Developing the Sector Allocation Chart

In this example we look at how to create the Sector Allocation chart. We will be using the DEMO_HoldingsDetails Block for this purpose (refer the Transformation Block sample at the end). Below guideline shows how to create the Data Object and fabricate the Asset Allocation Pie Chart.

• Give an appropriate name for the Data Object

  

• Set your Source Data Block

  

• Apply Data Settings one by one

  

• Set the Column names one by one as needed and we only need below 3 Columns

  

• Set the Grouping as shown below

  

• Set the Summary as shown below

  

Fabricating the table (2 Columns by 3 Rows table structure) 

Fabricating the Sector Allocation Pie Chart

Insert a native PowerPoint Pie Chart and fabricate as shown below

Sample Python Transformation Block (DEMO_HoldingsDetails)  

object_blueprint= {"PORTFOLIOCODE":"",

                "CURRENCYCODE":"",

                "CURRENCY":"",

                "ISSUERNAME":"",

                "ISSUENAME":"",

                "ISSUEDISPLAYNAME":"",

                "COSTBASIS":"",

                "QUANTITY":"",

                "MARKETVALUEWITHOUTACCRUEDINCOME":"",

                "ACCRUEDINCOME":"",

                "MARKETVALUE":"",

                "LOCALMARKETVALUE":"",

                "UNREALIZEDGAINSLOSSES":"",

                "DIVIDENDYIELD":"",

                "ESTIMATEDANNUALINCOME":"",

                "HOLDINGSPERIOD":"",

                "PORTFOLIOWEIGHT":"",

                "PRICE":"",

                "BOOKVALUE":"",

                "LOCALBOOKVALUE":"",

                "ASSETCLASSNAME":"",

                "ISSUETYPE":"",

                "ISINCODE":"",

                "CUSIP":"",

                "FIGIID":"",

                "TICKER":"",

                "RISKCOUNTRYCODE":"",

                "RISKCOUNTRY":"",

                "HQCOUNTRYCODE":"",

                "HQCOUNTRY":"",

                "ISSUECOUNTRYCODE":"",

                "ISSUECOUNTRY":"",

                "REGIONCLASSIFICATIONSCHEME":"",

                "REGIONNAME":"",

                "PRIMARYSECTORSCHEME":"",

                "PRIMARYSECTORNAME":"",

                "PRIMARYSUBSECTORNAME":"",

                "PRIMARYINDUSTRYGROUPNAME":"",

                "PRIMARYINDUSTRYNAME":"",

                "PRIMARYSUBINDUSTRYNAME":"",

                "SECONDARYSECTORSCHEME":"",

                "SECONDARYSECTORNAME":"",

                "SECONDARYSUBSECTORNAME":"",

                "SECONDARYINDUSTRYGROUPNAME":"",

                "SECONDARYINDUSTRYNAME":"",

                "SECONDARYSUBINDUSTRYNAME":"",

                "CUSTOMCLASSIFICATION1":"",

                "CUSTOMCLASSIFICATION1NAME":"",

                "PERCENTAGE":"",

                "UNITCOST":""

                }

AccountCode = ""

AsofDate = ""

CurrencyCode=""

SectorScheme=""

WithCash=""

ShowCash=""

if (params.__len__() > 0):

    AccountCode = params["AccountCode"]

    AsofDate = params["AsofDate"]

    CurrencyCode=params["CurrencyCode"]

    SectorScheme=params["SectorScheme"]

    WithCash=params["WithCash"]

    ShowCash=params["ShowCash"]


basedata_df = pd.DataFrame(read("DEMO_SnowFlakeHoldingsDetailsExtract",{"AccountCode": AccountCode, "AsofDate": AsofDate, "CurrencyCode":CurrencyCode, "SectorScheme":SectorScheme})["data"])

currencycode=read("DEMO_SnowflakeGetPortfolioBaseCurrency",{"AccountCode": AccountCode})["data"]

baseCurrencyCode=currencycode[0]["BASECURRENCYCODE"]

baseCurrencyName=currencycode[0]["BASECURRENCYNAME"]

# Include or Exclude Cash

# data_df= pd.DataFrame()


if WithCash== "yes":

    data_df=basedata_df

else:

    data_df = basedata_df.loc[basedata_df['ASSETCLASSNAME'] != 'Cash and Equiv.'].reset_index()   

# Convert String Market Value into Float

data_df["MARKETVALUE"] = data_df["MARKETVALUE"].astype(float)

data_df["COSTBASIS"] = data_df["COSTBASIS"].astype(float)

data_df["QUANTITY"] = data_df["QUANTITY"].astype(float)


# calculate total marketvalue

# totalMV=0.00

total_mv = data_df["MARKETVALUE"].sum()

data_df['TotalMV']=total_mv

data_df['PERCENTAGE'] = data_df.apply(lambda row: row["MARKETVALUE"]/row["TotalMV"], axis=1)

data_df['UNITCOST'] = data_df.apply(lambda row: row["COSTBASIS"]/row["QUANTITY"], axis=1)

 
if WithCash == "yes" and ShowCash != "yes":

    data_df = data_df.loc[data_df['ASSETCLASSNAME'] != 'Cash and Equiv.']


del_column_list = []

for col_name in data_df.columns:

    if col_name not in object_blueprint.keys():

        del_column_list.append(col_name)

data_df = data_df.drop(del_column_list, axis=1)

output_list = data_df.to_dict(orient='records')

# Add output paramets
outputValue = dict()

outputValue["BaseCurrencyCode"] =  baseCurrencyCode

outputValue["BaseCurrencyName"] = baseCurrencyName


response["data"] = output_list

response["values"]= outputValue