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Historical Data

GMO Coin provides historical trading data that you can download for analysis and backtesting purposes.

Overview​

Historical data is available in CSV format and contains executed trade information. This data can be useful for:

  • Market analysis
  • Trading strategy backtesting
  • Research and data science projects
  • Compliance and record-keeping

Accessing Historical Data​

You can download the historical data (.CSV file) from:

URL: https://api.coin.z.com/data/trades/

Data Format​

The historical data is provided in CSV (Comma-Separated Values) format, which can be easily imported into spreadsheet applications, databases, or data analysis tools.

CSV Structure​

The CSV files contain the following information for each trade:

FieldDescription
TimestampDate and time when the trade was executed
SymbolTrading pair (e.g., BTC_JPY, ETH_JPY)
SideBuy or Sell
PriceExecution price
SizeTrade size/quantity

Example Data​

timestamp,symbol,side,price,size
2025-12-25T12:00:00.123Z,BTC_JPY,BUY,5000000,0.1
2025-12-25T12:00:01.456Z,BTC_JPY,SELL,5000100,0.05
2025-12-25T12:00:02.789Z,ETH_JPY,BUY,350000,1.5

Data Availability​

Time Range​

Historical data is available from the inception of each trading pair. The data is continuously updated with new trades.

Update Frequency​

The historical data files are updated periodically. For real-time trading data, use the WebSocket API or REST API endpoints instead.

Using Historical Data​

For Analysis​

Historical data can be used with various analysis tools:

  • Python: Use pandas for data analysis

    import pandas as pd
    df = pd.read_csv('trades.csv')
    df['timestamp'] = pd.to_datetime(df['timestamp'])

  • Excel: Import CSV directly into Microsoft Excel or Google Sheets

  • Databases: Import into SQL databases for complex queries

    COPY trades FROM 'trades.csv' DELIMITER ',' CSV HEADER;

For Backtesting​

Use historical data to test trading strategies:

import pandas as pd

# Load historical data
df = pd.read_csv('trades.csv', parse_dates=['timestamp'])

# Example: Calculate moving averages
df['price_ma_20'] = df['price'].rolling(window=20).mean()
df['price_ma_50'] = df['price'].rolling(window=50).mean()

# Implement your strategy
def backtest_strategy(df):
    # Your backtesting logic here
    pass

For Research​

Historical data provides valuable insights:

  • Price trends and patterns
  • Trading volume analysis
  • Market volatility studies
  • Correlation between different trading pairs

Limitations​

Not for Real-Time Trading​

Historical data is not suitable for real-time trading applications. For real-time data, use:

Data Accuracy​

While GMO Coin strives to provide accurate historical data, always verify critical information through official API endpoints when making trading decisions.

Alternative Data Sources​

REST API - Trades​

For recent trade history with more control over the time range:

Endpoint: GET /public/v1/trades

Parameters:

  • symbol (required): Trading symbol
  • page (optional): Page number
  • count (optional): Number of records per page

See Trades API for details.

WebSocket API - Trades​

For real-time trade notifications:

Channel: trades

Symbol: Specify the trading pair to subscribe to

See WebSocket Trades for details.

Data Usage Guidelines​

Terms of Service​

When using historical data:

  1. Comply with GMO Coin's Terms of Service
  2. Do not redistribute the data without permission
  3. Use the data responsibly and ethically
  4. Respect rate limits when accessing the download service

Best Practices​

  1. Download During Off-Peak Hours: To avoid impacting service performance, download large datasets during off-peak hours

  2. Cache Locally: Store downloaded data locally to avoid repeated downloads

  3. Incremental Updates: Only download new data instead of the entire dataset each time

  4. Data Validation: Verify data integrity after download

    # Check for missing timestamps
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    df = df.sort_values('timestamp')
    time_diff = df['timestamp'].diff()
    anomalies = time_diff[time_diff > pd.Timedelta(minutes=5)]

Support​

If you encounter issues with historical data:

  1. Check the data file format and structure
  2. Verify your download URL is correct
  3. Ensure you have a stable internet connection
  4. Contact GMO Coin support if problems persist

For API inquiries, please visit the GMO Coin Support Center.