI'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrsi\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'SMA_20'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msma\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    114\u001b[0m }\n\u001b[1;32m    115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/core.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcandles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcycles\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mimport\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     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverlap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mperformance\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/momentum/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msmi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msmi\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqueeze\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0msqueeze_pro\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msqueeze_pro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstc\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstoch\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pandas_ta/momentum/squeeze_pro.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# -*- coding: utf-8 -*-\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mNaN\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnpNaN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmomentum\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas_ta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moverlap\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mema\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mImportError\u001b[0m: cannot import name 'NaN' from 'numpy' (/usr/local/lib/python3.11/dist-packages/numpy/__init__.py)",
+            "",
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
+          ],
+          "errorDetails": {
+            "actions": [
+              {
+                "action": "open_url",
+                "actionText": "Open Examples",
+                "url": "/notebooks/snippets/importing_libraries.ipynb"
+              }
+            ]
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4e0bc93e"
+      },
+      "source": [
+        "## Visualize the data\n",
+        "\n",
+        "### Subtask:\n",
+        "Create an interactive candlestick chart of the OHLCV data using `plotly`, and optionally add the calculated technical indicators to the chart.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b6f75a31"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Create the candlestick chart and add technical indicators using plotly.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "6dafee77",
+        "outputId": "a30f9ae6-67a0-478e-c0d0-278c665ca72c"
+      },
+      "source": [
+        "import plotly.graph_objects as go\n",
+        "\n",
+        "fig = go.Figure()\n",
+        "\n",
+        "fig.add_trace(go.Candlestick(x=df.index,\n",
+        "                             open=df['open'],\n",
+        "                             high=df['high'],\n",
+        "                             low=df['low'],\n",
+        "                             close=df['close'],\n",
+        "                             name='Candlestick'))\n",
+        "\n",
+        "if 'RSI' in df.columns:\n",
+        "    fig.add_trace(go.Scatter(x=df.index, y=df['RSI'], mode='lines', name='RSI'))\n",
+        "\n",
+        "if 'SMA_20' in df.columns:\n",
+        "    fig.add_trace(go.Scatter(x=df.index, y=df['SMA_20'], mode='lines', name='SMA_20'))\n",
+        "\n",
+        "fig.update_layout(\n",
+        "    title='BTC/USDT Candlestick Chart with Technical Indicators',\n",
+        "    xaxis_title='Date',\n",
+        "    yaxis_title='Price'\n",
+        ")\n",
+        "\n",
+        "fig.show()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.35.2.min.js/"></script>                <div id=\"6af3b14e-85c3-43e9-b3b8-323a48faaa44\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"6af3b14e-85c3-43e9-b3b8-323a48faaa44\")) {                    Plotly.newPlot(                        \"6af3b14e-85c3-43e9-b3b8-323a48faaa44\",                        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+              "                            \n",
+              "var gd = document.getElementById('6af3b14e-85c3-43e9-b3b8-323a48faaa44');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "19c4f030"
+      },
+      "source": [
+        "## Enable ai interaction (conceptual)\n",
+        "\n",
+        "### Subtask:\n",
+        "Outline how you could potentially integrate AI for natural language prompting and analysis, acknowledging the complexity of building a robust natural language interface within the notebook.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "945966a1"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Outline how AI could be integrated for natural language processing and analysis, acknowledging the complexity of building a robust natural language interface within the notebook, as per the instructions.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ed6d27e8",
+        "outputId": "eed1f96c-03e8-4590-83e6-bc54d187a0fc"
+      },
+      "source": [
+        "print(\"\"\"\n",
+        "Conceptual Outline for AI Integration (Natural Language Processing and Analysis):\n",
+        "\n",
+        "1.  **Natural Language Understanding (NLU):**\n",
+        "    *   Develop or integrate an NLU model to process user input in natural language. This model would need to identify key entities (e.g., \"RSI\", \"Moving Average\", \"50 days\", \"BTC/USDT\") and the user's intent (e.g., \"query value\", \"plot indicator\", \"change asset\").\n",
+        "    *   This involves techniques like tokenization, part-of-speech tagging, named entity recognition, and dependency parsing to understand the structure and meaning of the user's query.\n",
+        "\n",
+        "2.  **Intent Recognition and Slot Filling:**\n",
+        "    *   Based on the NLU output, classify the user's intent. Possible intents could include:\n",
+        "        *   Get current value of an indicator.\n",
+        "        *   Plot an indicator on the chart.\n",
+        "        *   Change the asset or timeframe.\n",
+        "        *   Perform a specific analysis (e.g., \"Find divergence\").\n",
+        "    *   Extract relevant parameters (slots) from the query, such as the indicator name, time period, asset symbol, etc.\n",
+        "\n",
+        "3.  **Mapping to Code Execution:**\n",
+        "    *   Once the intent and slots are identified, map them to corresponding Python functions or code snippets within the notebook. For example, the intent \"Get current value of RSI\" with the slot \"RSI\" would map to accessing the last value of the 'RSI' column in the DataFrame.\n",
+        "    *   Queries like \"Plot the 20-day SMA\" would trigger the calculation of the SMA if not already present and add a scatter trace to the plotly figure.\n",
+        "\n",
+        "4.  **Generating Responses:**\n",
+        "    *   Formulate a natural language response to the user's query. This could be displaying the requested data, confirming an action has been taken (like updating the chart), or asking for clarification if the query is ambiguous.\n",
+        "\n",
+        "**Potential Applications of NLP in this Context:**\n",
+        "\n",
+        "*   **Querying Data and Indicators:** Users could ask questions like:\n",
+        "    *   \"What is the latest closing price?\"\n",
+        "    *   \"Tell me the current value of the 14-day RSI.\"\n",
+        "    *   \"Show me the 50-day simple moving average.\"\n",
+        "    *   \"What was the volume yesterday?\"\n",
+        "*   **Customizing Visualizations:** Users could request modifications to the chart:\n",
+        "    *   \"Add the Bollinger Bands to the chart.\"\n",
+        "    *   \"Change the timeframe to 4 hours.\"\n",
+        "    *   \"Plot the MACD indicator.\"\n",
+        "*   **Performing Basic Analysis:** Users could initiate simple analytical tasks:\n",
+        "    *   \"Check if BTC is overbought based on RSI.\"\n",
+        "    *   \"Find the highest price in the last week.\"\n",
+        "\n",
+        "**Challenges and Complexities:**\n",
+        "\n",
+        "*   **Robust Language Parsing:** Natural language is highly variable and ambiguous. Handling synonyms, variations in phrasing, and grammatical errors requires sophisticated parsing capabilities.\n",
+        "*   **Intent Recognition Accuracy:** Accurately identifying the user's true intent is crucial. Misinterpreting an intent can lead to incorrect actions.\n",
+        "*   **Mapping Complexity:** Mapping a wide range of natural language queries to specific code functions can be complex and requires a comprehensive understanding of the available operations and data.\n",
+        "*   **Context Management:** Understanding follow-up questions or queries that depend on previous interactions adds another layer of complexity.\n",
+        "*   **Error Handling:** Gracefully handling queries that cannot be understood or executed is essential for a good user experience.\n",
+        "*   **Domain Specificity:** Training an NLP model to understand financial and trading terminology requires a specialized dataset.\n",
+        "\n",
+        "**Scope Limitation:**\n",
+        "\n",
+        "Building a fully functional and robust natural language interface capable of handling a wide range of queries and complexities is a significant undertaking. It would likely involve:\n",
+        "\n",
+        "*   Developing or integrating a dedicated NLP engine or framework.\n",
+        "*   Creating a comprehensive knowledge base of possible intents, entities, and their mappings to code.\n",
+        "*   Extensive training data for the NLU and intent recognition models.\n",
+        "*   A more sophisticated architecture for managing the interaction flow and state.\n",
+        "\n",
+        "Therefore, a full implementation of such an AI interaction system is beyond the scope of this notebook, which focuses on the core data fetching, analysis, and visualization aspects. This outline provides a conceptual framework for how such an integration *could* be approached.\n",
+        "\"\"\")"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Conceptual Outline for AI Integration (Natural Language Processing and Analysis):\n",
+            "\n",
+            "1.  **Natural Language Understanding (NLU):**\n",
+            "    *   Develop or integrate an NLU model to process user input in natural language. This model would need to identify key entities (e.g., \"RSI\", \"Moving Average\", \"50 days\", \"BTC/USDT\") and the user's intent (e.g., \"query value\", \"plot indicator\", \"change asset\").\n",
+            "    *   This involves techniques like tokenization, part-of-speech tagging, named entity recognition, and dependency parsing to understand the structure and meaning of the user's query.\n",
+            "\n",
+            "2.  **Intent Recognition and Slot Filling:**\n",
+            "    *   Based on the NLU output, classify the user's intent. Possible intents could include:\n",
+            "        *   Get current value of an indicator.\n",
+            "        *   Plot an indicator on the chart.\n",
+            "        *   Change the asset or timeframe.\n",
+            "        *   Perform a specific analysis (e.g., \"Find divergence\").\n",
+            "    *   Extract relevant parameters (slots) from the query, such as the indicator name, time period, asset symbol, etc.\n",
+            "\n",
+            "3.  **Mapping to Code Execution:**\n",
+            "    *   Once the intent and slots are identified, map them to corresponding Python functions or code snippets within the notebook. For example, the intent \"Get current value of RSI\" with the slot \"RSI\" would map to accessing the last value of the 'RSI' column in the DataFrame.\n",
+            "    *   Queries like \"Plot the 20-day SMA\" would trigger the calculation of the SMA if not already present and add a scatter trace to the plotly figure.\n",
+            "\n",
+            "4.  **Generating Responses:**\n",
+            "    *   Formulate a natural language response to the user's query. This could be displaying the requested data, confirming an action has been taken (like updating the chart), or asking for clarification if the query is ambiguous.\n",
+            "\n",
+            "**Potential Applications of NLP in this Context:**\n",
+            "\n",
+            "*   **Querying Data and Indicators:** Users could ask questions like:\n",
+            "    *   \"What is the latest closing price?\"\n",
+            "    *   \"Tell me the current value of the 14-day RSI.\"\n",
+            "    *   \"Show me the 50-day simple moving average.\"\n",
+            "    *   \"What was the volume yesterday?\"\n",
+            "*   **Customizing Visualizations:** Users could request modifications to the chart:\n",
+            "    *   \"Add the Bollinger Bands to the chart.\"\n",
+            "    *   \"Change the timeframe to 4 hours.\"\n",
+            "    *   \"Plot the MACD indicator.\"\n",
+            "*   **Performing Basic Analysis:** Users could initiate simple analytical tasks:\n",
+            "    *   \"Check if BTC is overbought based on RSI.\"\n",
+            "    *   \"Find the highest price in the last week.\"\n",
+            "\n",
+            "**Challenges and Complexities:**\n",
+            "\n",
+            "*   **Robust Language Parsing:** Natural language is highly variable and ambiguous. Handling synonyms, variations in phrasing, and grammatical errors requires sophisticated parsing capabilities.\n",
+            "*   **Intent Recognition Accuracy:** Accurately identifying the user's true intent is crucial. Misinterpreting an intent can lead to incorrect actions.\n",
+            "*   **Mapping Complexity:** Mapping a wide range of natural language queries to specific code functions can be complex and requires a comprehensive understanding of the available operations and data.\n",
+            "*   **Context Management:** Understanding follow-up questions or queries that depend on previous interactions adds another layer of complexity.\n",
+            "*   **Error Handling:** Gracefully handling queries that cannot be understood or executed is essential for a good user experience.\n",
+            "*   **Domain Specificity:** Training an NLP model to understand financial and trading terminology requires a specialized dataset.\n",
+            "\n",
+            "**Scope Limitation:**\n",
+            "\n",
+            "Building a fully functional and robust natural language interface capable of handling a wide range of queries and complexities is a significant undertaking. It would likely involve:\n",
+            "\n",
+            "*   Developing or integrating a dedicated NLP engine or framework.\n",
+            "*   Creating a comprehensive knowledge base of possible intents, entities, and their mappings to code.\n",
+            "*   Extensive training data for the NLU and intent recognition models.\n",
+            "*   A more sophisticated architecture for managing the interaction flow and state.\n",
+            "\n",
+            "Therefore, a full implementation of such an AI interaction system is beyond the scope of this notebook, which focuses on the core data fetching, analysis, and visualization aspects. This outline provides a conceptual framework for how such an integration *could* be approached.\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2cb545c0"
+      },
+      "source": [
+        "## Generate trading signals (conceptual)\n",
+        "\n",
+        "### Subtask:\n",
+        "Discuss how you could approach generating trading signals based on indicators and parameters, and the potential for connecting to exchanges via webhooks/APIs, again noting the complexity of a full implementation.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f8e3d064"
+      },
+      "source": [
+        "**Reasoning**:\n",
+        "Write a detailed explanation of how trading signals could be generated from technical indicators, explain how these signals could theoretically be used to trigger trading actions, outline potential mechanisms for connecting to exchanges, discuss the complexities and risks, and state that a full implementation is beyond the scope.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bf8f0e40",
+        "outputId": "39f3b449-9ab8-4deb-a5d9-348be3a23dfe"
+      },
+      "source": [
+        "print(\"\"\"\n",
+        "Conceptual Outline for Trading Signal Generation and Execution:\n",
+        "\n",
+        "1.  **Trading Signal Generation:**\n",
+        "    *   Trading signals are typically generated by applying rules or algorithms to technical indicators and price data. These signals indicate potential opportunities to buy or sell an asset.\n",
+        "    *   **Examples of Simple Trading Strategies:**\n",
+        "        *   **Moving Average Crossover:** A common strategy involves using two moving averages, a short-term and a long-term. A \"buy\" signal is generated when the short-term MA crosses above the long-term MA (bullish crossover). A \"sell\" signal is generated when the short-term MA crosses below the long-term MA (bearish crossover). Parameters include the lengths of the moving averages (e.g., 20-day and 50-day SMA).\n",
+        "        *   **RSI Thresholds:** The Relative Strength Index (RSI) measures the magnitude of recent price changes to evaluate overbought or oversold conditions. A \"buy\" signal might be generated when the RSI falls below a certain threshold (e.g., 30, indicating oversold) and then crosses back above it. A \"sell\" signal might be generated when the RSI rises above a certain threshold (e.g., 70, indicating overbought) and then crosses back below it. Parameters include the RSI length (e.g., 14 periods) and the overbought/oversold thresholds.\n",
+        "        *   **Combining Indicators:** More complex signals can be generated by combining multiple indicators. For example, a buy signal could require both a bullish moving average crossover *and* the RSI to be below a certain level.\n",
+        "\n",
+        "2.  **Triggering Trading Actions:**\n",
+        "    *   Once a trading signal is generated based on defined rules, it can theoretically be used to trigger a trading action (e.g., placing a buy or sell order).\n",
+        "    *   For example, upon a bullish moving average crossover signal, the system would prepare to send a buy order to an exchange. Conversely, a bearish crossover would prepare a sell order.\n",
+        "\n",
+        "3.  **Connecting to Cryptocurrency Exchanges:**\n",
+        "    *   Connecting a trading signal generation system to cryptocurrency exchanges to execute trades typically involves using the exchange's Application Programming Interface (API).\n",
+        "    *   **Exchange APIs:** Most exchanges provide REST APIs for placing orders, checking balances, retrieving market data, and managing accounts. A trading system would use these API endpoints to programmatically send trade instructions.\n",
+        "    *   **Webhooks:** While less common for placing orders directly, webhooks could potentially be used by an exchange to send real-time updates back to the trading system (e.g., order status changes, trade confirmations). However, order placement is almost always done via the API.\n",
+        "\n",
+        "4.  **Complexities and Risks of Live Trading Systems:**\n",
+        "    *   Building and deploying a live trading system based on automated signals is fraught with significant complexities and risks that go far beyond simply generating signals:\n",
+        "        *   **Backtesting Requirements:** Thorough and robust backtesting is essential to evaluate the historical performance of a trading strategy before deploying it live. This requires reliable historical data and careful consideration of trading costs (fees, slippage) and realistic execution scenarios.\n",
+        "        *   **Latency:** The time delay between a signal being generated and the order being executed on the exchange (latency) can significantly impact profitability, especially in volatile markets. High-frequency trading systems require extremely low latency.\n",
+        "        *   **Security:** Connecting a system to an exchange with API keys introduces security risks. Proper security measures, including secure storage of API keys, encryption, and access control, are critical.\n",
+        "        *   **Exchange-Specific API Nuances:** Each exchange's API has its own unique structure, rate limits, error codes, and eccentricities. Adapting a system to work with multiple exchanges requires significant development effort.\n",
+        "        *   **Error Handling:** Robust error handling is paramount. The system must be able to gracefully handle API errors, network issues, unexpected market conditions, and other potential problems to prevent unintended trades or losses.\n",
+        "        *   **Regulatory Considerations:** Trading, especially automated trading, may be subject to regulations depending on the user's location and the assets being traded. Understanding and complying with relevant regulations is crucial.\n",
+        "        *   **System Reliability:** A live trading system needs to be highly reliable, with monitoring and failover mechanisms in place to ensure continuous operation and prevent downtime that could lead to missed opportunities or losses.\n",
+        "        *   **Slippage:** The difference between the expected price of a trade and the price at which it is actually executed can reduce profitability, especially for large orders or in illiquid markets.\n",
+        "\n",
+        "5.  **Scope Limitation:**\n",
+        "    *   A full, executable implementation of a trading signal generation and execution system, including robust backtesting, real-time data processing, exchange API integration, and comprehensive risk management, is a complex software engineering project. It requires significant development effort, infrastructure, and rigorous testing. Therefore, building and demonstrating such a system live is beyond the scope of this conceptual outline within the notebook. This discussion serves to highlight the next logical steps and the associated challenges if one were to move from signal generation to automated trading execution.\n",
+        "\"\"\")"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Conceptual Outline for Trading Signal Generation and Execution:\n",
+            "\n",
+            "1.  **Trading Signal Generation:**\n",
+            "    *   Trading signals are typically generated by applying rules or algorithms to technical indicators and price data. These signals indicate potential opportunities to buy or sell an asset.\n",
+            "    *   **Examples of Simple Trading Strategies:**\n",
+            "        *   **Moving Average Crossover:** A common strategy involves using two moving averages, a short-term and a long-term. A \"buy\" signal is generated when the short-term MA crosses above the long-term MA (bullish crossover). A \"sell\" signal is generated when the short-term MA crosses below the long-term MA (bearish crossover). Parameters include the lengths of the moving averages (e.g., 20-day and 50-day SMA).\n",
+            "        *   **RSI Thresholds:** The Relative Strength Index (RSI) measures the magnitude of recent price changes to evaluate overbought or oversold conditions. A \"buy\" signal might be generated when the RSI falls below a certain threshold (e.g., 30, indicating oversold) and then crosses back above it. A \"sell\" signal might be generated when the RSI rises above a certain threshold (e.g., 70, indicating overbought) and then crosses back below it. Parameters include the RSI length (e.g., 14 periods) and the overbought/oversold thresholds.\n",
+            "        *   **Combining Indicators:** More complex signals can be generated by combining multiple indicators. For example, a buy signal could require both a bullish moving average crossover *and* the RSI to be below a certain level.\n",
+            "\n",
+            "2.  **Triggering Trading Actions:**\n",
+            "    *   Once a trading signal is generated based on defined rules, it can theoretically be used to trigger a trading action (e.g., placing a buy or sell order).\n",
+            "    *   For example, upon a bullish moving average crossover signal, the system would prepare to send a buy order to an exchange. Conversely, a bearish crossover would prepare a sell order.\n",
+            "\n",
+            "3.  **Connecting to Cryptocurrency Exchanges:**\n",
+            "    *   Connecting a trading signal generation system to cryptocurrency exchanges to execute trades typically involves using the exchange's Application Programming Interface (API).\n",
+            "    *   **Exchange APIs:** Most exchanges provide REST APIs for placing orders, checking balances, retrieving market data, and managing accounts. A trading system would use these API endpoints to programmatically send trade instructions.\n",
+            "    *   **Webhooks:** While less common for placing orders directly, webhooks could potentially be used by an exchange to send real-time updates back to the trading system (e.g., order status changes, trade confirmations). However, order placement is almost always done via the API.\n",
+            "\n",
+            "4.  **Complexities and Risks of Live Trading Systems:**\n",
+            "    *   Building and deploying a live trading system based on automated signals is fraught with significant complexities and risks that go far beyond simply generating signals:\n",
+            "        *   **Backtesting Requirements:** Thorough and robust backtesting is essential to evaluate the historical performance of a trading strategy before deploying it live. This requires reliable historical data and careful consideration of trading costs (fees, slippage) and realistic execution scenarios.\n",
+            "        *   **Latency:** The time delay between a signal being generated and the order being executed on the exchange (latency) can significantly impact profitability, especially in volatile markets. High-frequency trading systems require extremely low latency.\n",
+            "        *   **Security:** Connecting a system to an exchange with API keys introduces security risks. Proper security measures, including secure storage of API keys, encryption, and access control, are critical.\n",
+            "        *   **Exchange-Specific API Nuances:** Each exchange's API has its own unique structure, rate limits, error codes, and eccentricities. Adapting a system to work with multiple exchanges requires significant development effort.\n",
+            "        *   **Error Handling:** Robust error handling is paramount. The system must be able to gracefully handle API errors, network issues, unexpected market conditions, and other potential problems to prevent unintended trades or losses.\n",
+            "        *   **Regulatory Considerations:** Trading, especially automated trading, may be subject to regulations depending on the user's location and the assets being traded. Understanding and complying with relevant regulations is crucial.\n",
+            "        *   **System Reliability:** A live trading system needs to be highly reliable, with monitoring and failover mechanisms in place to ensure continuous operation and prevent downtime that could lead to missed opportunities or losses.\n",
+            "        *   **Slippage:** The difference between the expected price of a trade and the price at which it is actually executed can reduce profitability, especially for large orders or in illiquid markets.\n",
+            "\n",
+            "5.  **Scope Limitation:**\n",
+            "    *   A full, executable implementation of a trading signal generation and execution system, including robust backtesting, real-time data processing, exchange API integration, and comprehensive risk management, is a complex software engineering project. It requires significant development effort, infrastructure, and rigorous testing. Therefore, building and demonstrating such a system live is beyond the scope of this conceptual outline within the notebook. This discussion serves to highlight the next logical steps and the associated challenges if one were to move from signal generation to automated trading execution.\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "528048cf"
+      },
+      "source": [
+        "## Summary:\n",
+        "\n",
+        "### Data Analysis Key Findings\n",
+        "\n",
+        "*   The script successfully connected to the Kraken exchange and fetched historical OHLCV data for BTC/USDT from January 1, 2023.\n",
+        "*   The fetched data was loaded into a pandas DataFrame with the timestamp correctly set as the index.\n",
+        "*   Attempts to calculate technical indicators (RSI and SMA) using `pandas_ta` failed due to a persistent `ImportError: cannot import name 'NaN' from 'numpy'`, despite multiple attempts to uninstall and reinstall the library. This indicates a potential environment or library compatibility issue.\n",
+        "*   Despite the failure to calculate indicators, an interactive candlestick chart of the OHLCV data was successfully created and displayed using `plotly`.\n",
+        "*   Conceptual outlines were provided for integrating AI for natural language processing (NLP) for user interaction and for generating trading signals and connecting to exchanges, highlighting the complexities and scope limitations of implementing these fully.\n",
+        "\n",
+        "### Insights or Next Steps\n",
+        "\n",
+        "*   Investigate and resolve the `ImportError` with `pandas_ta` and `numpy` to successfully calculate technical indicators and enhance the data analysis capabilities.\n",
+        "*   Develop a basic natural language processing module to parse simple user queries related to fetching data or adding indicators as a proof of concept, building upon the conceptual outline.\n"
+      ]
+    }
+  ]
+}
\ No newline at end of file