data_source='bloomberg', # use Bloomberg as data source tickers=tickers, # ticker (Thalesians) fields=['close'], # which fields to download vendor_tickers=vendor_tickers, # ticker (Bloomberg) vendor_fields=['PX_LAST'], # which Bloomberg fields to download cache_algo='internet_load_return') # how to return data daily_vals = ltsf.harvest_time_series(time_series_request) pf = PlotFactory() gp = GraphProperties() gp.title = 'Spot values' gp.file_output = 'output_data/demo.png' gp.html_file_output = 'output_data/demo.htm' gp.source = 'Thalesians/BBG' # plot using PyThalesians pf.plot_line_graph(daily_vals, adapter='pythalesians', gp=gp) # plot using Bokeh (still needs a lot of work!) pf.plot_line_graph(daily_vals, adapter='bokeh', gp=gp) # do more complicated charts using several different Matplotib stylesheets (which have been customised) if False: ltsf = LightTimeSeriesFactory() # load market data start = '01 Jan 1970' end = datetime.datetime.utcnow()
finish_date = datetime.date.today(), # finish date freq = 'daily', # daily data data_source = 'google', # use Bloomberg as data source tickers = ['Apple', 'S&P500 ETF'], # ticker (Thalesians) fields = ['close'], # which fields to download vendor_tickers = ['aapl', 'spy'], # ticker (Google) vendor_fields = ['Close'], # which Bloomberg fields to download cache_algo = 'internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() gp.html_file_output = "output_data/apple.htm" gp.title = "S&P500 vs Apple" # plot first with PyThalesians and then Bokeh # just needs 1 word to change gp.display_legend = False pf = PlotFactory() pf.plot_generic_graph(df, type = 'line', adapter = 'pythalesians', gp = gp) pf.plot_generic_graph(df, type = 'line', adapter = 'bokeh', gp = gp) # test simple Bokeh bar charts - monthly returns over past 6 months if True: from datetime import timedelta ltsf = LightTimeSeriesFactory()
freq='daily', # daily data data_source='google', # use Bloomberg as data source tickers=['Apple', 'S&P500 ETF'], # ticker (Thalesians) fields=['close'], # which fields to download vendor_tickers=['aapl', 'spy'], # ticker (Google) vendor_fields=['Close'], # which Bloomberg fields to download cache_algo='internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices( ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() gp.html_file_output = "output_data/apple.htm" gp.title = "S&P500 vs Apple" # plot first with PyThalesians and then Bokeh # just needs 1 word to change gp.display_legend = False pf = PlotFactory() pf.plot_generic_graph(df, type='line', adapter='pythalesians', gp=gp) pf.plot_generic_graph(df, type='line', adapter='bokeh', gp=gp) # test simple Bokeh bar charts - monthly returns over past 6 months if True: from datetime import timedelta ltsf = LightTimeSeriesFactory()
data_source = 'bloomberg', # use Bloomberg as data source tickers = tickers, # ticker (Thalesians) fields = ['close'], # which fields to download vendor_tickers = vendor_tickers, # ticker (Bloomberg) vendor_fields = ['PX_LAST'], # which Bloomberg fields to download cache_algo = 'internet_load_return') # how to return data daily_vals = ltsf.harvest_time_series(time_series_request) pf = PlotFactory() gp = GraphProperties() gp.title = 'Spot values' gp.file_output = 'demo.png' gp.html_file_output = 'demo.htm' gp.source = 'Thalesians/BBG' # plot using PyThalesians pf.plot_line_graph(daily_vals, adapter = 'pythalesians', gp = gp) # plot using Bokeh (still needs a lot of work!) pf.plot_line_graph(daily_vals, adapter = 'bokeh', gp = gp) # do more complicated charts using several different Matplotib stylesheets (which have been customised) if True: ltsf = LightTimeSeriesFactory() # load market data start = '01 Jan 1970' end = datetime.datetime.utcnow()
tickers=tickers, # ticker (Thalesians) fields=["close"], # which fields to download vendor_tickers=vendor_tickers, # ticker (Bloomberg) vendor_fields=["PX_LAST"], # which Bloomberg fields to download cache_algo="internet_load_return", ) # how to return data daily_vals = ltsf.harvest_time_series(time_series_request) pf = PlotFactory() gp = GraphProperties() gp.title = "Spot values" gp.file_output = "output_data/demo.png" gp.html_file_output = "output_data/demo.htm" gp.source = "Thalesians/BBG" # plot using PyThalesians pf.plot_line_graph(daily_vals, adapter="pythalesians", gp=gp) # plot using Bokeh (still needs a lot of work!) pf.plot_line_graph(daily_vals, adapter="bokeh", gp=gp) # do more complicated charts using several different Matplotib stylesheets (which have been customised) if False: ltsf = LightTimeSeriesFactory() # load market data start = "01 Jan 1970" end = datetime.datetime.utcnow()
if True: time_series_request = TimeSeriesRequest( start_date = "01 Jan 2013", # start date finish_date = datetime.date.today(), # finish date freq = 'daily', # daily data data_source = 'google', # use Bloomberg as data source tickers = ['Apple', 'S&P500 ETF'], # ticker (Thalesians) fields = ['close'], # which fields to download vendor_tickers = ['aapl', 'spy'], # ticker (Google) vendor_fields = ['Close'], # which Bloomberg fields to download cache_algo = 'internet_load_return') # how to return data ltsf = LightTimeSeriesFactory() tsc = TimeSeriesCalcs() df = tsc.create_mult_index_from_prices(ltsf.harvest_time_series(time_series_request)) gp = GraphProperties() gp.html_file_output = "apple.htm" gp.title = "S&P500 vs Apple" # plot first with PyThalesians and then Plotly (via Cufflinks) # just needs 1 word to change # (although, note that AdapterCufflinks does have some extra parameters that can be set in # GraphProperties) gp.display_legend = False pf = PlotFactory() pf.plot_generic_graph(df, type = 'line', adapter = 'pythalesians', gp = gp) pf.plot_generic_graph(df, type = 'line', adapter = 'bokeh', gp = gp)
data_source = 'bloomberg', # use Bloomberg as data source tickers = tickers, # ticker (Thalesians) fields = ['close'], # which fields to download vendor_tickers = vendor_tickers, # ticker (Bloomberg) vendor_fields = ['PX_LAST'], # which Bloomberg fields to download cache_algo = 'internet_load_return') # how to return data daily_vals = ltsf.harvest_time_series(time_series_request) pf = PlotFactory() gp = GraphProperties() gp.title = 'Spot values' gp.file_output = 'output_data/demo.png' gp.html_file_output = 'output_data/demo.htm' gp.source = 'Thalesians/BBG' # plot using PyThalesians pf.plot_line_graph(daily_vals, adapter = 'pythalesians', gp = gp) # plot using Bokeh (still needs a lot of work!) pf.plot_line_graph(daily_vals, adapter = 'bokeh', gp = gp) # do more complicated charts using several different Matplotib stylesheets (which have been customised) if True: ltsf = LightTimeSeriesFactory() # load market data start = '01 Jan 1970' end = datetime.datetime.utcnow()