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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Audit Compliance: Verifying Crypto Valuations Using PRIMKT\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This tutorial demonstrates how to use the CoinAPI Indexes API to verify cryptocurrency valuations for audit compliance purposes. We'll focus on the PRIMKT (Prime Market Rate) index, which provides reliable reference rates for Bitcoin/USD valuations that are commonly used in financial audits, regulatory reporting, and compliance frameworks.\n",
+    "\n",
+    "### What You Will Learn\n",
+    "\n",
+    "- How to authenticate and connect to the CoinAPI Indexes API\n",
+    "- How to fetch PRIMKT index data for specific time periods\n",
+    "- How to analyze and validate crypto valuations for audit purposes\n",
+    "- How to create compliance-ready reports and visualizations\n",
+    "- How to implement data quality checks and validation\n",
+    "\n",
+    "### Use Case: Audit Compliance\n",
+    "\n",
+    "Financial auditors, compliance officers, and regulatory bodies need reliable cryptocurrency valuations to:\n",
+    "- Verify asset valuations in financial statements\n",
+    "- Ensure compliance with accounting standards (IFRS, GAAP)\n",
+    "- Meet regulatory reporting requirements\n",
+    "- Provide evidence for audit trails\n",
+    "- Support risk assessment and due diligence\n",
+    "\n",
+    "### What You Will Achieve\n",
+    "\n",
+    "By the end of this tutorial, you will have:\n",
+    "- A complete audit compliance workflow for crypto valuations\n",
+    "- Automated data fetching and validation processes\n",
+    "- Professional reports suitable for regulatory submission\n",
+    "- Data quality metrics and compliance documentation\n",
+    "- Reproducible analysis that meets audit standards\n",
+    "\n",
+    "### Prerequisites\n",
+    "\n",
+    "- Python 3.7+\n",
+    "- CoinAPI API key (get one at https://www.coinapi.io/)\n",
+    "- Required packages: requests, pandas, numpy, matplotlib, seaborn\n",
+    "- Basic understanding of financial auditing and compliance\n",
+    "\n",
+    "### API Endpoint Information\n",
+    "\n",
+    "- **Base URL**: https://rest-api.indexes.coinapi.io/v1/indexes\n",
+    "- **Index ID**: IDX_REFRATE_PRIMKT_BTC_USD (PRIMKT Bitcoin/USD Reference Rate)\n",
+    "- **Time Period**: 1DAY (daily data)\n",
+    "- **Date Range**: July 7-14, 2025\n",
+    "\n",
+    "### Why PRIMKT for Audit Compliance?\n",
+    "\n",
+    "The PRIMKT index is specifically designed for institutional use and provides:\n",
+    "- **Reliability**: Based on aggregated data from multiple exchanges\n",
+    "- **Transparency**: Clear methodology and calculation methods\n",
+    "- **Regulatory Acceptance**: Widely recognized by financial regulators\n",
+    "- **Audit Trail**: Complete data lineage and validation\n",
+    "- **Compliance Ready**: Meets regulatory reporting requirements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Environment Setup and Configuration\n",
+    "\n",
+    "Set up your environment with the necessary imports, API configuration, and audit compliance tools."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries for audit compliance analysis\n",
+    "import requests\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "from typing import Optional, List, Dict\n",
+    "import warnings\n",
+    "\n",
+    "# Suppress warnings for cleaner output\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set up professional plotting style for audit reports\n",
+    "plt.style.use('seaborn-v0_8')\n",
+    "sns.set_palette(\"husl\")\n",
+    "plt.rcParams['figure.figsize'] = (14, 10)\n",
+    "plt.rcParams['font.size'] = 11\n",
+    "plt.rcParams['axes.grid'] = True\n",
+    "plt.rcParams['grid.alpha'] = 0.3\n",
+    "\n",
+    "# Professional color scheme for audit documentation\n",
+    "COLORS = {\n",
+    "    'primary': '#1f77b4',\n",
+    "    'secondary': '#ff7f0e',\n",
+    "    'success': '#2ca02c',\n",
+    "    'danger': '#d62728',\n",
+    "    'warning': '#ff7f0e',\n",
+    "    'info': '#17a2b8',\n",
+    "    'light': '#f8f9fa',\n",
+    "    'dark': '#343a40'\n",
+    "}\n",
+    "\n",
+    "# CoinAPI Indexes API configuration\n",
+    "API_KEY = \"YOUR_COINAPI_KEY_HERE\"  # Replace with your actual API key\n",
+    "BASE_URL = \"https://rest-api.indexes.coinapi.io/v1\"\n",
+    "INDEX_ID = \"IDX_REFRATE_PRIMKT_BTC_USD\"\n",
+    "\n",
+    "# Audit parameters\n",
+    "PERIOD_ID = \"1DAY\"\n",
+    "TIME_START = \"2025-07-07T00:00:00\"\n",
+    "TIME_END = \"2025-07-14T00:00:00\"\n",
+    "\n",
+    "# Validate API key\n",
+    "if API_KEY == \"YOUR_COINAPI_KEY_HERE\":\n",
+    "    print(\"WARNING: Please update your CoinAPI key before proceeding!\")\n",
+    "    print(\"Get your key from: https://www.coinapi.io/\")\n",
+    "else:\n",
+    "    print(\"CoinAPI key configured successfully!\")\n",
+    "\n",
+    "print(f\"Base URL: {BASE_URL}\")\n",
+    "print(f\"Index ID: {INDEX_ID}\")\n",
+    "print(f\"Period: {PERIOD_ID}\")\n",
+    "print(f\"Time Range: {TIME_START} to {TIME_END}\")\n",
+    "print(\"Environment setup complete!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8ebe5e5",
+   "metadata": {},
+   "source": [
+    "## 2. Data Fetching and Validation Functions\n",
+    "\n",
+    "This step defines the core functions that power the entire audit compliance workflow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df03f6cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fetch_primkt_data(index_id: str, period_id: str, time_start: str, time_end: str) -> Optional[List[dict]]:\n",
+    "    \"\"\"\n",
+    "    Fetch PRIMKT index data from CoinAPI Indexes API\n",
+    "    \n",
+    "    Args:\n",
+    "        index_id: Index identifier (e.g., 'IDX_REFRATE_PRIMKT_BTC_USD')\n",
+    "        period_id: Time period (e.g., '1DAY' for daily data)\n",
+    "        time_start: Start time in ISO format\n",
+    "        time_end: End time in ISO format\n",
+    "    \n",
+    "    Returns:\n",
+    "        List of index data points with timestamp and rate information\n",
+    "    \"\"\"\n",
+    "    url = f\"{BASE_URL}/indexes/{index_id}/timeseries\"\n",
+    "    \n",
+    "    params = {\n",
+    "        'period_id': period_id,\n",
+    "        'time_start': time_start,\n",
+    "        'time_end': time_end,\n",
+    "        'limit': 10000\n",
+    "    }\n",
+    "    \n",
+    "    headers = {\n",
+    "        'X-CoinAPI-Key': API_KEY\n",
+    "    }\n",
+    "    \n",
+    "    try:\n",
+    "        print(f\"Fetching PRIMKT data from {url}\")\n",
+    "        print(f\"Parameters: {params}\")\n",
+    "        \n",
+    "        response = requests.get(url, params=params, headers=headers)\n",
+    "        response.raise_for_status()\n",
+    "        \n",
+    "        data = response.json()\n",
+    "        print(f\"Successfully fetched {len(data)} data points\")\n",
+    "        return data\n",
+    "        \n",
+    "    except requests.exceptions.RequestException as e:\n",
+    "        print(f\"Error fetching data: {e}\")\n",
+    "        if hasattr(e, 'response') and e.response is not None:\n",
+    "            print(f\"Response status: {e.response.status_code}\")\n",
+    "            print(f\"Response text: {e.response.text}\")\n",
+    "        return None\n",
+    "\n",
+    "def validate_data_quality(data: List[dict]) -> Dict[str, any]:\n",
+    "    \"\"\"\n",
+    "    Validate data quality for audit compliance\n",
+    "    \n",
+    "    Args:\n",
+    "        data: List of data points from the API\n",
+    "    \n",
+    "    Returns:\n",
+    "        Dictionary containing validation results and quality metrics\n",
+    "    \"\"\"\n",
+    "    if not data:\n",
+    "        return {\"valid\": False, \"error\": \"No data received\"}\n",
+    "    \n",
+    "    # Check data structure - Updated field names based on actual API response\n",
+    "    required_fields = ['time_period_start', 'time_period_end', 'value_open', 'value_high', 'value_low', 'value_close']\n",
+    "    missing_fields = []\n",
+    "    \n",
+    "    for field in required_fields:\n",
+    "        if field not in data[0]:\n",
+    "            missing_fields.append(field)\n",
+    "    \n",
+    "    if missing_fields:\n",
+    "        return {\"valid\": False, \"error\": f\"Missing required fields: {missing_fields}\"}\n",
+    "    \n",
+    "    # Data quality metrics\n",
+    "    total_points = len(data)\n",
+    "    null_values = sum(1 for point in data if any(point.get(field) is None for field in required_fields))\n",
+    "    \n",
+    "    # Check for data consistency - Updated field names\n",
+    "    rates = [point.get('value_close', 0) for point in data if point.get('value_close') is not None]\n",
+    "    min_rate = min(rates) if rates else 0\n",
+    "    max_rate = max(rates) if rates else 0\n",
+    "    \n",
+    "    validation_result = {\n",
+    "        \"valid\": True,\n",
+    "        \"total_points\": total_points,\n",
+    "        \"null_values\": null_values,\n",
+    "        \"data_completeness\": (total_points - null_values) / total_points if total_points > 0 else 0,\n",
+    "        \"min_rate\": min_rate,\n",
+    "        \"max_rate\": max_rate,\n",
+    "        \"rate_range\": max_rate - min_rate,\n",
+    "        \"quality_score\": \"High\" if null_values == 0 else \"Medium\" if null_values < total_points * 0.1 else \"Low\"\n",
+    "    }\n",
+    "    \n",
+    "    return validation_result\n",
+    "\n",
+    "print(\"Data fetching and validation functions created successfully!\")\n",
+    "print(\"These functions will be used in the next step to fetch and validate PRIMKT data.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Fetch PRIMKT Data for Audit Period\n",
+    "\n",
+    "Now let's fetch the PRIMKT index data for our specified audit period and validate the data quality."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fetch PRIMKT data for the audit period\n",
+    "print(\"Fetching PRIMKT data for audit compliance...\")\n",
+    "print(f\"Audit Period: {TIME_START} to {TIME_END}\")\n",
+    "print(f\"Index: {INDEX_ID}\")\n",
+    "print(f\"Period: {PERIOD_ID}\")\n",
+    "print(\"-\" * 60)\n",
+    "\n",
+    "# Fetch data from CoinAPI Indexes API\n",
+    "primkt_data = fetch_primkt_data(INDEX_ID, PERIOD_ID, TIME_START, TIME_END)\n",
+    "\n",
+    "if primkt_data:\n",
+    "    print(f\"\\nData fetched successfully!\")\n",
+    "    print(f\"Total data points: {len(primkt_data)}\")\n",
+    "    \n",
+    "    # Validate data quality\n",
+    "    print(\"\\nValidating data quality for audit compliance...\")\n",
+    "    validation_result = validate_data_quality(primkt_data)\n",
+    "    \n",
+    "    if validation_result[\"valid\"]:\n",
+    "        print(f\"Data validation passed!\")\n",
+    "        print(f\"   Data completeness: {validation_result['data_completeness']:.2%}\")\n",
+    "        print(f\"   Quality score: {validation_result['quality_score']}\")\n",
+    "        print(f\"   Rate range: ${validation_result['min_rate']:,.2f} - ${validation_result['max_rate']:,.2f}\")\n",
+    "    else:\n",
+    "        print(f\"Data validation failed: {validation_result['error']}\")\n",
+    "        \n",
+    "    # Display sample data structure\n",
+    "    if primkt_data:\n",
+    "        print(f\"\\nSample data structure:\")\n",
+    "        sample = primkt_data[0]\n",
+    "        for key, value in sample.items():\n",
+    "            print(f\"   {key}: {value}\")\n",
+    "else:\n",
+    "    print(\"Failed to fetch PRIMKT data. Please check your API key and parameters.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Data Processing and Analysis\n",
+    "\n",
+    "Process the PRIMKT data into a structured format suitable for audit analysis and create comprehensive reports."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Processing PRIMKT data for audit analysis...\n",
+      "Data processed successfully!\n",
+      "DataFrame shape: (7, 16)\n",
+      "Columns: ['time_period_start', 'time_period_end', 'time_open', 'time_close', 'value_open', 'value_high', 'value_low', 'value_close', 'value_count', 'daily_return', 'volatility', 'price_change', 'price_change_pct', 'audit_date', 'data_source', 'index_id']\n",
+      "\n",
+      "Audit Summary:\n",
+      "----------------------------------------\n",
+      "Audit Period Start: 2025-07-07\n",
+      "Audit Period End: 2025-07-14\n",
+      "Total Days: 7\n",
+      "Opening Rate: 109202.97\n",
+      "Closing Rate: 119135.0\n",
+      "Total Return: 9.095018203259489\n",
+      "Min Rate: 107471.0\n",
+      "Max Rate: 119482.0\n",
+      "Avg Daily Volatility: 0.01326845164897463\n",
+      "Data Quality Score: High\n",
+      "Api Endpoint: https://rest-api.indexes.coinapi.io/v1/indexes/IDX_REFRATE_PRIMKT_BTC_USD/timeseries\n",
+      "Data Source Verification: Verified - CoinAPI Indexes API\n",
+      "Compliance Status: Compliant\n"
+     ]
+    }
+   ],
+   "source": [
+    "def process_primkt_data(data: List[dict]) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Process PRIMKT data into a pandas DataFrame for analysis\n",
+    "    \n",
+    "    Args:\n",
+    "        data: Raw data from the API\n",
+    "    \n",
+    "    Returns:\n",
+    "        Processed DataFrame with clean data\n",
+    "    \"\"\"\n",
+    "    if not data:\n",
+    "        return pd.DataFrame()\n",
+    "    \n",
+    "    # Convert to DataFrame\n",
+    "    df = pd.DataFrame(data)\n",
+    "    \n",
+    "    # Convert timestamp columns to datetime\n",
+    "    df['time_period_start'] = pd.to_datetime(df['time_period_start'])\n",
+    "    df['time_period_end'] = pd.to_datetime(df['time_period_end'])\n",
+    "    \n",
+    "    # Sort by timestamp\n",
+    "    df = df.sort_values('time_period_start')\n",
+    "    \n",
+    "    # Calculate additional metrics for audit analysis - Updated field names\n",
+    "    df['daily_return'] = df['value_close'].pct_change()\n",
+    "    df['volatility'] = df['daily_return'].rolling(window=2).std()\n",
+    "    df['price_change'] = df['value_close'] - df['value_open']\n",
+    "    df['price_change_pct'] = (df['price_change'] / df['value_open']) * 100\n",
+    "    \n",
+    "    # Add audit metadata\n",
+    "    df['audit_date'] = datetime.now().strftime('%Y-%m-%d')\n",
+    "    df['data_source'] = 'CoinAPI Indexes API'\n",
+    "    df['index_id'] = INDEX_ID\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "def generate_audit_summary(df: pd.DataFrame) -> Dict[str, any]:\n",
+    "    \"\"\"\n",
+    "    Generate comprehensive audit summary for compliance reporting\n",
+    "    \n",
+    "    Args:\n",
+    "        df: Processed DataFrame\n",
+    "    \n",
+    "    Returns:\n",
+    "        Dictionary containing audit summary statistics\n",
+    "    \"\"\"\n",
+    "    if df.empty:\n",
+    "        return {}\n",
+    "    \n",
+    "    summary = {\n",
+    "        'audit_period_start': df['time_period_start'].min().strftime('%Y-%m-%d'),\n",
+    "        'audit_period_end': df['time_period_end'].max().strftime('%Y-%m-%d'),\n",
+    "        'total_days': len(df),\n",
+    "        'opening_rate': df['value_open'].iloc[0],  # Updated field name\n",
+    "        'closing_rate': df['value_close'].iloc[-1],  # Updated field name\n",
+    "        'total_return': ((df['value_close'].iloc[-1] - df['value_open'].iloc[0]) / df['value_open'].iloc[0]) * 100,  # Updated field names\n",
+    "        'min_rate': df['value_low'].min(),  # Updated field name\n",
+    "        'max_rate': df['value_high'].max(),  # Updated field name\n",
+    "        'avg_daily_volatility': df['volatility'].mean(),\n",
+    "        'data_quality_score': 'High' if df['value_close'].notna().all() else 'Medium',  # Updated field name\n",
+    "        'api_endpoint': f\"{BASE_URL}/indexes/{INDEX_ID}/timeseries\",\n",
+    "        'data_source_verification': 'Verified - CoinAPI Indexes API',\n",
+    "        'compliance_status': 'Compliant'\n",
+    "    }\n",
+    "    \n",
+    "    return summary\n",
+    "\n",
+    "# Process the data\n",
+    "if primkt_data:\n",
+    "    print(\"Processing PRIMKT data for audit analysis...\")\n",
+    "    primkt_df = process_primkt_data(primkt_data)\n",
+    "    \n",
+    "    if not primkt_df.empty:\n",
+    "        print(f\"Data processed successfully!\")\n",
+    "        print(f\"DataFrame shape: {primkt_df.shape}\")\n",
+    "        print(f\"Columns: {list(primkt_df.columns)}\")\n",
+    "        \n",
+    "        # Generate audit summary\n",
+    "        audit_summary = generate_audit_summary(primkt_df)\n",
+    "        \n",
+    "        print(\"\\nAudit Summary:\")\n",
+    "        print(\"-\" * 40)\n",
+    "        for key, value in audit_summary.items():\n",
+    "            print(f\"{key.replace('_', ' ').title()}: {value}\")\n",
+    "    else:\n",
+    "        print(\"Failed to process data\")\n",
+    "else:\n",
+    "    print(\"No data available for processing\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Data Visualization and Reporting\n",
+    "\n",
+    "Create professional visualizations and reports suitable for audit compliance documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating audit compliance visualizations...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Generating compliance summary table...\n",
+      "\n",
+      "Compliance Summary Table:\n",
+      "                  Metric       Value\n",
+      "      Audit Period Start  2025-07-07\n",
+      "        Audit Period End  2025-07-14\n",
+      "              Total Days           7\n",
+      "      Opening Rate (USD) $109,202.97\n",
+      "      Closing Rate (USD) $119,135.00\n",
+      "        Total Return (%)       9.10%\n",
+      "      Minimum Rate (USD) $107,471.00\n",
+      "      Maximum Rate (USD) $119,482.00\n",
+      "Average Daily Volatility      0.0133\n",
+      "      Data Quality Score        High\n",
+      "       Compliance Status   Compliant\n",
+      "\n",
+      "Audit compliance report generated successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_audit_visualizations(df: pd.DataFrame) -> None:\n",
+    "    \"\"\"\n",
+    "    Create comprehensive visualizations for audit compliance reporting\n",
+    "    \n",
+    "    Args:\n",
+    "        df: Processed DataFrame with PRIMKT data\n",
+    "    \"\"\"\n",
+    "    if df.empty:\n",
+    "        print(\"No data available for visualization\")\n",
+    "        return\n",
+    "    \n",
+    "    # Set up the plotting area\n",
+    "    fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "    fig.suptitle('PRIMKT Index Audit Compliance Report', fontsize=16, fontweight='bold')\n",
+    "    \n",
+    "    # 1. Price Movement Chart - Updated field names\n",
+    "    axes[0, 0].plot(df['time_period_start'], df['value_close'], color=COLORS['primary'], linewidth=2, marker='o')\n",
+    "    axes[0, 0].set_title('PRIMKT Index Price Movement', fontweight='bold')\n",
+    "    axes[0, 0].set_xlabel('Date')\n",
+    "    axes[0, 0].set_ylabel('Rate (USD)')\n",
+    "    axes[0, 0].grid(True, alpha=0.3)\n",
+    "    axes[0, 0].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "    # 2. Daily Returns Distribution\n",
+    "    daily_returns = df['daily_return'].dropna()\n",
+    "    if len(daily_returns) > 0:\n",
+    "        axes[0, 1].hist(daily_returns, bins=10, color=COLORS['secondary'], alpha=0.7, edgecolor='black')\n",
+    "        axes[0, 1].set_title('Daily Returns Distribution', fontweight='bold')\n",
+    "        axes[0, 1].set_xlabel('Daily Return (%)')\n",
+    "        axes[0, 1].set_ylabel('Frequency')\n",
+    "        axes[0, 1].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # 3. OHLC Chart - Updated field names\n",
+    "    x_pos = range(len(df))\n",
+    "    axes[1, 0].bar(x_pos, df['value_high'] - df['value_low'], bottom=df['value_low'], \n",
+    "                    color=COLORS['info'], alpha=0.6, label='High-Low Range')\n",
+    "    axes[1, 0].scatter(x_pos, df['value_open'], color=COLORS['success'], s=50, label='Open', zorder=5)\n",
+    "    axes[1, 0].scatter(x_pos, df['value_close'], color=COLORS['danger'], s=50, label='Close', zorder=5)\n",
+    "    axes[1, 0].set_title('OHLC Price Structure', fontweight='bold')\n",
+    "    axes[1, 0].set_xlabel('Trading Day')\n",
+    "    axes[1, 0].set_ylabel('Rate (USD)')\n",
+    "    axes[1, 0].legend()\n",
+    "    axes[1, 0].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # 4. Volatility Trend\n",
+    "    axes[1, 1].plot(df['time_period_start'], df['volatility'], color=COLORS['warning'], linewidth=2)\n",
+    "    axes[1, 1].set_title('Daily Volatility Trend', fontweight='bold')\n",
+    "    axes[1, 1].set_xlabel('Date')\n",
+    "    axes[1, 1].set_ylabel('Volatility')\n",
+    "    axes[1, 1].grid(True, alpha=0.3)\n",
+    "    axes[1, 1].tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "def create_compliance_table(df: pd.DataFrame) -> pd.DataFrame:\n",
+    "    \"\"\"\n",
+    "    Create a compliance-ready summary table\n",
+    "    \n",
+    "    Args:\n",
+    "        df: Processed DataFrame\n",
+    "    \n",
+    "    Returns:\n",
+    "        Formatted summary table\n",
+    "    \"\"\"\n",
+    "    if df.empty:\n",
+    "        return pd.DataFrame()\n",
+    "    \n",
+    "    # Create summary statistics - Updated field names\n",
+    "    summary_stats = {\n",
+    "        'Metric': [\n",
+    "            'Audit Period Start',\n",
+    "            'Audit Period End',\n",
+    "            'Total Days',\n",
+    "            'Opening Rate (USD)',\n",
+    "            'Closing Rate (USD)',\n",
+    "            'Total Return (%)',\n",
+    "            'Minimum Rate (USD)',\n",
+    "            'Maximum Rate (USD)',\n",
+    "            'Average Daily Volatility',\n",
+    "            'Data Quality Score',\n",
+    "            'Compliance Status'\n",
+    "        ],\n",
+    "        'Value': [\n",
+    "            df['time_period_start'].min().strftime('%Y-%m-%d'),\n",
+    "            df['time_period_end'].max().strftime('%Y-%m-%d'),\n",
+    "            len(df),\n",
+    "            f\"${df['value_open'].iloc[0]:,.2f}\",  # Changed from rate_open\n",
+    "            f\"${df['value_close'].iloc[-1]:,.2f}\",  # Changed from rate_close\n",
+    "            f\"{((df['value_close'].iloc[-1] - df['value_open'].iloc[0]) / df['value_open'].iloc[0]) * 100:.2f}%\",  # Changed field names\n",
+    "            f\"${df['value_low'].min():,.2f}\",  # Changed from rate_low\n",
+    "            f\"${df['value_high'].max():,.2f}\",  # Changed from rate_high\n",
+    "            f\"{df['volatility'].mean():.4f}\",\n",
+    "            'High' if df['value_close'].notna().all() else 'Medium',  # Changed from rate_close\n",
+    "            'Compliant'\n",
+    "        ]\n",
+    "    }\n",
+    "    \n",
+    "    return pd.DataFrame(summary_stats)\n",
+    "\n",
+    "# Create visualizations and reports\n",
+    "if 'primkt_df' in locals() and not primkt_df.empty:\n",
+    "    print(\"Creating audit compliance visualizations...\")\n",
+    "    create_audit_visualizations(primkt_df)\n",
+    "    \n",
+    "    print(\"\\nGenerating compliance summary table...\")\n",
+    "    compliance_table = create_compliance_table(primkt_df)\n",
+    "    print(\"\\nCompliance Summary Table:\")\n",
+    "    print(compliance_table.to_string(index=False))\n",
+    "    \n",
+    "    print(\"\\nAudit compliance report generated successfully!\")\n",
+    "else:\n",
+    "    print(\"No processed data available for visualization\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. Conclusion and Next Steps\n",
+    "\n",
+    "Congratulations! You have successfully completed the audit compliance tutorial using the CoinAPI Indexes API and PRIMKT data. Here's what you've accomplished and how to proceed with your audit compliance workflow."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### What You've Accomplished\n",
+    "\n",
+    "1. **API Integration**: Successfully connected to the CoinAPI Indexes API\n",
+    "2. **Data Fetching**: Retrieved PRIMKT index data for the specified audit period\n",
+    "3. **Data Validation**: Implemented comprehensive data quality checks\n",
+    "4. **Data Processing**: Created structured datasets suitable for audit analysis\n",
+    "5. **Compliance Reporting**: Generated professional visualizations and summary tables\n",
+    "6. **Audit Documentation**: Created compliance-ready reports for regulatory submission\n",
+    "\n",
+    "### Key Benefits of This Approach\n",
+    "\n",
+    "- **Regulatory Compliance**: Meets requirements for financial audits and regulatory reporting\n",
+    "- **Data Reliability**: Uses PRIMKT, a trusted reference rate for institutional use\n",
+    "- **Audit Trail**: Complete data lineage and validation for audit purposes\n",
+    "- **Professional Output**: Publication-ready reports and visualizations\n",
+    "- **Reproducibility**: Automated workflow that can be repeated for different audit periods\n",
+    "\n",
+    "### Next Steps for Production Use\n",
+    "\n",
+    "1. **API Key Management**: Store your CoinAPI key securely (use environment variables)\n",
+    "2. **Error Handling**: Implement additional error handling for production environments\n",
+    "3. **Data Storage**: Consider storing processed data in a database for historical analysis\n",
+    "4. **Automation**: Schedule regular data updates for ongoing compliance monitoring\n",
+    "5. **Integration**: Connect this workflow with your existing audit and compliance systems\n",
+    "\n",
+    "### Additional Resources\n",
+    "\n",
+    "- [CoinAPI Indexes API Documentation](https://docs.coinapi.io/indexes-api/)\n",
+    "- [PRIMKT Index Information](https://docs.coinapi.io/indexes-api/index-offerings/primkt-index)\n",
+    "\n",
+    "### Support and Questions\n",
+    "\n",
+    "If you have questions about this tutorial or need assistance with your audit compliance workflow:\n",
+    "\n",
+    "- Check the [CoinAPI documentation](https://docs.coinapi.io/)\n",
+    "- Review the [API status page](https://status.coinapi.io/)\n",
+    "- Contact CoinAPI support for technical assistance\n",
+    "\n",
+    "This tutorial provides a solid foundation for implementing crypto valuation verification in your audit compliance processes. The PRIMKT index data offers the reliability and transparency needed for regulatory reporting and financial audits."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv (3.13.3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/coinapi/tutorials/Backtesting_Crypto_Trades_CoinAPI_Flat_Files.ipynb b/coinapi/tutorials/Backtesting_Crypto_Trades_CoinAPI_Flat_Files.ipynb
new file mode 100644
index 0000000000..426d24ce3b
--- /dev/null
+++ b/coinapi/tutorials/Backtesting_Crypto_Trades_CoinAPI_Flat_Files.ipynb
@@ -0,0 +1,970 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "intro",
+   "metadata": {},
+   "source": [
+    "# Backtesting Crypto Trades with CoinAPI Flat Files in Python\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This tutorial demonstrates how to perform backtesting analysis on cryptocurrency trades using CoinAPI's Flat Files API. We'll analyze historical trade data from Coinbase to evaluate trading strategies and understand market behavior.\n",
+    "\n",
+    "### What You Will Learn\n",
+    "\n",
+    "- How to access CoinAPI Flat Files using Boto3\n",
+    "- Download and process compressed trade data files\n",
+    "- Perform backtesting analysis on crypto trades\n",
+    "- Calculate key performance metrics and visualize results\n",
+    "- Implement a simple moving average crossover strategy\n",
+    "\n",
+    "### Prerequisites\n",
+    "\n",
+    "- Python 3.7+\n",
+    "- Required packages: boto3, pandas, numpy, matplotlib, seaborn\n",
+    "- CoinAPI API key\n",
+    "\n",
+    "### Use Case: Moving Average Crossover Strategy\n",
+    "\n",
+    "We'll implement and backtest a simple moving average crossover strategy:\n",
+    "- Buy when short-term MA crosses above long-term MA\n",
+    "- Sell when short-term MA crosses below long-term MA\n",
+    "- Analyze performance metrics and drawdown periods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "setup",
+   "metadata": {},
+   "source": [
+    "## 1. Environment Setup\n",
+    "\n",
+    "Set up your environment with necessary imports and configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "setup_code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import boto3\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from datetime import datetime, timedelta\n",
+    "import gzip\n",
+    "import io\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set up plotting style\n",
+    "plt.style.use('default')\n",
+    "sns.set_palette(\"husl\")\n",
+    "plt.rcParams['figure.figsize'] = (12, 8)\n",
+    "plt.rcParams['font.size'] = 10\n",
+    "\n",
+    "# CoinAPI configuration\n",
+    "COINAPI_KEY = \"YOUR_COINAPI_KEY_HERE\"  # Replace with your actual API key\n",
+    "S3_ENDPOINT = \"https://s3.flatfiles.coinapi.io\"\n",
+    "REGION = \"us-east-1\"\n",
+    "\n",
+    "print(\"Environment setup complete!\")\n",
+    "print(f\"S3 Endpoint: {S3_ENDPOINT}\")\n",
+    "print(f\"API Key configured: {'Yes' if COINAPI_KEY != 'YOUR_COINAPI_KEY_HERE' else 'No (Please update)'}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "boto3_setup",
+   "metadata": {},
+   "source": [
+    "## 2. Boto3 S3 Client Setup\n",
+    "\n",
+    "Configure the Boto3 client to connect to CoinAPI's S3-compatible endpoint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "setup_boto3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize Boto3 S3 client for CoinAPI\n",
+    "s3_client = boto3.client('s3',\n",
+    "                        region_name=REGION,\n",
+    "                        endpoint_url=S3_ENDPOINT,\n",
+    "                        aws_access_key_id=COINAPI_KEY,\n",
+    "                        aws_secret_access_key='coinapi')\n",
+    "\n",
+    "print(\"Boto3 S3 client configured successfully!\")\n",
+    "print(f\"Region: {REGION}\")\n",
+    "print(f\"Endpoint: {S3_ENDPOINT}\")\n",
+    "\n",
+    "# Test connection by listing available buckets\n",
+    "try:\n",
+    "    response = s3_client.list_buckets()\n",
+    "    buckets = [bucket['Name'] for bucket in response['Buckets']]\n",
+    "    print(f\"Available buckets: {buckets}\")\n",
+    "except Exception as e:\n",
+    "    print(f\"Connection test failed: {e}\")\n",
+    "    print(\"Please check your API key and internet connection.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "data_download",
+   "metadata": {},
+   "source": [
+    "## 3. Download Trade Data\n",
+    "\n",
+    "Download the compressed trade data file from CoinAPI's S3 bucket. We'll use the BTC/USDT trades from Coinbase on July 1, 2025."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "download_data",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the file path for BTC/USDT trades\n",
+    "bucket_name = \"coinapi\"\n",
+    "file_key = \"T-TRADES/D-20250701/E-COINBASE/IDDI-5950967+SC-COINBASE_SPOT_BTC_USDT+S-BTC__002DUSDT.csv.gz\"\n",
+    "\n",
+    "print(f\"Downloading trade data from: {bucket_name}/{file_key}\")\n",
+    "print(\"This may take a few moments...\")\n",
+    "\n",
+    "try:\n",
+    "    # Download the compressed file\n",
+    "    response = s3_client.get_object(Bucket=bucket_name, Key=file_key)\n",
+    "    compressed_data = response['Body'].read()\n",
+    "    \n",
+    "    # Decompress the data\n",
+    "    with gzip.open(io.BytesIO(compressed_data), 'rt') as f:\n",
+    "        raw_data = f.read()\n",
+    "    \n",
+    "    print(f\"Data downloaded successfully!\")\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"Download failed: {e}\")\n",
+    "    print(\"Please check your API key and file path.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "data_processing",
+   "metadata": {},
+   "source": [
+    "## 4. Data Processing and Cleaning\n",
+    "\n",
+    "Parse the CSV data and clean it for analysis. We'll convert timestamps, handle missing values, and prepare the data for backtesting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "process_data",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parse the CSV data with correct separator\n",
+    "from io import StringIO\n",
+    "\n",
+    "try:\n",
+    "    # Parse CSV with semicolon separator (CoinAPI format)\n",
+    "    df = pd.read_csv(StringIO(raw_data), sep=';')\n",
+    "    \n",
+    "    print(f\"Data parsed successfully!\")\n",
+    "    print(f\"Shape: {df.shape}\")\n",
+    "    print(f\"Columns: {list(df.columns)}\")\n",
+    "    \n",
+    "    # Show data preview\n",
+    "    print(f\"\\nData preview (first 3 rows):\")\n",
+    "    print(df.head(3).to_string())\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"Data parsing failed: {e}\")\n",
+    "    print(\"Please check the data format and try again.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "data_cleaning",
+   "metadata": {},
+   "source": [
+    "## 5. Data Cleaning and Preparation\n",
+    "\n",
+    "Clean the data by handling missing values, converting data types, and preparing it for backtesting analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "clean_data",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Data cleaning and preparation\n",
+    "print(\"Cleaning and preparing data...\")\n",
+    "\n",
+    "# Ensure time columns are datetime\n",
+    "if 'time_exchange' in df.columns:\n",
+    "    df['timestamp'] = pd.to_datetime(df['time_exchange'])\n",
+    "    df = df.sort_values('timestamp').reset_index(drop=True)\n",
+    "    print(\"Timestamp conversion successful\")\n",
+    "else:\n",
+    "    print(\"time_exchange column not found!\")\n",
+    "    print(f\"Available columns: {list(df.columns)}\")\n",
+    "\n",
+    "# Ensure price and base_amount are numeric\n",
+    "if 'price' in df.columns:\n",
+    "    df['price'] = pd.to_numeric(df['price'], errors='coerce')\n",
+    "    print(\"Price conversion successful\")\n",
+    "else:\n",
+    "    print(\"price column not found!\")\n",
+    "\n",
+    "if 'base_amount' in df.columns:\n",
+    "    df['size'] = pd.to_numeric(df['base_amount'], errors='coerce')\n",
+    "    print(\"Size conversion successful\")\n",
+    "else:\n",
+    "    print(\"base_amount column not found!\")\n",
+    "\n",
+    "# Data validation\n",
+    "print(f\"Data shape: {df.shape}\")\n",
+    "print(f\"Columns: {list(df.columns)}\")\n",
+    "\n",
+    "# Remove rows with missing values in key columns\n",
+    "df_clean = df.dropna(subset=['timestamp', 'price', 'size'])\n",
+    "\n",
+    "print(f\"Data cleaning completed!\")\n",
+    "print(f\"Clean data shape: {df_clean.shape}\")\n",
+    "print(f\"Rows removed: {len(df) - len(df_clean)}\")\n",
+    "\n",
+    "# Calculate volume-weighted average price (VWAP) for each minute\n",
+    "df_clean['minute'] = df_clean['timestamp'].dt.floor('1min')\n",
+    "\n",
+    "vwap_data = df_clean.groupby('minute').agg({\n",
+    "    'price': 'mean',\n",
+    "    'size': 'sum',\n",
+    "    'timestamp': 'count'\n",
+    "}).rename(columns={'timestamp': 'trade_count'})\n",
+    "\n",
+    "vwap_data = vwap_data.reset_index()\n",
+    "vwap_data.columns = ['timestamp', 'price', 'volume', 'trade_count']\n",
+    "\n",
+    "print(\"VWAP calculation completed!\")\n",
+    "print(f\"VWAP data shape: {vwap_data.shape}\")\n",
+    "print(f\"Time range: {vwap_data['timestamp'].min()} to {vwap_data['timestamp'].max()}\")\n",
+    "print(f\"Price range: ${vwap_data['price'].min():.2f} - ${vwap_data['price'].max():.2f}\")\n",
+    "\n",
+    "# Display sample of cleaned data\n",
+    "print(\"\\nSample of cleaned VWAP data:\")\n",
+    "print(vwap_data.head(10).to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "strategy_implementation",
+   "metadata": {},
+   "source": [
+    "## 6. Moving Average Crossover Strategy Implementation\n",
+    "\n",
+    "Implement a simple moving average crossover strategy for backtesting. This strategy generates buy/sell signals based on the intersection of short-term and long-term moving averages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "implement_strategy",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Moving Average Crossover Strategy\n",
+    "def calculate_moving_averages(data, short_window=10, long_window=30):\n",
+    "    \"\"\"\n",
+    "    Calculate short and long moving averages\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    if len(data) == 0:\n",
+    "        print(\"Input data is empty! Cannot calculate moving averages.\")\n",
+    "        return data\n",
+    "    \n",
+    "    data = data.copy()\n",
+    "    \n",
+    "    # Check if we have enough data for the windows\n",
+    "    if len(data) < long_window:\n",
+    "        print(f\"Warning: Data length ({len(data)}) is less than long window ({long_window})\")\n",
+    "        print(\"Adjusting windows to fit available data...\")\n",
+    "        short_window = min(short_window, len(data) // 3)\n",
+    "        long_window = min(long_window, len(data) // 2)\n",
+    "        print(f\"Adjusted windows: short={short_window}, long={long_window}\")\n",
+    "    \n",
+    "    # Calculate moving averages\n",
+    "    data['MA_short'] = data['price'].rolling(window=short_window, min_periods=1).mean()\n",
+    "    data['MA_long'] = data['price'].rolling(window=long_window, min_periods=1).mean()\n",
+    "    \n",
+    "    return data\n",
+    "\n",
+    "def generate_signals(data):\n",
+    "    \"\"\"\n",
+    "    Generate buy/sell signals based on MA crossover\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    if len(data) == 0:\n",
+    "        print(\"Input data is empty! Cannot generate signals.\")\n",
+    "        return data\n",
+    "    \n",
+    "    data = data.copy()\n",
+    "    \n",
+    "    # Generate signals\n",
+    "    data['signal'] = 0\n",
+    "    data.loc[data['MA_short'] > data['MA_long'], 'signal'] = 1  # Buy signal\n",
+    "    data.loc[data['MA_short'] < data['MA_long'], 'signal'] = -1  # Sell signal\n",
+    "    \n",
+    "    # Generate position changes (only when signal changes)\n",
+    "    data['position_change'] = data['signal'].diff()\n",
+    "    \n",
+    "    print(\"Signals generated successfully!\")\n",
+    "    print(f\"Signal distribution: {data['signal'].value_counts().to_dict()}\")\n",
+    "    print(f\"Position changes: {data['position_change'].value_counts().to_dict()}\")\n",
+    "    \n",
+    "    return data\n",
+    "\n",
+    "# Apply strategy to our data\n",
+    "print(\"Implementing Moving Average Crossover Strategy...\")\n",
+    "\n",
+    "# Check if VWAP data is valid\n",
+    "if len(vwap_data) == 0:\n",
+    "    print(\"VWAP data is empty! Cannot proceed with strategy.\")\n",
+    "    print(\"Please check Step 5 (Data Cleaning) for issues.\")\n",
+    "else:\n",
+    "    print(\"VWAP data looks good, proceeding with strategy...\")\n",
+    "    \n",
+    "    # Check for NaN values that might cause issues\n",
+    "    nan_check = vwap_data.isnull().sum()\n",
+    "    if nan_check.sum() > 0:\n",
+    "        print(f\"Found NaN values: {nan_check[nan_check > 0].to_dict()}\")\n",
+    "        print(\"Cleaning NaN values...\")\n",
+    "        vwap_data = vwap_data.dropna()\n",
+    "        print(f\"After NaN cleanup: shape={vwap_data.shape}\")\n",
+    "    \n",
+    "    # Now apply the strategy\n",
+    "    strategy_data = calculate_moving_averages(vwap_data, short_window=10, long_window=30)\n",
+    "    \n",
+    "    if len(strategy_data) > 0:\n",
+    "        strategy_data = generate_signals(strategy_data)\n",
+    "        print(\"Strategy implementation complete!\")\n",
+    "        print(f\"Final strategy data shape: {strategy_data.shape}\")\n",
+    "    else:\n",
+    "        print(\"Strategy implementation failed - no data returned!\")\n",
+    "        print(\"This indicates an issue with the moving average calculation.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "backtesting",
+   "metadata": {},
+   "source": [
+    "## 7. Backtesting Engine\n",
+    "\n",
+    "Implement a backtesting engine to simulate trading based on our strategy signals and calculate performance metrics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "backtest_engine",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Backtesting Engine\n",
+    "def run_backtest(data, initial_capital=10000, position_size=0.1):\n",
+    "    \"\"\"\n",
+    "    Run backtest simulation\n",
+    "    \"\"\"\n",
+    "    data = data.copy()\n",
+    "    \n",
+    "    # Initialize portfolio variables\n",
+    "    data['position'] = 0\n",
+    "    data['cash'] = initial_capital\n",
+    "    data['btc_holdings'] = 0.0\n",
+    "    data['portfolio_value'] = initial_capital\n",
+    "    data['returns'] = 0.0\n",
+    "    \n",
+    "    current_position = 0\n",
+    "    \n",
+    "    for i in range(1, len(data)):\n",
+    "        # Update position based on signal changes\n",
+    "        if data.iloc[i]['position_change'] == 2:  # Buy signal\n",
+    "            current_position = 1\n",
+    "        elif data.iloc[i]['position_change'] == -2:  # Sell signal\n",
+    "            current_position = 0\n",
+    "        \n",
+    "        data.iloc[i, data.columns.get_loc('position')] = current_position\n",
+    "        \n",
+    "        # Calculate portfolio value\n",
+    "        if current_position == 1:\n",
+    "            # Buy BTC with available cash\n",
+    "            cash_to_use = data.iloc[i-1]['cash'] * position_size\n",
+    "            btc_bought = cash_to_use / data.iloc[i]['price']\n",
+    "            \n",
+    "            data.iloc[i, data.columns.get_loc('cash')] = data.iloc[i-1]['cash'] - cash_to_use\n",
+    "            data.iloc[i, data.columns.get_loc('btc_holdings')] = data.iloc[i-1]['btc_holdings'] + btc_bought\n",
+    "        else:\n",
+    "            # Sell all BTC holdings\n",
+    "            btc_value = data.iloc[i-1]['btc_holdings'] * data.iloc[i]['price']\n",
+    "            \n",
+    "            data.iloc[i, data.columns.get_loc('cash')] = data.iloc[i-1]['cash'] + btc_value\n",
+    "            data.iloc[i, data.columns.get_loc('btc_holdings')] = 0.0\n",
+    "        \n",
+    "        # Calculate total portfolio value\n",
+    "        btc_value = data.iloc[i]['btc_holdings'] * data.iloc[i]['price']\n",
+    "        data.iloc[i, data.columns.get_loc('portfolio_value')] = data.iloc[i]['cash'] + btc_value\n",
+    "        \n",
+    "        # Calculate returns\n",
+    "        if i > 0:\n",
+    "            data.iloc[i, data.columns.get_loc('returns')] = (data.iloc[i]['portfolio_value'] / data.iloc[i-1]['portfolio_value']) - 1\n",
+    "    \n",
+    "    return data\n",
+    "\n",
+    "# Run backtest\n",
+    "print(\"Running backtest simulation...\")\n",
+    "\n",
+    "# Check if we have the required columns\n",
+    "required_cols = ['timestamp', 'price', 'position_change']\n",
+    "missing_cols = [col for col in required_cols if col not in strategy_data.columns]\n",
+    "\n",
+    "if missing_cols:\n",
+    "    print(f\"Missing required columns: {missing_cols}\")\n",
+    "    print(\"Creating missing columns...\")\n",
+    "    \n",
+    "    # Create position_change column if it doesn't exist\n",
+    "    if 'position_change' not in strategy_data.columns:\n",
+    "        strategy_data['position_change'] = 0\n",
+    "        print(\"Created position_change column\")\n",
+    "    \n",
+    "    # Create any other missing columns\n",
+    "    for col in missing_cols:\n",
+    "        if col not in strategy_data.columns:\n",
+    "            strategy_data[col] = 0\n",
+    "            print(f\"Created {col} column\")\n",
+    "\n",
+    "# Now run the backtest\n",
+    "try:\n",
+    "    backtest_results = run_backtest(strategy_data, initial_capital=10000, position_size=0.1)\n",
+    "    \n",
+    "    print(\"Backtest completed successfully!\")\n",
+    "    print(f\"Initial capital: $10,000\")\n",
+    "    \n",
+    "    # Check if we have any data\n",
+    "    if len(backtest_results) > 0:\n",
+    "        print(f\"Final portfolio value: ${backtest_results['portfolio_value'].iloc[-1]:.2f}\")\n",
+    "        print(f\"Total return: {((backtest_results['portfolio_value'].iloc[-1] / 10000) - 1) * 100:.2f}%\")\n",
+    "        \n",
+    "        # Display backtest summary\n",
+    "        print(\"\\nBacktest Summary:\")\n",
+    "        summary_cols = ['timestamp', 'price', 'position', 'cash', 'btc_holdings', 'portfolio_value']\n",
+    "        # Only show columns that exist\n",
+    "        available_summary_cols = [col for col in summary_cols if col in backtest_results.columns]\n",
+    "        print(backtest_results[available_summary_cols].tail(10))\n",
+    "        \n",
+    "    else:\n",
+    "        print(\"Backtest results are empty!\")\n",
+    "        print(\"This might indicate an issue with the backtest logic or input data\")\n",
+    "        \n",
+    "except Exception as e:\n",
+    "    print(f\"Backtest failed with error: {e}\")\n",
+    "    print(\"Trying simplified backtest...\")\n",
+    "    \n",
+    "    # Fallback: Simple backtest\n",
+    "    def simple_backtest(data, initial_capital=10000):\n",
+    "        \"\"\"Simplified backtest for debugging\"\"\"\n",
+    "        if len(data) == 0:\n",
+    "            return pd.DataFrame()\n",
+    "        \n",
+    "        result = data.copy()\n",
+    "        result['portfolio_value'] = initial_capital\n",
+    "        result['position'] = 0\n",
+    "        result['cash'] = initial_capital\n",
+    "        result['btc_holdings'] = 0.0\n",
+    "        \n",
+    "        # Simple logic: just track portfolio value\n",
+    "        for i in range(len(result)):\n",
+    "            if i > 0:\n",
+    "                # Simple return calculation\n",
+    "                price_change = (result.iloc[i]['price'] / result.iloc[i-1]['price']) - 1\n",
+    "                result.iloc[i, result.columns.get_loc('portfolio_value')] = \\\n",
+    "                    result.iloc[i-1]['portfolio_value'] * (1 + price_change * 0.1)\n",
+    "        \n",
+    "        return result\n",
+    "    \n",
+    "    # Try simplified version\n",
+    "    simple_results = simple_backtest(strategy_data, initial_capital=10000)\n",
+    "    \n",
+    "    if len(simple_results) > 0:\n",
+    "        print(\"Simplified backtest successful!\")\n",
+    "        print(f\"Final portfolio value: ${simple_results['portfolio_value'].iloc[-1]:.2f}\")\n",
+    "        backtest_results = simple_results\n",
+    "    else:\n",
+    "        print(\"Even simplified backtest failed!\")\n",
+    "        print(\"Please check the input data structure\")\n",
+    "        backtest_results = pd.DataFrame()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "performance_analysis",
+   "metadata": {},
+   "source": [
+    "## 8. Performance Analysis and Metrics\n",
+    "\n",
+    "Calculate key performance metrics including returns, volatility, Sharpe ratio, maximum drawdown, and other risk-adjusted measures."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "calculate_metrics",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Calculating performance metrics...\n",
+      "Detected data frequency: hour\n",
+      "Annualization factor: 8760\n",
+      "Performance metrics calculated!\n",
+      "\n",
+      "Strategy Performance Summary:\n",
+      "==================================================\n",
+      "Total Return             : -0.93%\n",
+      "Annualized Return        : -8.22%\n",
+      "Volatility (Annualized)  : 1.79%\n",
+      "Sharpe Ratio             : -4.58\n",
+      "Max Drawdown             : -1.24%\n",
+      "Win Rate                 : 19.34%\n",
+      "Avg Win                  : 0.02%\n",
+      "Avg Loss                 : -0.02%\n",
+      "Profit Factor            : 0.80\n",
+      "Data Frequency           : hour\n",
+      "Avg Holding Period       : 23.6 hours\n",
+      "==================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Performance Metrics Calculation\n",
+    "def calculate_performance_metrics(data):\n",
+    "    \"\"\"\n",
+    "    Calculate comprehensive performance metrics with automatic crypto data frequency detection\n",
+    "    \"\"\"\n",
+    "    # Remove first row (no returns)\n",
+    "    returns_data = data.iloc[1:].copy()\n",
+    "    \n",
+    "    # Detect data frequency automatically for proper annualization\n",
+    "    if len(data) > 1:\n",
+    "        time_diff = data['timestamp'].iloc[1] - data['timestamp'].iloc[0]\n",
+    "        time_diff_seconds = time_diff.total_seconds()\n",
+    "        \n",
+    "        if time_diff_seconds <= 60:  # Minute data\n",
+    "            annualization_factor = 365 * 24 * 60  # Minutes per year (crypto trades 24/7)\n",
+    "            frequency_name = \"minute\"\n",
+    "        elif time_diff_seconds <= 3600:  # Hour data\n",
+    "            annualization_factor = 365 * 24  # Hours per year\n",
+    "            frequency_name = \"hour\"\n",
+    "        elif time_diff_seconds <= 86400:  # Daily data\n",
+    "            annualization_factor = 365  # Days per year (crypto trades 24/7)\n",
+    "            frequency_name = \"day\"\n",
+    "        else:  # Weekly or longer\n",
+    "            annualization_factor = 52  # Weeks per year\n",
+    "            frequency_name = \"week\"\n",
+    "    else:\n",
+    "        annualization_factor = 365  # Default fallback for crypto\n",
+    "        frequency_name = \"unknown\"\n",
+    "    \n",
+    "    print(f\"Detected data frequency: {frequency_name}\")\n",
+    "    print(f\"Annualization factor: {annualization_factor}\")\n",
+    "    \n",
+    "    # Basic metrics\n",
+    "    total_return = (data['portfolio_value'].iloc[-1] / data['portfolio_value'].iloc[0]) - 1\n",
+    "    annualized_return = total_return * (annualization_factor / len(returns_data))\n",
+    "    \n",
+    "    # Volatility (annualized)\n",
+    "    volatility = returns_data['returns'].std() * np.sqrt(annualization_factor)\n",
+    "    \n",
+    "    # Sharpe Ratio (assuming 0% risk-free rate for crypto)\n",
+    "    sharpe_ratio = annualized_return / volatility if volatility > 0 else 0\n",
+    "    \n",
+    "    # Maximum Drawdown\n",
+    "    cumulative_returns = (1 + returns_data['returns']).cumprod()\n",
+    "    running_max = cumulative_returns.expanding().max()\n",
+    "    drawdown = (cumulative_returns - running_max) / running_max\n",
+    "    max_drawdown = drawdown.min()\n",
+    "    \n",
+    "    # Win/Loss ratio\n",
+    "    positive_returns = returns_data[returns_data['returns'] > 0]['returns']\n",
+    "    negative_returns = returns_data[returns_data['returns'] < 0]['returns']\n",
+    "    \n",
+    "    win_rate = len(positive_returns) / len(returns_data) if len(returns_data) > 0 else 0\n",
+    "    avg_win = positive_returns.mean() if len(positive_returns) > 0 else 0\n",
+    "    avg_loss = negative_returns.mean() if len(negative_returns) > 0 else 0\n",
+    "    \n",
+    "    # Profit Factor\n",
+    "    total_profit = positive_returns.sum() if len(positive_returns) > 0 else 0\n",
+    "    total_loss = abs(negative_returns.sum()) if len(negative_returns) > 0 else 0\n",
+    "    profit_factor = total_profit / total_loss if total_loss > 0 else float('inf')\n",
+    "    \n",
+    "    # Crypto-specific metrics\n",
+    "    # Calculate average holding period in the detected time units\n",
+    "    if 'position_change' in data.columns:\n",
+    "        position_changes = data[data['position_change'] != 0]\n",
+    "        if len(position_changes) > 1:\n",
+    "            avg_holding_period = len(returns_data) / len(position_changes)\n",
+    "        else:\n",
+    "            avg_holding_period = len(returns_data)\n",
+    "    else:\n",
+    "        avg_holding_period = len(returns_data)\n",
+    "    \n",
+    "    return {\n",
+    "        'Total Return': f\"{total_return:.2%}\",\n",
+    "        'Annualized Return': f\"{annualized_return:.2%}\",\n",
+    "        'Volatility (Annualized)': f\"{volatility:.2%}\",\n",
+    "        'Sharpe Ratio': f\"{sharpe_ratio:.2f}\",\n",
+    "        'Max Drawdown': f\"{max_drawdown:.2%}\",\n",
+    "        'Win Rate': f\"{win_rate:.2%}\",\n",
+    "        'Avg Win': f\"{avg_win:.2%}\",\n",
+    "        'Avg Loss': f\"{avg_loss:.2%}\",\n",
+    "        'Profit Factor': f\"{profit_factor:.2f}\",\n",
+    "        'Data Frequency': frequency_name,\n",
+    "        'Avg Holding Period': f\"{avg_holding_period:.1f} {frequency_name}s\"\n",
+    "    }\n",
+    "\n",
+    "# Calculate metrics\n",
+    "print(\"Calculating performance metrics...\")\n",
+    "\n",
+    "performance_metrics = calculate_performance_metrics(backtest_results)\n",
+    "\n",
+    "print(\"Performance metrics calculated!\")\n",
+    "print(\"\\nStrategy Performance Summary:\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "for metric, value in performance_metrics.items():\n",
+    "    print(f\"{metric:25}: {value}\")\n",
+    "\n",
+    "print(\"=\" * 50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "visualization",
+   "metadata": {},
+   "source": [
+    "## 9. Data Visualization\n",
+    "\n",
+    "Create comprehensive visualizations to understand the strategy performance, including price charts, moving averages, portfolio value, and drawdown analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "create_plots",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🎨 Creating visualizations...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x1600 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Visualizations created successfully!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create comprehensive visualizations\n",
+    "print(\"Creating visualizations...\")\n",
+    "\n",
+    "# Create subplots\n",
+    "fig, axes = plt.subplots(3, 1, figsize=(14, 16))\n",
+    "\n",
+    "# 1. Price and Moving Averages\n",
+    "axes[0].plot(strategy_data['timestamp'], strategy_data['price'], label='BTC Price', alpha=0.7, linewidth=1)\n",
+    "axes[0].plot(strategy_data['timestamp'], strategy_data['MA_short'], label='Short MA (10)', linewidth=2)\n",
+    "axes[0].plot(strategy_data['timestamp'], strategy_data['MA_long'], label='Long MA (30)', linewidth=2)\n",
+    "\n",
+    "# Highlight buy/sell signals\n",
+    "buy_signals = strategy_data[strategy_data['position_change'] == 2]\n",
+    "sell_signals = strategy_data[strategy_data['position_change'] == -2]\n",
+    "\n",
+    "axes[0].scatter(buy_signals['timestamp'], buy_signals['price'], \n",
+    "               color='green', marker='^', s=100, label='Buy Signal', zorder=5)\n",
+    "axes[0].scatter(sell_signals['timestamp'], sell_signals['price'], \n",
+    "               color='red', marker='v', s=100, label='Sell Signal', zorder=5)\n",
+    "\n",
+    "axes[0].set_title('BTC/USDT Price with Moving Average Crossover Signals', fontsize=14, fontweight='bold')\n",
+    "axes[0].set_ylabel('Price (USDT)', fontsize=12)\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# 2. Portfolio Value Over Time\n",
+    "axes[1].plot(backtest_results['timestamp'], backtest_results['portfolio_value'], \n",
+    "            label='Portfolio Value', color='blue', linewidth=2)\n",
+    "axes[1].axhline(y=10000, color='red', linestyle='--', alpha=0.7, label='Initial Capital')\n",
+    "axes[1].set_title('Portfolio Value Over Time', fontsize=14, fontweight='bold')\n",
+    "axes[1].set_ylabel('Portfolio Value (USDT)', fontsize=12)\n",
+    "axes[1].legend()\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "# 3. Drawdown Analysis\n",
+    "returns_data = backtest_results.iloc[1:].copy()\n",
+    "cumulative_returns = (1 + returns_data['returns']).cumprod()\n",
+    "running_max = cumulative_returns.expanding().max()\n",
+    "drawdown = (cumulative_returns - running_max) / running_max\n",
+    "\n",
+    "axes[2].fill_between(returns_data['timestamp'], drawdown, 0, \n",
+    "                    color='red', alpha=0.3, label='Drawdown')\n",
+    "axes[2].plot(returns_data['timestamp'], drawdown, color='red', linewidth=1)\n",
+    "axes[2].axhline(y=0, color='black', linestyle='-', alpha=0.5)\n",
+    "axes[2].set_title('Portfolio Drawdown Analysis', fontsize=14, fontweight='bold')\n",
+    "axes[2].set_ylabel('Drawdown (%)', fontsize=12)\n",
+    "axes[2].set_xlabel('Time', fontsize=12)\n",
+    "axes[2].legend()\n",
+    "axes[2].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Visualizations created successfully!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "additional_analysis",
+   "metadata": {},
+   "source": [
+    "## 10. Additional Analysis: Strategy Comparison\n",
+    "\n",
+    "Compare our strategy performance against a simple buy-and-hold approach to understand the relative effectiveness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "strategy_comparison",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🔄 Comparing strategy performance...\n",
+      "✅ Strategy comparison completed!\n",
+      "\n",
+      "📊 Strategy Performance Comparison:\n",
+      "============================================================\n",
+      "                   Metric Buy & Hold MA Crossover\n",
+      "             Total Return     -1.32%       -0.93%\n",
+      "              Final Value   $9867.74     $9906.82\n",
+      "Performance vs Buy & Hold   Baseline       -29.5%\n",
+      "============================================================\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Strategy Comparison: MA Crossover vs Buy & Hold\n",
+    "print(\"🔄 Comparing strategy performance...\")\n",
+    "\n",
+    "# Calculate buy & hold performance\n",
+    "initial_price = strategy_data['price'].iloc[0]\n",
+    "final_price = strategy_data['price'].iloc[-1]\n",
+    "buy_hold_return = (final_price / initial_price) - 1\n",
+    "\n",
+    "# Our strategy return\n",
+    "strategy_return = (backtest_results['portfolio_value'].iloc[-1] / 10000) - 1\n",
+    "\n",
+    "# Create comparison DataFrame\n",
+    "comparison_data = pd.DataFrame({\n",
+    "    'Metric': ['Total Return', 'Final Value', 'Performance vs Buy & Hold'],\n",
+    "    'Buy & Hold': [f\"{buy_hold_return:.2%}\", f\"${10000 * (1 + buy_hold_return):.2f}\", 'Baseline'],\n",
+    "    'MA Crossover': [f\"{strategy_return:.2%}\", f\"${backtest_results['portfolio_value'].iloc[-1]:.2f}\", \n",
+    "                    f\"{((strategy_return - buy_hold_return) / buy_hold_return * 100):.1f}%\" if buy_hold_return != 0 else 'N/A']\n",
+    "})\n",
+    "\n",
+    "print(\"Strategy comparison completed!\")\n",
+    "print(\"\\nStrategy Performance Comparison:\")\n",
+    "print(\"=\" * 60)\n",
+    "print(comparison_data.to_string(index=False))\n",
+    "print(\"=\" * 60)\n",
+    "\n",
+    "# Create comparison chart\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "# Portfolio values over time\n",
+    "plt.plot(backtest_results['timestamp'], backtest_results['portfolio_value'], \n",
+    "         label='MA Crossover Strategy', linewidth=2, color='blue')\n",
+    "\n",
+    "# Buy & hold line\n",
+    "buy_hold_values = 10000 * (strategy_data['price'] / initial_price)\n",
+    "plt.plot(strategy_data['timestamp'], buy_hold_values, \n",
+    "         label='Buy & Hold', linewidth=2, color='red', linestyle='--')\n",
+    "\n",
+    "plt.title('Strategy Performance Comparison: MA Crossover vs Buy & Hold', \n",
+    "          fontsize=16, fontweight='bold')\n",
+    "plt.xlabel('Time', fontsize=12)\n",
+    "plt.ylabel('Portfolio Value (USDT)', fontsize=12)\n",
+    "plt.legend(fontsize=12)\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conclusion",
+   "metadata": {},
+   "source": [
+    "## 11. Conclusion and Insights\n",
+    "\n",
+    "Summarize the backtesting results and provide actionable insights for strategy improvement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "final_insights",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Analyzing final results and generating insights...\n",
+      "Analysis complete!\n",
+      "\n",
+      "Key Insights:\n",
+      "==================================================\n",
+      "Total trades executed: 40\n",
+      "Average holding period: 24.9 hour\n",
+      "Strategy outperformance: -29.5%\n",
+      "Sharpe ratio: -4.58\n",
+      "Maximum drawdown: -1.24%\n",
+      "==================================================\n",
+      "\n",
+      "Recommendations for Strategy Improvement:\n",
+      "1. Optimize MA periods based on market conditions\n",
+      "2. Add stop-loss mechanisms to limit drawdowns\n",
+      "3. Implement position sizing based on volatility\n",
+      "4. Consider adding filters for trend strength\n",
+      "5. Test on different timeframes and assets\n",
+      "\n",
+      "Next Steps:\n",
+      "• Test with different parameter combinations\n",
+      "• Analyze performance across different market conditions\n",
+      "• Implement risk management features\n",
+      "• Consider machine learning enhancements\n",
+      "\n",
+      "Additional Resources:\n",
+      "• CoinAPI Documentation: https://docs.coinapi.io/\n",
+      "• Flat Files API: https://docs.coinapi.io/flat-files-api/\n",
+      "• Trading Strategy Literature\n",
+      "\n",
+      "Tutorial completed successfully!\n",
+      "Happy backtesting!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Final Insights and Recommendations\n",
+    "print(\"Analyzing final results and generating insights...\")\n",
+    "\n",
+    "# Calculate additional insights\n",
+    "total_trades = len(buy_signals) + len(sell_signals)\n",
+    "avg_holding_period = len(strategy_data) / total_trades if total_trades > 0 else 0\n",
+    "\n",
+    "print(\"Analysis complete!\")\n",
+    "print(\"\\nKey Insights:\")\n",
+    "print(\"=\" * 50)\n",
+    "print(f\"Total trades executed: {total_trades}\")\n",
+    "print(f\"Average holding period: {avg_holding_period:.1f} {performance_metrics.get('Data Frequency', 'time units')}\")\n",
+    "print(f\"Strategy outperformance: {comparison_data.iloc[2]['MA Crossover']}\")\n",
+    "print(f\"Sharpe ratio: {performance_metrics['Sharpe Ratio']}\")\n",
+    "print(f\"Maximum drawdown: {performance_metrics['Max Drawdown']}\")\n",
+    "print(\"=\" * 50)\n",
+    "\n",
+    "print(\"\\nRecommendations for Strategy Improvement:\")\n",
+    "print(\"1. Optimize MA periods based on market conditions\")\n",
+    "print(\"2. Add stop-loss mechanisms to limit drawdowns\")\n",
+    "print(\"3. Implement position sizing based on volatility\")\n",
+    "print(\"4. Consider adding filters for trend strength\")\n",
+    "print(\"5. Test on different timeframes and assets\")\n",
+    "\n",
+    "print(\"\\nNext Steps:\")\n",
+    "print(\"• Test with different parameter combinations\")\n",
+    "print(\"• Analyze performance across different market conditions\")\n",
+    "print(\"• Implement risk management features\")\n",
+    "print(\"• Consider machine learning enhancements\")\n",
+    "\n",
+    "print(\"\\nAdditional Resources:\")\n",
+    "print(\"• CoinAPI Documentation: https://docs.coinapi.io/\")\n",
+    "print(\"• Flat Files API: https://docs.coinapi.io/flat-files-api/\")\n",
+    "print(\"• Trading Strategy Literature\")\n",
+    "\n",
+    "print(\"\\nTutorial completed successfully!\")\n",
+    "print(\"Happy backtesting!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv (3.13.3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/coinapi/tutorials/Getting_started_Flat_Files.ipynb b/coinapi/tutorials/Getting_started_Flat_Files.ipynb
deleted file mode 100644
index d92c524f14..0000000000
--- a/coinapi/tutorials/Getting_started_Flat_Files.ipynb
+++ /dev/null
@@ -1,364 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "intro",
-   "metadata": {},
-   "source": [
-    "# Getting started: Flat Files\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "This tutorial demonstrates how to work with CoinAPI's Flat Files S3 API using curl commands. The Flat Files API provides access to historical cryptocurrency data stored in Amazon S3 buckets, allowing you to download large datasets efficiently.\n",
-    "\n",
-    "### What You Will Learn\n",
-    "\n",
-    "- How to list objects in the S3 bucket\n",
-    "- How to filter objects by prefix\n",
-    "- How to download specific data files\n",
-    "- Understanding the file structure and naming conventions\n",
-    "\n",
-    "### Prerequisites\n",
-    "\n",
-    "- curl command-line tool\n",
-    "- CoinAPI API key\n",
-    "- Basic understanding of REST APIs\n",
-    "\n",
-    "### API Documentation\n",
-    "\n",
-    "For detailed information, visit: https://docs.coinapi.io/flat-files-api/s3-api/"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "setup",
-   "metadata": {},
-   "source": [
-    "## 1. Environment Setup\n",
-    "\n",
-    "Set up your environment with the necessary configuration for CoinAPI Flat Files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "setup_code",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "✅ Environment setup complete!\n",
-      "📊 Using S3 base URL: http://s3.flatfiles.coinapi.io\n",
-      "🔑 API Key configured: Yes\n",
-      "\n",
-      "📋 Ready to work with CoinAPI Flat Files!\n",
-      "\n",
-      "💡 Remember to replace YOUR_API_KEY_HERE with your actual CoinAPI key\n",
-      "🔧 subprocess module imported for executing curl commands\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import required libraries\n",
-    "import subprocess\n",
-    "import json\n",
-    "import os\n",
-    "from datetime import datetime\n",
-    "\n",
-    "# Configuration for CoinAPI Flat Files\n",
-    "API_KEY = \"YOUR_COINAPI_KEY_HERE\"  # Replace with your actual API key\n",
-    "BASE_URL = \"http://s3.flatfiles.coinapi.io\"  # Correct endpoint\n",
-    "\n",
-    "print(\"✅ Environment setup complete!\")\n",
-    "print(f\"📊 Using S3 base URL: {BASE_URL}\")\n",
-    "print(f\"🔑 API Key configured: {'Yes' if API_KEY != 'YOUR_API_KEY_HERE' else 'No (Please update)'}\")\n",
-    "print(\"\\n📋 Ready to work with CoinAPI Flat Files!\")\n",
-    "print(\"\\n💡 Remember to replace YOUR_API_KEY_HERE with your actual CoinAPI key\")\n",
-    "print(\"🔧 subprocess module imported for executing curl commands\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "list_objects",
-   "metadata": {},
-   "source": [
-    "## 2. Listing Objects in the Bucket\n",
-    "\n",
-    "First, let's explore the available data by listing objects in the S3 bucket. This helps you understand what data is available and how it's organized.\n",
-    "\n",
-    "**Command to run:**\n",
-    "```bash\n",
-    "curl -H 'Authorization: YOUR_API_KEY' 'http://s3.flatfiles.coinapi.io/bucket/?list-type=2'\n",
-    "```\n",
-    "\n",
-    "This command will return an XML response listing all objects in the bucket. The response will include:\n",
-    "- File keys (paths)\n",
-    "- Last modified dates\n",
-    "- File sizes\n",
-    "- Storage class information\n",
-    "\n",
-    "**Note:** The response may be truncated if there are many files. You can use the `max-keys` parameter to control the number of results."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "list_bucket",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🔍 Listing all objects in the bucket...\n",
-      "\n",
-      "📋 Running command:\n",
-      "\n",
-      "📊 Response received:\n",
-      "<ListBucketResult xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xmlns:xsd=\"http://www.w3.org/2001/XMLSchema\"><ContentLength>0</ContentLength><HttpStatusCode>OK</HttpStatusCode><IsTruncated>false</IsTruncated><Prefix /><MaxKeys>0</MaxKeys><CommonPrefixes><Prefix>T-TRADES/</Prefix></CommonPrefixes><CommonPrefixes><Prefix>T-QUOTES/</Prefix></CommonPrefixes><CommonPrefixes><Prefix>T-LIMITBOOK_FULL/</Prefix></CommonPrefixes><Delimiter>/</Delimiter><KeyCount>0</KeyCount></ListBucketResult>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Display the curl command for listing objects\n",
-    "print(\"🔍 Listing all objects in the bucket...\")\n",
-    "print(\"\\n📋 Running command:\")\n",
-    "\n",
-    "# Execute the command\n",
-    "try:\n",
-    "    result = subprocess.run([\n",
-    "        'curl', \n",
-    "        '-H', f'Authorization: {API_KEY}', \n",
-    "        f'{BASE_URL}/bucket/?list-type=2'\n",
-    "    ], capture_output=True, text=True, timeout=30)\n",
-    "    \n",
-    "    if result.returncode == 0:\n",
-    "        print(\"\\n📊 Response received:\")\n",
-    "        # Show only first few lines to avoid overwhelming output\n",
-    "        lines = result.stdout.split('\\n')\n",
-    "        for i, line in enumerate(lines[:10]):  # Show first 10 lines\n",
-    "            print(line)\n",
-    "        if len(lines) > 10:\n",
-    "            print(f\"... and {len(lines) - 10} more lines\")\n",
-    "    else:\n",
-    "        print(f\"\\n❌ Error: {result.stderr}\")\n",
-    "        \n",
-    "except Exception as e:\n",
-    "    print(f\"\\n❌ Failed to execute command: {e}\")\n",
-    "    print(\"💡 Make sure you have curl installed and your API key is valid\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "filter_objects",
-   "metadata": {},
-   "source": [
-    "## 3. Filtering Objects by Prefix\n",
-    "\n",
-    "Now let's filter objects by a specific prefix to find data for a particular date and exchange. This is useful when you want to focus on specific data types or time periods.\n",
-    "\n",
-    "**Command to run:**\n",
-    "```bash\n",
-    "curl -H 'Authorization: YOUR_API_KEY' 'http://s3.flatfiles.coinapi.io/bucket/?list-type=2&prefix=T-LIMITBOOK_FULL/D-20250102/E-BINANCE/'\n",
-    "```\n",
-    "\n",
-    "This will return only files that match the specified prefix pattern."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "filter_prefix",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "🔍 Filtering objects with prefix: T-LIMITBOOK_FULL/D-20250102/E-BINANCE/\n",
-      "\n",
-      "📋 Running command:\n",
-      "\n",
-      "📊 Response received:\n",
-      "<ListBucketResult xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" 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00</LastModified><Size>6988591</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6881853+SC-BINANCE_SPOT_COMBO_TRY+S-COMBOTRY.csv.gz</Key><LastModified>2025-01-03T00:15:43+00:00</LastModified><Size>932165</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6883496+SC-BINANCE_SPOT_LTC_TRY+S-LTCTRY.csv.gz</Key><LastModified>2025-01-03T00:15:52+00:00</LastModified><Size>2708983</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6883497+SC-BINANCE_SPOT_RAD_TRY+S-RADTRY.csv.gz</Key><LastModified>2025-01-03T00:15:55+00:00</LastModified><Size>576885</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6888966+SC-BINANCE_SPOT_BTC_ARS+S-BTCARS.csv.gz</Key><LastModified>2025-01-03T00:15:41+00:00</LastModified><Size>2488625</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6888967+SC-BINANCE_SPOT_OPTIM_TRY+S-OPTRY.csv.gz</Key><LastModified>2025-01-03T00:15:54+00:00</LastModified><Size>2380590</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6888968+SC-BINANCE_SPOT_PAXG_TRY+S-PAXGTRY.csv.gz</Key><LastModified>2025-01-03T00:15:54+00:00</LastModified><Size>2219192</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6892216+SC-BINANCE_SPOT_MAV_BTC+S-MAVBTC.csv.gz</Key><LastModified>2025-01-03T00:15:50+00:00</LastModified><Size>488499</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6892218+SC-BINANCE_SPOT_MAV_USDT+S-MAVUSDT.csv.gz</Key><LastModified>2025-01-03T00:15:56+00:00</LastModified><Size>7409002</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6894844+SC-BINANCE_SPOT_PENDLE_BTC+S-PENDLEBTC.csv.gz</Key><LastModified>2025-01-03T00:15:55+00:00</LastModified><Size>4785227</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-6894845+SC-BINANCE_SPOT_PENDLE_USDT+S-PENDLEUSDT.csv.gz</Key><LastModified>2025-01-03T00:16:03+00:00</LastModified><Size>18121928</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-7281421+SC-BINANCE_SPOT_BCH_BNB+S-BCHBNB.csv.gz</Key><LastModified>2025-01-03T00:15:37+00:00</LastModified><Size>379939</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-7281426+SC-BINANCE_SPOT_BCH_BTC+S-BCHBTC.csv.gz</Key><LastModified>2025-01-03T00:15:41+00:00</LastModified><Size>6016126</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-7281435+SC-BINANCE_SPOT_BCH_USDC_6F1B1B+S-BCHUSDC.csv.gz</Key><LastModified>2025-01-03T00:15:40+00:00</LastModified><Size>3227315</Size></Contents><Contents><Key>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-7308400+SC-BINANCE_SPOT_MAV_TRY+S-MAVTRY.csv.gz</Key><LastModified>2025-01-03T00:15:50+00:00</LastModified><Size>405238</Size></Contents><Prefix>T-LIMITBOOK_FULL/D-20250102/E-BINANCE/</Prefix><MaxKeys>0</MaxKeys><Delimiter>/</Delimiter><KeyCount>0</KeyCount></ListBucketResult>\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Filter objects by prefix for specific date and exchange\n",
-    "prefix = \"T-LIMITBOOK_FULL/D-20250102/E-BINANCE/\"\n",
-    "\n",
-    "print(f\"🔍 Filtering objects with prefix: {prefix}\")\n",
-    "print(\"\\n📋 Running command:\")\n",
-    "\n",
-    "# Execute the command\n",
-    "try:\n",
-    "    result = subprocess.run([\n",
-    "        'curl', \n",
-    "        '-H', f'Authorization: {API_KEY}', \n",
-    "        f'{BASE_URL}/bucket/?list-type=2&prefix={prefix}'\n",
-    "    ], capture_output=True, text=True, timeout=30)\n",
-    "    \n",
-    "    if result.returncode == 0:\n",
-    "        print(\"\\n📊 Response received:\")\n",
-    "        # Show only first few lines\n",
-    "        lines = result.stdout.split('\\n')\n",
-    "        for i, line in enumerate(lines[:8]):  # Show first 8 lines\n",
-    "            print(line)\n",
-    "        if len(lines) > 8:\n",
-    "            print(f\"... and {len(lines) - 8} more lines\")\n",
-    "    else:\n",
-    "        print(f\"\\n❌ Error: {result.stderr}\")\n",
-    "        \n",
-    "except Exception as e:\n",
-    "    print(f\"\\n❌ Failed to execute command: {e}\")\n",
-    "    print(\"💡 Make sure you have curl installed and your API key is valid\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "download_file",
-   "metadata": {},
-   "source": [
-    "## 4. Downloading a Specific File\n",
-    "\n",
-    "Now let's download a specific file. We'll use the example file path: `T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-138123+SC-BINANCE_SPOT_BTC_USDT+S-BTCUSDT.csv.gz`\n",
-    "\n",
-    "This file contains limit book data for BTC/USDT on Binance from September 4, 2022.\n",
-    "\n",
-    "**Command to run:**\n",
-    "```bash\n",
-    "curl -H 'Authorization: YOUR_API_KEY' 'http://s3.flatfiles.coinapi.io/bucket/T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-138123+SC-BINANCE_SPOT_BTC_USDT+S-BTCUSDT.csv.gz' -o btc_usdt_limitbook_20250102.csv.gz\n",
-    "```\n",
-    "\n",
-    "This will download the file and save it locally as `btc_usdt_limitbook_20250102.csv.gz`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "download_specific",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "📥 Downloading file: T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-138123+SC-BINANCE_SPOT_BTC_USDT+S-BTCUSDT.csv.gz\n",
-      "\n",
-      "📋 Running command:\n",
-      "\n",
-      "✅ File downloaded successfully as: btc_usdt_limitbook_20220904.csv.gz\n",
-      "💡 The file is compressed (gzip format). You can decompress it using: gunzip {output_file}\n",
-      "\n",
-      "📊 First few lines of downloaded file:\n",
-      "time_exchange;time_coinapi;update_type;is_buy;entry_px;entry_sx;order_id\n",
-      "00:00:20.3516459;00:00:20.3516460;SNAPSHOT;0;94619.96;0.88898;\n",
-      "00:00:20.3516459;00:00:20.3516460;SNAPSHOT;1;94619.95;5.42067;\n",
-      "00:00:20.3516459;00:00:20.3516460;SNAPSHOT;0;94619.98;0.00012;\n",
-      "00:00:20.3516459;00:00:20.3516460;SNAPSHOT;1;94619.94;0.00018;\n",
-      "... and 23094549 more lines\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Download a specific file\n",
-    "file_key = \"T-LIMITBOOK_FULL/D-20250102/E-BINANCE/IDDI-138123+SC-BINANCE_SPOT_BTC_USDT+S-BTCUSDT.csv.gz\"\n",
-    "output_file = \"btc_usdt_limitbook_20250102.csv.gz\"\n",
-    "\n",
-    "print(f\"📥 Downloading file: {file_key}\")\n",
-    "print(\"\\n📋 Running command:\")\n",
-    "\n",
-    "# Execute the command\n",
-    "try:\n",
-    "    result = subprocess.run([\n",
-    "        'curl', \n",
-    "        '-H', f'Authorization: {API_KEY}', \n",
-    "        f'{BASE_URL}/bucket/{file_key}',\n",
-    "        '-o', output_file\n",
-    "    ], capture_output=True, text=True, timeout=600)\n",
-    "    \n",
-    "    if result.returncode == 0:\n",
-    "        print(f\"\\n✅ File downloaded successfully as: {output_file}\")\n",
-    "        print(\"💡 The file is compressed (gzip format). You can decompress it using: gunzip {output_file}\")\n",
-    "        \n",
-    "        # Show first few lines of the downloaded file (if it exists)\n",
-    "        try:\n",
-    "            if os.path.exists(output_file):\n",
-    "                import gzip\n",
-    "                with gzip.open(output_file, 'rt') as gz:\n",
-    "                    lines = gz.readlines()\n",
-    "                    print(f\"\\n📊 First few lines of downloaded file:\")\n",
-    "                    for i, line in enumerate(lines[:5]):\n",
-    "                        print(line.strip())\n",
-    "                    if len(lines) > 5:\n",
-    "                        print(f\"... and {len(lines) - 5} more lines\")\n",
-    "            else:\n",
-    "                print(\"\\n⚠️  File was not downloaded (check API key and file path)\")\n",
-    "        except Exception as e:\n",
-    "            print(f\"\\n⚠️  Could not read file contents: {e}\")\n",
-    "    else:\n",
-    "        print(f\"\\n❌ Error: {result.stderr}\")\n",
-    "        \n",
-    "except Exception as e:\n",
-    "    print(f\"\\n❌ Failed to execute command: {e}\")\n",
-    "    print(\"💡 Make sure you have curl installed and your API key is valid\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "conclusion",
-   "metadata": {},
-   "source": [
-    "## 5. Conclusion\n",
-    "\n",
-    "You've successfully learned how to work with CoinAPI's Flat Files S3 API using curl commands. This powerful interface allows you to access historical cryptocurrency data efficiently.\n",
-    "\n",
-    "### Key Takeaways\n",
-    "\n",
-    "- **S3 API Access**: Use standard S3 API calls with your CoinAPI key\n",
-    "- **File Organization**: Data is organized by type, date, exchange, and symbol\n",
-    "- **Prefix Filtering**: Use prefixes to efficiently find specific data\n",
-    "- **Compressed Format**: Files are gzip compressed to save bandwidth\n",
-    "- **Sequence Numbers**: Use sequence numbers to ensure data continuity\n",
-    "\n",
-    "### Next Steps\n",
-    "\n",
-    "1. **Explore Different Data Types**: Try accessing different data types like trades, quotes, or limit book\n",
-    "2. **Automate Downloads**: Create scripts to download data for specific time periods\n",
-    "3. **Data Processing**: Use tools like pandas to analyze the downloaded CSV files\n",
-    "4. **Real-time Integration**: Combine with real-time APIs for comprehensive analysis\n",
-    "\n",
-    "### Additional Resources\n",
-    "\n",
-    "- [CoinAPI Flat Files Documentation](https://docs.coinapi.io/flat-files-api/s3-api/)\n",
-    "---\n",
-    "\n",
-    "**Happy data mining! 📈📊🔍**"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "venv",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/coinapi/tutorials/Historical_OHLCV_REST_Build_Candlestick_Charts.ipynb b/coinapi/tutorials/Historical_OHLCV_REST_Build_Candlestick_Charts.ipynb
new file mode 100644
index 0000000000..0ae85250f6
--- /dev/null
+++ b/coinapi/tutorials/Historical_OHLCV_REST_Build_Candlestick_Charts.ipynb
@@ -0,0 +1,439 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "intro",
+   "metadata": {},
+   "source": [
+    "# Historical OHLCV from REST: Build Candlestick Charts\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This tutorial demonstrates how to fetch historical OHLCV (Open, High, Low, Close, Volume) data from the CoinAPI REST API and create professional candlestick charts. You'll learn how to retrieve historical price data for cryptocurrency pairs and visualize them using matplotlib and mplfinance.\n",
+    "\n",
+    "### What You Will Learn\n",
+    "\n",
+    "- How to authenticate with the CoinAPI REST API\n",
+    "- How to fetch historical OHLCV data with specific parameters\n",
+    "- How to process and clean the API response data\n",
+    "- How to create professional candlestick charts\n",
+    "- How to analyze price trends and patterns\n",
+    "\n",
+    "### Prerequisites\n",
+    "\n",
+    "- Python 3.7+\n",
+    "- Required packages: requests, pandas, numpy, matplotlib, mplfinance\n",
+    "- CoinAPI API key (free tier available)\n",
+    "\n",
+    "### Setup Instructions\n",
+    "\n",
+    "1. Install required packages: `pip install requests pandas numpy matplotlib mplfinance`\n",
+    "2. Get your free API key from [CoinAPI](https://www.coinapi.io/)\n",
+    "3. Replace API_KEY placeholder in the code below"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "setup",
+   "metadata": {},
+   "source": [
+    "## 1. Environment Setup\n",
+    "\n",
+    "Set up your environment with necessary imports and configuration for working with the CoinAPI REST API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "setup_code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required libraries\n",
+    "import requests\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import mplfinance as mpf\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "\n",
+    "# Set up plotting style\n",
+    "plt.style.use('default')\n",
+    "plt.rcParams['figure.figsize'] = (14, 8)\n",
+    "plt.rcParams['font.size'] = 10\n",
+    "\n",
+    "# API configuration\n",
+    "API_KEY = \"YOUR_API_KEY_HERE\"  # Replace with your actual API key\n",
+    "BASE_URL = \"https://rest.coinapi.io/v1\"\n",
+    "\n",
+    "# Editable parameters\n",
+    "SYMBOL_ID = \"BINANCE_SPOT_BTC_USDT\"\n",
+    "PERIOD_ID = \"1HRS\"\n",
+    "TIME_START = \"2025-07-07T00:00:00\"\n",
+    "TIME_END = \"2025-07-14T00:00:00\"\n",
+    "LIMIT = 200\n",
+    "\n",
+    "print(\"Environment setup complete!\")\n",
+    "print(f\"Using API base URL: {BASE_URL}\")\n",
+    "print(f\"API Key configured: {'Yes' if API_KEY != 'YOUR_API_KEY_HERE' else 'No (Please update)'}\")\n",
+    "print(f\"Target symbol: {SYMBOL_ID}\")\n",
+    "print(f\"Time period: {TIME_START} to {TIME_END}\")\n",
+    "print(f\"Data interval: {PERIOD_ID}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "data_fetching",
+   "metadata": {},
+   "source": [
+    "## 2. Fetch Historical OHLCV Data\n",
+    "\n",
+    "Now we'll fetch historical OHLCV data from the CoinAPI REST API using the specified parameters. We'll make a request to the `/ohlcv/:symbol_id/history` endpoint with our query parameters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fetch_data",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fetch_ohlcv_data(symbol_id, period_id, time_start, time_end, limit):\n",
+    "    \"\"\"\n",
+    "    Fetch historical OHLCV data from CoinAPI REST API\n",
+    "    \n",
+    "    Args:\n",
+    "        symbol_id (str): Trading pair identifier (e.g., BINANCE_SPOT_BTC_USDT)\n",
+    "        period_id (str): Time period for each candle (e.g., 1HRS, 1DAY)\n",
+    "        time_start (str): Start time in ISO format\n",
+    "        time_end (str): End time in ISO format\n",
+    "        limit (int): Maximum number of candles to return\n",
+    "    \n",
+    "    Returns:\n",
+    "        dict: API response with OHLCV data\n",
+    "    \"\"\"\n",
+    "    endpoint = f\"/ohlcv/{symbol_id}/history\"\n",
+    "    url = BASE_URL + endpoint\n",
+    "    \n",
+    "    # Query parameters\n",
+    "    params = {\n",
+    "        'period_id': period_id,\n",
+    "        'time_start': time_start,\n",
+    "        'time_end': time_end,\n",
+    "        'limit': limit\n",
+    "    }\n",
+    "    \n",
+    "    # Headers with API key\n",
+    "    headers = {\n",
+    "        'X-CoinAPI-Key': API_KEY\n",
+    "    }\n",
+    "    \n",
+    "    try:\n",
+    "        response = requests.get(url, params=params, headers=headers)\n",
+    "        response.raise_for_status()\n",
+    "        return response.json()\n",
+    "    except requests.exceptions.RequestException as e:\n",
+    "        print(f\"Error fetching data: {e}\")\n",
+    "        return None\n",
+    "\n",
+    "# Fetch the data\n",
+    "print(\"Fetching historical OHLCV data...\")\n",
+    "ohlcv_data = fetch_ohlcv_data(SYMBOL_ID, PERIOD_ID, TIME_START, TIME_END, LIMIT)\n",
+    "\n",
+    "if ohlcv_data:\n",
+    "    print(f\"Successfully fetched {len(ohlcv_data)} OHLCV records\")\n",
+    "    print(\"Sample data structure:\")\n",
+    "    if ohlcv_data:\n",
+    "        print(f\"First record: {ohlcv_data[0]}\")\n",
+    "else:\n",
+    "    print(\"Failed to fetch data. Please check your API key and parameters.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "analysis",
+   "metadata": {},
+   "source": [
+    "## 3. Data Processing and Analysis\n",
+    "\n",
+    "Now we'll process the raw API response data into a pandas DataFrame and perform some basic analysis. We'll convert timestamps, calculate additional metrics, and prepare the data for visualization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "analyze_data",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_ohlcv_data(raw_data):\n",
+    "    \"\"\"\n",
+    "    Process raw OHLCV data into a pandas DataFrame\n",
+    "    \n",
+    "    Args:\n",
+    "        raw_data (list): Raw API response data\n",
+    "    \n",
+    "    Returns:\n",
+    "        pd.DataFrame: Processed OHLCV data\n",
+    "    \"\"\"\n",
+    "    if not raw_data:\n",
+    "        return None\n",
+    "    \n",
+    "    # Convert to DataFrame\n",
+    "    df = pd.DataFrame(raw_data)\n",
+    "    \n",
+    "    # Convert timestamp to datetime\n",
+    "    df['time_period_start'] = pd.to_datetime(df['time_period_start'])\n",
+    "    df['time_period_end'] = pd.to_datetime(df['time_period_end'])\n",
+    "    \n",
+    "    # Set time_period_start as index\n",
+    "    df.set_index('time_period_start', inplace=True)\n",
+    "    \n",
+    "    # Sort by time\n",
+    "    df.sort_index(inplace=True)\n",
+    "    \n",
+    "    # Calculate additional metrics\n",
+    "    df['price_change'] = df['price_close'] - df['price_open']\n",
+    "    df['price_change_pct'] = (df['price_change'] / df['price_open']) * 100\n",
+    "    df['body_size'] = abs(df['price_close'] - df['price_open'])\n",
+    "    df['upper_shadow'] = df['price_high'] - df[['price_open', 'price_close']].max(axis=1)\n",
+    "    df['lower_shadow'] = df[['price_open', 'price_close']].min(axis=1) - df['price_low']\n",
+    "    \n",
+    "    return df\n",
+    "\n",
+    "# Process the data\n",
+    "if ohlcv_data:\n",
+    "    df = process_ohlcv_data(ohlcv_data)\n",
+    "    \n",
+    "    if df is not None:\n",
+    "        print(\"Data processing complete!\")\n",
+    "        print(f\"DataFrame shape: {df.shape}\")\n",
+    "        print(\"\\nFirst few rows:\")\n",
+    "        print(df.head())\n",
+    "        \n",
+    "        print(\"\\nData summary:\")\n",
+    "        print(df.describe())\n",
+    "        \n",
+    "        print(\"\\nPrice statistics:\")\n",
+    "        print(f\"Price range: ${df['price_low'].min():.2f} - ${df['price_high'].max():.2f}\")\n",
+    "        print(f\"Average volume: {df['volume_traded'].mean():.2f}\")\n",
+    "        print(f\"Total price change: {df['price_change'].sum():.2f} USDT\")\n",
+    "        print(f\"Average price change: {df['price_change_pct'].mean():.2f}%\")\n",
+    "    else:\n",
+    "        print(\"Failed to process data.\")\n",
+    "else:\n",
+    "    print(\"No data to process.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "visualization",
+   "metadata": {},
+   "source": [
+    "## 4. Creating Professional Candlestick Charts\n",
+    "\n",
+    "Now we'll create professional candlestick charts using mplfinance, which is specifically designed for financial data visualization. We'll create multiple chart types to analyze different aspects of the price data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "create_plots",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating candlestick chart...\n",
+      "OHLCV DataFrame shape: (168, 5)\n",
+      "OHLCV DataFrame columns: ['Open', 'High', 'Low', 'Close', 'Volume']\n",
+      "\n",
+      "First few rows of OHLCV data:\n",
+      "                                Open       High        Low      Close  \\\n",
+      "time_period_start                                                       \n",
+      "2025-07-07 00:00:00+00:00  109203.85  109288.02  108800.01  108823.07   \n",
+      "2025-07-07 01:00:00+00:00  108823.07  109089.00  108679.75  109019.12   \n",
+      "2025-07-07 02:00:00+00:00  109019.12  109499.99  109019.12  109364.52   \n",
+      "2025-07-07 03:00:00+00:00  109364.53  109700.00  109364.52  109389.47   \n",
+      "2025-07-07 04:00:00+00:00  109389.46  109447.54  109128.72  109128.73   \n",
+      "\n",
+      "                              Volume  \n",
+      "time_period_start                     \n",
+      "2025-07-07 00:00:00+00:00  253.53776  \n",
+      "2025-07-07 01:00:00+00:00  299.50777  \n",
+      "2025-07-07 02:00:00+00:00  433.77607  \n",
+      "2025-07-07 03:00:00+00:00  326.47933  \n",
+      "2025-07-07 04:00:00+00:00  184.06878  \n",
+      "Candlestick chart created and saved as 'candlestick_chart.png'\n",
+      "\n",
+      "Creating price analysis charts...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1200 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Price analysis charts created and saved as 'price_analysis_charts.png'\n",
+      "\n",
+      "All visualizations complete!\n"
+     ]
+    }
+   ],
+   "source": [
+    "def create_candlestick_chart(df, title=\"BTC/USDT Candlestick Chart\"):\n",
+    "    \"\"\"\n",
+    "    Create a professional candlestick chart using mplfinance\n",
+    "    \n",
+    "    Args:\n",
+    "        df (pd.DataFrame): OHLCV data with datetime index\n",
+    "        title (str): Chart title\n",
+    "    \"\"\"\n",
+    "    # Create a clean DataFrame with only the required OHLCV columns\n",
+    "    # The actual column names from the API are: price_open, price_high, price_low, price_close, volume_traded\n",
+    "    ohlcv_df = df[['price_open', 'price_high', 'price_low', 'price_close', 'volume_traded']].copy()\n",
+    "    \n",
+    "    # Rename columns to the standard OHLCV format expected by mplfinance\n",
+    "    ohlcv_df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
+    "    \n",
+    "    print(\"OHLCV DataFrame shape:\", ohlcv_df.shape)\n",
+    "    print(\"OHLCV DataFrame columns:\", ohlcv_df.columns.tolist())\n",
+    "    print(\"\\nFirst few rows of OHLCV data:\")\n",
+    "    print(ohlcv_df.head())\n",
+    "    \n",
+    "    # Create the candlestick chart\n",
+    "    mpf.plot(ohlcv_df, \n",
+    "             type='candle', \n",
+    "             title=title,\n",
+    "             ylabel='Price (USDT)',\n",
+    "             volume=True,\n",
+    "             style='charles',\n",
+    "             figsize=(14, 8),\n",
+    "             panel_ratios=(3, 1),\n",
+    "             savefig='candlestick_chart.png')\n",
+    "    \n",
+    "    print(\"Candlestick chart created and saved as 'candlestick_chart.png'\")\n",
+    "\n",
+    "def create_price_analysis_charts(df):\n",
+    "    \"\"\"\n",
+    "    Create additional analysis charts\n",
+    "    \"\"\"\n",
+    "    fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
+    "    fig.suptitle('BTC/USDT Price Analysis', fontsize=16, fontweight='bold')\n",
+    "    \n",
+    "    # Price trend over time\n",
+    "    axes[0, 0].plot(df.index, df['price_close'], linewidth=2, color='blue')\n",
+    "    axes[0, 0].set_title('Price Trend Over Time')\n",
+    "    axes[0, 0].set_ylabel('Price (USDT)')\n",
+    "    axes[0, 0].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Volume analysis\n",
+    "    axes[0, 1].bar(df.index, df['volume_traded'], alpha=0.7, color='green')\n",
+    "    axes[0, 1].set_title('Trading Volume')\n",
+    "    axes[0, 1].set_ylabel('Volume')\n",
+    "    axes[0, 1].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Price change distribution\n",
+    "    axes[1, 0].hist(df['price_change_pct'], bins=20, alpha=0.7, color='orange', edgecolor='black')\n",
+    "    axes[1, 0].set_title('Price Change Distribution')\n",
+    "    axes[1, 0].set_xlabel('Price Change (%)')\n",
+    "    axes[1, 0].set_ylabel('Frequency')\n",
+    "    axes[1, 0].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Body size vs volume scatter\n",
+    "    axes[1, 1].scatter(df['body_size'], df['volume_traded'], alpha=0.6, color='purple')\n",
+    "    axes[1, 1].set_title('Body Size vs Volume')\n",
+    "    axes[1, 1].set_xlabel('Candle Body Size (USDT)')\n",
+    "    axes[1, 1].set_ylabel('Volume')\n",
+    "    axes[1, 1].grid(True, alpha=0.3)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.savefig('price_analysis_charts.png', dpi=300, bbox_inches='tight')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print(\"Price analysis charts created and saved as 'price_analysis_charts.png'\")\n",
+    "\n",
+    "# Create the charts\n",
+    "if 'df' in locals() and df is not None:\n",
+    "    print(\"Creating candlestick chart...\")\n",
+    "    create_candlestick_chart(df)\n",
+    "    \n",
+    "    print(\"\\nCreating price analysis charts...\")\n",
+    "    create_price_analysis_charts(df)\n",
+    "    \n",
+    "    print(\"\\nAll visualizations complete!\")\n",
+    "else:\n",
+    "    print(\"No data available for visualization. Please run the data processing cell first.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conclusion",
+   "metadata": {},
+   "source": [
+    "## 5. Conclusion and Next Steps\n",
+    "\n",
+    "Congratulations! You've successfully fetched historical OHLCV data from the CoinAPI REST API and built professional candlestick charts.\n",
+    "\n",
+    "### What We Accomplished\n",
+    "\n",
+    "- Successfully connected to the CoinAPI REST API using proper authentication\n",
+    "- Fetched historical OHLCV data for BTC/USDT with specific parameters\n",
+    "- Processed and analyzed the raw API response data\n",
+    "- Created professional candlestick charts using mplfinance\n",
+    "- Generated additional analysis charts for deeper insights\n",
+    "- Saved all visualizations as high-quality image files\n",
+    "\n",
+    "### Key Takeaways\n",
+    "\n",
+    "- The CoinAPI REST API provides clean, structured OHLCV data\n",
+    "- Proper data processing is essential for financial analysis\n",
+    "- Volume analysis provides valuable insights into market activity\n",
+    "- Historical data analysis helps identify patterns and trends\n",
+    "\n",
+    "\n",
+    "### API Endpoint Reference\n",
+    "\n",
+    "- **Base URL**: `https://rest.coinapi.io/v1`\n",
+    "- **Endpoint**: `/ohlcv/:symbol_id/history`\n",
+    "- **Documentation**: [CoinAPI OHLCV Historical Data](https://docs.coinapi.io/market-data/rest-api/ohlcv/historical-data)\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Happy analyzing!**"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv (3.13.3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/coinapi/tutorials/How_to_create_the_right_endpoint_for_Market_Data_REST_API.ipynb b/coinapi/tutorials/How_to_create_the_right_endpoint_for_Market_Data_REST_API.ipynb
deleted file mode 100644
index 3af59cd762..0000000000
--- a/coinapi/tutorials/How_to_create_the_right_endpoint_for_Market_Data_REST_API.ipynb
+++ /dev/null
@@ -1,407 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "intro",
-   "metadata": {},
-   "source": [
-    "# How to Create the Right Endpoint for Market Data REST API\n",
-    "\n",
-    "## Introduction\n",
-    "\n",
-    "This tutorial will guide you through creating proper endpoints for the CoinAPI.io Market Data REST API. You'll learn how to structure API requests, handle authentication, and retrieve different types of market data including metadata, OHLCV, and trades data.\n",
-    "\n",
-    "### What You Will Learn\n",
-    "\n",
-    "- How to properly structure REST API endpoints\n",
-    "- Authentication using X-CoinAPI-Key header\n",
-    "- Retrieving metadata (assets, symbols, exchanges)\n",
-    "- Using metadata to fetch OHLCV and trades data\n",
-    "- Best practices for API endpoint construction\n",
-    "\n",
-    "### Prerequisites\n",
-    "\n",
-    "- Basic understanding of REST APIs\n",
-    "- Familiarity with curl commands\n",
-    "- Terminal or command line access\n",
-    "\n",
-    "### API Documentation Reference\n",
-    "\n",
-    "- [CoinAPI.io Market Data API Documentation](https://docs.coinapi.io/market-data/)\n",
-    "- Base URL: `https://rest.coinapi.io`\n",
-    "- Authentication: X-CoinAPI-Key header"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "setup",
-   "metadata": {},
-   "source": [
-    "## 1. Understanding the API Structure\n",
-    "\n",
-    "Before creating endpoints, let's understand the CoinAPI.io Market Data API structure:\n",
-    "\n",
-    "### Base URL\n",
-    "```\n",
-    "https://rest.coinapi.io\n",
-    "```\n",
-    "\n",
-    "### Authentication\n",
-    "In the examples, we will be using the `X-CoinAPI-Key` header for authentication:\n",
-    "```\n",
-    "X-CoinAPI-Key: YOUR_API_KEY_HERE\n",
-    "```\n",
-    "\n",
-    "### Common Endpoint Categories\n",
-    "1. **Metadata Endpoints** - Get information about available data\n",
-    "2. **Latest/Current Data Endpoints** - Retrieve latest or current market data\n",
-    "3. **Historical Data Endpoints** - Access historical information"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "metadata_endpoints",
-   "metadata": {},
-   "source": [
-    "## 2. Metadata Endpoints - The Foundation\n",
-    "\n",
-    "Metadata endpoints provide essential information about available data. These are crucial for building dynamic applications.\n",
-    "\n",
-    "### 2.1 List All Assets\n",
-    "\n",
-    "**Endpoint:** `/v1/assets`\n",
-    "**Purpose:** Get all available assets (cryptocurrencies, fiat currencies)\n",
-    "\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     https://rest.coinapi.io/v1/assets\n",
-    "```\n",
-    "\n",
-    "**Sample Response:**\n",
-    "```json\n",
-    "[\n",
-    "  {\n",
-    "    \"asset_id\": \"BTC\",\n",
-    "    \"name\": \"Bitcoin\",\n",
-    "    \"type_is_crypto\": 1,\n",
-    "    \"data_quote_start\": \"2014-02-24T17:43:05.0000000Z\",\n",
-    "    \"data_quote_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"data_orderbook_start\": \"2014-02-24T17:43:05.0000000Z\",\n",
-    "    \"data_orderbook_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"data_trade_start\": \"2010-07-17T23:09:17.0000000Z\",\n",
-    "    \"data_trade_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"data_symbols_count\": 1000,\n",
-    "    \"volume_1hrs_usd\": 1234567890.12,\n",
-    "    \"volume_1day_usd\": 9876543210.98,\n",
-    "    \"volume_1mth_usd\": 123456789012.34,\n",
-    "    \"price_usd\": 45000.00\n",
-    "  }\n",
-    "]\n",
-    "```\n",
-    "\n",
-    "### 2.2 List All Symbols\n",
-    "\n",
-    "**Endpoint:** `/v1/symbols`\n",
-    "**Purpose:** Get all available trading pairs and symbols\n",
-    "\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     https://rest.coinapi.io/v1/symbols\n",
-    "```\n",
-    "\n",
-    "**Sample Response:**\n",
-    "```json\n",
-    "[\n",
-    "  {\n",
-    "    \"symbol_id\": \"BINANCE_SPOT_BTC_USDT\",\n",
-    "    \"exchange_id\": \"BINANCE\",\n",
-    "    \"symbol_type\": \"SPOT\",\n",
-    "    \"asset_id_base\": \"BTC\",\n",
-    "    \"asset_id_quote\": \"USDT\",\n",
-    "    \"data_start\": \"2017-08-17\",\n",
-    "    \"data_end\": \"2024-01-01\",\n",
-    "    \"data_quote_start\": \"2017-08-17T00:00:00.0000000Z\",\n",
-    "    \"data_quote_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"data_orderbook_start\": \"2017-08-17T00:00:00.0000000Z\",\n",
-    "    \"data_orderbook_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"data_trade_start\": \"2017-08-17T00:00:00.0000000Z\",\n",
-    "    \"data_trade_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"volume_1hrs\": 123.45,\n",
-    "    \"volume_1day\": 1234.56,\n",
-    "    \"volume_1mth\": 12345.67,\n",
-    "    \"price\": 45000.00\n",
-    "  }\n",
-    "]\n",
-    "```\n",
-    "\n",
-    "### 2.3 List All Exchanges\n",
-    "\n",
-    "**Endpoint:** `/v1/exchanges`\n",
-    "**Purpose:** Get all available exchanges\n",
-    "\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     https://rest.coinapi.io/v1/exchanges\n",
-    "```\n",
-    "\n",
-    "**Sample Response:**\n",
-    "```json\n",
-    "[\n",
-    "  {\n",
-    "    \"exchange_id\": \"BINANCE\",\n",
-    "    \"name\": \"Binance\",\n",
-    "    \"website\": \"https://www.binance.com\",\n",
-    "    \"data_start\": \"2017-08-17\",\n",
-    "    \"data_end\": \"2024-01-01\",\n",
-    "    \"data_quote_start\": \"2017-08-17T00:00:00.0000000Z\",\n",
-    "    \"data_quote_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"data_orderbook_start\": \"2017-08-17T00:00:00.0000000Z\",\n",
-    "    \"data_orderbook_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"data_trade_start\": \"2017-08-17T00:00:00.0000000Z\",\n",
-    "    \"data_trade_end\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"data_symbols_count\": 1000,\n",
-    "    \"volume_1hrs_usd\": 1234567890.12,\n",
-    "    \"volume_1day_usd\": 9876543210.98,\n",
-    "    \"volume_1mth_usd\": 123456789012.34\n",
-    "  }\n",
-    "]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "market_data_endpoints",
-   "metadata": {},
-   "source": [
-    "## 3. Market Data Endpoints - Using Metadata\n",
-    "\n",
-    "Now that we understand the metadata structure, let's use it to fetch actual market data.\n",
-    "\n",
-    "### 3.1 OHLCV Data (Open, High, Low, Close, Volume)\n",
-    "\n",
-    "**Endpoint:** `/v1/ohlcv/{symbol_id}/history`\n",
-    "**Purpose:** Get historical OHLCV data for a specific symbol\n",
-    "\n",
-    "**Parameters:**\n",
-    "- `symbol_id`: From metadata (e.g., \"BINANCE_SPOT_BTC_USDT\")\n",
-    "- `period_id`: Time period (1MIN, 5MIN, 1HRS, 1DAY, etc.)\n",
-    "- `time_start`: Start time (ISO 8601 format)\n",
-    "- `time_end`: End time (ISO 8601 format)\n",
-    "- `limit`: Number of data points (max 100000)\n",
-    "\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     \"https://rest.coinapi.io/v1/ohlcv/BINANCE_SPOT_BTC_USDT/history?period_id=1DAY&time_start=2024-01-01T00:00:00&time_end=2024-01-07T00:00:00&limit=7\"\n",
-    "```\n",
-    "\n",
-    "**Sample Response:**\n",
-    "```json\n",
-    "[\n",
-    "  {\n",
-    "    \"time_period_start\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"time_period_end\": \"2024-01-02T00:00:00.0000000Z\",\n",
-    "    \"time_open\": \"2024-01-01T00:00:00.0000000Z\",\n",
-    "    \"time_close\": \"2024-01-01T23:59:59.0000000Z\",\n",
-    "    \"price_open\": 45000.00,\n",
-    "    \"price_high\": 45500.00,\n",
-    "    \"price_low\": 44800.00,\n",
-    "    \"price_close\": 45200.00,\n",
-    "    \"volume_traded\": 1234.56,\n",
-    "    \"trades_count\": 5678\n",
-    "  }\n",
-    "]\n",
-    "```\n",
-    "\n",
-    "### 3.2 Trades Data\n",
-    "\n",
-    "**Endpoint:** `/v1/trades/{symbol_id}/history`\n",
-    "**Purpose:** Get historical trades for a specific symbol\n",
-    "\n",
-    "**Parameters:**\n",
-    "- `symbol_id`: From metadata (e.g., \"BINANCE_SPOT_BTC_USDT\")\n",
-    "- `time_start`: Start time (ISO 8601 format)\n",
-    "- `time_end`: End time (ISO 8601 format)\n",
-    "- `limit`: Number of trades (max 100000)\n",
-    "\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     \"https://rest.coinapi.io/v1/trades/BINANCE_SPOT_BTC_USDT/history?time_start=2024-01-01T00:00:00&time_end=2024-01-01T01:00:00&limit=100\"\n",
-    "```\n",
-    "\n",
-    "**Sample Response:**\n",
-    "```json\n",
-    "[\n",
-    "  {\n",
-    "    \"sequence\": 123456789,\n",
-    "    \"symbol_id\": \"BINANCE_SPOT_BTC_USDT\",\n",
-    "    \"time_exchange\": \"2024-01-01T00:00:01.1234567Z\",\n",
-    "    \"time_coinapi\": \"2024-01-01T00:00:01.1234567Z\",\n",
-    "    \"uuid\": \"12345678-1234-1234-1234-123456789012\",\n",
-    "    \"price\": 45000.00,\n",
-    "    \"size\": 0.1,\n",
-    "    \"taker_side\": \"BUY\"\n",
-    "  }\n",
-    "]\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "endpoint_best_practices",
-   "metadata": {},
-   "source": [
-    "## 4. Best Practices for Endpoint Construction\n",
-    "\n",
-    "### 4.1 URL Structure\n",
-    "\n",
-    "**Good Practice:**\n",
-    "```bash\n",
-    "https://rest.coinapi.io/v1/ohlcv/BINANCE_SPOT_BTC_USDT/history?period_id=1DAY&time_start=2024-01-01T00:00:00&limit=7\n",
-    "```\n",
-    "\n",
-    "**Components:**\n",
-    "- Base URL: `https://rest.coinapi.io`\n",
-    "- API Version: `/v1`\n",
-    "- Resource: `/ohlcv`\n",
-    "- Identifier: `/{symbol_id}`\n",
-    "- Action: `/history`\n",
-    "- Query Parameters: `?param1=value1&param2=value2`\n",
-    "\n",
-    "### 4.2 Parameter Handling\n",
-    "\n",
-    "**Required Parameters:**\n",
-    "- Always include `X-CoinAPI-Key` header\n",
-    "- Use proper symbol_id from metadata\n",
-    "- Specify time ranges for historical data\n",
-    "\n",
-    "**Optional Parameters:**\n",
-    "- Use `limit` to control response size\n",
-    "- Include `period_id` for OHLCV data\n",
-    "- Add filters when available\n",
-    "\n",
-    "### 4.3 Error Handling\n",
-    "\n",
-    "**Common HTTP Status Codes:**\n",
-    "- `200`: Success\n",
-    "- `400`: Bad Request (invalid parameters)\n",
-    "- `401`: Unauthorized (invalid API key)\n",
-    "- `429`: Rate Limit Exceeded\n",
-    "- `500`: Server Error\n",
-    "\n",
-    "**Error Response Example:**\n",
-    "```json\n",
-    "{\n",
-    "  \"error\": \"Invalid symbol_id\",\n",
-    "  \"status\": 400\n",
-    "}\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "advanced_techniques",
-   "metadata": {},
-   "source": [
-    "## 5. Advanced Endpoint Techniques\n",
-    "\n",
-    "### 5.1 Filtering\n",
-    "\n",
-    "**Filter by Exchange:**\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     \"https://rest.coinapi.io/v1/symbols?filter_exchange_id=BINANCE\"\n",
-    "```\n",
-    "\n",
-    "**Filter by Asset:**\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     \"https://rest.coinapi.io/v1/symbols?filter_asset_id=BTC\"\n",
-    "```\n",
-    "\n",
-    "### 6.2 Real-time Data\n",
-    "\n",
-    "**Latest OHLCV:**\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     https://rest.coinapi.io/v1/ohlcv/BINANCE_SPOT_BTC_USDT/latest?period_id=1MIN&limit=1000&include_empty_items=true\n",
-    "```\n",
-    "\n",
-    "**Latest Trades:**\n",
-    "```bash\n",
-    "curl -H \"X-CoinAPI-Key: YOUR_API_KEY\" \\\n",
-    "     https://rest.coinapi.io/v1/trades/BINANCE_SPOT_BTC_USDT/latest?limit=1000&include_id=false\n",
-    "```\n",
-    "\n",
-    "\n",
-    "**Best Practices:**\n",
-    "- Implement exponential backoff\n",
-    "- Cache metadata responses\n",
-    "- Use appropriate time intervals"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "conclusion",
-   "metadata": {},
-   "source": [
-    "## 6. Conclusion\n",
-    "\n",
-    "### Key Takeaways\n",
-    "\n",
-    "- **Metadata First**: Always start with metadata endpoints to understand available data\n",
-    "- **Proper Authentication**: Use X-CoinAPI-Key header or any other authentication method you prefer for all API requests\n",
-    "- **URL Structure**: Follow the pattern: base/version/resource/identifier/action\n",
-    "- **Parameter Validation**: Ensure all required parameters are included\n",
-    "- **Error Handling**: Implement proper error handling for different HTTP status codes\n",
-    "- **Rate Limiting**: Monitor rate limits and implement appropriate delays\n",
-    "\n",
-    "### Endpoint Construction Checklist\n",
-    "\n",
-    "✅ Base URL: `https://rest.coinapi.io`  \n",
-    "✅ API Version: `/v1`  \n",
-    "✅ Resource Path: `/resource`  \n",
-    "✅ Identifier: `/{symbol_id}` or `/{asset_id}`  \n",
-    "✅ Action: `/history`, `/current`, `/latest`  \n",
-    "✅ Query Parameters: `?param=value`  \n",
-    "✅ Headers: `X-CoinAPI-Key: YOUR_KEY`  \n",
-    "\n",
-    "### Next Steps\n",
-    "\n",
-    "1. **Explore More Endpoints**: Try orderbook, quotes, and other market data endpoints\n",
-    "2. **Build Applications**: Use these endpoints to create trading applications or data analysis tools\n",
-    "3. **Optimize Performance**: Implement caching and efficient data fetching strategies\n",
-    "4. **Monitor Usage**: Track API usage and implement rate limiting in your applications\n",
-    "\n",
-    "### Additional Resources\n",
-    "\n",
-    "- [CoinAPI.io Documentation](https://docs.coinapi.io/market-data/)\n",
-    "- [API Status Page](https://status.coinapi.io/)\n",
-    "\n",
-    "---\n",
-    "\n",
-    "**Happy API building! 🚀📊**"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/coinapi/tutorials/Market_Trends_VWAP_Intraday_Analysis.ipynb b/coinapi/tutorials/Market_Trends_VWAP_Intraday_Analysis.ipynb
new file mode 100644
index 0000000000..19a47b98e8
--- /dev/null
+++ b/coinapi/tutorials/Market_Trends_VWAP_Intraday_Analysis.ipynb
@@ -0,0 +1,781 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "title",
+   "metadata": {},
+   "source": [
+    "# Market Trends: Graphing VWAP for Intraday Trend Identification\n",
+    "\n",
+    "## Introduction\n",
+    "\n",
+    "This tutorial demonstrates how to analyze Volume Weighted Average Price (VWAP) data from cryptocurrency markets to identify intraday trading trends and market patterns. Using the CoinAPI Indexes API, we'll fetch VWAP data for Bitcoin and create visualizations to help traders make informed decisions.\n",
+    "\n",
+    "### What You Will Learn\n",
+    "\n",
+    "- How to connect to the CoinAPI Indexes API and fetch VWAP data\n",
+    "- Techniques for analyzing intraday market trends using VWAP\n",
+    "- Methods to identify support and resistance levels from VWAP data\n",
+    "- How to create professional charts for market analysis\n",
+    "- Best practices for interpreting VWAP data in cryptocurrency trading\n",
+    "\n",
+    "### Prerequisites\n",
+    "\n",
+    "- Python 3.8+\n",
+    "- Required packages: requests, pandas, numpy, matplotlib, seaborn\n",
+    "- CoinAPI API key (free tier available)\n",
+    "- Basic understanding of cryptocurrency markets and technical analysis\n",
+    "\n",
+    "### Overview\n",
+    "\n",
+    "This tutorial will walk you through fetching VWAP data for Bitcoin over a 24-hour period, analyzing the data for trend identification, and creating visualizations that highlight key market patterns. By the end, you'll have a comprehensive understanding of how to use VWAP data for intraday trading decisions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "setup",
+   "metadata": {},
+   "source": [
+    "## 1. Environment Setup and Configuration\n",
+    "\n",
+    "Set up your environment with necessary imports, configuration, and initial setup for the CoinAPI Indexes API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "setup_code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import required packages\n",
+    "import requests\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from datetime import datetime, timedelta\n",
+    "import json\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Configure plotting style\n",
+    "plt.style.use('default')\n",
+    "sns.set_palette(\"husl\")\n",
+    "plt.rcParams['figure.figsize'] = (14, 10)\n",
+    "plt.rcParams['font.size'] = 11\n",
+    "plt.rcParams['axes.grid'] = True\n",
+    "plt.rcParams['grid.alpha'] = 0.3\n",
+    "\n",
+    "# Configuration variables for CoinAPI Indexes API\n",
+    "API_BASE_URL = \"https://rest-api.indexes.coinapi.io/v1\"\n",
+    "INDEX_ID = \"IDX_REFRATE_VWAP_BTC\"\n",
+    "PERIOD_ID = \"1HRS\"\n",
+    "TIME_START = \"2025-07-07T00:00:00\"\n",
+    "TIME_END = \"2025-07-08T00:00:00\"\n",
+    "\n",
+    "# API key configuration - Replace with your actual API key\n",
+    "API_KEY = \"YOUR_COINAPI_KEY_HERE\"  # Replace with your actual API key\n",
+    "\n",
+    "# Validate API key\n",
+    "if API_KEY == \"YOUR_COINAPI_KEY_HERE\":\n",
+    "    print(\"WARNING: Please update your CoinAPI key before proceeding!\")\n",
+    "    print(\"Get your key from: https://www.coinapi.io/\")\n",
+    "else:\n",
+    "    print(\"CoinAPI key configured successfully!\")\n",
+    "\n",
+    "print(f\"Base URL: {API_BASE_URL}\")\n",
+    "print(f\"Index ID: {INDEX_ID}\")\n",
+    "print(f\"Period: {PERIOD_ID}\")\n",
+    "print(f\"Time Range: {TIME_START} to {TIME_END}\")\n",
+    "print(\"Environment setup complete!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "data_loading",
+   "metadata": {},
+   "source": [
+    "## 2. Data Loading and API Configuration\n",
+    "\n",
+    "Configure the API request and fetch VWAP data from the CoinAPI Indexes API. We'll retrieve hourly VWAP data for Bitcoin over a 24-hour period."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "data_loading_code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# API endpoint configuration\n",
+    "endpoint = f\"{API_BASE_URL}/indexes/{INDEX_ID}/timeseries\"\n",
+    "\n",
+    "# Query parameters\n",
+    "params = {\n",
+    "    'period_id': PERIOD_ID,\n",
+    "    'time_start': TIME_START,\n",
+    "    'time_end': TIME_END\n",
+    "}\n",
+    "\n",
+    "# Headers for authentication\n",
+    "headers = {\n",
+    "    'X-CoinAPI-Key': API_KEY\n",
+    "}\n",
+    "\n",
+    "print(f\"API Endpoint: {endpoint}\")\n",
+    "print(f\"Index ID: {INDEX_ID}\")\n",
+    "print(f\"Time Period: {TIME_START} to {TIME_END}\")\n",
+    "print(f\"Data Granularity: {PERIOD_ID}\")\n",
+    "\n",
+    "# Make the API request\n",
+    "try:\n",
+    "    response = requests.get(endpoint, params=params, headers=headers)\n",
+    "    response.raise_for_status()\n",
+    "    \n",
+    "    # Parse the response\n",
+    "    data = response.json()\n",
+    "    print(f\"API request successful!\")\n",
+    "    print(f\"Retrieved {len(data)} data points\")\n",
+    "    \n",
+    "except requests.exceptions.RequestException as e:\n",
+    "    print(f\"API request failed: {e}\")\n",
+    "    if response.status_code == 401:\n",
+    "        print(\"   This usually means an invalid or missing API key\")\n",
+    "    elif response.status_code == 429:\n",
+    "        print(\"   Rate limit exceeded. Please wait before making another request\")\n",
+    "    data = []\n",
+    "\n",
+    "# Display sample data structure (first few records)\n",
+    "if data:\n",
+    "    print(\"Sample data structure:\")\n",
+    "    print(json.dumps(data[0], indent=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "data_preparation",
+   "metadata": {},
+   "source": [
+    "## 3. Data Preparation and Cleaning\n",
+    "\n",
+    "Transform the raw API response into a structured DataFrame for analysis. We'll clean the data and prepare it for trend analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "data_preparation_code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert API response to DataFrame\n",
+    "if data:\n",
+    "    df = pd.DataFrame(data)\n",
+    "    \n",
+    "    # Display initial DataFrame info\n",
+    "    print(\"Initial DataFrame Info:\")\n",
+    "    print(f\"Shape: {df.shape}\")\n",
+    "    print(f\"Columns: {list(df.columns)}\")\n",
+    "    \n",
+    "    # Convert timestamp to datetime\n",
+    "    df['time_period_start'] = pd.to_datetime(df['time_period_start'])\n",
+    "    df['time_period_end'] = pd.to_datetime(df['time_period_end'])\n",
+    "    \n",
+    "    # Sort by timestamp\n",
+    "    df = df.sort_values('time_period_start').reset_index(drop=True)\n",
+    "    \n",
+    "    # Extract key metrics - using the correct field names from API response\n",
+    "    df['vwap'] = df['value_close'].astype(float)  # Use value_close as VWAP\n",
+    "    df['open_price'] = df['value_open'].astype(float)\n",
+    "    df['high_price'] = df['value_high'].astype(float)\n",
+    "    df['low_price'] = df['value_low'].astype(float)\n",
+    "    \n",
+    "    # Create additional time-based features\n",
+    "    df['hour'] = df['time_period_start'].dt.hour\n",
+    "    df['day_period'] = df['time_period_start'].dt.strftime('%Y-%m-%d')\n",
+    "    \n",
+    "    # Calculate price changes and returns\n",
+    "    df['price_change'] = df['vwap'].diff()\n",
+    "    df['price_change_pct'] = df['vwap'].pct_change() * 100\n",
+    "    \n",
+    "    # Calculate moving averages for trend analysis\n",
+    "    df['vwap_ma_3'] = df['vwap'].rolling(window=3).mean()\n",
+    "    df['vwap_ma_6'] = df['vwap'].rolling(window=6).mean()\n",
+    "    \n",
+    "    print(\"Data preparation complete!\")\n",
+    "    print(f\"Time range: {df['time_period_start'].min()} to {df['time_period_start'].max()}\")\n",
+    "    print(f\"VWAP range: ${df['vwap'].min():,.2f} to ${df['vwap'].max():,.2f}\")\n",
+    "    \n",
+    "    # Display cleaned data\n",
+    "    print(\"Cleaned DataFrame (first 5 rows):\")\n",
+    "    display_cols = ['time_period_start', 'vwap', 'open_price', 'high_price', 'low_price', 'price_change', 'price_change_pct']\n",
+    "    print(df[display_cols].head())\n",
+    "    \n",
+    "else:\n",
+    "    print(\"No data available for processing\")\n",
+    "    df = pd.DataFrame()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "exploration",
+   "metadata": {},
+   "source": [
+    "## 4. Data Exploration and Statistical Analysis\n",
+    "\n",
+    "Explore the VWAP data to understand its characteristics, identify patterns, and prepare for trend analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "exploration_code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VWAP Data Statistics:\n",
+      "==================================================\n",
+      "                vwap     open_price     high_price      low_price  \\\n",
+      "count      24.000000      24.000000      24.000000      24.000000   \n",
+      "mean   108573.379842  108611.826546  108798.214494  108413.350748   \n",
+      "std       487.180011     499.258850     476.923460     517.733021   \n",
+      "min    107887.196847  107887.196847  108103.593595  107515.941048   \n",
+      "25%    108145.429041  108145.252626  108344.732049  107980.537742   \n",
+      "50%    108592.114473  108672.728802  108743.663519  108450.940633   \n",
+      "75%    109030.891673  109052.394369  109131.652198  108789.287625   \n",
+      "max    109406.030850  109406.030851  109717.158653  109381.626665   \n",
+      "\n",
+      "       price_change  price_change_pct  \n",
+      "count     23.000000         23.000000  \n",
+      "mean     -24.923036         -0.022775  \n",
+      "std      211.491656          0.194708  \n",
+      "min     -327.318242         -0.302241  \n",
+      "25%     -174.182104         -0.160582  \n",
+      "50%      -34.937068         -0.032144  \n",
+      "75%      127.426745          0.117738  \n",
+      "max      349.336300          0.320363  \n",
+      "Time-based Analysis:\n",
+      "==================================================\n",
+      "Hourly VWAP Statistics:\n",
+      "           vwap                           open_price high_price  low_price  \\\n",
+      "           mean std        min        max       mean        max        min   \n",
+      "hour                                                                         \n",
+      "0     108841.65 NaN  108841.65  108841.65  109213.20  109308.84  108819.13   \n",
+      "1     109043.86 NaN  109043.86  109043.86  108841.72  109113.39  108701.81   \n",
+      "2     109393.20 NaN  109393.20  109393.20  109043.86  109533.65  109042.19   \n",
+      "3     109406.03 NaN  109406.03  109406.03  109395.95  109717.16  109381.63   \n",
+      "4     109136.93 NaN  109136.93  109136.93  109406.03  109467.61  109136.82   \n",
+      "5     109084.01 NaN  109084.01  109084.01  109136.90  109218.02  109034.66   \n",
+      "6     108789.72 NaN  108789.72  108789.72  109090.02  109186.44  108779.34   \n",
+      "7     109078.05 NaN  109078.05  109078.05  108789.72  109080.02  108667.48   \n",
+      "8     109026.57 NaN  109026.57  109026.57  109077.99  109099.61  108889.85   \n",
+      "9     108867.79 NaN  108867.79  108867.79  109026.57  109027.07  108774.77   \n",
+      "10    108690.20 NaN  108690.20  108690.20  108836.97  108913.40  108683.29   \n",
+      "11    108655.26 NaN  108655.26  108655.26  108690.20  108815.02  108588.16   \n",
+      "12    108348.99 NaN  108348.99  108348.99  108655.26  108672.31  108313.72   \n",
+      "13    108528.97 NaN  108528.97  108528.97  108349.05  108563.43  108012.34   \n",
+      "14    108231.77 NaN  108231.77  108231.77  108528.97  108656.40  107969.94   \n",
+      "15    108297.08 NaN  108297.08  108297.08  108231.78  108636.07  108223.62   \n",
+      "16    107969.76 NaN  107969.76  107969.76  108296.93  108502.65  107909.32   \n",
+      "17    107943.16 NaN  107943.16  107943.16  107969.76  108356.83  107804.67   \n",
+      "18    108018.03 NaN  108018.03  108018.03  107944.42  108103.59  107515.94   \n",
+      "19    108057.97 NaN  108057.97  108057.97  108017.84  108304.25  107997.57   \n",
+      "20    107887.20 NaN  107887.20  107887.20  108056.81  108151.55  107871.28   \n",
+      "21    108174.58 NaN  108174.58  108174.58  107887.20  108224.69  107802.06   \n",
+      "22    108021.92 NaN  108021.92  108021.92  108174.73  108196.70  107984.07   \n",
+      "23    108268.42 NaN  108268.42  108268.42  108021.96  108308.45  108016.77   \n",
+      "\n",
+      "     price_change_pct  \n",
+      "                 mean  \n",
+      "hour                   \n",
+      "0                 NaN  \n",
+      "1                0.19  \n",
+      "2                0.32  \n",
+      "3                0.01  \n",
+      "4               -0.25  \n",
+      "5               -0.05  \n",
+      "6               -0.27  \n",
+      "7                0.27  \n",
+      "8               -0.05  \n",
+      "9               -0.15  \n",
+      "10              -0.16  \n",
+      "11              -0.03  \n",
+      "12              -0.28  \n",
+      "13               0.17  \n",
+      "14              -0.27  \n",
+      "15               0.06  \n",
+      "16              -0.30  \n",
+      "17              -0.02  \n",
+      "18               0.07  \n",
+      "19               0.04  \n",
+      "20              -0.16  \n",
+      "21               0.27  \n",
+      "22              -0.14  \n",
+      "23               0.23  \n",
+      "Trend Analysis:\n",
+      "==================================================\n",
+      "Overall VWAP Change: $-573.23 (-0.53%)\n",
+      "Overall Trend: BEARISH (Price decreased)\n",
+      "Volatility (Std Dev of Returns): 0.19%\n",
+      "Average Price Range: $384.86\n",
+      "Maximum Price Range: $686.46\n",
+      "\n",
+      "Additional Market Insights:\n",
+      "==================================================\n",
+      "Highest VWAP: $109,406.03 at 03:00\n",
+      "Lowest VWAP: $109,406.03 at 03:00\n",
+      "Average Hourly Price Change: -0.02%\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not df.empty:\n",
+    "    # Basic statistics\n",
+    "    print(\"VWAP Data Statistics:\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    stats = df[['vwap', 'open_price', 'high_price', 'low_price', 'price_change', 'price_change_pct']].describe()\n",
+    "    print(stats)\n",
+    "    \n",
+    "    # Time-based analysis\n",
+    "    print(\"Time-based Analysis:\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    # Hourly statistics\n",
+    "    hourly_stats = df.groupby('hour').agg({\n",
+    "        'vwap': ['mean', 'std', 'min', 'max'],\n",
+    "        'open_price': 'mean',\n",
+    "        'high_price': 'max',\n",
+    "        'low_price': 'min',\n",
+    "        'price_change_pct': 'mean'\n",
+    "    }).round(2)\n",
+    "    \n",
+    "    print(\"Hourly VWAP Statistics:\")\n",
+    "    print(hourly_stats)\n",
+    "    \n",
+    "    # Trend identification\n",
+    "    print(\"Trend Analysis:\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    # Overall trend direction\n",
+    "    first_vwap = df['vwap'].iloc[0]\n",
+    "    last_vwap = df['vwap'].iloc[-1]\n",
+    "    total_change = last_vwap - first_vwap\n",
+    "    total_change_pct = (total_change / first_vwap) * 100\n",
+    "    \n",
+    "    print(f\"Overall VWAP Change: ${total_change:,.2f} ({total_change_pct:+.2f}%)\")\n",
+    "    \n",
+    "    if total_change > 0:\n",
+    "        print(\"Overall Trend: BULLISH (Price increased)\")\n",
+    "    elif total_change < 0:\n",
+    "        print(\"Overall Trend: BEARISH (Price decreased)\")\n",
+    "    else:\n",
+    "        print(\"Overall Trend: SIDEWAYS (No change)\")\n",
+    "    \n",
+    "    # Volatility analysis\n",
+    "    volatility = df['price_change_pct'].std()\n",
+    "    print(f\"Volatility (Std Dev of Returns): {volatility:.2f}%\")\n",
+    "    \n",
+    "    # Price range analysis (replacing volume analysis)\n",
+    "    price_range = df['high_price'] - df['low_price']\n",
+    "    avg_price_range = price_range.mean()\n",
+    "    max_price_range = price_range.max()\n",
+    "    print(f\"Average Price Range: ${avg_price_range:,.2f}\")\n",
+    "    print(f\"Maximum Price Range: ${max_price_range:,.2f}\")\n",
+    "    \n",
+    "    # Additional insights\n",
+    "    print(\"\\nAdditional Market Insights:\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    # Identify highest and lowest VWAP periods\n",
+    "    max_vwap_idx = df['vwap'].idxmax()\n",
+    "    min_vwap_idx = df['vwap'].idxmax()\n",
+    "    \n",
+    "    print(f\"Highest VWAP: ${df.loc[max_vwap_idx, 'vwap']:,.2f} at {df.loc[max_vwap_idx, 'time_period_start'].strftime('%H:%M')}\")\n",
+    "    print(f\"Lowest VWAP: ${df.loc[min_vwap_idx, 'vwap']:,.2f} at {df.loc[min_vwap_idx, 'time_period_start'].strftime('%H:%M')}\")\n",
+    "    \n",
+    "    # Calculate average hourly price change\n",
+    "    avg_hourly_change = df['price_change_pct'].mean()\n",
+    "    print(f\"Average Hourly Price Change: {avg_hourly_change:+.2f}%\")\n",
+    "    \n",
+    "else:\n",
+    "    print(\"No data available for exploration\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "core_analysis",
+   "metadata": {},
+   "source": [
+    "## 5. Core Analysis: VWAP Trend Identification\n",
+    "\n",
+    "Perform the main analysis to identify intraday trends, support/resistance levels, and key trading patterns from the VWAP data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "core_analysis_code",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VWAP Trend Analysis:\n",
+      "==================================================\n",
+      "Trend Distribution:\n",
+      "  Bearish: 13 periods (54.2%)\n",
+      "  Bullish: 9 periods (37.5%)\n",
+      "  Neutral: 2 periods (8.3%)\n",
+      "Support and Resistance Analysis:\n",
+      "  Support Levels Found: 6\n",
+      "  Resistance Levels Found: 6\n",
+      "  Key Support Levels:\n",
+      "    $107,887.20 at 20:00\n",
+      "    $107,943.16 at 17:00\n",
+      "    $108,021.92 at 22:00\n",
+      "  Key Resistance Levels:\n",
+      "    $109,406.03 at 03:00\n",
+      "    $109,078.05 at 07:00\n",
+      "    $108,528.97 at 13:00\n",
+      "Price Range Analysis:\n",
+      "  High Range Periods (>75th percentile): 6 periods\n",
+      "  Average Price Change in High Range: -0.01%\n",
+      "Momentum Analysis:\n",
+      "  Current 3-period Momentum: +0.35%\n",
+      "  Momentum is positive (bullish)\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not df.empty:\n",
+    "    print(\"VWAP Trend Analysis:\")\n",
+    "    print(\"=\" * 50)\n",
+    "    \n",
+    "    # Identify trend changes\n",
+    "    df['trend'] = 'neutral'\n",
+    "    df.loc[df['vwap'] > df['vwap_ma_3'], 'trend'] = 'bullish'\n",
+    "    df.loc[df['vwap'] < df['vwap_ma_3'], 'trend'] = 'bearish'\n",
+    "    \n",
+    "    # Count trend periods\n",
+    "    trend_counts = df['trend'].value_counts()\n",
+    "    print(\"Trend Distribution:\")\n",
+    "    for trend, count in trend_counts.items():\n",
+    "        percentage = (count / len(df)) * 100\n",
+    "        print(f\"  {trend.capitalize()}: {count} periods ({percentage:.1f}%)\")\n",
+    "    \n",
+    "    # Identify support and resistance levels\n",
+    "    print(\"Support and Resistance Analysis:\")\n",
+    "    \n",
+    "    # Support levels (local minima)\n",
+    "    support_levels = []\n",
+    "    for i in range(1, len(df) - 1):\n",
+    "        if (df['vwap'].iloc[i] < df['vwap'].iloc[i-1] and \n",
+    "            df['vwap'].iloc[i] < df['vwap'].iloc[i+1]):\n",
+    "            support_levels.append({\n",
+    "                'time': df['time_period_start'].iloc[i],\n",
+    "                'price': df['vwap'].iloc[i],\n",
+    "                'price_range': df['high_price'].iloc[i] - df['low_price'].iloc[i]\n",
+    "            })\n",
+    "    \n",
+    "    # Resistance levels (local maxima)\n",
+    "    resistance_levels = []\n",
+    "    for i in range(1, len(df) - 1):\n",
+    "        if (df['vwap'].iloc[i] > df['vwap'].iloc[i-1] and \n",
+    "            df['vwap'].iloc[i] > df['vwap'].iloc[i+1]):\n",
+    "            resistance_levels.append({\n",
+    "                'time': df['time_period_start'].iloc[i],\n",
+    "                'price': df['vwap'].iloc[i],\n",
+    "                'price_range': df['high_price'].iloc[i] - df['low_price'].iloc[i]\n",
+    "            })\n",
+    "    \n",
+    "    print(f\"  Support Levels Found: {len(support_levels)}\")\n",
+    "    print(f\"  Resistance Levels Found: {len(resistance_levels)}\")\n",
+    "    \n",
+    "    # Display key levels\n",
+    "    if support_levels:\n",
+    "        print(\"  Key Support Levels:\")\n",
+    "        for level in sorted(support_levels, key=lambda x: x['price'])[:3]:\n",
+    "            print(f\"    ${level['price']:,.2f} at {level['time'].strftime('%H:%M')}\")\n",
+    "    \n",
+    "    if resistance_levels:\n",
+    "        print(\"  Key Resistance Levels:\")\n",
+    "        for level in sorted(resistance_levels, key=lambda x: x['price'], reverse=True)[:3]:\n",
+    "            print(f\"    ${level['price']:,.2f} at {level['time'].strftime('%H:%M')}\")\n",
+    "    \n",
+    "    # Price range analysis (replacing volume analysis)\n",
+    "    print(\"Price Range Analysis:\")\n",
+    "    \n",
+    "    # High price range periods\n",
+    "    price_range = df['high_price'] - df['low_price']\n",
+    "    range_threshold = price_range.quantile(0.75)\n",
+    "    high_range_periods = df[price_range > range_threshold]\n",
+    "    \n",
+    "    if not high_range_periods.empty:\n",
+    "        avg_price_change_high_range = high_range_periods['price_change_pct'].mean()\n",
+    "        print(f\"  High Range Periods (>75th percentile): {len(high_range_periods)} periods\")\n",
+    "        print(f\"  Average Price Change in High Range: {avg_price_change_high_range:+.2f}%\")\n",
+    "    \n",
+    "    # Momentum analysis\n",
+    "    print(\"Momentum Analysis:\")\n",
+    "    \n",
+    "    # Calculate momentum indicators\n",
+    "    df['momentum'] = df['vwap'] - df['vwap'].shift(3)\n",
+    "    df['momentum_pct'] = (df['momentum'] / df['vwap'].shift(3)) * 100\n",
+    "    \n",
+    "    current_momentum = df['momentum_pct'].iloc[-1]\n",
+    "    print(f\"  Current 3-period Momentum: {current_momentum:+.2f}%\")\n",
+    "    \n",
+    "    if current_momentum > 0:\n",
+    "        print(\"  Momentum is positive (bullish)\")\n",
+    "    else:\n",
+    "        print(\"  Momentum is negative (bearish)\")\n",
+    "    \n",
+    "else:\n",
+    "    print(\"No data available for analysis\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "visualization",
+   "metadata": {},
+   "source": [
+    "## 6. Visualization and Results\n",
+    "\n",
+    "Create comprehensive visualizations to present the VWAP analysis results, including trend charts, support/resistance levels, and volume analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "visualization_code",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1600x1400 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Summary Statistics Table:\n",
+      "================================================================================\n",
+      "              Metric       Value\n",
+      "       Total Periods          24\n",
+      "       Starting VWAP $108,841.65\n",
+      "         Ending VWAP $108,268.42\n",
+      "        Total Change    $-573.23\n",
+      "      Total Change %      -0.53%\n",
+      " Average Price Range     $384.86\n",
+      "Volatility (Std Dev)       0.19%\n",
+      "     Bullish Periods   9 (37.5%)\n",
+      "     Bearish Periods  13 (54.2%)\n",
+      "      Support Levels           6\n",
+      "   Resistance Levels           6\n",
+      "    Current Momentum      +0.35%\n"
+     ]
+    }
+   ],
+   "source": [
+    "if not df.empty:\n",
+    "    # Create a comprehensive visualization\n",
+    "    fig, axes = plt.subplots(3, 1, figsize=(16, 14))\n",
+    "    fig.suptitle('Bitcoin VWAP Intraday Analysis - Market Trends and Patterns', \n",
+    "                 fontsize=16, fontweight='bold')\n",
+    "    \n",
+    "    # Plot 1: VWAP Price with Moving Averages and Trend\n",
+    "    ax1 = axes[0]\n",
+    "    ax1.plot(df['time_period_start'], df['vwap'], 'b-', linewidth=2, label='VWAP', alpha=0.8)\n",
+    "    ax1.plot(df['time_period_start'], df['vwap_ma_3'], 'r--', linewidth=1.5, label='3-Period MA', alpha=0.7)\n",
+    "    ax1.plot(df['time_period_start'], df['vwap_ma_6'], 'g--', linewidth=1.5, label='6-Period MA', alpha=0.7)\n",
+    "    \n",
+    "    # Color code by trend\n",
+    "    for i in range(len(df)):\n",
+    "        if df['trend'].iloc[i] == 'bullish':\n",
+    "            ax1.scatter(df['time_period_start'].iloc[i], df['vwap'].iloc[i], \n",
+    "                       color='green', s=30, alpha=0.6)\n",
+    "        elif df['trend'].iloc[i] == 'bearish':\n",
+    "            ax1.scatter(df['time_period_start'].iloc[i], df['vwap'].iloc[i], \n",
+    "                       color='red', s=30, alpha=0.6)\n",
+    "    \n",
+    "    # Add support and resistance levels\n",
+    "    if support_levels:\n",
+    "        support_prices = [level['price'] for level in support_levels]\n",
+    "        support_times = [level['time'] for level in support_levels]\n",
+    "        ax1.scatter(support_times, support_prices, color='blue', s=100, \n",
+    "                   marker='^', label='Support Levels', zorder=5)\n",
+    "    \n",
+    "    if resistance_levels:\n",
+    "        resistance_prices = [level['price'] for level in resistance_levels]\n",
+    "        resistance_times = [level['time'] for level in resistance_levels]\n",
+    "        ax1.scatter(resistance_times, resistance_prices, color='red', s=100, \n",
+    "                   marker='v', label='Resistance Levels', zorder=5)\n",
+    "    \n",
+    "    ax1.set_title('VWAP Price Action with Trend Indicators', fontweight='bold')\n",
+    "    ax1.set_ylabel('VWAP Price (USD)', fontweight='bold')\n",
+    "    ax1.legend()\n",
+    "    ax1.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Plot 2: Price Range Analysis (replacing Volume Analysis)\n",
+    "    ax2 = axes[1]\n",
+    "    price_range = df['high_price'] - df['low_price']\n",
+    "    bars = ax2.bar(df['time_period_start'], price_range, alpha=0.7, \n",
+    "                   color='skyblue', edgecolor='navy', linewidth=0.5)\n",
+    "    \n",
+    "    # Highlight high range periods\n",
+    "    if not high_range_periods.empty:\n",
+    "        high_range_times = high_range_periods['time_period_start']\n",
+    "        high_range_values = high_range_periods['high_price'] - high_range_periods['low_price']\n",
+    "        ax2.bar(high_range_times, high_range_values, color='orange', \n",
+    "               alpha=0.8, label='High Range Periods')\n",
+    "    \n",
+    "    ax2.set_title('Price Range by Hour (High-Low Spread)', fontweight='bold')\n",
+    "    ax2.set_ylabel('Price Range (USD)', fontweight='bold')\n",
+    "    ax2.legend()\n",
+    "    ax2.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Plot 3: Price Changes and Momentum\n",
+    "    ax3 = axes[2]\n",
+    "    \n",
+    "    # Price changes\n",
+    "    colors = ['green' if x > 0 else 'red' if x < 0 else 'gray' for x in df['price_change_pct']]\n",
+    "    bars3 = ax3.bar(df['time_period_start'], df['price_change_pct'], \n",
+    "                    color=colors, alpha=0.7, edgecolor='black', linewidth=0.5)\n",
+    "    \n",
+    "    # Add momentum line\n",
+    "    ax3_twin = ax3.twinx()\n",
+    "    ax3_twin.plot(df['time_period_start'], df['momentum_pct'], 'purple', \n",
+    "                  linewidth=2, label='3-Period Momentum', alpha=0.8)\n",
+    "    \n",
+    "    ax3.set_title('Price Changes and Momentum', fontweight='bold')\n",
+    "    ax3.set_ylabel('Price Change (%)', fontweight='bold')\n",
+    "    ax3_twin.set_ylabel('Momentum (%)', fontweight='bold', color='purple')\n",
+    "    ax3.axhline(y=0, color='black', linestyle='-', alpha=0.5)\n",
+    "    ax3.grid(True, alpha=0.3)\n",
+    "    \n",
+    "    # Format x-axis for all subplots\n",
+    "    for ax in axes:\n",
+    "        ax.xaxis.set_major_formatter(plt.matplotlib.dates.DateFormatter('%H:%M'))\n",
+    "        ax.tick_params(axis='x', rotation=45)\n",
+    "    \n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    # Create summary statistics table\n",
+    "    print(\"Summary Statistics Table:\")\n",
+    "    print(\"=\" * 80)\n",
+    "    \n",
+    "    summary_data = {\n",
+    "        'Metric': [\n",
+    "            'Total Periods',\n",
+    "            'Starting VWAP',\n",
+    "            'Ending VWAP',\n",
+    "            'Total Change',\n",
+    "            'Total Change %',\n",
+    "            'Average Price Range',\n",
+    "            'Volatility (Std Dev)',\n",
+    "            'Bullish Periods',\n",
+    "            'Bearish Periods',\n",
+    "            'Support Levels',\n",
+    "            'Resistance Levels',\n",
+    "            'Current Momentum'\n",
+    "        ],\n",
+    "        'Value': [\n",
+    "            len(df),\n",
+    "            f\"${df['vwap'].iloc[0]:,.2f}\",\n",
+    "            f\"${df['vwap'].iloc[-1]:,.2f}\",\n",
+    "            f\"${total_change:+,.2f}\",\n",
+    "            f\"{total_change_pct:+.2f}%\",\n",
+    "            f\"${price_range.mean():,.2f}\",\n",
+    "            f\"{df['price_change_pct'].std():.2f}%\",\n",
+    "            f\"{trend_counts.get('bullish', 0)} ({trend_counts.get('bullish', 0)/len(df)*100:.1f}%)\",\n",
+    "            f\"{trend_counts.get('bearish', 0)} ({trend_counts.get('bearish', 0)/len(df)*100:.1f}%)\",\n",
+    "            len(support_levels),\n",
+    "            len(resistance_levels),\n",
+    "            f\"{current_momentum:+.2f}%\"\n",
+    "        ]\n",
+    "    }\n",
+    "    \n",
+    "    summary_df = pd.DataFrame(summary_data)\n",
+    "    print(summary_df.to_string(index=False))\n",
+    "    \n",
+    "else:\n",
+    "    print(\"No data available for visualization\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "conclusion",
+   "metadata": {},
+   "source": [
+    "## 7. Conclusion and Next Steps\n",
+    "\n",
+    "Summarize what we've accomplished and suggest next steps for further exploration of VWAP analysis in cryptocurrency markets.\n",
+    "\n",
+    "### Summary\n",
+    "\n",
+    "In this tutorial, we successfully analyzed Bitcoin VWAP data using the CoinAPI Indexes API to identify intraday market trends. We fetched hourly VWAP data over a 24-hour period, performed comprehensive trend analysis, identified support and resistance levels, and created visualizations that highlight key market patterns.\n",
+    "\n",
+    "### Key Takeaways\n",
+    "\n",
+    "- VWAP data provides valuable insights into intraday market trends and price action\n",
+    "- Moving averages help identify trend direction and momentum changes\n",
+    "- Support and resistance levels can be identified from local price extremes\n",
+    "- Volume analysis helps validate price movements and identify high-impact periods\n",
+    "- The CoinAPI Indexes API offers reliable access to cryptocurrency market data\n",
+    "\n",
+    "### Next Steps\n",
+    "\n",
+    "To expand your VWAP analysis capabilities, consider:\n",
+    "\n",
+    "1. **Multi-timeframe Analysis**: Compare VWAP data across different periods (1H, 4H, 1D)\n",
+    "2. **Cross-Asset Comparison**: Analyze VWAP patterns across multiple cryptocurrencies\n",
+    "3. **Advanced Indicators**: Incorporate RSI, MACD, or Bollinger Bands with VWAP\n",
+    "4. **Backtesting Strategies**: Test trading strategies based on VWAP signals\n",
+    "5. **Real-time Monitoring**: Set up automated alerts for VWAP breakouts\n",
+    "6. **Risk Management**: Develop position sizing based on VWAP volatility\n",
+    "\n",
+    "### Additional Resources\n",
+    "\n",
+    "- [CoinAPI Documentation](https://docs.coinapi.io/)\n",
+    "\n",
+    "### Trading Disclaimer\n",
+    "\n",
+    "This tutorial is for educational purposes only. The analysis and visualizations provided should not be considered as financial advice. Always conduct your own research and consider consulting with financial professionals before making trading decisions. Cryptocurrency markets are highly volatile and involve substantial risk."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv (3.13.3)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/coinapi/tutorials/README.md b/coinapi/tutorials/README.md
index be647077c2..bf03c9fc83 100755
--- a/coinapi/tutorials/README.md
+++ b/coinapi/tutorials/README.md
@@ -4,14 +4,27 @@ This directory contains a collection of tutorials demonstrating various use case
 
 ## Available Tutorials
 
-- **How to Create the Right Endpoint for Market Data API**
-  - *Description:* Learn authentication, metadata retrieval, and how to build dynamic requests for OHLCV and trades data using curl commands.
-  - *File:* `How_to_create_the_right_endpoint_for_Market_Data_REST_API.ipynb`
+- **Audit Compliance PRIMKT Tutorial**
+  - *Product:* Indexes API - https://docs.coinapi.io/indexes-api/index-offerings/primkt-index
+  - *Description:* Learn about audit compliance and PRIMKT functionality with comprehensive examples and demonstrations.
+  - *File:* `Audit_Compliance_PRIMKT_Tutorial.ipynb`
 
-- **Getting Started: Flat Files**
-  - *Description:* A tutorial demonstrating how to list, filter, and download historical Flat Files.
-  - *File:* `Getting_started_Flat_Files.ipynb`
+- **Market Trends VWAP Intraday Analysis**
+  - *Product:* Indexes API - https://docs.coinapi.io/indexes-api/index-offerings/vwap-index
+  - *Description:* Tutorial on analyzing market trends using VWAP (Volume Weighted Average Price) for intraday trading strategies.
+  - *File:* `Market_Trends_VWAP_Intraday_Analysis.ipynb`
+
+- **Backtesting Crypto Trades with CoinAPI Flat Files**
+  - *Product:* Flat Files - https://docs.coinapi.io/flat-files-api/data-types/trades
+  - *Description:* Comprehensive tutorial on backtesting cryptocurrency trades using CoinAPI flat files data for strategy validation and performance analysis.
+  - *File:* `Backtesting_Crypto_Trades_CoinAPI_Flat_Files.ipynb`
+
+- **Historical OHLCV REST - Build Candlestick Charts**
+  - *Product:* Market Data API - https://docs.coinapi.io/market-data/rest-api/ohlcv/historical-data
+  - *Description:* Learn how to retrieve historical OHLCV data via REST API and create candlestick charts for market analysis.
+  - *File:* `Historical_OHLCV_REST_Build_Candlestick_Charts.ipynb`
 
 - **Price Trend Analysis with Volatility Indicators**
+  - *Product:* Market Data API - https://docs.coinapi.io/market-data/rest-api/exchange-rates/
   - *Description:* Learn to calculate volatility indicators (ATR, normalized volatility), detect market trends, and create comprehensive visualizations with statistical analysis of cryptocurrency price patterns.
   - *File:* `Price_Trend_Analysis.ipynb`