cryptory

Retrieve historical cryptocurrency and other related data.

cryptory integrates various sources of historical crypto data, so that you can perform analysis and build models without having to worry about knowing different packages and APIs. Current data sources include:

• Daily historical prices
• Additional cryptocurrency information (transaction fees, active adressess, etc.)
• Reddit metrics (e.g. subscriber growth)
• Google Trends (via Pytrends)
• Stock market
• Foreign exchange rates
• Commodity prices

Installation

$ pip install cryptory

Compatibility

• Python 2.7+
• Python 3

Dependencies

• pandas>=0.23.0
• numpy>=1.14.0
• pytrends>=4.4.0
• beautifulsoup4>=4.0.0

How to Use

Consult the documentation help(Cryptory) for more information on its usage.

Basic Usage

# load packagefromcryptoryimportCryptory# initialise object # pull data from start of 2017 to present daymy_cryptory=Cryptory(from_date="2017-01-01")
# get historical bitcoin prices from coinmarketcapmy_cryptory.extract_coinmarketcap("bitcoin")

# get daily subscriber numbers to the bitcoin reddit pagemy_cryptory.extract_reddit_metrics(subreddit="bitcoin",
metric="total-subscribers")

# google trends- bitcoin search resultsmy_cryptory.get_google_trends(kw_list=["bitcoin"])

# dow jones price (market code from yahoo finance)my_cryptory.get_stock_prices(market="%5EDJI")

# USD/EUR exchange ratemy_cryptory.get_exchange_rates(from_currency="USD", to_currency="EUR")

# get historical commodity pricesmy_cryptory.get_metal_prices()

Advanced Usage

As all cryptory methods return a pandas dataframe, it's relatively easy to combine results and perform more complex calculations.

# generate price correlation matriximportpandasaspdimportseabornassnsimportmatplotlib.pyplotaspltall_coins_df=my_cryptory.extract_bitinfocharts("btc")
# coins of interestbitinfocoins= ["btc", "eth", "xrp", "bch", "ltc", "dash", "xmr", "doge"]
forcoininbitinfocoins[1:]:
all_coins_df=all_coins_df.merge(my_cryptory.extract_bitinfocharts(coin), on="date", how="left")
# date column not need for upcoming calculationsall_coins_df=all_coins_df.drop('date', axis=1)
corr=all_coins_df.pct_change().corr(method='pearson')
fig, ax=plt.subplots(figsize=(7,5)) sns.heatmap(corr, xticklabels=[col.replace("_price", "") forcolincorr.columns.values],
yticklabels=[col.replace("_price", "") forcolincorr.columns.values],
annot_kws={"size": 16})
plt.show()

# overlay bitcoin price and google searches for bitcoinbtc_google=my_cryptory.get_google_trends(kw_list=['bitcoin']).merge(
my_cryptory.extract_coinmarketcap('bitcoin')[['date','close']], on='date', how='inner')
# need to scale columns (min-max scaling)btc_google[['bitcoin','close']] = (
btc_google[['bitcoin', 'close']]-btc_google[['bitcoin', 'close']].min())/(
btc_google[['bitcoin', 'close']].max()-btc_google[['bitcoin', 'close']].min())
fig, ax1=plt.subplots(1, 1, figsize=(9, 3))
ax1.set_xticks([datetime.date(j,i,1) foriinrange(1,13,2) forjinrange(2017,2019)])
ax1.set_xticklabels([datetime.date(j,i,1).strftime('%b %d %Y') foriinrange(1,13,2) forjinrange(2017,2019)])
ax1.plot(btc_google['date'].astype(datetime.datetime),
btc_google['close'], label='bitcoin', color='#FF9900')
ax1.plot(btc_google['date'].astype(datetime.datetime),
btc_google['bitcoin'], label="bitcoin (google search)", color='#4885ed')
ax1.legend(bbox_to_anchor=(0.1, 1), loc=2, borderaxespad=0., ncol=2, prop={'size': 14})
plt.show()

Issues & Suggestions

cryptory relies quite strongly on scraping, which means that it can break quite easily. If you spot something not working, then raise an issue. Also, if you have any suggestions or criticism, you can also raise an issue.