def load_data():
ticker = 'EURUSD' # will use in plot titles later (and for creating Plotly URL)
##### download intraday EUR/USD data from Bloomberg or CSV file
source = "Bloomberg"
source = "CSV"
csv_file = None
data_downloader = DataDownloader()
start_date = datetime.datetime.utcnow() - timedelta(days = 120)
freq = 'intraday'
if source == 'Bloomberg':
vendor_ticker = 'EURUSD BGN Curncy'
elif source == 'CSV':
# you can get free FX intraday data from Gain Capital (if you don't have Bloomberg)
vendor_ticker = 'EURUSD'
csv_file = 'D:/EURUSD.csv'
return data_downloader.download_time_series(vendor_ticker, ticker, start_date, source, csv_file = csv_file, freq = freq)
def streaming_fx_study():
# Learn about API authentication here: https://plot.ly/python/getting-started
# Find your api_key here: https://plot.ly/settings/api
plotly_username = "******"
plotly_api_key = "XXXX"
# Learn about stream id here at: http://help.plot.ly/documentation/python/streaming-tutorial/
# Find your stream_id here: https://plot.ly/settings/api
stream_id = "XXXX"
plotly.tools.set_credentials_file(username=plotly_username, api_key=plotly_api_key)
# Code below based on https://plot.ly/python/streaming-line-tutorial/
max_points = 50000
data_downloader = DataDownloader()
ticker = 'EURUSD'; vendor_ticker = 'EURUSD=X'
postfix = '-test'
# Make instance of stream id object
stream = plotly.graph_objs.Stream(
token = stream_id, # (!) link stream id to 'token' key
maxpoints = max_points # (!) keep a max of 80 pts on screen
)
trace1 = plotly.graph_objs.Scatter(
x = [],
y = [],
mode = 'lines',
stream = stream # (!) embed stream id, 1 per trace
)
data = Data([trace1])
# Add title to layout object
layout = Layout(title = ticker)
# Make a figure object
fig = Figure(data = data, layout = layout)
# Send fig to Plotly, initialize streaming plot, open new tab
py.iplot(fig, filename=ticker + "-stream" + postfix)
# Make instance of the Stream link object, with same stream id as Stream id object
s = py.Stream(stream_id)
# Open the stream
s.open()
#### Grab live FX prices from Yahoo & plot point by point
#### till termination
while True:
x = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f')
try:
y = data_downloader.download_live_quote(vendor_ticker, 'Yahoo')
s.write(dict(x = x, y = y))
except: pass
time.sleep(1) # sleep for 1 second
def vol_study():
# Learn about API authentication here: https://plot.ly/python/getting-started
# Find your api_key here: https://plot.ly/settings/api
plotly_username = "******"
plotly_api_key = "XXXXX"
plotly.tools.set_credentials_file(username = plotly_username, api_key = plotly_api_key)
ticker = 'EURUSD' # will use in plot titles later (and for creating Plotly URL)
##### Download intraday EUR/USD data from Bloomberg or CSV file
source = "Bloomberg"
source = "CSV"
csv_file = None
plot_helper = PlotHelper()
data_downloader = DataDownloader()
start_date = datetime.datetime.utcnow() - timedelta(days = 120)
freq = 'intraday'
if source == 'Bloomberg':
vendor_ticker = 'EURUSD BGN Curncy'
elif source == 'CSV':
vendor_ticker = 'EURUSD'
csv_file = 'D:/EURUSD.csv'
spot = data_downloader.download_time_series(vendor_ticker, ticker, start_date, source, csv_file = csv_file, freq = freq)
#### Calculate 1 day realised vol on EUR/USD data from Bloomberg using different data frequency (1 min, ..., 60 min)
minute_freq = [1, 5, 10, 30, 60]
realised_vol = None
for min in minute_freq:
spot_min = spot.loc[spot.index.minute % min == 0]
rets = spot_min / spot_min.shift(1) - 1
realised_vol_min = pandas.rolling_std(rets, 1440.0 / min) * math.sqrt(252.0 * (1440.0 / min)) * 100
realised_vol_min.columns = [str(min) + 'min']
if realised_vol is None: realised_vol = realised_vol_min
else:
realised_vol = realised_vol.join(realised_vol_min, how = 'outer')
# reduce the number of points to plot
realised_vol = realised_vol.loc[realised_vol.index.minute % 60 == 0]
xaxis = 'Date'
yaxis = 'Daily Realised Vol'
source_label = "Source: @thalesians/BBG"
# Using varying shades of blue for each line (helped by colorlover library)
title = ticker + ' Realised Vol 1D Window' + '<BR>' + source_label
realised_vol.index = pandas.to_datetime(realised_vol.index.values)
# also apply graduated color scheme of blues (from light to dark)
# see http://moderndata.plot.ly/color-scales-in-ipython-notebook/ for details on colorlover package
# which allows you to set scales
fig = Figure(data = plot_helper.convert_df_plotly(realised_vol, gradcolor = 'Blues', addmarker=False),
layout = plot_helper.create_layout(title, xaxis, yaxis),
)
filename = 'realised-vol-freq-' + str(ticker) + str(postfix)
plot_url = py.iplot(fig, filename = filename)
#### Calculate Realised Vol on S&P500 data from Yahoo and compare with VIX index
source = 'Yahoo'
start_date = datetime.datetime.utcnow() - timedelta(days = 365)
spx = data_downloader.download_time_series('^GSPC', 'S&P500', start_date, source)
vix = data_downloader.download_time_series('^VIX', 'VIX', start_date, source)
# calculate realised vol on S&P500 (and shift it to be aligned to VIX - implied vol)
spx_realised_vol = pandas.rolling_std(spx / spx.shift(1) - 1, 20) * math.sqrt(252) * 100
spx_realised_vol = spx_realised_vol.shift(-20)
vol = spx_realised_vol.join(vix, how = 'outer')
source_label = "Source: @thalesians/Yahoo"
title = "Comparing S&P500 1M realised vol with VIX" + '<BR>' + source_label
xaxis = 'Date'
yaxis = 'Vol'
fig = Figure(data = plot_helper.convert_df_plotly(vol, addmarker = True),
layout = plot_helper.create_layout(title, xaxis, yaxis),
)
plot_url = py.iplot(fig, filename = 'sp500-vix-comparison-' + str(postfix))
