This is a script that fetches all historical OHLC data from GDAX.
The data has a 1-minute interval and can be used to carry out further in-depth analysis of market trends. All traded products (BTC, BCH, ETH, LTC) will be imported into a local SQLite database. The import will begin from the earliest trading date (see below). The script can be invoked periodically (for example with a cronjob) to fetch the latest data. It will automatically resume from the latest saved date.
API rate limits are respected (currently the limit is 3 req/s, the script will do 1 req/s). It will also re-try fetching and continue to the next symbol after 3 failed attempts.
# Bitcoin
BTC-EUR: 2015-04-23 | BTC-USD: 2015-01-08 | BTC-GBP: 2015-04-21
# Ethereum
ETH-BTC: 2016-05-18 | ETH-EUR: 2017-05-23 | ETH-USD: 2016-05-18
# Litecoin
LTC-BTC: 2016-08-17 | LTC-USD: 2016-08-17 | LTC-EUR: 2017-05-22
# Bitcoin Cash
BCH-BTC: 2018-01-17 | BCH-USD: 2017-12-20 | BCH-EUR: 2018-01-24
The script has been tested with Python 3.5 and 3.6.
You need the sqlite3 library installed on your system. On macOS you can use homebrew: brew install sqlite3.
To install the development environment, clone the repo and run:
$ pipenv install
You can build the Docker image with:
$ docker build -t gdax .
➤ pipenv run python main.py --help
usage: main.py [-h] [-l LOGLEVEL] [-s START_DATE] [-p PRODUCT] db_file
GDAX Fetcher
positional arguments:
db_file sqlite3 db file path
optional arguments:
-h, --help show this help message and exit
-l LOGLEVEL, --loglevel LOGLEVEL
Loglevel. DEBUG|INFO|WARNING|ERROR|CRITICAL
-s START_DATE, --start-date START_DATE
Process candles since given date. YYYY-mm-dd format
-p PRODUCT, --product PRODUCT
Which product to update
You can run the script with the following command:
$ pipenv run python main.py db.sqlite3
Or if you prefer Docker:
$ docker run --rm -v $(pwd)/data:/data python main.py /data/db.sqlite3
This will start fetching data into the db.sqlite3 file. If it's the first run it will start from the earliest trading day. You can abort the script at any time and it will resume fetching from the last saved state.
2018-05-06 20:37:07,173 INFO Updating ['BCH-BTC', 'BCH-USD', 'BCH-EUR', 'BTC-EUR', 'BTC-USD', 'BTC-GBP', 'ETH-BTC', 'ETH-EUR', 'ETH-USD', 'LTC-BTC', 'LTC-USD', 'LTC-EUR']
2018-05-06 20:37:07,175 INFO No previous data found. Importing full history
2018-05-06 20:37:07,175 INFO 1/12 | BCH-BTC | starting from 2018-01-17 00:00:00
2018-05-06 20:37:12,996 INFO BCH-BTC | importing 2018-01-18
2018-05-06 20:37:19,931 INFO BCH-BTC | importing 2018-01-19
2018-05-06 20:37:25,257 INFO BCH-BTC | importing 2018-01-20
If you don't want to fetch all history for every product you can use the --start-date and --product flags. For example this will fetch the OHLC data for ETH-USD starting from March 2018:
$ pipenv run python main.py -p ETH-USD -s 2018-03-01 debug_db.sqlite3
2018-05-06 20:45:00,656 INFO Updating ['ETH-USD']
2018-05-06 20:45:00,660 INFO 1/1 | ETH-USD | starting from 2018-03-01 00:00:00
The data will be stored in an SQLite3 database table called candles. It has the following structure:
market
time
open
high
low
close
volume
You can use the sqlite3 cli to inspect the data::
$ sqlite3 gdax.sqlite3
sqlite> SELECT * FROM candles;
BCH-BTC|1516400040|0.15418|0.15418|0.15418|0.15418|0.25917758
BCH-BTC|1516399920|0.15375|0.15375|0.15375|0.15375|0.0539563
BCH-BTC|1516399800|0.15376|0.15376|0.15375|0.15375|5
BCH-BTC|1516399620|0.1538|0.1538|0.15376|0.15376|2.5832529299999996
BCH-BTC|1516399560|0.15439|0.15439|0.15439|0.15439|0.09186857
The data can easily be exported to CSV:
$ sqlite3 gdax.sqlite3
sqlite> .mode csv
sqlite> .headers on
sqlite> .output data.csv
sqlite> SELECT * FROM candles;
sqlite> .quit
The data will be in a file called data.csv:
$ head data.csv
market,time,open,high,low,close,volume
BCH-BTC,1516219140,0.15032,0.15127,0.15032,0.15127,5.197920569999999
BCH-BTC,1516219080,0.15015,0.15032,0.15,0.15032,10.3002287
BCH-BTC,1516218900,0.15015,0.15015,0.15011,0.15011,2.2113686899999996
BCH-BTC,1516218840,0.15042,0.15042,0.15,0.15011,2.59691718
BCH-BTC,1516218780,0.15042,0.15042,0.15042,0.15042,5.030391000000001
To import the data into Pandas you have two options. Either use the Sqlite3 db directly:
import pandas as pd
import sqlite3
# Connect to database
con = sqlite3.connect('gdax.sqlite3')
c = con.cursor()
# Read data into pandas dataframe
sql = 'SELECT * FROM candles'
df = pd.read_sql_query(sql, con, index_col='time', parse_dates={'time': 's'})
df = df.astype(float)Or export the data into a CSV file and use the read_csv method (less boilerplate code):
import pandas as pd
df = pd.read_csv('gdax.csv', index_col='time', parse_dates=True)
df.index = pd.to_datetime(df.index, unit='s')