BTC Futures Data for Systemic Backtesting

This repository contains various BTC Futures datasets formatted for use in systemic backtesting models. The data includes a variety of metrics across different time intervals, facilitating comprehensive analysis and strategy testing for BTC Futures contracts.

Data Overview

The dataset is designed to help traders and developers backtest trading strategies using historical data of BTC Futures. The core data points include:

• Open Interest (OI)
• Sum of Open Interest Value
• Close Price (OHLC Data)

Additionally, the data is available in various intervals such as 5-minute, 4-hour, and more, making it suitable for high-frequency and medium-term backtesting.

Main Files:

1. BTC Oldata-4h-interval: Contains 4-hour interval data with fields for open interest, sumOpenInterestValue, and close.

2. BTC Oidata (01-06-2024 - 01-07-2024): A collection of open interest data for BTC Futures from June 1, 2024, to July 1, 2024, broken down in various time intervals.

3. BTC Price (5min interval): High-frequency price data of BTC Futures with 5-minute interval granularity, including open, high, low, and close (OHLC) data for backtesting short-term strategies.

4. BTC Funding: Funding rate data relevant for analyzing trends in futures markets and its potential impact on price movements.

Key Fields:

• symbol: BTCUSDT, denoting Bitcoin against USDT pair.
• openInterest: Reflects the total number of outstanding futures contracts.
• sumOpenInterestValue: Cumulative value of the open interest contracts.
• close: The closing price of the BTC Futures at the specified interval.
• timestamp: Unix timestamp in milliseconds.

Usage

The data in this repository is useful for:

1. Backtesting: Use the historical data to test trading strategies, assess risk management techniques, and optimize trade executions.
2. Analysis of Open Interest: Open interest can indicate momentum and provide insights into the market direction.
3. Funding Rate Analysis: Correlate funding rates with price movements to assess potential liquidation points.
4. Systematic Trading Models: Build and test systematic strategies using multiple data sources and various metrics (price, OI, funding, etc.).

Example Code for Data Loading

import json

# Load BTC Futures data
with open('path_to_data_file.json', 'r') as file:
    btc_data = json.load(file)

# Example of accessing a specific data point
print(btc_data[0]['symbol'])  # Output: BTCUSDT
print(btc_data[0]['sumOpenInterest'])  # Output: Total open interest value