def main():
global currencyList
n = 0
curDay = strftime("%d-%m-%Y", gmtime())
l = len(currencyList)
#l = 1
while n < l:
# initialise scraper
scraper = CmcScraper(currencyList[n], '01-01-2000', curDay)
# # get data as list of list
headers, data = scraper.get_data()
# # export the data to csv
scraper.export_csv('history/'+currencyList[n]+".csv")
# # get dataframe for the data
df = scraper.get_dataframe()
x = []
y = []
with open('history/'+currencyList[n]+'.csv', 'r') as f:
sreader = csv.reader(f)
i = 0
for row in sreader:
if i > 0 :
x.append(i)
y.append((float(row[2]) + float(row[3]))/2)
i = i + 1
prediction = predict(y,currencyList[n])
print currencyList[n] + ' => ' + str(prediction)
y.reverse()
data = {}
data["pointStart"] = 1230764400000
data["pointInterval"] = 3600000
data["dataLength"] = len(y)
data["data"] = y
data["prediction"] = prediction
json_data = json.dumps(data)
#print json_data
file = open('public/'+currencyList[n]+'.json', 'w')
file.write(json_data)
file.close()
n = n+1
import streamlit as st
from cryptocmd import CmcScraper
import plotly.express as px
st.write('# 비트코인 BTC 데이터')
scrppper = CmcScraper('BTC', '01-01-2021', '07-01-2021') # %d-%m-%Y
df = scrppper.get_dataframe()
fig_close = px.line(df, x='Date', y=['Open', 'High', 'Low', 'Close'], title='가격')
fig_volume = px.line(df, x='Date', y=['Volume'], title='거래량')
st.plotly_chart(fig_close)
st.plotly_chart(fig_volume)
import streamlit as st
from cryptocmd import CmcScraper
import plotly.express as px
from datetime import datetime
st.write('#Cryptocurrency Web App')
st.sidebar.header('Menu')
name = st.sidebar.selectbox('Name', ['BTC', 'ETH', 'USDT'])
start_date = st.sidebar.date_input('start date', datetime(2021, 1, 1))
end_date = st.sidebar.date_input('end date', datetime(2021, 1, 25))
scrapper = CmcScraper(name, start_date.strftime('%d-%m-%Y'),
end_date.strftime('%d-%m-%Y'))
df = scrapper.get_dataframe()
fig_close = px.line(df,
x='Date',
y=['Open', 'High', 'Low', 'Close'],
title='Price')
fig_volume = px.line(df, x='Date', y=['Volume'], title='Volume')
st.plotly_chart(fig_close)
st.plotly_chart(fig_volume)
def get_historical_quotes(self) -> pd.DataFrame:
scraper = CmcScraper(self.ticker)
df = scraper.get_dataframe()
return pd.DataFrame({'_id' : 'null', 'Ticker' : self.ticker, 'TimeStampID' :
df['Date'], 'Price_USD' : df['Close'], 'Price_BTC' : 'null',
'MarketCap_USD' : df['Market Cap'], 'Volume24hr_USD' : df['Volume']})
def get_historical_data(crypto, first_day, last_day):
scraper = CmcScraper(crypto, first_day, last_day)
historical_data_df = scraper.get_dataframe().set_index("Date")
return historical_data_df
def load_data(selected_ticker):
init_scraper = CmcScraper(selected_ticker)
df = init_scraper.get_dataframe()
min_date = pd.to_datetime(min(df['Date']))
max_date = pd.to_datetime(max(df['Date']))
return min_date, max_date
elif date_range == "Specific date range":
### Initialise scraper with time interval
start_date = st.sidebar.date_input('Select start date:',
min_value=min_date,
max_value=max_date,
value=min_date)
end_date = st.sidebar.date_input('Select end date:',
min_value=min_date,
max_value=max_date,
value=max_date)
scraper = CmcScraper(selected_ticker, str(start_date.strftime("%d-%m-%Y")),
str(end_date.strftime("%d-%m-%Y")))
### Pandas dataFrame for the same data
data = scraper.get_dataframe()
st.subheader('Raw data')
st.write(data.head())
### Plot functions
def plot_raw_data():
fig = go.Figure()
fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name="Close"))
fig.layout.update(title_text='Time Series data with Rangeslider',
xaxis_rangeslider_visible=True)
st.plotly_chart(fig)
def plot_raw_data_log():
def updateCoins(coinCodes):
"""Update the H5 Repository with Coin Data
Keyword arguments:
coinList - Set of CoinCodes
"""
global apph5datafile
coinList = []
if coinCodes == '*':
print("You asked to update all tracked coins")
coinList = config.get('Active', 'Coins').split(' ')
else:
coinList = coinCodes
print("You asked to list the last week for coins %s" % (coinList))
print("You asked to update coins %s" % (coinList))
for coin in coinList:
havecointable = True
if not os.path.exists(apph5datafile):
print("Creating Datafile %s" % apph5datafile)
havecointable = False
# if not ('/'+coin.upper()) in storedcoins:
# print("Coin %s not in dataset" % coin.upper())
# havecointable = False
if havecointable:
try:
hdf = read_hdf(apph5datafile, coin)
hdf['date_ranked'] = hdf['Date'].rank(ascending=1)
print "Last element from %s is dated %s" % (
coin, str(hdf.at[0, 'Date'])[:10])
lastdate = datetime.strptime(
str(hdf.at[0, 'Date'])[:10], "%Y-%m-%d")
nextday = timedelta(1)
nextday = lastdate + nextday
delta = (datetime.today() - timedelta(1)) - nextday
print("Difference from yesterday to last update ", delta)
except KeyError:
print("No data found for this Coin")
nextday = (datetime.today() - timedelta(2 * 365))
lastdate = datetime.today() - timedelta(1)
print "Updating data from %s to %s" % (str(nextday), str(lastdate))
startdate = '{:%d-%m-%Y}'.format(nextday)
enddate = '{:%d-%m-%Y}'.format(lastdate)
scraper = CmcScraper(coin, startdate, enddate)
else:
scraper = CmcScraper(coin.upper())
# get data as list of list
try:
headers, data = scraper.get_data()
# get dataframe for the data
df = scraper.get_dataframe()
print df
# df.to_hdf('pluton.h5',coin,append=True,format='t')
df.to_hdf(apph5datafile, coin.upper())
except ValueError:
print("No data found")
return
f= pd.read_csv(data)
print(f)
!pip install cryptocmd
from cryptocmd import CmcScraper
# initialise scraper
scraper1 = CmcScraper('XRP', '08-08-2015', '12-04-2018')
scraper2 = CmcScraper('ETH', '08-08-2015', '12-04-2018')
scraper3 = CmcScraper('DASH', '08-08-2015', '12-04-2018')
scraper4 = CmcScraper('LTC', '08-08-2015', '12-04-2018')
scraper5 = CmcScraper('XLM', '08-08-2015', '12-04-2018')
# get dataframe for the data
df1 = scraper1.get_dataframe()
df2 = scraper2.get_dataframe()
df3 = scraper3.get_dataframe()
df4 = scraper4.get_dataframe()
df5 = scraper5.get_dataframe()
Y = f['BCHAIN-MKPRU'][0:978]
X = pd.concat([df1['Close'], df2['Close'], df3['Close'] ,df4['Close'], df5['Close'],f[0:979]], axis=1)
X= X[1:]
from scipy import stats
X = stats.zscore(X)
Y = stats.zscore(Y)
X.shape
t=range(1,979)
plt.plot(t, np.flipud(Y), color='darkorange', label='data')
def download_data_scrapper(from_symbol):
scraper = CmcScraper(from_symbol)
data = scraper.get_dataframe()
return data
def CryptoForecast(SelectCrypto):
# get the data
# cryptodata = pdr.get_data_yahoo(
# [SelectCrypto + "-" + "USD"], start=firstdate, end=todate
# ).reset_index()
# cryptodata.columns = cryptodata.columns.get_level_values(0)
scraper = CmcScraper(SelectCrypto, firstdate, todate)
cryptodata = scraper.get_dataframe()
cryp_pro = cryptodata[["Date", "Close"]]
cryp_pro = cryp_pro.rename(columns={"Date": "ds", "Close": "y"})
# Fit the Model
model = Prophet()
model.fit(cryp_pro)
# make the prediction
future = model.make_future_dataframe(periods=60)
forecast = model.predict(future)
# prepare data for plotting
forecast = forecast[["ds", "yhat", "yhat_lower", "yhat_upper"]]
forecast = forecast.rename(columns={"ds": "Date"})
plot_data = cryptodata.merge(forecast, on="Date", how="outer").sort_values(by="Date", ascending=False)
plot_data["Volume"] = plot_data["Volume"].replace(np.nan, 0)
plot_data = plot_data.iloc[0:150,:]
# make the plot
fig_forecast = px.scatter(
plot_data,
x="Date",
y="Close",
size="Volume",
title=SelectCrypto,
labels={"Date": "Date", "Close": "Price in USD"},
template="plotly_dark",
#height = 500,
).update_traces(mode="lines+markers", marker=dict(color="green", opacity=0.4))
fig_forecast["data"][0]["showlegend"]=True
fig_forecast["data"][0]["name"]="Actual Price"
fig_forecast.add_trace(
go.Scatter(
x=plot_data["Date"],
y=plot_data["yhat"],
mode="lines",
name="Predicted Price",
)
)
fig_forecast.add_trace(
go.Scatter(
x=plot_data["Date"],
y=plot_data["yhat_upper"],
mode="lines",
line=dict(dash="dot", color="dodgerblue"),
name="Upper Band",
)
)
fig_forecast.add_trace(
go.Scatter(
x=plot_data["Date"],
y=plot_data["yhat_lower"],
mode="lines",
line=dict(dash="dot"),
name="Lower Band",
)
)
fig_forecast.update_layout(legend=dict(orientation="h", x=1.02, y=1.02, xanchor="right", yanchor="bottom"))
return fig_forecast
