btc-mc-EMA-plot.py
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| importdatetimeasdt | |
| importrequests | |
| importio | |
| importpandasaspd | |
| importmatplotlib.pyplotasplt | |
| frommatplotlib.pyplotimportfigure | |
| frommatplotlibimportticker | |
| frommatplotlib.tickerimport (MultipleLocator, FormatStrFormatter, AutoMinorLocator) | |
| %matplotlibinline | |
| # Author: Adam Anderson, https://github.com/84adam | |
| # Shout Out to: Benjamin Kircher, https://github.com/bkircher for help with formatting, data download | |
| # function to download CSV file from URL; convert to Pandas DataFrame | |
| defget_csv_from_url(url): | |
| s=requests.get(url).content | |
| df=pd.read_csv(io.StringIO(s.decode('utf-8'))) | |
| returndf | |
| if__name__=='__main__': | |
| # data source: coinmetrics.io - all BTC community data (CSV) | |
| url="https://coinmetrics.io/newdata/btc.csv" | |
| # download data | |
| print("Downloading data...") | |
| df=get_csv_from_url(url) | |
| # select columns | |
| df=df[['time', 'CapMrktCurUSD']] | |
| # rename columns | |
| df=df.rename(columns={"CapMrktCurUSD": "market cap"}) | |
| # convert 'time' (object series) to timestamp format | |
| df['time'] =df['time'].astype('datetime64[ns]') | |
| # divide market cap by 1 billion | |
| df['market cap'] =df['market cap'] /1000000000 | |
| # rename for convenience | |
| x=df['time'] | |
| y=df['market cap'] | |
| # calculate Exponential Moving Averages (EMAs) [Periods: 5, 10, 20, 50, 100, 200] | |
| # enter (#-days) per period for EMAs, e.g. '7' = 1 week/period | |
| per=7# one week | |
| df['EMA_5p'] =df.iloc[:,1].ewm(span=(5*per),adjust=True).mean() | |
| df['EMA_10p'] =df.iloc[:,1].ewm(span=(10*per),adjust=True).mean() | |
| df['EMA_20p'] =df.iloc[:,1].ewm(span=(20*per),adjust=True).mean() | |
| df['EMA_50p'] =df.iloc[:,1].ewm(span=(50*per),adjust=True).mean() | |
| df['EMA_100p'] =df.iloc[:,1].ewm(span=(100*per),adjust=True).mean() | |
| df['EMA_200p'] =df.iloc[:,1].ewm(span=(200*per),adjust=True).mean() | |
| # rename for convenience | |
| y5=df['EMA_5p'] | |
| y20=df['EMA_20p'] | |
| y10=df['EMA_10p'] | |
| y50=df['EMA_50p'] | |
| y100=df['EMA_100p'] | |
| y200=df['EMA_200p'] | |
| # set starting and ending dates | |
| # start = len(df['time']) - (365*1+1) # one year ago | |
| # start = len(df['time']) - (365*2+1) # two years ago | |
| # start = len(df['time']) - (365*3+1) # three years ago | |
| start=1308# market cap >= $100M from this date onwards: '2012-08-03' | |
| end=len(x) -1# last date in series | |
| start_date=df['time'][start] | |
| end_date=df['time'][end] | |
| # get start/end market cap values (Billions USD) | |
| start_mc=y[start] | |
| end_mc=y[end-1] | |
| # set ranges for plotting on chart | |
| x=x[start:end] | |
| y=y[start:end] | |
| y5=y5[start:end] | |
| y10=y10[start:end] | |
| y20=y20[start:end] | |
| y50=y50[start:end] | |
| y100=y100[start:end] | |
| y200=y200[start:end] | |
| # set figure size | |
| plt.figure(figsize=[24,12]) | |
| ax=plt.subplot(1, 1, 1) | |
| # plot values in log scale | |
| ax.semilogy(x, y, label='BTC Market Cap', linewidth=3) | |
| ax.plot(x, y5, label='EMA 5') | |
| ax.plot(x, y10, label='EMA 10') | |
| ax.plot(x, y20, label='EMA 20') | |
| ax.plot(x, y50, label='EMA 50') | |
| ax.plot(x, y100, label='EMA 100') | |
| ax.plot(x, y200, label='EMA 200') | |
| # set y-scale limits: $100 million to $1.4 trillion | |
| ax.set_ylim(0.1, 1400) | |
| # ax.set_ylim(50, 300) | |
| # set x-scale limits: | |
| # start/end dates 10 days before/after specified `start_date`/`end_date` | |
| ax.set_xlim(start_date-dt.timedelta(days=10), end_date+dt.timedelta(days=10)) | |
| # ax.set_xlim(start_date - dt.timedelta(days=5), end_date + dt.timedelta(days=5)) | |
| # set Y-axis tick formats | |
| ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.0f')) | |
| ax.yaxis.set_minor_formatter(ticker.FormatStrFormatter('%.0f')) | |
| ax.tick_params(which='major', width=3, length=8) | |
| ax.tick_params(which='minor', width=2, length=5) | |
| # for the minor ticks, show only every 100 billion mark | |
| ax.yaxis.set_minor_locator(MultipleLocator(200)) | |
| plt.grid(True) | |
| plt.legend(loc=2, fontsize=16) | |
| plt.xlabel(f'Date Range: {start_date.strftime("%m/%d/%Y")} (\${start_mc:.2f}B) - {end_date.strftime("%m/%d/%Y")} (\${end_mc:.2f}B)', fontsize=16) | |
| plt.ylabel('Market Cap [Billions USD]', fontsize=16) | |
| plt.title("Bitcoin Market Cap vs. Weekly EMAs - Aug. 2012 to Present", fontsize=21) | |
| plt.show() |