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      Python: CloudQuant Charting

      CloudQuant Charting Documentation

      Imports

      From liberator import charting

      Histogram(**kwargs)

      The Histogram function will generate a chart distribution/histogram chart that is branded with CloudQuant logo and colors.

      returns 

      plotly.graph_objs._figure.Figure

      Required Keyword Arguments:

      • "df" = the pandas data frame with the data you want to graph
      • "col" = (string) dataframe column name you wish to graph

      Optional Keyword Arguments:

      • "title" = (string) The title at the top of the chart. title can contain some html tags like: 
        •  <b></b><i></i> and <br>.
           note: not all html tags are supported.
      • "xlabel" = (string) the label for the x axis. xlabel can contain some html tags like: 
        •  <b></b><i></i> and <br>.
      • "ylabel" = (string) the label for the y axis. ylabel can contain some html tags like: 
        •  <b></b><i></i> and <br>.
      • "histnorm" = (string) One of 'percent', 'probability', 'density', or 'probability density'. If None, the output of histfunc is used as is. If 'probability', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins. If 'percent', the output of histfunc for a given bin is divided by the sum of the output of histfunc for all bins and multiplied by 100. If 'density', the output of histfunc for a given bin is divided by the size of the bin. If 'probability density', the output of histfunc for a given bin is normalized such that it corresponds to the probability that a random event whose distribution is described by the output of histfunc will fall into that bin.
      • "width" = Horizontal size (in px). Defaults to 800. Cannot be less than 400
      • "height" = Vertical size (in px) . Defaults to 800. Cannot be less than 400

      Charting overlays of another series - Optional

      💡 note: it is a good idea to rename the overlay_col to a name that looks better visually

      • "overlay_df" = the pandas data frame with the data you compare in the graph
      • "overlay_col" = (string) dataframe column name you wish to compare in graph

      Example Histogram

      import liberator #CloudQuant Data Liberator
      import pandas as pd #Pandas data frames
      from liberator import charting #cloudquant charting
      # query liberator to get the latest daily data for a list of stocks
      res = liberator.query(name="daily_bars")

      # convert the results to a dataframe
      df_bars = liberator.get_dataframe(res)
      # sort the market data by volume descending
      df_bars = df_bars.sort_values(by = ['volume'], ascending=[False])
      #calculate the change (close - open) for the market data in the data frame
      change = df_bars.close - df_bars.open
      df_bars["change"] = change
      # create a histogram chart of the change price of the top 100 (by volume) stocks
      change = df_bars.close - df_bars.open 
      fig = charting.Histogram(df = df_bars[0:100], col = "change", 
      title="<b>Change Px Distribution</b> <br>of last trading day's top 100 High Volume Stocks",
      width = 800, height = 400, histnorm = "probability")
      fig.show()

       

      Candlestick(**kwargs)

      The Candlestick function will generate a Candlestick chart that is branded with CloudQuant logo and colors.

      returns either a single or an array of:

      plotly.graph_objs._figure.Figure

      Required Keyword Arguments:

      • "df" = the pandas data frame with the OHLC data you want to graph This is typically a liberator query for "Daily Bars" or "Minute Bars"
        • NOTE: the data frame must have at least 20 bars of data

      💡 note: the data frame must have at least 20 bars of data

      Optional Keyword Arguments:

      • "title" = (string) The title at the top of the chart. title can contain some HTML tags like: 
        •  <b></b><i></i> and <br>.
      • "xlabel" = (string) the label for the x axis. xlabel can contain some html tags like: 
        •  <b></b><i></i> and <br>.
        • xlabel defaults to "Time (EST - New York)"
      • "ylabel" = (string) the label for the y axis. ylabel can contain some HTML tags like: 
        •  <b></b><i></i> and <br>.
        • defaults to "Price $ - US Dollars"
      • "width" = Horizontal size (in px). Defaults to 800. Cannot be less than 700
      • "height" = Vertical size (in px) . Defaults to 800. Cannot be less than 400
      • "open" = the name of the open column. Defaults to "open"
      • "high" = the name of the high column. Defaults to "high"
      • "low" = the name of the low column. Defaults to "low"
      • "close" = the name of the close column. Defaults to "close"
      • "timestamp" = the name of the timestamp or date column. Defaults to "timestamp"

      Showing a trade (must be round turn with both entry and exit values)

      • "entry_px" = numeric entry price
      • "entry_time" = timestamp of the entry trade
      • "close_px" = numeric closing price
      • "close_time" = timestsamp of the closing trade
      • "entry_side" = The entry action "b" = buy or "s" = sell Entry side is used to compute the color for the shown trade.

      Example Candlestick Chart

      import liberator
      import cloudquantcharting as charting
      import pandas as pd
      symbol = "XLE"
      res = liberator.query(symbols = symbol, name="daily_bars", back_to = '5/1/2020')
      df_bars = liberator.get_dataframe(res)

      fig = charting.Candlestick(df=df_bars, overlay_df=gaspx, 
                                 width = 800, height= 600, xlabel = "Date")
      fig.show()

      xle

      Secondary Y Axis - To overlay a signal

      • "overlay_df" = the pandas data frame with the data you compare in the graph

      • "overlay_col" = (list) or (string) the list of column names or the single dataframe column name you wish to compare in graph. These will appear as thin solid lines on the graph.

        note: line names will use the column names in the legend

        note: all secondary axis values should be in the same numeric range otherwise the secondary axis will be useless.

      • "overlay_col2" = (list) or (string) the list of column names or the single dataframe column name you wish to compare in graph. These will appear as thin dashed lines on the graph.

        note: line names will use the column names in the legend

        note: all secondary axis values should be in the same numeric range otherwise the secondary axis will be useless.

      Example Candlestick chart with a Data overlay

      import liberator #CloudQuant Data Liberator
      import pandas as pd #Pandas data frames
      from liberator import charting #cloudquant charting
      from datetime import datetime
      from datetime import timedelta
      back_to = (datetime.now() + timedelta(days=-360) ).strftime('%Y-%m-%d 23:59:59') #get the start date for N days ago
      symbol = "SPY"
      # query liberator to get the market data for our symbol for the last N days
      res = liberator.query(name="daily_bars", symbols=symbol, back_to=back_to)
      # convert the results to a dataframe
      df_bars = liberator.get_dataframe(res)

      # Read Retail gas prices
      res = liberator.query( name="eia_gas_prices", back_to = back_to)
      gaspx = liberator.get_dataframe(res)



      # chart the market data showing a second Y-Axis 
      fig = charting.Candlestick(df=df_bars, overlay_df=gaspx, 
                                 overlay_col = "retail_gas_price", 
                                 height= 700, width=800,
                                xlabel = "Date",
                                title = "SPY vs Retail Gas Prices")
      fig.show()

      spy_vs_gas_px

      Studies for Candlestick Charts

      💡 Important Note for Studies. If you request Studies you MAY receive more than one figure in your return set. Some studies return a second line chart. See the supported studies section below.

      A good example of coding for variable types of output from this function is:

             res = Candlestick(df=df, study=MyStudy, study_columns=[ "high", "low", "close", "volume"])
             if isinstance(res, list):
                 for f in res:
                     f.show(renderer="png")
             else:
                 res.show(renderer="png")

      Supported Studies That return ONE FIGURE

          "BBANDS":"Bollinger Bands",  - Arguments = close
          "DEMA":"2x Exponential MA",  - Arguments = close
          "EMA":"Exponential MA",  - Arguments = close
          "HT_TRENDLINE":"Hilbert Transform",  - Arguments = close
          "KAMA":"Kaufman MA",  - Arguments = close
          "MA":"Moving average",  - Arguments = close
          "MAMA":"MESA Adaptive MA",  - Arguments = close
          "MIDPOINT":"MidPoint",  - Arguments = close
          "MIDPRICE":"Midpoint Price",   - Arguments = high, low
          "SAR":"Parabolic SAR",   - Arguments = high, low
          "SAREXT":"Parabolic SAR Ext",   - Arguments = high, low
          "SMA":"Simple MA",  - Arguments = close
          "T3":"T3 Moving Average",  - Arguments = close
          "TEMA":"T3 Exponential MA",  - Arguments = close
          "TRIMA":"Triangular MA",  - Arguments = close
          "WMA":"Weighted MA",  - Arguments = close

      Supported Studies That return AN ARRAY of FIGURES

          "ADX":"Avg Dir Move Index",   - Arguments = high, low, close 
          "ADXR":"AvgDir MoveIdx Rating",   - Arguments = high, low, close 
          "APO":"ABS PX Oscillator",   - Arguments = close
          "AROON":"Aroon",   - Arguments = high, low
          "AROONOSC":"Aroon Oscillator",   - Arguments = high, low
          "BOP":"Balance Of Power",    - Arguments = open, high, low, close 
          "CCI":"Cmdty Channel Idx",   - Arguments = high, low, close 
          "CMO":"Chande Momentum Osc",   - Arguments = close
          "DX":"Directnl Move Idx",   - Arguments = high, low, close 
          "MACD":"MA Convg/Diverg",   - Arguments = close
          "MACDEXT":"MACD EXT",  - Arguments = close
          "MFI":"Money Flow Index",    - Arguments = high, low, close, volume
          "MINUS_DI":"Minus DI",   - Arguments = high, low, close 
          "MINUS_DM":"Minus DM",   - Arguments = high, low
          "MOM":"Momentum",  - Arguments = close
          "PLUS_DI":"Plus DI",   - Arguments = high, low, close 
          "PLUS_DM":"Plus DM",   - Arguments = high, low
          "PPO":"%PX Oscillator",  - Arguments = close
          "ROC":"Rate of change",  - Arguments = close
          "ROCP":"Rate Δ %",  - Arguments = close
          "ROCR":"Rate Δ ratio",  - Arguments = close
          "ROCR100":"Rate Δ ratio 100",  - Arguments = close
          "RSI":"RSI",  - Arguments = close
          "STOCH":"Stochastic",   - Arguments = high, low, close 
          "STOCHF":"Stochastic Fast",   - Arguments = high, low, close 
          "STOCHRSI":"Stochastic RSI",  - Arguments = close
          "TRIX":"1-day ROC",  - Arguments = close
          "ULTOSC":"Ultimate Oscillator",   - Arguments = high, low, close 
          "WILLR":"Williams %R",   - Arguments = high, low, close 
          "ATR":"Avg True Range",   - Arguments = high, low, close 
          "MACDFIX":"MA Convergence/Divergence ",   - Arguments = close
          "NATR":"Normalized Average True Range",   - Arguments = high, low, close 
          "TRANGE": "True Range" ,   - Arguments = high, low, close

      LineChart(**kwargs)

      The LineChart function will generate a line chart that is branded with CloudQuant logo and colors.

      returns 

      plotly.graph_objs._figure.Figure

      Required Keyword Arguments:

      • "df" = the pandas data frame with the data you want to graph
      • "cols" = List containing (string) names of the dataframe column name you wish to graph as a line. If the cols is an empty list then it will chart any applicable overlays and studies.
      • "x_column" = (string) the name of the dataframe column name you wish to use as the x axis. If not supplied then the x_column will default to the index of the dataframe.

      Optional Keyword Arguments:

      • "title" = (string) The title at the top of the chart. title can contain some html tags like  
        • <b></b><i></i> and <br>.
          note: not all html tags are supported.
      • "xlabel" = (string) the label for the x axis. xlabel can contain some html tags like  
        • <b></b><i></i> and <br>.
      • "ylabel" = (string) the label for the y axis. ylabel can contain some html tags like  
        • <b></b><i></i> and <br>.
      • "width" = Horizontal size (in px). Defaults to 800. Cannot be less than 400
      • "height" = Vertical size (in px) . Defaults to 800. Cannot be less than 300

      Secondary Y Axis:

      • "y2axis_name" = (string) the name of the secondary axis that will display on the right hand side of the chart. Adding this argument indicates that you want to add a second y axis. Without it no second axis will be created.
      • overlay_df = the data frame that you want to plot. Important Note this dataframe must have a column with the same name and format as the X axis in your original dataframe (df)
      • overlay_cols = [list of strings] this list of strings contains the names of the second Y axis data plots. example ["col1", "col2", "col3"]

      Studies:

      NOTE You must specify x_column to chart the studies, even if the x_column is timestamp

      • "study" = Name of the study - See list of supported studies
      • "study_columns" = column names list from the dataframe specified in "df" to perform the study upon in order. If you wish to show a study you must specify the study_column.
      • "timeperiod" = the number of time periods in the study

      • Supported Studies

                     "BBANDS": #Bollinger Bands (defaults to 5 time periods)
                     "MAMA": #MESA Adaptive Moving Average  (defaults to 5 time periods)
                     "DEMA": #Double Exponential Moving Average (defaults to 30 time periods)
                     "EMA": #Exponential Moving Average (defaults to 30 time periods)
                     "HT_TRENDLINE": #Hilbert Transform - Instantaneous Trendline (no time periods)
                     "KAMA": #Kaufman Adaptive Moving Average (defaults to 30 time periods)
                     "MA": #Moving average (defaults to 30 time periods)
                     "MIDPOINT": #MidPoint over period (defaults to 14 time periods)
                     "MIDPRICE": #Midpoint Price over period (defaults to 14 time periods)
                     "SAR": #Parabolic SAR  (no time periods)
                     "SAREXT": #Parabolic SAR - Extended  (no time periods)
                     "SMA": #Simple Moving Average (defaults to 30 time periods)
                     "T3": #Triple Exponential Moving Average (T3)  (no time periods)
                     "TEMA": #Triple Exponential Moving Average (defaults to 30 time periods)
                     "TRIMA": #Triangular Moving Average (defaults to 30 time periods)
                     "WMA": #Weighted Moving Average (defaults to 30 time periods)

         

      Line Chart Example

      import cloudquantcharting
      import liberator
      from datetime import datetime as dt
      res = liberator.query( symbols='AMZN', name='Twitter', back_to = '12/22/2020 08:00:00', as_of='12/22/2020 16:00:00')
      twitter_df = liberator.get_dataframe(res)
      fig = cloudquantcharting.LineChart(df=twitter_df, cols = ['s-score', 's-buzz', 's', 's-mean', 'sv-score','s-dispersion','s-buzz','s-delta'], x_column = "timestamp")
      fig.show()

      BarChart(**kwargs)

      The BarChart function will generate a bar chart that is branded with CloudQuant logo and colors.

      returns 

      plotly.graph_objs._figure.Figure

      Required Keyword Arguments:

      • "df" = the pandas data frame with the data you want to graph
      • "xcol" = (string) dataframe column name you wish to graph as the X Axis
        • note: of a value appears in the xcol more than once it will cause a stacked bar for that given value.
      • "ycol" = (string) dataframe column name you wish to graph as the Y Axis. This should be a numeric column

      Optional Keyword Arguments:

      • "title" = (string) The title at the top of the chart. title can contain some html tags like 
        • <b></b><i></i> and <br>. 
      • "xlabel" = (string) the label for the x axis. xlabel can contain some html tags like  
        • <b></b><i></i>
      • "ylabel" = (string) the label for the y axis. ylabel can contain some html tags like  
        • <b></b> and <i></i>
      • "width" = Horizontal size (in px). Defaults to 800. Cannot be less than 400
      • "height" = Vertical size (in px) . Defaults to 400. Cannot be less than 300
      • "singlecolor" = (bool) Should the bar chart be a single color (True or False). defaults to True.
      • "orientation" = 'v' or 'h'. Defaults to v as vertical.

      Barchart Example

      import liberator #CloudQuant Data Liberator
      import pandas as pd #Pandas data frames
      from liberator import charting #cloudquant charting
      res = liberator.query(name="daily_bars") # query liberator to get the latest daily data for a list of stocks
      df_bars = liberator.get_dataframe(res) # convert the results to a dataframe
      df_bars = df_bars.sort_values(by = ['volume'], ascending=[False]) # sort the market data by volume descending
      fig = charting.BarChart(df = df_bars[0:20], xcol = "symbol", ycol="spread", title="Spread Price of High Volume Stocks", width = 800, height = 400)# create a bar chart of the spread price of the top 20 (by volume) stocks
      fig.show()

       

      PieChart(**kwargs)

      The Pie function will generate a Pie chart that is branded with CloudQuant logo and colors.

      returns 

      plotly.graph_objs._figure.Figure

      Required Keyword Arguments:

      • "df" = the pandas data frame with the data you want to graph
      • "labelcol" = (string) dataframe column name you wish to graph
      • "valuecol" = (string) dataframe numeric column use for the pie slice size

      Optional Keyword Arguments:

      • "title" = (string) The title at the top of the chart. title can contain some html tags like 

        • <b></b><i></i> and <br>. #note: not all html tags are supported.

      • "width" = Horizontal size (in px). Defaults to 800. Cannot be less than 400

      • "height" = Vertical size (in px) . Defaults to 400. Cannot be less than 300

      • "hole" = size of the donut hole (between 0 and 1). 0.0 equals no donut hole.

      • "colors" = Color sequence to use. Defaults to CQ Color palette. Optionally one of ['reds', 'greens', 'blues']

      PieChart Example

      import liberator #CloudQuant Data Liberator
      import pandas as pd #Pandas data frames
      from liberator import charting #cloudquant charting
      #normally you would use liberator to query a data 
      values = [ ['Jan', '150000', 'USD'], ['Feb', '200000', 'USD'], ['Mar','330000', 'USD'], ['Apr', '440000', 'USD'],['May', '555000', 'USD'],['June', '355000', 'USD'], 
      ['July', '150000', 'USD'], ['Aug', '130000', 'USD'], ['Sept','330000', 'USD'], ['Oct', '440000', 'USD'], ['Nov', '130000', 'USD'], ['Dec','330000', 'USD']]
      df = pd.DataFrame(values, columns=["month", "sales", "currency"])
      title = "<B>Sales</b><br>by month" #titles can use some html tags to make them look nicer
      fig = charting.PieChart(df=df, labelcol='month', valuecol="sales",title = title, height = 700)
      fig.show()

       

       

      GroupedBarChart(**kwargs)

      The GroupedBarChart function will generate a grouped bar chart that is branded with CloudQuant logo and colors.

      returns 

      plotly.graph_objs._figure.Figure

      Required Keyword Arguments:

      • "df" = the pandas data frame with the data you want to graph
      • "groups" = (string) dataframe column name you wish to graph as the group bar.
      • "values" = (list) a list of dataframe column names you wish to graph as the values. This should be a numeric column

      Optional Keyword Arguments:

      • "title" = (string) The title at the top of the chart. title can contain some html tags like 
        <b></b><i></i> and <br> #Please note: not all html tags are supported.
      • "group_label" = (string) the glabel for the x axis. The group_label can contain some html tags like
      <b></b><i></i> and <br>.
      • "value_label" = (string) the label for the y axis. The value_label can contain some html tags like  
        • <b></b><i></i> and <br>.
      • "width" = Horizontal size (in px). Defaults to 800. Cannot be less than 400
      • "height" = Vertical size (in px) . Defaults to 400. Cannot be less than 300
      • "colors" = A list of colors. Bars' color would iterate the list as a circle. Default set cq_colors list.
      • "orientation" = 'v' or 'h'. Defaults to v as vertical.

      Example GroupedBarChart

      import liberator #CloudQuant Data Liberator
      import pandas as pd #Pandas data frames
      from liberator import charting #cloudquant charting
      dataset_name = 'reddit_wallstreetbets_comments'
      #get the last known values for WSB
      df = liberator.get_dataframe(liberator.query(name = dataset_name))
      df = df.loc[df['vader_body_sentiment_compound'] > 0]
      # sort the dataframe
      df = df.sort_values(by = ['vader_body_sentiment_compound'], ascending=[False])
      #chart the top 5 sentiment scores
      charting.GroupedBarChart(df=df[0:5], groups="symbol", 
                               values = ["vader_body_sentiment_pos",    "vader_body_sentiment_compound", "textblob_body_sentiment_subjectivity"],
                              title="WS Bets Sentiment", group_label = "Symbol", value_label = "sentiment score")

      grouped_bar

       

      ScatterPlot (**kwargs)

      The ScatterPlot function will generate a scatter plot chart that is branded with CloudQuant logo and colors.

      returns 

      plotly.graph_objs._figure.Figure

      Required Keyword Arguments

      • "df" = the pandas data frame with the data you want to graph
      • "x_column" = (string) dataframe numeric column name you wish to graph on the horizontal axis
      • "y_column" = (string) or (list) of strings with the name of the dataframe numeric column name you wish to graph on the vertical axis

      Optional Keyword Arguments

      • "title" = (string) The title at the top of the chart. title can contain some html tags like  
        • <b></b><i></i> and <br> # Please note: not all html tags are supported.
      • "width" = Horizontal size (in px). Defaults to 800. Cannot be less than 400
      • "height" = Vertical size (in px) . Defaults to 400. Cannot be less than 300
      • "xlabel" = the text to appear with the horizontal axis
      • "ylabel" = the text to appear with the vertical axis
      • "size_column" = the numerical column that will indicate the size of the scatter plot bubble. If the size_column contains negative values then the bubbles will color green and red for positive and negative.
      • "size_multiplier" = numeric multiplier to change the size of the bubble.
        • 💡 Important note, use the size_multiplier to set different sizes of the bubbles. Often the bubbles will be too small or fill the whole chart space. The size_multiplier can be used to correct this problem. 

      Example ScatterPlot

      import liberator #CloudQuant Data Liberator
      import pandas as pd #Pandas data frames
      from liberator import charting #cloudquant charting
      dataset_name = 'reddit_wallstreetbets_comments'
      #get the last known values for WSB
      df = liberator.get_dataframe(liberator.query(name = dataset_name))
      df = df.loc[df['vader_body_sentiment_compound'] > 0]
      # sort the dataframe
      df = df.sort_values(by = ['vader_body_sentiment_compound'], ascending=[False])
      #plot the scatter plot
      fig = charting.ScatterPlot(df=df[0:200], x_column = "textblob_body_sentiment_subjectivity",  y_column = ['vader_body_sentiment_neu'],
                      title="WS Bets Sentiment" ,size_multiplier = .15, width = 800, height = 800   )
      fig.show()

      scatter

       

       

      addNotes(thefig, notes)

      addNotes will add a callout to the chart that allows you to highlight 1:N interesting points on the chart
       

      returns 

      plotly.graph_objs._figure.Figure

      required arguments:

      • thefig: a figure that has been returned by one of the CloudQuant Charting functions.
      • notes: an list of dictionary items that contain the following:
              {'x':your_x_coordinate,

              'y':your_x_coordinate, 

              'note':your_note_in_string_format,

              }

      example addNotes

      ###############################################################################
      # Import CloudQuant Data Liberator™ and query market data and then display the market data in a 
      # Candlestick chart using cloudquantcharting 
      #
      import liberator #CloudQuant Data Liberator
      import pandas as pd #Pandas data frames
      from liberator import charting #cloudquant charting
      from datetime import datetime
      from datetime import timedelta
      back_to = (datetime.now() + timedelta(days=-2) ).strftime('%Y-%m-%d 23:59:59') #get the start date for N days ago
      symbol = "AAPL"
      # query liberator to get the market data for our symbol for the last N days
      res = liberator.query(name="minute_bars", symbols=symbol, back_to=back_to) 
      # convert the results to a dataframe
      df_bars = liberator.get_dataframe(res)
      # chart the market data
      fig = charting.Candlestick(df=df_bars, title=symbol)


      mydata = []
      data = {'x':df_bars.timestamp[7],'y':df_bars.open[7], 'note':"T1"}
      mydata.append(data)

      data = {'x':df_bars.timestamp[155],'y':df_bars.open[155], 'note':"T2"}
      mydata.append(data)

      data = {'x':df_bars.timestamp[1000],'y':df_bars.open[1000], 'note':"T3"}
      mydata.append(data)

      fig = charting.addNotes(fig, mydata)
      fig.show()

      addnotes