first init

portfolioPerf.sln

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+
+Microsoft Visual Studio Solution File, Format Version 12.00
+# Visual Studio 15
+VisualStudioVersion = 15.0.27130.2003
+MinimumVisualStudioVersion = 10.0.40219.1
+Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "portfolioPerf", "portfolioPerf\portfolioPerf.pyproj", "{A9A2E519-01E9-4960-A2D4-6B01ED7DE4E1}"
+EndProject
+Global
+	GlobalSection(SolutionConfigurationPlatforms) = preSolution
+		Debug|Any CPU = Debug|Any CPU
+		Release|Any CPU = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(ProjectConfigurationPlatforms) = postSolution
+		{A9A2E519-01E9-4960-A2D4-6B01ED7DE4E1}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{A9A2E519-01E9-4960-A2D4-6B01ED7DE4E1}.Release|Any CPU.ActiveCfg = Release|Any CPU
+	EndGlobalSection
+	GlobalSection(SolutionProperties) = preSolution
+		HideSolutionNode = FALSE
+	EndGlobalSection
+	GlobalSection(ExtensibilityGlobals) = postSolution
+		SolutionGuid = {DFA9180E-8E80-42C4-8516-5EF179715B1C}
+	EndGlobalSection
+EndGlobal

portfolioPerf/currencies.csv

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+asset,currency
+AT0000A18XM4 SW,CHF
+BE0974268972 BB,EUR
+US0527691069 US,USD
+DE0007164600 GR,EUR
+US6092071058 US,USD

portfolioPerf/portfolioPerf.py

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+import numpy as np
+import pandas as pd
+import datetime
+
+
+weights = pd.read_csv('weights.csv',sep=',', encoding='utf8', parse_dates=['date'], dayfirst=False, index_col='date')
+prices = pd.read_csv('prices.csv',sep=',', encoding='utf8', parse_dates=['date'], dayfirst=False, index_col='date')
+currencies = pd.read_csv('currencies.csv',sep=',', encoding='utf8', dayfirst=False, index_col='asset')
+exchanges = pd.read_csv('exchanges.csv',sep=',', encoding='utf8', parse_dates=['date'], dayfirst=False, index_col='date')
+
+
+class PortfolioPerformance:
+    def calculate_asset_performance(start_date, end_date):
+        start_date = pd.Timestamp(start_date)
+        end_date = pd.Timestamp(end_date)
+        next_start_date = start_date + datetime.timedelta(days=1)                    
+        
+        price = prices[start_date : end_date]           
+        weight = weights[next_start_date : end_date]
+        
+        price = price.fillna(method='pad')              
+        price = price.fillna(method='bfill')
+        weight = weight.fillna(method='pad')
+        weight = weight.fillna(method='bfill')
+
+        Ri = pd.DataFrame(columns = price.columns)
+        P = pd.Series(1., index = pd.date_range(start_date, end_date))
+        
+        for i in range(len(price) - 1):                 
+            Ri.loc[i] = price.apply(lambda row: (row.iloc[i+1] - row.iloc[i]) / row.iloc[i])         
+        Ri.index = pd.date_range(next_start_date, end_date)
+        
+        Ri = Ri.mul(weight)                             
+        R = Ri.sum(axis=1)
+
+        for i in range(len(P) - 1):
+            P.iloc[i+1] = P.iloc[i]*(1+R.iloc[i])
+
+        print("Asset Performance:"+ '\n',P)
+        return (P)
+
+
+    def calculate_currency_performance(start_date, end_date):
+        start_date = pd.Timestamp(start_date)
+        end_date = pd.Timestamp(end_date)
+        next_start_date = start_date + datetime.timedelta(days=1)
+
+        exchange = exchanges[start_date : end_date]
+        weight = weights[next_start_date : end_date]
+
+        exchange = exchange.fillna(method='pad')
+        exchange = exchange.fillna(method='bfill')
+        weight = weight.fillna(method='pad')
+        weight = weight.fillna(method='bfill')
+        
+        ex = pd.DataFrame(columns = currencies.index)
+        CRi = pd.DataFrame(columns = currencies.index)
+        CP = pd.Series(1., index = pd.date_range(start_date, end_date))
+
+        for i in range(len(exchange.columns)):
+            for j in range(len(currencies)):
+                if ((exchange.columns[i] == currencies.iloc[j]).bool()):
+                    ex.iloc[ :, j] = exchange.iloc[ :, i]
+
+        for i in range(len(exchange) - 1):
+            CRi.loc[i] = ex.apply(lambda row: (row.iloc[i+1] - row.iloc[i]) / row.iloc[i])
+        CRi.index = pd.date_range(next_start_date, end_date)
+        
+        CRi = CRi.mul(weight)
+        CR = CRi.sum(axis = 1)
+
+        for i in range(len(CP) - 1):
+            CP.iloc[i+1] = CP.iloc[i] * (1 + CR.iloc[i])
+
+        print("Currency Performance:"+ '\n', CP)
+        return (CP)
+
+    def calculate_total_performance(start_date, end_date):
+        start_date = pd.Timestamp(start_date)
+        end_date = pd.Timestamp(end_date)
+        next_start_date = start_date + datetime.timedelta(days=1)
+
+        price = prices[start_date : end_date]
+        exchange = exchanges[start_date : end_date]
+        weight = weights[next_start_date : end_date]
+        
+        price = price.fillna(method='pad')
+        price = price.fillna(method='bfill')
+        exchange = exchange.fillna(method='pad')
+        exchange = exchange.fillna(method='bfill')
+        weight = weight.fillna(method='pad')
+        weight = weight.fillna(method='bfill')
+
+        ex = pd.DataFrame(columns = currencies.index)
+        TRi = pd.DataFrame(columns = currencies.index)
+        TP = pd.Series(1., index = pd.date_range(start_date, end_date))
+
+        for i in range(len(exchange.columns)):
+            for j in range(len(currencies)):
+                if ((exchange.columns[i] == currencies.iloc[j]).bool()):
+                    ex.iloc[ :, j] = exchange.iloc[ :, i]
+        ex = ex.mul(price)
+
+        for i in range(len(exchange) - 1):
+            TRi.loc[i] = ex.apply(lambda row: (row.iloc[i+1] - row.iloc[i]) / row.iloc[i])
+        TRi.index = pd.date_range(next_start_date, end_date)
+        
+        TRi = TRi.mul(weight)
+        TR = TRi.sum(axis = 1)
+
+        for i in range(len(TP) - 1):
+            TP.iloc[i+1] = TP.iloc[i] * (1 + TR.iloc[i])
+
+        print("Total Performance:"+ '\n', TP)
+        return (TP)

portfolioPerf/portfolioPerf.pyproj

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+<Project DefaultTargets="Build" xmlns="http://schemas.microsoft.com/developer/msbuild/2003" ToolsVersion="4.0">
+  <PropertyGroup>
+    <Configuration Condition=" '$(Configuration)' == '' ">Debug</Configuration>
+    <SchemaVersion>2.0</SchemaVersion>
+    <ProjectGuid>a9a2e519-01e9-4960-a2d4-6b01ed7de4e1</ProjectGuid>
+    <ProjectHome>.</ProjectHome>
+    <StartupFile>portfolioPerf.py</StartupFile>
+    <SearchPath>
+    </SearchPath>
+    <WorkingDirectory>.</WorkingDirectory>
+    <OutputPath>.</OutputPath>
+    <Name>portfolioPerf</Name>
+    <RootNamespace>portfolioPerf</RootNamespace>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)' == 'Debug' ">
+    <DebugSymbols>true</DebugSymbols>
+    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
+  </PropertyGroup>
+  <PropertyGroup Condition=" '$(Configuration)' == 'Release' ">
+    <DebugSymbols>true</DebugSymbols>
+    <EnableUnmanagedDebugging>false</EnableUnmanagedDebugging>
+  </PropertyGroup>
+  <ItemGroup>
+    <Compile Include="portfolioPerf.py" />
+    <Compile Include="test_portfolioPerf.py">
+      <SubType>Code</SubType>
+    </Compile>
+  </ItemGroup>
+  <Import Project="$(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets" />
+  <!-- Uncomment the CoreCompile target to enable the Build command in
+       Visual Studio and specify your pre- and post-build commands in
+       the BeforeBuild and AfterBuild targets below. -->
+  <!--<Target Name="CoreCompile" />-->
+  <Target Name="BeforeBuild">
+  </Target>
+  <Target Name="AfterBuild">
+  </Target>
+</Project>

portfolioPerf/test_portfolioPerf.py

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+import unittest
+import pandas as pd
+from portfolioPerf import PortfolioPerformance
+
+class Test_test_portfolioPerf(unittest.TestCase):
+    def test_asset_performance(self):
+        P = pd.Series([1.000000, 1.007755, 1.015940, 1.019302], index = pd.date_range('20140113', '20140116'))
+        self.assertTrue((PortfolioPerformance.calculate_asset_performance('20140113', '20140116') == P).bool)
+
+    def test_currency_performance(self):
+        CP = pd.Series([1.000000, 0.999568, 0.997234, 0.997369], index = pd.date_range('20140113', '20140116'))
+        self.assertTrue((PortfolioPerformance.calculate_currency_performance('20140113', '20140116') == CP).bool)
+
+    def test_total_performance(self):
+        TP = pd.Series([1.000000, 0.999568, 0.997234, 0.997369], index = pd.date_range('20140113', '20140116'))
+        self.assertTrue((PortfolioPerformance.calculate_total_performance('20140113', '20140116') == TP).bool)
+
+if __name__ == '__main__':
+    unittest.main()