Pull Gemini OHLCV Timeseries in Python for any Symbol or Timeframe via API

April 24, 2023, 6:49 p.m.by CryptoDataDownload

Basic Overview

Gemini is a popular cryptocurrency exchange that provides OHLVC (Open, High, Low, Volume, Close) market data via their API for a wide range of digital assets. This data is essential for traders and investors who want to analyze price trends and make informed decisions about buying and selling cryptocurrencies. Gemini's OHLVC data includes information on historical prices, trading volumes, and other market metrics, which can be accessed through their API or through third-party services that use their data. The accuracy and reliability of Gemini's OHLVC data make it a valuable resource for price analysis.

Code

The code sample will demonstrate how to access the Gemini API in less than 25 lines of Python code; retrieve Open, High, Low, Close and volume information for any pair on Gemini, add a human-readable date column, then save the results to a CSV file. It also allows you to toggle to different timeframes to use. (change the "timeframe" variable in line 24). Timeframes available are: 1 minute (1m), 5 minute (5m), 15 minute (15m), 30 minute (30m), one hour (1hr), six hour (6hr), and finally, daily (1day). Every line is commented for your benefit. Feel free to modify to fit your purpose

# CryptoDataDownload
# No Express Warranty
import pandas as pd
import requests
import json

def main(symbol='btcusd', time_frame='1day'):
    symbol = symbol.lower()  # change to lowercase symbol name (how Gemini API likes it)
    url = f'https://api.gemini.com/v2/candles/{symbol}/{time_frame}'    # URL endpoint
    response = requests.get(url)  # get the request from Gemini/ action the url param

if response.status_code == 200:  # check the response
        data = json.loads(response.text)  # if its good, load the json response
        # these next few lines will take the json response and turn it into a pandas dataframe
        data_pd = pd.DataFrame(data, columns=['unix', 'open', 'high', 'low', 'close', 'volume'])  # load to dataframe
        data_pd['date'] = pd.to_datetime(data_pd['unix'], unit='ms')   # add column for human readable date
        filename = f'Gemini_{symbol}_{time_frame}.csv'   # filename to save as
        data_pd.to_csv(filename, index=False)   # write csv file
        print(f"File saved: {filename}")

if __name__ == "__main__":
    SYM = 'btcusd'   # symbol to use, no slash used
    timeframe = '1day'   # must be in this list [1m, 5m, 15m, 30m, 1hr, 6hr, 1day]
    main(symbol=SYM, time_frame=timeframe)

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THE PERFORMANCE OF TRADING SYSTEMS IS BASED ON THE USE OF COMPUTERIZED SYSTEM LOGIC. IT IS HYPOTHETICAL. PLEASE NOTE THE FOLLOWING DISCLAIMER. CFTC RULE 4.41: HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT BEEN EXECUTED, THE RESULTS MAY HAVE UNDER-OR-OVER COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFIT OR LOSSES SIMILAR TO THOSE SHOWN. U.S. GOVERNMENT REQUIRED DISCLAIMER: COMMODITY FUTURES TRADING COMMISSION. FUTURES AND OPTIONS TRADING HAS LARGE POTENTIAL REWARDS, BUT ALSO LARGE POTENTIAL RISK. YOU MUST BE AWARE OF THE RISKS AND BE WILLING TO ACCEPT THEM IN ORDER TO INVEST IN THE FUTURES AND OPTIONS MARKETS. DON’T TRADE WITH MONEY YOU CAN’T AFFORD TO LOSE. THIS IS NEITHER A SOLICITATION NOR AN OFFER TO BUY/SELL FUTURES OR OPTIONS. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE DISCUSSED ON THIS WEBSITE. THE PAST PERFORMANCE OF ANY TRADING SYSTEM OR METHODOLOGY IS NOT NECESSARILY INDICATIVE OF FUTURE RESULTS.

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