1# Import required libraries
2import requests
3import pandas as pd
4import numpy as np
5import matplotlib.pyplot as plt
6import mplfinance as mpf
7from datetime import datetime, timedelta
8import json
9
10# Set up plotting style
11plt.style.use('default')
12plt.rcParams['figure.figsize'] = (14, 8)
13plt.rcParams['font.size'] = 10
14
15# API configuration
16API_KEY = "YOUR_API_KEY_HERE"  # Replace with your actual API key
17BASE_URL = "https://rest.coinapi.io/v1"
18
19# Editable parameters
20SYMBOL_ID = "BINANCE_SPOT_BTC_USDT"
21PERIOD_ID = "1HRS"
22TIME_START = "2025-07-07T00:00:00"
23TIME_END = "2025-07-14T00:00:00"
24LIMIT = 200
25
26print("Environment setup complete!")
27print(f"Using API base URL: {BASE_URL}")
28print(f"API Key configured: {'Yes' if API_KEY != 'YOUR_API_KEY_HERE' else 'No (Please update)'}")
29print(f"Target symbol: {SYMBOL_ID}")
30print(f"Time period: {TIME_START} to {TIME_END}")
31print(f"Data interval: {PERIOD_ID}")

1def fetch_ohlcv_data(symbol_id, period_id, time_start, time_end, limit):
2    """
3    Fetch historical OHLCV data from CoinAPI REST API
4    
5    Args:
6        symbol_id (str): Trading pair identifier (e.g., BINANCE_SPOT_BTC_USDT)
7        period_id (str): Time period for each candle (e.g., 1HRS, 1DAY)
8        time_start (str): Start time in ISO format
9        time_end (str): End time in ISO format
10        limit (int): Maximum number of candles to return
11    
12    Returns:
13        dict: API response with OHLCV data
14    """
15    endpoint = f"/ohlcv/{symbol_id}/history"
16    url = BASE_URL + endpoint
17    
18    # Query parameters
19    params = {
20        'period_id': period_id,
21        'time_start': time_start,
22        'time_end': time_end,
23        'limit': limit
24    }
25    
26    # Headers with API key
27    headers = {
28        'X-CoinAPI-Key': API_KEY
29    }
30    
31    try:
32        response = requests.get(url, params=params, headers=headers)
33        response.raise_for_status()
34        return response.json()
35    except requests.exceptions.RequestException as e:
36        print(f"Error fetching data: {e}")
37        return None
38
39# Fetch the data
40print("Fetching historical OHLCV data...")
41ohlcv_data = fetch_ohlcv_data(SYMBOL_ID, PERIOD_ID, TIME_START, TIME_END, LIMIT)
42
43if ohlcv_data:
44    print(f"Successfully fetched {len(ohlcv_data)} OHLCV records")
45    print("Sample data structure:")
46    if ohlcv_data:
47        print(f"First record: {ohlcv_data[0]}")
48else:
49    print("Failed to fetch data. Please check your API key and parameters.")

1def process_ohlcv_data(raw_data):
2    """
3    Process raw OHLCV data into a pandas DataFrame
4    
5    Args:
6        raw_data (list): Raw API response data
7    
8    Returns:
9        pd.DataFrame: Processed OHLCV data
10    """
11    if not raw_data:
12        return None
13    
14    # Convert to DataFrame
15    df = pd.DataFrame(raw_data)
16    
17    # Convert timestamp to datetime
18    df['time_period_start'] = pd.to_datetime(df['time_period_start'])
19    df['time_period_end'] = pd.to_datetime(df['time_period_end'])
20    
21    # Set time_period_start as index
22    df.set_index('time_period_start', inplace=True)
23    
24    # Sort by time
25    df.sort_index(inplace=True)
26    
27    # Calculate additional metrics
28    df['price_change'] = df['price_close'] - df['price_open']
29    df['price_change_pct'] = (df['price_change'] / df['price_open']) * 100
30    df['body_size'] = abs(df['price_close'] - df['price_open'])
31    df['upper_shadow'] = df['price_high'] - df[['price_open', 'price_close']].max(axis=1)
32    df['lower_shadow'] = df[['price_open', 'price_close']].min(axis=1) - df['price_low']
33    
34    return df
35
36# Process the data
37if ohlcv_data:
38    df = process_ohlcv_data(ohlcv_data)
39    
40    if df is not None:
41        print("Data processing complete!")
42        print(f"DataFrame shape: {df.shape}")
43        print("\nFirst few rows:")
44        print(df.head())
45        
46        print("\nData summary:")
47        print(df.describe())
48        
49        print("\nPrice statistics:")
50        print(f"Price range: ${df['price_low'].min():.2f} - ${df['price_high'].max():.2f}")
51        print(f"Average volume: {df['volume_traded'].mean():.2f}")
52        print(f"Total price change: {df['price_change'].sum():.2f} USDT")
53        print(f"Average price change: {df['price_change_pct'].mean():.2f}%")
54    else:
55        print("Failed to process data.")
56else:
57    print("No data to process.")

1def create_candlestick_chart(df, title="BTC/USDT Candlestick Chart"):
2    """
3    Create a professional candlestick chart using mplfinance
4    
5    Args:
6        df (pd.DataFrame): OHLCV data with datetime index
7        title (str): Chart title
8    """
9    # Create a clean DataFrame with only the required OHLCV columns
10    # The actual column names from the API are: price_open, price_high, price_low, price_close, volume_traded
11    ohlcv_df = df[['price_open', 'price_high', 'price_low', 'price_close', 'volume_traded']].copy()
12    
13    # Rename columns to the standard OHLCV format expected by mplfinance
14    ohlcv_df.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
15    
16    print("OHLCV DataFrame shape:", ohlcv_df.shape)
17    print("OHLCV DataFrame columns:", ohlcv_df.columns.tolist())
18    print("\nFirst few rows of OHLCV data:")
19    print(ohlcv_df.head())
20    
21    # Create the candlestick chart
22    mpf.plot(ohlcv_df, 
23             type='candle', 
24             title=title,
25             ylabel='Price (USDT)',
26             volume=True,
27             style='charles',
28             figsize=(14, 8),
29             panel_ratios=(3, 1),
30             savefig='candlestick_chart.png')
31    
32    print("Candlestick chart created and saved as 'candlestick_chart.png'")
33
34def create_price_analysis_charts(df):
35    """
36    Create additional analysis charts
37    """
38    fig, axes = plt.subplots(2, 2, figsize=(16, 12))
39    fig.suptitle('BTC/USDT Price Analysis', fontsize=16, fontweight='bold')
40    
41    # Price trend over time
42    axes[0, 0].plot(df.index, df['price_close'], linewidth=2, color='blue')
43    axes[0, 0].set_title('Price Trend Over Time')
44    axes[0, 0].set_ylabel('Price (USDT)')
45    axes[0, 0].grid(True, alpha=0.3)
46    
47    # Volume analysis
48    axes[0, 1].bar(df.index, df['volume_traded'], alpha=0.7, color='green')
49    axes[0, 1].set_title('Trading Volume')
50    axes[0, 1].set_ylabel('Volume')
51    axes[0, 1].grid(True, alpha=0.3)
52    
53    # Price change distribution
54    axes[1, 0].hist(df['price_change_pct'], bins=20, alpha=0.7, color='orange', edgecolor='black')
55    axes[1, 0].set_title('Price Change Distribution')
56    axes[1, 0].set_xlabel('Price Change (%)')
57    axes[1, 0].set_ylabel('Frequency')
58    axes[1, 0].grid(True, alpha=0.3)
59    
60    # Body size vs volume scatter
61    axes[1, 1].scatter(df['body_size'], df['volume_traded'], alpha=0.6, color='purple')
62    axes[1, 1].set_title('Body Size vs Volume')
63    axes[1, 1].set_xlabel('Candle Body Size (USDT)')
64    axes[1, 1].set_ylabel('Volume')
65    axes[1, 1].grid(True, alpha=0.3)
66    
67    plt.tight_layout()
68    plt.savefig('price_analysis_charts.png', dpi=300, bbox_inches='tight')
69    plt.show()
70    
71    print("Price analysis charts created and saved as 'price_analysis_charts.png'")
72
73# Create the charts
74if 'df' in locals() and df is not None:
75    print("Creating candlestick chart...")
76    create_candlestick_chart(df)
77    
78    print("\nCreating price analysis charts...")
79    create_price_analysis_charts(df)
80    
81    print("\nAll visualizations complete!")
82else:
83    print("No data available for visualization. Please run the data processing cell first.")