Historical data
This page contains some samples for downloading historical data of your quant trading system.
Yahoo Finance API
There are many libraries for downloading historical data of stock markets from Yahoo finance, you can try some of them to discovery which you prefer:
pandas_datareader (with a stable documentation and it works for many sources)
yfinance (with a nice table of the arguments of the history method)
Exchange rates (forex) from Yahoo finance
[1]:
!pip install pandas_datareader
from pandas_datareader import data as pdr
cross_from = ['EUR', 'USD', 'CAD']
cross_to = ['USD', 'JPY', 'USD']
symbols = [x + y + '=X' for x, y in zip(cross_from, cross_to)]
data = pdr.DataReader(symbols, 'yahoo')
data
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[1]:
| Attributes | Adj Close | Close | High | Low | Open | Volume | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Symbols | EURUSD=X | USDJPY=X | CADUSD=X | EURUSD=X | USDJPY=X | CADUSD=X | EURUSD=X | USDJPY=X | CADUSD=X | EURUSD=X | USDJPY=X | CADUSD=X | EURUSD=X | USDJPY=X | CADUSD=X | EURUSD=X | USDJPY=X | CADUSD=X |
| Date | ||||||||||||||||||
| 2016-01-13 | 1.084399 | 117.830002 | 0.701508 | 1.084399 | 117.830002 | 0.701508 | 1.088400 | 118.362999 | 0.704871 | 1.080600 | 117.779999 | 0.700334 | 1.084399 | 117.831001 | 0.701459 | 0.0 | 0.0 | 0.0 |
| 2016-01-14 | 1.088601 | 117.425003 | 0.696816 | 1.088601 | 117.425003 | 0.696816 | 1.095000 | 118.161003 | 0.697515 | 1.083999 | 117.309998 | 0.694734 | 1.088803 | 117.400002 | 0.696811 | 0.0 | 0.0 | 0.0 |
| 2016-01-15 | 1.085894 | 118.203003 | 0.697126 | 1.085894 | 118.203003 | 0.697126 | 1.098200 | 118.255997 | 0.697253 | 1.085776 | 116.650002 | 0.687474 | 1.085776 | 118.223000 | 0.697204 | 0.0 | 0.0 | 0.0 |
| 2016-01-18 | 1.092001 | 116.989998 | 0.685965 | 1.092001 | 116.989998 | 0.685965 | 1.092705 | 117.433998 | 0.690274 | 1.087600 | 116.931000 | 0.685857 | 1.091906 | 117.010002 | 0.685857 | 0.0 | 0.0 | 0.0 |
| 2016-01-19 | 1.089503 | 117.384003 | 0.687545 | 1.089503 | 117.384003 | 0.687545 | 1.091000 | 118.099998 | 0.692953 | 1.086200 | 117.266998 | 0.687238 | 1.089503 | 117.397003 | 0.687564 | 0.0 | 0.0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2021-01-05 | 1.225160 | 103.125000 | 0.782411 | 1.225160 | 103.125000 | 0.782411 | 1.229483 | 103.180000 | 0.787402 | 1.224995 | 102.671997 | 0.781959 | 1.225295 | 103.141998 | 0.782473 | 0.0 | 0.0 | 0.0 |
| 2021-01-06 | 1.230027 | 102.678001 | 0.788973 | 1.230027 | 102.678001 | 0.788973 | 1.235025 | 103.431000 | 0.791766 | 1.226693 | 102.589996 | 0.786250 | 1.229861 | 102.699997 | 0.788868 | 0.0 | 0.0 | 0.0 |
| 2021-01-07 | 1.234111 | 103.024002 | 0.789553 | 1.234111 | 103.024002 | 0.789553 | 1.234568 | 103.950996 | 0.789640 | 1.224665 | 102.956001 | 0.785379 | 1.233776 | 103.028000 | 0.789472 | 0.0 | 0.0 | 0.0 |
| 2021-01-08 | 1.227144 | 103.790001 | 0.788407 | 1.227144 | 103.790001 | 0.788407 | 1.228215 | 104.078003 | 0.789952 | 1.221493 | 103.616997 | 0.786201 | 1.226873 | 103.794998 | 0.788308 | 0.0 | 0.0 | 0.0 |
| 2021-01-11 | 1.216841 | 104.209999 | 0.782411 | 1.216841 | 104.209999 | 0.782411 | 1.223990 | 104.249001 | 0.788644 | 1.215806 | 103.686996 | 0.782142 | 1.222643 | 103.948997 | 0.788277 | 0.0 | 0.0 | 0.0 |
1281 rows × 18 columns
Other asset classes from Yahoo finance
[2]:
symbol = 'AMZN'
start = '1997-05-15'
end = '2020-09-30'
period = 'daily'
[3]:
from pandas_datareader import data as pdr
data = pdr.DataReader(symbol, 'yahoo', start, end)
len(data) # 5884 # until 2020-09-30 included
#data.to_csv('pdr_data.csv')
data
[3]:
| High | Low | Open | Close | Volume | Adj Close | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 1997-05-15 | 2.500000 | 1.927083 | 2.437500 | 1.958333 | 72156000.0 | 1.958333 |
| 1997-05-16 | 1.979167 | 1.708333 | 1.968750 | 1.729167 | 14700000.0 | 1.729167 |
| 1997-05-19 | 1.770833 | 1.625000 | 1.760417 | 1.708333 | 6106800.0 | 1.708333 |
| 1997-05-20 | 1.750000 | 1.635417 | 1.729167 | 1.635417 | 5467200.0 | 1.635417 |
| 1997-05-21 | 1.645833 | 1.375000 | 1.635417 | 1.427083 | 18853200.0 | 1.427083 |
| ... | ... | ... | ... | ... | ... | ... |
| 2020-09-24 | 3069.300049 | 2965.000000 | 2977.790039 | 3019.790039 | 5529400.0 | 3019.790039 |
| 2020-09-25 | 3101.540039 | 2999.000000 | 3054.860107 | 3095.129883 | 4615200.0 | 3095.129883 |
| 2020-09-28 | 3175.040039 | 3117.169922 | 3148.850098 | 3174.050049 | 4224200.0 | 3174.050049 |
| 2020-09-29 | 3188.260010 | 3132.540039 | 3175.389893 | 3144.879883 | 3495800.0 | 3144.879883 |
| 2020-09-30 | 3212.879883 | 3133.989990 | 3141.139893 | 3148.729980 | 4896100.0 | 3148.729980 |
5884 rows × 6 columns
[4]:
import pandas
!pip install yfinance
import yfinance
data = yfinance.Ticker(symbol)
data.info
df = pandas.DataFrame(data.history(period='max'))
len(df) # 5885 # until 2020-09-30 included + headline
#df.to_csv('yfinance_data.ticker.csv')
df
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[4]:
| Open | High | Low | Close | Volume | Dividends | Stock Splits | |
|---|---|---|---|---|---|---|---|
| Date | |||||||
| 1997-05-15 | 2.437500 | 2.500000 | 1.927083 | 1.958333 | 72156000 | 0 | 0.0 |
| 1997-05-16 | 1.968750 | 1.979167 | 1.708333 | 1.729167 | 14700000 | 0 | 0.0 |
| 1997-05-19 | 1.760417 | 1.770833 | 1.625000 | 1.708333 | 6106800 | 0 | 0.0 |
| 1997-05-20 | 1.729167 | 1.750000 | 1.635417 | 1.635417 | 5467200 | 0 | 0.0 |
| 1997-05-21 | 1.635417 | 1.645833 | 1.375000 | 1.427083 | 18853200 | 0 | 0.0 |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2021-01-04 | 3270.000000 | 3272.000000 | 3144.020020 | 3186.629883 | 4411400 | 0 | 0.0 |
| 2021-01-05 | 3166.010010 | 3223.379883 | 3165.060059 | 3218.510010 | 2655500 | 0 | 0.0 |
| 2021-01-06 | 3146.479980 | 3197.510010 | 3131.159912 | 3138.379883 | 4394800 | 0 | 0.0 |
| 2021-01-07 | 3157.000000 | 3208.540039 | 3155.000000 | 3162.159912 | 3514500 | 0 | 0.0 |
| 2021-01-08 | 3180.000000 | 3190.639893 | 3142.199951 | 3182.699951 | 3534300 | 0 | 0.0 |
5953 rows × 7 columns
[5]:
data = yfinance.download(symbol, start=start, end=end, group_by='ticker')
len(data) # 5883 # until 2020-09-30 excluded
#data.to_csv('yfinance_data.download.csv')
data
[*********************100%***********************] 1 of 1 completed
[5]:
| Open | High | Low | Close | Adj Close | Volume | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 1997-05-15 | 2.437500 | 2.500000 | 1.927083 | 1.958333 | 1.958333 | 72156000 |
| 1997-05-16 | 1.968750 | 1.979167 | 1.708333 | 1.729167 | 1.729167 | 14700000 |
| 1997-05-19 | 1.760417 | 1.770833 | 1.625000 | 1.708333 | 1.708333 | 6106800 |
| 1997-05-20 | 1.729167 | 1.750000 | 1.635417 | 1.635417 | 1.635417 | 5467200 |
| 1997-05-21 | 1.635417 | 1.645833 | 1.375000 | 1.427083 | 1.427083 | 18853200 |
| ... | ... | ... | ... | ... | ... | ... |
| 2020-09-23 | 3120.429932 | 3127.000000 | 2992.379883 | 2999.860107 | 2999.860107 | 5652700 |
| 2020-09-24 | 2977.790039 | 3069.300049 | 2965.000000 | 3019.790039 | 3019.790039 | 5529400 |
| 2020-09-25 | 3054.860107 | 3101.540039 | 2999.000000 | 3095.129883 | 3095.129883 | 4615200 |
| 2020-09-28 | 3148.850098 | 3175.040039 | 3117.169922 | 3174.050049 | 3174.050049 | 4224200 |
| 2020-09-29 | 3175.389893 | 3188.260010 | 3132.540039 | 3144.879883 | 3144.879883 | 3495800 |
5883 rows × 6 columns
[6]:
yfinance.pdr_override()
data = pdr.get_data_yahoo(symbol, start=start, end=end)
len(data) # 5883 # until 2020-09-30 excluded
#data.to_csv('yfpdr_data.csv')
data
[*********************100%***********************] 1 of 1 completed
[6]:
| Open | High | Low | Close | Adj Close | Volume | |
|---|---|---|---|---|---|---|
| Date | ||||||
| 1997-05-15 | 2.437500 | 2.500000 | 1.927083 | 1.958333 | 1.958333 | 72156000 |
| 1997-05-16 | 1.968750 | 1.979167 | 1.708333 | 1.729167 | 1.729167 | 14700000 |
| 1997-05-19 | 1.760417 | 1.770833 | 1.625000 | 1.708333 | 1.708333 | 6106800 |
| 1997-05-20 | 1.729167 | 1.750000 | 1.635417 | 1.635417 | 1.635417 | 5467200 |
| 1997-05-21 | 1.635417 | 1.645833 | 1.375000 | 1.427083 | 1.427083 | 18853200 |
| ... | ... | ... | ... | ... | ... | ... |
| 2020-09-23 | 3120.429932 | 3127.000000 | 2992.379883 | 2999.860107 | 2999.860107 | 5652700 |
| 2020-09-24 | 2977.790039 | 3069.300049 | 2965.000000 | 3019.790039 | 3019.790039 | 5529400 |
| 2020-09-25 | 3054.860107 | 3101.540039 | 2999.000000 | 3095.129883 | 3095.129883 | 4615200 |
| 2020-09-28 | 3148.850098 | 3175.040039 | 3117.169922 | 3174.050049 | 3174.050049 | 4224200 |
| 2020-09-29 | 3175.389893 | 3188.260010 | 3132.540039 | 3144.879883 | 3144.879883 | 3495800 |
5883 rows × 6 columns
[7]:
!pip install yahoofinancials
from yahoofinancials import YahooFinancials
data = YahooFinancials(symbol)
json = data.get_historical_price_data(start_date=start, end_date=end, time_interval=period)
df = pandas.DataFrame(json[symbol]['prices'])
len(df) # 5883 # until 2020-09-30 excluded and seconds instead of date
#df.to_csv('yahoofinancials_data.csv')
df
Requirement already satisfied: yahoofinancials in /opt/conda/lib/python3.8/site-packages (1.6)
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Requirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.8/site-packages (from beautifulsoup4->yahoofinancials) (2.0.1)
[7]:
| date | high | low | open | close | volume | adjclose | formatted_date | |
|---|---|---|---|---|---|---|---|---|
| 0 | 863703000 | 2.500000 | 1.927083 | 2.437500 | 1.958333 | 72156000 | 1.958333 | 1997-05-15 |
| 1 | 863789400 | 1.979167 | 1.708333 | 1.968750 | 1.729167 | 14700000 | 1.729167 | 1997-05-16 |
| 2 | 864048600 | 1.770833 | 1.625000 | 1.760417 | 1.708333 | 6106800 | 1.708333 | 1997-05-19 |
| 3 | 864135000 | 1.750000 | 1.635417 | 1.729167 | 1.635417 | 5467200 | 1.635417 | 1997-05-20 |
| 4 | 864221400 | 1.645833 | 1.375000 | 1.635417 | 1.427083 | 18853200 | 1.427083 | 1997-05-21 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5878 | 1600867800 | 3127.000000 | 2992.379883 | 3120.429932 | 2999.860107 | 5652700 | 2999.860107 | 2020-09-23 |
| 5879 | 1600954200 | 3069.300049 | 2965.000000 | 2977.790039 | 3019.790039 | 5529400 | 3019.790039 | 2020-09-24 |
| 5880 | 1601040600 | 3101.540039 | 2999.000000 | 3054.860107 | 3095.129883 | 4615200 | 3095.129883 | 2020-09-25 |
| 5881 | 1601299800 | 3175.040039 | 3117.169922 | 3148.850098 | 3174.050049 | 4224200 | 3174.050049 | 2020-09-28 |
| 5882 | 1601386200 | 3188.260010 | 3132.540039 | 3175.389893 | 3144.879883 | 3495800 | 3144.879883 | 2020-09-29 |
5883 rows × 8 columns
[8]:
!pip install yahoo_fin
from yahoo_fin import stock_info as si
data = si.get_data(symbol)
len(data) # 5885 # until 2020-09-30 included (+ headline)
#data.to_csv('yahoo_fin_data.csv')
data
Requirement already satisfied: yahoo_fin in /opt/conda/lib/python3.8/site-packages (0.8.6)
Warning - Certain functionality
requires requests_html, which is not installed.
Install using:
pip install requests_html
After installation, you may have to restart your Python session.
[8]:
| open | high | low | close | adjclose | volume | ticker | |
|---|---|---|---|---|---|---|---|
| 1997-05-15 | 2.437500 | 2.500000 | 1.927083 | 1.958333 | 1.958333 | 72156000 | AMZN |
| 1997-05-16 | 1.968750 | 1.979167 | 1.708333 | 1.729167 | 1.729167 | 14700000 | AMZN |
| 1997-05-19 | 1.760417 | 1.770833 | 1.625000 | 1.708333 | 1.708333 | 6106800 | AMZN |
| 1997-05-20 | 1.729167 | 1.750000 | 1.635417 | 1.635417 | 1.635417 | 5467200 | AMZN |
| 1997-05-21 | 1.635417 | 1.645833 | 1.375000 | 1.427083 | 1.427083 | 18853200 | AMZN |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2021-01-04 | 3270.000000 | 3272.000000 | 3144.020020 | 3186.629883 | 3186.629883 | 4411400 | AMZN |
| 2021-01-05 | 3166.010010 | 3223.379883 | 3165.060059 | 3218.510010 | 3218.510010 | 2655500 | AMZN |
| 2021-01-06 | 3146.479980 | 3197.510010 | 3131.159912 | 3138.379883 | 3138.379883 | 4394800 | AMZN |
| 2021-01-07 | 3157.000000 | 3208.540039 | 3155.000000 | 3162.159912 | 3162.159912 | 3514500 | AMZN |
| 2021-01-08 | 3180.000000 | 3190.639893 | 3142.199951 | 3182.699951 | 3182.699951 | 3534300 | AMZN |
5953 rows × 7 columns
Exchange rates (forex) from Oanda
Oanda API allows to download the historical data with the API key of a free Oanda account: you can find the Oanda library or you can use many other backtesting libraries for this.
But this is a general tutorial, so you can find the data by oanda.URL that the getSymbols method of R quantmod library uses. When you use this method, you can only download the historical data of the last six months.
[9]:
import json
import pandas
import requests
from datetime import datetime, timedelta
base_url = 'https://www.oanda.com/fx-for-business/historical-rates/api/data/update/?&source=OANDA&adjustment=0&base_currency={to_currency}&start_date={start_date}&end_date={end_date}&period={period}&price=mid&view=table"e_currency_0={from_currency}'
today = datetime.strftime(datetime.now(), '%Y-%m-%d')
def get_oanda_currency_historical_rates(base_url, start, end, quote_currency, base_currency, period):
url = base_url.format(from_currency=quote_currency, to_currency=base_currency, start_date=start, end_date=end, period=period)
response = json.loads(requests.get(url).content.decode('utf-8'))
return pandas.DataFrame(response['widget'][0]['data'])
# the last 6 months
df = get_oanda_currency_historical_rates(base_url, datetime.strftime(datetime.now() - timedelta(180), '%Y-%m-%d'), today, 'EUR', 'USD', period)
df
[9]:
| 0 | 1 | |
|---|---|---|
| 0 | 1610236800000 | 0.818336 |
| 1 | 1610150400000 | 0.818270 |
| 2 | 1610064000000 | 0.816854 |
| 3 | 1609977600000 | 0.813786 |
| 4 | 1609891200000 | 0.812080 |
| ... | ... | ... |
| 175 | 1595116800000 | 0.875000 |
| 176 | 1595030400000 | 0.875028 |
| 177 | 1594944000000 | 0.876130 |
| 178 | 1594857600000 | 0.877077 |
| 179 | 1594771200000 | 0.875957 |
180 rows × 2 columns
[10]:
# the last 60 days
df = get_oanda_currency_historical_rates(base_url, datetime.strftime(datetime.now() - timedelta(60), '%Y-%m-%d'), today, 'EUR', 'USD', period)
df.head()
[10]:
| 0 | 1 | |
|---|---|---|
| 0 | 1610236800000 | 0.818336 |
| 1 | 1610150400000 | 0.818270 |
| 2 | 1610064000000 | 0.816854 |
| 3 | 1609977600000 | 0.813786 |
| 4 | 1609891200000 | 0.812080 |
Crypto currencies from CoinMarketCap
CoinMarketCap API allows to download the historical data with the API key of a free CoinMarketCap account: you can find the CoinMarketCap library or you can use many other backtesting libraries for this.
Like for the all assets, there are many libraries that they implement Coinmarketcap API, but many are open source so the updates could be slowly:
the CoinMarketCap library have the error message JSONDecodeError(‘Expecting value: line 1 column 1 (char 0)’)
the CCXT library uses an old version of CoinMarketCap API
Below you can find a sample with sandbox API of CoinMarketCap.
[11]:
import json
import pandas
import requests
base_url = 'https://sandbox-api.coinmarketcap.com/v1/'
def get_coinmarketcap_data(url):
response = json.loads(requests.get(url).content.decode('utf-8'))
return pandas.DataFrame(response['data'])
# returns all active cryptocurrencies
url = base_url + 'cryptocurrency/map'
marketcap_map = get_coinmarketcap_data(url)
marketcap_map
[11]:
| id | name | symbol | slug | is_active | rank | platform | |
|---|---|---|---|---|---|---|---|
| 0 | 1 | Bitcoin | BTC | bitcoin | 1 | 1 | None |
| 1 | 2 | Litecoin | LTC | litecoin | 1 | 5 | None |
| 2 | 3 | Namecoin | NMC | namecoin | 1 | 310 | None |
| 3 | 4 | Terracoin | TRC | terracoin | 1 | 926 | None |
| 4 | 5 | Peercoin | PPC | peercoin | 1 | 346 | None |
| ... | ... | ... | ... | ... | ... | ... | ... |
| 2322 | 4266 | Monarch | MT | monarch | 1 | 2059 | {'id': 1027, 'name': 'Ethereum', 'symbol': 'ET... |
| 2323 | 4268 | NewYork Exchange | NYE | newyork-exchange | 1 | 2012 | None |
| 2324 | 4273 | Sessia | KICKS | sessia | 1 | 2118 | {'id': 1027, 'name': 'Ethereum', 'symbol': 'ET... |
| 2325 | 4275 | Cocos-BCX | COCOS | cocos-bcx | 1 | 1960 | {'id': 1027, 'name': 'Ethereum', 'symbol': 'ET... |
| 2326 | 4276 | Defi | DEFI | defi | 1 | 1965 | {'id': 1027, 'name': 'Ethereum', 'symbol': 'ET... |
2327 rows × 7 columns
[12]:
# returns last market data of all active cryptocurrencies
url = base_url + 'cryptocurrency/listings/latest?convert=EUR'
last_marketcap = get_coinmarketcap_data(url)
last_marketcap
[12]:
| id | name | symbol | slug | num_market_pairs | date_added | tags | max_supply | circulating_supply | total_supply | platform | cmc_rank | last_updated | quote | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | Bitcoin | BTC | bitcoin | 7919 | 2013-04-28T00:00:00.000Z | [mineable] | 2.100000e+07 | 1.790601e+07 | 1.790601e+07 | None | 1 | 2019-08-30T18:51:28.000Z | {'EUR': {'price': 8708.442730269642, 'volume_2... |
| 1 | 1027 | Ethereum | ETH | ethereum | 5629 | 2015-08-07T00:00:00.000Z | [mineable] | NaN | 1.075379e+08 | 1.075379e+08 | None | 2 | 2019-08-30T18:51:21.000Z | {'EUR': {'price': 153.68597253794135, 'volume_... |
| 2 | 52 | XRP | XRP | ripple | 449 | 2013-08-04T00:00:00.000Z | [] | 1.000000e+11 | 4.293287e+10 | 9.999137e+10 | None | 3 | 2019-08-30T18:51:03.000Z | {'EUR': {'price': 0.23181863120417767, 'volume... |
| 3 | 1831 | Bitcoin Cash | BCH | bitcoin-cash | 378 | 2017-07-23T00:00:00.000Z | [mineable] | 2.100000e+07 | 1.797598e+07 | 1.797598e+07 | None | 4 | 2019-08-30T18:51:08.000Z | {'EUR': {'price': 256.03512635975414, 'volume_... |
| 4 | 2 | Litecoin | LTC | litecoin | 538 | 2013-04-28T00:00:00.000Z | [mineable] | 8.400000e+07 | 6.314712e+07 | 6.314712e+07 | None | 5 | 2019-08-30T18:51:04.000Z | {'EUR': {'price': 58.62607480610576, 'volume_2... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 95 | 3709 | Grin | GRIN | grin | 53 | 2019-01-27T00:00:00.000Z | [mineable] | NaN | 1.954596e+07 | 1.954596e+07 | None | 96 | 2019-08-30T18:51:18.000Z | {'EUR': {'price': 1.7584409339670577, 'volume_... |
| 96 | 2694 | Nexo | NEXO | nexo | 22 | 2018-05-01T00:00:00.000Z | [] | NaN | 5.600000e+08 | 1.000000e+09 | {'id': 1027, 'name': 'Ethereum', 'symbol': 'ET... | 97 | 2019-08-30T18:51:12.000Z | {'EUR': {'price': 0.06125607986488225, 'volume... |
| 97 | 2900 | Project Pai | PAI | project-pai | 17 | 2018-07-05T00:00:00.000Z | [] | NaN | 1.451234e+09 | 1.618050e+09 | None | 98 | 2019-08-30T18:51:14.000Z | {'EUR': {'price': 0.022456686270928363, 'volum... |
| 98 | 2137 | Electroneum | ETN | electroneum | 20 | 2017-11-02T00:00:00.000Z | [mineable] | 2.100000e+10 | 9.792946e+09 | 9.792946e+09 | None | 99 | 2019-08-30T18:51:08.000Z | {'EUR': {'price': 0.0032193992228962184, 'volu... |
| 99 | 1681 | PRIZM | PZM | prizm | 7 | 2017-05-19T00:00:00.000Z | [] | 6.000000e+09 | 9.820737e+07 | 9.820737e+07 | None | 100 | 2019-08-30T18:51:05.000Z | {'EUR': {'price': 0.3204834704804656, 'volume_... |
100 rows × 14 columns
[13]:
# returns last market data of Bitcoin
url = base_url + 'cryptocurrency/quotes/latest?id=1&convert=EUR'
last_bitcoin = get_coinmarketcap_data(url)
last_bitcoin
[13]:
| 1 | |
|---|---|
| circulating_supply | 17906012 |
| cmc_rank | 1 |
| date_added | 2013-04-28T00:00:00.000Z |
| id | 1 |
| is_active | 1 |
| is_fiat | 0 |
| is_market_cap_included_in_calc | 1 |
| last_updated | 2019-08-30T18:51:28.000Z |
| max_supply | 21000000 |
| name | Bitcoin |
| num_market_pairs | 7919 |
| platform | None |
| quote | {'EUR': {'price': 8708.442730269642, 'volume_2... |
| slug | bitcoin |
| symbol | BTC |
| tags | [mineable] |
| total_supply | 17906012 |
[14]:
# returns historical data from a time_start to a time_end
url = base_url + 'cryptocurrency/quotes/historical?id=1&convert=EUR&time_start=2020-05-21T12:14&time_end=2020-05-21T12:44'
data = get_coinmarketcap_data(url)
data
[14]:
| id | name | symbol | is_fiat | quotes | |
|---|---|---|---|---|---|
| 0 | 1 | Bitcoin | BTC | 0 | {'timestamp': '2020-05-21T12:19:03.000Z', 'quo... |
| 1 | 1 | Bitcoin | BTC | 0 | {'timestamp': '2020-05-21T12:24:02.000Z', 'quo... |
| 2 | 1 | Bitcoin | BTC | 0 | {'timestamp': '2020-05-21T12:29:03.000Z', 'quo... |
| 3 | 1 | Bitcoin | BTC | 0 | {'timestamp': '2020-05-21T12:34:03.000Z', 'quo... |
| 4 | 1 | Bitcoin | BTC | 0 | {'timestamp': '2020-05-21T12:39:01.000Z', 'quo... |
| 5 | 1 | Bitcoin | BTC | 0 | {'timestamp': '2020-05-21T12:44:02.000Z', 'quo... |
Other asset classes from Quandl
You can see details of asset classes of Quandl, by its research, for example crude oil.
Quandl API allows to download the historical data with the API key of a free Quandl account: you can find the Quandl library (doc) or you can use many other backtesting libraries for this.
However, it is possible to execute 50 calls per day without API key.
[15]:
symbol = 'CHRIS/CME_QM1'
import pandas
from datetime import datetime
base_url = 'https://www.quandl.com/api/v3/datasets/{symbol}.csv?start_date={start_date}&end_date={end_date}&collapse={period}'
today = datetime.strftime(datetime.now(), '%Y-%m-%d')
def get_quandl_financial_data(base_url, symbol, start, end, period):
url = base_url.format(symbol=symbol, start_date=start, end_date=end, period=period)
return pandas.read_csv(url)
# returns all free columns values
df = get_quandl_financial_data(base_url, symbol, start, end, period)
df
[15]:
| Date | Open | High | Low | Last | Change | Settle | Volume | Previous Day Open Interest | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 2020-09-30 | 39.225 | 40.375 | 38.675 | 39.850 | 0.93 | 40.22 | 10605.0 | 1252.0 |
| 1 | 2020-09-29 | 40.575 | 40.700 | 38.425 | 39.075 | -1.31 | 39.29 | 12589.0 | 784.0 |
| 2 | 2020-09-28 | 40.125 | 40.800 | 39.775 | 40.600 | 0.35 | 40.60 | 8194.0 | 787.0 |
| 3 | 2020-09-25 | 40.175 | 40.625 | 39.700 | 40.075 | -0.06 | 40.25 | 7024.0 | 879.0 |
| 4 | 2020-09-24 | 39.600 | 40.350 | 39.125 | 40.200 | 0.38 | 40.31 | 8349.0 | 869.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1646 | 2014-03-12 | 99.500 | 99.575 | 97.550 | 97.990 | 2.04 | 97.99 | 9437.0 | 2462.0 |
| 1647 | 2014-03-11 | 100.925 | 101.500 | 99.350 | 100.030 | 1.09 | 100.03 | 7337.0 | 2300.0 |
| 1648 | 2014-03-07 | 101.900 | 102.900 | 101.575 | 102.580 | 1.02 | 102.58 | 5304.0 | 2315.0 |
| 1649 | 2014-03-06 | 101.000 | 102.050 | 100.150 | 101.560 | 0.11 | 101.56 | 6850.0 | 2383.0 |
| 1650 | 2014-03-05 | 103.325 | 103.525 | 100.825 | 101.450 | 1.88 | 101.45 | 7459.0 | 2305.0 |
1651 rows × 9 columns
[16]:
# returns only open values
df['Open'] # or
df = get_quandl_financial_data(base_url + '&column_index=1', symbol, start, end, period)
df
[16]:
| Date | Open | |
|---|---|---|
| 0 | 2020-09-30 | 39.225 |
| 1 | 2020-09-29 | 40.575 |
| 2 | 2020-09-28 | 40.125 |
| 3 | 2020-09-25 | 40.175 |
| 4 | 2020-09-24 | 39.600 |
| ... | ... | ... |
| 1646 | 2014-03-12 | 99.500 |
| 1647 | 2014-03-11 | 100.925 |
| 1648 | 2014-03-07 | 101.900 |
| 1649 | 2014-03-06 | 101.000 |
| 1650 | 2014-03-05 | 103.325 |
1651 rows × 2 columns
[17]:
!pip install quandl
import quandl
# returns all free columns values
data = quandl.get(symbol)
data
Requirement already satisfied: quandl in /opt/conda/lib/python3.8/site-packages (3.5.3)
Requirement already satisfied: requests>=2.7.0 in /opt/conda/lib/python3.8/site-packages (from quandl) (2.25.1)
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Requirement already satisfied: numpy>=1.8 in /opt/conda/lib/python3.8/site-packages (from quandl) (1.19.4)
Requirement already satisfied: pandas>=0.14 in /opt/conda/lib/python3.8/site-packages (from quandl) (1.1.5)
Requirement already satisfied: pytz>=2017.2 in /opt/conda/lib/python3.8/site-packages (from pandas>=0.14->quandl) (2020.5)
Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests>=2.7.0->quandl) (2020.12.5)
Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests>=2.7.0->quandl) (1.26.2)
Requirement already satisfied: chardet<5,>=3.0.2 in /opt/conda/lib/python3.8/site-packages (from requests>=2.7.0->quandl) (4.0.0)
Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests>=2.7.0->quandl) (2.10)
[17]:
| Open | High | Low | Last | Change | Settle | Volume | Previous Day Open Interest | |
|---|---|---|---|---|---|---|---|---|
| Date | ||||||||
| 2014-03-05 | 103.325 | 103.525 | 100.825 | 101.450 | 1.88 | 101.45 | 7459.0 | 2305.0 |
| 2014-03-06 | 101.000 | 102.050 | 100.150 | 101.560 | 0.11 | 101.56 | 6850.0 | 2383.0 |
| 2014-03-07 | 101.900 | 102.900 | 101.575 | 102.580 | 1.02 | 102.58 | 5304.0 | 2315.0 |
| 2014-03-11 | 100.925 | 101.500 | 99.350 | 100.030 | 1.09 | 100.03 | 7337.0 | 2300.0 |
| 2014-03-12 | 99.500 | 99.575 | 97.550 | 97.990 | 2.04 | 97.99 | 9437.0 | 2462.0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2021-01-04 | 48.400 | 49.850 | 47.200 | 47.350 | -0.90 | 47.62 | 21845.0 | 1350.0 |
| 2021-01-05 | 47.400 | 50.200 | 47.275 | 49.850 | 2.31 | 49.93 | 23473.0 | 1799.0 |
| 2021-01-06 | 49.825 | 50.925 | 49.475 | 50.525 | 0.70 | 50.63 | 16675.0 | 1908.0 |
| 2021-01-07 | 50.525 | 51.275 | 50.400 | 50.950 | 0.20 | 50.83 | 10814.0 | 1800.0 |
| 2021-01-08 | 50.925 | 52.750 | 50.825 | 52.750 | 1.41 | 52.24 | 10770.0 | 1833.0 |
1720 rows × 8 columns
[18]:
# returns only open values
data['Open']
[18]:
Date
2014-03-05 103.325
2014-03-06 101.000
2014-03-07 101.900
2014-03-11 100.925
2014-03-12 99.500
...
2021-01-04 48.400
2021-01-05 47.400
2021-01-06 49.825
2021-01-07 50.525
2021-01-08 50.925
Name: Open, Length: 1720, dtype: float64