Before diving into the code, ensure that you have the necessary tools installed. You’ll need Python and the bitfinex library. Install it using:

Let’s start by examining the Python code you provided:

import datetime

import time

import pandas as pd

We begin by importing the required libraries: bitfinex for interacting with the Bitfinex API, datetime for handling date and time, time for managing delays, and pandas for data manipulation.

Here, we specify the cryptocurrency pair of interest (pair), the desired timeframe (TIMEFRAME), and the timeframe in seconds (TIMEFRAME_S).

# Define query parameters

pair = 'BTCUSDT'

TIMEFRAME = '1h'

TIMEFRAME_S = 60

Set the start and end dates for the historical data. The dates are converted to milliseconds since the epoch for compatibility with the Bitfinex API.

# Define the start date

t_start = datetime.datetime(2023, 10, 7, 0, 0)

t_start = time.mktime(t_start.timetuple()) * 1000

# Define the end date

t_stop = datetime.datetime(2023, 10, 9, 0, 0)

t_stop = time.mktime(t_stop.timetuple()) * 1000

Now, let’s take a closer look at the fetch_data function:

# Function to fetch data

def fetch_data(start, stop, symbol, interval, TIMEFRAME_S):

limit = 1000

api_v2 = bitfinex.bitfinex_v2.api_v2()

hour = TIMEFRAME_S * 1000

step = hour * limit

data = []

total_steps = (stop - start) / hour

while total_steps > 0:

if total_steps < limit:

step = total_steps * hour

end = start + step

data += api_v2.candles(symbol=symbol, interval=interval, limit=limit, start=start, end=end)

print(pd.to_datetime(start, unit='ms'), pd.to_datetime(end, unit='ms'), "steps left:", total_steps)

start = start + step

total_steps -= limit

time.sleep(1.5)

return data

This function is designed to retrieve historical candlestick data from Bitfinex. It employs a strategy of breaking down the data retrieval into smaller chunks to adhere to Bitfinex’s limit on the number of data points per request.

Now, let’s fetch the data using the defined parameters:

# Fetching data using the defined parameters

result = fetch_data(t_start, t_stop, pair, TIMEFRAME, TIMEFRAME_S)

The fetch_data function is called with the specified parameters.

# Creating a DataFrame from the fetched data

names = ['Date', 'Open', 'Close', 'High', 'Low', 'Volume']

df = pd.DataFrame(result, columns=names)

# Dropping duplicates, converting Date to datetime, and setting it as the index

df.drop_duplicates(inplace=True)

df['Date'] = pd.to_datetime(df['Date'], unit='ms')

df.set_index('Date', inplace=True)

df.sort_index(inplace=True)

# Save the DataFrame to a CSV file

df.to_csv(f"{pair}_{TIMEFRAME}.csv")

The fetched data is organized into a DataFrame, duplicates are removed, and the data is saved to a CSV file named after the cryptocurrency pair and timeframe.

With this Python code, you can easily retrieve historical data from Bitfinex, enabling you to perform in-depth analyses and gain insights into cryptocurrency price movements. Feel free to customize the code based on your specific needs and explore further with additional data analysis techniques.

This tutorial provides a solid foundation for working with historical cryptocurrency data and opens up possibilities for more advanced analyses. Happy coding!