diff --git a/.github/workflows/SonarCloud.yml b/.github/workflows/SonarCloud.yml deleted file mode 100644 index 93975f32..00000000 --- a/.github/workflows/SonarCloud.yml +++ /dev/null @@ -1,30 +0,0 @@ -name: SonarCloud analysis -on: - push: - pull_request: - workflow_dispatch: - -permissions: - pull-requests: read # allows SonarCloud to decorate PRs with analysis results - -jobs: - Analysis: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - with: - fetch-depth: 0 # Shallow clones should be disabled for a better relevancy of analysis - - - name: Analyze with SonarCloud - uses: SonarSource/sonarcloud-github-action@v2.0.2 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # Needed to get PR information - SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }} # Generate a token on Sonarcloud.io, add it to the secrets of this repo with the name SONAR_TOKEN - with: - # Additional arguments for the SonarScanner CLI - args: > - -Dsonar.projectKey=mihakralj_QuanTAlib - -Dsonar.organization=mihakralj - -Dsonar.sources=. - -Dsonar.verbose=false diff --git a/.github/workflows/main_automation.yml b/.github/workflows/main_automation.yml index f8450eee..e45f7eb5 100644 --- a/.github/workflows/main_automation.yml +++ b/.github/workflows/main_automation.yml @@ -1,137 +1,174 @@ -name: CI/CD Pipeline - +name: Stage/build/test/release/publish on: workflow_dispatch: push: - branches: [main, dev] + branches: + - main pull_request: - branches: [main, dev] - -env: - DOTNET_VERSION: '8.0.x' - JAVA_VERSION: '11' - JAVA_DISTRIBUTION: 'zulu' + branches: + - main jobs: - build_test_publish: + build_test: + #runs-on: windows-latest runs-on: ubuntu-latest steps: - - uses: actions/checkout@v3 + - name: Checkout + uses: actions/checkout@v3 with: fetch-depth: 0 - - name: Setup environment +############## Install tools + + - name: Create Quantower folder at root run: | sudo mkdir -p /Quantower/ sudo chmod -R 777 /Quantower - - name: Setup .NET + - name: Install .NET uses: actions/setup-dotnet@v3 with: - dotnet-version: ${{ env.DOTNET_VERSION }} + dotnet-version: '8.x' + dotnet-quality: 'preview' - - name: Setup Java - uses: actions/setup-java@v3 + - name: Install GitVersion + uses: gittools/actions/gitversion/setup@v0 with: - java-version: ${{ env.JAVA_VERSION }} - distribution: ${{ env.JAVA_DISTRIBUTION }} - - - name: Install tools - run: | - dotnet tool install --global GitVersion.Tool - dotnet tool install --global dotnet-sonarscanner --version 8.0.3 - dotnet tool install --global dotnet-coverage + versionSpec: '6.x' + includePrerelease: true - name: Determine Version - id: gitversion - run: | - dotnet-gitversion - echo "::set-output name=MajorMinorPatch::$(dotnet-gitversion /output json /showvariable MajorMinorPatch)" - echo "::set-output name=FullSemVer::$(dotnet-gitversion /output json /showvariable FullSemVer)" - echo "::set-output name=SemVer::$(dotnet-gitversion /output json /showvariable SemVer)" + id: gitversion + uses: gittools/actions/gitversion/execute@v0 + with: + useConfigFile: true + #configFilePath: GitVersion.yml + updateAssemblyInfo: true - - name: Start Sonar analysis - if: github.ref == 'refs/heads/dev' +############## Install more tools + + - name: Install JDK11 for Sonar Scanner + uses: actions/setup-java@v3 + with: + java-version: 11 + distribution: 'zulu' + + - name: Install JetBrains + run: dotnet tool install JetBrains.dotCover.GlobalTool --global + - name: Install Sonar Scanner + run: dotnet tool install dotnet-sonarscanner --global + - name: Install dotnet-coverage + run: dotnet tool install dotnet-coverage --global + + - name: Sonar start env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }} + run: dotnet sonarscanner begin /o:"mihakralj" /k:"mihakralj_QuanTAlib" + /d:sonar.login="${{ secrets.SONAR_TOKEN }}" + /d:sonar.host.url="https://sonarcloud.io" + /d:sonar.cs.dotcover.reportsPaths=dotcover* + +############# Build and test + + - name: Build Main branch of QuanTAlib DLL + if: ${{ github.ref != 'refs/heads/dev' }} + run: dotnet build ./lib/quantalib.csproj --configuration Release --nologo -p:PackageVersion=${{ steps.gitversion.outputs.MajorMinorPatch }} + - name: Build dev branch of QuanTAlib DLL + if: ${{ github.ref == 'refs/heads/dev' }} + run: dotnet build ./lib/quantalib.csproj --configuration Release --nologo -p:PackageVersion=${{ steps.gitversion.outputs.FullSemVer }} + - name: Build Averages DLL + run: dotnet build ./quantower/Averages/Averages.csproj --configuration Release --nologo + - name: Build Statistics DLL + run: dotnet build ./quantower/Statistics/Statistics.csproj --configuration Release --nologo + - name: Build SyntheticVendor DLL + run: dotnet build ./SyntheticVendor/SyntheticVendor.csproj --configuration Release --nologo + + - name: DotCover Test HTML + if: ${{ github.ref == 'refs/heads/dev' }} + run: dotnet dotcover test tests/tests.csproj --dcReportType=HTML --dcoutput=./dotcover.html + - name: DotCover Test XML + if: ${{ github.ref == 'refs/heads/dev' }} + run: dotnet dotcover test tests/tests.csproj --dcReportType=DetailedXML --dcoutput=./dotcover.xml --verbosity=Detailed + - name: Coverlet Test + if: ${{ github.ref == 'refs/heads/dev' }} + run: dotnet test -p:CollectCoverage=true --collect:"XPlat Code Coverage" --results-directory "./" + +############## Report to Sonar/CodeCov/Codacy + + - name: Move coverage report to project root + if: ${{ github.ref == 'refs/heads/dev' }} run: | - dotnet sonarscanner begin /o:"mihakralj" /k:"mihakralj_QuanTAlib" \ - /d:sonar.token="${{ secrets.SONAR_TOKEN }}" \ - /d:sonar.scanner.scanAll=false \ - /d:sonar.host.url="https://sonarcloud.io" \ - /d:sonar.cs.opencover.reportsPaths="**/TestResults/coverage.opencover.xml" \ - /d:sonar.cs.vscoveragexml.reportsPaths=coverage.xml + report=$(find . -name '*coverage.cobertura.xml' | head -1) + mv "$report" ./coverage.cobertura.xml - - - name: Build projects - run: | - dotnet build ./lib/quantalib.csproj --configuration Release --nologo \ - -p:PackageVersion=${{ github.ref == 'refs/heads/dev' && steps.gitversion.outputs.FullSemVer || steps.gitversion.outputs.MajorMinorPatch }} - dotnet build ./quantower/Averages/Averages.csproj --configuration Release --nologo - dotnet build ./quantower/Statistics/Statistics.csproj --configuration Release --nologo - dotnet build ./SyntheticVendor/SyntheticVendor.csproj --configuration Release --nologo - - - name: Run tests with coverage - if: github.ref == 'refs/heads/dev' - run: | - dotnet test --verbosity normal /p:CollectCoverage=true /p:CoverletOutputFormat=opencover /p:CoverletOutput="./TestResults/" - dotnet-coverage collect "dotnet test" -f xml -o "coverage.xml" - - - name: Generate and process coverage report - if: github.ref == 'refs/heads/dev' - uses: danielpalme/ReportGenerator-GitHub-Action@5.1.26 - with: - reports: '**/TestResults/coverage.opencover.xml' - targetdir: 'coveragereport' - reporttypes: 'HtmlInline;Cobertura' - - - name: Upload coverage to Codacy - if: github.ref == 'refs/heads/dev' + - name: Upload to Codacy + if: ${{ github.ref == 'refs/heads/dev' }} uses: codacy/codacy-coverage-reporter-action@v1 with: project-token: ${{ secrets.CODACY_PROJECT_TOKEN }} - coverage-reports: "coveragereport/Cobertura.xml" + coverage-reports: "*cover*.xml" - - name: Upload coverage to Codecov - if: github.ref == 'refs/heads/dev' + - name: Upload to Codecov + if: ${{ github.ref == 'refs/heads/dev' }} uses: codecov/codecov-action@v3 with: - files: coveragereport/Cobertura.xml + files: cover* verbose: true - - name: Finish Sonar analysis - if: github.ref == 'refs/heads/dev' + - name: Upload to Sonar + if: ${{ github.ref == 'refs/heads/dev' }} env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }} run: dotnet sonarscanner end /d:sonar.login="${{ secrets.SONAR_TOKEN }}" - - name: Publish release assets +############## Publish dev release + + - name: Publish dev release assets + if: ${{ github.ref == 'refs/heads/dev' }} uses: SourceSprint/upload-multiple-releases@1.0.7 env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} with: - prerelease: ${{ github.ref == 'refs/heads/dev' }} + prerelease: true overwrite: true - release_name: ${{ github.ref == 'refs/heads/dev' && steps.gitversion.outputs.SemVer || steps.gitversion.outputs.MajorMinorPatch }} - tag_name: ${{ steps.gitversion.outputs.SemVer }} + release_name: ${{ steps.gitversion.outputs.SemVer }} + tag_name: prerelease release_config: | + lib/bin/Release/QuanTAlib.dll quantower/Averages/bin/Release/Averages.dll quantower/Statistics/bin/Release/Statistics.dll SyntheticVendor/bin/Release/SyntheticVendor.dll - - name: Push package to myget.org - run: | - dotnet nuget push 'lib/bin/Release/QuanTAlib.*.nupkg' \ - --api-key ${{ secrets.MYGET_DEPLOY_KEY_QUANTALIB }} \ - --source https://www.myget.org/F/quantalib/api/v2/package \ + - name: Push package to myget.org + run: dotnet nuget push 'lib/bin/Release/QuanTAlib.*.nupkg' + --api-key ${{ secrets.MYGET_DEPLOY_KEY_QUANTALIB }} + --source https://www.myget.org/F/quantalib/api/v2/package + --skip-duplicate + +############## Publish main release + + - name: Publish main release assets + if: ${{ github.ref == 'refs/heads/main' }} + uses: SourceSprint/upload-multiple-releases@1.0.7 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + prerelease: false + overwrite: true + release_name: ${{ steps.gitversion.outputs.MajorMinorPatch }} + tag_name: latest + release_config: | + lib/bin/Release/QuanTAlib.dll + quantower/Averages/bin/Release/Averages.dll + quantower/Statistics/bin/Release/Statistics.dll + SyntheticVendor/bin/Release/SyntheticVendor.dll + + - name: Push package to nuget.org + if: ${{ github.ref == 'refs/heads/main' }} + run: dotnet nuget push 'lib/bin/Release/QuanTAlib.*.nupkg' + --api-key ${{ secrets.NUGET_DEPLOY_KEY_QUANTLIB }} + --source https://api.nuget.org/v3/index.json --skip-duplicate - - name: Push package to nuget.org - if: github.ref == 'refs/heads/main' - run: | - dotnet nuget push 'lib/bin/Release/QuanTAlib.*.nupkg' \ - --api-key ${{ secrets.NUGET_DEPLOY_KEY_QUANTLIB }} \ - --source https://api.nuget.org/v3/index.json \ - --skip-duplicate \ No newline at end of file diff --git a/.github/workflows/sonarcloud.yml b/.github/workflows/sonarcloud.yml deleted file mode 100644 index 93975f32..00000000 --- a/.github/workflows/sonarcloud.yml +++ /dev/null @@ -1,30 +0,0 @@ -name: SonarCloud analysis -on: - push: - pull_request: - workflow_dispatch: - -permissions: - pull-requests: read # allows SonarCloud to decorate PRs with analysis results - -jobs: - Analysis: - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v3 - with: - fetch-depth: 0 # Shallow clones should be disabled for a better relevancy of analysis - - - name: Analyze with SonarCloud - uses: SonarSource/sonarcloud-github-action@v2.0.2 - env: - GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # Needed to get PR information - SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }} # Generate a token on Sonarcloud.io, add it to the secrets of this repo with the name SONAR_TOKEN - with: - # Additional arguments for the SonarScanner CLI - args: > - -Dsonar.projectKey=mihakralj_QuanTAlib - -Dsonar.organization=mihakralj - -Dsonar.sources=. - -Dsonar.verbose=false diff --git a/.gitignore b/.gitignore index 97c271bd..dc4496da 100644 --- a/.gitignore +++ b/.gitignore @@ -395,7 +395,4 @@ FodyWeavers.xsd *.msp # JetBrains Rider -*.sln.iml -*.dcvr - -dotcover.html +*.sln.iml \ No newline at end of file diff --git a/.sonarlint/QuanTAlib.json b/.sonarlint/QuanTAlib.json deleted file mode 100644 index 482e31c0..00000000 --- a/.sonarlint/QuanTAlib.json +++ /dev/null @@ -1,4 +0,0 @@ -{ - "SonarCloudOrganization": "mihakralj", - "ProjectKey": "mihakralj_QuanTAlib" -} \ No newline at end of file diff --git a/Directory.Build.props b/Directory.Build.props index 48962b26..5d40b927 100644 --- a/Directory.Build.props +++ b/Directory.Build.props @@ -7,38 +7,31 @@ true preview false + false true AnyCPU False bin\$(Configuration)\ + False full true true true snupkg + AnyCPU true - - - true - true - true - true - true - false - true - true - - - - all - runtime; build; native; contentfiles; analyzers - + + + all + runtime; build; native; contentfiles; analyzers + D:\Quantower $([System.IO.Directory]::GetDirectories("$(QuantowerRoot)\TradingPlatform", "v1*")[0]) + \ No newline at end of file diff --git a/QuanTAlib.sln b/QuanTAlib.sln index a6ac4ee3..44dd6e71 100644 --- a/QuanTAlib.sln +++ b/QuanTAlib.sln @@ -1,13 +1,13 @@ - + Microsoft Visual Studio Solution File, Format Version 12.00 # Visual Studio Version 17 VisualStudioVersion = 17.0.31903.59 MinimumVisualStudioVersion = 10.0.40219.1 Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "quantalib", "lib\quantalib.csproj", "{584E06A9-CEB4-476A-85CC-6A8FF3974AE2}" EndProject -Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Tests", "Tests\Tests.csproj", "{D85FEBB4-B651-466F-85CC-FD902378D4D2}" +Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "tests", "tests\tests.csproj", "{D85FEBB4-B651-466F-85CC-FD902378D4D2}" EndProject -Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MovingAverages", "quantower\Averages\Averages.csproj", "{32CC09CC-26E3-4FCE-8932-C0513C4AD766}" +Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MovingAverages", "quantower\averages\Averages.csproj", "{32CC09CC-26E3-4FCE-8932-C0513C4AD766}" EndProject Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "SyntheticVendor", "SyntheticVendor\SyntheticVendor.csproj", "{20B1B5F1-8C36-4668-B0AE-951C13AE197B}" EndProject @@ -49,4 +49,3 @@ Global {B6D3EB11-63B6-430F-B526-E1981B3D8214} = {A8D9AE68-24E3-476C-BB98-244541BB4B43} EndGlobalSection EndGlobal - diff --git a/SyntheticVendor/SyntheticVendor.cs b/SyntheticVendor/SyntheticVendor.cs index a770055d..58ad336b 100644 --- a/SyntheticVendor/SyntheticVendor.cs +++ b/SyntheticVendor/SyntheticVendor.cs @@ -1,7 +1,6 @@ using System; using System.Collections.Generic; using System.Threading; -using System.Security.Cryptography; using TradingPlatform.BusinessLayer; using TradingPlatform.BusinessLayer.Integration; @@ -74,50 +73,31 @@ namespace SyntheticVendorNamespace CreateMessageSymbol("W17", "2 Geometric Brownian motion", "QT", "USD", SymbolType.Synthetic) }; - /* - Bond, - CFD, - Crypto, - Debentures, - Equities, - ETF, - FixedIncome, - Forex, - Forward, - Futures, - Indexes, - Options, - Spot, - Synthetic, - Swap, - Warrants, +/* + Bond, + CFD, + Crypto, + Debentures, + Equities, + ETF, + FixedIncome, + Forex, + Forward, + Futures, + Indexes, + Options, + Spot, + Synthetic, + Swap, + Warrants, - */ +*/ } - public static VendorMetaData GetVendorMetaData() - { - return new VendorMetaData - { - VendorName = "Synthetic Vendor", - VendorDescription = "A synthetic vendor for testing and demonstration purposes", - GetDefaultConnections = () => - { - var defaultConnection = Vendor.CreateDefaultConnectionInfo( - "Synthetic Connection", - "Synthetic Vendor", - "", // Replace with actual path if you have a logo - allowCreateCustomConnections: true - ); - return new List { defaultConnection }; - } - }; - } - - private static MessageSymbol CreateMessageSymbol( + private MessageSymbol CreateMessageSymbol( string id, string name, string exchangeId, @@ -147,7 +127,27 @@ namespace SyntheticVendorNamespace return messageSymbol; } - private static MessageSymbol CreateMessageSymbol(string id, string name, string exchangeId, string assetId, SymbolType type) + public static VendorMetaData GetVendorMetaData() + { + return new VendorMetaData() + { + VendorName = "Synthetic Vendor", + VendorDescription = "A synthetic vendor for testing and demonstration purposes", + GetDefaultConnections = () => + { + var defaultConnection = Vendor.CreateDefaultConnectionInfo( + "Synthetic Connection", + "Synthetic Vendor", + "", // Replace with actual path if you have a logo + allowCreateCustomConnections: true + ); + return new List { defaultConnection }; + } + }; + } + + + private MessageSymbol CreateMessageSymbol(string id, string name, string exchangeId, string assetId, SymbolType type) { return new MessageSymbol(id) { @@ -173,26 +173,26 @@ namespace SyntheticVendorNamespace LotStep = 0.01, MaxLot = 1000000, SymbolType = type - /* - SymbolType.Unknown, - [EnumMember] Forex, - [EnumMember] Equities, - [EnumMember] CFD, - [EnumMember] Indexes, - [EnumMember] Futures, - [EnumMember] Options, - [EnumMember] ETF, - [EnumMember] Crypto, - [EnumMember] Synthetic, - [EnumMember] Spot, - [EnumMember] Forward, - [EnumMember] FixedIncome, - [EnumMember] Warrants, +/* + SymbolType.Unknown, + [EnumMember] Forex, + [EnumMember] Equities, + [EnumMember] CFD, + [EnumMember] Indexes, + [EnumMember] Futures, + [EnumMember] Options, + [EnumMember] ETF, + [EnumMember] Crypto, + [EnumMember] Synthetic, + [EnumMember] Spot, + [EnumMember] Forward, + [EnumMember] FixedIncome, + [EnumMember] Warrants, - [EnumMember] Debentures, - [EnumMember] Bond, - [EnumMember] Swap, - */ + [EnumMember] Debentures, + [EnumMember] Bond, + [EnumMember] Swap, +*/ }; } @@ -213,7 +213,7 @@ namespace SyntheticVendorNamespace public override PingResult Ping() { - return new PingResult + return new PingResult() { State = PingEnum.Connected, PingTime = TimeSpan.FromMilliseconds(2), @@ -226,7 +226,12 @@ namespace SyntheticVendorNamespace public override void OnConnected(CancellationToken token) { - throw new NotImplementedException(); + // This method is called after a successful connection + // You can initialize resources or start any necessary processes here + base.OnConnected(token); + + // For example, you might want to push some initial messages or data + // PushMessage(new MessageVendorEvent("SyntheticVendor connected successfully")); } @@ -262,10 +267,7 @@ namespace SyntheticVendorNamespace var historyItems = new List(); var symbolId = requestParameters.SymbolId; - if (string.IsNullOrEmpty(symbolId)) - { - return historyItems; - } + if (string.IsNullOrEmpty(symbolId)) return historyItems; DateTime from = requestParameters.FromTime; DateTime to = requestParameters.ToTime; @@ -282,9 +284,7 @@ namespace SyntheticVendorNamespace { DateTime intervalEnd = currentTime.AddTicks(periodTimeSpan.Ticks * MAX_ITEMS_PER_REQUEST); if (intervalEnd > to) - { intervalEnd = to; - } while (currentTime <= intervalEnd) { @@ -293,10 +293,7 @@ namespace SyntheticVendorNamespace currentTime = currentTime.Add(periodTimeSpan); - if (requestParameters.CancellationToken.IsCancellationRequested) - { - return historyItems; - } + if (requestParameters.CancellationToken.IsCancellationRequested) return historyItems; } currentTime = intervalEnd; @@ -309,6 +306,7 @@ namespace SyntheticVendorNamespace { switch (symbolId) { + //case "W0": return GenerateConstant; case "W1": return GenerateSpike; case "W2": return GenerateDiracDelta; case "W3": return GenerateSquareWave; @@ -333,7 +331,7 @@ namespace SyntheticVendorNamespace public override HistoryMetadata GetHistoryMetadata(CancellationToken cancellationToken) { - return new HistoryMetadata + return new HistoryMetadata() { AllowedHistoryTypes = new HistoryType[] { @@ -362,58 +360,71 @@ namespace SyntheticVendorNamespace } - /*******************************************************************************************************************************************/ - /*******************************************************************************************************************************************/ - /*******************************************************************************************************************************************/ - /*******************************************************************************************************************************************/ - /*******************************************************************************************************************************************/ - /*******************************************************************************************************************************************/ - /*******************************************************************************************************************************************/ +/*******************************************************************************************************************************************/ +/*******************************************************************************************************************************************/ +/*******************************************************************************************************************************************/ +/*******************************************************************************************************************************************/ +/*******************************************************************************************************************************************/ +/*******************************************************************************************************************************************/ +/*******************************************************************************************************************************************/ - private HistoryItemBar GenerateSpike(DateTime time, TimeSpan slice) + private HistoryItemBar GenerateSpike(DateTime time, TimeSpan slice) + { + // Ensure we're working with UTC time + DateTime utcTime = time.ToUniversalTime(); + + // Calculate the number of hours since the epoch + double hoursSinceEpoch = (utcTime - new DateTime(1970, 1, 1, 0, 0, 0, DateTimeKind.Utc)).TotalHours; + + // Calculate the position within the 25-hour cycle + int cyclePosition = (int)Math.Floor(hoursSinceEpoch % 25); + + // Determine if this is a spike hour (hour 24 in the cycle) or the hour after + bool isSpike = cyclePosition == 24; + bool isAfterSpike = cyclePosition == 0; + + double openValue, closeValue; + if (isSpike) { - // Ensure we're working with UTC time - DateTime utcTime = time.ToUniversalTime(); - - // Calculate the number of hours since the epoch - double hoursSinceEpoch = (utcTime - new DateTime(1970, 1, 1, 0, 0, 0, DateTimeKind.Utc)).TotalHours; - - // Calculate the position within the 25-hour cycle - int cyclePosition = (int)Math.Floor(hoursSinceEpoch % 25); - - // Determine if this is a spike hour (hour 24 in the cycle) or the hour after - bool isSpike = cyclePosition == 24; - bool isAfterSpike = cyclePosition == 0; - - double openValue, closeValue; - if (isSpike) - { - openValue = 0; - closeValue = 100; - } - else if (isAfterSpike) - { - openValue = 100; - closeValue = 0; - } - else - { - openValue = closeValue = 0.000001; - } - - return new HistoryItemBar - { - TicksLeft = time.Ticks, - TicksRight = time.Add(slice).Ticks - 1, - Open = openValue, - High = Math.Max(openValue, closeValue), - Low = Math.Min(openValue, closeValue), - Close = closeValue, - Volume = Math.Abs(closeValue - openValue), - Ticks = time.Add(slice).Ticks - time.Ticks - }; + openValue = 0; + closeValue = 100; + } + else if (isAfterSpike) + { + openValue = 100; + closeValue = 0; + } + else + { + openValue = closeValue = 0.000001; } + return new HistoryItemBar + { + TicksLeft = time.Ticks, + TicksRight = time.Add(slice).Ticks - 1, + Open = openValue, + High = Math.Max(openValue, closeValue), + Low = Math.Min(openValue, closeValue), + Close = closeValue, + Volume = Math.Abs(closeValue - openValue), + Ticks = time.Add(slice).Ticks - time.Ticks + }; + } + + + + + private static readonly double[] distributionValues = new double[] + { + 0.010, // Extreme left tail + 0.050, // Left tail + 0.200, // Left of center + 0.480, // Center (peak) + 0.200, // Right of center + 0.050, // Right tail + 0.010 // Extreme right tail + }; private HistoryItemBar GenerateDiracDelta(DateTime time, TimeSpan slice) { @@ -486,7 +497,7 @@ namespace SyntheticVendorNamespace double value = 50 + 50 * Math.Sin(cyclePosition * frequency); // Oscillate between 0 and 100 double nextValue = 50 + 50 * Math.Sin((cyclePosition + slice.TotalMinutes) * frequency); - double factor = 0.6 * Math.Abs(nextValue - value); + double factor = 0.6 * Math.Abs (nextValue - value); return new HistoryItemBar { @@ -494,8 +505,8 @@ namespace SyntheticVendorNamespace TicksRight = time.Add(slice).Ticks - 1, Open = value, - High = Math.Max(value, nextValue) + factor, - Low = Math.Min(value, nextValue) - factor, + High = Math.Max(value, nextValue)+factor, + Low = Math.Min(value, nextValue)-factor, Close = nextValue, Volume = Math.Abs(nextValue - value) * 100, // Volume proportional to price change @@ -540,7 +551,6 @@ namespace SyntheticVendorNamespace openValue = 0.0001; closeValue = 100; } - else { } return new HistoryItemBar { @@ -597,6 +607,26 @@ namespace SyntheticVendorNamespace }; } + private HistoryItemBar GeneratePulseWave(DateTime time, TimeSpan slice) + { + double hours = (time - DateTime.UnixEpoch).TotalHours; + double period = 24; // 24-hour period + double position = hours % period; + double value = position < period / 5 ? 100 : -100; // 20% duty cycle + + return new HistoryItemBar + { + TicksLeft = time.Ticks, + TicksRight = time.Add(slice).Ticks - 1, + Open = value, + High = 100, + Low = -100, + Close = value, + Volume = 100, + Ticks = 100 + }; + } + private HistoryItemBar GenerateTriangleWave(DateTime time, TimeSpan slice) { double hours = (time - DateTime.UnixEpoch).TotalHours; @@ -749,7 +779,7 @@ namespace SyntheticVendorNamespace } - readonly Random random = new Random(); + Random random = new Random(); private double currentAmplitude = 100; private HistoryItemBar GenerateAMSignal(DateTime time, TimeSpan slice) { @@ -757,6 +787,9 @@ namespace SyntheticVendorNamespace double period = 12.0; double frequency = 2 * Math.PI / period; // Frequency for a 5-hour period + // Determine the start of the current 5-hour cycle + double cycleStartTime = Math.Floor(hours / period) * period; + // Calculate the phase of the signal within the current 5-hour cycle double phase = frequency * (hours % period); @@ -791,8 +824,8 @@ namespace SyntheticVendorNamespace private double currentFrequency = Math.PI / 220.0; // Initial frequency - private double accumulatedPhase; - private double lastCloseValue; // To store the last close value + private double accumulatedPhase = 0; + private double lastCloseValue = 0; // To store the last close value private HistoryItemBar GenerateFMSignal(DateTime time, TimeSpan slice) { @@ -882,81 +915,81 @@ namespace SyntheticVendorNamespace - private double previousClose = 50; - private const double meanPrice = 50; +private double previousClose = 50; +private const double meanPrice = 50; - private HistoryItemBar GeneratePinkNoise(DateTime time, TimeSpan slice) +private HistoryItemBar GeneratePinkNoise(DateTime time, TimeSpan slice) +{ + double volatility = 2; + double meanReversionStrength = 0.1; + + // Generate open price + double openNoise = GeneratePinkNoiseValue(); + double open = previousClose + volatility * openNoise + meanReversionStrength * (meanPrice - previousClose); + + // Generate close price + double closeNoise = GeneratePinkNoiseValue(); + double close = open + volatility * closeNoise + meanReversionStrength * (meanPrice - open); + + // Determine High and Low + double high = Math.Max(open, close); + double low = Math.Min(open, close); + + // Add variation to High and Low + double highNoise = Math.Abs(GeneratePinkNoiseValue()); + high += volatility * highNoise; + + double lowNoise = Math.Abs(GeneratePinkNoiseValue()); + low -= volatility * lowNoise; + + double volume = Math.Abs(GeneratePinkNoiseValue()) * 1000 + 100; + + // Update previous close for the next iteration + previousClose = close; + + return new HistoryItemBar + { + TicksLeft = time.Ticks, + TicksRight = time.Add(slice).Ticks - 1, + Open = open, + High = high, + Low = low, + Close = close, + Volume = volume, + Ticks = slice.Ticks + }; +} + + + private const int NumOctaves = 6; + private double[] pinkNoiseState = new double[NumOctaves]; + private double GeneratePinkNoiseValue() + { + double total = 0; + + for (int i = 0; i < NumOctaves; i++) { - double volatility = 2; - double meanReversionStrength = 0.1; - - // Generate open price - double openNoise = GeneratePinkNoiseValue(); - double open = previousClose + volatility * openNoise + meanReversionStrength * (meanPrice - previousClose); - - // Generate close price - double closeNoise = GeneratePinkNoiseValue(); - double close = open + volatility * closeNoise + meanReversionStrength * (meanPrice - open); - - // Determine High and Low - double high = Math.Max(open, close); - double low = Math.Min(open, close); - - // Add variation to High and Low - double highNoise = Math.Abs(GeneratePinkNoiseValue()); - high += volatility * highNoise; - - double lowNoise = Math.Abs(GeneratePinkNoiseValue()); - low -= volatility * lowNoise; - - double volume = Math.Abs(GeneratePinkNoiseValue()) * 1000 + 100; - - // Update previous close for the next iteration - previousClose = close; - - return new HistoryItemBar - { - TicksLeft = time.Ticks, - TicksRight = time.Add(slice).Ticks - 1, - Open = open, - High = high, - Low = low, - Close = close, - Volume = volume, - Ticks = slice.Ticks - }; + double white = random.NextDouble() * 2 - 1; + pinkNoiseState[i] = (pinkNoiseState[i] + white) * 0.5; + total += pinkNoiseState[i] * Math.Pow(2, -i); } - - private const int NumOctaves = 6; - private readonly double[] pinkNoiseState = new double[NumOctaves]; - private double GeneratePinkNoiseValue() - { - double total = 0; - - for (int i = 0; i < NumOctaves; i++) - { - double white = random.NextDouble() * 2 - 1; - pinkNoiseState[i] = (pinkNoiseState[i] + white) * 0.5; - total += pinkNoiseState[i] * Math.Pow(2, -i); - } - - // Normalize - return total / NumOctaves; - } + // Normalize + return total / NumOctaves; + } - private double lastValue; +private double lastValue = 0; - private HistoryItemBar GenerateBrownNoise(DateTime time, TimeSpan slice) - { - double dt = slice.TotalDays / 365.0; // Time step in years - double sigma = 25.0; // Annual volatility +private HistoryItemBar GenerateBrownNoise(DateTime time, TimeSpan slice) +{ + double dt = slice.TotalDays / 365.0; // Time step in years + double sigma = 25.0; // Annual volatility - double increment = GenerateGaussian(0, sigma * Math.Sqrt(dt)); - double open = lastValue * (1 + GenerateGaussian(0, 0.05)); - double close = open + increment; + double increment = GenerateGaussian(0, sigma * Math.Sqrt(dt)); + double open = lastValue * (1 + GenerateGaussian(0, 0.05)); + double close = open + increment; // Simulate intra-period high and low double high = Math.Max(open, close); @@ -964,83 +997,83 @@ namespace SyntheticVendorNamespace double low = Math.Min(open, close); low -= low * Math.Abs(GenerateGaussian(0, 0.06)); - lastValue = close; + lastValue = close; - return new HistoryItemBar - { - TicksLeft = time.Ticks, - TicksRight = time.Add(slice).Ticks - 1, - Open = open, - High = high, - Low = low, - Close = close, - Volume = Math.Abs(close - open) * 1000, // Simplified volume calculation - Ticks = slice.Ticks - }; - } - // Helper method to generate Gaussian distributed random numbers - private double GenerateGaussian(double mean, double stdDev) - { - double u1 = 1.0 - random.NextDouble(); // Uniform(0,1] random doubles - double u2 = 1.0 - random.NextDouble(); - double randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2); - return mean + stdDev * randStdNormal; - } + return new HistoryItemBar + { + TicksLeft = time.Ticks, + TicksRight = time.Add(slice).Ticks - 1, + Open = open, + High = high, + Low = low, + Close = close, + Volume = Math.Abs(close - open) * 1000, // Simplified volume calculation + Ticks = slice.Ticks + }; +} +// Helper method to generate Gaussian distributed random numbers +private double GenerateGaussian(double mean, double stdDev) +{ + double u1 = 1.0 - random.NextDouble(); // Uniform(0,1] random doubles + double u2 = 1.0 - random.NextDouble(); + double randStdNormal = Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2); + return mean + stdDev * randStdNormal; +} - private double GBMLastClose = 100; // Starting price - private readonly double GBMMu = 0.05; // Annual drift - private readonly double GBMSigma = 0.2; // Annual volatility +private double GBMLastClose = 100; // Starting price +private double GBMMu = 0.05; // Annual drift +private double GBMSigma = 0.2; // Annual volatility - private HistoryItemBar GenerateGBM(DateTime time, TimeSpan slice) - { - // Convert time slice to years - double dt = slice.TotalDays / 365.0; +private HistoryItemBar GenerateGBM(DateTime time, TimeSpan slice) +{ + // Convert time slice to years + double dt = slice.TotalDays / 365.0; - // Generate a random normal variable for the main price movement - double epsilon = GenerateGaussian(0, 1); + // Generate a random normal variable for the main price movement + double epsilon = GenerateGaussian(0, 1); - // Calculate the price movement using GBM equation - double drift = (GBMMu - 0.5 * GBMSigma * GBMSigma) * dt; - double diffusion = GBMSigma * Math.Sqrt(dt) * epsilon; - double returnValue = Math.Exp(drift + diffusion); + // Calculate the price movement using GBM equation + double drift = (GBMMu - 0.5 * GBMSigma * GBMSigma) * dt; + double diffusion = GBMSigma * Math.Sqrt(dt) * epsilon; + double returnValue = Math.Exp(drift + diffusion); - // Add variability between previous close and current open - double openVariability = GBMLastClose * GBMSigma * Math.Sqrt(dt) * GenerateGaussian(0, 1) * 0.1; - double open = GBMLastClose + openVariability; + // Add variability between previous close and current open + double openVariability = GBMLastClose * GBMSigma * Math.Sqrt(dt) * GenerateGaussian(0, 1) * 0.1; + double open = GBMLastClose + openVariability; - // Calculate new close price - double close = open * returnValue; + // Calculate new close price + double close = open * returnValue; - // Generate High and Low values - double highLowRange = Math.Max(Math.Abs(close - open), GBMLastClose * GBMSigma * Math.Sqrt(dt) * Math.Abs(GenerateGaussian(0, 1))); - double high = Math.Max(open, close) + highLowRange * 0.5; - double low = Math.Min(open, close) - highLowRange * 0.5; + // Generate High and Low values + double highLowRange = Math.Max(Math.Abs(close - open), GBMLastClose * GBMSigma * Math.Sqrt(dt) * Math.Abs(GenerateGaussian(0, 1))); + double high = Math.Max(open, close) + highLowRange * 0.5; + double low = Math.Min(open, close) - highLowRange * 0.5; - // Generate volume (you may want to adjust this based on your needs) - double volume = Math.Max(100, 1000 * Math.Abs(close - open) + 500 * GenerateGaussian(0, 1)); + // Generate volume (you may want to adjust this based on your needs) + double volume = Math.Max(100, 1000 * Math.Abs(close - open) + 500 * GenerateGaussian(0, 1)); - // Update last close for next iteration - GBMLastClose = close; + // Update last close for next iteration + GBMLastClose = close; - return new HistoryItemBar - { - TicksLeft = time.Ticks, - TicksRight = time.Add(slice).Ticks - 1, - Open = open, - High = high, - Low = low, - Close = close, - Volume = volume, - Ticks = slice.Ticks - }; - } + return new HistoryItemBar + { + TicksLeft = time.Ticks, + TicksRight = time.Add(slice).Ticks - 1, + Open = open, + High = high, + Low = low, + Close = close, + Volume = volume, + Ticks = slice.Ticks + }; +} private double FBMLastClose = 100; // Starting price - private readonly double FBMHurst = 0.85; // Hurst parameter (0.5 < H < 1 for persistent fBm) - private readonly double FBMSigma = 0.25; // Volatility parameter - private readonly double FBMDrift = 0.001; // drift + private double FBMHurst = 0.85; // Hurst parameter (0.5 < H < 1 for persistent fBm) + private double FBMSigma = 0.25; // Volatility parameter + private double FBMDrift = 0.001; // drift private HistoryItemBar GenerateFBM(DateTime time, TimeSpan slice) { diff --git a/Tests/Tests.csproj b/Tests/Tests.csproj index ff7867e3..0f30adf3 100644 --- a/Tests/Tests.csproj +++ b/Tests/Tests.csproj @@ -4,15 +4,7 @@ QuanTAlib.Tests QuanTAlib.Tests - - - all - runtime; build; native; contentfiles; analyzers - - - all - runtime; build; native; contentfiles; analyzers - + all @@ -20,11 +12,11 @@ - + @@ -34,7 +26,6 @@ --> - diff --git a/Tests/test_Trady.cs b/Tests/test_Trady.cs index 8037b8e3..95e9e9b1 100644 --- a/Tests/test_Trady.cs +++ b/Tests/test_Trady.cs @@ -2,7 +2,7 @@ using Xunit; using Trady.Analysis.Indicator; using Trady.Core; using Trady.Core.Infrastructure; -namespace QuanTAlib; +using QuanTAlib; public class TradyTests { diff --git a/Tests/test_Tulip.cs b/Tests/test_Tulip.cs index 159902ae..5b7cb558 100644 --- a/Tests/test_Tulip.cs +++ b/Tests/test_Tulip.cs @@ -1,6 +1,6 @@ using Xunit; using Tulip; -namespace QuanTAlib; +using QuanTAlib; public class TulipTests { @@ -23,7 +23,7 @@ public class TulipTests iterations = 3; skip = 500; data = feed.Close.v.ToArray(); - outdata = new double[data.Length]; + outdata = new double[data.Count()]; } [Fact] @@ -66,11 +66,12 @@ public class TulipTests double[][] arrout = [outdata]; Tulip.Indicators.ema.Run(inputs: arrin, options: [period], outputs: arrout); - Assert.Equal(QL.Length, arrout[0].Length); + Assert.Equal(QL.Length, arrout[0].Count()); for (int i = QL.Length - 1; i > skip*2; i--) //Initial Tulip Ema value is (wrongly) set to the first input value - therefore large skip { double QL_item = QL[i].Value; double TU = arrout[0][i]; + //Assert.InRange(TU - QL_item, -range, range); Assert.True(Math.Abs(TU - QL_item) <= range, $"Assertion failed at index {i} for period {period}: TU = {TU}, QL_item = {QL_item}, delta = {TU-QL_item}"); } diff --git a/Tests/test_consistency.cs b/Tests/test_consistency.cs index ea6ae40d..72d482cd 100644 --- a/Tests/test_consistency.cs +++ b/Tests/test_consistency.cs @@ -1,5 +1,5 @@ using Xunit; -namespace QuanTAlib; +using QuanTAlib; public class Consistency { @@ -312,6 +312,31 @@ public class Consistency } } + /* + [Fact] + public void Jma_isNew() + { + int p = (int)rnd.Next(2, 100); + Jma ma1 = new(p); + Jma ma2 = new(p); + for (int i = 0; i < series_len; i++) + { + TValue item1 = new(Time: DateTime.Now, Value: rnd.Next(-100, 100), IsNew: true); + ma1.Calc(item1); + for (int j = 0; j < corrections; j++) + { + item1 = new(Time: DateTime.Now, Value: rnd.Next(-100, 100), IsNew: false); + ma1.Calc(item1); + } + ma2.Calc(new TValue(item1.Time, item1.Value, IsNew: true)); + + //Assert.Equal(ma1.Value, ma2.Value); + Assert.True(ma1.Value == ma2.Value, $"Assertion failed at p={p}, Value={item1.Value}. ma1.Value={ma1.Value}, ma2.Value={ma2.Value}"); + } + } + */ + + [Fact] public void Kama_isNew() { @@ -417,6 +442,7 @@ public class Consistency [Fact] public void Qema_isNew() { + int p = (int)rnd.Next(2, 100); Qema ma1 = new(); Qema ma2 = new(); for (int i = 0; i < series_len; i++) @@ -657,7 +683,7 @@ public class Consistency [Fact] public void Kurtosis_isNew() { - int p = (int)rnd.Next(5, 100); + int p = (int)rnd.Next(2, 100); Kurtosis ma1 = new(p); Kurtosis ma2 = new(p); for (int i = 0; i < series_len; i++) diff --git a/Tests/test_skender.stock.cs b/Tests/test_skender.stock.cs index 9602b2d5..a0932094 100644 --- a/Tests/test_skender.stock.cs +++ b/Tests/test_skender.stock.cs @@ -1,6 +1,6 @@ using Xunit; using Skender.Stock.Indicators; -namespace QuanTAlib; +using QuanTAlib; public class SkenderTests { @@ -284,6 +284,7 @@ public class SkenderTests { for (int run = 0; run < iterations; run++) { + //period = rnd.Next(50) + 5; Mama ma = new(fastLimit: 0.5, slowLimit: 0.05); TSeries QL = new(); foreach (TBar item in feed) diff --git a/Tests/test_talib.cs b/Tests/test_talib.cs index 3d57d44a..82e267c9 100644 --- a/Tests/test_talib.cs +++ b/Tests/test_talib.cs @@ -1,6 +1,6 @@ using Xunit; using TALib; -namespace QuanTAlib; +using QuanTAlib; public class TAlibTests { @@ -22,7 +22,7 @@ public class TAlibTests feed.Add(10000); iterations = 3; data = feed.Close.v.ToArray(); - TALIB = new double[data.Length]; + TALIB = new double[data.Count()]; } @@ -37,11 +37,11 @@ public class TAlibTests foreach (TBar item in feed) { QL.Add(ma.Calc(new TValue(item.Time, item.Close))); } Core.Sma(data, 0, QL.Length - 1, TALIB, out int outBegIdx, out _, period); - Assert.Equal(QL.Length, TALIB.Length); + Assert.Equal(QL.Length, TALIB.Count()); for (int i = QL.Length - 1; i > period; i--) { double TL = i < outBegIdx ? double.NaN : TALIB[i - outBegIdx]; - Assert.InRange(TL - QL[i].Value, -range, range); + Assert.InRange(TALIB[i - outBegIdx] - QL[i].Value, -range, range); } } } @@ -57,7 +57,7 @@ public class TAlibTests foreach (TBar item in feed) { QL.Add(ma.Calc(new TValue(item.Time, item.Close))); } Core.Ema(data, 0, QL.Length - 1, TALIB, out int outBegIdx, out _, period); - Assert.Equal(QL.Length, TALIB.Length); + Assert.Equal(QL.Length, TALIB.Count()); for (int i = QL.Length - 1; i > period; i--) { double TL = i < outBegIdx ? double.NaN : TALIB[i - outBegIdx]; @@ -77,7 +77,7 @@ public class TAlibTests foreach (TBar item in feed) { QL.Add(ma.Calc(new TValue(item.Time, item.Close))); } Core.Dema(data, 0, QL.Length - 1, TALIB, out int outBegIdx, out _, period); - Assert.Equal(QL.Length, TALIB.Length); + Assert.Equal(QL.Length, TALIB.Count()); for (int i = QL.Length - 1; i > period*20; i--) { double TL = i < outBegIdx ? double.NaN : TALIB[i - outBegIdx]; @@ -97,7 +97,7 @@ public class TAlibTests foreach (TBar item in feed) { QL.Add(ma.Calc(new TValue(item.Time, item.Close))); } Core.Tema(data, 0, QL.Length - 1, TALIB, out int outBegIdx, out _, period); - Assert.Equal(QL.Length, TALIB.Length); + Assert.Equal(QL.Length, TALIB.Count()); for (int i = QL.Length - 1; i > period*20; i--) { double TL = i < outBegIdx ? double.NaN : TALIB[i - outBegIdx]; @@ -106,6 +106,29 @@ public class TAlibTests } } +//TODO fix WMA +/* + [Fact] + public void WMA() + { + for (int run = 0; run < iterations; run++) + { + period = rnd.Next(50) + 5; + Wma ma = new(period); + TSeries QL = new(); + foreach (TBar item in feed) + { QL.Add(ma.Calc(new TValue(item.Time, item.Close))); } + Core.Wma(data, 0, QL.Length - 1, TALIB, out int outBegIdx, out _, period); + Assert.Equal(QL.Length, TALIB.Count()); + for (int i = QL.Length - 1; i > period*3; i--) + { + double TL = i < outBegIdx ? double.NaN : TALIB[i - outBegIdx]; + Assert.InRange(TALIB[i - outBegIdx] - QL[i].Value, -range, range); + } + } + } + */ + [Fact] public void T3() { @@ -117,7 +140,7 @@ public class TAlibTests foreach (TBar item in feed) { QL.Add(ma.Calc(new TValue(item.Time, item.Close))); } Core.T3(data, 0, QL.Length - 1, TALIB, out int outBegIdx, out _, optInTimePeriod: period, optInVFactor: 0.7); - Assert.Equal(QL.Length, TALIB.Length); + Assert.Equal(QL.Length, TALIB.Count()); for (int i = QL.Length - 1; i > period*20; i--) { double TL = i < outBegIdx ? double.NaN : TALIB[i - outBegIdx]; diff --git a/archive/.editorconfig b/archive/.editorconfig new file mode 100644 index 00000000..b99bec2f --- /dev/null +++ b/archive/.editorconfig @@ -0,0 +1,16 @@ +# Top-most EditorConfig file +root = true + +[*.{cs,vb}] +# Suppress S3776 (Cognitive Complexity) +dotnet_diagnostic.S3776.severity = none +# Suppress CA1416 (Platform Compatibility) +dotnet_diagnostic.CA1416.severity = none +dotnet_style_parentheses_in_control_flow_statements = always_for_clarity:suggestion +csharp_new_line_before_open_brace = none +csharp_new_line_before_else = false +csharp_new_line_before_catch = false +csharp_new_line_before_finally = false +csharp_new_line_before_members_in_object_initializers = false +csharp_new_line_before_members_in_anonymous_types = false +csharp_new_line_between_query_expression_clauses = false \ No newline at end of file diff --git a/archive/.gitignore b/archive/.gitignore new file mode 100644 index 00000000..f7e3d0a2 --- /dev/null +++ b/archive/.gitignore @@ -0,0 +1,358 @@ +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. +## +## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore + +# User-specific files +*.rsuser +*.suo +*.user +*.userosscache +*.sln.docstates +.vscode/ +.fleet/ +*.deps.json +.Sandbox/ +#.sonarlint/ +.DS_Store + +# User-specific files (MonoDevelop/Xamarin Studio) +*.userprefs + +# Mono auto generated files +mono_crash.* + +# Build results +[Dd]ebug/ +[Dd]ebugPublic/ +[Rr]elease/ +[Rr]eleases/ +x64/ +x86/ +[Aa][Rr][Mm]/ +[Aa][Rr][Mm]64/ +bld/ +[Bb]in/ +[Oo]bj/ +[Ll]og/ +[Ll]ogs/ + +# Visual Studio 2015/2017 cache/options directory +.vs/ +# Uncomment if you have tasks that create the project's static files in wwwroot +#wwwroot/ + +# Visual Studio 2017 auto generated files +Generated\ Files/ + +# MSTest test Results +[Tt]est[Rr]esult*/ +[Bb]uild[Ll]og.* + +# NUnit +*.VisualState.xml +TestResult.xml +nunit-*.xml + +# Build Results of an ATL Project +[Dd]ebugPS/ +[Rr]eleasePS/ +dlldata.c + +# Benchmark Results +BenchmarkDotNet.Artifacts/ + +# .NET Core +project.lock.json +project.fragment.lock.json +artifacts/ + +# StyleCop +StyleCopReport.xml + +# Files built by Visual Studio +*_i.c +*_p.c +*_h.h +*.ilk +*.meta +*.obj +*.iobj +*.pch +*.pdb +*.ipdb +*.pgc +*.pgd +*.rsp +*.sbr +*.tlb +*.tli +*.tlh +*.tmp +*.tmp_proj +*_wpftmp.csproj +*.log +*.vspscc +*.vssscc +.builds +*.pidb +*.svclog +*.scc + +# Chutzpah Test files +_Chutzpah* + +# Visual C++ cache files +ipch/ +*.aps +*.ncb +*.opendb +*.opensdf +*.sdf +*.cachefile +*.VC.db +*.VC.VC.opendb + +# Visual Studio profiler +*.psess +*.vsp +*.vspx +*.sap + +# Visual Studio Trace Files +*.e2e + +# TFS 2012 Local Workspace +$tf/ + +# Guidance Automation Toolkit +*.gpState + +# ReSharper is a .NET coding add-in +_ReSharper*/ +*.[Rr]e[Ss]harper +*.DotSettings.user + +# TeamCity is a build add-in +_TeamCity* + +# DotCover is a Code Coverage Tool +*.dotCover + +# AxoCover is a Code Coverage Tool +.axoCover/* +!.axoCover/settings.json + +# Visual Studio code coverage results +*.coverage +*.coveragexml + +# NCrunch +_NCrunch_* +.*crunch*.local.xml +nCrunchTemp_* + +# MightyMoose +*.mm.* +AutoTest.Net/ + +# Web workbench (sass) +.sass-cache/ + +# Installshield output folder +[Ee]xpress/ + +# DocProject is a documentation generator add-in +DocProject/buildhelp/ +DocProject/Help/*.HxT +DocProject/Help/*.HxC +DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.[Pp]ublish.xml +*.azurePubxml +# Note: Comment the next line if you want to checkin your web deploy settings, +# but database connection strings (with potential passwords) will be unencrypted +*.pubxml +*.publishproj + +# Microsoft Azure Web App publish settings. Comment the next line if you want to +# checkin your Azure Web App publish settings, but sensitive information contained +# in these scripts will be unencrypted +PublishScripts/ + +# NuGet Packages +*.nupkg +# NuGet Symbol Packages +*.snupkg +# The packages folder can be ignored because of Package Restore +**/[Pp]ackages/* +# except build/, which is used as an MSBuild target. +!**/[Pp]ackages/build/ +# Uncomment if necessary however generally it will be regenerated when needed +#!**/[Pp]ackages/repositories.config +# NuGet v3's project.json files produces more ignorable files +*.nuget.props +*.nuget.targets + +# Microsoft Azure Build Output +csx/ +*.build.csdef + +# Microsoft Azure Emulator +ecf/ +rcf/ + +# Windows Store app package directories and files +AppPackages/ +BundleArtifacts/ +Package.StoreAssociation.xml +_pkginfo.txt +*.appx +*.appxbundle +*.appxupload + +# Visual Studio cache files +# files ending in .cache can be ignored +*.[Cc]ache +# but keep track of directories ending in .cache +!?*.[Cc]ache/ + +# Others +ClientBin/ +~$* +*~ +*.dbmdl +*.dbproj.schemaview +*.jfm +*.pfx +*.publishsettings +orleans.codegen.cs + +# Including strong name files can present a security risk +# (https://github.com/github/gitignore/pull/2483#issue-259490424) +#*.snk + +# Since there are multiple workflows, uncomment next line to ignore bower_components +# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622) +#bower_components/ + +# RIA/Silverlight projects +Generated_Code/ + +# Backup & report files from converting an old project file +# to a newer Visual Studio version. Backup files are not needed, +# because we have git ;-) +_UpgradeReport_Files/ +Backup*/ +UpgradeLog*.XML +UpgradeLog*.htm +ServiceFabricBackup/ +*.rptproj.bak + +# SQL Server files +*.mdf +*.ldf +*.ndf + +# Business Intelligence projects +*.rdl.data +*.bim.layout +*.bim_*.settings +*.rptproj.rsuser +*- [Bb]ackup.rdl +*- [Bb]ackup ([0-9]).rdl +*- [Bb]ackup ([0-9][0-9]).rdl + +# Microsoft Fakes +FakesAssemblies/ + +# GhostDoc plugin setting file +*.GhostDoc.xml + +# Node.js Tools for Visual Studio +.ntvs_analysis.dat +node_modules/ + +# Visual Studio 6 build log +*.plg + +# Visual Studio 6 workspace options file +*.opt + +# Visual Studio 6 auto-generated workspace file (contains which files were open etc.) +*.vbw + +# Visual Studio LightSwitch build output +**/*.HTMLClient/GeneratedArtifacts +**/*.DesktopClient/GeneratedArtifacts +**/*.DesktopClient/ModelManifest.xml +**/*.Server/GeneratedArtifacts +**/*.Server/ModelManifest.xml +_Pvt_Extensions + +# Paket dependency manager +.paket/paket.exe +paket-files/ + +# FAKE - F# Make +.fake/ + +# CodeRush personal settings +.cr/personal + +# Python Tools for Visual Studio (PTVS) +__pycache__/ +*.pyc + +# Cake - Uncomment if you are using it +# tools/** +# !tools/packages.config + +# Tabs Studio +*.tss + +# Telerik's JustMock configuration file +*.jmconfig + +# BizTalk build output +*.btp.cs +*.btm.cs +*.odx.cs +*.xsd.cs + +# OpenCover UI analysis results +OpenCover/ + +# Azure Stream Analytics local run output +ASALocalRun/ + +# MSBuild Binary and Structured Log +*.binlog + +# NVidia Nsight GPU debugger configuration file +*.nvuser + +# MFractors (Xamarin productivity tool) working folder +.mfractor/ + +# Local History for Visual Studio +.localhistory/ + +# BeatPulse healthcheck temp database +healthchecksdb + +# Backup folder for Package Reference Convert tool in Visual Studio 2017 +MigrationBackup/ + +# Ionide (cross platform F# VS Code tools) working folder +.ionide/ +dotCover.Output.dcvr +/Tests/GlobalSuppressions.cs diff --git a/archive/.refactoring/base.cs b/archive/.refactoring/base.cs new file mode 100644 index 00000000..530d38d2 --- /dev/null +++ b/archive/.refactoring/base.cs @@ -0,0 +1,237 @@ +using System; + +public readonly record struct TValue(DateTime Time, double Value, bool IsNew = true, bool IsHot = true) +{ + public DateTime Time { get; init; } = Time; + public double Value { get; init; } = Value; + public bool IsNew { get; init; } = IsNew; + public bool IsHot { get; init; } = IsHot; + + public TValue() : this(DateTime.UtcNow, 0) { } + public TValue(double value) : this(DateTime.UtcNow, value) { } + public TValue((DateTime time, double value) tuple) : this(tuple.time, tuple.value) { } + + public static implicit operator double(TValue tv) => tv.Value; + public static implicit operator DateTime(TValue tv) => tv.Time; + public static implicit operator TValue(double value) => new TValue(DateTime.UtcNow, value); + + public override string ToString() => $"[{Time:yyyy-MM-dd HH:mm:ss}: {Value:F2}]"; +} + + +public readonly record struct TBar(DateTime Time, double Open, double High, double Low, double Close, double Volume, bool IsNew = true) +{ + public DateTime Time { get; init; } = Time; + public double Open { get; init; } = Open; + public double High { get; init; } = High; + public double Low { get; init; } = Low; + public double Close { get; init; } = Close; + public double Volume { get; init; } = Volume; + public bool IsNew { get; init; } = IsNew; + + public TBar() : this(DateTime.UtcNow, 0, 0, 0, 0, 0) { } + public TBar(double open, double high, double low, double close, double volume) : this(DateTime.UtcNow, open, high, low, close, volume) { } + public TBar((DateTime time, double open, double high, double low, double close, double volume) tuple) : this(tuple.time, tuple.open, tuple.high, tuple.low, tuple.close, tuple.volume) { } + + public override string ToString() => $"[{Time:yyyy-MM-dd HH:mm:ss}: O={Open:F2}, H={High:F2}, L={Low:F2}, C={Close:F2}, V={Volume:F2}]"; +} + +///////////////////// +/// +///////////////////// + +public class GBM_Feed +{ + private readonly double _mu; + private readonly double _sigma; + private readonly Random _random; + private double _lastClose; + private double _lastHigh; + private double _lastLow; + + public GBM_Feed(double initialPrice, double mu, double sigma) + { + _lastClose = initialPrice; + _lastHigh = initialPrice; + _lastLow = initialPrice; + _mu = mu; + _sigma = sigma; + _random = Random.Shared; + } + + public TBar Generate(bool IsNew = true) + { + DateTime time = DateTime.UtcNow; + double dt = 1.0 / 252; // Assuming daily steps in a trading year of 252 days + double drift = (_mu - 0.5 * _sigma * _sigma) * dt; + double diffusion = _sigma * Math.Sqrt(dt) * NormalRandom(); + double newClose = _lastClose * Math.Exp(drift + diffusion); + + double open = _lastClose; + double high = Math.Max(open, newClose) * (1 + _random.NextDouble() * 0.01); + double low = Math.Min(open, newClose) * (1 - _random.NextDouble() * 0.01); + double volume = 1000 + _random.NextDouble() * 1000; // Random volume between 1000 and 2000 + + if (!IsNew) + { + high = Math.Max(_lastHigh, high); + low = Math.Min(_lastLow, low); + } + else + { + _lastClose = newClose; + } + + _lastHigh = high; + _lastLow = low; + + return new TBar(time, open, high, low, newClose, volume, IsNew); + } + + private double NormalRandom() + { + // Box-Muller transform to generate standard normal random variable + double u1 = 1.0 - _random.NextDouble(); // Uniform(0,1] random doubles + double u2 = 1.0 - _random.NextDouble(); + return Math.Sqrt(-2.0 * Math.Log(u1)) * Math.Sin(2.0 * Math.PI * u2); + } +} + + +/// +/// //////////////// +/// + +public class EMA +{ + private double lastEma, lastEmaCandidate, k; + private int period, i; + public TValue Value { get; private set; } + public bool IsHot { get; private set; } + + public EMA(int period) { + Init(period); + } + + public void Init(int period) + { + this.period = period; + this.k = 2.0 / (period + 1); + this.lastEma = this.lastEmaCandidate = double.NaN; + this.i = 0; + } + public TValue Update(TValue input, bool IsNew = true) { + double ema; + + if (double.IsNaN(lastEma)) { lastEma = input.Value; } + + if (IsNew) { + lastEma = lastEmaCandidate; + i++; + } + + double kk = (i= period; + Value = new TValue(input.Time, ema, IsNew, IsHot); + return Value; + } +} + +///////////////// +/// + +public class SMA +{ + private CircularBuffer buffer; + private int period; + private double sum; + public TValue Value { get; private set; } + public bool IsHot { get; private set; } + + public SMA(int period) + { + Init(period); + } + + public void Init(int period) + { + this.period = period; + this.buffer = new CircularBuffer(period); + this.sum = 0; + this.IsHot = false; + this.Value = default; + } + + public TValue Update(TValue input, bool IsNew = true) + { + if (IsNew) + { + if (buffer.Count == period) { + sum -= buffer[0]; + } + buffer.Add(input); + sum += input.Value; + } else { + if (buffer.Count > 0) { + sum -= buffer[buffer.Count - 1]; + sum += input.Value; + buffer[buffer.Count - 1] = input; + } else { + buffer.Add(input); + sum += input.Value; + } + } + + double sma = buffer.Count > 0 ? sum / buffer.Count : double.NaN; + IsHot = buffer.Count >= period; + Value = new TValue(input.Time, sma, IsNew, IsHot); + return Value; + } +} + +///////////////////// +/// +///////////////////// + + +public class CircularBuffer +{ + private double[] _buffer; + private int _start; + private int _size; + + public CircularBuffer(int capacity) { + _buffer = new double[capacity]; + _start = 0; + _size = 0; + } + + public int Capacity => _buffer.Length; + public int Count => _size; + + public void Add(double item) { + if (_size < Capacity) { + _buffer[(_start + _size) % Capacity] = item; + _size++; + } else { + _buffer[_start] = item; + _start = (_start + 1) % Capacity; + } + } + + public double this[int index] { + get { + if (index < 0 || index >= _size) + throw new IndexOutOfRangeException(); + return _buffer[(_start + index) % Capacity]; + } + set { + if (index < 0 || index >= _size) + throw new IndexOutOfRangeException(); + _buffer[(_start + index) % Capacity] = value; + } + } +} \ No newline at end of file diff --git a/archive/.refactoring/test.dib b/archive/.refactoring/test.dib new file mode 100644 index 00000000..d2277e66 --- /dev/null +++ b/archive/.refactoring/test.dib @@ -0,0 +1,163 @@ +#!meta + +{"kernelInfo":{"defaultKernelName":"csharp","items":[{"aliases":[],"name":"csharp"}]}} + +#!csharp + +#r "\bin\Debug\calculations.dll" +using QuanTAlib; + +#!csharp + +TValue vv = new(10); +display(vv.ToString()); +display(vv.IsHot); + +TBar bb = new(1,1,1,1,10); +display(bb.ToString()); +display(bb.IsNew); + +#!csharp + +int i=10; +SMA sma = new(i); +Console.WriteLine($"{"Close",10} {"SMA(" + i + ")",10}"); +for (int i = 0; i < 20; i++) +{ + TValue c =(double)i+1; + sma.Update(10000,true); + sma.Update(1,false); + sma.Update(-1000,false); + sma.Update(c,false); + + Console.WriteLine($"{i+1} {(double)c,10:F2} {(double)sma.Value,10:F2} {sma.Value.IsHot}"); +} + +#!csharp + +public class Emitter { + private Random random = new Random(); + public event EventHandler> Pub; + public void Emit() { + DateTime now = DateTime.Now; + double randomValue = random.NextDouble() * 100; // Generates a random number between 0 and 100 + TValue value = new TValue(now, randomValue); + + EventArg eventArg = new EventArg(value, true, true); + OnValuePub(eventArg); + } + protected virtual void OnValuePub(EventArg eventArg) { + Pub?.Invoke(this, eventArg); + } +} + +public class BarEmitter +{ + private Random random = new Random(); + public event EventHandler> Pub; + private double lastClose = 100.0; // Starting price + + public void Emit() + { + double open = lastClose; + double close = open * (1 + (random.NextDouble() - 0.5) * 0.02); // +/- 1% change + double high = Math.Max(open, close) * (1 + random.NextDouble() * 0.005); // Up to 0.5% higher + double low = Math.Min(open, close) * (1 - random.NextDouble() * 0.005); // Up to 0.5% lower + double volume = random.NextDouble() * 1000000; // Random volume between 0 and 1,000,000 + + TBar bar = new TBar(DateTime.Now, open, high, low, close, volume); + lastClose = close; + + EventArg eventArg = new EventArg(bar, true, true); + OnBarPub(eventArg); + } + + protected virtual void OnBarPub(EventArg eventArg) + { + Pub?.Invoke(this, eventArg); + } +} + + +public class Listener +{ + public void Sub(object sender, EventArgs e) + { + if (e is EventArg tValueArg) { + Console.WriteLine($"TValue: {tValueArg.Data.Value:F2}"); + } else if (e is EventArg tBarArg) { + Console.WriteLine($"TBar: o={tBarArg.Data.Open:F2}, v={tBarArg.Data.Volume:F2}"); + } else { + Console.WriteLine($"Unknown type: {e.GetType().Name}"); + } + } +} + +#!csharp + +Emitter em1 = new(); +BarEmitter em2 = new(); +Listener list = new(); + +em1.Pub += list.Sub; +em2.Pub += list.Sub; + +// Emit 5 random values +for (int i = 0; i < 3; i++) { + em1.Emit(); + em2.Emit(); +} + +#!csharp + +public abstract class Indicator { + protected Indicator() { + Init(); } + public virtual void Init() {} + public virtual TValue Calc(TValue input, bool isNew=true, bool isHot=true) { + return new TValue(); + } +} + +public class EMA : Indicator +{ + private double lastEma, lastEmaCandidate, k; + private int period, i; + + public EMA(int period) { + Init(period); + } + + public void Init(int period) + { + this.period = period; + this.k = 2.0 / (period + 1); + this.lastEma = this.lastEmaCandidate = double.NaN; + this.i = 0; + } + + public override TValue Calc(TValue input, bool isNew = true, bool isHot = true) { + double ema; + + if (double.IsNaN(lastEma)) { lastEma = lastEmaCandidate = input.Value; } + + if (isNew) { + lastEma = lastEmaCandidate; + i++; + } + + double kk = (i>=period)?k:(2.0/(i+1)); + ema = lastEma + kk * (input.Value - lastEma); + lastEmaCandidate = ema; + + return new TValue(input.Timestamp, ema); + } +} + +#!csharp + +EMA ema = new(3); +display(ema.Calc(100)); +display(ema.Calc(0,false)); +display(ema.Calc(100,false)); +display(ema.Calc(0)); diff --git a/archive/.sonarlint/QuanTAlib.ruleset b/archive/.sonarlint/QuanTAlib.ruleset new file mode 100644 index 00000000..c546ccdb --- /dev/null +++ b/archive/.sonarlint/QuanTAlib.ruleset @@ -0,0 +1,5 @@ + + + + + \ No newline at end of file diff --git a/archive/.sonarlint/QuanTAlib.slconfig b/archive/.sonarlint/QuanTAlib.slconfig new file mode 100644 index 00000000..601908c4 --- /dev/null +++ b/archive/.sonarlint/QuanTAlib.slconfig @@ -0,0 +1,19 @@ +{ + "ServerUri": "https://sonarcloud.io/", + "Organization": { + "Key": "mihakralj", + "Name": "Miha Kralj" + }, + "ProjectKey": "mihakralj_QuanTAlib", + "ProjectName": "QuanTAlib", + "Profiles": { + "CSharp": { + "ProfileKey": "AYBIHuVX3Y_jZnooaQv1", + "ProfileTimestamp": "2022-04-20T17:57:57Z" + }, + "Secrets": { + "ProfileKey": "AYXoTKve9Ao2yLWbNVCT", + "ProfileTimestamp": "2023-01-25T09:40:14Z" + } + } +} \ No newline at end of file diff --git a/archive/.sonarlint/mihakralj_quantalib/CSharp/SonarLint.xml b/archive/.sonarlint/mihakralj_quantalib/CSharp/SonarLint.xml new file mode 100644 index 00000000..90bc98df --- /dev/null +++ b/archive/.sonarlint/mihakralj_quantalib/CSharp/SonarLint.xml @@ -0,0 +1,89 @@ + + + + + sonar.cs.analyzeGeneratedCode + false + + + sonar.cs.file.suffixes + .cs + + + sonar.cs.ignoreHeaderComments + true + + + sonar.cs.roslyn.ignoreIssues + false + + + + + S107 + + + max + 7 + + + + + S110 + + + max + 5 + + + + + S1479 + + + maximum + 30 + + + + + S2342 + + + flagsAttributeFormat + ^([A-Z]{1,3}[a-z0-9]+)*([A-Z]{2})?s$ + + + format + ^([A-Z]{1,3}[a-z0-9]+)*([A-Z]{2})?$ + + + + + S2436 + + + max + 2 + + + maxMethod + 3 + + + + + S3776 + + + propertyThreshold + 3 + + + threshold + 15 + + + + + \ No newline at end of file diff --git a/archive/.sonarlint/mihakralj_quantalib_secrets_settings.json b/archive/.sonarlint/mihakralj_quantalib_secrets_settings.json new file mode 100644 index 00000000..1a0a4ae7 --- /dev/null +++ b/archive/.sonarlint/mihakralj_quantalib_secrets_settings.json @@ -0,0 +1,32 @@ +{ + "sonarlint.rules": { + "secrets:S6338": { + "level": "On", + "severity": "Blocker" + }, + "secrets:S6337": { + "level": "On", + "severity": "Blocker" + }, + "secrets:S6290": { + "level": "On", + "severity": "Blocker" + }, + "secrets:S6334": { + "level": "On", + "severity": "Blocker" + }, + "secrets:S6336": { + "level": "On", + "severity": "Blocker" + }, + "secrets:S6335": { + "level": "On", + "severity": "Blocker" + }, + "secrets:S6292": { + "level": "On", + "severity": "Blocker" + } + } +} \ No newline at end of file diff --git a/archive/.sonarlint/mihakralj_quantalibcsharp.ruleset b/archive/.sonarlint/mihakralj_quantalibcsharp.ruleset new file mode 100644 index 00000000..5ad478ec --- /dev/null +++ b/archive/.sonarlint/mihakralj_quantalibcsharp.ruleset @@ -0,0 +1,390 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/archive/.sonarlint/sonar.settings.json b/archive/.sonarlint/sonar.settings.json new file mode 100644 index 00000000..10d827d5 --- /dev/null +++ b/archive/.sonarlint/sonar.settings.json @@ -0,0 +1 @@ +{"sonar.exclusions":[],"sonar.global.exclusions":["**/build-wrapper-dump.json"],"sonar.inclusions":[]} \ No newline at end of file diff --git a/archive/Calculations/Basics/ADD_Series.cs b/archive/Calculations/Basics/ADD_Series.cs new file mode 100644 index 00000000..6d0436fe --- /dev/null +++ b/archive/Calculations/Basics/ADD_Series.cs @@ -0,0 +1,32 @@ +namespace QuanTAlib; +using System; + +/* +ADD - adding TSeries+TSeries together, or TSeries+double, or double+TSeries + +Remarks: + Most of scaffolding is packaged in abstracty class Pair_TSeries_Indicator. + + */ + +public class ADD_Series : Pair_TSeries_Indicator +{ + public ADD_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + public ADD_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + } + public ADD_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + } + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, TValue1.v + TValue2.v); + if (update) { base[base.Count - 1] = result; } else { base.Add(result); } + } +} diff --git a/archive/Calculations/Basics/CORR_Series.cs b/archive/Calculations/Basics/CORR_Series.cs new file mode 100644 index 00000000..b5209361 --- /dev/null +++ b/archive/Calculations/Basics/CORR_Series.cs @@ -0,0 +1,52 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +CORR: Pearson's Correlation Coefficient + PCC is a measure of linear correlation between two sets of data. + It is the ratio between the covariance of two variables and the product of + their standard deviations; it is essentially a normalized measurement of + the covariance, such that the result always has a value between −1 and 1. + +Sources: + https://en.wikipedia.org/wiki/Pearson_correlation_coefficient + + */ + +public class CORR_Series : Pair_TSeries_Indicator +{ + public CORR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + + private readonly System.Collections.Generic.List _x = new(); + private readonly System.Collections.Generic.List _xx = new(); + private readonly System.Collections.Generic.List _y = new(); + private readonly System.Collections.Generic.List _yy = new(); + private readonly System.Collections.Generic.List _xy = new(); + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + Add_Replace_Trim(_x, TValue1.v, _p, update); + Add_Replace_Trim(_xx, TValue1.v * TValue1.v, _p, update); + Add_Replace_Trim(_y, TValue2.v, _p, update); + Add_Replace_Trim(_yy, TValue2.v * TValue2.v, _p, update); + Add_Replace_Trim(_xy, TValue1.v * TValue2.v, _p, update); + + double _sumx = _x.Sum(); + double _sumxx = _xx.Sum(); + double _sumy = _y.Sum(); + double _sumyy = _yy.Sum(); + double _sumxy = _xy.Sum(); + + double _covar = (_sumxx - _sumx * _sumx / _p) * (_sumyy - _sumy * _sumy / _p); + double _cor = (_covar != 0) ? (_sumxy - _sumx * _sumy / _p) / Math.Sqrt(_covar) : 0.0; + + var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _cor); + if (update) { base[base.Count - 1] = result; } else { base.Add(result); } + + } +} diff --git a/archive/Calculations/Basics/COVAR_Series.cs b/archive/Calculations/Basics/COVAR_Series.cs new file mode 100644 index 00000000..ee700873 --- /dev/null +++ b/archive/Calculations/Basics/COVAR_Series.cs @@ -0,0 +1,48 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +COVAR: Covariance + Covariance is defined as the expected value (or mean) of the product + of their deviations from their individual expected values. + +Sources: + https://en.wikipedia.org/wiki/Covariance + + */ + + +public class COVAR_Series : Pair_TSeries_Indicator +{ + public COVAR_Series(TSeries d1, TSeries d2, int period, bool useNaN = false) : base(d1, d2, period, useNaN) + { + if (base._d1.Count > 0 && base._d2.Count > 0) + { + for (int i = 0; i < base._d1.Count; i++) + { + this.Add(base._d1[i], base._d2[i], false); + } + } + } + + private readonly System.Collections.Generic.List _x = new(); + private readonly System.Collections.Generic.List _y = new(); + private readonly System.Collections.Generic.List _xy = new(); + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + BufferTrim(_x, TValue1.v, _p, update); + BufferTrim(_y, TValue2.v, _p, update); + BufferTrim(_xy, TValue1.v * TValue2.v, _p, update); + + double _avgx = _x.Average(); + double _avgy = _y.Average(); + double _avgxy = _xy.Average(); + double _covar = _avgxy - (_avgx * _avgy); + + var result = (TValue1.t, (this.Count < this._p - 1 && this._NaN) ? double.NaN : _covar); + if (update) { base[base.Count - 1] = result; } else { base.Add(result); } + } +} diff --git a/archive/Calculations/Basics/DIV_Series.cs b/archive/Calculations/Basics/DIV_Series.cs new file mode 100644 index 00000000..962e4f6f --- /dev/null +++ b/archive/Calculations/Basics/DIV_Series.cs @@ -0,0 +1,32 @@ +namespace QuanTAlib; +using System; + +/* +DIV - divide TSeries/TSeries , or TSeries/double, or double/TSeries + +Remarks: + Most of scaffolding is packaged in abstracty class Pair_TSeries_Indicator. + */ + +public class DIV_Series : Pair_TSeries_Indicator +{ + public DIV_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + public DIV_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + } + public DIV_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + } + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, + (TValue2.v is not 0) ? TValue1.v / TValue2.v : Double.PositiveInfinity); + if (update) { base[base.Count - 1] = result; } else { base.Add(result); } + } +} \ No newline at end of file diff --git a/archive/Calculations/Basics/MUL_Series.cs b/archive/Calculations/Basics/MUL_Series.cs new file mode 100644 index 00000000..b2bae613 --- /dev/null +++ b/archive/Calculations/Basics/MUL_Series.cs @@ -0,0 +1,30 @@ +namespace QuanTAlib; +using System; + +/* +MUL - multiply TSeries*TSeries together, or TSeries*double, or double*TSeries + + */ + +public class MUL_Series : Pair_TSeries_Indicator +{ + public MUL_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + public MUL_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + } + public MUL_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + } + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, + TValue1.v * TValue2.v); + if (update) { base[base.Count - 1] = result; } else { base.Add(result); } + } +} \ No newline at end of file diff --git a/archive/Calculations/Basics/SUB_Series.cs b/archive/Calculations/Basics/SUB_Series.cs new file mode 100644 index 00000000..e4333ec6 --- /dev/null +++ b/archive/Calculations/Basics/SUB_Series.cs @@ -0,0 +1,31 @@ +namespace QuanTAlib; +using System; + +/* +SUB - subtracting TSeries-TSeries, or TSeries-double, or double-TSeries + + */ + + +public class SUB_Series : Pair_TSeries_Indicator +{ + public SUB_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + public SUB_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + } + public SUB_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + } + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, + TValue1.v - TValue2.v); + if (update) { base[base.Count - 1] = result; } else { base.Add(result); } + } +} \ No newline at end of file diff --git a/archive/Calculations/Calculations.csproj b/archive/Calculations/Calculations.csproj new file mode 100644 index 00000000..701f67f7 --- /dev/null +++ b/archive/Calculations/Calculations.csproj @@ -0,0 +1,80 @@ + + + + QuanTAlib + 0.2.30 + 0.2.30 + 0.2.30 + Library of TA Calculations, Charts and Strategies for Quantower + Quantitative Technical Analysis Library in C# for Quantower + git + https://github.com/mihakralj/QuanTAlib + true + Miha Kralj + Miha Kralj + Apache-2.0 + readme.md + net8.0;net7.0 + disable + preview + disable + true + en-US + QuanTAlib + QuanTAlib + True + AnyCPU + False + full + True + True + + Indicators;Stock;Market;Technical;Analysis;Algorithmic;Trading;Trade;Trend;Momentum;Finance;Algorithm;Algo; + AlgoTrading;Financial;Strategy;Chart;Charting;Oscillator;Overlay;Equity;Bitcoin;Crypto;Cryptocurrency;Forex; + Quantitative;Historical;Quotes; + + + + + + + + + + full + True + 7 + True + anycpu + + + full + True + 7 + True + anycpu + + + QuanTAlib2.png + https://raw.githubusercontent.com/mihakralj/QuanTAlib/main/.github/QuanTAlib2.png + True + ..\.sonarlint\mihakralj_quantalibcsharp.ruleset + 0.2.1-dev.2 + + + + + + + True + + + + + True + False + + + + + \ No newline at end of file diff --git a/archive/Calculations/ClassStructures/Pair_TSeries_Abstract.cs b/archive/Calculations/ClassStructures/Pair_TSeries_Abstract.cs new file mode 100644 index 00000000..5d1dc59f --- /dev/null +++ b/archive/Calculations/ClassStructures/Pair_TSeries_Abstract.cs @@ -0,0 +1,157 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +Abstract classes with all scaffolding required to build indicators. + All abstracts support period, NaN, and all permutations of Add() methods. + Indicator classess need to implement: + - Chaining constructor (Abstract's constructor executes first) + - Default Add(value) class + - optional Add(series) bulk insert class (for optimization of historical analysis) + + Single_TSeries_Indicator - one single-value TSeries in, one TSeries out. + Pair_TSeries_Indicator - Two TSeries in, one TSeries out. (includes simple semaphoring) + Single_TBars_Indicator - One OHLCV TBars in, one TSeries out. + + */ + +public abstract class Pair_TSeries_Indicator : TSeries +{ + protected readonly int _p; + protected readonly bool _NaN; + protected readonly TSeries _d1; + protected readonly TSeries _d2; + protected readonly double _dd1, _dd2; + + // Chainable Constructors - add them at the end of primary constructors if needed + protected Pair_TSeries_Indicator(TSeries source1, TSeries source2, int period, bool useNaN) + { + _p = period; + _NaN = useNaN; + _d1 = source1; + _d2 = source2; + _dd1 = double.NaN; + _dd2 = double.NaN; + _d1.Pub += Sub; + _d2.Pub += Sub; + } + + protected Pair_TSeries_Indicator(TSeries source1, TSeries source2) + { + _d1 = source1; + _d2 = source2; + _dd1 = double.NaN; + _dd2 = double.NaN; + _d1.Pub += Sub; + _d2.Pub += Sub; + } + + protected Pair_TSeries_Indicator(TSeries source1, double dd2) + { + _d1 = source1; + _d2 = new TSeries(); + _dd1 = double.NaN; + _dd2 = dd2; + _d1.Pub += Sub; + } + + protected Pair_TSeries_Indicator(double dd1, TSeries source2) + { + _d1 = new TSeries(); + _d2 = source2; + _dd1 = dd1; + _dd2 = double.NaN; + _d2.Pub += Sub; + } + + // overridable Add(Tvalue, Tvalue) method to add/update a single value at the end of the list + public virtual void Add((DateTime t, double v) TValue1, (DateTime t, double v) TValue2, bool update) + { + base.Add((TValue1.t, 0), update); + // default inserts zeros + } + + // potentially overridable Add() bulk variations (could be replaced with faster bulk algos) + public virtual void Add(TSeries d1, TSeries d2) + { + for (var i = 0; i < d1.Count; i++) + { + Add(d1[i], d2[i], false); + } + } + + public virtual void Add(TSeries d1, double dd2) + { + for (var i = 0; i < d1.Count; i++) + { + Add(d1[i], (d1[i].t, dd2), false); + } + } + + public virtual void Add(double dd1, TSeries d2) + { + for (var i = 0; i < d2.Count; i++) + { + Add((d2[i].t, dd1), d2[i], false); + } + } + + public void Add((DateTime t, double v) TValue1, (DateTime t, double v) TValue2) + { + Add(TValue1, TValue2, false); + } + + public void Add(bool update) + { + if (_dd1 is double.NaN && _dd2 is double.NaN) + { + // (Series, Series) + if (update || (_d1.Count > Count && _d2.Count > Count)) + { + Add(_d1[_d1.Count - 1], _d2[_d2.Count - 1], update); + } + } + else if (_dd2 is not double.NaN && _dd1 is double.NaN) + { + // (Series, Double) + Add(_d1[_d1.Count - 1], (_d1[_d1.Count - 1].t, _dd2), update); + } + else + { + // (Double, Series) + Add((_d2[_d2.Count - 1].t, _dd1), _d2[_d2.Count - 1], update); + } + } + + public void Add() + { + Add(false); + } + + public new void Sub(object source, TSeriesEventArgs e) + { + Add(e.update); + } + + protected static void Add_Replace(List l, double v, bool update) + { + if (update) + { + l[l.Count - 1] = v; + } + else + { + l.Add(v); + } + } + + protected static void Add_Replace_Trim(List l, double v, int p, bool update) + { + Add_Replace(l, v, update); + if (l.Count > p && p != 0) + { + l.RemoveAt(0); + } + } +} diff --git a/archive/Calculations/Feeds/Alphavantage_Feed.cs b/archive/Calculations/Feeds/Alphavantage_Feed.cs new file mode 100644 index 00000000..f2bcc60e --- /dev/null +++ b/archive/Calculations/Feeds/Alphavantage_Feed.cs @@ -0,0 +1,55 @@ +namespace QuanTAlib; +using System; +using System.Text.Json; + +/* +Alphavantage - Free API to collect 100 recent daily quotes. It requires a (free) API key + Get API key at https://www.alphavantage.co/support/#api-key + Parameters: + Symbol: stock ("AAPL"), + APIkey: unique Alphavantage API key + + +*/ +public class Alphavantage_Feed : TBars +{ + public enum Interval { Month, Week, Day, Hour, Min30, Min15, Min5, Min1 } + public Alphavantage_Feed(string Symbol = "IBM", string APIkey = "demo") + { + System.Net.Http.HttpClient client = new(); + + string req = "https://www.alphavantage.co/query?function=TIME_SERIES_DAILY_ADJUSTED" + "&symbol=" + Symbol + "&apikey=" + APIkey; + var msg = client.GetStringAsync(req).Result; + var jres = JsonSerializer.Deserialize(msg).RootElement; + jres.TryGetProperty("Time Series (Daily)", out JsonElement json); + + if (json.ValueKind == JsonValueKind.Undefined) { throw new InvalidOperationException("Stock symbol " + Symbol + " not found"); } + foreach (var val in json.EnumerateObject()) { base.Add(GetOHLC(val)); } + base.Reverse(); + } + private static (DateTime t, double o, double h, double l, double c, double v) GetOHLC(JsonProperty json) + { + double o, h, l, c, v; + o = h = l = c = v = 0; + DateTime date = Convert.ToDateTime(json.Name); + foreach (var val in json.Value.EnumerateObject()) + { + switch (val.Name) + { + case "1. open": o = Convert.ToDouble(val.Value.ToString()); break; + case "1b. open (USD)": o = Convert.ToDouble(val.Value.ToString()); break; + case "2. high": h = Convert.ToDouble(val.Value.ToString()); break; + case "2b. high (USD)": h = Convert.ToDouble(val.Value.ToString()); break; + case "3. low": l = Convert.ToDouble(val.Value.ToString()); break; + case "3b. low (USD)": l = Convert.ToDouble(val.Value.ToString()); break; + case "4. close": c = Convert.ToDouble(val.Value.ToString()); break; + case "4b. close (USD)": c = Convert.ToDouble(val.Value.ToString()); break; + case "5. adjusted close": c = Convert.ToDouble(val.Value.ToString()); break; + case "5. volume": v = Convert.ToDouble(val.Value.ToString()); break; + case "6. volume": v = Convert.ToDouble(val.Value.ToString()); break; + default: o = 0; h = 0; l = 0; c = 0; v = 0; break; + } + } + return (date, o, h, l, c, v); + } +} \ No newline at end of file diff --git a/archive/Calculations/Feeds/GBM_Feed.cs b/archive/Calculations/Feeds/GBM_Feed.cs new file mode 100644 index 00000000..c99a282f --- /dev/null +++ b/archive/Calculations/Feeds/GBM_Feed.cs @@ -0,0 +1,67 @@ +namespace QuanTAlib; +using System; + +/* +GBM - Geometric Brownian Motion is a random simulator of market movement, returning List + GBM can be used for testing indicators, validation and Monte Carlo simulations of strategies. + + Sample usage: + GBM-Random data = new(); // generates 1 year (252) list of bars + GBM-Random data = new(Bars: 1000); // generates 1,000 bars + GBM-Random data = new(Bars: 252, Volatility: 0.05, Drift: 0.0005, Seed: 100.0) + + Parameters + Bars: number of bars (quotes) requested + Volatility: how dymamic/volatile the series should be; default is 1 + Drift: incremental drift due to annual interest rate; default is 5% + Seed: starting value of the random series; should not be 0 + + */ + +public class GBM_Feed : TBars +{ + private double seed; + readonly double drift, volatility; + readonly int precision; + public GBM_Feed(int Bars = 252, double Volatility = 1.0, double Drift = 0.05, double Seed = 100.0, int Precision = 2) + { + this.seed = Seed; + volatility = Volatility * 0.01; + drift = Drift * 0.01; + precision = Precision; + for (int i = 0; i < Bars; i++) + { + DateTime Timestamp = DateTime.Today.AddDays(i - Bars); + this.Add(Timestamp); + } + } + + public void Add(bool update = false) { this.Add(DateTime.Now, update); } + public void Add(DateTime timestamp, bool update = false) + { + double Open = GBM_value(seed, volatility * volatility, drift, precision); + double Close = GBM_value(Open, volatility, drift, precision); + + double OCMax = Math.Max(Open, Close); + double High = (GBM_value(seed, volatility * 0.5, 0, precision)); + High = (High < OCMax) ? (2 * OCMax) - High : High; + + double OCMin = Math.Min(Open, Close); + double Low = (GBM_value(seed, volatility * 0.5, 0, precision)); + Low = (Low > OCMin) ? (2 * OCMin) - Low : Low; + + double Volume = GBM_value(seed * 10, volatility * 2, Drift: 0, precision: 1); + + base.Add((timestamp, Open, High, Low, Close, Volume), update); + seed = Close; + } + + private static double GBM_value(double Seed, double Volatility, double Drift, int precision) + { + Random rnd = new(); + double U1 = 1.0 - rnd.NextDouble(); + double U2 = 1.0 - rnd.NextDouble(); + double Z = Math.Sqrt(-2.0 * Math.Log(U1)) * Math.Sin(2.0 * Math.PI * U2); + return Math.Round(Seed * Math.Exp(Drift - (Volatility * Volatility * 0.5) + (Volatility * Z)), digits: precision); + } +} \ No newline at end of file diff --git a/archive/Calculations/Feeds/RND_Feed.cs b/archive/Calculations/Feeds/RND_Feed.cs new file mode 100644 index 00000000..c6e311af --- /dev/null +++ b/archive/Calculations/Feeds/RND_Feed.cs @@ -0,0 +1,28 @@ +namespace QuanTAlib; +using System; + +/* +Random Bars generator - used for testing, validation and fun + Returns 'bars' number of candles that follow common market movement. + volatility defines how 'jumpy' is the series of + startvalue defines beginning closing price that then guides the rest of series + + */ + +public class RND_Feed : TBars +{ + public RND_Feed(int Bars, double Volatility = 0.05, double Startvalue = 100.0) + { + Random rnd = new(); + double c = Startvalue; + for (int i = 0; i < Bars; i++) + { + double o = Math.Round(c + (c * (((Volatility * 0.1) * rnd.NextDouble()) - 0.005)), 2); + double h = Math.Round(o + (c * Volatility * rnd.NextDouble()), 2); + double l = Math.Round(o - (c * Volatility * rnd.NextDouble()), 2); + c = Math.Round(l + ((h - l) * rnd.NextDouble()), 2); + double v = Math.Round(1000 * rnd.NextDouble(), 2); + this.Add(DateTime.Today.AddDays(i - Bars), o, h, l, c, v); + } + } +} \ No newline at end of file diff --git a/archive/Calculations/Feeds/Yahoo_Feed.cs b/archive/Calculations/Feeds/Yahoo_Feed.cs new file mode 100644 index 00000000..1d87e97d --- /dev/null +++ b/archive/Calculations/Feeds/Yahoo_Feed.cs @@ -0,0 +1,50 @@ +namespace QuanTAlib; +using System; +using System.Text.Json; + +/* +Yahoo Finance - Free API feed to collect daily market quotes + Parameters: + Symbol: stock symbol (default: "IBM") + Period: number of days of collected history (default: 252) + Usage: + Yahoo_Feed ticker = new("MSFT", 20) + + +*/ +public class Yahoo_Feed : TBars +{ + public Yahoo_Feed(string Symbol = "IBM", int Period = 252) + { + Period = (int)(Period * 1.45); + string requestUrl = "https://query1.finance.yahoo.com/v8/finance/chart/" + + Symbol + "?interval=1d&period1=" + + (int)new DateTimeOffset(DateTime.UtcNow.AddDays(-Period + 1)).ToUnixTimeSeconds() + "&period2=" + + (int)new DateTimeOffset(DateTime.UtcNow).ToUnixTimeSeconds(); + System.Net.Http.HttpClient client = new(); + var msg = client.GetStringAsync(requestUrl).Result; + var jresult = JsonSerializer.Deserialize(msg).RootElement; + + jresult.TryGetProperty("chart", out JsonElement json); + json.TryGetProperty("result", out json); + json[0].TryGetProperty("timestamp", out JsonElement datetime); + json[0].TryGetProperty("indicators", out json); + json.TryGetProperty("quote", out json); + json[0].TryGetProperty("open", out JsonElement open); + json[0].TryGetProperty("high", out JsonElement high); + json[0].TryGetProperty("low", out JsonElement low); + json[0].TryGetProperty("close", out JsonElement close); + json[0].TryGetProperty("volume", out JsonElement volume); + + for (int i = 0; i < datetime.GetArrayLength(); i++) + { + DateTime d = DateTimeOffset.FromUnixTimeSeconds(long.Parse(datetime[i].GetRawText())).DateTime; + double o = Math.Round(double.Parse(open[i].GetRawText()), 3); + double h = Math.Round(double.Parse(high[i].GetRawText()), 3); + double l = Math.Round(double.Parse(low[i].GetRawText()), 3); + double c = Math.Round(double.Parse(close[i].GetRawText()), 3); + double v = Math.Round(double.Parse(volume[i].GetRawText()), 3); + base.Add(d, o, h, l, c, v); + } + } +} diff --git a/archive/Calculations/Logic/COMPARE_Series.cs b/archive/Calculations/Logic/COMPARE_Series.cs new file mode 100644 index 00000000..70d26902 --- /dev/null +++ b/archive/Calculations/Logic/COMPARE_Series.cs @@ -0,0 +1,39 @@ +namespace QuanTAlib; +using System; + +/* +COMPARE - Generates +1 if A is above B, -1 if A is below B and 0 if A=B + + + */ + +public class COMPARE_Series : Pair_TSeries_Indicator +{ + + public COMPARE_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + public COMPARE_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + } + public COMPARE_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + } + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + + double val = TValue1.v > TValue2.v ? 1 : -1; + val = TValue1.v == TValue2.v ? 0 : val; + (System.DateTime t, double v) over = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, TValue1.v > TValue2.v ? 1 : val); + if (update) { base[^1] = over; } + else { base.Add(over); } + + + } +} + + diff --git a/archive/Calculations/Logic/CROSS_Series.cs b/archive/Calculations/Logic/CROSS_Series.cs new file mode 100644 index 00000000..b9a40d9b --- /dev/null +++ b/archive/Calculations/Logic/CROSS_Series.cs @@ -0,0 +1,49 @@ +namespace QuanTAlib; +using System; + +/* +OVER - Generates +1 if A is above B, -1 if A is below B and 0 if A=B + +Remarks: + OVER.Cross generates 1 when A breaks B from below and -1 when A breaks B from above + + */ + +public class CROSS_Series : Pair_TSeries_Indicator +{ + public TSeries Cross { get; set; } = new(); + + private double _previous = double.NaN; + public CROSS_Series(TSeries d1, TSeries d2) : base(d1, d2) + { + if (base._d1.Count > 0 && base._d2.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], base._d2[i], false); } } + } + public CROSS_Series(TSeries d1, double dd2) : base(d1, dd2) + { + if (base._d1.Count > 0) { for (int i = 0; i < base._d1.Count; i++) { this.Add(base._d1[i], (base._d1[i].t, dd2), false); } } + } + public CROSS_Series(double dd1, TSeries d2) : base(dd1, d2) + { + if (base._d2.Count > 0) { for (int i = 0; i < base._d2.Count; i++) { this.Add((base._d2[i].t, dd1), base._d2[i], false); } } + } + + public override void Add((System.DateTime t, double v) TValue1, (System.DateTime t, double v) TValue2, bool update) + { + + double val = TValue1.v > TValue2.v ? 1 : -1; + val = TValue1.v == TValue2.v ? 0 : val; + double over = TValue1.v > TValue2.v ? 1 : val; + + val = (_previous < over) ? 1 : -1; + val = ((_previous == over) || Double.IsNaN(this._previous) || (this._previous == 0)) ? 0 : val; + (System.DateTime t, double v) result = ((TValue1.t > TValue2.t) ? TValue1.t : TValue2.t, val); + + this._previous = over; + + if (update) { base[^1] = result; } + else { base.Add(result); } + + } +} + + diff --git a/archive/Calculations/Logic/EQUITY_Series.cs b/archive/Calculations/Logic/EQUITY_Series.cs new file mode 100644 index 00000000..8323feb7 --- /dev/null +++ b/archive/Calculations/Logic/EQUITY_Series.cs @@ -0,0 +1,91 @@ +namespace QuanTAlib; +using System; + +/* +EQUITY - Generates P&L portfolio based on trades signals and equity prices + + */ + + +//base prices: bars.close +//trade signals: trades +//optional: long, short, long&short +//optional: warmup period: warmup + +/* + +public class EQUITY_Series : Single_TSeries_Indicator { + readonly TSeries inmarket; //for every bar + private readonly TSeries _price; + private double _equity; + private readonly double _capital; + + readonly int _warmup; + double _cash; + int _units; + private bool _longbuy, _longsell; + double _long_order, _open_order; + double _investment_value; + short _inmarket; + + public EQUITY_Series(TSeries signal, TSeries price, int warmup = 0, double capital = 1000) : base(signal, period: 0, useNaN: false) { + _capital = capital; + _cash = _capital; + _investment_value = 0; + _warmup = (warmup > 0) ? warmup : 1; + + inmarket = new(); + _longbuy = _longsell = false; + _open_order = 0; + _inmarket = 0; + _units = 0; + _long_order = 0; + + _price = price; //we buy on the Open price of the NEXT bar + _long_order = 0; + + if (base._data.Count > 0) { base.Add(base._data); } + } + + public override void Add((System.DateTime t, double v) TValue, bool update) { + + if (this.Count > _warmup) { + + // harvest the gain-loss from previous day + _investment_value = _units * _price[this.Count - 1].v; + _equity = _cash + _investment_value; + + + //execute orders from previous bar + if (_longbuy && _inmarket == 0) { //time to execute the long buy + _units = (int)(_cash / _price[this.Count - 1].v); + _long_order = _units * _price[this.Count - 1].v; + _cash -= _long_order; + _open_order = _long_order; + _equity = _cash + _open_order; + _inmarket = 1; + _longbuy = false; + } + + if (_longsell && _inmarket == 1) { //time to execute the long sell + _long_order = (_units * _price[this.Count - 1].v); + _cash += _long_order; + _units = 0; + + _open_order = 0; + _equity = _cash + _open_order; + _inmarket = 0; + _longsell = false; + } + + if (_inmarket == 0 && TValue.v == 1) { _longbuy = true; } //out of market, enter long + if (_inmarket == 1 && TValue.v == -1) { _longsell = true; } //long market, exit long + + //Console.WriteLine($"{TValue.v,3}\t {(_inmarket)} : {_cash,10:f2} + {_units*_price[^1].v,7:f2} = {_equity-_capital:f2}"); + } + inmarket.Add((TValue.t, (double)_inmarket)); + base.Add((TValue.t, _equity), update, _NaN); + } +} + +*/ \ No newline at end of file diff --git a/archive/Calculations/Logic/TOrders.cs b/archive/Calculations/Logic/TOrders.cs new file mode 100644 index 00000000..ec681ec5 --- /dev/null +++ b/archive/Calculations/Logic/TOrders.cs @@ -0,0 +1,38 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Collections.ObjectModel; +using System.Data; +using System.Linq; + + +public enum OType +{ + NIL = 0, // No position + BTO = 1, // Buy to Open + STC = 2, // Sell to Close + STO = 3, // Sell to Open + BTC = 4, // Buy to Close + END = 5, // Exit the trade +} + + +public class TOrders : List<(DateTime t, OType o)> +{ + + public void Add((DateTime t, OType o) TOrder, bool update = false) + { + if (update) { this[^1] = TOrder; } + else { base.Add(TOrder); } + OnEvent(update); + } + + + protected virtual void OnEvent(bool update = false) + { + Pub?.Invoke(this, new TSeriesEventArgs { update = update }); + } + public delegate void NewDataEventHandler(object source, TSeriesEventArgs args); + public event NewDataEventHandler Pub; + +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ADL_Series.cs b/archive/Calculations/_Updated/ADL_Series.cs new file mode 100644 index 00000000..9873a1cc --- /dev/null +++ b/archive/Calculations/_Updated/ADL_Series.cs @@ -0,0 +1,79 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ADL: Chaikin Accumulation/Distribution Line + ADL is a volume-based indicator that measures the cumulative Money Flow Volume: + + 1. Money Flow Multiplier = [(Close - Low) - (High - Close)] /(High - Low) + 2. Money Flow Volume = Money Flow Multiplier x Volume for the Period + 3. ADL = Previous ADL + Current Period's Money Flow Volume + +Sources: + https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line + + */ + +public class ADL_Series : TSeries +{ + protected readonly TBars _data; + private double _lastadl, _lastlastadl; + + //core constructors + public ADL_Series() + { + Name = $"ADL()"; + _lastadl = _lastlastadl = 0; + } + public ADL_Series(TBars source) + { + _data = source; + Name = $"ADL({(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _lastadl = _lastlastadl = 0; + _data.Pub += Sub; + Add(data: _data); + } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) { this._lastadl = this._lastlastadl; } + else { this._lastlastadl = this._lastadl; } + + double _adl = 0; + double tmp = TBar.h - TBar.l; + if (tmp > 0.0) + { + _adl = _lastadl + ((2 * TBar.c - TBar.l - TBar.h) / tmp * TBar.v); + } + _lastadl = _adl; + + var ret = (TBar.t, _adl); + return base.Add(ret, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _lastadl = _lastlastadl = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ADOSC_Series.cs b/archive/Calculations/_Updated/ADOSC_Series.cs new file mode 100644 index 00000000..b3ebc2c1 --- /dev/null +++ b/archive/Calculations/_Updated/ADOSC_Series.cs @@ -0,0 +1,100 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ADOSC: Chaikin Accumulation/Distribution Oscillator + ADO measures the momentum of ADL using the difference between slow (10-day) EMA(ADL) + and fast (3-day) EMA(ADL): + + Chaikin A/D Oscillator is defined as 3-day EMA of ADL minus 10-day EMA of ADL + +Sources: + https://school.stockcharts.com/doku.php?id=technical_indicators:chaikin_oscillator + + */ + +public class ADOSC_Series : TSeries +{ + protected readonly TBars _data; + private readonly double _k1, _k2; + private double _lastema1, _lastlastema1, _lastema2, _lastlastema2; + private double _lastadl, _lastlastadl; + + //core constructors + public ADOSC_Series(int shortPeriod, int longPeriod, bool useNaN = false) + { + Name = $"ADOSC()"; + _k1 = 2.0 / (shortPeriod + 1); + _k2 = 2.0 / (longPeriod + 1); + _lastadl = _lastlastadl = _lastema1 = _lastlastema1 = _lastema2 = _lastlastema2 = 0; + } + public ADOSC_Series(TBars source, int shortPeriod, int longPeriod, bool useNaN = false) : this(shortPeriod, longPeriod, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _lastadl = _lastlastadl = 0; + _data.Pub += Sub; + Add(data: _data); + } + + public ADOSC_Series() : this(shortPeriod: 3, longPeriod: 10, useNaN: false) { } + + public ADOSC_Series(TBars source) : this(source, shortPeriod: 3, longPeriod: 10, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + + if (update) + { + _lastadl = _lastlastadl; + _lastema1 = _lastlastema1; + _lastema2 = _lastlastema2; + } + + double _adl = 0; + double tmp = TBar.h - TBar.l; + if (tmp > 0.0) { _adl = _lastadl + ((2 * TBar.c - TBar.l - TBar.h) / tmp * TBar.v); } + if (this.Count == 0) { _lastema1 = _lastema2 = _adl; } + + double _ema1 = (_adl - _lastema1) * _k1 + _lastema1; + double _ema2 = (_adl - _lastema2) * _k2 + _lastema2; + + _lastlastadl = _lastadl; + _lastadl = _adl; + _lastlastema1 = _lastema1; + _lastema1 = _ema1; + _lastlastema2 = _lastema2; + _lastema2 = _ema2; + + double _adosc = _ema1 - _ema2; + + var ret = (TBar.t, _adosc); + return base.Add(ret, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _lastadl = _lastlastadl = _lastema1 = _lastlastema1 = _lastema2 = _lastlastema2 = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ALMA_Series.cs b/archive/Calculations/_Updated/ALMA_Series.cs new file mode 100644 index 00000000..5c62c0dd --- /dev/null +++ b/archive/Calculations/_Updated/ALMA_Series.cs @@ -0,0 +1,129 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +ALMA: Arnaud Legoux Moving Average + The ALMA moving average uses the curve of the Normal (Gauss) distribution, which + can be shifted from 0 to 1. This allows regulating the smoothness and high + sensitivity of the indicator. Sigma is another parameter that is responsible for + the shape of the curve coefficients. This moving average reduces lag of the data + in conjunction with smoothing to reduce noise. + + +Sources: + https://phemex.com/academy/what-is-arnaud-legoux-moving-averages + https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/ + + Discrepancy with Pandas-TA (but passes the validation with Skender.GetAlma) + */ + +public class ALMA_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + private readonly System.Collections.Generic.List _buffer = new(); + private readonly System.Collections.Generic.List _weight; + private double _norm; + private readonly double _offset, _sigma; + + //core constructors + public ALMA_Series(int period, double offset, double sigma, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"ALMA({period})"; + _offset = offset; + _sigma = sigma; + _weight = new(); + } + public ALMA_Series(TSeries source, int period, double offset, double sigma, bool useNaN) : this(period, offset, sigma, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + public ALMA_Series() : this(period: 0, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(int period) : this(period: period, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TBars source) : this(source: source.Close, period: 0, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TBars source, int period) : this(source: source.Close, period: period, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TBars source, int period, double offset, double sigma, bool useNaN) : this(source.Close, period: period, offset: offset, sigma: sigma, useNaN: false) { } + public ALMA_Series(TSeries source) : this(source, period: 0, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TSeries source, int period) : this(source: source, period: period, offset: 0.85, sigma: 6.0, useNaN: false) { } + public ALMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, offset: 0.85, sigma: 6.0, useNaN: useNaN) { } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, double.NaN), update); + } + + BufferTrim(_buffer, TValue.v, _period, update); + if (_weight.Count < _buffer.Count) + { + for (var i = 0; i < _buffer.Count - _weight.Count; i++) + { + _weight.Add(0.0); + } + } + + + if (_buffer.Count <= _period || _period == 0) + { + var _len = _buffer.Count; + _norm = 0; + var _m = _offset * (_len - 1); + var _s = _len / _sigma; + for (var i = 0; i < _len; i++) + { + var _wt = Math.Exp(-((i - _m) * (i - _m)) / (2 * _s * _s)); + _weight[i] = _wt; + _norm += _wt; + } + } + + double _weightedSum = 0; + for (var i = 0; i < _buffer.Count; i++) + { + _weightedSum += _weight[i] * _buffer[i]; + } + + var _alma = _weightedSum / _norm; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _alma); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _weight.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ATRP_Series.cs b/archive/Calculations/_Updated/ATRP_Series.cs new file mode 100644 index 00000000..9a4fee50 --- /dev/null +++ b/archive/Calculations/_Updated/ATRP_Series.cs @@ -0,0 +1,97 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ATRP: Average True Range Percent + Average True Range Percent is (ATR/Close Price)*100. + This normalizes so it can be compared to other stocks. + +Sources: + https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/atrp + + */ + +public class ATRP_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private double _k; + private int _len; + private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum; + + //core constructors + public ATRP_Series(int period, bool useNaN) + { + _period = period; + _k = 1.0 / (double)(_period); + _NaN = useNaN; + _len = 0; + Name = $"ATRP({period})"; + } + public ATRP_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public ATRP_Series() : this(period: 1, useNaN: false) { } + public ATRP_Series(int period) : this(period: period, useNaN: false) { } + public ATRP_Series(TBars source) : this(source, period: 1, useNaN: false) { } + public ATRP_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; } + else + { + _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; + _k = (_period == 0) ? 1 / (double)_len : _k; + _len++; + } + + if (_len == 1) { _cm1 = TBar.c; } + double d1 = Math.Abs(TBar.h - TBar.l); + double d2 = Math.Abs(_cm1 - TBar.h); + double d3 = Math.Abs(_cm1 - TBar.l); + (DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); + _cm1 = TBar.c; + + double _atr = 0; + if (this.Count == 0) { _atr = d.v; } + else if (this.Count < _period + 1) { _sum += d.v; _atr = _sum / (this.Count); } + else { _atr = _k * (d.v - _lastatr) + _lastatr; } + _lastatr = _atr; + double _atrp = 100 * (_atr / TBar.c); + + var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _atrp); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ATR_Series.cs b/archive/Calculations/_Updated/ATR_Series.cs new file mode 100644 index 00000000..c7e36b0a --- /dev/null +++ b/archive/Calculations/_Updated/ATR_Series.cs @@ -0,0 +1,98 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ATR: wildeR Moving Average + The average true range (ATR) is a price volatility indicator + showing the average price variation of assets within a given time period. + +Sources: + https://en.wikipedia.org/wiki/Average_true_range + https://www.tradingview.com/wiki/Average_True_Range_(ATR) + https://www.investopedia.com/terms/a/atr.asp + + */ + +public class ATR_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private double _k; + private int _len; + private double _lastatr, _lastlastatr, _cm1, _lastcm1, _sum, _oldsum; + + //core constructors + public ATR_Series(int period, bool useNaN) + { + _period = period; + _k = 1.0 / (double)(_period); + _NaN = useNaN; + _len = 0; + Name = $"ATR({period})"; + } + public ATR_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public ATR_Series() : this(period: 1, useNaN: false) { } + public ATR_Series(int period) : this(period: period, useNaN: false) { } + public ATR_Series(TBars source) : this(source, period: 1, useNaN: false) { } + public ATR_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) { _lastatr = _lastlastatr; _cm1 = _lastcm1; _sum = _oldsum; } + else + { + _lastlastatr = _lastatr; _lastcm1 = _cm1; _oldsum = _sum; + _k = (_period == 0) ? 1 / (double)_len : _k; + _len++; + } + + if (_len == 1) { _cm1 = TBar.c; } + double d1 = Math.Abs(TBar.h - TBar.l); + double d2 = Math.Abs(_cm1 - TBar.h); + double d3 = Math.Abs(_cm1 - TBar.l); + (DateTime t, double v) d = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); + _cm1 = TBar.c; + + double _atr = 0; + if (this.Count == 0) { _atr = d.v; } + else if (this.Count < _period + 1) { _sum += d.v; _atr = _sum / (this.Count); } + else { _atr = _k * (d.v - _lastatr) + _lastatr; } + _lastatr = _atr; + + var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _atr); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/BBANDS_Series.cs b/archive/Calculations/_Updated/BBANDS_Series.cs new file mode 100644 index 00000000..1750def4 --- /dev/null +++ b/archive/Calculations/_Updated/BBANDS_Series.cs @@ -0,0 +1,121 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +BBANDS: Bollinger Bands® + Price channels created by John Bollinger, depict volatility as standard deviation boundary + line range from a moving average of price. The bands automatically widen when volatility + increases and contract when volatility decreases. Their dynamic nature allows them to be + used on different securities with the standard settings. + + Mid Band = simple moving average (SMA) + Upper Band = SMA + (standard deviation of price x multiplier) + Lower Band = SMA - (standard deviation of price x multiplier) + Bandwidth = Width of the channel: (Upper-Lower)/SMA + %B = The location of the data point within the channel: (Price-Lower)/(Upper/Lower) + Z-Score = number of standard deviations of the data point from SMA + +Sources: + https://www.investopedia.com/terms/b/bollingerbands.asp + https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands + +Note: + Bollinger Bands® is a registered trademark of John A. Bollinger. + + */ + +public class BBANDS_Series : TSeries +{ + protected readonly int _period; + protected readonly double _multiplier; + protected readonly bool _NaN; + protected readonly TSeries _data; + public SMA_Series Mid { get; } + public TSeries Upper { get; } + public TSeries Lower { get; } + public TSeries PercentB { get; } + public TSeries Bandwidth { get; } + public TSeries Zscore { get; } + private readonly SDEV_Series _sdev; + + //core constructors + public BBANDS_Series(int period, double multiplier, bool useNaN) + { + _period = period; + _multiplier = multiplier; + _NaN = useNaN; + Name = $"BBANDS({period})"; + } + public BBANDS_Series(TSeries source, int period, double multiplier, bool useNaN) : this(period, multiplier, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + Upper = new("BB_Up"); + Lower = new("BB_Low"); + Bandwidth = new("BBandwidth"); + PercentB = new("%BBandwidth"); + Zscore = new("Zscore"); + + Mid = new(period, false); + _sdev = new(period, false); + + _data.Pub += Sub; + Add(_data); + } + + public BBANDS_Series() : this(period: 0, multiplier: 2.0, useNaN: false) { } + public BBANDS_Series(int period) : this(period: period, multiplier: 2.0, useNaN: false) { } + public BBANDS_Series(TBars source) : this(source: source.Close, period: 0, multiplier: 2.0, useNaN: false) { } + public BBANDS_Series(TBars source, int period) : this(source: source.Close, period: period, multiplier: 2.0, useNaN: false) { } + public BBANDS_Series(TBars source, int period, double multiplier, bool useNaN) : this(source.Close, period: period, multiplier: multiplier, useNaN: false) { } + public BBANDS_Series(TSeries source) : this(source, period: 0, useNaN: false) { } + public BBANDS_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + public BBANDS_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, multiplier: 2.0, useNaN: useNaN) { } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + var _mid = Mid.Add(TValue, update); + var _sd = this._sdev.Add(TValue, update); + var _upper = Upper.Add((TValue.t, _mid.v + _sd.v * _multiplier), update); + var _lower = Lower.Add((TValue.t, _mid.v - _sd.v * _multiplier), update); + double _pbdnd = TValue.v - _lower.v; + double _pbdvr = _upper.v - _lower.v; + PercentB.Add((TValue.t, _pbdnd / _pbdvr), update); + Zscore.Add((TValue.t, (TValue.v - _mid.v) / _sd.v), update); + Bandwidth.Add((TValue.t, _pbdvr / _mid.v), update); + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _pbdvr / _mid.v); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + //reset calculation + public override void Reset() + { + Mid.Clear(); + _sdev.Clear(); + Upper.Clear(); + Lower.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/BIAS_Series.cs b/archive/Calculations/_Updated/BIAS_Series.cs new file mode 100644 index 00000000..f8bcb0a3 --- /dev/null +++ b/archive/Calculations/_Updated/BIAS_Series.cs @@ -0,0 +1,81 @@ +namespace QuanTAlib; +using System; + +/* +BIAS: Rate of change between the source and a moving average. + Bias is a statistical term which means a systematic deviation from the actual value. + +BIAS = (close - SMA) / SMA + = (close / SMA) - 1 + +Sources: + https://en.wikipedia.org/wiki/Bias_of_an_estimator + + */ + +public class BIAS_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly SMA_Series _sma; + + //core constructors + public BIAS_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"BIAS({period})"; + _sma = new(period, false); + } + public BIAS_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public BIAS_Series() : this(period: 0, useNaN: false) { } + public BIAS_Series(int period) : this(period: period, useNaN: false) { } + public BIAS_Series(TBars source) : this(source.Close, 0, false) { } + public BIAS_Series(TBars source, int period) : this(source.Close, period, false) { } + public BIAS_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public BIAS_Series(TSeries source) : this(source, 0, false) { } + public BIAS_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + var _s = _sma.Add(TValue, update); + double _bias = (TValue.v / ((_s.v != 0) ? _s.v : 1)) - 1; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _bias); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _sma.Reset(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/CCI_Series.cs b/archive/Calculations/_Updated/CCI_Series.cs new file mode 100644 index 00000000..4ee2d959 --- /dev/null +++ b/archive/Calculations/_Updated/CCI_Series.cs @@ -0,0 +1,97 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +CCI: Commodity Channel Index + Commodity Channel Index is a momentum oscillator used to primarily identify overbought + and oversold levels relative to a mean. CCI measures the current price level relative + to an average price level over a given period of time: + - CCI is relatively high when prices are far above their average. + - CCI is relatively low when prices are far below their average. + Using this method, CCI can be used to identify overbought and oversold levels. + +Sources: + https://www.investopedia.com/terms/c/commoditychannelindex.asp + https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/cci + + */ + +public class CCI_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private readonly System.Collections.Generic.List _tp = new(); + + //core constructors + public CCI_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"CCI({period})"; + } + public CCI_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public CCI_Series() : this(period: 2, useNaN: false) { } + public CCI_Series(int period) : this(period: period, useNaN: false) { } + public CCI_Series(TBars source) : this(source, period: 2, useNaN: false) { } + public CCI_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + double _tpItem = (TBar.h + TBar.l + TBar.c) / 3.0; + if (update) + { + this._tp[this._tp.Count - 1] = _tpItem; + } + else + { + this._tp.Add(_tpItem); + } + if (this._tp.Count > this._period) { this._tp.RemoveAt(0); } + + // average TP over _tp buffer + double _avgTp = _tp.Average(); + + // average Deviation over _tp buffer + double _avgDv = 0; + for (int i = 0; i < this._tp.Count; i++) { _avgDv += Math.Abs(_avgTp - this._tp[i]); } + _avgDv /= this._tp.Count; + + double _cci = (_avgDv == 0) ? 0 : (this._tp[this._tp.Count - 1] - _avgTp) / (0.015 * _avgDv); + var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _cci); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _tp.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/CMO_Series.cs b/archive/Calculations/_Updated/CMO_Series.cs new file mode 100644 index 00000000..36476603 --- /dev/null +++ b/archive/Calculations/_Updated/CMO_Series.cs @@ -0,0 +1,100 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +CMO: Chande Momentum Oscillator + Chande Momentum Oscillator (also known as CMO indicator) was developed by Tushar S. Chande + CMO is similar to other momentum oscillators (e.g. RSI or Stochastics). Alike RSI oscillator, + the CMO values move in the range from -100 to +100 points and its aim is to detect the + overbought and oversold market conditions. CMO calculates the price momentum on both the up + days as well as the down days. The CMO calculation is based on non-smoothed price values + meaning that it can reach its extremes more frequently and the short-time swings are more visible. + +Sources: + https://www.technicalindicators.net/indicators-technical-analysis/144-cmo-chande-momentum-oscillator + + */ + +public class CMO_Series : TSeries +{ + private readonly System.Collections.Generic.List _buff_up = new(); + private readonly System.Collections.Generic.List _buff_dn = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private double _plast_value, _last_value; + + //core constructors + public CMO_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"CMO({period})"; + } + public CMO_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public CMO_Series() : this(period: 0, useNaN: false) { } + public CMO_Series(int period) : this(period: period, useNaN: false) { } + public CMO_Series(TBars source) : this(source.Close, 0, false) { } + public CMO_Series(TBars source, int period) : this(source.Close, period, false) { } + public CMO_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public CMO_Series(TSeries source) : this(source, 0, false) { } + public CMO_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) { _last_value = _plast_value; } else { _plast_value = _last_value; } + BufferTrim(buffer: _buff_up, (TValue.v > _last_value) ? TValue.v - _last_value : 0, period: _period, update: update); + BufferTrim(buffer: _buff_dn, (TValue.v < _last_value) ? _last_value - TValue.v : 0, period: _period, update: update); + _last_value = TValue.v; + double _cmo_up = 0; + double _cmo_dn = 0; + for (int i = 0; i < Math.Min(_buff_up.Count, _buff_dn.Count); i++) + { + _cmo_up += _buff_up[i]; + _cmo_dn += _buff_dn[i]; + } + double _cmo = 100 * (_cmo_up - _cmo_dn) / (_cmo_up + _cmo_dn); + if (_cmo_up + _cmo_dn == 0) { _cmo = 0; } + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _cmo); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buff_up.Clear(); + _buff_dn.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/CUSUM_Series.cs b/archive/Calculations/_Updated/CUSUM_Series.cs new file mode 100644 index 00000000..eef69d44 --- /dev/null +++ b/archive/Calculations/_Updated/CUSUM_Series.cs @@ -0,0 +1,80 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +CUSUM: Cumulative Sum (aka Running Total) + SUM across a period provides a rolling sum of all values across the period. + If SUM values would be divided with period, the output would be SMA() + +Sources: + https://en.wikipedia.org/wiki/CUSUM + */ + +public class CUSUM_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public CUSUM_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"CUSUM({period})"; + } + public CUSUM_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public CUSUM_Series() : this(period: 0, useNaN: false) { } + public CUSUM_Series(int period) : this(period: period, useNaN: false) { } + public CUSUM_Series(TBars source) : this(source.Close, 0, false) { } + public CUSUM_Series(TBars source, int period) : this(source.Close, period, false) { } + public CUSUM_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public CUSUM_Series(TSeries source) : this(source, period: 0, useNaN: false) { } + public CUSUM_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sum = 0; + for (int i = 0; i < _buffer.Count; i++) { _sum += _buffer[i]; } + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sum); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/DECAY_Series.cs b/archive/Calculations/_Updated/DECAY_Series.cs new file mode 100644 index 00000000..287b3f45 --- /dev/null +++ b/archive/Calculations/_Updated/DECAY_Series.cs @@ -0,0 +1,93 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +DECAY: + Linear decay can be modeled by a straight line with a negative slope of 1/period. + The value decreases in a straight line from the last maximum to 0. + Decay = Last Max - distance/period + + Exponential decay is modeled as an exponential curve with diminishing factor of + 1-1/p + + */ + +public class DECAY_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly bool _exp; + private double _pdecay, _ppdecay; + private readonly double _dfactor; + + //core constructors + public DECAY_Series(int period, bool exponential, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"DECAY({period})"; + _exp = exponential; + _dfactor = (_exp) ? 1.0 - 1.0 / (double)_period : 1 / (double)_period; + _pdecay = _ppdecay = 0; + } + public DECAY_Series(TSeries source, int period, bool exponential, bool useNaN) : this(period, exponential, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public DECAY_Series() : this(period: 0, exponential: false, useNaN: false) { } + public DECAY_Series(int period) : this(period: period, exponential: false, useNaN: false) { } + public DECAY_Series(TBars source) : this(source.Close, period: 0, exponential: false, useNaN: false) { } + public DECAY_Series(TBars source, int period) : this(source.Close, period: period, exponential: false, useNaN: false) { } + public DECAY_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, exponential: false, useNaN) { } + public DECAY_Series(TSeries source) : this(source, period: 0, exponential: false, useNaN: false) { } + public DECAY_Series(TSeries source, int period) : this(source: source, period: period, exponential: false, useNaN: false) { } + public DECAY_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, exponential: false, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + if (update) { _pdecay = _ppdecay; } + else { _ppdecay = _pdecay; } + + if (this.Count == 0) { _pdecay = TValue.v; } + double _decay = Math.Max(TValue.v, Math.Max((_exp) ? _pdecay * _dfactor : _pdecay - _dfactor, 0)); + _pdecay = _decay; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _decay); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _pdecay = _ppdecay = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/DEMA_Series.cs b/archive/Calculations/_Updated/DEMA_Series.cs new file mode 100644 index 00000000..1288d666 --- /dev/null +++ b/archive/Calculations/_Updated/DEMA_Series.cs @@ -0,0 +1,144 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +DEMA: Double Exponential Moving Average + DEMA uses EMA(EMA()) to calculate smoother Exponential moving average. + +Sources: + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/double-exponential-moving-average-dema/ + +Remark: + ema1 = EMA(close, length) + ema2 = EMA(ema1, length) + DEMA = 2 * ema1 - ema2 + + */ + +public class DEMA_Series : TSeries +{ + private double _k; + private double _sum, _oldsum; + private double _lastema1, _oldema1, _lastema2, _oldema2; + private int _len; + private readonly bool _useSMA; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructor + public DEMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"DEMA({period})"; + _k = 2.0 / (_period + 1); + _len = 0; + _sum = _oldsum = _lastema1 = _lastema2 = 0; + } + //generic constructors (source) + + public DEMA_Series() : this(0, false, true) { } + public DEMA_Series(int period) : this(period, false, true) { } + public DEMA_Series(TBars source) : this(source.Close, 0, false) { } + public DEMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public DEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public DEMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public DEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public DEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastema1 = _oldema1; + _lastema2 = _oldema2; + _sum = _oldsum; + } + else + { + _oldema1 = _lastema1; + _oldema2 = _lastema2; + _oldsum = _sum; + _len++; + } + + if (_period == 0) + { + _k = 2.0 / (_len + 1); + } + + double _ema1, _ema2, _dema; + if (Count == 0) + { + _ema1 = _ema2 = _sum = TValue.v; + } + else if (_len <= _period && _useSMA && _period != 0) + { + _sum += TValue.v; + _ema1 = _sum / Math.Min(_len, _period); + _ema2 = _ema1; + } + else + { + _ema1 = (TValue.v - _lastema1) * _k + _lastema1; + _ema2 = (_ema1 - _lastema2) * _k + _lastema2; + } + + _dema = 2 * _ema1 - _ema2; + + _lastema1 = double.IsNaN(_ema1) ? _lastema1 : _ema1; + _lastema2 = double.IsNaN(_ema2) ? _lastema2 : _ema2; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _dema); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) + { + return (DateTime.Today, double.NaN); + } + + foreach (var item in data) + { + Add(item, false); + } + + return _data.Last; + } + + public (DateTime t, double v) Add(bool update) + { + return Add(_data.Last, update); + } + + public (DateTime t, double v) Add() + { + return Add(_data.Last, false); + } + + private new void Sub(object source, TSeriesEventArgs e) + { + Add(_data.Last, e.update); + } + + //reset calculation + public override void Reset() + { + _sum = _oldsum = _lastema1 = _lastema2 = 0; + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/DWMA_Series.cs b/archive/Calculations/_Updated/DWMA_Series.cs new file mode 100644 index 00000000..7c03b8a9 --- /dev/null +++ b/archive/Calculations/_Updated/DWMA_Series.cs @@ -0,0 +1,143 @@ +namespace QuanTAlib; + +using System; +using System.Collections.Generic; +using System.Threading.Tasks; + +/* +DWMA: Double Weighted Moving Average + The weights are decreasing over the period with p^2 decay + and the most recent data has the heaviest weight. + + */ + +public class DWMA_Series : TSeries +{ + private readonly List _buffer = new(); + private List _weights; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + protected int _len; + + //core constructors + public DWMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"DWMA({period})"; + _len = 0; + _weights = CalculateWeights(_period); + } + + public DWMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + public DWMA_Series() : this(0, false) + { + } + + public DWMA_Series(int period) : this(period, false) + { + } + + public DWMA_Series(TBars source) : this(source.Close, 0, false) + { + } + + public DWMA_Series(TBars source, int period) : this(source.Close, period, false) + { + } + + public DWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) + { + } + + public DWMA_Series(TSeries source, int period) : this(source, period, false) + { + } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(_buffer, TValue.v, _period, update); + if (_period == 0) + { + _len++; + _weights = CalculateWeights(_len); + } + + double _dwma = 0, _wsum = 0; + var bufferCount = _buffer.Count; + + var lockObj = new object(); + Parallel.For(0, bufferCount, i => + { + var temp = _buffer[i] * _weights[i]; + lock (lockObj) + { + _dwma += temp; + _wsum += _weights[i]; + } + }); + _dwma /= _wsum; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _dwma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) + { + return (DateTime.Today, double.NaN); + } + + foreach (var item in data) + { + Add(item, false); + } + + return _data.Last; + } + + public (DateTime t, double v) Add(bool update) + { + return Add(_data.Last, update); + } + + public (DateTime t, double v) Add() + { + return Add(_data.Last, false); + } + + private new void Sub(object source, TSeriesEventArgs e) + { + Add(_data.Last, e.update); + } + + //calculating weights + private static List CalculateWeights(int period) + { + var weights = new List(period); + for (var i = 0; i < period; i++) + { + weights.Add((i + 1) * (i + 1)); + } + + return weights; + } + + //reset calculation + public override void Reset() + { + _len = 0; + _buffer.Clear(); + _weights = CalculateWeights(_period); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/EMA_Series.cs b/archive/Calculations/_Updated/EMA_Series.cs new file mode 100644 index 00000000..7f49566d --- /dev/null +++ b/archive/Calculations/_Updated/EMA_Series.cs @@ -0,0 +1,136 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +EMA: Exponential Moving Average + EMA needs very short history buffer and calculates the EMA value using just the + previous EMA value. The weight of the new datapoint (k) is k = 2 / (period-1) + +Sources: + https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages + https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp + https://blog.fugue88.ws/archives/2017-01/The-correct-way-to-start-an-Exponential-Moving-Average-EMA + +Issues: + There is no consensus what the first EMA value should be - a zero, a first + datapoint, or an average of the initial Period bars. All three starting methods + converge within 20+ bars to the same moving average. Most implementations (including this one) + use SMA() for the first Period bars as a seeding value for EMA. + + */ + +public class EMA_Series : TSeries +{ + private double _k; + private double _lastema, _oldema; + private double _sum, _oldsum; + private int _len; + private readonly bool _useSMA; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + + public EMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"EMA({period})"; + _k = 2.0 / (_period + 1); + _len = 0; + _sum = _oldsum = _lastema = _oldema = 0; + } + public EMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public EMA_Series() : this(0, false, true) { } + public EMA_Series(int period) : this(period, false, true) { } + public EMA_Series(TBars source) : this(source.Close, 0, false) { } + public EMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public EMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public EMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public EMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastema = _oldema; + _sum = _oldsum; + } + else + { + _oldema = _lastema; + _oldsum = _sum; + _len++; + } + + double _ema = 0; + if (_period == 0) + { + _k = 2.0 / (_len + 1); + } + + if (Count == 0) + { + _ema = _sum = TValue.v; + } + else if (_len <= _period && _useSMA && _period != 0) + { + _sum += TValue.v; + if (_period != 0 && _len > _period) + { + _sum -= _data[Count - _period - (update ? 1 : 0)].v; + } + + _ema = _sum / Math.Min(_len, _period); + } + else + { + _ema = _k * (TValue.v - _lastema) + _lastema; + } + + _lastema = double.IsNaN(_ema) ? _lastema : _ema; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ema); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _sum = _oldsum = _lastema = _oldema = 0; + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ENTROPY_Series.cs b/archive/Calculations/_Updated/ENTROPY_Series.cs new file mode 100644 index 00000000..14da1862 --- /dev/null +++ b/archive/Calculations/_Updated/ENTROPY_Series.cs @@ -0,0 +1,97 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +ENTROPY: + Introduced by Claude Shannon in 1948, entropy measures the unpredictability + of the data, or equivalently, of its average information. + +Calculation: + P = close / Σ(close) + ENTROPY = Σ(-P * Log(P) / Log(base)) + +Sources: + https://en.wikipedia.org/wiki/Entropy_(information_theory) + https://math.stackexchange.com/questions/3428693/how-to-calculate-entropy-from-a-set-of-correlated-samples + + */ + +public class ENTROPY_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly double _logbase; + private readonly System.Collections.Generic.List _buffer = new(); + private readonly System.Collections.Generic.List _buff2 = new(); + + //core constructors + public ENTROPY_Series(int period, double logbase, bool useNaN) + { + _period = period; + _NaN = useNaN; + _logbase = logbase; + Name = $"ENTROPY({period})"; + } + public ENTROPY_Series(TSeries source, int period, double logbase, bool useNaN) : this(period, logbase, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public ENTROPY_Series() : this(period: 0, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(int period) : this(period: period, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TBars source) : this(source.Close, period: 0, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TBars source, int period) : this(source.Close, period, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, logbase: 2.0, useNaN: useNaN) { } + public ENTROPY_Series(TSeries source) : this(source, period: 0, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TSeries source, int period) : this(source: source, period: period, logbase: 2.0, useNaN: false) { } + public ENTROPY_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, logbase: 2.0, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + double _sum = _buffer.Sum(); + double _pp = this._buffer[^1] / _sum; + double _ppp = -_pp * Math.Log(_pp) / Math.Log(this._logbase); + BufferTrim(_buff2, _ppp, _period, update); + double _entp = _buff2.Sum(); + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _entp); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _buff2.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/FWMA_Series.cs b/archive/Calculations/_Updated/FWMA_Series.cs new file mode 100644 index 00000000..eceecc6b --- /dev/null +++ b/archive/Calculations/_Updated/FWMA_Series.cs @@ -0,0 +1,105 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Threading.Tasks; +using System.Numerics; +using System.Linq; + +/* +FWMA: Fibonacci's Weighted Moving Average is similar to a Weighted Moving Average + (WMA) where the weights are based on the Fibonacci Sequence. + + */ +public class FWMA_Series : TSeries +{ + private readonly List _buffer = new(); + private List _weights; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + protected int _len; + + public FWMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"FWMA({period})"; + _len = 0; + _weights = CalculateWeights(_period); + } + + public FWMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + public FWMA_Series() : this(period: 0, useNaN: false) { } + public FWMA_Series(int period) : this(period: period, useNaN: false) { } + public FWMA_Series(TBars source) : this(source.Close, 0, false) { } + public FWMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public FWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public FWMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + if (_period == 0) + { + _len++; + _weights = CalculateWeights(_len); + } + double _fwma = 0; + double totalWeights = _weights.Sum(); + object lockObj = new object(); + Parallel.For(0, _buffer.Count, i => + { + double temp = _buffer[i] * _weights[i]; + lock (lockObj) { _fwma += temp; } + }); + _fwma /= totalWeights; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _fwma); + return base.Add(res, update); + } + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + private static List CalculateWeights(int period) + { + //to prevent overflow, max period can be no more than 1476 + period = (period > 1476) ? 1476 : period; + List weights = new List(period); + BigInteger a = 0; + BigInteger b = 1; + for (int i = 0; i < period; i++) + { + BigInteger temp = a; + a = b; + b = temp + b; + weights.Add((double)Decimal.Parse(a.ToString())); + } + return weights; + } + + public override void Reset() + { + _weights = CalculateWeights(_period); + _buffer.Clear(); + } +} diff --git a/archive/Calculations/_Updated/HEMA_Series.cs b/archive/Calculations/_Updated/HEMA_Series.cs new file mode 100644 index 00000000..0d45962f --- /dev/null +++ b/archive/Calculations/_Updated/HEMA_Series.cs @@ -0,0 +1,133 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +HEMA: Hull-EMA Moving Average - a hybrid indicator + Modified HUll Moving Average; instead of using WMA (Weighted MA) for calculation, + HEMA uses EMA for Hull's formula: + +EMA1 = EMA(n/2) of price - where k = 4/(n/2 +1) +EMA2 = EMA(n) of price - where k = 3/(n+1) +Raw HMA = (2 * EMA1) - EMA2 +EMA3 = EMA(sqrt(n)) of Raw HMA - where k = 2/(sqrt(n)+1) + */ + +public class HEMA_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private double _k1, _k2, _k3; + private int _len; + private double _lastema1, _oldema1; + private double _lastema2, _oldema2; + private double _lasthema, _oldhema; + + //core constructors + public HEMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"HEMA({period})"; + (_k1, _k2, _k3) = CalculateK(_period); + _len = 0; + _lastema1 = _oldema1 = _lastema2 = _oldema2 = _lasthema = _oldhema = 0; + } + public HEMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public HEMA_Series() : this(period: 0, useNaN: false) { } + public HEMA_Series(int period) : this(period: period, useNaN: false) { } + public HEMA_Series(TBars source) : this(source.Close, 0, false) { } + public HEMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public HEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public HEMA_Series(TSeries source) : this(source, 0, false) { } + public HEMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastema1 = _oldema1; + _lastema2 = _oldema2; + _lasthema = _oldhema; + } + else + { + _oldema1 = _lastema1; + _oldema2 = _lastema2; + _oldhema = _lasthema; + } + double _ema1, _ema2, _hema; + if (_period == 0) + { + _len++; + (_k1, _k2, _k3) = CalculateK(_len); + } + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, double.NaN), update); + } + else if (this.Count == 0) + { + _ema1 = _ema2 = _hema = TValue.v; + } + else + { + _ema1 = _k1 * (TValue.v - _lastema1) + _lastema1; + _ema2 = _k2 * (TValue.v - _lastema2) + _lastema2; + _hema = _k3 * (((2 * _ema1) - _ema2) - _lasthema) + _lasthema; + } + + _lastema1 = _ema1; + _lastema2 = _ema2; + _lasthema = _hema; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hema); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _lastema1 = _lastema2 = _lasthema = 0; + _oldema1 = _oldema2 = _oldhema = 0; + _len = 0; + } + + public static (double k1, double k2, double k3) CalculateK(int len) + { + double k1 = 8 / (double)(len + 7); + double k2 = 3 / (double)(len + 2); + double k3 = 2 / Math.Sqrt(len + 3); + + return (k1, k2, k3); + } + +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/HMA_Series.cs b/archive/Calculations/_Updated/HMA_Series.cs new file mode 100644 index 00000000..30d9087a --- /dev/null +++ b/archive/Calculations/_Updated/HMA_Series.cs @@ -0,0 +1,98 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +HMA: Hull Moving Average + Developed by Alan Hull, an extremely fast and smooth moving average; almost + eliminates lag altogether and manages to improve smoothing at the same time. + +Sources: + https://alanhull.com/hull-moving-average + https://school.stockcharts.com/doku.php?id=technical_indicators:hull_moving_average + +WMA1 = WMA(n/2) of price +WMA2 = WMA(n) of price +Raw HMA = (2 * WMA1) - WMA2 +HMA = WMA(sqrt(n)) of Raw HMA + + */ + +public class HMA_Series : TSeries +{ + protected int _period, _period2, _psqrt; + protected readonly bool _NaN; + protected readonly TSeries _data; + protected WMA_Series _wma1, _wma2, _wma3; + + //core constructors + public HMA_Series(int period, bool useNaN) + { + _period = period; + _period2 = period / 2; + _psqrt = (int)Math.Sqrt(period); + _NaN = useNaN; + _wma1 = new(Math.Max(_period2, 1), false); + _wma2 = new(Math.Max(_period, 1), false); + _wma3 = new(Math.Max(_psqrt, 1), useNaN); + Name = $"HMA({period})"; + } + public HMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public HMA_Series() : this(period: 0, useNaN: false) { } + public HMA_Series(int period) : this(period: period, useNaN: false) { } + public HMA_Series(TBars source) : this(source.Close, 0, false) { } + public HMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public HMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public HMA_Series(TSeries source) : this(source, 0, false) { } + public HMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (_period == 0) + { + _wma1.Len = this.Count / 2; + _wma2.Len = this.Count; + _wma1.Len = (int)Math.Sqrt(this.Count); + } + double _w1 = _wma1.Add(TValue, update).v; + double _w2 = _wma2.Add(TValue, update).v; + double _hma = _wma3.Add((2 * _w1) - _w2, update).v; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _wma1.Reset(); + _wma2.Reset(); + _wma3.Reset(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/HWMA_Series.cs b/archive/Calculations/_Updated/HWMA_Series.cs new file mode 100644 index 00000000..b58ea0b2 --- /dev/null +++ b/archive/Calculations/_Updated/HWMA_Series.cs @@ -0,0 +1,146 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +HWMA: Holt-Winter Moving Average + Indicator HWMA (Holt-Winter Moving Average) is a three-parameter moving + average by the Holt-Winter method; Holt-Winters Exponential Smoothing is + used for forecasting time series data that exhibits both a trend and a + seasonal variation. + + +Sources: + https://timeseriesreasoning.com/contents/holt-winters-exponential-smoothing/ + https://www.mql5.com/en/code/20856 + +nA - smoothed series (from 0 to 1) +nB - assess the trend (from 0 to 1) +nC - assess seasonality (from 0 to 1) + +Heuristic for determining alpha, beta, and gamma from period: + alpha = 2 / (1 + period) + beta = 1 / period + gamma = 1 / period + +F[i] = (1-nA) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + nA * Price[i] +V[i] = (1-nB) * (V[i-1] + A[i-1]) + nB * (F[i] - F[i-1]) +A[i] = (1-nC) * A[i-1] + nC * (V[i] - V[i-1]) +HWMA[i] = F[i] + V[i] + 0.5 * A[i] + + */ + +public class HWMA_Series : TSeries +{ + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + double _nA, _nB, _nC; + double _pF, _pV, _pA; + double _ppF, _ppV, _ppA; + + //core constructors + + public HWMA_Series(double nA, double nB, double nC, bool useNaN) + { + _period = (int)((2 - nA) / nA); + _nA = nA; + _nB = nB; + _nC = nC; + _NaN = useNaN; + Name = $"HWMA({_period})"; + _len = 0; + } + public HWMA_Series(TSeries source, double nA, double nB, double nC, bool useNaN = false) : this(nA, nB, nC, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public HWMA_Series() : this(period: 0, useNaN: false) { } + public HWMA_Series(int period) : this(period, useNaN: false) { } + public HWMA_Series(int period, bool useNaN) : this(nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN) + { + _period = period; + } + public HWMA_Series(TBars source) : this(source.Close, period: 0, useNaN: false) { } + public HWMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public HWMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public HWMA_Series(TSeries source, int period) : this(source, period, false) { } + public HWMA_Series(TSeries source, int period, bool useNaN) : this(source, nA: 2 / (1 + (double)period), nB: 1 / (double)period, nC: 1 / (double)period, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + double _F, _V, _A; + if (_len == 0) { _pF = TValue.v; _pA = _pV = 0; } + + if (update) { _pF = _ppF; _pV = _ppV; _pA = _ppA; } + else + { + _ppF = _pF; + _ppV = _pV; + _ppA = _pA; + _len++; + } + + if (_period == 0) + { + _nA = 2 / (1 + (double)_len); + _nB = 1 / (double)_len; + _nC = 1 / (double)_len; + } + if (_period == 1) + { + _nA = 1; + _nB = 0; + _nC = 0; + } + + _F = (1 - _nA) * (_pF + _pV + 0.5 * _pA) + _nA * TValue.v; + _V = (1 - _nB) * (_pV + _pA) + _nB * (_F - _pF); + _A = (1 - _nC) * _pA + _nC * (_V - _pV); + + double _hwma = _F + _V + 0.5 * _A; + _pF = _F; + _pV = _V; + _pA = _A; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _hwma); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/JMA_Series.cs b/archive/Calculations/_Updated/JMA_Series.cs new file mode 100644 index 00000000..8d5b9af0 --- /dev/null +++ b/archive/Calculations/_Updated/JMA_Series.cs @@ -0,0 +1,176 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +JMA: Jurik Moving Average + Mark Jurik's Moving Average (JMA) attempts to eliminate noise to see the + underlying activity. It has extremely low lag, is very smooth and is responsive + to market gaps. + +Sources: + https://c.mql5.com/forextsd/forum/164/jurik_1.pdf + https://www.prorealcode.com/prorealtime-indicators/jurik-volatility-bands/ + +Issues: + Real JMA algorithm is not published and this formula is derived through + deduction and reverse analysis of JMA behavior. It is really close, but not + exact - published JMA tests against JMA.CSV fail with small deviation. The + original algo is slightly different, yet this approximation is close enough. + + */ + +public class JMA_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly System.Collections.Generic.List volty_short = new(); + private readonly System.Collections.Generic.List vsum_buff = new(); + private readonly double pr; + private double upperBand, lowerBand, vsum, Kv; + private double prev_ma1, prev_det0, prev_det1, prev_vsum, prev_jma; + private double p_upperBand, p_lowerBand, p_Kv, p_prev_ma1, p_prev_det0, p_prev_det1, p_prev_vsum, p_prev_jma; + private readonly int _voltyS, _voltyL; + + //core constructors + public JMA_Series(int period, double phase, int vshort, int vlong, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"JMA({period})"; + upperBand = lowerBand = prev_ma1 = prev_det0 = prev_det1 = prev_vsum = prev_jma = Kv = 0.0; + pr = (phase * 0.01) + 1.5; + if (phase < -100) { pr = 0.5; } + if (phase > 100) { pr = 2.5; } + _voltyS = vshort; + _voltyL = vlong; + } + + public JMA_Series(TSeries source, int period, double phase, int vshort, int vlong, bool useNaN) : this(period, phase, vshort, vlong, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public JMA_Series() : this(period: 0, phase: 0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(int period) : this(period: period, phase: 0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TBars source) : this(source.Close, period: 0, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TBars source, int period) : this(source.Close, period, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, phase: 0.0, vshort: 10, vlong: 65, useNaN: useNaN) { } + public JMA_Series(TSeries source) : this(source, period: 0, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TSeries source, int period) : this(source: source, period: period, phase: 0.0, vshort: 10, vlong: 65, useNaN: false) { } + public JMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, phase: 0.0, vshort: 10, vlong: 65, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (this.Count == 0) { prev_ma1 = prev_jma = TValue.v; } + if (update) + { + upperBand = p_upperBand; + lowerBand = p_lowerBand; + Kv = p_Kv; + prev_vsum = p_prev_vsum; + prev_ma1 = p_prev_ma1; + prev_det0 = p_prev_det0; + prev_det1 = p_prev_det1; + prev_jma = p_prev_jma; + } + else + { + p_upperBand = upperBand; + p_lowerBand = lowerBand; + p_Kv = Kv; + p_prev_vsum = prev_vsum; + p_prev_ma1 = prev_ma1; + p_prev_det0 = prev_det0; + p_prev_det1 = prev_det1; + p_prev_jma = prev_jma; + } + + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, double.NaN), update); + } + + // from Tvalue to volty + double del1 = TValue.v - upperBand; + double del2 = TValue.v - lowerBand; + upperBand = (del1 > 0) ? TValue.v : TValue.v - (Kv * del1); + lowerBand = (del2 < 0) ? TValue.v : TValue.v - (Kv * del2); + double volty = Math.Abs(del1) > Math.Abs(del2) ? Math.Abs(del1) : + (Math.Abs(del1) < Math.Abs(del2) ? Math.Abs(del2) : + Math.Abs(0.5 * (del1 + del2))); + + //// from volty to avolty + if (update) { volty_short[volty_short.Count - 1] = volty; } + else { volty_short.Add(volty); } + if (volty_short.Count > _voltyS) { volty_short.RemoveAt(0); } + vsum = prev_vsum + 0.1 * (volty - volty_short.First()); + prev_vsum = vsum; + if (update) { vsum_buff[vsum_buff.Count - 1] = vsum; } + else { vsum_buff.Add(vsum); } + if (vsum_buff.Count > _voltyL) { vsum_buff.RemoveAt(0); } + double avolty = 0; + for (int i = 0; i < vsum_buff.Count; i++) { avolty += vsum_buff[i]; } + avolty /= vsum_buff.Count; + + /// from avolty to rolty + double rvolty = (avolty != 0) ? volty / avolty : 0; + double len1 = (Math.Log(Math.Sqrt(_period)) / Math.Log(2.0)) + 2; + if (len1 < 0) { len1 = 0; } + + double pow1 = Math.Max(len1 - 2.0, 0.5); + if (rvolty > Math.Pow(len1, 1.0 / pow1)) { rvolty = Math.Pow(len1, 1.0 / pow1); } + if (rvolty < 1) { rvolty = 1; } + + //// from rvolty to second smoothing + double pow2 = Math.Pow(rvolty, pow1); + double beta = 0.45 * (_period - 1) / (0.45 * (_period - 1) + 2); + Kv = Math.Pow(beta, Math.Sqrt(pow2)); + double alpha = Math.Pow(beta, pow2); + double ma1 = (1 - alpha) * TValue.v + alpha * prev_ma1; + prev_ma1 = ma1; + + double det0 = (1 - beta) * (TValue.v - ma1) + beta * prev_det0; + prev_det0 = det0; + double ma2 = ma1 + pr * det0; + + double det1 = ((1 - alpha) * (1 - alpha) * (ma2 - prev_jma)) + (alpha * alpha * prev_det1); + prev_det1 = det1; + double jma = prev_jma + det1; + prev_jma = jma; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : jma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + upperBand = lowerBand = prev_ma1 = prev_det0 = prev_det1 = prev_vsum = prev_jma = Kv = 0.0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/KAMA_Series.cs b/archive/Calculations/_Updated/KAMA_Series.cs new file mode 100644 index 00000000..f839d4aa --- /dev/null +++ b/archive/Calculations/_Updated/KAMA_Series.cs @@ -0,0 +1,118 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +KAMA: Kaufman's Adaptive Moving Average + Created in 1988 by American quantitative finance theorist Perry J. Kaufman and is known as + Kaufman's Adaptive Moving Average (KAMA). Even though the method was developed as early as 1972, + it was not until the popular book titled "Trading Systems and Methods" that it was made widely + available to the public. Unlike other conventional moving averages systems, the Kaufman's Adaptive + Moving Average, considers market volatility apart from price fluctuations. + + KAMA[i] = KAMA[i-1] + SC * ( price - KAMA[i-1] ) + +Sources: + https://www.tutorialspoint.com/kaufman-s-adaptive-moving-average-kama-formula-and-how-does-it-work + https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/kaufmans-adaptive-moving-average-kama/ + https://www.technicalindicators.net/indicators-technical-analysis/152-kama-kaufman-adaptive-moving-average + +Remark: + If useNaN:true argument is provided, KAMA starts calculating values from [period] bar onwards. + Without useNaN argument (default setting), KAMA starts calculating values from bar 1 - and yields + slightly different results for the first 50 bars - and then converges with the other one. + + */ + +public class KAMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + private double _lastkama, _lastlastkama; + private readonly double _scFast, _scSlow; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public KAMA_Series(int period, int fast, int slow, bool useNaN) + { + _period = period; + _NaN = useNaN; + _scFast = 2.0 / (((period < fast) ? period : fast) + 1); + _scSlow = 2.0 / (slow + 1); + _lastkama = _lastlastkama = 0; + Name = $"KAMA({period})"; + } + public KAMA_Series(TSeries source, int period, int fast, int slow, bool useNaN) : this(period, fast, slow, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public KAMA_Series() : this(period: 0, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(int period) : this(period: period, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TBars source) : this(source.Close, period: 0, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TBars source, int period) : this(source.Close, period: period, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period: period, fast: 2, slow: 30, useNaN: useNaN) { } + public KAMA_Series(TSeries source) : this(source, period: 0, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TSeries source, int period) : this(source: source, period: period, fast: 2, slow: 30, useNaN: false) { } + public KAMA_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, fast: 2, slow: 30, useNaN: useNaN) { } + public KAMA_Series(TSeries source, int period, int fast, int slow) : this(source: source, period: period, fast: fast, slow: slow, useNaN: false) { } + + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + if (update) { _lastkama = _lastlastkama; } + else { _lastlastkama = _lastkama; } + BufferTrim(buffer: _buffer, value: TValue.v, period: _period + 1, update: update); + + double _kama = 0; + if (this.Count < _period) { _kama = TValue.v; } + else + { + double _change = Math.Abs(_buffer[^1] - _buffer[(_buffer.Count > _period + 1) ? 1 : 0]); + double _sumpv = 0; + for (int i = 1; i < _buffer.Count; i++) { _sumpv += Math.Abs(_buffer[(_buffer.Count > 0) ? i : 0] - _buffer[i - 1]); } + double _er = (_sumpv == 0) ? 0 : _change / _sumpv; + double _sc = (_er * (_scFast - _scSlow)) + _scSlow; + _kama = (_lastkama + (_sc * _sc * (TValue.v - _lastkama))); + } + _lastkama = _kama; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _kama); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _lastkama = _lastlastkama = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/KURTOSIS_Series.cs b/archive/Calculations/_Updated/KURTOSIS_Series.cs new file mode 100644 index 00000000..f632407a --- /dev/null +++ b/archive/Calculations/_Updated/KURTOSIS_Series.cs @@ -0,0 +1,107 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +KURTOSIS: Kurtosis of population + Kurtosis characterizes the relative peakedness or flatness of a distribution + compared with the normal distribution. Positive kurtosis indicates a relatively + peaked distribution. Negative kurtosis indicates a relatively flat distribution. + + The normal curve is called Mesokurtic curve. If the curve of a distribution is + more outlier prone (or heavier-tailed) than a normal or mesokurtic curve then + it is referred to as a Leptokurtic curve. If a curve is less outlier prone (or + lighter-tailed) than a normal curve, it is called as a platykurtic curve. + +Calculation: + sum4 = Σ(close-SMA)^4 + sum2 = (Σ(close-SMA)^2)^2 + KURTOSIS = length * (sum4/sum2) + +Sources: + https://en.wikipedia.org/wiki/Kurtosis + https://stats.oarc.ucla.edu/other/mult-pkg/faq/general/faq-whats-with-the-different-formulas-for-kurtosis/ + + */ + +public class KURTOSIS_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly System.Collections.Generic.List _buffer = new(); + + //core constructors + public KURTOSIS_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"KURTOSIS({period})"; + } + public KURTOSIS_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public KURTOSIS_Series() : this(period: 0, useNaN: false) { } + public KURTOSIS_Series(int period) : this(period: period, useNaN: false) { } + public KURTOSIS_Series(TSeries source) : this(source, period: 0, useNaN: false) { } + public KURTOSIS_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + double _n = _buffer.Count; + double _avg = _buffer.Average(); + + double _s2 = 0; + double _s4 = 0; + for (int i = 0; i < this._buffer.Count; i++) + { + _s2 += (_buffer[i] - _avg) * (_buffer[i] - _avg); + _s4 += (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg) * (_buffer[i] - _avg); + } + + double _Vx = _s2 / (_n - 1); + double _kurt = (_n > 3) ? + (_n * (_n + 1) * _s4) / (_Vx * _Vx * (_n - 3) * (_n - 1) * (_n - 2)) - (3 * (_n - 1) * (_n - 1) / ((_n - 2) * (_n - 3))) //using Sheskin Algo + : (_s2 * _s2) / _n - 3; //using Snedecor and Cochran (1967) algo + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _kurt); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MACD_Series.cs b/archive/Calculations/_Updated/MACD_Series.cs new file mode 100644 index 00000000..c23e1eca --- /dev/null +++ b/archive/Calculations/_Updated/MACD_Series.cs @@ -0,0 +1,88 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MACD: Moving Average Convergence/Divergence + Moving average convergence divergence (MACD) is a trend-following momentum + indicator that shows the relationship between two moving averages of a series. + The MACD is calculated by subtracting the 26-period exponential moving average (EMA) + from the 12-period EMA. MACD Signal is 9-day EMA of MACD. + + */ + +public class MACD_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + + protected readonly int _slow, _fast, _signal; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly EMA_Series _TSlow; + private readonly EMA_Series _TFast; + public EMA_Series Signal { get; } + + //core constructors + public MACD_Series(int slow = 26, int fast = 12, int signal = 9, bool useNaN = false) + { + _slow = slow; + _fast = fast; + _signal = signal; + _NaN = useNaN; + Name = $"MACD({slow},{fast},{signal})"; + _TSlow = new(slow, useNaN: false, useSMA: true); + _TFast = new(fast, useNaN: false, useSMA: true); + Signal = new(signal, useNaN: false, useSMA: true); + } + public MACD_Series(TSeries source, int slow, int fast, int signal, bool useNaN) : this(slow, fast, signal, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MACD_Series(TSeries source) : this(source: source, slow: 26, fast: 12, signal: 9, useNaN: false) { } + public MACD_Series(TSeries source, int slow, int fast, int signal) : this(source: source, slow: slow, fast: fast, signal: signal, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + var _sslow = _TSlow.Add(TValue, update); + var _sfast = _TFast.Add(TValue, update); + Signal.Add((TValue.t, _sfast.v - _sslow.v)); + + var res = (TValue.t, Count < _fast - 1 && _NaN ? double.NaN : _sfast.v - _sslow.v); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MAD_Series.cs b/archive/Calculations/_Updated/MAD_Series.cs new file mode 100644 index 00000000..23a7bf05 --- /dev/null +++ b/archive/Calculations/_Updated/MAD_Series.cs @@ -0,0 +1,88 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MAD: Mean Absolute Deviation + Also known as AAD - Average Absolute Deviation, to differentiate it from Median Absolute Deviation + MAD defines the degree of variation across the series. + +Calculation: + MAD = Σ(|close-SMA|) / period + +Sources: + https://en.wikipedia.org/wiki/Average_absolute_deviation + + */ + +public class MAD_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MAD_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MAD({period})"; + } + public MAD_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAD_Series() : this(period: 0, useNaN: false) { } + public MAD_Series(int period) : this(period: period, useNaN: false) { } + public MAD_Series(TBars source) : this(source.Close, 0, false) { } + public MAD_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAD_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAD_Series(TSeries source) : this(source, 0, false) { } + public MAD_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + double _mad = 0; + for (int i = 0; i < _buffer.Count; i++) { _mad += Math.Abs(_buffer[i] - _sma); } + _mad /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mad); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MAE_Series.cs b/archive/Calculations/_Updated/MAE_Series.cs new file mode 100644 index 00000000..566b47c2 --- /dev/null +++ b/archive/Calculations/_Updated/MAE_Series.cs @@ -0,0 +1,86 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MAE: Mean Absolute Error + Defined as a Mean (Average) of the absolute difference between actual and estimated values. + MAE = (1/n) * Σ|y_i - MA_i| + +Sources: + https://en.wikipedia.org/wiki/Mean_absolute_error + + */ + +public class MAE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MAE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MSE({period})"; + } + public MAE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAE_Series() : this(period: 0, useNaN: false) { } + public MAE_Series(int period) : this(period: period, useNaN: false) { } + public MAE_Series(TBars source) : this(source.Close, 0, false) { } + public MAE_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAE_Series(TSeries source) : this(source, 0, false) { } + public MAE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _mae = 0; + for (int i = 0; i < _buffer.Count; i++) { _mae += Math.Abs(_buffer[i] - _sma); } + _mae /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mae); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MAMA_Series.cs b/archive/Calculations/_Updated/MAMA_Series.cs new file mode 100644 index 00000000..538f66a2 --- /dev/null +++ b/archive/Calculations/_Updated/MAMA_Series.cs @@ -0,0 +1,207 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +MAMA: MESA Adaptive Moving Average + Created by John Ehlers, the MAMA indicator is a 5-period adaptive moving average of + high/low price that uses classic electrical radio-frequency signal processing algorithms + to reduce noise. + + KAMAi = KAMAi - 1 + SC * ( price - KAMAi-1 ) + +Sources: + https://mesasoftware.com/papers/MAMA.pdf + https://www.tradingview.com/script/foQxLbU3-Ehlers-MESA-Adaptive-Moving-Average-LazyBear/ + + */ + +public class MAMA_Series : TSeries +{ + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + private double sumPr; + private double fastl, slowl; + private (double i, double i1, double i2, double i3, double i4, double i5, double i6, double io) pr, i1, q1, sm, dt; + private (double i, double i1, double io) i2, q2, re, im, pd, ph, mama, fama; + public TSeries Fama { get; } + private double mamaseed, famaseed; + + //core constructors + + public MAMA_Series(double fastlimit, double slowlimit, bool useNaN) + { + _period = (int)(2 / fastlimit) - 1; + fastl = fastlimit; + slowl = slowlimit; + Fama = new TSeries(); + _NaN = useNaN; + Name = $"MAMA({_period})"; + _len = 0; + } + public MAMA_Series(TSeries source, double fastlimit, double slowlimit, bool useNaN = false) : this(fastlimit, slowlimit, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAMA_Series() : this(period: 0, useNaN: false) { } + public MAMA_Series(int period) : this(period, useNaN: false) { } + public MAMA_Series(int period, bool useNaN) : this(fastlimit: 2 / (period + 1), slowlimit: 0.2 / (period + 1), useNaN) + { + _period = period; + } + public MAMA_Series(TBars source) : this(source.Close, period: 0, useNaN: false) { } + public MAMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAMA_Series(TSeries source, int period) : this(source, period, false) { } + public MAMA_Series(TSeries source, int period, bool useNaN) : this(source, fastlimit: 2 / ((double)period + 1), slowlimit: 0.2 / ((double)period + 1), useNaN: useNaN) { } + + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + if (!update) + { + // roll forward (oldx = x) + pr.io = pr.i6; pr.i6 = pr.i5; pr.i5 = pr.i4; pr.i4 = pr.i3; pr.i3 = pr.i2; pr.i2 = pr.i1; pr.i1 = pr.i; + i1.io = i1.i6; i1.i6 = i1.i5; i1.i5 = i1.i4; i1.i4 = i1.i3; i1.i3 = i1.i2; i1.i2 = i1.i1; i1.i1 = i1.i; + q1.io = q1.i6; q1.i6 = q1.i5; q1.i5 = q1.i4; q1.i4 = q1.i3; q1.i3 = q1.i2; q1.i2 = q1.i1; q1.i1 = q1.i; + dt.io = dt.i6; dt.i6 = dt.i5; dt.i5 = dt.i4; dt.i4 = dt.i3; dt.i3 = dt.i2; dt.i2 = dt.i1; dt.i1 = dt.i; + sm.io = sm.i6; sm.i6 = sm.i5; sm.i5 = sm.i4; sm.i4 = sm.i3; sm.i3 = sm.i2; sm.i2 = sm.i1; sm.i1 = sm.i; + i2.io = i2.i1; i2.i1 = i2.i; q2.io = q2.i1; q2.i1 = q2.i; + re.io = re.i1; re.i1 = re.i; im.io = im.i1; im.i1 = im.i; + pd.io = pd.i1; pd.i1 = pd.i; ph.io = ph.i1; ph.i1 = ph.i; + mama.io = mama.i1; mama.i1 = mama.i; + fama.io = fama.i1; + fama.i1 = fama.i; + _len++; + } + if (_period == 0) + { + fastl = 2 / (double)_len; + slowl = fastl * 0.1; + } + if (_period == 1) + { + fastl = 1; + slowl = 1; + } + var i = _len - 1; + pr.i = TValue.v; + if (i > 5) + { + var adj = 0.075 * pd.i1 + 0.54; + + // smooth and detrender + sm.i = (4 * pr.i + 3 * pr.i1 + 2 * pr.i2 + pr.i3) / 10; + dt.i = (0.0962 * sm.i + 0.5769 * sm.i2 - 0.5769 * sm.i4 - 0.0962 * sm.i6) * adj; + + // in-phase and quadrature + q1.i = (0.0962 * dt.i + 0.5769 * dt.i2 - 0.5769 * dt.i4 - 0.0962 * dt.i6) * adj; + i1.i = dt.i3; + + // advance the phases by 90 degrees + double jI = (0.0962 * i1.i + 0.5769 * i1.i2 - 0.5769 * i1.i4 - 0.0962 * i1.i6) * adj; + double jQ = (0.0962 * q1.i + 0.5769 * q1.i2 - 0.5769 * q1.i4 - 0.0962 * q1.i6) * adj; + + // phasor addition for 3-bar averaging + i2.i = i1.i - jQ; + q2.i = q1.i + jI; + + i2.i = 0.2 * i2.i + 0.8 * i2.i1; // smoothing it + q2.i = 0.2 * q2.i + 0.8 * q2.i1; + + // homodyne discriminator + re.i = i2.i * i2.i1 + q2.i * q2.i1; + im.i = i2.i * q2.i1 - q2.i * i2.i1; + + re.i = 0.2 * re.i + 0.8 * re.i1; // smoothing it + im.i = 0.2 * im.i + 0.8 * im.i1; + + // calculate period + pd.i = im.i != 0 && re.i != 0 ? 6.283185307179586 / Math.Atan(im.i / re.i) : 0d; + + // adjust period to thresholds + pd.i = pd.i > 1.5 * pd.i1 ? 1.5 * pd.i1 : pd.i; + pd.i = pd.i < 0.67 * pd.i1 ? 0.67 * pd.i1 : pd.i; + pd.i = pd.i < 6d ? 6d : pd.i; + pd.i = pd.i > 50d ? 50d : pd.i; + + // smooth the period + pd.i = 0.2 * pd.i + 0.8 * pd.i1; + + // determine phase position + ph.i = i1.i != 0 ? Math.Atan(q1.i / i1.i) * 57.29577951308232 : 0; + + // change in phase + var delta = Math.Max(ph.i1 - ph.i, 1d); + + // adaptive alpha value + var alpha = Math.Max(fastl / delta, slowl); + + // final indicators + mama.i = alpha * (pr.i - mama.i1) + mama.i1; + fama.i = 0.5d * alpha * (mama.i - fama.i1) + fama.i1; + } + else + { + sumPr += pr.i; + pd.i = sm.i = dt.i = i1.i = q1.i = i2.i = q2.i = re.i = im.i = ph.i = 0; + mama.i = fama.i = sumPr / (i + 1); + + if (_len == 1) + { + mamaseed = famaseed = TValue.v; + } + else + { + mamaseed = fastl * (TValue.v - mamaseed) + mamaseed; + famaseed = slowl * (TValue.v - famaseed) + famaseed; + } + } + + double _fama = (i > 5) ? fama.i : famaseed; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _fama); + Fama.Add(res, update); + double _mama = (i > 5) ? mama.i : mamaseed; + res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mama); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MAPE_Series.cs b/archive/Calculations/_Updated/MAPE_Series.cs new file mode 100644 index 00000000..0ac76546 --- /dev/null +++ b/archive/Calculations/_Updated/MAPE_Series.cs @@ -0,0 +1,95 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MAPE: Mean Absolute Percentage Error + Measures the size of the error in percentage terms + +Calculation: + MAPE = Σ(|close – SMA| / |close|) / n + +Sources: + https://en.wikipedia.org/wiki/Mean_absolute_percentage_error + +Remark: + returns infinity if any of observations is 0. + Use SMAPE or WMAPE instead to avoid division-by-zero in MAPE + + */ + +public class MAPE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MAPE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MAPE({period})"; + } + public MAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAPE_Series() : this(period: 0, useNaN: false) { } + public MAPE_Series(int period) : this(period: period, useNaN: false) { } + public MAPE_Series(TBars source) : this(source.Close, 0, false) { } + public MAPE_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAPE_Series(TSeries source) : this(source, 0, false) { } + public MAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _mape = 0; + for (int i = 0; i < _buffer.Count; i++) + { + _mape += (_buffer[i] != 0) ? Math.Abs(_buffer[i] - _sma) / Math.Abs(_buffer[i]) : double.PositiveInfinity; + } + _mape /= (_buffer.Count > 0) ? _buffer.Count : 1; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mape); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MAX_Series.cs b/archive/Calculations/_Updated/MAX_Series.cs new file mode 100644 index 00000000..17aa9c71 --- /dev/null +++ b/archive/Calculations/_Updated/MAX_Series.cs @@ -0,0 +1,77 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MAX - Maximum value in the given period in the series. + If period = 0 => period = full length of the series + + */ + +public class MAX_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MAX_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MAX({period})"; + } + public MAX_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MAX_Series() : this(period: 0, useNaN: false) { } + public MAX_Series(int period) : this(period: period, useNaN: false) { } + public MAX_Series(TBars source) : this(source.Close, 0, false) { } + public MAX_Series(TBars source, int period) : this(source.Close, period, false) { } + public MAX_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MAX_Series(TSeries source) : this(source, 0, false) { } + public MAX_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _max = _buffer.Max(); + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _max); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MEDIAN_Series.cs b/archive/Calculations/_Updated/MEDIAN_Series.cs new file mode 100644 index 00000000..e431c979 --- /dev/null +++ b/archive/Calculations/_Updated/MEDIAN_Series.cs @@ -0,0 +1,95 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MED - Median value + Median of numbers is the middlemost value of the given set of numbers. + It separates the higher half and the lower half of a given data sample. + At least half of the observations are smaller than or equal to median + and at least half of the observations are greater than or equal to the median. + + If the number of values is odd, the middlemost observation of the sorted + list is the median of the given data. If the number of values is even, + median is the average of (n/2)th and [(n/2) + 1]th values of the sorted list. + + If period = 0 => period is max + +Sources: + https://corporatefinanceinstitute.com/resources/knowledge/other/median/ + https://en.wikipedia.org/wiki/Median + + */ + +public class MEDIAN_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MEDIAN_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MEDIAN({period})"; + } + public MEDIAN_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MEDIAN_Series() : this(period: 0, useNaN: false) { } + public MEDIAN_Series(int period) : this(period: period, useNaN: false) { } + public MEDIAN_Series(TBars source) : this(source.Close, 0, false) { } + public MEDIAN_Series(TBars source, int period) : this(source.Close, period, false) { } + public MEDIAN_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MEDIAN_Series(TSeries source) : this(source, 0, false) { } + public MEDIAN_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + System.Collections.Generic.List _s = new(this._buffer); + _s.Sort(); + int _p1 = _s.Count / 2; + int _p2 = Math.Max(0, (_s.Count / 2) - 1); + double _med = (_s.Count % 2 != 0) ? _s[_p1] : (_s[_p1] + _s[_p2]) / 2; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _med); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MIDPOINT_Series.cs b/archive/Calculations/_Updated/MIDPOINT_Series.cs new file mode 100644 index 00000000..ba98b3b3 --- /dev/null +++ b/archive/Calculations/_Updated/MIDPOINT_Series.cs @@ -0,0 +1,81 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MIDPOINT: Midpoint value (max+min)/2 in the given period in the series. + If period = 0 => period = full length of the series + +Sources: + https://thefaqblog.com/what-is-the-midpoint-in-statistics/ + + */ + +public class MIDPOINT_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MIDPOINT_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MIDPOINT({period})"; + } + public MIDPOINT_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MIDPOINT_Series() : this(period: 0, useNaN: false) { } + public MIDPOINT_Series(int period) : this(period: period, useNaN: false) { } + public MIDPOINT_Series(TBars source) : this(source.Close, 0, false) { } + public MIDPOINT_Series(TBars source, int period) : this(source.Close, period, false) { } + public MIDPOINT_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MIDPOINT_Series(TSeries source) : this(source, 0, false) { } + public MIDPOINT_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _max = _buffer.Max(); + double _min = _buffer.Min(); + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : (_max + _min) * 0.5); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MIDPRICE_Series.cs b/archive/Calculations/_Updated/MIDPRICE_Series.cs new file mode 100644 index 00000000..851b23fd --- /dev/null +++ b/archive/Calculations/_Updated/MIDPRICE_Series.cs @@ -0,0 +1,74 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +MIDPRICE: Midpoint price (highhest high + lowest low)/2 in the given period in the series. + If period = 0 => period = full length of the series + + */ + +public class MIDPRICE_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private readonly System.Collections.Generic.List _bufferhi = new(); + private readonly System.Collections.Generic.List _bufferlo = new(); + + //core constructors + public MIDPRICE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MIDPRICE({period})"; + } + public MIDPRICE_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public MIDPRICE_Series() : this(period: 2, useNaN: false) { } + public MIDPRICE_Series(int period) : this(period: period, useNaN: false) { } + public MIDPRICE_Series(TBars source) : this(source, period: 2, useNaN: false) { } + public MIDPRICE_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + BufferTrim(_bufferhi, TBar.h, _period, update); + BufferTrim(_bufferlo, TBar.l, _period, update); + double _mid = (_bufferhi.Max() + _bufferlo.Min()) * 0.5; + + var res = (TBar.t, Count < _period - 1 && _NaN ? double.NaN : _mid); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _bufferhi.Clear(); + _bufferlo.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MIN_Series.cs b/archive/Calculations/_Updated/MIN_Series.cs new file mode 100644 index 00000000..d3d7bb73 --- /dev/null +++ b/archive/Calculations/_Updated/MIN_Series.cs @@ -0,0 +1,77 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MIN - Minimum value in the given period in the series. + If period = 0 => period = full length of the series + + */ + +public class MIN_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MIN_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MAX({period})"; + } + public MIN_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MIN_Series() : this(period: 0, useNaN: false) { } + public MIN_Series(int period) : this(period: period, useNaN: false) { } + public MIN_Series(TBars source) : this(source.Close, 0, false) { } + public MIN_Series(TBars source, int period) : this(source.Close, period, false) { } + public MIN_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MIN_Series(TSeries source) : this(source, 0, false) { } + public MIN_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _max = _buffer.Min(); + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _max); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/MSE_Series.cs b/archive/Calculations/_Updated/MSE_Series.cs new file mode 100644 index 00000000..34440185 --- /dev/null +++ b/archive/Calculations/_Updated/MSE_Series.cs @@ -0,0 +1,85 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +MSE: Mean Square Error + Defined as a Mean (Average) of the Square of the difference between actual and estimated values. + +Sources: + https://en.wikipedia.org/wiki/Mean_squared_error + + */ + +public class MSE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public MSE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"MSE({period})"; + } + public MSE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public MSE_Series() : this(period: 0, useNaN: false) { } + public MSE_Series(int period) : this(period: period, useNaN: false) { } + public MSE_Series(TBars source) : this(source.Close, 0, false) { } + public MSE_Series(TBars source, int period) : this(source.Close, period, false) { } + public MSE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public MSE_Series(TSeries source) : this(source, 0, false) { } + public MSE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _mse = 0; + for (int i = 0; i < _buffer.Count; i++) { _mse += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _mse /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _mse); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/OBV_Series.cs b/archive/Calculations/_Updated/OBV_Series.cs new file mode 100644 index 00000000..2025034e --- /dev/null +++ b/archive/Calculations/_Updated/OBV_Series.cs @@ -0,0 +1,106 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +OBV: On-Balance Volume + On-balance volume (OBV) is a technical trading momentum indicator that uses volume flow to predict + changes in stock price. Joseph Granville first developed the OBV metric in the 1963 book + Granville's New Key to Stock Market Profits. + + | +volume; if close > close[previous] + OBV = OBV[previous] + | 0; if close = close[previous] + | -volume; if close < close[previous] + +Sources: + https://www.investopedia.com/terms/o/onbalancevolume.asp + https://www.tradingview.com/wiki/On_Balance_Volume_(OBV) + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/ + https://www.motivewave.com/studies/on_balance_volume.htm + +Note: + There is no consensus on what is the first OBV value in the series: + - TA-LIB uses the first volume: OBV[0] = volume[0] + - Skender stock library uses 0: OBV[0] = 0 + + */ + +public class OBV_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TBars _data; + private double _lastobv, _lastlastobv; + private double _lastclose, _lastlastclose; + + //core constructors + public OBV_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"OBV({period})"; + this._lastobv = this._lastlastobv = 0; + this._lastclose = this._lastlastclose = 0; + } + public OBV_Series(TBars source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(data: _data); + } + public OBV_Series() : this(period: 2, useNaN: false) { } + public OBV_Series(int period) : this(period: period, useNaN: false) { } + public OBV_Series(TBars source) : this(source, period: 2, useNaN: false) { } + public OBV_Series(TBars source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + + if (update) + { + this._lastobv = this._lastlastobv; + this._lastclose = this._lastlastclose; + } + + double _obv = this._lastobv; + if (TBar.c > this._lastclose) { _obv += TBar.v; } + if (TBar.c < this._lastclose) { _obv -= TBar.v; } + + this._lastlastobv = this._lastobv; + this._lastobv = _obv; + + this._lastlastclose = this._lastclose; + this._lastclose = TBar.c; + + var res = (TBar.t, (this.Count < this._period && this._NaN) ? double.NaN : _obv); + return base.Add(res, update); + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + this._lastobv = this._lastlastobv = 0; + this._lastclose = this._lastlastclose = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/RMA_Series.cs b/archive/Calculations/_Updated/RMA_Series.cs new file mode 100644 index 00000000..a1b94259 --- /dev/null +++ b/archive/Calculations/_Updated/RMA_Series.cs @@ -0,0 +1,133 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +RMA: wildeR Moving Average + J. Welles Wilder introduced RMA as an alternative to EMA. RMA's weight (k) is + set as 1/period, giving less weight to the new data compared to EMA. + +Sources: + https://archive.org/details/newconceptsintec00wild/page/23/mode/2up + https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing + https://www.incrediblecharts.com/indicators/wilder_moving_average.php + +Issues: + Pandas-TA library calculates RMA using straight Exponential Weighted Mean: + pandas.ewm().mean() and returns incorrect first (period) of bars compared to + published formula. This implementation passess the validation test in Wilder's book. + + */ + +public class RMA_Series : TSeries +{ + private double _k; + private double _lastrma, _oldrma; + private double _sum, _oldsum; + private readonly bool _useSMA; + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructor + public RMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"RMA({period})"; + _k = 1.0 / (double)(this._period); + _len = 0; + _sum = _oldsum = _lastrma = _oldrma = 0; + } + //generic constructors (source) + + public RMA_Series() : this(0, false, true) { } + public RMA_Series(int period) : this(period, false, true) { } + public RMA_Series(TBars source) : this(source.Close, 0, false) { } + public RMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public RMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public RMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public RMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public RMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastrma = _oldrma; + _sum = _oldsum; + } + else + { + _oldrma = _lastrma; + _oldsum = _sum; + _len++; + } + + double _rma = 0; + if (_period == 0) + { + _k = 1.0 / (double)(this._len); + } + + if (Count == 0) + { + _rma = _sum = TValue.v; + + } + else if (_len <= _period && _useSMA && _period != 0) + { + _sum += TValue.v; + if (_period != 0 && _len > _period) + { + _sum -= _data[Count - _period - (update ? 1 : 0)].v; + } + _rma = _sum / Math.Min(_len, _period); + } + else + { + _rma = _k * (TValue.v - _lastrma) + _lastrma; + } + + _lastrma = double.IsNaN(_rma) ? _lastrma : _rma; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rma); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _sum = _oldsum = _lastrma = _oldrma = 0; + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/RSI_Series.cs b/archive/Calculations/_Updated/RSI_Series.cs new file mode 100644 index 00000000..6a037b28 --- /dev/null +++ b/archive/Calculations/_Updated/RSI_Series.cs @@ -0,0 +1,134 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +RSI: Relative Strength Index + Created by J. Welles Wilder, the Relative Strength Index measures strength + of the winning/losing streak over N lookback periods on a scale of 0 to 100, + to depict overbought and oversold conditions. + +Sources: + https://www.investopedia.com/terms/r/rsi.asp + + */ + +public class RSI_Series : TSeries +{ + private readonly System.Collections.Generic.List _gain = new(); + private readonly System.Collections.Generic.List _loss = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private double _avgGain, _avgLoss, _lastValue; + private double _avgGain_o, _avgLoss_o, _lastValue_o; + private int i; + + //core constructors + public RSI_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"RSI({period})"; + i = 0; + } + public RSI_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public RSI_Series() : this(period: 0, useNaN: false) { } + public RSI_Series(int period) : this(period: period, useNaN: false) { } + public RSI_Series(TBars source) : this(source.Close, 0, false) { } + public RSI_Series(TBars source, int period) : this(source.Close, period, false) { } + public RSI_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public RSI_Series(TSeries source) : this(source, 0, false) { } + public RSI_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + + double _rsi = 0; + if (update) + { + _lastValue = _lastValue_o; + _avgGain = _avgGain_o; + _avgLoss = _avgLoss_o; + } + else + { + _lastValue_o = _lastValue; + _avgGain_o = _avgGain; + _avgLoss_o = _avgLoss; + } + + if (i == 0) { _lastValue = TValue.v; } + + double _gainval = (TValue.v > _lastValue) ? TValue.v - _lastValue : 0; + BufferTrim(_gain, _gainval, _period, update); + double _lossval = (TValue.v < _lastValue) ? _lastValue - TValue.v : 0; + BufferTrim(_loss, _lossval, _period, update); + _lastValue = TValue.v; + + // calculate RSI + if (i > _period && _period != 0) + { + _avgGain = ((_avgGain * (_period - 1)) + _gain[^1]) / _period; + _avgLoss = ((_avgLoss * (_period - 1)) + _loss[^1]) / _period; + if (_avgLoss > 0) + { + double rs = _avgGain / _avgLoss; + _rsi = 100 - (100 / (1 + rs)); + } + else { _rsi = 100; } + } + // initialize average gain + else + { + double _sumGain = 0; + for (int p = 0; p < _gain.Count; p++) { _sumGain += _gain[p]; } + double _sumLoss = 0; + for (int p = 0; p < _loss.Count; p++) { _sumLoss += _loss[p]; } + + _avgGain = _sumGain / _gain.Count; + _avgLoss = _sumLoss / _loss.Count; + + _rsi = (_avgLoss > 0) ? 100 - (100 / (1 + (_avgGain / _avgLoss))) : 100; + } + if (!update) { i++; } + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _rsi); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + i = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/SDEV_Series.cs b/archive/Calculations/_Updated/SDEV_Series.cs new file mode 100644 index 00000000..88fe0ce1 --- /dev/null +++ b/archive/Calculations/_Updated/SDEV_Series.cs @@ -0,0 +1,91 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SDEV: Population Standard Deviation + Population Standard Deviation is the square root of the biased variance, also knons as + Uncorrected Sample Standard Deviation + +Sources: + https://en.wikipedia.org/wiki/Standard_deviation#Uncorrected_sample_standard_deviation + +Remark: + SDEV (Population Standard Deviation) is also known as a biased/uncorrected Standard Deviation. + For unbiased version that uses Bessel's correction, use SDEV instead. + + */ + +public class SDEV_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public SDEV_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SDEV({period})"; + } + public SDEV_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SDEV_Series() : this(period: 0, useNaN: false) { } + public SDEV_Series(int period) : this(period: period, useNaN: false) { } + public SDEV_Series(TBars source) : this(source.Close, 0, false) { } + public SDEV_Series(TBars source, int period) : this(source.Close, period, false) { } + public SDEV_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SDEV_Series(TSeries source) : this(source, 0, false) { } + public SDEV_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _var = 0; + for (int i = 0; i < _buffer.Count; i++) { _var += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _var /= this._buffer.Count; + double _sdev = Math.Sqrt(_var); + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sdev); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/SLOPE_Series.cs b/archive/Calculations/_Updated/SLOPE_Series.cs new file mode 100644 index 00000000..c47b8650 --- /dev/null +++ b/archive/Calculations/_Updated/SLOPE_Series.cs @@ -0,0 +1,130 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SLOPE: Slope of linear regression (using Least Square Method) + Linear Regression provides a slope of a straight line that is the best approximation of the given set of data. + The method of least squares is a standard approach in linear regression analysis to approximate the solution + by minimizing the sum of the squares of the residuals made in the results of each individual equation. + +Additional outputs provided by LINREG: + .Intercept - y-intercept point of the best fit line + .RSquared - R-Squared (R²), Coefficient of Determination + .StdDev - Standard Deviation of data over given periods + + y = Slope * x + Intercept + +Sources: + https://en.wikipedia.org/wiki/Least_squares + + */ + +public class SLOPE_Series : TSeries +{ + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly TSeries p_Intercept = new(); + private readonly TSeries p_RSquared = new(); + private readonly TSeries p_StdDev = new(); + private readonly System.Collections.Generic.List _buffer = new(); + public TSeries Intercept => p_Intercept; + public TSeries RSquared => p_RSquared; + public TSeries StdDev => p_StdDev; + //core constructors + public SLOPE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SLOPE({period})"; + } + public SLOPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SLOPE_Series() : this(period: 0, useNaN: false) { } + public SLOPE_Series(int period) : this(period: period, useNaN: false) { } + public SLOPE_Series(TBars source) : this(source.Close, 0, false) { } + public SLOPE_Series(TBars source, int period) : this(source.Close, period, false) { } + public SLOPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SLOPE_Series(TSeries source) : this(source, 0, false) { } + public SLOPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + int _len = this._buffer.Count; + + // get averages for period + double sumX = 0; + double sumY = 0; + + for (int p = 0; p < _len; p++) + { + sumX += this.Count - _len + 2 + p; + sumY += _buffer[p]; + } + double avgX = sumX / _len; + double avgY = sumY / _len; + + // least squares method + double sumSqX = 0; + double sumSqY = 0; + double sumSqXY = 0; + + for (int p = 0; p < _len; p++) + { + double devX = this.Count - _len + 2 + p - avgX; + double devY = _buffer[p] - avgY; + + sumSqX += devX * devX; + sumSqY += devY * devY; + sumSqXY += devX * devY; + } + + double _slope = sumSqXY / sumSqX; + double _intercept = avgY - (_slope * avgX); + + // calculate Standard Deviation and R-Squared + double stdDevX = Math.Sqrt(sumSqX / _len); + double stdDevY = Math.Sqrt(sumSqY / _len); + double _StdDev = stdDevY; + + double arrr = (stdDevX * stdDevY != 0) ? sumSqXY / (stdDevX * stdDevY) / _len : 0; + double _RSquared = arrr * arrr; + + var ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _intercept); + p_Intercept.Add(ret, update); + + ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _StdDev); + p_StdDev.Add(ret, update); + + ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _RSquared); + p_RSquared.Add(ret, update); + + ret = (TValue.t, this.Count < this._period - 1 && this._NaN ? double.NaN : _slope); + return base.Add(ret, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/SMAPE_Series.cs b/archive/Calculations/_Updated/SMAPE_Series.cs new file mode 100644 index 00000000..a2e65e05 --- /dev/null +++ b/archive/Calculations/_Updated/SMAPE_Series.cs @@ -0,0 +1,84 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SMAPE: Symmetric Mean Absolute Percentage Error + Measures the size of the error in percentage terms + +Sources: + https://en.wikipedia.org/wiki/Symmetric_mean_absolute_percentage_error + + */ + +public class SMAPE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public SMAPE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SMAPE({period})"; + } + public SMAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SMAPE_Series() : this(period: 0, useNaN: false) { } + public SMAPE_Series(int period) : this(period: period, useNaN: false) { } + public SMAPE_Series(TBars source) : this(source.Close, 0, false) { } + public SMAPE_Series(TBars source, int period) : this(source.Close, period, false) { } + public SMAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SMAPE_Series(TSeries source) : this(source, 0, false) { } + public SMAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + double _smape = 0; + for (int i = 0; i < _buffer.Count; i++) { _smape += Math.Abs(_buffer[i] - _sma) / (Math.Abs(_buffer[i]) + Math.Abs(_sma)); } + _smape /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _smape); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/SMA_Series.cs b/archive/Calculations/_Updated/SMA_Series.cs new file mode 100644 index 00000000..e4c7b9e6 --- /dev/null +++ b/archive/Calculations/_Updated/SMA_Series.cs @@ -0,0 +1,112 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SMA: Simple Moving Average + The weights are equally distributed across the period, resulting in a mean() of + the data within the period + +Sources: + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/ + https://stats.stackexchange.com/a/24739 + +Remark: + This calc doesn't use LINQ or SUM() or any of (slow) iterative methods. It is not as fast as TA-LIB + implementation, but it does allow incremental additions of inputs and real-time calculations of SMA() + + */ +public class SMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + + private double _sum, _oldsum; + private readonly int _period; + private readonly TSeries _data; + protected readonly bool _NaN; + + //core constructor + public SMA_Series(int period, bool useNaN) + { + _period = Math.Max(0, period); + _NaN = useNaN; + Name = $"SMA({period})"; + _sum = _oldsum = 0; + } + public SMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SMA_Series() : this(0, false) { } + public SMA_Series(int period) : this(period, false) { } + public SMA_Series(TBars source) : this(source.Close, 0, false) { } + public SMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public SMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SMA_Series(TSeries source) : this(source, 0, false) { } + public SMA_Series(TSeries source, int period) : this(source, period, false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return (TValue.t, double.NaN); + } + else + { + if (update && _buffer.Count > 0) + { + _sum -= _buffer[^1]; + _buffer[^1] = TValue.v; + _oldsum = _sum; + } + else + { + _buffer.Add(TValue.v); + _oldsum = _sum; + } + + _sum += TValue.v; + if (_period != 0 && _buffer.Count > _period) + { + _sum -= _buffer[0]; + _buffer.RemoveAt(0); + } + } + + double _div = _period == 0 ? _buffer.Count : Math.Min(_buffer.Count, _period); + var _sma = _sum / _div; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _sma); + return base.Add(res, update); + } + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + + //reset calculation + public override void Reset() + { + _sum = _oldsum = 0; + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/SMMA_Series.cs b/archive/Calculations/_Updated/SMMA_Series.cs new file mode 100644 index 00000000..cfba2a57 --- /dev/null +++ b/archive/Calculations/_Updated/SMMA_Series.cs @@ -0,0 +1,105 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Linq; + +/* +SMMA: Smoothed Moving Average + The Smoothed Moving Average (SMMA) is a combination of a SMA and an EMA. It gives the recent prices + an equal weighting as the historic prices as it takes all available price data into account. + The main advantage of a smoothed moving average is that it removes short-term fluctuations. + + SMMA(i) = (SMMA-1*(N-1) + CLOSE (i)) / N + +Sources: + https://blog.earn2trade.com/smoothed-moving-average + https://guide.traderevolution.com/traderevolution/mobile-applications/phone/android/technical-indicators/moving-averages/smma-smoothed-moving-average + https://www.chartmill.com/documentation/technical-analysis-indicators/217-MOVING-AVERAGES-%7C-The-Smoothed-Moving-Average-%28SMMA%29 + + */ + +public class SMMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private double _lastsmma, _lastlastsmma; + + //core constructors + public SMMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SMMA({period})"; + } + public SMMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SMMA_Series() : this(period: 0, useNaN: false) { } + public SMMA_Series(int period) : this(period: period, useNaN: false) { } + public SMMA_Series(TBars source) : this(source.Close, 0, false) { } + public SMMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public SMMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SMMA_Series(TSeries source) : this(source, 0, false) { } + public SMMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, double.NaN), update); + } + + double _smma = 0; + if (update) { this._lastsmma = this._lastlastsmma; } + + if (this.Count < this._period) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + _smma = _buffer.Average(); + } + else + { + _smma = ((_lastsmma * (_period - 1)) + TValue.v) / _period; + } + + this._lastlastsmma = this._lastsmma; + this._lastsmma = _smma; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _smma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + this._lastsmma = this._lastlastsmma = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/SSDEV_Series.cs b/archive/Calculations/_Updated/SSDEV_Series.cs new file mode 100644 index 00000000..ae2a10de --- /dev/null +++ b/archive/Calculations/_Updated/SSDEV_Series.cs @@ -0,0 +1,91 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +SSDEV: (Corrected) Sample Standard Deviation + Sample Standard Deviaton uses Bessel's correction to correct the bias in the variance. + +Sources: + https://en.wikipedia.org/wiki/Standard_deviation#Corrected_sample_standard_deviation + Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + +Remark: + SSDEV (Sample Standard Deviation) is also known as a unbiased/corrected Standard Deviation. + For a population/biased/uncorrected Standard Deviation, use PSDEV instead + + */ + +public class SSDEV_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public SSDEV_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SSDEV({period})"; + } + public SSDEV_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SSDEV_Series() : this(period: 0, useNaN: false) { } + public SSDEV_Series(int period) : this(period: period, useNaN: false) { } + public SSDEV_Series(TBars source) : this(source.Close, 0, false) { } + public SSDEV_Series(TBars source, int period) : this(source.Close, period, false) { } + public SSDEV_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SSDEV_Series(TSeries source) : this(source, 0, false) { } + public SSDEV_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _svar = 0; + for (int i = 0; i < this._buffer.Count; i++) { _svar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _svar /= (_buffer.Count > 1) ? _buffer.Count - 1 : 1; // Bessel's correction + double _ssdev = Math.Sqrt(_svar); + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _ssdev); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/SVAR_Series.cs b/archive/Calculations/_Updated/SVAR_Series.cs new file mode 100644 index 00000000..3438c9d5 --- /dev/null +++ b/archive/Calculations/_Updated/SVAR_Series.cs @@ -0,0 +1,90 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +VAR: Population Variance + Population variance without Bessel's correction + +Sources: + https://en.wikipedia.org/wiki/Variance + Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + +Remark: + VAR (Population Variance) is also known as a biased Sample Variance. For unbiased + sample variance use SVAR instead. + + */ + +public class SVAR_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public SVAR_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"SVAR({period})"; + } + public SVAR_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public SVAR_Series() : this(period: 0, useNaN: false) { } + public SVAR_Series(int period) : this(period: period, useNaN: false) { } + public SVAR_Series(TBars source) : this(source.Close, 0, false) { } + public SVAR_Series(TBars source, int period) : this(source.Close, period, false) { } + public SVAR_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public SVAR_Series(TSeries source) : this(source, 0, false) { } + public SVAR_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _svar = 0; + for (int i = 0; i < this._buffer.Count; i++) { _svar += (this._buffer[i] - _sma) * (this._buffer[i] - _sma); } + _svar /= (this._buffer.Count > 1) ? this._buffer.Count - 1 : 1; // Bessel's correction + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _svar); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/T3_Series.cs b/archive/Calculations/_Updated/T3_Series.cs new file mode 100644 index 00000000..b203f818 --- /dev/null +++ b/archive/Calculations/_Updated/T3_Series.cs @@ -0,0 +1,173 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Numerics; + +/* +T3: Tillson T3 Moving Average + Tim Tillson described it in "Technical Analysis of Stocks and Commodities", January 1998 in the + article "Better Moving Averages". Tillson’s moving average becomes a popular indicator of + technical analysis as it gets less lag with the price chart and its curve is considerably smoother. + +Sources: + https://technicalindicators.net/indicators-technical-analysis/150-t3-moving-average + http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/ + */ + +public class T3_Series : TSeries +{ + private readonly double _k, _k1m, _c1, _c2, _c3, _c4; + private readonly System.Collections.Generic.List _buffer1 = new(); + private readonly System.Collections.Generic.List _buffer2 = new(); + private readonly System.Collections.Generic.List _buffer3 = new(); + private readonly System.Collections.Generic.List _buffer4 = new(); + private readonly System.Collections.Generic.List _buffer5 = new(); + private readonly System.Collections.Generic.List _buffer6 = new(); + private readonly bool _useSMA; + private double _lastema1, _lastema2, _lastema3, _lastema4, _lastema5, _lastema6; + private double _llastema1, _llastema2, _llastema3, _llastema4, _llastema5, _llastema6; + protected int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public T3_Series(int period, double vfactor, bool useSMA, bool useNaN) + { + _period = period; + _len = 0; + _NaN = useNaN; + Name = $"T3({period})"; + _useSMA = useSMA; + double _a = vfactor; //0.7; //0.618 + _c1 = -_a * _a * _a; + _c2 = 3 * _a * _a + 3 * _a * _a * _a; + _c3 = -6 * _a * _a - 3 * _a - 3 * _a * _a * _a; + _c4 = 1 + 3 * _a + _a * _a * _a + 3 * _a * _a; + + _k = 2.0 / (_period + 1); + _k1m = 1.0 - _k; + _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = _lastema4 = _llastema4 = _lastema5 = _llastema5 = _lastema5 = _llastema5 = 0; + } + public T3_Series(TSeries source, int period, double vfactor, bool useSMA, bool useNaN) : this(period, vfactor, useSMA, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public T3_Series() : this(period: 0, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(int period) : this(period: period, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TBars source) : this(source.Close, 0, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TBars source, int period) : this(source.Close, period, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TBars source, int period, bool useNaN) : this(source.Close, period, vfactor: 0.7, useSMA: true, useNaN: useNaN) { } + public T3_Series(TBars source, int period, double vfactor, bool useNaN) : this(source.Close, period, vfactor: vfactor, useSMA: true, useNaN: useNaN) { } + public T3_Series(TBars source, int period, bool useSMA, bool useNaN) : this(source.Close, period, vfactor: 0.7, useSMA: useSMA, useNaN: useNaN) { } + public T3_Series(TSeries source) : this(source, 0, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TSeries source, int period) : this(source: source, period: period, vfactor: 0.7, useSMA: true, useNaN: false) { } + public T3_Series(TSeries source, int period, bool useNaN) : this(source: source, period: period, vfactor: 0.7, useSMA: true, useNaN: useNaN) { } + public T3_Series(TSeries source, int period, double vfactor) : this(source: source, period: period, vfactor: vfactor, useSMA: true, useNaN: false) { } + public T3_Series(TSeries source, int period, double vfactor, bool useNaN) : this(source: source, period: period, vfactor: vfactor, useSMA: true, useNaN: useNaN) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + double _ema1, _ema2, _ema3, _ema4, _ema5, _ema6; + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + if (update) { _lastema1 = _llastema1; _lastema2 = _llastema2; _lastema3 = _llastema3; _lastema4 = _llastema4; _lastema5 = _llastema5; _lastema6 = _llastema6; } + else { _llastema1 = _lastema1; _llastema2 = _lastema2; _llastema3 = _lastema3; _llastema4 = _lastema4; _llastema5 = _lastema5; _llastema6 = _lastema6; } + + if (_len == 0) { _lastema1 = _lastema2 = _lastema3 = _lastema4 = _lastema5 = _lastema6 = TValue.v; } + + + if ((_len < _period) && _useSMA) + { + BufferTrim(_buffer1, TValue.v, _period, update); + _ema1 = 0; + for (int i = 0; i < _buffer1.Count; i++) { _ema1 += _buffer1[i]; } + _ema1 /= _buffer1.Count; + + BufferTrim(_buffer2, _ema1, _period, update); + _ema2 = 0; + for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; } + _ema2 /= _buffer2.Count; + + BufferTrim(_buffer3, _ema2, _period, update); + _ema3 = 0; + for (int i = 0; i < _buffer3.Count; i++) { _ema3 += _buffer3[i]; } + _ema3 /= _buffer3.Count; + + BufferTrim(_buffer4, _ema3, _period, update); + _ema4 = 0; + for (int i = 0; i < _buffer4.Count; i++) { _ema4 += _buffer4[i]; } + _ema4 /= _buffer4.Count; + + BufferTrim(_buffer5, _ema4, _period, update); + _ema5 = 0; + for (int i = 0; i < _buffer5.Count; i++) { _ema5 += _buffer5[i]; } + _ema5 /= _buffer5.Count; + + BufferTrim(_buffer6, _ema5, _period, update); + _ema6 = 0; + for (int i = 0; i < _buffer6.Count; i++) { _ema6 += _buffer6[i]; } + _ema6 /= _buffer6.Count; + } + else + { + _ema1 = (TValue.v * this._k) + (this._lastema1 * this._k1m); + _ema2 = (_ema1 * this._k) + (this._lastema2 * this._k1m); + _ema3 = (_ema2 * this._k) + (this._lastema3 * this._k1m); + _ema4 = (_ema3 * this._k) + (this._lastema4 * this._k1m); + _ema5 = (_ema4 * this._k) + (this._lastema5 * this._k1m); + _ema6 = (_ema5 * this._k) + (this._lastema6 * this._k1m); + } + _len++; + _lastema1 = _ema1; + _lastema2 = _ema2; + _lastema3 = _ema3; + _lastema4 = _ema4; + _lastema5 = _ema5; + _lastema6 = _ema6; + + double _T3 = _c1 * _ema6 + _c2 * _ema5 + _c3 * _ema4 + _c4 * _ema3; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _T3); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = _lastema4 = _llastema4 = _lastema5 = _llastema5 = _lastema5 = _llastema5 = 0; + _buffer1.Clear(); + _buffer2.Clear(); + _buffer3.Clear(); + _buffer4.Clear(); + _buffer5.Clear(); + _buffer6.Clear(); + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/TBars.cs b/archive/Calculations/_Updated/TBars.cs new file mode 100644 index 00000000..2d72df30 --- /dev/null +++ b/archive/Calculations/_Updated/TBars.cs @@ -0,0 +1,153 @@ +namespace QuanTAlib; +using System; + +/* +TBars class - includes all series for common data used in indicators and other calculations. + Has a bit limited overloading and casting (compared to TSeries) + Includes Select(int) method to simplify choosing the most optimal data source for indicators + Includes the most basic pricing calcs: HL2, OC2, OHL3, HLC3, OHLC4, HLCC4 + (it is 'cheaper' to calculate them once during data capture than each time during data analysis) + + */ + +public class TBars : System.Collections.Generic.List<(DateTime t, double o, double h, double l, double c, double v)> +{ + public string Name { get; set; } + private readonly TSeries _open = new("open"); + private readonly TSeries _high = new("high"); + private readonly TSeries _low = new("low"); + private readonly TSeries _close = new("close"); + private readonly TSeries _volume = new("volume"); + private readonly TSeries _hl2 = new("HL2"); + private readonly TSeries _oc2 = new("OC2"); + private readonly TSeries _ohl3 = new("OHL3"); + private readonly TSeries _hlc3 = new("HLC3"); + private readonly TSeries _ohlc4 = new("OHLC4"); + private readonly TSeries _hlcc4 = new("HLCC4"); + + public TSeries Open => this._open; + public TSeries High => this._high; + public TSeries Low => this._low; + public TSeries Close => this._close; + public TSeries Volume => this._volume; + public TSeries HL2 => this._hl2; + public TSeries OC2 => this._oc2; + public TSeries OHL3 => this._ohl3; + public TSeries HLC3 => this._hlc3; + public TSeries OHLC4 => this._ohlc4; + public TSeries HLCC4 => this._hlcc4; + + public TBars() { } + + public TBars(string Name) + { + this.Name = Name; + } + + public (DateTime t, double o, double h, double l, double c, double v) Last => this[^1]; + public TBars Tail(int count = 10) + { + TBars outBars = new(); + if (count > this.Count) { count = this.Count; } + for (int i = this.Count - count; i < this.Count; i++) { outBars.Add(this[i]); } + return outBars; + } + public TSeries Select(int source) + { + return source switch + { + 0 => _open, + 1 => _high, + 2 => _low, + 3 => _close, + 4 => _hl2, + 5 => _oc2, + 6 => _ohl3, + 7 => _hlc3, + 8 => _ohlc4, + _ => _hlcc4, + }; + } + public static string SelectStr(int source) + { + return source switch + { + 0 => "Open", + 1 => "High", + 2 => "Low", + 3 => "Close", + 4 => "HL2", + 5 => "OC2", + 6 => "OHL3", + 7 => "HLC3", + 8 => "OHLC4", + _ => "HLCC4", + }; + } + + public virtual (DateTime t, double v) Add((double o, double h, double l, double c, double v) p, bool update = false) => + Add((t: (this.Count == 0) ? DateTime.Today : this[^1].t.AddDays(1), p.o, p.h, p.l, p.c, p.v), update); + + public virtual (DateTime t, double v) Add(double o, double h, double l, double c, double v, bool update = false) => + Add((o, h, l, c, v), update); + + public virtual (DateTime t, double v) Add(DateTime t, double o, double h, double l, double c, double v, bool update = false) => + this.Add((t, o, h, l, c, v), update); + + public virtual (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) { this[^1] = TBar; } else { base.Add(TBar); } + + _open.Add((TBar.t, TBar.o), update); + _high.Add((TBar.t, TBar.h), update); + _low.Add((TBar.t, TBar.l), update); + _close.Add((TBar.t, TBar.c), update); + _volume.Add((TBar.t, TBar.v), update); + _hl2.Add((TBar.t, (TBar.h + TBar.l) * 0.5), update); + _oc2.Add((TBar.t, (TBar.o + TBar.c) * 0.5), update); + _ohl3.Add((TBar.t, (TBar.o + TBar.h + TBar.l) * 0.333333333333333), update); + _hlc3.Add((TBar.t, (TBar.h + TBar.l + TBar.c) * 0.333333333333333), update); + _ohlc4.Add((TBar.t, (TBar.o + TBar.h + TBar.l + TBar.c) * 0.25), update); + _hlcc4.Add((TBar.t, (TBar.h + TBar.l + TBar.c + TBar.c) * 0.25), update); + + this.OnEvent(update); + return (TBar.t, (TBar.o + TBar.h + TBar.l + TBar.c) * 0.25); + } + + public delegate void NewDataEventHandler(object source, TSeriesEventArgs args); + public event NewDataEventHandler Pub; + protected virtual void OnEvent(bool update = false) + { + if (Pub != null && Pub.Target != this) + { + Pub(this, new TSeriesEventArgs { update = update }); + } + } + + public void Sub(object source, TSeriesEventArgs e) + { + TBars ss = (TBars)source; if (ss.Count > 1) + { + for (int i = 0; i < ss.Count; i++) { this.Add(ss[i]); } + } + else + { + this.Add(ss[^1], e.update); + } + } + + /// common helpers + public static void BufferTrim(System.Collections.Generic.List buffer, double value, int period, bool update) + { + if (!update) + { + buffer.Add(value); + if (buffer.Count > period && period > 0) { buffer.RemoveAt(0); } + return; + } + buffer[^1] = value; + } + public virtual void Reset() + { + } +} diff --git a/archive/Calculations/_Updated/TEMA_Series.cs b/archive/Calculations/_Updated/TEMA_Series.cs new file mode 100644 index 00000000..f1720fad --- /dev/null +++ b/archive/Calculations/_Updated/TEMA_Series.cs @@ -0,0 +1,134 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +TEMA: Triple Exponential Moving Average + TEMA uses EMA(EMA(EMA())) to calculate less laggy Exponential moving average. + +Sources: + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/ + +Remark: + ema1 = EMA(close, length) + ema2 = EMA(ema1, length) + ema3 = EMA(ema2, length) + TEMA = 3 * (ema1 - ema2) + ema3 + + */ + +public class TEMA_Series : TSeries +{ + private double _k; + private double _sum, _oldsum; + private double _lastema1, _oldema1, _lastema2, _oldema2, _lastema3, _oldema3; + private int _len; + private readonly bool _useSMA; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructor + public TEMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"TEMA({period})"; + _k = 2.0 / (_period + 1); + _len = 0; + _sum = _oldsum = _lastema1 = _lastema2 = _lastema3 = 0; + } + public TEMA_Series() : this(0, false, true) { } + public TEMA_Series(int period) : this(period, false, true) { } + public TEMA_Series(TBars source) : this(source.Close, 0, false) { } + public TEMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public TEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public TEMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public TEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public TEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + _lastema1 = _oldema1; + _lastema2 = _oldema2; + _lastema3 = _oldema3; + _sum = _oldsum; + } + else + { + _oldema1 = _lastema1; + _oldema2 = _lastema2; + _oldema3 = _lastema3; + _oldsum = _sum; + _len++; + } + + if (_period == 0) { _k = 2.0 / (_len + 1); } + + double _ema1, _ema2, _ema3, _tema; + if (this.Count == 0) + { + _ema1 = _ema2 = _ema3 = _sum = TValue.v; + } + else if (_len <= _period && _useSMA && _period != 0) + { + _sum += TValue.v; + _ema1 = _sum / Math.Min(_len, _period); + _ema2 = _ema1; + _ema3 = _ema2; + } + else + { + _ema1 = (TValue.v - _lastema1) * _k + _lastema1; + _ema2 = (_ema1 - _lastema2) * _k + _lastema2; + _ema3 = (_ema2 - _lastema3) * _k + _lastema3; + } + + _tema = (3 * (_ema1 - _ema2)) + _ema3; + + _lastema1 = Double.IsNaN(_ema1) ? _lastema1 : _ema1; + _lastema2 = Double.IsNaN(_ema2) ? _lastema2 : _ema2; + _lastema3 = Double.IsNaN(_ema3) ? _lastema3 : _ema3; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _tema); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _sum = _oldsum = _lastema1 = _lastema2 = 0; + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/TRIMA_Series.cs b/archive/Calculations/_Updated/TRIMA_Series.cs new file mode 100644 index 00000000..3c972143 --- /dev/null +++ b/archive/Calculations/_Updated/TRIMA_Series.cs @@ -0,0 +1,94 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +TRIMA: Triangular Moving Average + A weighted moving average where the shape of the weights are triangular and the greatest + weight is in the middle of the period, + +Sources: + https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triangular-moving-average-trima/ + +Remark: + trima = sma(sma(signal, n/2), n/2) + + */ + +public class TRIMA_Series : TSeries +{ + private readonly int _p1a, _p1b; + private readonly SMA_Series sma, trima; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public TRIMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"xMA({period})"; + _p1a = (int)Math.Floor((period * 0.5) + 1); + _p1b = (int)Math.Ceiling(0.5 * period); + sma = new(_p1a); + trima = new(_p1b); + + } + public TRIMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public TRIMA_Series() : this(period: 0, useNaN: false) { } + public TRIMA_Series(int period) : this(period: period, useNaN: false) { } + public TRIMA_Series(TBars source) : this(source.Close, 0, false) { } + public TRIMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public TRIMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public TRIMA_Series(TSeries source) : this(source, 0, false) { } + public TRIMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + + var _sma = sma.Add(TValue, update); + var _trima = trima.Add(_sma, update); + + var res = (_trima.t, Count < _period - 1 && _NaN ? double.NaN : _trima.v); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + sma.Reset(); + trima.Reset(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/TRIX_Series.cs b/archive/Calculations/_Updated/TRIX_Series.cs new file mode 100644 index 00000000..490eeccb --- /dev/null +++ b/archive/Calculations/_Updated/TRIX_Series.cs @@ -0,0 +1,132 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +TRIX: Triple Exponential Average Oscillator + Developed by Jack Hutson in the early 1980s, the triple exponential average (TRIX) + has become a popular technical analysis tool to aid chartists in spotting diversions + and directional cues in stock trading patterns. + +Sources: + https://www.investopedia.com/terms/t/trix.asp + + */ + +public class TRIX_Series : TSeries +{ + private readonly double _k; + private readonly System.Collections.Generic.List _buffer1 = new(); + private readonly System.Collections.Generic.List _buffer2 = new(); + private readonly System.Collections.Generic.List _buffer3 = new(); + private double _lastema1, _lastema2, _lastema3; + private double _llastema1, _llastema2, _llastema3; + private int _len; + private readonly bool _useSMA; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + + public TRIX_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + _useSMA = useSMA; + Name = $"TRIX({period})"; + _k = 2.0 / (_period + 1); + _len = 0; + _lastema1 = _llastema1 = _lastema2 = _llastema2 = _lastema3 = _llastema3 = 0; + } + public TRIX_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public TRIX_Series() : this(0, false, true) { } + public TRIX_Series(int period) : this(period, false, true) { } + public TRIX_Series(TBars source) : this(source.Close, 0, false) { } + public TRIX_Series(TBars source, int period) : this(source.Close, period, false) { } + public TRIX_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public TRIX_Series(TSeries source, int period) : this(source, period, false, true) { } + public TRIX_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (double.IsNaN(TValue.v)) + { + return base.Add((TValue.t, Double.NaN), update); + } + if (_len == 0) { _lastema1 = _lastema2 = _lastema3 = TValue.v; } + if (update) { _lastema1 = _llastema1; _lastema2 = _llastema2; _lastema3 = _llastema3; } + else + { + _llastema1 = _lastema1; _llastema2 = _lastema2; _llastema3 = _lastema3; _len++; + } + + double _ema1, _ema2, _ema3; + if ((this.Count < _period) && _useSMA) + { + BufferTrim(_buffer1, TValue.v, _period, update); + _ema1 = 0; + for (int i = 0; i < _buffer1.Count; i++) { _ema1 += _buffer1[i]; } + _ema1 /= _buffer1.Count; + + BufferTrim(_buffer2, _ema1, _period, update); + _ema2 = 0; + for (int i = 0; i < _buffer2.Count; i++) { _ema2 += _buffer2[i]; } + _ema2 /= _buffer2.Count; + + BufferTrim(_buffer3, _ema2, _period, update); + _ema3 = 0; + for (int i = 0; i < _buffer3.Count; i++) { _ema3 += _buffer3[i]; } + _ema3 /= _buffer3.Count; + } + else + { + _ema1 = (TValue.v - _lastema1) * _k + _lastema1; + _ema2 = (_ema1 - _lastema2) * _k + _lastema2; + _ema3 = (_ema2 - _lastema3) * _k + _lastema3; + } + double _trix = 100 * (_ema3 - _lastema3) / _lastema3; + _lastema1 = _ema1; + _lastema2 = _ema2; + _lastema3 = _ema3; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _trix); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _len = 0; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/TR_Series.cs b/archive/Calculations/_Updated/TR_Series.cs new file mode 100644 index 00000000..49d942ce --- /dev/null +++ b/archive/Calculations/_Updated/TR_Series.cs @@ -0,0 +1,91 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +TR: True Range + True Range was introduced by J. Welles Wilder in his book New Concepts in Technical Trading Systems. + It measures the daily range plus any gap from the closing price of the preceding day. + +Calculation: + d1 = ABS(High - Low) + d2 = ABS(High - Previous close) + d3 = ABS(Previous close - Low) + TR = MAX(d1,d2,d3) + +Sources: + https://www.macroption.com/true-range/ + + */ + +public class TR_Series : TSeries +{ + protected readonly TBars _data; + private double _cm1, _cm1_o; + + //core constructors + public TR_Series() + { + Name = $"TR()"; + _cm1 = _cm1_o = double.NaN; + } + public TR_Series(TBars source) + { + _data = source; + Name = $"TR({(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _cm1 = _cm1_o = double.NaN; + _data.Pub += Sub; + Add(data: _data); + } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + + if (update) + { + _cm1 = _cm1_o; + } + else + { + _cm1_o = _cm1; + } + + if (_cm1 is double.NaN) + { + _cm1 = TBar.c; + } + + double d1 = Math.Abs(TBar.h - TBar.l); + double d2 = Math.Abs(_cm1 - TBar.h); + double d3 = Math.Abs(_cm1 - TBar.l); + _cm1 = TBar.c; + var ret = (TBar.t, Math.Max(d1, Math.Max(d2, d3))); + return base.Add(ret, update); + + } + + public new void Add(TBars data) + { + foreach (var item in data) { Add(item, false); } + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TBar: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TBar: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TBar: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _cm1 = _cm1_o = double.NaN; + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/TSeries.cs b/archive/Calculations/_Updated/TSeries.cs new file mode 100644 index 00000000..4763e6c3 --- /dev/null +++ b/archive/Calculations/_Updated/TSeries.cs @@ -0,0 +1,137 @@ +namespace QuanTAlib; +using System; +using System.Collections.Generic; +using System.Collections.ObjectModel; +using System.Data; +using System.Linq; + +/* +TSeries is the cornerstone of all QuanTAlib classes. + TSeries is a single List of tuples (time, value) and contains several operators, casts, overloads + and other helpers that simplify usage of library. + Think of TSeries as an equivalent of Numpy array. + + - includes Length property (to mimic array's method) + - includes publishing and subscribing methods that attach to events + + */ +public class TSeriesEventArgs : EventArgs +{ + public bool update { get; set; } +} + +public class TSeries : List<(DateTime t, double v)> +{ + private readonly (DateTime t, double v) Default = (DateTime.MinValue, double.NaN); + public IEnumerable t => this.Select(item => item.t); + public IEnumerable v => this.Select(item => item.v); + public (DateTime t, double v) Last => Count > 0 ? this[^1] : Default; + + public int Length => Count; + public string Name { get; set; } + public int Keep = 0; + + public TSeries() + { + this.Name = "data"; + } + + public TSeries(string Name) + { + this.Name = Name; + } + + public virtual (DateTime t, double v) Add(double v, bool update = false) + { + return Add((t: Count == 0 ? DateTime.Today : this[^1].t.AddDays(1), v), update); + } + + public virtual (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + if (update) + { + this[^1] = TValue; + } + else + { + base.Add(TValue); + } + + OnEvent(update); + return TValue; + } + + public virtual (DateTime t, double v) Add((DateTime t, double o, double h, double l, double c, double v) TBar, bool update = false) + { + if (update) + { + this[this.Count - 1] = (TBar.t, TBar.c); + } + else + { + base.Add((TBar.t, TBar.c)); + } + + OnEvent(update); + return (TBar.t, TBar.c); + } + + public virtual (DateTime t, double v) Add(TSeries data) + { + foreach (var item in data) { Add(item); } + return data.Last; + } + + public virtual (DateTime t, double v) Add(TBars data) + { + foreach (var item in data) { Add(item.c, false); } + return (data.Last.t, data.Last.c); + } + + public void Sub(object source, TSeriesEventArgs e) + { + var data = (TSeries)source; + if (data == null) { return; } + foreach (var item in data) { Add(item); } + } + + public delegate void NewEventHandler(object source, TSeriesEventArgs args); + + public event NewEventHandler Pub; + + protected virtual void OnEvent(bool update = false) + { + if (Keep > 0) + { + TrimToSize(keep: Keep); + } + Pub?.Invoke(this, new TSeriesEventArgs { update = update }); + } + + /// common helpers + public static void BufferTrim(List buffer, double value, int period, bool update) + { + if (!update) + { + buffer.Add(value); + if (buffer.Count > period && period > 0) { buffer.RemoveAt(0); } + return; + } + buffer[^1] = value; + } + public virtual void Reset() + { + } + + public void TrimToSize(int keep) + { + if (keep >= this.Count) + { + return; // No need to trim if the series is already smaller than or equal to n + } + + // Remove elements from the beginning of the list + int elementsToRemove = this.Count - keep; + RemoveRange(0, elementsToRemove); + } +} diff --git a/archive/Calculations/_Updated/VAR_Series.cs b/archive/Calculations/_Updated/VAR_Series.cs new file mode 100644 index 00000000..4f03e12d --- /dev/null +++ b/archive/Calculations/_Updated/VAR_Series.cs @@ -0,0 +1,90 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +VAR: Population Variance + Population variance without Bessel's correction + +Sources: + https://en.wikipedia.org/wiki/Variance + Bessel's correction: https://en.wikipedia.org/wiki/Bessel%27s_correction + +Remark: + VAR (Population Variance) is also known as a biased Sample Variance. For unbiased + sample variance use SVAR instead. + + */ + +public class VAR_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public VAR_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"VAR({period})"; + } + public VAR_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public VAR_Series() : this(period: 0, useNaN: false) { } + public VAR_Series(int period) : this(period: period, useNaN: false) { } + public VAR_Series(TBars source) : this(source.Close, 0, false) { } + public VAR_Series(TBars source, int period) : this(source.Close, period, false) { } + public VAR_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public VAR_Series(TSeries source) : this(source, 0, false) { } + public VAR_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _pvar = 0; + for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _pvar /= this._buffer.Count; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _pvar); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/WMAPE_Series.cs b/archive/Calculations/_Updated/WMAPE_Series.cs new file mode 100644 index 00000000..55935b85 --- /dev/null +++ b/archive/Calculations/_Updated/WMAPE_Series.cs @@ -0,0 +1,92 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +WMAPE: Weighted Mean Absolute Percentage Error + Measures the size of the error in percentage terms. Improves problems with MAPE + when there are zero or close-to-zero values because there would be a division by zero + or values of MAPE tending to infinity. + +Sources: + https://en.wikipedia.org/wiki/WMAPE + + */ + +public class WMAPE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public WMAPE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"WMAPE({period})"; + } + public WMAPE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public WMAPE_Series() : this(period: 0, useNaN: false) { } + public WMAPE_Series(int period) : this(period: period, useNaN: false) { } + public WMAPE_Series(TBars source) : this(source.Close, 0, false) { } + public WMAPE_Series(TBars source, int period) : this(source.Close, period, false) { } + public WMAPE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public WMAPE_Series(TSeries source) : this(source, 0, false) { } + public WMAPE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + + double _sma = _buffer.Average(); + + double _div = 0; + double _wmape = 0; + for (int i = 0; i < _buffer.Count; i++) + { + _wmape += Math.Abs(_buffer[i] - _sma); + _div += Math.Abs(_buffer[i]); + } + _wmape = (_div != 0) ? _wmape / _div : double.PositiveInfinity; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _wmape); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/WMA_Series.cs b/archive/Calculations/_Updated/WMA_Series.cs new file mode 100644 index 00000000..7fbf16a1 --- /dev/null +++ b/archive/Calculations/_Updated/WMA_Series.cs @@ -0,0 +1,117 @@ +namespace QuanTAlib; + +using System; +using System.Collections.Generic; +using System.Linq; +using System.Threading; +using System.Threading.Tasks; + +/* +WMA: (linearly) Weighted Moving Average + The weights are linearly decreasing over the period and the most recent data has + the heaviest weight. + +Sources: + https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/weighted-moving-average-wma/ + https://www.technicalindicators.net/indicators-technical-analysis/83-moving-averages-simple-exponential-weighted + + */ + +public class WMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + private System.Collections.Generic.List _weights; + protected int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + protected int _len; + public int Len + { + get { return _len; } + set { _len = value; } + } + + //core constructors + public WMA_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"WMA({period})"; + _len = 1; + _weights = CalculateWeights(_period); + } + public WMA_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public WMA_Series() : this(period: 0, useNaN: false) { } + public WMA_Series(int period) : this(period: period, useNaN: false) { } + public WMA_Series(TBars source) : this(source.Close, 0, false) { } + public WMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public WMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public WMA_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + if (_period == 0) + { + _weights = CalculateWeights(_len); + _len++; + } + double _wma = 0; + double totalWeights = (_buffer.Count * (_buffer.Count + 1)) * 0.5; + object lockObj = new object(); + Parallel.For(0, _buffer.Count, i => + { + double temp = _buffer[i] * this._weights[i]; + lock (lockObj) { _wma += temp; } + }); + _wma /= totalWeights; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _wma); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //calculating weights + private static List CalculateWeights(int period) + { + List weights = new List(period); + for (int i = 0; i < period; i++) + { + weights.Add(i + 1); + } + return weights; + } + + //reset calculation + public override void Reset() + { + _len = 0; + _weights = CalculateWeights(_period); + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ZLEMA_Series.cs b/archive/Calculations/_Updated/ZLEMA_Series.cs new file mode 100644 index 00000000..676e7fe5 --- /dev/null +++ b/archive/Calculations/_Updated/ZLEMA_Series.cs @@ -0,0 +1,105 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +ZLEMA: Zero Lag Exponential Moving Average + The Zero lag exponential moving average (ZLEMA) indicator was created by John + Ehlers and Ric Way. + +The formula for a given N-Day period and for a given Data series is: + Lag = (Period-1)/2 + Ema Data = {Data+(Data-Data(Lag days ago)) + ZLEMA = EMA (EmaData,Period) + +Remark: + The idea is do a regular exponential moving average (EMA) calculation but on a + de-lagged data instead of doing it on the regular data. Data is de-lagged by + removing the data from "lag" days ago thus removing (or attempting to remove) + the cumulative lag effect of the moving average. + + */ + +public class ZLEMA_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly EMA_Series _ema; + + //core constructor + public ZLEMA_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + Name = $"ZLEMA({period})"; + _len = 1; + _ema = new(period); + } + //generic constructors (source) + + public ZLEMA_Series() : this(0, false, true) { } + public ZLEMA_Series(int period) : this(period, false, true) { } + public ZLEMA_Series(TBars source) : this(source.Close, 0, false) { } + public ZLEMA_Series(TBars source, int period) : this(source.Close, period, false) { } + public ZLEMA_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public ZLEMA_Series(TSeries source, int period) : this(source, period, false, true) { } + public ZLEMA_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public ZLEMA_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + int _lag; + if (_period == 0) + { + _lag = (int)((_len - 1) * 0.5); + _len++; + } + else { _lag = (int)((_period - 1) * 0.5); } + _lag = Math.Min(_lag, _buffer.Count - 1); + _lag = Math.Max(_lag, 0) + 1; + double _zlValue = 2 * TValue.v - _buffer[^_lag]; + double _zlema = _ema.Add((TValue.t, _zlValue), update).v; + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zlema); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _ema.Reset(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ZL_Series.cs b/archive/Calculations/_Updated/ZL_Series.cs new file mode 100644 index 00000000..ee7aa4ce --- /dev/null +++ b/archive/Calculations/_Updated/ZL_Series.cs @@ -0,0 +1,100 @@ +namespace QuanTAlib; + +using System; +using System.Linq; + +/* +ZL: Zero Lag + Data is de-lagged by removing the data from “lag” days ago, thus removing + (or attempting to) the cumulative effect of the moving average. + +Calculation: + Lag = (Period-1)/2 + ZL = Data + (Data - Data(Lag days ago) ) + +Sources: + https://mudrex.com/blog/zero-lag-ema-trading-strategy/ + + */ + +public class ZL_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + private int _len; + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + private readonly EMA_Series _ema; + + //core constructor + public ZL_Series(int period, bool useNaN, bool useSMA) + { + _period = period; + _NaN = useNaN; + Name = $"ZL({period})"; + _len = 1; + _ema = new(period); + } + //generic constructors (source) + + public ZL_Series() : this(0, false, true) { } + public ZL_Series(int period) : this(period, false, true) { } + public ZL_Series(TBars source) : this(source.Close, 0, false) { } + public ZL_Series(TBars source, int period) : this(source.Close, period, false) { } + public ZL_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public ZL_Series(TSeries source, int period) : this(source, period, false, true) { } + public ZL_Series(TSeries source, int period, bool useNaN) : this(source, period, useNaN, true) { } + public ZL_Series(TSeries source, int period, bool useNaN, bool useSMA) : this(period, useNaN, useSMA) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + int _lag; + if (_period == 0) + { + _lag = (int)((_len - 1) * 0.5); + _len++; + } + else { _lag = (int)((_period - 1) * 0.5); } + _lag = Math.Min(_lag, _buffer.Count - 1); + _lag = Math.Max(_lag, 0) + 1; + double _zlValue = 2 * TValue.v - _buffer[^_lag]; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zlValue); + return base.Add(res, update); + } + + //variation of Add() + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + _ema.Reset(); + } +} \ No newline at end of file diff --git a/archive/Calculations/_Updated/ZSCORE_Series.cs b/archive/Calculations/_Updated/ZSCORE_Series.cs new file mode 100644 index 00000000..52c45d70 --- /dev/null +++ b/archive/Calculations/_Updated/ZSCORE_Series.cs @@ -0,0 +1,97 @@ +using System.Linq; + +namespace QuanTAlib; +using System; +using System.Collections.Generic; + +/* +ZSCORE: number of standard deviations from SMA + Z-score describes a value's relationship to the mean of a series, as measured in + terms of standard deviations from the mean. If a Z-score is 0, it indicates that + the data point's score is identical to the mean score. A Z-score of 1.0 would + indicate a value that is one standard deviation from the mean. Z-scores may be + positive or negative, with a positive value indicating the score is above the + mean and a negative score indicating it is below the mean. + +Sources: + https://en.wikipedia.org/wiki/Z-score + https://www.investopedia.com/terms/z/zscore.asp + +Calculation: + std = std * STDEV(close, length) + mean = SMA(close, length) + ZSCORE = (close - mean) / std + + */ + +public class ZSCORE_Series : TSeries +{ + private readonly System.Collections.Generic.List _buffer = new(); + protected readonly int _period; + protected readonly bool _NaN; + protected readonly TSeries _data; + + //core constructors + public ZSCORE_Series(int period, bool useNaN) + { + _period = period; + _NaN = useNaN; + Name = $"ZSCORE({period})"; + } + public ZSCORE_Series(TSeries source, int period, bool useNaN) : this(period, useNaN) + { + _data = source; + Name = Name.Substring(0, Name.IndexOf(")")) + $", {(string.IsNullOrEmpty(_data.Name) ? "data" : _data.Name)})"; + _data.Pub += Sub; + Add(_data); + } + public ZSCORE_Series() : this(period: 0, useNaN: false) { } + public ZSCORE_Series(int period) : this(period: period, useNaN: false) { } + public ZSCORE_Series(TBars source) : this(source.Close, 0, false) { } + public ZSCORE_Series(TBars source, int period) : this(source.Close, period, false) { } + public ZSCORE_Series(TBars source, int period, bool useNaN) : this(source.Close, period, useNaN) { } + public ZSCORE_Series(TSeries source) : this(source, 0, false) { } + public ZSCORE_Series(TSeries source, int period) : this(source: source, period: period, useNaN: false) { } + + ////////////////// + // core Add() algo + public override (DateTime t, double v) Add((DateTime t, double v) TValue, bool update = false) + { + BufferTrim(buffer: _buffer, value: TValue.v, period: _period, update: update); + double _sma = _buffer.Average(); + + double _pvar = 0; + for (int i = 0; i < _buffer.Count; i++) { _pvar += (_buffer[i] - _sma) * (_buffer[i] - _sma); } + _pvar /= this._buffer.Count; + double _psdev = Math.Sqrt(_pvar); + double _zscore = (_psdev == 0) ? 1 : (TValue.v - _sma) / _psdev; + + var res = (TValue.t, Count < _period - 1 && _NaN ? double.NaN : _zscore); + return base.Add(res, update); + } + + public override (DateTime t, double v) Add(TSeries data) + { + if (data == null) { return (DateTime.Today, Double.NaN); } + foreach (var item in data) { Add(item, false); } + return _data.Last; + } + public (DateTime t, double v) Add(bool update) + { + return this.Add(TValue: _data.Last, update: update); + } + public (DateTime t, double v) Add() + { + return Add(TValue: _data.Last, update: false); + } + private new void Sub(object source, TSeriesEventArgs e) + { + Add(TValue: _data.Last, update: e.update); + } + + //reset calculation + public override void Reset() + { + _buffer.Clear(); + } +} \ No newline at end of file diff --git a/archive/Indicators/Charts/2MACross_chart.cs b/archive/Indicators/Charts/2MACross_chart.cs new file mode 100644 index 00000000..d655896b --- /dev/null +++ b/archive/Indicators/Charts/2MACross_chart.cs @@ -0,0 +1,298 @@ +using System; +using System.Drawing; +using System.Linq; +using TradingPlatform.BusinessLayer; +namespace QuanTAlib; + +public class MovingAverage_chart : Indicator +{ + #region Parameters + [InputParameter("MA1: Type:", 0, variants: new object[] + { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, + "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] + private int MA1type = 15; + + [InputParameter("MA1: Smoothing period:", 1, 1, 999, 1, 1)] + private int MA1Period = 10; + + [InputParameter("MA1: Data source:", 2, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int MA1DataSource = 3; + + [InputParameter("MA2: Type:", 3, variants: new object[] + { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, + "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] + private int MA2type = 16; + + [InputParameter("MA2: Smoothing period:", 4, 1, 999, 1, 1)] + private int MA2Period = 50; + + [InputParameter("MA2: Data source:", 5, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int MA2DataSource = 8; + + [InputParameter("Long trades", 6)] + private bool LongTrades = true; + + [InputParameter("Short trades", 6)] + private bool ShortTrades = true; + + #endregion Parameters + + protected HistoricalData History; + private TBars bars; + + /////// + private TSeries MA1, MA2; + private CROSS_Series trades; + private COMPARE_Series overunder; + + /////// + + public MovingAverage_chart() + { + this.SeparateWindow = false; + this.Name = "MAs Crossover"; + this.AddLineSeries("MA1", Color.LimeGreen, 2, LineStyle.Solid); + this.AddLineSeries("MA2", Color.OrangeRed, 2, LineStyle.Solid); + } + + protected override void OnInit() + { + this.bars = new(); + this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); + for (int i = this.History.Count - 1; i >= 0; i--) + { + var rec = this.History[i, SeekOriginHistory.Begin]; + bars.Add(rec.TimeLeft, rec[PriceType.Open], + rec[PriceType.High], rec[PriceType.Low], + rec[PriceType.Close], rec[PriceType.Volume]); + } + this.Name = "MAs Cross: [ "; + switch (MA1type) + { + case 0: + MA1 = new SMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"SMA"; + break; + case 1: + MA1 = new EMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"EMA"; + break; + case 2: + MA1 = new WMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"WMA"; + break; + case 3: + MA1 = new T3_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"T3"; + break; + case 4: + MA1 = new SMMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"SMMA"; + break; + case 5: + MA1 = new TRIMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"TRIMA"; + break; + case 6: + MA1 = new DWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"DWMA"; + break; + case 7: + MA1 = new FWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period); + this.Name += $"FWMA"; + break; + case 8: + MA1 = new DEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"DEMA"; + break; + case 9: + MA1 = new TEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"TEMA"; + break; + case 10: + MA1 = new ALMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"ALMA"; + break; + case 11: + MA1 = new HMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"HMA"; + break; + case 12: + MA1 = new HEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"HEMA"; + break; + case 13: + double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA1Period); + MA1 = new MAMA_Series(source: bars.Select(this.MA1DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); + this.Name += $"MAMA"; + break; + case 14: + MA1 = new KAMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"KAMA"; + break; + case 15: + MA1 = new ZLEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"ZLEMA"; + break; + default: + MA1 = new JMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"JMA"; + break; + } + + this.Name = this.Name + $" ({MA1Period}:{TBars.SelectStr(this.MA1DataSource)}) : "; + + switch (MA2type) + { + case 0: + MA2 = new SMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"SMA"; + break; + case 1: + MA2 = new EMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"EMA"; + break; + case 2: + MA2 = new WMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"WMA"; + break; + case 3: + MA2 = new T3_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"T3"; + break; + case 4: + MA2 = new SMMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"SMMA"; + break; + case 5: + MA2 = new TRIMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"TRIMA"; + break; + case 6: + MA2 = new DWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"DWMA"; + break; + case 7: + MA2 = new FWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period); + this.Name += $"FWMA"; + break; + case 8: + MA2 = new DEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"DEMA"; + break; + case 9: + MA2 = new TEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"TEMA"; + break; + case 10: + MA2 = new ALMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"ALMA"; + break; + case 11: + MA2 = new HMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"HMA"; + break; + case 12: + MA2 = new HEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"HEMA"; + break; + case 13: + double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA2Period); + MA2 = new MAMA_Series(source: bars.Select(this.MA2DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); + this.Name += $"MAMA"; + break; + case 14: + MA2 = new KAMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"KAMA"; + break; + case 15: + MA2 = new ZLEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"ZLEMA"; + break; + default: + MA2 = new JMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"JMA"; + break; + } + this.Name += $"({MA2Period}:{TBars.SelectStr(this.MA2DataSource)}) ]"; + + int maxKeep = Math.Max(Math.Max(this.MA1Period, this.MA2Period), 100); + MA1.Keep = maxKeep; + MA2.Keep = maxKeep; + trades.Keep = maxKeep; + overunder.Keep = maxKeep; + + overunder = new(MA1, MA2); + trades = new(MA1, MA2); + } + + protected override void OnUpdate(UpdateArgs args) + { + bool update = !(args.Reason == UpdateReason.NewBar || + args.Reason == UpdateReason.HistoricalBar); + this.bars.Add(this.Time(), this.GetPrice(PriceType.Open), + this.GetPrice(PriceType.High), + this.GetPrice(PriceType.Low), + this.GetPrice(PriceType.Close), + this.GetPrice(PriceType.Volume), update); + this.SetValue(this.MA1[^1].v, lineIndex: 0); + this.SetValue(this.MA2[^1].v, lineIndex: 1); + + if (trades[^1].v == 1) + { + this.EndCloud(0, 1, Color.Empty); + if (LongTrades) + { + this.LinesSeries[0].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.UpArrow)); + this.BeginCloud(0, 1, Color.FromArgb(127, Color.Green)); + } + if (ShortTrades) + { + this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.DownArrow)); + } + } + if (trades[^1].v == -1) + { + this.EndCloud(0, 1, Color.Empty); + if (ShortTrades) + { + this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.UpArrow)); + this.BeginCloud(0, 1, Color.FromArgb(127, Color.Red)); + } + if (LongTrades) + { + this.LinesSeries[0].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); + } + } + } + public override void OnPaintChart(PaintChartEventArgs args) + { + base.OnPaintChart(args); + if (this.CurrentChart == null) { return; } + Graphics graphics = args.Graphics; + var mainWindow = this.CurrentChart.MainWindow; + int leftIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left)); + int rightIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right))); + int historycount = HistoricalData.Count; + int ymax = mainWindow.ClientRectangle.Height; + int xmax = mainWindow.ClientRectangle.Width; + + /* + for (int i = leftIndex; i <= rightIndex; i++) { + int xi = (int)Math.Round(mainWindow.CoordinatesConverter.GetChartX(Time(Count - 1 - i))); + int width = this.CurrentChart.BarsWidth; + int height = (int)((equity[i+historycount].v) *proportion); + + Brush bb = Brushes.DarkSlateGray; + bb = (overunder[i+historycount].v>0 && LongTrades)? Brushes.Green : bb; + bb = (overunder[i + historycount].v < 0 && ShortTrades) ? Brushes.Red : bb; + + graphics.FillRectangle(bb, xi, ymax - height, width, height); + } + */ + } +} diff --git a/archive/Indicators/Charts/2MASlope_chart.cs b/archive/Indicators/Charts/2MASlope_chart.cs new file mode 100644 index 00000000..db75d157 --- /dev/null +++ b/archive/Indicators/Charts/2MASlope_chart.cs @@ -0,0 +1,320 @@ +using System; +using System.Drawing; +using System.Linq; +using TradingPlatform.BusinessLayer; +namespace QuanTAlib; + +public class MovingAverageSlope_chart : Indicator +{ + #region Parameters + [InputParameter("MA1: Type:", 0, variants: new object[] + { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, + "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] + private int MA1type = 16; + + [InputParameter("MA1: Smoothing period:", 1, 1, 999, 1, 1)] + private int MA1Period = 10; + + [InputParameter("MA1: Data source:", 2, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int MA1DataSource = 3; + + [InputParameter("MA2: Type:", 3, variants: new object[] + { "SMA", 0, "EMA", 1, "WMA", 2, "T3", 3, "SMMA", 4, "TRIMA", 5, "DWMA", 6, "FWMA", 7, "DEMA", 8, "TEMA", 9, + "ALMA", 10, "HMA", 11, "HEMA", 12, "MAMA", 13, "KAMA", 14, "ZLEMA", 15, "JMA", 16})] + private int MA2type = 6; + + [InputParameter("MA2: Smoothing period:", 4, 1, 999, 1, 1)] + private int MA2Period = 50; + + [InputParameter("MA2: Data source:", 5, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int MA2DataSource = 8; + + [InputParameter("Data required for slope calc:", 6, 2, 10, 1, 1)] + private int SlopePeriod = 3; + + [InputParameter("Long trades", 7)] + private bool LongTrades = true; + + [InputParameter("Short trades", 8)] + private bool ShortTrades; + + #endregion Parameters + + protected HistoricalData History; + private TBars bars; + + /////// + private TSeries MA1, MA2; + private SLOPE_Series sMA1, sMA2; + private CROSS_Series sig1, sig2; + + private bool inLong, inShort; + /////// + + public MovingAverageSlope_chart() + { + this.SeparateWindow = false; + this.Name = "Slopes convergence"; + this.AddLineSeries("MA1", Color.DarkSlateGray, 2, LineStyle.Solid); + this.AddLineSeries("MA2", Color.DarkSlateGray, 2, LineStyle.Solid); + } + + protected override void OnInit() + { + this.bars = new(); + this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); + for (int i = this.History.Count - 1; i >= 0; i--) + { + var rec = this.History[i, SeekOriginHistory.Begin]; + bars.Add(rec.TimeLeft, rec[PriceType.Open], + rec[PriceType.High], rec[PriceType.Low], + rec[PriceType.Close], rec[PriceType.Volume]); + } + this.Name = "Slopes convergence: [ "; + switch (MA1type) + { + case 0: + MA1 = new SMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"SMA"; + break; + case 1: + MA1 = new EMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"EMA"; + break; + case 2: + MA1 = new WMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"WMA"; + break; + case 3: + MA1 = new T3_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"T3"; + break; + case 4: + MA1 = new SMMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"SMMA"; + break; + case 5: + MA1 = new TRIMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"TRIMA"; + break; + case 6: + MA1 = new DWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"DWMA"; + break; + case 7: + MA1 = new FWMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period); + this.Name += $"FWMA"; + break; + case 8: + MA1 = new DEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"DEMA"; + break; + case 9: + MA1 = new TEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"TEMA"; + break; + case 10: + MA1 = new ALMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"ALMA"; + break; + case 11: + MA1 = new HMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"HMA"; + break; + case 12: + MA1 = new HEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"HEMA"; + break; + case 13: + double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA1Period); + MA1 = new MAMA_Series(source: bars.Select(this.MA1DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); + this.Name += $"MAMA"; + break; + case 14: + MA1 = new KAMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"KAMA"; + break; + case 15: + MA1 = new ZLEMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"ZLEMA"; + break; + default: + MA1 = new JMA_Series(source: bars.Select(this.MA1DataSource), period: this.MA1Period, useNaN: false); + this.Name += $"JMA"; + break; + } + + this.Name = this.Name + $" ({MA1Period}:{TBars.SelectStr(this.MA1DataSource)}) : "; + + switch (MA2type) + { + case 0: + MA2 = new SMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"SMA"; + break; + case 1: + MA2 = new EMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"EMA"; + break; + case 2: + MA2 = new WMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"WMA"; + break; + case 3: + MA2 = new T3_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"T3"; + break; + case 4: + MA2 = new SMMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"SMMA"; + break; + case 5: + MA2 = new TRIMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"TRIMA"; + break; + case 6: + MA2 = new DWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"DWMA"; + break; + case 7: + MA2 = new FWMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period); + this.Name += $"FWMA"; + break; + case 8: + MA2 = new DEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"DEMA"; + break; + case 9: + MA2 = new TEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"TEMA"; + break; + case 10: + MA2 = new ALMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"ALMA"; + break; + case 11: + MA2 = new HMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"HMA"; + break; + case 12: + MA2 = new HEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"HEMA"; + break; + case 13: + double factor = 1.015 * Math.Exp(-0.043 * (double)this.MA2Period); + MA2 = new MAMA_Series(source: bars.Select(this.MA2DataSource), fastlimit: factor, slowlimit: factor * 0.1, useNaN: false); + this.Name += $"MAMA"; + break; + case 14: + MA2 = new KAMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"KAMA"; + break; + case 15: + MA2 = new ZLEMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"ZLEMA"; + break; + default: + MA2 = new JMA_Series(source: bars.Select(this.MA2DataSource), period: this.MA2Period, useNaN: false); + this.Name += $"JMA"; + break; + } + this.Name += $"({MA2Period}:{TBars.SelectStr(this.MA2DataSource)}) ]"; + + sMA1 = new(MA1, SlopePeriod); + sMA2 = new(MA2, SlopePeriod); + sig1 = new(sMA1, 0); + sig2 = new(sMA2, 0); + + int maxKeep = Math.Max(Math.Max(this.MA1Period, this.MA2Period), 100); + + MA1.Keep = maxKeep; + MA2.Keep = maxKeep; + sMA1.Keep = maxKeep; + sMA2.Keep = maxKeep; + sig1.Keep = maxKeep; + sig2.Keep = maxKeep; + } + + protected override void OnUpdate(UpdateArgs args) + { + bool update = !(args.Reason == UpdateReason.NewBar || + args.Reason == UpdateReason.HistoricalBar); + this.bars.Add(this.Time(), this.Open(), this.High(), this.Low(), this.Close(), this.Volume(), update); + this.SetValue(this.MA1[^1].v, lineIndex: 0); + this.SetValue(this.MA2[^1].v, lineIndex: 1); + + Color s1Color = (this.sMA1[^1].v > 0) ? Color.LimeGreen : Color.OrangeRed; + Color s2Color = (this.sMA2[^1].v > 0) ? Color.LimeGreen : Color.OrangeRed; + + this.LinesSeries[0].SetMarker(0, s1Color); + this.LinesSeries[1].SetMarker(0, s2Color); + + if (sig1[^1].v > 0 || sig2[^1].v > 0) + { + if (sMA1[^1].v >= 0 && sMA2[^1].v >= 0 && LongTrades) + { + inLong = true; + this.BeginCloud(0, 1, Color.FromArgb(127, Color.DarkGreen)); + this.LinesSeries[(this.MA1[^1].v < this.MA2[^1].v) ? 0 : 1].SetMarker(0, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.UpArrow)); + } + else + { + this.EndCloud(0, 1, Color.Empty); + if (inShort && this.Count > 1) + { + this.LinesSeries[(this.MA1[^1].v < this.MA2[^1].v) ? 1 : 0].SetMarker(1, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.DownArrow)); + inShort = false; + } + } + } + + if (sig1[^1].v < 0 || sig2[^1].v < 0) + { + if (sMA1[^1].v <= 0 && sMA2[^1].v <= 0 && ShortTrades) + { + inShort = true; + this.BeginCloud(0, 1, Color.FromArgb(100, Color.Red)); + this.LinesSeries[(this.MA1[^1].v > this.MA2[^1].v) ? 0 : 1].SetMarker(0, new IndicatorLineMarker(Color.OrangeRed, upperIcon: IndicatorLineMarkerIconType.UpArrow)); + } + else + { + this.EndCloud(0, 1, Color.Empty); + if (inLong && this.Count > 1) + { + LinesSeries[(this.MA1[^1].v > this.MA2[^1].v) ? 1 : 0].SetMarker(1, new IndicatorLineMarker(Color.LimeGreen, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); + inLong = false; + } + } + } + } + public override void OnPaintChart(PaintChartEventArgs args) + { + base.OnPaintChart(args); + if (this.CurrentChart == null) { return; } + Graphics graphics = args.Graphics; + var mainWindow = this.CurrentChart.MainWindow; + int leftIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left)); + int rightIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right))); + /* + int historycount = HistoricalData.Count; + int ymax = mainWindow.ClientRectangle.Height; + + + for (int i = leftIndex; i <= rightIndex; i++) { + int xi = (int)Math.Round(mainWindow.CoordinatesConverter.GetChartX(Time(Count - 1 - i))); + int width = this.CurrentChart.BarsWidth; + int height = (int)((equity[i+historycount].v) *proportion); + + Brush bb = Brushes.DarkSlateGray; + bb = (overunder[i+historycount].v>0 && LongTrades)? Brushes.Green : bb; + bb = (overunder[i + historycount].v < 0 && ShortTrades) ? Brushes.Red : bb; + + graphics.FillRectangle(bb, xi, ymax - height, width, height); + } + */ + } +} diff --git a/archive/Indicators/Charts/JMA_chart.cs b/archive/Indicators/Charts/JMA_chart.cs new file mode 100644 index 00000000..1f7ec9aa --- /dev/null +++ b/archive/Indicators/Charts/JMA_chart.cs @@ -0,0 +1,104 @@ +using System; +using System.Diagnostics; +using System.Drawing; +using System.Linq; +using TradingPlatform.BusinessLayer; +using TradingPlatform.BusinessLayer.Chart; +namespace QuanTAlib; + +public class JMA_chart : Indicator +{ + #region Parameters + + [InputParameter("Data source", 0, variants: new object[] + { "Open", 0, "High", 1, "Low", 2, "Close", 3, "HL2", 4, "OC2", 5, + "OHL3", 6, "HLC3", 7, "OHLC4", 8, "Weighted (HLCC4)", 9 })] + private int DataSource = 3; + + [InputParameter("Smoothing period", 1, 1, 999, 1, 1)] + private int Period = 9; + + [InputParameter("Volatility short", 2, 3, 50, 1, 1)] + private int Vshort = 10; + + [InputParameter("Volatility long", 3, 20, 500, 1, 1)] + private int Vlong = 65; + + [InputParameter("Phase", 4, -100, 100, 1, 2)] + private double Jphase; + + #endregion Parameters + + /////// + private JMA_Series indicator; + /////// + + protected TBars bars; + protected IChartWindow mainWindow; + protected Graphics graphics; + protected int firstOnScreenBarIndex, lastOnScreenBarIndex; + protected HistoricalData History; + protected int HistPeriod; + public JMA_chart() + { + Name = "JMA - Jurik Moving Avg"; + Description = "Jurik Moving Average description"; + AddLineSeries(lineName: "JMA", lineColor: Color.Yellow, lineWidth: 3, lineStyle: LineStyle.Solid); + SeparateWindow = false; + HistPeriod = Period; + } + + + protected override void OnInit() + { + base.OnInit(); + bars = new(); + var dur1 = this.HistoricalData.FromTime; + var dur = this.HistoricalData.Period.Duration.TotalSeconds * (HistPeriod * 4); //seconds of two periods + + this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); + + for (int i = this.History.Count - 1; i >= 0; i--) + { + + var rec = this.History[i, SeekOriginHistory.Begin]; + + bars.Add(rec.TimeLeft, rec[PriceType.Open], + rec[PriceType.High], rec[PriceType.Low], + rec[PriceType.Close], rec[PriceType.Volume]); + } + + indicator = new(source: bars.Select(DataSource), period: Period, phase: Jphase, vshort: Vshort, vlong: Vlong, useNaN: true); + indicator.Keep = Math.Max(Period, 100); + } + + protected override void OnUpdate(UpdateArgs args) + { + base.OnUpdate(args); + bars.Add(Time(), GetPrice(PriceType.Open), + GetPrice(PriceType.High), + GetPrice(PriceType.Low), + GetPrice(PriceType.Close), + GetPrice(PriceType.Volume), + update: !(args.Reason == UpdateReason.NewBar || args.Reason == UpdateReason.HistoricalBar)); + + this.SetValue(indicator[^1].v, lineIndex: 0); + } + public override void OnPaintChart(PaintChartEventArgs args) + { + base.OnPaintChart(args); + if (this.CurrentChart == null) + { + return; + } + + graphics = args.Graphics; + mainWindow = this.CurrentChart.MainWindow; + + DateTime leftTime = mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Left); + DateTime rightTime = mainWindow.CoordinatesConverter.GetTime(mainWindow.ClientRectangle.Right); + firstOnScreenBarIndex = (int)mainWindow.CoordinatesConverter.GetBarIndex(leftTime); + lastOnScreenBarIndex = (int)Math.Ceiling(mainWindow.CoordinatesConverter.GetBarIndex(rightTime)); + } + +} diff --git a/archive/Indicators/Charts/TrailingStop.cs b/archive/Indicators/Charts/TrailingStop.cs new file mode 100644 index 00000000..ad480826 --- /dev/null +++ b/archive/Indicators/Charts/TrailingStop.cs @@ -0,0 +1,102 @@ +using System; +using System.Diagnostics; +using System.Drawing; +using System.Linq; +using TradingPlatform.BusinessLayer; +namespace QuanTAlib; + +public class TrailingStop_chart : Indicator +{ + #region Parameters + + [InputParameter("Period", 0, 1, 100, 1, 1)] + protected int _period = 30; + + [InputParameter("Factor", 1, 1, 100, 0.1, 1)] + protected double _factor = 10; + + [InputParameter("Long TS", 2)] + private bool _LongTS = true; + + [InputParameter("Short TS", 3)] + private bool _ShortTS = true; + + #endregion Parameters + + /////// + private HistoricalData History; + private TBars bars; + private ATR_Series _atr; + private double _tslineL, _ratchetL, _tslineS, _ratchetS; + + /////// + + public TrailingStop_chart() + { + Name = $"ATR Trailing Stop"; + AddLineSeries(lineName: "TrailingATR Long", lineColor: Color.Yellow, lineWidth: 1, lineStyle: LineStyle.Dot); + AddLineSeries(lineName: "Ratchet Long", lineColor: Color.Yellow, lineWidth: 3, lineStyle: LineStyle.Solid); + + AddLineSeries(lineName: "TrailingATR Short", lineColor: Color.Yellow, lineWidth: 1, lineStyle: LineStyle.Dot); + AddLineSeries(lineName: "Ratchet Short", lineColor: Color.Yellow, lineWidth: 3, lineStyle: LineStyle.Solid); + + SeparateWindow = false; + } + + + protected override void OnInit() + { + this.Name = $"Trailing Stop (ATR:{_period}, Mult:{_factor:f2})"; + this.bars = new(); + + this.History = this.Symbol.GetHistory(period: this.HistoricalData.Period, fromTime: HistoricalData.FromTime); + for (int i = this.History.Count - 1; i >= 0; i--) + { + var rec = this.History[i, SeekOriginHistory.Begin]; + bars.Add(rec.TimeLeft, rec[PriceType.Open], + rec[PriceType.High], rec[PriceType.Low], + rec[PriceType.Close], rec[PriceType.Volume]); + } + _atr = new(source: bars, _period, useNaN: true); + _ratchetL = Double.NegativeInfinity; + _ratchetS = Double.PositiveInfinity; + + this.LinesSeries[0].Visible = _LongTS; + this.LinesSeries[1].Visible = _LongTS; + this.LinesSeries[2].Visible = _ShortTS; + this.LinesSeries[3].Visible = _ShortTS; + } + + protected override void OnUpdate(UpdateArgs args) + { + bool update = !(args.Reason == UpdateReason.NewBar || + args.Reason == UpdateReason.HistoricalBar); + this.bars.Add(this.Time(), this.GetPrice(PriceType.Open), + this.GetPrice(PriceType.High), + this.GetPrice(PriceType.Low), + this.GetPrice(PriceType.Close), + this.GetPrice(PriceType.Volume), update); + + _tslineL = bars.High[^1].v - (_factor * _atr[^1].v); + _ratchetL = Math.Max(_tslineL, _ratchetL); + if (_ratchetL > bars.Low[^1].v) + { + this.LinesSeries[1].SetMarker(0, new IndicatorLineMarker(Color.Yellow, bottomIcon: IndicatorLineMarkerIconType.DownArrow)); + _ratchetL = _tslineL; + } + + _tslineS = bars.High[^1].v + (_factor * _atr[^1].v); + _ratchetS = Math.Min(_tslineS, _ratchetS); + if (_ratchetS < bars.High[^1].v) + { + this.LinesSeries[3].SetMarker(0, new IndicatorLineMarker(Color.Yellow, upperIcon: IndicatorLineMarkerIconType.UpArrow)); + _ratchetS = _tslineS; + } + + this.SetValue(_tslineL, lineIndex: 0); + this.SetValue(_ratchetL, lineIndex: 1); + this.SetValue(_tslineS, lineIndex: 2); + this.SetValue(_ratchetS, lineIndex: 3); + } +} + diff --git a/archive/Indicators/Indicators.csproj b/archive/Indicators/Indicators.csproj new file mode 100644 index 00000000..5b9da593 --- /dev/null +++ b/archive/Indicators/Indicators.csproj @@ -0,0 +1,56 @@ + + + net7.0 + preview + false + AnyCPU + Indicator + QuanTAlib_Indicators + QuanTAlib + embedded + AnyCPU + disable + False + ..\.sonarlint\mihakralj_quantalibcsharp.ruleset + 0.2.1.0 + 0.2.1.0 + 0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d + 0.2.1-dev.2 + NETSDK1057 + true + NETSDK1057 + + + True + 3 + True + anycpu + full + + + embedded + True + 3 + True + anycpu + + + + + + + + + + + + + QuanTAlib\%(RecursiveDir)%(Filename)%(Extension) + + + + + ..\.github\TradingPlatform.BusinessLayer.dll + + + \ No newline at end of file diff --git a/archive/QuanTAlib.sln_old b/archive/QuanTAlib.sln_old new file mode 100644 index 00000000..a2d92cf3 --- /dev/null +++ b/archive/QuanTAlib.sln_old @@ -0,0 +1,24 @@ +Microsoft Visual Studio Solution File, Format Version 12.00 +# Visual Studio Version 17 +VisualStudioVersion = 17.2.32210.308 +MinimumVisualStudioVersion = 10.0.40219.1 +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Calculations", "v2\calculations.csproj", "{AAE21F8A-9BC2-4647-A9EB-4DC86C569080}" +EndProject +Global + GlobalSection(SolutionConfigurationPlatforms) = preSolution + Debug|Any CPU = Debug|Any CPU + Release|Any CPU = Release|Any CPU + EndGlobalSection + GlobalSection(ProjectConfigurationPlatforms) = postSolution + {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Debug|Any CPU.Build.0 = Debug|Any CPU + {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Release|Any CPU.ActiveCfg = Release|Any CPU + {AAE21F8A-9BC2-4647-A9EB-4DC86C569080}.Release|Any CPU.Build.0 = Release|Any CPU + EndGlobalSection + GlobalSection(SolutionProperties) = preSolution + HideSolutionNode = FALSE + EndGlobalSection + GlobalSection(ExtensibilityGlobals) = postSolution + SolutionGuid = {E5592DC2-0542-45B2-A0CF-C6B1EDC72B87} + EndGlobalSection +EndGlobal \ No newline at end of file diff --git a/archive/Strategies/Strategies.csproj b/archive/Strategies/Strategies.csproj new file mode 100644 index 00000000..e665dc50 --- /dev/null +++ b/archive/Strategies/Strategies.csproj @@ -0,0 +1,53 @@ + + + net7.0 + preview + false + AnyCPU + Strategy + QuanTAlib_Strategies + QuanTAlib + embedded + AnyCPU + disable + False + ..\.sonarlint\mihakralj_quantalibcsharp.ruleset + 0.2.1.0 + 0.2.1.0 + 0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d + 0.2.1-dev.2 + NETSDK1057 + true + NETSDK1057 + + + True + 3 + True + anycpu + full + + + embedded + True + 3 + True + anycpu + + + + + + + QuanTAlib\%(RecursiveDir)%(Filename)%(Extension) + + + + + + + + ..\.github\TradingPlatform.BusinessLayer.dll + + + \ No newline at end of file diff --git a/archive/Tests/Basic tests/Indicators.cs b/archive/Tests/Basic tests/Indicators.cs new file mode 100644 index 00000000..431aa94f --- /dev/null +++ b/archive/Tests/Basic tests/Indicators.cs @@ -0,0 +1,156 @@ +using Xunit; +using System; +using QuanTAlib; + +namespace Basics; +#nullable disable +public class Indicators +{ + private static Type[] maSeriesTypes = new Type[] + { + typeof(SMA_Series), + typeof(EMA_Series), + typeof(DEMA_Series), + typeof(TEMA_Series), + typeof(WMA_Series), + typeof(ALMA_Series), + typeof(DWMA_Series), + typeof(FWMA_Series), + typeof(HMA_Series), + typeof(ZLEMA_Series), + typeof(RMA_Series), + typeof(HEMA_Series), + typeof(JMA_Series), + typeof(CUSUM_Series), + typeof(SMMA_Series), + typeof(T3_Series), + typeof(KAMA_Series), + typeof(TRIMA_Series), + typeof(MAMA_Series), + typeof(HWMA_Series), + }; + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Name_exists(Type classType) + { + TSeries data = new("Data") { 1, 2, 3 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + Assert.NotEmpty(MA_Series.Name); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Series_Length(Type classType) + { + GBM_Feed feed = new(1000); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + Assert.Equal(1000, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Return_data(Type classType) + { + TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + var result = MA_Series.Add(20); + Assert.Equal(result.v, MA_Series.Last.v); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Update(Type classType) + { + TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + var pre_update = MA_Series.Last.v; + + double pre_data = data.Last.v; + data.Add(20, true); + data.Add(pre_data, true); + + Assert.Equal(pre_update, MA_Series.Last.v); + Assert.Equal(data.Count, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_zero(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 0, false) as TSeries; + Assert.Equal(data.Count, MA_Series.Count); + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Reset(Type classType) + { + GBM_Feed feed = new(10); + TSeries data = feed.OHLC4; + var MA_Series = Activator.CreateInstance(classType, data, 10, false) as TSeries; + MA_Series.Reset(); + data.Add(0); + Assert.Equal(data.Last.v, MA_Series.Last.v); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_one(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 1, false) as TSeries; + Assert.InRange(MA_Series.Last.v - data.Last.v, -10e-6, 10e-6); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void NaN_test(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.True(double.IsNaN(MA_Series[0].v)); + Assert.True(double.IsNaN(MA_Series[8].v)); + Assert.False(double.IsNaN(MA_Series[9].v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Edge_numbers(Type classType) + { + TSeries data = new() { double.Epsilon, double.PositiveInfinity, double.MaxValue, double.NegativeInfinity }; + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.Equal(4, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void handling_NaN(Type classType) + { + TSeries data = new("Name") { 1, 2, 3, 4, 5, 6, double.NaN, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }; + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + public static IEnumerable MASeriesData() + { + foreach (var type in maSeriesTypes) + { + yield return new object[] { type }; + } + } +} +#nullable restore \ No newline at end of file diff --git a/archive/Tests/Basic tests/Oscillators.cs b/archive/Tests/Basic tests/Oscillators.cs new file mode 100644 index 00000000..fcf86f06 --- /dev/null +++ b/archive/Tests/Basic tests/Oscillators.cs @@ -0,0 +1,161 @@ +using Xunit; +using System; +using System.Runtime.InteropServices; +using QuanTAlib; + +namespace Basics; +#nullable disable +public class Oscillators +{ + private static Type[] maSeriesTypes = new[] + { + typeof(BIAS_Series), + typeof(MAX_Series), + typeof(MIN_Series), + typeof(MIDPOINT_Series), + typeof(ZL_Series), + typeof(DECAY_Series), + typeof(ENTROPY_Series), + typeof(KURTOSIS_Series), + typeof(MAD_Series), + typeof(MAPE_Series), + typeof(MAE_Series), + typeof(MSE_Series), + typeof(SDEV_Series), + typeof(SMAPE_Series), + typeof(WMAPE_Series), + typeof(SSDEV_Series), + typeof(VAR_Series), + typeof(SVAR_Series), + typeof(MEDIAN_Series), + typeof(ZSCORE_Series), + typeof(CMO_Series), + typeof(RSI_Series), + typeof(TRIX_Series), + typeof(BBANDS_Series), +}; + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Name_exists(Type classType) + { + TSeries data = new("Data") { 1, 2, 3 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + Assert.NotEmpty(MA_Series.Name); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Series_Length(Type classType) + { + GBM_Feed feed = new(1000); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + Assert.Equal(1000, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Return_data(Type classType) + { + TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + var result = MA_Series.Add(20); + Assert.Equal(result.v, MA_Series.Last.v); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Update(Type classType) + { + TSeries data = new() { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 }; + + var MA_Series = Activator.CreateInstance(classType, data, 5, false) as TSeries; + var pre_update = MA_Series.Last.v; + + double pre_data = data.Last.v; + data.Add(20, true); + data.Add(pre_data, true); + + Assert.Equal(pre_update, MA_Series.Last.v); + Assert.Equal(data.Count, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_zero(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 0, false) as TSeries; + Assert.Equal(data.Count, MA_Series.Count); + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Reset(Type classType) + { + GBM_Feed feed = new(10); + TSeries data = feed.OHLC4; + var MA_Series = Activator.CreateInstance(classType, data, 10, false) as TSeries; + MA_Series.Reset(); + data.Add(1); + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_one(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 1, false) as TSeries; + Assert.False(double.IsNaN(MA_Series[^1].v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void NaN_test(Type classType) + { + GBM_Feed feed = new(100); + TSeries data = feed.OHLC4; + + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.True(double.IsNaN(MA_Series[0].v)); + Assert.True(double.IsNaN(MA_Series[8].v)); + Assert.False(double.IsNaN(MA_Series[9].v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Edge_numbers(Type classType) + { + TSeries data = new() { double.Epsilon, double.PositiveInfinity, double.MaxValue, double.NegativeInfinity }; + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.Equal(4, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void handling_NaN(Type classType) + { + TSeries data = new("Name") { 1, 2, 3, 4, 5, 6, double.NaN, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 }; + var MA_Series = Activator.CreateInstance(classType, data, 10, true) as TSeries; + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + public static IEnumerable MASeriesData() + { + foreach (var type in maSeriesTypes) + { + yield return new object[] { type }; + } + } +} +#nullable restore \ No newline at end of file diff --git a/archive/Tests/Basic tests/TBars_input.cs b/archive/Tests/Basic tests/TBars_input.cs new file mode 100644 index 00000000..02489ff7 --- /dev/null +++ b/archive/Tests/Basic tests/TBars_input.cs @@ -0,0 +1,97 @@ +using Xunit; +using System; +using System.Runtime.InteropServices; +using QuanTAlib; + +namespace Basics; +#nullable disable +public class TBars +{ + private static Type[] maSeriesTypes = new Type[] + { + typeof(ATR_Series), + typeof(ATRP_Series), + typeof(TR_Series), + typeof(ADL_Series), + typeof(CCI_Series), + typeof(OBV_Series), + typeof(ADOSC_Series), + typeof(MIDPRICE_Series), + }; + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Name_exists(Type classType) + { + GBM_Feed data = new(10); + + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + Assert.NotEmpty(MA_Series.Name); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Series_Length(Type classType) + { + GBM_Feed data = new(1000); + + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + Assert.Equal(1000, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Return_data(Type classType) + { + GBM_Feed data = new(10); + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + var result = MA_Series.Add((DateTime.Today, 1, 2, 3, 4, 5)); + Assert.Equal(result.v, MA_Series.Last.v); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Update(Type classType) + { + GBM_Feed data = new(10); + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + var pre_update = MA_Series.Last; + + var pre_data = data.Last; + data.Add((DateTime.Today, 1, 2, 3, 4, 5), true); + data.Add(pre_data, true); + + Assert.Equal(pre_update.v, MA_Series.Last.v); + Assert.Equal(data.Count, MA_Series.Count); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Reset(Type classType) + { + GBM_Feed data = new(10); + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + MA_Series.Reset(); + data.Add(); + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + [Theory] + [MemberData(nameof(MASeriesData))] + public void Period_default(Type classType) + { + GBM_Feed data = new(100); + + var MA_Series = Activator.CreateInstance(classType, data) as TSeries; + Assert.False(double.IsNaN(MA_Series.Last.v)); + } + + public static IEnumerable MASeriesData() + { + foreach (var type in maSeriesTypes) + { + yield return new object[] { type }; + } + } +} +#nullable restore \ No newline at end of file diff --git a/archive/Tests/Pairs/ADD_Test.cs b/archive/Tests/Pairs/ADD_Test.cs new file mode 100644 index 00000000..2c86e5a8 --- /dev/null +++ b/archive/Tests/Pairs/ADD_Test.cs @@ -0,0 +1,64 @@ +using Xunit; +using System; +using QuanTAlib; + +namespace Pairs; +public class ADD_Test +{ + [Fact] + public void ADDSeriesSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + ADD_Series c = new(a, b); + Assert.Equal(5, c.Last().v); + } + + [Fact] + public void ADDSeriesDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + ADD_Series c = new(a, 10.0); + Assert.Equal(15, c.Last().v); + } + + [Fact] + public void ADDDoubleSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + ADD_Series c = new(10.0, a); + Assert.Equal(15, c.Last().v); + } + + [Fact] + public void ADDEventing_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + ADD_Series c = new(a, b); + a.Add(2); + b.Add(2); + Assert.Equal(4, c.Last().v); + } + + [Fact] + public void ADDUpdateDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + double b = 10; + ADD_Series c = new(a, b); + a.Add(0, true); + Assert.Equal(10, c.Last().v); + } + + [Fact] + public void ADDUpdating_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + ADD_Series c = new(a, b); + a.Add(10, true); + b.Add(10, true); + Assert.Equal(20, c.Last().v); + } +} diff --git a/archive/Tests/Pairs/DIV_Test.cs b/archive/Tests/Pairs/DIV_Test.cs new file mode 100644 index 00000000..0b011bc6 --- /dev/null +++ b/archive/Tests/Pairs/DIV_Test.cs @@ -0,0 +1,64 @@ +using Xunit; +using System; +using QuanTAlib; + +namespace Pairs; +public class DIV_Test +{ + [Fact] + public void DIVSeriesSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15 }; + TSeries b = new() { 5, 4, 3, 2, 1, 3 }; + DIV_Series c = new(a, b); + Assert.Equal(5, c.Last().v); + } + + [Fact] + public void DIVSeriesDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; + DIV_Series c = new(a, 0); + Assert.Equal(double.PositiveInfinity, c.Last().v); + } + + [Fact] + public void DIVDoubleSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 3.0 }; + DIV_Series c = new(12.0, a); + Assert.Equal(4.0, c.Last().v); + } + + [Fact] + public void DIVEventing_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + DIV_Series c = new(a, b); + a.Add(12.0); + b.Add(2); + Assert.Equal(6.0, c.Last().v); + } + + [Fact] + public void DIVUpdatewDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15 }; + double b = 2; + DIV_Series c = new(a, b); + a.Add(10, true); + Assert.Equal(5, c.Last().v); + } + + [Fact] + public void DIVUpdating_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 1 }; + DIV_Series c = new(a, b); + a.Add(10, true); + b.Add(2, true); + Assert.Equal(5, c.Last().v); + } +} diff --git a/archive/Tests/Pairs/MUL_Test.cs b/archive/Tests/Pairs/MUL_Test.cs new file mode 100644 index 00000000..cc1c9718 --- /dev/null +++ b/archive/Tests/Pairs/MUL_Test.cs @@ -0,0 +1,64 @@ +using Xunit; +using System; +using QuanTAlib; + +namespace Pairs; +public class MUL_Test +{ + [Fact] + public void MULSeriesSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 1 }; + MUL_Series c = new(a, b); + Assert.Equal(5, c.Last().v); + } + + [Fact] + public void MULSeriesDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + MUL_Series c = new(a, 10.0); + Assert.Equal(50, c.Last().v); + } + + [Fact] + public void MULDoubleSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + MUL_Series c = new(5.0, a); + Assert.Equal(25, c.Last().v); + } + + [Fact] + public void MULEventing_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + MUL_Series c = new(a, b); + a.Add(2); + b.Add(5); + Assert.Equal(10, c.Last().v); + } + + [Fact] + public void MULUpdateDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + double b = 10; + MUL_Series c = new(a, b); + a.Add(2, true); + Assert.Equal(20, c.Last().v); + } + + [Fact] + public void MULUpdating_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + MUL_Series c = new(a, b); + a.Add(10, true); + b.Add(10, true); + Assert.Equal(100, c.Last().v); + } +} diff --git a/archive/Tests/Pairs/SUB_Test.cs b/archive/Tests/Pairs/SUB_Test.cs new file mode 100644 index 00000000..2a96d81d --- /dev/null +++ b/archive/Tests/Pairs/SUB_Test.cs @@ -0,0 +1,64 @@ +using Xunit; +using System; +using QuanTAlib; + +namespace Pairs; +public class SUB_Test +{ + [Fact] + public void SUBSeriesSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 1 }; + SUB_Series c = new(a, b); + Assert.Equal(4, c.Last().v); + } + + [Fact] + public void SUBSeriesDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; + SUB_Series c = new(a, 10.0); + Assert.Equal(5.0, c.Last().v); + } + + [Fact] + public void SUBDoubleSeries_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15.0 }; + SUB_Series c = new(10.0, a); + Assert.Equal(-5.0, c.Last().v); + } + + [Fact] + public void SUBEventing_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 0 }; + SUB_Series c = new(a, b); + a.Add(7.0); + b.Add(2); + Assert.Equal(5.0, c.Last().v); + } + + [Fact] + public void SUBUpdatewDouble_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 15 }; + double b = 10; + SUB_Series c = new(a, b); + a.Add(1, true); + Assert.Equal(-9, c.Last().v); + } + + [Fact] + public void SUBUpdating_Test() + { + TSeries a = new() { 0, 1, 2, 3, 4, 5 }; + TSeries b = new() { 5, 4, 3, 2, 1, 1 }; + SUB_Series c = new(a, b); + a.Add(10, true); + b.Add(0, true); + Assert.Equal(10, c.Last().v); + } +} diff --git a/archive/Tests/Pairs/TBars_Test.cs b/archive/Tests/Pairs/TBars_Test.cs new file mode 100644 index 00000000..cae4ecec --- /dev/null +++ b/archive/Tests/Pairs/TBars_Test.cs @@ -0,0 +1,112 @@ +using Xunit; +using System; +using QuanTAlib; + +namespace Bars; +public class TBars_Test +{ + [Fact] + public void InsertingTuple() + { + TBars s = new() { (t: DateTime.Today, o: double.Epsilon, h: double.NaN, l: Double.MaxValue, c: Double.NegativeInfinity, v: Double.PositiveInfinity) }; + var tup = (t: DateTime.Today, o: double.Epsilon, h: double.NaN, l: Double.MaxValue, + c: Double.NegativeInfinity, v: Double.PositiveInfinity); + Assert.Equal(tup, s[^1]); + } + + [Fact] + public void Casting_Parameters() + { + TBars s = new() + { + { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1, false } + }; + Assert.Equal(0.1, s[^1].o); + Assert.Equal(1.1, s[^1].h); + Assert.Equal(2.1, s[^1].l); + Assert.Equal(3.1, s[^1].c); + Assert.Equal(4.1, s[^1].v); + Assert.Equal(DateTime.Today, s[^1].t); + Assert.Single(s); + } + + [Fact] + public void Updating_Value() + { + TBars s = new() + { + { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1 } + }; + s.Add(DateTime.Today, 1.0, 1.0, 1.0, 1.0, 1.0, update: false); + s.Add(DateTime.Today, 0.0, 0.0, 0.0, 0.0, 0.0, update: true); + Assert.Equal(0.0, s[^1].o); + Assert.Equal(0.0, s[^1].h); + Assert.Equal(0.0, s[^1].l); + Assert.Equal(0.0, s[^1].c); + Assert.Equal(0.0, s[^1].v); + Assert.Equal(2, s.Count); + } + [Fact] + public void Extracting_TSeries() + { + TBars s = new() + { + { DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1 }, + { DateTime.Today, 2.1, 3.1, 4.1, 5.1, 6.1 } + }; + + TSeries t = s.Open; + Assert.Equal(t.t, s.Open.t); + Assert.Equal(t.v, s.Open.v); + + t = s.High; + Assert.Equal(t.t, s.High.t); + Assert.Equal(t.v, s.High.v); + + t = s.Low; + Assert.Equal(t.t, s.Low.t); + Assert.Equal(t.v, s.Low.v); + + t = s.Close; + Assert.Equal(t.t, s.Close.t); + Assert.Equal(t.v, s.Close.v); + + t = s.Volume; + Assert.Equal(t.t, s.Volume.t); + Assert.Equal(t.v, s.Volume.v); + + t = s.HL2; + Assert.Equal(t.t, s.HL2.t); + Assert.Equal(t.v, s.HL2.v); + + t = s.OC2; + Assert.Equal(t.t, s.OC2.t); + Assert.Equal(t.v, s.OC2.v); + + t = s.OHL3; + Assert.Equal(t.t, s.OHL3.t); + Assert.Equal(t.v, s.OHL3.v); + + t = s.HLC3; + Assert.Equal(t.t, s.HLC3.t); + Assert.Equal(t.v, s.HLC3.v); + + t = s.OHLC4; + Assert.Equal(t.t, s.OHLC4.t); + Assert.Equal(t.v, s.OHLC4.v); + + t = s.HLCC4; + Assert.Equal(t.t, s.HLCC4.t); + Assert.Equal(t.v, s.HLCC4.v); + } + [Fact] + public void Broadcasting_Events() + { + TBars s = new() { (DateTime.Today, 2.1, 3.1, 4.1, 5.1, 6.1) }; + TSeries t = new(); + s.Close.Pub += t.Sub; + s.Add(DateTime.Today, 0.1, 1.1, 2.1, 3.1, 4.1, false); + Assert.Equal(s.Close.v, t.v); + Assert.Equal(s.Close.Count, t.Count); + } +} diff --git a/archive/Tests/Tests.csproj b/archive/Tests/Tests.csproj new file mode 100644 index 00000000..41673a8b --- /dev/null +++ b/archive/Tests/Tests.csproj @@ -0,0 +1,45 @@ + + + net7.0 + preview + enable + enable + false + AnyCPU;x64 + 0.2.1.0 + 0.2.1.0 + 0.2.1-dev.2+Branch.dev.Sha.cb5fe2dc86a78fe9358da810d17952c82299ed3d + 0.2.1-dev.2 + $(NoWarn);NETSDK1057 + true + + + + runtime; build; native; contentfiles; analyzers; buildtransitive + all + + + + + runtime; build; native; contentfiles; analyzers; buildtransitive + all + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/archive/Tests/Validations/Trends/Pandas_TA.cs b/archive/Tests/Validations/Trends/Pandas_TA.cs new file mode 100644 index 00000000..4656dc13 --- /dev/null +++ b/archive/Tests/Validations/Trends/Pandas_TA.cs @@ -0,0 +1,484 @@ +using Xunit; +using System; +using QuanTAlib; +using System.Runtime.InteropServices; +using System.Runtime.InteropServices.Marshalling; +using Python.Runtime; + +namespace Validations; + +public class PandasTA : IDisposable +{ + private bool disposed = false; + private readonly GBM_Feed bars; + private readonly Random rnd = new(); + private readonly int period, skip; + private readonly int digits; + private readonly dynamic np; + private readonly dynamic ta; + private readonly dynamic pd; + private readonly dynamic df; + + public PandasTA() + { + bars = new GBM_Feed(5000, 0.8, 0.0); + period = rnd.Next(28) + 3; + skip = period + 50; + digits = 8; + + var pythonDLL = PythonLibrary.Locate(); + Runtime.PythonDLL = pythonDLL; + PythonEngine.Initialize(); + + np = Py.Import("numpy"); + pd = Py.Import("pandas"); + ta = Py.Import("pandas_ta"); + + string[] cols = { "open", "high", "low", "close", "volume" }; + var ary = new double[bars.Count, 5]; + for (var i = 0; i < bars.Count; i++) + { + ary[i, 0] = bars.Open[i].v; + ary[i, 1] = bars.High[i].v; + ary[i, 2] = bars.Low[i].v; + ary[i, 3] = bars.Close[i].v; + ary[i, 4] = bars.Volume[i].v; + } + + df = ta.DataFrame(data: np.array(ary), index: np.array(bars.Close.t), columns: np.array(cols)); + } + + public void Dispose() + { + Dispose(true); + PythonEngine.Shutdown(); + GC.SuppressFinalize(this); + } + + ~PandasTA() + { + Dispose(false); + } + + protected virtual void Dispose(bool disposing) + { + if (!disposed) + { + disposed = true; + } + } + + [Fact] + private void ADL() + { + ADL_Series QL = new(bars); + var pta = df.ta.ad(high: df.high, low: df.low, close: df.close, volume: df.volume); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void BBANDS() + { + BBANDS_Series QL = new(bars.Close, period); + var pta = df.ta.bbands(close: df.close, length: period).to_numpy(); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL.Lower[i].v; + var PanTA_item = (double)pta[i][0]; //lower + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Mid[i].v; + PanTA_item = (double)pta[i][1]; //mid + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Upper[i].v; + PanTA_item = (double)pta[i][2]; //upper + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void BIAS() + { + BIAS_Series QL = new(bars.Close, period, false); + var pta = df.ta.bias(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void CCI() + { + CCI_Series QL = new(bars, period, false); + var pta = df.ta.cci(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void DEMA() + { + DEMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.dema(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void EMA() + { + EMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.ema(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void ENTROPY() + { + ENTROPY_Series QL = new(bars.Close, period, false); + var pta = df.ta.entropy(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void HL2() + { + var pta = df.ta.hl2(high: df.high, low: df.low); + for (var i = bars.HL2.Length - 1; i > skip; i--) + { + var QL_item = bars.HL2[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void HLC3() + { + var pta = df.ta.hlc3(high: df.high, low: df.low, close: df.close); + for (var i = bars.HLC3.Length; i > skip; i--) + { + var QL_item = bars.HLC3[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void HMA() + { + HMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.hma(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void KURTOSIS() + { + KURTOSIS_Series QL = new(bars.Close, period, false); + var pta = df.ta.kurtosis(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void MACD() + { + MACD_Series QL = new(bars.Close, 26, 12, 9, false); + var pta = df.ta.macd(close: df.close).to_numpy(); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1][0]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Signal[i - 1].v; + PanTA_item = (double)pta[i - 1][2]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void MAD() + { + MAD_Series QL = new(bars.Close, period, false); + var pta = df.ta.mad(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void MEDIAN() + { + MEDIAN_Series QL = new(bars.Close, period); + var pta = df.ta.median(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void OBV() + { + OBV_Series QL = new(bars); + var pta = df.ta.obv(close: df.close, volume: df.volume); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void OHLC4() + { + var pta = df.ta.ohlc4(open: df.open, high: df.high, low: df.low, close: df.close); + for (var i = bars.OHLC4.Length; i > skip; i--) + { + var QL_item = bars.OHLC4[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void SDEV() + { + SDEV_Series QL = new(bars.Close, period, false); + var pta = df.ta.stdev(close: df.close, length: period, ddof: 0); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void SMA() + { + SMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.sma(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void SSDEV() + { + SSDEV_Series QL = new(bars.Close, period, false); + var pta = df.ta.stdev(close: df.close, length: period, ddof: 1); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void SVARIANCE() + { + SVAR_Series QL = new(bars.Close, period); + var pta = df.ta.variance(close: df.close, length: period, ddof: 1); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void TEMA() + { + TEMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.tema(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void TR() + { + TR_Series QL = new(bars); + var pta = df.ta.true_range(high: df.high, low: df.low, close: df.close); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void TRIMA() + { + // TODO: return length to variable length (period) when Pandas-TA fixes trima to calculate even periods right + TRIMA_Series QL = new(bars.Close, 11); + var pta = df.ta.trima(close: df.close, length: 11); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void VARIANCE() + { + VAR_Series QL = new(bars.Close, period); + var pta = df.ta.variance(close: df.close, length: period, ddof: 0); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void WMA() + { + WMA_Series QL = new(bars.Close, period, false); + var pta = df.ta.wma(close: df.close, length: period); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + private void ZSCORE() + { + ZSCORE_Series QL = new(bars.Close, period, false); + var pta = df.ta.zscore(close: df.close, length: period, ddof: 0); + for (var i = QL.Length - 1; i > skip; i--) + { + var QL_item = QL[i - 1].v; + var PanTA_item = (double)pta[i - 1]; + Assert.InRange(PanTA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } +} + +public static class PythonLibrary +{ + public static string Locate() + { + if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows)) + { + string[] paths = Environment.GetEnvironmentVariable("PATH")?.Split(';') ?? Array.Empty(); + foreach (string path in paths) + { + string[] pythonDLLs = Directory.GetFiles(path, "python3*.dll"); + if (pythonDLLs.Length > 0) + { + foreach (string item in pythonDLLs) + { + if (!item.EndsWith("python3.dll", StringComparison.OrdinalIgnoreCase)) + { + return item; + } + } + + } + } + throw new FileNotFoundException("Python library not found in PATH"); + } + else if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux)) + { + return "/usr/lib/x86_64-linux-gnu/libpython3.10.so"; + /* + List pythonLibraries = new List(); + List directoriesToSearch = new List { "/home/runner/.local/lib" }; // Add more directories as needed + string filePattern = "libpython3.*.so"; + SearchFiles(directoriesToSearch, filePattern, pythonLibraries); + + if (pythonLibraries.Count > 0) { + return pythonLibraries[0]; + } + else { + throw new FileNotFoundException("Python library not found"); + } + */ + } + + else if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX)) + { + throw new NotSupportedException("Not supported yet"); + } + + else { throw new NotSupportedException("Unsupported operating system"); } + } + static void SearchFiles(List directoriesToSearch, string filePattern, List foundFiles) + { + foreach (string directory in directoriesToSearch) + { + if (Directory.Exists(directory)) + { + try + { + string[] files = Directory.GetFiles(directory, filePattern, SearchOption.AllDirectories); + foundFiles.AddRange(files); + } + catch (Exception e) + { + Console.WriteLine("Error searching in directory: " + directory + " - " + e.Message); + } + } + } + } +} \ No newline at end of file diff --git a/archive/Tests/Validations/Trends/Skender.cs b/archive/Tests/Validations/Trends/Skender.cs new file mode 100644 index 00000000..393f55f1 --- /dev/null +++ b/archive/Tests/Validations/Trends/Skender.cs @@ -0,0 +1,489 @@ +using System; +using QuanTAlib; +using Skender.Stock.Indicators; +using Xunit; + +namespace Validations; +public class Skender +{ + private readonly GBM_Feed bars; + private readonly Random rnd = new(); + private readonly int period, digits, skip; + private readonly IEnumerable quotes; + + + public Skender() + { + bars = new(Bars: 10000, Volatility: 0.5, Drift: 0.0, Precision: 2); + period = rnd.Next(30) + 5; + digits = 6; //minimizing rounding errors in type conversions + skip = period + 2; + + quotes = bars.Select(q => new Quote + { + Date = q.t, + Open = (decimal)q.o, + High = (decimal)q.h, + Low = (decimal)q.l, + Close = (decimal)q.c, + Volume = (decimal)q.v + }); + } + + /* + [Fact] + public void ADL() + { + ADL_Series QL = new(bars); + var SK = quotes.GetAdl().Select(i => i.Adl); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1)!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); + } + } + */ + [Fact] + public void ALMA() + { + ALMA_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetAlma(period).Select(i => i.Alma.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void ATR() + { + ATR_Series QL = new(bars, period: period, useNaN: false); + var SK = quotes.GetAtr(period).Select(i => i.Atr.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void ATRP() + { + ATRP_Series QL = new(bars, period, false); + var SK = quotes.GetAtr(period).Select(i => i.Atrp.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void BBANDS() + { + BBANDS_Series QL = new(bars.Close, period, 2.0, useNaN: false); + var SK = quotes.GetBollingerBands(period, 2.0); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL.Mid[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Sma!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Upper[i - 1].v; + SK_item = SK.ElementAt(i - 1).UpperBand!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Lower[i - 1].v; + SK_item = SK.ElementAt(i - 1).LowerBand!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Bandwidth[i - 1].v; + SK_item = SK.ElementAt(i - 1).Width!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.PercentB[i - 1].v; + SK_item = SK.ElementAt(i - 1).PercentB!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Zscore[i - 1].v; + SK_item = SK.ElementAt(i - 1).ZScore!.Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void CCI() + { + CCI_Series QL = new(bars, period, false); + var SK = quotes.GetCci(period).Select(i => i.Cci.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void CMO() + { + CMO_Series QL = new(bars.Close, period, false); + var SK = quotes.GetCmo(period).Select(i => i.Cmo.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void CORR() + { + CORR_Series QL = new(bars.High, bars.Low, period, false); + var SK = quotes.Use(CandlePart.High).GetCorrelation(quotes.Use(CandlePart.Low), period).Select(i => i.Correlation.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void COVAR() + { + COVAR_Series QL = new(bars.High, bars.Low, period, false); + var SK = quotes.Use(CandlePart.High).GetCorrelation(quotes.Use(CandlePart.Low), period).Select(i => i.Covariance.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void DEMA() + { + DEMA_Series QL = new(bars.Close, period, false, useSMA: true); + var SK = quotes.GetDema(period).Select(i => i.Dema.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void EMA() + { + EMA_Series QL = new(bars.Close, period, false); + var SK = quotes.GetEma(lookbackPeriods: period).Select(i => i.Ema.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void HL2() + { + TSeries QL = bars.HL2; + var SK = quotes.GetBaseQuote(CandlePart.HL2).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void HLC3() + { + TSeries QL = bars.HLC3; + var SK = quotes.GetBaseQuote(CandlePart.HLC3).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void HMA() + { + HMA_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetHma(period).Select(i => i.Hma.Null2NaN()!); + for (int i = QL.Length; i > skip * 2; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + + [Fact] + public void KAMA() + { + // TODO: check precision of KAMA() + KAMA_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetKama(period).Select(i => i.Kama.Null2NaN()!); + for (int i = QL.Length; i > skip + 2; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void SLOPE() + { + SLOPE_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetSlope(period); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = (double)SK.ElementAt(i - 1).Slope!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Intercept[i - 1].v; + SK_item = (double)SK.ElementAt(i - 1).Intercept!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.RSquared[i - 1].v; + SK_item = (double)SK.ElementAt(i - 1).RSquared!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.StdDev[i - 1].v; + SK_item = (double)SK.ElementAt(i - 1).StdDev!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void MACD() + { + MACD_Series QL = new(bars.Close, 26, 12, 9, useNaN: false); + var SK = quotes.GetMacd(12, 26, 9); + for (int i = QL.Length; i > 27; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Macd.Null2NaN()!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + //QL_item = QL.Signal[i - 1].v; + //SK_item = SK.ElementAt(i - 1).Signal.Null2NaN()!; + //Assert.InRange(SK_item! - QL_item, -Math.Pow(10,-digits), Math.Pow(10,-digits)); + } + } + [Fact] + public void MAD() + { + MAD_Series QL = new(bars.Close, period, false); + var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mad.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void MAMA() + { + MAMA_Series QL = new(bars.HL2, fastlimit: 0.5, slowlimit: 0.05); + var SK = quotes.GetMama(fastLimit: 0.5, slowLimit: 0.05); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Mama.Null2NaN()!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + QL_item = QL.Fama[i - 1].v; + SK_item = SK.ElementAt(i - 1).Fama.Null2NaN()!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void MAPE() + { + MAPE_Series QL = new(bars.Close, period, false); + var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mape.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void MSE() + { + MSE_Series QL = new(bars.Close, period, false); + var SK = quotes.GetSmaAnalysis(period).Select(i => i.Mse.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void OBV() + { + OBV_Series QL = new(bars, period, false); + var SK = quotes.GetObv(period).Select(i => i.Obv!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL.Last().v; + // adding volume[0] to OBV to pass the test and keep compatibility with TA-LIB + double SK_item = SK.Last()! + (double)quotes.First().Volume!; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void OC2() + { + TSeries QL = bars.OC2; + var SK = quotes.GetBaseQuote(CandlePart.OC2).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void OHL3() + { + TSeries QL = bars.OHL3; + var SK = quotes.GetBaseQuote(CandlePart.OHL3).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void OHLC4() + { + TSeries QL = bars.OHLC4; + var SK = quotes.GetBaseQuote(CandlePart.OHLC4).ToList(); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1).Value; + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void RSI() + { + RSI_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetRsi(period).Select(i => i.Rsi.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void SDEV() + { + SDEV_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetStdDev(period).Select(i => i.StdDev.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void SMA() + { + SMA_Series QL = new(bars.Close, period, false); + var SK = quotes.GetSma(period).Select(i => i.Sma.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void SMMA() + { + SMMA_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetSmma(period).Select(i => i.Smma.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void T3() + { + T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, false); + var SK = quotes.GetT3(lookbackPeriods: period, volumeFactor: 0.7).Select(i => i.T3.Null2NaN()!); + for (int i = QL.Length; i > period * 15; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void TRIX() + { + TRIX_Series QL = new(bars.Close, period, false); + var SK = quotes.GetTrix(period).Select(i => i.Trix.Null2NaN()!); + for (int i = QL.Length; i > period * 12; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void TEMA() + { + TEMA_Series QL = new(bars.Close, period, false); + var SK = quotes.GetTema(period).Select(i => i.Tema.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void TR() + { + TR_Series QL = new(bars); + var SK = quotes.GetTr().Select(i => i.Tr.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void WMA() + { + WMA_Series QL = new(bars.Close, period, false); + var SK = quotes.GetWma(period).Select(i => i.Wma.Null2NaN()!); + for (int i = QL.Length; i > skip * 2; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + [Fact] + public void ZSCORE() + { + ZSCORE_Series QL = new(bars.Close, period, useNaN: false); + var SK = quotes.GetStdDev(period).Select(i => i.ZScore.Null2NaN()!); + for (int i = QL.Length; i > skip; i--) + { + double QL_item = QL[i - 1].v; + double SK_item = SK.ElementAt(i - 1); + Assert.InRange(SK_item! - QL_item, -Math.Pow(10, -digits), Math.Pow(10, -digits)); + } + } + +} diff --git a/archive/Tests/Validations/Trends/TA_LIB.cs b/archive/Tests/Validations/Trends/TA_LIB.cs new file mode 100644 index 00000000..0e2c3654 --- /dev/null +++ b/archive/Tests/Validations/Trends/TA_LIB.cs @@ -0,0 +1,487 @@ +using Xunit; +using System; +using TALib; +using QuanTAlib; + +namespace Validations; +public class Ta_Lib +{ + private readonly GBM_Feed bars; + private readonly Random rnd = new(); + private readonly int period, digits, skip; + private readonly double[] TALIB; + private readonly double[] TALIB2; + private readonly double[] inopen; + private readonly double[] inhigh; + private readonly double[] inlow; + private readonly double[] inclose; + private readonly double[] involume; + + public Ta_Lib() + { + bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3); + period = rnd.Next(28) + 3; + skip = period + 2; + digits = 9; + + TALIB = new double[bars.Count]; + TALIB2 = new double[bars.Count]; + inopen = bars.Open.v.ToArray(); + inhigh = bars.High.v.ToArray(); + inlow = bars.Low.v.ToArray(); + inclose = bars.Close.v.ToArray(); + involume = bars.Volume.v.ToArray(); + } + + [Fact] + public void ADD() + { + ADD_Series QL = new(bars.Open, bars.Close); + Core.Add(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ADL() + { + ADL_Series QL = new(bars); + Core.Ad(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > 0; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ADOSC() + { + ADOSC_Series QL = new(bars, 3, 10, false); + Core.AdOsc(inhigh, inlow, inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip * 2; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ATR() + { + ATR_Series QL = new(bars, period: period, useNaN: false); + Core.Atr(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + public void BBANDS() + { + double[] outMiddle = new double[bars.Count]; + double[] outUpper = new double[bars.Count]; + double[] outLower = new double[bars.Count]; + BBANDS_Series QL = new(bars.Close, period: period, multiplier: 2.0, false); + Core.Bbands(inclose, 0, bars.Count - 1, outRealUpperBand: outUpper, outRealMiddleBand: outMiddle, outRealLowerBand: outLower, out int outBegIdx, out _, optInTimePeriod: period, optInNbDevUp: 2.0, optInNbDevDn: 2.0); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL.Upper[i].v; + double TA_item = outUpper[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); + QL_item = QL.Mid[i].v; + TA_item = outMiddle[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); + QL_item = QL.Lower[i].v; + TA_item = outLower[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), high: Math.Exp(-digits)); + } + } + [Fact] + public void CCI() + { + CCI_Series QL = new(bars, period, false); + Core.Cci(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + /* CMO in TA-LIB is not valid + [Fact] + public void CMO() { + CMO_Series QL = new(bars.Close, period, false); + Core.Cmo(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + */ + [Fact] + public void CORR() + { + CORR_Series QL = new(bars.Open, bars.Close, period); + Core.Correl(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, optInTimePeriod: period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void DEMA() + { + DEMA_Series QL = new(bars.Close, period, false, useSMA: false); + Core.Dema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > period * 10; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void DIV() + { + DIV_Series QL = new(bars.Open, bars.Close); + Core.Div(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void EMA() + { + EMA_Series QL = new(bars.Close, period, false); + Core.Ema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HL2() + { + TSeries QL = bars.HL2; + Core.MedPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HLC3() + { + TSeries QL = bars.HLC3; + Core.TypPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HLCC4() + { + TSeries QL = bars.HLCC4; + Core.WclPrice(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void KAMA() + { + KAMA_Series QL = new(bars.Close, period, fast: 2, slow: 30); + Core.Kama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period); + for (int i = QL.Length - 1; i > skip * 15; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MACD() + { + double[] macdSignal = new double[bars.Count]; + double[] macdHist = new double[bars.Count]; + MACD_Series QL = new(bars.Close, slow: 26, fast: 12, signal: 9, false); + // TA-LIB runs EMA without SMA, leaving first 100 values for convergence + Core.Macd(inclose, 0, bars.Count - 1, outMacd: TALIB, outMacdSignal: macdSignal, outMacdHist: macdHist, out int outBegIdx, out _, optInFastPeriod: 12, optInSlowPeriod: 26, optInSignalPeriod: 9); + for (int i = QL.Length - 1; i > 100; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Signal[i].v; + TA_item = macdSignal[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + /* + [Fact] + public void MAMA() + { + MAMA_Series QL = new(bars.Close, fastlimit: 0.5, slowlimit: 0.05); + Core.Mama(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outMama: TALIB, outFama: TALIB2, outBegIdx: out int outBegIdx, outNbElement: out _, optInFastLimit: 0.5, optInSlowLimit: 0.05); + for (int i = QL.Length - 1; i > skip * 10; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits-1), Math.Exp(-digits-1)); + } + } + */ + [Fact] + public void MAX() + { + MAX_Series QL = new(bars.Close, period, false); + Core.Max(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MIDPOINT() + { + MIDPOINT_Series QL = new(bars.Close, period, false); + Core.MidPoint(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MIDPRICE() + { + MIDPRICE_Series QL = new(bars, period, false); + Core.MidPrice(inhigh, inlow, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MIN() + { + MIN_Series QL = new(bars.Close, period, false); + Core.Min(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MUL() + { + MUL_Series QL = new(bars.Open, bars.Close); + Core.Mult(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void OBV() + { + OBV_Series QL = new(bars, period, false); + Core.Obv(inclose, involume, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void OHLC4() + { + TSeries QL = bars.OHLC4; + Core.AvgPrice(inopen, inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void RSI() + { + RSI_Series QL = new(bars.Close, period, false); + Core.Rsi(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SDEV() + { + SDEV_Series QL = new(bars.Close, period, false); + Core.StdDev(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SMA() + { + SMA_Series QL = new(bars.Close, period, false); + Core.Sma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SUB() + { + SUB_Series QL = new(bars.Open, bars.Close); + Core.Sub(inopen, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SUM() + { + CUSUM_Series QL = new(bars.Close, period, false); + Core.Sum(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void T3() + { + T3_Series QL = new(source: bars.Close, period: period, vfactor: 0.7, useNaN: false); + Core.T3(inReal: inclose, startIdx: 0, endIdx: bars.Count - 1, outReal: TALIB, outBegIdx: out int outBegIdx, outNbElement: out _, optInTimePeriod: period, optInVFactor: 0.7); + for (int i = QL.Length - 1; i > period * 10; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TEMA() + { + TEMA_Series QL = new(bars.Close, period, false); + Core.Tema(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip * 15; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TR() + { + TR_Series QL = new(bars); + Core.TRange(inhigh, inlow, inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TRIMA() + { + TRIMA_Series QL = new(bars.Close, period, false); + Core.Trima(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TRIX() + { + TRIX_Series QL = new(bars.Close, period, useNaN: false, useSMA: true); + Core.Trix(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > period * 10; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void VAR() + { + VAR_Series QL = new(bars.Close, period, false); + Core.Var(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip * 15; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void WMA() + { + WMA_Series QL = new(bars.Close, period, false); + Core.Wma(inclose, 0, bars.Count - 1, TALIB, out int outBegIdx, out _, period); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TA_item = TALIB[i - outBegIdx]; + Assert.InRange(TA_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + +} diff --git a/archive/Tests/Validations/Trends/Tulip.cs b/archive/Tests/Validations/Trends/Tulip.cs new file mode 100644 index 00000000..fc36b359 --- /dev/null +++ b/archive/Tests/Validations/Trends/Tulip.cs @@ -0,0 +1,578 @@ +using Xunit; +using System; +using Tulip; +using QuanTAlib; + +namespace Validations; +public class Tulip_Test +{ + private readonly GBM_Feed bars; + private readonly Random rnd = new(); + private readonly int period, digits, skip; + private readonly double[] outdata; + private readonly double[] inopen; + private readonly double[] inhigh; + private readonly double[] inlow; + private readonly double[] inclose; + private readonly double[] involume; + + public Tulip_Test() + { + bars = new(Bars: 5000, Volatility: 0.8, Drift: 0.0, Precision: 3); + period = rnd.Next(28) + 3; + skip = period + 5; + digits = 8; + + outdata = new double[bars.Count]; + inopen = bars.Open.v.ToArray(); + inhigh = bars.High.v.ToArray(); + inlow = bars.Low.v.ToArray(); + inclose = bars.Close.v.ToArray()!; + involume = bars.Volume.v.ToArray()!; + + } + [Fact] + public void ADL() + { + double[][] arrin = { inhigh, inlow, inclose, involume }; + double[][] arrout = { outdata }; + ADL_Series QL = new(bars); + Tulip.Indicators.ad.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ADD() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + ADD_Series QL = new(bars.High, bars.Low); + Tulip.Indicators.add.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ADOSC() + { + double[][] arrin = { inhigh, inlow, inclose, involume }; + double[][] arrout = { outdata }; + int s = 3; + ADOSC_Series QL = new(bars, s, period, false); + Tulip.Indicators.adosc.Run(inputs: arrin, options: new double[] { s, period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ATR() + { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + + ATR_Series QL = new(bars, period: period, useNaN: false); + Tulip.Indicators.atr.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + //Tulip ATR doesn't use warm-up SMA, compensating with 200 warming bars + for (int i = QL.Length - 1; i > 200 + skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void BBANDS() + { + double[][] arrin = { inclose }; + double[] outmid = new double[bars.Count]; + double[] outlower = new double[bars.Count]; + double[] outupper = new double[bars.Count]; + double[][] arrout = { outlower, outmid, outupper }; + BBANDS_Series QL = new(bars.Close, period, 2, false); + Tulip.Indicators.bbands.Run(inputs: arrin, options: new double[] { period, 2 }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL.Lower[i].v; + double TU_item = outlower[i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Mid[i].v; + TU_item = outmid[i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + QL_item = QL.Upper[i].v; + TU_item = outupper[i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + /* + [Fact] + public void CCI() { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + CCI_Series QL = new(bars, period, useNaN: false); + Tulip.Indicators.cci.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) { + double QL_item = QL[i].v; + double TU_item = outdata[i - period + 1]; + Assert.Equal(QL_item,TU_item); + //Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + */ + [Fact] + public void CMO() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + CMO_Series QL = new(bars.Close, period, useNaN: false); + Tulip.Indicators.cmo.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void DECAY() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + DECAY_Series QL = new(bars.Close, period, useNaN: false); + Tulip.Indicators.decay.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 200; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void DEMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + DEMA_Series QL = new(bars.Close, period, useNaN: false, useSMA: false); + Tulip.Indicators.dema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 200; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - (period + period - 2)]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void DIV() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + DIV_Series QL = new(bars.High, bars.Low); + Tulip.Indicators.div.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void EDECAY() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + DECAY_Series QL = new(bars.Close, period, exponential: true, useNaN: false); + Tulip.Indicators.edecay.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 200; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void EMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + // Tulip EMA doesn't use SMA to warm-up + EMA_Series QL = new(bars.Close, period, false, useSMA: false); + Tulip.Indicators.ema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HL2() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + + TSeries QL = bars.HL2; + Tulip.Indicators.medprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HLC3() + { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + + TSeries QL = bars.HLC3; + Tulip.Indicators.typprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void HLCC4() + { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + + TSeries QL = bars.HLCC4; + Tulip.Indicators.wcprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + public void HMA() + { + int p = 10; + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + HMA_Series QL = new(bars.Close, p, false); + Tulip.Indicators.hma.Run(inputs: arrin, options: new double[] { p }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 2; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - p - 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits - 2), Math.Exp(-digits - 2)); + } + } + + [Fact] + public void KAMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + KAMA_Series QL = new(bars.Close, period); + Tulip.Indicators.kama.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > 250; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + + [Fact] + public void LINREG() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + SLOPE_Series QL = new(bars.Close, period); + Tulip.Indicators.linregslope.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MACD() + { + + double[] outsignal = new double[bars.Count]; + double[] outhist = new double[bars.Count]; + double[][] arrin = { inclose }; + double[][] arrout = { outdata, outsignal, outhist }; + MACD_Series QL = new(bars.Close, slow: 26, fast: 10, signal: 9); + Tulip.Indicators.macd.Run(inputs: arrin, options: new double[] { 10, 26, 9 }, outputs: arrout); + for (int i = QL.Length - 1; i > 150; i--) + { + double QL_item = QL[i].v; + double TU_item = outdata[i - 26 + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MAX() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + MAX_Series QL = new(bars.Close, period, false); + Tulip.Indicators.max.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MIN() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + MIN_Series QL = new(bars.Close, period, false); + Tulip.Indicators.min.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void MUL() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + MUL_Series QL = new(bars.High, bars.Low); + Tulip.Indicators.mul.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void OBV() + { + double[][] arrin = { inclose, involume }; + double[][] arrout = { outdata }; + OBV_Series QL = new(bars, period, false); + Tulip.Indicators.obv.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i] + arrin[1][0]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void OHLC4() + { + double[][] arrin = { inopen, inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + + TSeries QL = bars.OHLC4; + Tulip.Indicators.avgprice.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void RMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + RMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.wilders.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void RSI() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + RSI_Series QL = new(bars.Close, period, false); + Tulip.Indicators.rsi.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + SMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.sma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SDEV() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + SDEV_Series QL = new(bars.Close, period, false); + Tulip.Indicators.stddev.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SUB() + { + double[][] arrin = { inhigh, inlow }; + double[][] arrout = { outdata }; + SUB_Series QL = new(bars.High, bars.Low); + Tulip.Indicators.sub.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void SUM() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + CUSUM_Series QL = new(bars.Close, period, false); + Tulip.Indicators.sum.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TR() + { + double[][] arrin = { inhigh, inlow, inclose }; + double[][] arrout = { outdata }; + TR_Series QL = new(bars); + Tulip.Indicators.tr.Run(inputs: arrin, options: new double[] { }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TEMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + TEMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.tema.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 200; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - (period - 1) * 3]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void TRIMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + TRIMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.trima.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + /* + [Fact] + public void TRIX() { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + TRIX_Series QL = new(bars.Close, period); + Tulip.Indicators.trix.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > period+200; i--) { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - (period*3) + 2]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits+2), Math.Exp(-digits+2)); + } + } + */ + [Fact] + public void VAR() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + VAR_Series QL = new(bars.Close, period, false); + Tulip.Indicators.var.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void WMA() + { + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + WMA_Series QL = new(bars.Close, period, false); + Tulip.Indicators.wma.Run(inputs: arrin, options: new double[] { period }, outputs: arrout); + for (int i = QL.Length - 1; i > skip; i--) + { + double QL_item = QL[i].v; + double TU_item = arrout[0][i - period + 1]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits), Math.Exp(-digits)); + } + } + [Fact] + public void ZLEMA() + { + int p = 4; + double[][] arrin = { inclose }; + double[][] arrout = { outdata }; + ZLEMA_Series QL = new(bars.Close, p, false); + Tulip.Indicators.zlema.Run(inputs: arrin, options: new double[] { p }, outputs: arrout); + for (int i = QL.Length - 1; i > skip + 20; i--) + { + double QL_item = QL[i].v; + double TU_item = outdata[i]; + Assert.InRange(TU_item! - QL_item, -Math.Exp(-digits - 2), Math.Exp(-digits - 2)); + } + } +} diff --git a/archive/Tests/requirements.txt b/archive/Tests/requirements.txt new file mode 100644 index 00000000..2cc91dd6 --- /dev/null +++ b/archive/Tests/requirements.txt @@ -0,0 +1,6 @@ +pytz +six +python-dateutil +numpy +pandas +pandas-ta \ No newline at end of file diff --git a/archive/docs/.nojekyll b/archive/docs/.nojekyll new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/archive/docs/.nojekyll @@ -0,0 +1 @@ + diff --git a/archive/docs/ALMA.md b/archive/docs/ALMA.md new file mode 100644 index 00000000..0aa250a0 --- /dev/null +++ b/archive/docs/ALMA.md @@ -0,0 +1,5 @@ +# ALMA: Arnaud Legoux Moving Average + +period = 10 + +![Alt text](./img/ALMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/DEMA.md b/archive/docs/DEMA.md new file mode 100644 index 00000000..e82d1ef9 --- /dev/null +++ b/archive/docs/DEMA.md @@ -0,0 +1,5 @@ +# DEMA: Double Exponential Moving Average + +period = 10 + +![Alt text](./img/DEMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/DWMA.md b/archive/docs/DWMA.md new file mode 100644 index 00000000..9ba59690 --- /dev/null +++ b/archive/docs/DWMA.md @@ -0,0 +1,5 @@ +# DWMA: Double Weighted Moving Average + +period = 10 + +![Alt text](./img/DWMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/EMA.md b/archive/docs/EMA.md new file mode 100644 index 00000000..ba8091d8 --- /dev/null +++ b/archive/docs/EMA.md @@ -0,0 +1,50 @@ +# EMA: Exponential Moving Average + +Also known as exponentially weighted moving average, as it places greater weight on the most recent data points. +EMA reacts more agressively to recent data changes and calculates the current value using just the previous EMA value and current data point. The weight applied to the new value is typically $k = 2 / (period-1)$ + +## Calculation + +EMA is a rolling calculation requiring only one historical data point to calculate the current value and is denoted as ${EMA}_{p}{(data)}$ where $p$ represents the period and $data$ represents the list of data points. + +Some implementations of EMA calculate a seeding value of $EMA$ as a ${SMA}_{p}$ when $n < period$ - and start the $EMA$ calculation only after the warm-up period. QuanTAlib offers an option to enable/disable SMA warm-up. + +$$ +EMA_n = \left\{ \begin{array}{cl} +\frac{1}{p}\left( data_{n}-data_{n-p}\right)+SMA_{n-1} & : \ n \leq period \\ +{k}\times ({data_{n}} - EMA_{n-1}) + EMA_{n-1} & : \ n > period +\end{array} \right. +$$ + +## Behavior +![Alt text](./img/EMA_chart.svg) + +## Reference Calculation +period = 5 +``` +TSeries data = new() {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36, 85.53, 86.54, 86.89, 87.77, 87.29}; +EMA_Series ema = new(data, 5, useNaN: false); +EMA_Series ema_nan = new(data, 5, useNaN: true); +for (int i=0; i< data.Count; i++) + Console.WriteLine($"{i}\t{data[i].v,7:f2}\t{ema_nan[i].v,7:f3}\t{ema[i].v,7:f3}"); +``` +| #| Input | **QuanTAlib** | _TA-LIB_ | _Skender_ | _Pandas-TA_ | _Tulip_ | +|--|:--:|:--:|:--:|:--:|:--:|:--:| +|0| 81.59| **81.590**| _NaN_| _NaN_| _NaN_| _NaN_| +|1| 81.06| **81.840**| _NaN_| _NaN_| _NaN_| _NaN_| +|2| 82.87| **81.840**| _NaN_| _NaN_| _NaN_| _NaN_| +|3| 83.00| **82.130**| _NaN_| _NaN_| _NaN_| _NaN_| +|4| 83.61| **82.426**| _82.426_| _82.426_| _82.426_| _82.426_| +|5| 83.15| **82.667**| _82.667_| _82.667_| _82.667_|_82.667_| +|6| 82.84| **82.725**| _82.725_| _82.725_| _82.725_|_82.725_| +|7| 83.99| **83.147**| _83.147_| _83.147_| _83.147_|_83.147_| +|8| 84.55| **83.614**| _83.614_| _83.614_| _83.614_|_83.614_| +|9| 84.36| **83.863**| _83.863_| _83.863_| _83.863_|_83.863_| +|10| 85.53| **84.419**| _84.419_| _84.419_| _84.419_|_84.419_| +|11| 86.54| **85.126**| _85.126_| _85.126_| _85.126_|_85.126_| +|12| 86.89| **85.714**| _85.714_| _85.714_| _85.714_|_85.714_| +|13| 87.77| **86.399**| _86.399_| _86.399_| _86.399_|_86.399_| +|14| 87.29| **86.696**| _86.696_| _86.696_| _86.696_|_86.696_| +## References + +- https://en.wikipedia.org/wiki/Exponential_smoothing \ No newline at end of file diff --git a/archive/docs/FMA.md b/archive/docs/FMA.md new file mode 100644 index 00000000..50b42426 --- /dev/null +++ b/archive/docs/FMA.md @@ -0,0 +1,4 @@ +# FMA: Fibonacci Moving Average +period = 6 + +![Alt text](./img/FMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/HEMA.md b/archive/docs/HEMA.md new file mode 100644 index 00000000..5ede117a --- /dev/null +++ b/archive/docs/HEMA.md @@ -0,0 +1,4 @@ +# HEMA: Hull-Exponential Moving Average +period = 10 + +![Alt text](./img/HEMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/HMA.md b/archive/docs/HMA.md new file mode 100644 index 00000000..22761879 --- /dev/null +++ b/archive/docs/HMA.md @@ -0,0 +1,4 @@ +# HMA: Hull Moving Average +period = 10 + +![Alt text](./img/HMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/HWMA.md b/archive/docs/HWMA.md new file mode 100644 index 00000000..6102933e --- /dev/null +++ b/archive/docs/HWMA.md @@ -0,0 +1,4 @@ +# HWMA: Holt-Winter Moving Average +nA = 0.5; nB = 0.3; nC = 0.01; + +![Alt text](./img/HWMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/JMA.md b/archive/docs/JMA.md new file mode 100644 index 00000000..31c7de3e --- /dev/null +++ b/archive/docs/JMA.md @@ -0,0 +1,4 @@ +# JMA: Jurik Moving Average +period = 10 + +![Alt text](./img/JMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/KAMA.md b/archive/docs/KAMA.md new file mode 100644 index 00000000..594d4d10 --- /dev/null +++ b/archive/docs/KAMA.md @@ -0,0 +1,4 @@ +# KAMA: Kaufman's Adaptive Moving Average +period = 10 + +![Alt text](./img/KAMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/MAMA.md b/archive/docs/MAMA.md new file mode 100644 index 00000000..19dacbb0 --- /dev/null +++ b/archive/docs/MAMA.md @@ -0,0 +1,4 @@ +# MAMA: MESA Adaptive Moving Average +period = 10 + +![Alt text](./img/MAMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/QA.md b/archive/docs/QA.md new file mode 100644 index 00000000..3e56fcda --- /dev/null +++ b/archive/docs/QA.md @@ -0,0 +1,40 @@ +### Is QuanTAlib fast? + +Well, _no_, but actually *yes*. QuanTAlib works on an additive principle, meaning that even when served a full list of quotes, QuanTAlib will process them one item at the time, rolling forward throug the given time series of data. +- If the last bar is still forming (parameter `update: true`), QuanTAlib can easily recalculates the last entry as often as needed without any need to recalculate the history. +- If a new bar is added to the input, QuanTAlib will process that one item (default parameter `update: false`) and add that one result to the List. No recalculation of the history needed. + +So, if you test QuanTAlib with small set of 500 historical bars and calculate EMA(20) on it, the performance of QuanTAlib will be dead last compared to all other TA libraries. + +But when the system uses 10,000 or historical bars, updates the current data at every new tick, and adds a new bar every minute, QuanTAlib has no rivals; all other libraries will need to re-calculate the full length of the indicator on each update/addition to the time series, while QuanTAlib will just update the last entry or add a single new value to the series. No back calculations are performed - ever. Longer the input data series and more updates/additions it gets, the greater advantage there is for QuanTAlib. + +### Are results of QuanTAlib valid? + +QuanTAlib includes battery of tests to compare its results with four well-known and reviewed Technical Analysis libraries to assure accuracy and validity of results: + +- [TA-LIB](https://www.ta-lib.org/function.html) +- [Skender Stock Indicators](https://dotnet.stockindicators.dev/) +- [Pandas-TA](https://twopirllc.github.io/pandas-ta/) +- [Tulip Indicators](https://tulipindicators.org/) + +Not all indicators are implemented by all libraries - and sometimes results of the four reference libraries disagree with each other. Indicators that return equivalent result set to **all four** libraries are marked with ⭐ on [the list of indicators](indicators.md) - these are indicators that can be trusted most. Each verified equivalency of results is marked with ✔️, and each discrepancy is marked with ❌. + +For each discrepancy the research was done to establish the reason and to select the implementation that is the most faithful to the original description of the indicator. + +_For example, CMO indicator was described by Tushar S. Chande in his book The New Technical Trader, where he includes the example of calculating 10-day CMO on a given data. QuanTAlib, Skender.NET and Tulip libraries can all replicate the results, while TA-LIB and Pandas-TA return something very different:_ + +| #| Input | **QuanTAlib** | TA-LIB | Skender | Pandas-TA | Tulip | +|--|:--:|:--:|:--:|:--:|:--:|:--:| +| 0| 101.03|**0.00**| NaN| NaN| NaN| NaN| +| 1| 101.03|**0.00**| NaN| NaN| NaN| NaN| +| 2| 101.12|**100.00**| NaN| NaN| NaN| NaN| +| 3| 101.97|**100.00**| NaN| NaN| NaN| NaN| +| 4| 102.78|**100.00**| NaN| NaN| NaN| NaN| +| 5| 103.00|**100.00**| NaN| NaN| NaN| NaN| +| 6| 102.97|**96.87**| NaN| NaN| NaN| NaN| +| 7| 103.06|**97.01**| NaN| NaN| NaN| NaN| +| 8| 102.94|**85.91**| NaN| NaN| NaN| NaN| +| 9| 102.72|**69.23**| NaN| NaN| NaN| NaN| +|10| 102.75|**69.62**| 69.62| 69.62| ~55.22~| 69.62| +|11| 102.91|**71.43**| ~71.62~| 71.43| ~60.09~| 71.43| +|12| 102.97|**71.08**| ~72.42~| 71.08| ~61.93~| 71.08| \ No newline at end of file diff --git a/archive/docs/RMA.md b/archive/docs/RMA.md new file mode 100644 index 00000000..e33234d9 --- /dev/null +++ b/archive/docs/RMA.md @@ -0,0 +1,5 @@ +# RMA: wildeR Moving Average + +period = 10 + +![Alt text](./img/RMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/SMA.md b/archive/docs/SMA.md new file mode 100644 index 00000000..c84295b5 --- /dev/null +++ b/archive/docs/SMA.md @@ -0,0 +1,57 @@ +# SMA: Simple Moving Average + +SMA is is an arithmetic moving average where the weights in SMA are **equally** distributed across the given period, resulting in a mean() of the data within the period. + +## Calculation + +SMA is a rolling calculation that is looking backwards from the position ${n}$ and is denoted as ${SMA}_{p}{(data)}$ where $p$ represents the period and $data$ represents the list of data points: +$$ +SMA_p{(data)} = \frac{1}{p}\sum_{i=n-p+1}^{n} data_i +$$ +When calculating the value of the next $SMA_{p,next}$ while knowing previous SMA values, SMA calculation can be reduced to: +$$ +SMA_{p,next} = SMA_{p,prev}+\frac{1}{p}\left( data_{n+1}-data_{n+1-p}\right) +$$ + +## Implementation +`TSeries SMA_Series (TSeries source, int period = 0, bool useNaN = false)` + +- SMA_Series returns TSeries list +- `source`: input of type TSeries; SMA_Series automatically subscribes to events of new data added to the source +- `period`: optional size of a lookback window; if set to 0, SMA calculates cumulative average across the whole source +- `useNaN`: if set to _true_, SMA_Series will hide values within the initial period with NaN (for compatibility with other libraries) + +## Behavior +![Alt text](./img/SMA_chart.svg) +## Reference Calculation & Validation +period = 5 +``` +TSeries data = new() {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36, 85.53, 86.54, 86.89, 87.77, 87.29}; +SMA_Series sma = new(data, 5, useNaN: false); +``` + +| #| Input | **QuanTAlib** | _TA-LIB_ | _Skender_ | _Pandas-TA_ | _Tulip_ | +|--|:--:|:--:|:--:|:--:|:--:|:--:| +| 0| 212.80|**212.80**| _NaN_| _NaN_| _NaN_| _NaN_| +| 1| 214.06|**213.43**| _NaN_| _NaN_| _NaN_| _NaN_| +| 2| 213.89|**213.58**| _NaN_| _NaN_| _NaN_| _NaN_| +| 3| 214.66|**213.85**| _NaN_| _NaN_| _NaN_| _NaN_| +| 4| 213.95|**213.87**| _213.87_| _213.87_| _213.87_| _213.87_| +| 5| 213.95|**214.10**| _214.10_| _214.10_| _214.10_| _214.10_| +| 6| 214.55|**214.20**| _214.20_| _214.20_| _214.20_| _214.20_| +| 7| 214.02|**214.23**| _214.23_| _214.23_| _214.23_| _214.23_| +| 8| 214.51|**214.20**| _214.20_| _214.20_| _214.20_| _214.20_| +| 9| 213.75|**214.16**| _214.16_| _214.16_| _214.16_| _214.16_| +|10| 214.22|**214.21**| _214.21_| _214.21_| _214.21_| _214.21_| +|11| 213.43|**213.99**| _213.99_| _213.99_| _213.99_| _213.99_| +|12| 214.21|**214.02**| _214.02_| _214.02_| _214.02_| _214.02_| +|13| 213.66|**213.85**| _213.85_| _213.85_| _213.85_| _213.85_| +|14| 215.03|**214.11**| _214.11_| _214.11_| _214.11_| _214.11_| +|15| 216.89|**214.64**| _214.64_| _214.64_| _214.64_| _214.64_| +|16| 216.66|**215.29**| _215.29_| _215.29_| _215.29_| _215.29_| + + +## References + - https://en.wikipedia.org/wiki/Moving_average#Simple_moving_average + - Kaufman, Perry J. (2013) Trading Systems and Methods + - Murphy, J. (1999) Technical Analysis of the Financial Markets \ No newline at end of file diff --git a/archive/docs/SMMA.md b/archive/docs/SMMA.md new file mode 100644 index 00000000..9d5946f4 --- /dev/null +++ b/archive/docs/SMMA.md @@ -0,0 +1,5 @@ +# SMMA: Smoothed Moving Average + +period = 10 + +![Alt text](./img/SMMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/T3.md b/archive/docs/T3.md new file mode 100644 index 00000000..ae8f7082 --- /dev/null +++ b/archive/docs/T3.md @@ -0,0 +1,5 @@ +# T3: Tillson T3 Moving Average + +period = 10 + +![Alt text](./img/T3_chart.svg) \ No newline at end of file diff --git a/archive/docs/TEMA.md b/archive/docs/TEMA.md new file mode 100644 index 00000000..1d79871e --- /dev/null +++ b/archive/docs/TEMA.md @@ -0,0 +1,5 @@ +# TEMA: Triple Exponential Moving Average + +period = 10 + +![Alt text](./img/TEMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/TRIMA.md b/archive/docs/TRIMA.md new file mode 100644 index 00000000..712eefb1 --- /dev/null +++ b/archive/docs/TRIMA.md @@ -0,0 +1,5 @@ +# TRIMA: Triangular Moving Average + +period = 10 + +![Alt text](./img/TRIMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/Trading_example.ipynb b/archive/docs/Trading_example.ipynb new file mode 100644 index 00000000..6b1af863 --- /dev/null +++ b/archive/docs/Trading_example.ipynb @@ -0,0 +1,458 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Installed Packages
  • QuanTAlib, 0.2.1
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#r \"nuget: QuanTAlib\"\n", + "#r \"nuget: Plotly.NET;\"\n", + "#r \"nuget: Plotly.NET.Interactive;\"\n", + "\n", + "using QuanTAlib;\n", + "using Plotly.NET;\n", + "using Plotly.NET.LayoutObjects;" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + }, + "polyglot_notebook": { + "kernelName": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "string symbol=\"SPY\"; // we'll focus on SPY symbol\n", + "int warmup = 50; // we'll allow 50 bars to pass by with no trading - for warm-up\n", + "Yahoo_Feed bars = new(symbol,350); //collect bars of symbol from Yahoo feed\n", + "TSeries data = bars.Close; //we need just one average value - (Open+High+Low+CLose)/4\n", + "\n", + "//make a chart\n", + "var d = Chart2D.Chart.Candlestick(bars.Open.v.Skip(warmup).ToList(), bars.High.v.Skip(warmup).ToList(),\n", + "bars.Low.v.Skip(warmup).ToList(), bars.Close.v.Skip(warmup).ToList(), bars.Open.t.Skip(warmup).ToList(), symbol)\n", + " .WithSize(1200,400).WithMargin(Margin.init(30,10,40,30,1,false)).WithXAxisRangeSlider(RangeSlider.init(Visible:false)).WithTitle(symbol);\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "JMA_Series fastMA = new(data,12); //typically MACD uses EMA(12), but let's try with superior JMA(12)\n", + "JMA_Series slowMA = new(data,26); //likewise, let's use JMA(26) instead of EMA(26)\n", + "\n", + "//make a chart\n", + "var cfast = Chart2D.Chart.Line(fastMA.t.Skip(warmup).ToList(), fastMA.v.Skip(warmup).ToList(), false, \"Fast MA\").WithLineStyle(Width: 2, Color: Color.fromString(\"blue\"));\n", + "var cslow = Chart2D.Chart.Line(slowMA.t.Skip(warmup).ToList(), slowMA.v.Skip(warmup).ToList(), false, \"Slow MA\").WithLineStyle(Width: 2, Color: Color.fromString(\"red\"));\n", + "var chart = Chart.Combine(new []{cfast,cslow}).WithSize(1200,400).WithMargin(Margin.init(30,10,40,30,1,false))\n", + " .WithXAxisRangeSlider(RangeSlider.init(Visible:false)).WithTitle($\"slow MA and fast MA of {symbol} OHLC4\");\n", + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + }, + "polyglot_notebook": { + "kernelName": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SUB_Series MACD_line = new(fastMA, slowMA); //MACD line is just a SUBtraction fastMA-slowMA\n", + "JMA_Series signal_line = new(MACD_line, 9); //signal line is an EMA(9) of MACD; we use superior JMA(9) instead\n", + "\n", + "//make a chart\n", + "var cfast = Chart2D.Chart.Line(MACD_line.t.Skip(warmup).ToList(), MACD_line.v.Skip(warmup).ToList(), false, \"MACD\").WithLineStyle(Width: 2, Color: Color.fromString(\"orange\"));\n", + "var cslow = Chart2D.Chart.Line(signal_line.t.Skip(warmup).ToList(), signal_line.v.Skip(warmup).ToList(), false, \"signal\").WithLineStyle(Width: 2, Color: Color.fromString(\"green\"));\n", + "var chart = Chart.Combine(new []{cfast,cslow}).WithSize(1200,400).WithMargin(Margin.init(30,10,40,30,1,false))\n", + " .WithXAxisRangeSlider(RangeSlider.init(Visible:false)).WithTitle($\"MACD line and signal line of fastMA-slowMA\");\n", + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + }, + "polyglot_notebook": { + "kernelName": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "SUB_Series histogram = new(MACD_line, signal_line); //MACD histogram is an oscillator of MACD-signal\n", + "\n", + "//make a chart\n", + "var cfast = Chart2D.Chart.Column(values: histogram.v.Skip(warmup).ToList(), Keys: histogram.t.Skip(warmup).ToList())\n", + ".WithSize(1200,400).WithMargin(Margin.init(30,10,40,30,1,false)).WithXAxisRangeSlider(RangeSlider.init(Visible:false))\n", + ".WithTitle(\"MACD histogram of MACD-signal\");\n", + "cfast" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + }, + "polyglot_notebook": { + "kernelName": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "COMPARE_Series over = new(histogram,0); //generate over/under series when histogram is above/below zero\n", + "CROSS_Series trades = new(histogram,0); //generate a signal series where histogram crosses zero (from below and from above)\n", + "\n", + "//make a chart\n", + "var cover = Chart2D.Chart.Line(over.t.Skip(warmup).ToList(),over.v.Skip(warmup).ToList(),false,\"state\").WithLineStyle(Width: 1, Color: Color.fromString(\"blue\"));\n", + "var cbars = Chart2D.Chart.Area(trades.t.Skip(warmup).ToList(), trades.v.Skip(warmup).ToList(),false );\n", + "var chart = Chart.Combine(new []{cover,cbars}).WithSize(1200,400).WithMargin(Margin.init(30,10,40,30,1,false))\n", + " .WithXAxisRangeSlider(RangeSlider.init(Visible:false)).WithTitle(\"in-market signal and trading orders based on MACD histogram\");\n", + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + }, + "polyglot_notebook": { + "kernelName": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "EQUITY_Series folio = new(trades, data, Long:true, Short:false, Warmup:warmup); //generate equity curve from trades and\n", + "\n", + "//make a chart\n", + "var cbars = Chart2D.Chart.Area(folio.t.Skip(warmup).ToList(), folio.v.Skip(warmup).ToList(),false ).WithSize(1200,400).WithMargin(Margin.init(30,10,40,30,1,false))\n", + " .WithXAxisRangeSlider(RangeSlider.init(Visible:false)).WithTitle($\"Equity curve (long trades only) for MACD on {symbol}\");\n", + "cbars" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + }, + "polyglot_notebook": { + "kernelName": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
107.93999999999998
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "// some what-if examples\n", + "string symbol=\"IBM\";\n", + "int warmup = 50;\n", + "Yahoo_Feed bars = new(symbol,350);\n", + "TSeries data = bars.Close;\n", + "HEMA_Series fastMA = new(data,12);\n", + "JMA_Series slowMA = new(data,23,-100);\n", + "SUB_Series MACD_line = new(fastMA, slowMA);\n", + "JMA_Series signal_line = new(MACD_line, 9);\n", + "SUB_Series histogram = new(MACD_line, signal_line);\n", + "CROSS_Series trades = new(histogram,0);\n", + "EQUITY_Series folio = new(trades, data, Long:true, Short:false, Warmup:warmup);\n", + "folio[^1].v" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".NET (C#)", + "language": "C#", + "name": ".net-csharp" + }, + "language_info": { + "name": "polyglot-notebook" + }, + "polyglot_notebook": { + "kernelInfo": { + "defaultKernelName": "csharp", + "items": [ + { + "aliases": [ + "C#", + "c#" + ], + "languageName": "C#", + "name": "csharp" + }, + { + "aliases": [ + "frontend" + ], + "name": "vscode" + } + ] + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/archive/docs/WMA.md b/archive/docs/WMA.md new file mode 100644 index 00000000..42792053 --- /dev/null +++ b/archive/docs/WMA.md @@ -0,0 +1,49 @@ +# WMA: Weighted Moving Average +period = 10 + +![Alt text](./img/WMA_chart.svg) + +WMA is linearly weighted moving Average where the weights are linearly decreasing over the _period_ and the most recent data has the heaviest weight. + +## Calculation + +WMA is a rolling calculation that is looking backwards from the position ${n}$ and is denoted as ${WMA}_{p}{(data)}$ where $p$ represents the period, $w$ represents the assigned weight and $data$ represents the list of data points: +$$ +WMA_p{(data)} = \frac{1}{\sum w }\sum_{i=n-p+1}^{n} w_i data_i +$$ +Weights $w$ are linearly increasing from $1$ to $p$. For example, the weights $w$ for a $p=5$ would be {1, 2, 3, 4, 5} + + + +## Reference Calculation +period = 5 +``` +TSeries data = new() {81.59, 81.06, 82.87, 83.00, 83.61, 83.15, 82.84, 83.99, 84.55, 84.36, 85.53, 86.54, 86.89, 87.77, 87.29}; +WMA_Series wma = new(data, 5, useNaN: false); +WMA_Series wma_nan = new(data, 5, useNaN: true); +for (int i=0; i< data.Count; i++) + Console.WriteLine($"{i}\t{data[i].v,7:f2}\t{wma_nan[i].v,7:f3}\t{wma[i].v,7:f3}"); +``` + +|#|input|wma_NaN|wma| +|--|:--:|:--:|:--:| +|0| 81.59| NaN| 81.590| +|1| 81.06| NaN| 81.237| +|2| 82.87| NaN| 82.053| +|3| 83.00| NaN| 82.432| +|4| 83.61| 82.825| 82.825| +|5| 83.15| 83.066| 83.066| +|6| 82.84| 83.100| 83.100| +|7| 83.99| 83.399| 83.399| +|8| 84.55| 83.809| 83.809| +|9| 84.36| 84.053| 84.053| +|10| 85.53| 84.637| 84.637| +|11| 86.54| 85.399| 85.399| +|12| 86.89| 86.031| 86.031| +|13| 87.77| 86.763| 86.763| +|14| 87.29| 87.121| 87.121| + +## References + - https://en.wikipedia.org/wiki/Moving_average#Weighted_moving_average + - Kaufman, Perry J. (2013) Trading Systems and Methods + - Murphy, J. (1999) Technical Analysis of the Financial Markets \ No newline at end of file diff --git a/archive/docs/ZLEMA.md b/archive/docs/ZLEMA.md new file mode 100644 index 00000000..5df16918 --- /dev/null +++ b/archive/docs/ZLEMA.md @@ -0,0 +1,5 @@ +# ZLEMA: Zero-lag Exponential Moving Average + +period = 10 + +![Alt text](./img/ZLEMA_chart.svg) \ No newline at end of file diff --git a/archive/docs/_sidebar.md b/archive/docs/_sidebar.md new file mode 100644 index 00000000..ceb3a715 --- /dev/null +++ b/archive/docs/_sidebar.md @@ -0,0 +1,24 @@ +* [Home](/) + * [FAQ - Frequently asked questions answered](QA.md) + +* [List of all Indicators](indicators.md "Indicators coverage") + + * [SMA - Simple Moving Average](SMA.md) + * [EMA - Exponential Moving Average](EMA.md) + * [WMA - Weighted Moving Average](WMA.md) + * [T3 - Tillson T3 Exponential MA](T3.md) + * [SMMA - Smoothed Moving Average](SMMA.md) + * [TRIMA - Triangular Moving Average](TRIMA.md) + * [DWMA - Double Weighted Moving Average](DWMA.md) + * [FMA - Fibonacci Moving Average](FMA.md) + * [DEMA - Double Exponential MA](DEMA.md) + * [TEMA - Triple Exponential MA](TEMA.md) + * [ALMA - Arnaud Legoux Moving Average](ALMA.md) + * [HMA - Hull Moving Average](HMA.md) + * [HEMA - Hull/Exponential Moving Average](HEMA.md) + * [HWMA - Holt-Winter Moving Average](HWMA.md) + * [MAMA - MESA Adaptive Moving Average](MAMA.md) + * [KAMA - Kaufman Adaptive Moving Average](KAMA.md) + * [ZLEMA - Zero-Lag Exponential MA](ZLEMA.md) + * [JMA - Jurik Moving Average](JMA.md) + diff --git a/archive/docs/getting_started.ipynb b/archive/docs/getting_started.ipynb new file mode 100644 index 00000000..681f37e2 --- /dev/null +++ b/archive/docs/getting_started.ipynb @@ -0,0 +1,445 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Quick Start\n", + "\n", + "In order to use this .NET Interactive Notebook and play along with QuanTAlib (outside of making your own app or plugging QuanTAlib into Quantower platform), you will need:\n", + "\n", + "- Installed Visual Studio Code\n", + "- Installed .NET 6 SDK\n", + "- Installed .NET Interactive Notebooks extension\n", + "\n", + "**For impatient**, here is a simple example of calculating three moving averages - SMA(data), EMA(SMA(data)) and WMA(EMA(SMA(data))) from 10 days of AAPL stock data using QuanTAlib:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "index\t data\t\t sma(data)\t ema(sma(data))\t wma(ema(sma(data)))\n", + "0\t 2023-03-27\t 158.28\t\t 158.28\t\t NaN\n", + "1\t 2023-03-28\t 157.97\t\t 158.12\t\t NaN\n", + "2\t 2023-03-29\t 158.90\t\t 158.38\t\t NaN\n", + "3\t 2023-03-30\t 159.77\t\t 158.73\t\t NaN\n", + "4\t 2023-03-31\t 160.79\t\t 159.14\t\t 158.69\n", + "5\t 2023-04-03\t 162.37\t\t 160.22\t\t 159.25\n", + "6\t 2023-04-04\t 163.97\t\t 161.47\t\t 160.10\n", + "7\t 2023-04-05\t 164.56\t\t 162.50\t\t 161.07\n", + "8\t 2023-04-06\t 165.02\t\t 163.34\t\t 162.04\n" + ] + } + ], + "source": [ + "#r \"nuget:QuanTAlib;\"\n", + "using QuanTAlib;\n", + "\n", + "Yahoo_Feed aapl = new(\"AAPL\", 10);\n", + "TSeries data = aapl.Close;\n", + "SMA_Series sma = new(source: data, period: 5, useNaN: false);\n", + "EMA_Series ema = new(sma, period: 5); // by default, indicators expose all data, no NaN values\n", + "WMA_Series wma = new(ema, 5, useNaN: true); // for the final calculation we can hide early data with NaNs\n", + "\n", + "Console.Write($\"index\\t data\\t\\t sma(data)\\t ema(sma(data))\\t wma(ema(sma(data)))\\n\");\n", + "for (int i=0; iindexvalue0
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "var item1 = (DateTime.Today, 105.3); // (DateTime, Value) tuple\n", + "double item2 = 293.1; // a simple double\n", + "\n", + "TSeries data = new();\n", + "data.Add(item1); // adding tuple variable\n", + "data.Add(item2); // QuanTAlib stamps the (double) with current time\n", + "data.Add(0); // directly adding a number (stamped with current time)\n", + "data.Add((DateTime.Now.AddDays(-3), 10)); // adding a tuple with timestamp 3 days ago\n", + "\n", + "data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TSeries list can display only values (without timestamps) or only timestamps (without values) by using `.v` or `.t` properties" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
[ 105.3, 293.1, 0, 10 ]
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data.v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The last element on the list can be accessed by .Last() or by [^1] - and using `.t` (time) and `.v` (value) properties. Also, casting a TSeries into (double) will return the value of the last element" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
10
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "bool IsTheSame = data.Last().v == data[^1].v;\n", + "double lastvalue = data;\n", + "\n", + "lastvalue" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All indicators are just modified TSeries classes; they get all required input during class construction (source of the datafeed, period...) and they automatically subscribe to events of the datafeed. Whenever datafeed gets a new value, indicator will calculate its own value. Indicators are also event publishers, so other indicators can subscribe to their results, chaining indicators together:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
[ Infinity, 0.6666666666666666, 0.3333333333333333, 0.2, 0.14285714285714285, 0.1111111111111111, 0.09090909090909091, 0.07692307692307693, 0.06666666666666667, 0.058823529411764705, 0.25 ]
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "TSeries t1 = new() {0,1,2,3,4,5,6,7,8,9}; // t1 is loaded with data and activated as a publisher\n", + "EMA_Series t2 = new(t1, 3); // t2 will auto-load all history of t1 and wait for events from t1\n", + "ADD_Series t3 = new(t1, t2); // t3 is an ADDition of t1 and t2 - will also load history and wait for t2 events\n", + "DIV_Series t4 = new(1, t3); // t4 is calculating 1/t3 - and waiting for t3 events\n", + "\n", + "TSeries t5 = new(); // a wild indicator appeared! And it is empty!\n", + "t4.Pub += t5.Sub; // let us add a manual subscription to events coming from t4 - t5 is now listening to t4\n", + "t1.Add(0); // we add one new value to t1 - and trigger the full cascade of calculation! t5 is now full!\n", + "\n", + "t5.v" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# MACD compounded indicator\n", + "\n", + "With QuanTAlib we can chain indicators together, creating complex compounded indicators. For example, we can create Moving Average Convergence/Divergence (MACD) indicators by chaining all required operations in a sequence:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "dotnet_interactive": { + "language": "csharp" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.000974358974349343, -0.03456027049873228, -0.13792617985566447, -0.4729486712049916, -0.825402881197467, -0.8902360596814031, -0.9360607784903126, -0.7333381872239422 ... (79 more) ]
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Yahoo_Feed aapl = new(\"AAPL\", 100);\n", + "TSeries close = aapl.Close; // close will get data from history\n", + "EMA_Series slow = new(close,26); // slow gets data from slow through pub-sub eventing\n", + "EMA_Series fast = new(close,12); // fast gets data from slow (via eventing)\n", + "SUB_Series macd = new(fast,slow); // macd is a SUBtraction: fast-slow\n", + "EMA_Series signal = new(macd,9); // signal is EMA of macd\n", + "SUB_Series histogram = new(macd, signal); // histogram is SUBtraction macd-signal\n", + "\n", + "histogram.v\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".NET (C#)", + "language": "C#", + "name": ".net-csharp" + }, + "language_info": { + "name": "polyglot-notebook" + }, + "polyglot_notebook": { + "kernelInfo": { + "defaultKernelName": "csharp", + "items": [ + { + "aliases": [ + "C#", + "c#" + ], + "languageName": "C#", + "name": "csharp" + }, + { + "aliases": [], + "languageName": "KQL", + "name": "kql" + }, + { + "aliases": [ + "frontend" + ], + "name": "vscode" + } + ] + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/archive/docs/img/ALMA_chart.svg b/archive/docs/img/ALMA_chart.svg new file mode 100644 index 00000000..2e3ef221 --- /dev/null +++ b/archive/docs/img/ALMA_chart.svg @@ -0,0 +1 @@ +020406000.20.40.60.81020406000.20.40.60.8102040600102030020406001020300204060−1−0.500.510204060−1−0.500.510204060−1−0.500.510204060−0.4−0.200.20.40204060−1−0.500.50204060−1−0.500.510204060−1−0.500.51020406000.51020406001020300204060−1010204060−1010204060170172174176178 \ No newline at end of file diff --git a/archive/docs/img/DEMA_chart.svg b/archive/docs/img/DEMA_chart.svg new file mode 100644 index 00000000..16913aa6 --- /dev/null +++ b/archive/docs/img/DEMA_chart.svg @@ -0,0 +1 @@ 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b/archive/docs/index.html new file mode 100644 index 00000000..e6588dd8 --- /dev/null +++ b/archive/docs/index.html @@ -0,0 +1,34 @@ + + + + + Document + + + + + + + +
+ + + + + + + + + diff --git a/archive/docs/indicators.md b/archive/docs/indicators.md new file mode 100644 index 00000000..4c5301fe --- /dev/null +++ b/archive/docs/indicators.md @@ -0,0 +1,175 @@ +# Coverage + +⭐= Calculation is validated against several TA libraries + +✔️= Validation tests passed + +❌= Issue + +|**BASIC TRANSFORMS**|**QuanTAlib**|**TA-LIB**|**Skender**|**Pandas TA**|**Tulip**| +|--|:--:|:--:|:--:|:--:|:--:| +|OC2 - (Open+Close)/2|️ `.OC2`||✔️CandlePart.OC2|| +|⭐HL2 - Median Price|`.HL2`|✔️MEDPRICE|✔️CandlePart.HL2|✔️hl2|✔️medprice| +|⭐HLC3 - Typical Price|`.HLC3`|✔️TYPPRICE|✔️CandlePart.HLC3|✔️hlc3|✔️typprice| +|OHL3 - (Open+High+Low)/3|`.OHL3`||✔️CandlePart.OHL3|| +|⭐OHLC4 - Average Price|`.OHLC4`|✔️AVGPRICE|️✔️CandlePart.OHLC4|✔️ohlc4|✔️avgprice| +|HLCC4 - Weighted Price|`.HLCC4`|✔️WCLPRICE|||✔️wcprice| +|MIDPOINT - Midpoint value|`MIDPOINT_Series`|✔️MIDPOINT||midpoint| +|MIDPRICE - Midpoint price|`MIDPRICE_Series`|✔️MIDPRICE||midprice| +|MAX - Max value|`MAX_Series`|✔️MAX|||✔️max| +|MIN - Min value|`MIN_Series`|✔️MIN|||✔️min| +|SUM - Summation|`SUM_Series`|✔️SUM|||✔️sum| +|ADD - Addition|`ADD_Series`|✔️ADD|||✔️add| +|SUB - Subtraction|`SUB_Series`|✔️SUB|||✔️sub| +|MUL - Multiplication|`MUL_Series`|✔️MUL|||✔️mul| +|DIV - Division|`DIV_Series`|✔️DIV|||✔️div| +||||| +|**STATISTICS & NUMERICAL ANALYSIS**| +|||||| +|BIAS - Bias|`BIAS_Series`|||✔️bias| +|CORR - Pearson's Correlation Coefficient|`CORR_Series`|✔️CORREL|✔️GetCorrelation|| +|COVAR - Covariance|`COVAR_Series`||✔️GetCorrelation|| +|DECAY - Linear Decay|`DECAY_Series`|||decay|✔️decay| +|EDECAY - Exponential Decay|`DECAY_Series`|||decay|✔️edecay| +|ENTROPY - Entropy|`ENTROPY_Series`|||entropy|| +|KURTOSIS - Kurtosis|`KURT_Series`|||✔️kurtosis| +|SLOPE - Slope of Linear Regression|`SLOPE_Series`||✔️GetSlope||✔️linregslope| +|MAD - Mean Absolute Deviation|`MAD_Series`||✔️GetSmaAnalysis|✔️mad| +|MAE - Mean Absolute Error|`MAE_Series`|||| +|MAPE - Mean Absolute Percent Error|`MAPE_Series`||✔️GetSmaAnalysis|| +|MEDIAN - Median value|`MEDIAN_Series`|||✔️median| +|MSE - Mean Squared Error|`MSE_Series`||✔️GetSmaAnalysis|| +|SKEW - Skewness||||skew| +|⭐SDEV - Standard Deviation (Volatility)|`SDEV_Series`|✔️STDDEV|✔️GetStdDev|✔️stdev|✔️stddev| +|SSDEV - Sample Standard Deviation|`SSDEV_Series`|||✔️stdev| +|SMAPE - Symmetric Mean Absolute Percent Error|`SMAPE_Series`|||| +|VAR - Population Variance|`VAR_Series`|✔️VAR||✔️variance|✔️var| +|SVAR - Sample Variance|`SVAR_Series`|||✔️variance| +|QUANTILE - Quantile||||quantile| +|WMAPE - Weighted Mean Absolute Percent Error|`WMAPE_Series`|||| +|ZSCORE - Number of standard deviations from mean|`ZSCORE_Series`||✔️GetStdDev|✔️zscore| +|||||| +|**TREND INDICATORS & AVERAGES**| +|||||| +|AFIRMA - Autoregressive Finite Impulse Response Moving Average||||| +|ALMA - Arnaud Legoux Moving Average|`ALMA_Series`||✔️GetAlma|alma| +|DEMA - Double EMA Average|`DEMA_Series`|✔️DEMA|✔️GetDema|✔️dema|✔️dema| +|DWMA - Double WMA Average|`DWMA_Series`||||| +|⭐[EMA - Exponential Moving Average](EMA.md)|`EMA_Series`|✔️EMA|✔️GetEma|✔️ema|✔️ema| +|EPMA - Endpoint Moving Average|||GetEpma|| +|FRAMA - Fractal Adaptive Moving Average||||| +|FMA - Fibonacci's Weighted Moving Average|`FMA_Series`|||fwma| +|HILO - Gann High-Low Activator||||hilo| +|HEMA - Hull/EMA Average|`HEMA_Series`|||| +|Hilbert Transform Instantaneous Trendline||HT_TRENDLINE|GetHtTrendline|| +|⭐HMA - Hull Moving Average|`HMA_Series`||✔️GetHma|✔️hma|✔️hma| +|HWMA - Holt-Winter Moving Average|`HWMA_Series`|||✔️hwma| +|JMA - Jurik Moving Average|`JMA_Series`|||jma|| +|KAMA - Kaufman's Adaptive Moving Average|`KAMA_Series`|✔️KAMA|✔️GetKama|✔️kama|✔️kama| +|KDJ - KDJ Indicator (trend reversal)||||kdj| +|LSMA - Least Squares Moving Average|||GetEpma|| +|⭐MACD - Moving Average Convergence/Divergence|`MACD_Series`|✔️MACD|✔️GetMacd|✔️macd|✔️macd| +|MAMA - MESA Adaptive Moving Average|`MAMA_Series`|✔️MAMA|✔️GetMama|| +|MCGD - McGinley Dynamic||||mcgd| +|MMA - Modified Moving Average||||| +|PPMA - Pivot Point Moving Average||||| +|PWMA - Pascal's Weighted Moving Average||||pwma| +|⭐RMA - WildeR's Moving Average|`RMA_Series`|||✔️rma|✔️rma| +|SINWMA - Sine Weighted Moving Average||||sinwma| +|⭐[SMA - Simple Moving Average](SMA.md)|`SMA_Series`|✔️SMA|✔️GetSma|✔️sma|✔️sma| +|SMMA - Smoothed Moving Average|`SMMA_Series`||✔️GetSmma|| +|SSF - Ehler's Super Smoother Filter||||ssf| +|SUPERTREND - Supertrend||||supertrend| +|SWMA - Symmetric Weighted Moving Average||||swma| +|T3 - Tillson T3 Moving Average|`T3_Series`|✔️T3|✔️GetT3|✔️t3|| +|⭐TEMA - Triple EMA Average|`TEMA_Series`|✔️TEMA|✔️GetTema|✔️tema|✔️tema| +|⭐TRIMA - Triangular Moving Average|`TRIMA_Series`|✔️TRIMA||✔️trima|✔️trima| +|TSF - Time Series Forecast||TSF||| +|VIDYA - Variable Index Dynamic Average||||vidya|vidya| +|VORTEX - Vortex Indicator||||vortex| +|⭐WMA - Weighted Moving Average|`WMA_Series`|✔️WMA|✔️GetWma|✔️wma|✔️wma| +|ZLEMA - Zero Lag EMA Average|`ZLEMA_Series`|||✔️zlma|❌zlema| +|||||| +|**VOLATILITY INDICATORS**| +|||||| +|⭐ADL - Chaikin Accumulation Distribution Line|`ADL_Series`|✔️AD|✔️GetAdl|✔️ad|✔️ad| +|⭐ADOSC - Chaikin Accumulation Distribution Oscillator|`ADOSC_Series`|✔️ADOSC||✔️adosc|✔️adosc| +|⭐ATR - Average True Range|`ATR_Series`|✔️ATR|✔️GetAtr|✔️atr|✔️atr| +|ATRP - Average True Range Percent|`ATRP_Series`||✔️GetAtr|| +|BETA - Beta coefficient||BETA|GetBeta|| +|⭐BBANDS - Bollinger Bands®|`BBANDS_Series`|✔️BBANDS|✔️GetBollingerBands|✔️bbands|✔️bbands| +|CHAND - Chandelier Exit|||GetChandelier|| +|CRSI - Connor RSI|||GetConnorsRsi|| +|CVI - Chaikins Volatility|||||cvi| +|DON - Donchian Channels|||GetDonchian|| +|FCB - Fractal Chaos Bands|||GetFcb|| +|FISHER - Fisher Transform|||GetFcb||fisher| +|HV - Historical Volatility||||| +|ICH - Ichimoku|||GetIchimoku|| +|KEL - Keltner Channels|||GetKeltner|| +|NATR - Normalized Average True Range||NATR|GetAtr|| +|CHN - Price Channel Indicator||||| +|RSI - Relative Strength Index|`RSI_Series`|✔️RSI|✔️GetRsi|✔️rsi|✔️rsi| +|SAR - Parabolic Stop and Reverse||SAR|GetParabolicSar|| +|SRSI - Stochastic RSI||STOCHRSI|GetStochRsi|| +|STARC - Starc Bands||||| +|TR - True Range|`TR_Series`|✔️TRANGE|✔️GetTr|✔️true_range|✔️tr| +|UI - Ulcer Index||||| +|VSTOP - Volatility Stop||||| +|||||| +|**MOMENTUM INDICATORS & OSCILLATORS**| +|||||| +|AC - Acceleration Oscillator||||| +|ADX - Average Directional Movement Index||ADX|GetAdx||adx| +|ADXR - Average Directional Movement Index Rating||ADXR|GetAdx||adxr| +|AO - Awesome Oscillator|||GetAwesome||ao| +|APO - Absolute Price Oscillator||APO|||apo| +|AROON - Aroon oscillator||AROON|GetAroon||aroon| +|BOP - Balance of Power||BOP|GetBop||bop| +|CCI - Commodity Channel Index|`CCI_Series`|✔️CCI|✔️GetCci||❌cci| +|CFO - Chande Forcast Oscillator||||| +|CMO - Chande Momentum Oscillator|`CMO_Series`|❌CMO|✔️GetCmo|❌cmo|✔️cmo| +|COG - Center of Gravity||||| +|COPPOCK - Coppock Curve||||| +|CTI - Ehler's Correlation Trend Indicator||||| +|DPO - Detrended Price Oscillator|||GetDpo|| +|DMI - Directional Movement Index||DX|GetAdx|| +|EFI - Elder Ray's Force Index|||GetElderRay|| +|FOSC - Forecast oscillator|||||fosc| +|GAT - Alligator oscillator|||GetGator|| +|HURST - Hurst Exponent|||GetHurst|| +|KRI - Kairi Relative Index||||| +|KVO - Klinger Volume Oscillator|||||| +|MFI - Money Flow Index||MFI|GetMfi|| +|MOM - Momentum||MOM||| +|NVI - Negative Volume Index||||| +|PO - Price Oscillator||||| +|PPO - Percentage Price Oscillator||PPO||| +|PMO - Price Momentum Oscillator||||| +|PVI - Positive Volume Index||||| +|ROC - Rate of Change||MOM|GetRoc|| +|RVGI - Relative Vigor Index||||| +|SMI - Stochastic Momentum Index||||| +|STC - Schaff Trend Cycle||||| +|STOCH - Stochastic Oscillator||STOCH|GetStoch|| +|TRIX - 1-day ROC of TEMA|`TRIX_Series`|✔️TRIX|✔️GetTrix|✔️trix|❌trix| +|TSI - True Strength Index||||| +|UO - Ultimate Oscillator||ULTOSC|GetUltimate||ultosc| +|WILLR - Larry Williams' %R||WILLR|GetWilliamsR||willr| +|WGAT - Williams Alligator||||| +|||||| +|**VOLUME INDICATORS**| +|||||| +|AOBV - Archer On-Balance Volume||||| +|CMF - Chaikin Money Flow||||| +|EOM - Ease of Movement|||||emv| +|KVO - Klinger Volume Oscilaltor|||||kvo| +|OBV - On-Balance Volume|`OBV_Series`|✔️OBV|✔️GetObv|✔️obv|❌obv| +|PRS - Price Relative Strength|||| +|PVOL - Price-Volume||||| +|PVO - Percentage Volume Oscillator||||| +|PVR - Price Volume Rank||||| +|PVT - Price Volume Trend||||| +|VP - Volume Profile||||| +|VWAP - Volume Weighted Average Price||||| +|VWMA - Volume Weighted Moving Average|||||vwma| diff --git a/archive/docs/readme.md b/archive/docs/readme.md new file mode 100644 index 00000000..a8c3d92d --- /dev/null +++ b/archive/docs/readme.md @@ -0,0 +1,44 @@ +# QuanTAlib - quantitative technical indicators for Quantower and other C#-based trading platorms + +[![Lines of Code](https://sonarcloud.io/api/project_badges/measure?project=mihakralj_QuanTAlib&metric=ncloc)](https://sonarcloud.io/summary/overall?id=mihakralj_QuanTAlib) +[![Codacy grade](https://img.shields.io/codacy/grade/b1f9109222234c87bce45f1fd4c63aee?style=flat-square)](https://app.codacy.com/gh/mihakralj/QuanTAlib/dashboard) +[![codecov](https://codecov.io/gh/mihakralj/QuanTAlib/branch/main/graph/badge.svg?style=flat-square&token=YNMJRGKMTJ?style=flat-square)](https://codecov.io/gh/mihakralj/QuanTAlib) +[![Security Rating](https://sonarcloud.io/api/project_badges/measure?project=mihakralj_QuanTAlib&metric=security_rating)](https://sonarcloud.io/summary/new_code?id=mihakralj_QuanTAlib) +[![CodeFactor](https://www.codefactor.io/repository/github/mihakralj/quantalib/badge/main)](https://www.codefactor.io/repository/github/mihakralj/quantalib/overview/main) + +[![Nuget](https://img.shields.io/nuget/v/QuanTAlib?style=flat-square)](https://www.nuget.org/packages/QuanTAlib/) +![GitHub last commit](https://img.shields.io/github/last-commit/mihakralj/QuanTAlib) +[![Nuget](https://img.shields.io/nuget/dt/QuanTAlib?style=flat-square)](https://www.nuget.org/packages/QuanTAlib/) +[![GitHub watchers](https://img.shields.io/github/watchers/mihakralj/QuanTAlib?style=flat-square)](https://github.com/mihakralj/QuanTAlib/watchers) +[![.NET7.0](https://img.shields.io/badge/.NET-7.0%20%7C%206.0%20%7C%204.8-blue?style=flat-square)](https://dotnet.microsoft.com/en-us/download/dotnet/7.0) + +**Quan**titative **TA** **lib**rary (QuanTAlib) is a C# library of classess and methods for quantitative technical analysis useful for analyzing quotes with [Quantower](https://www.quantower.com/) and other C#-based trading platforms. + +**QuanTAlib** is written with some specific design criteria in mind - why there is '_yet another C# TA library_': + +- Prioritize **real-time data analysis** (series can add new data and indicator doesn't have to re-calculate the whole history) +- **Allow updates** to the last quote and adjusting the calculation to the still-forming bar +- **Calculate early data right** - output data is as valid as mathematically possible from the first value onwards + +![Alt text](./img/quotes.gif) + +If not obvious, QuanTAlib is intended for developers, and it does not focus on sources of OHLCV quotes. There are some very basic data feeds available to use in the learning process: `RND_Feed` and `GBM_Feed` for random data, `Yahoo_Feed` and `Alphavantage_Feed` for a quick grab of daily data of US stock market. + +See [Getting Started](https://github.com/mihakralj/QuanTAlib/blob/main/Docs/getting_started.ipynb) .NET interactive notebook to get a feel how library works. Developers can use QuanTAlib in [Polyglot Notebooks](https://marketplace.visualstudio.com/items?itemName=ms-dotnettools.dotnet-interactive-vscode) or in console apps, but the best usage of the library is with C#-enabled trading platforms - see **QuanTower_Charts** folder for Quantower examples and check **Releases** for compiled Quantower DLL. + +### Coverage + +[List of all indicators - current and planned](indicators.md) + +### Validation + +QuanTAlib uses validation tests with four other TA libraries to assure accuracy and validity of results: + +- [TA-LIB](https://www.ta-lib.org/function.html) +- [Skender Stock Indicators](https://dotnet.stockindicators.dev/) +- [Pandas-TA](https://twopirllc.github.io/pandas-ta/) +- [Tulip Indicators](https://tulipindicators.org/) + +### Questions + +[Some most common questions addressed](QA.md) \ No newline at end of file diff --git a/lib/averages/Alma.cs b/lib/averages/Alma.cs index 9d34a7a9..dd031ffd 100644 --- a/lib/averages/Alma.cs +++ b/lib/averages/Alma.cs @@ -17,15 +17,15 @@ public class Alma : AbstractBase private readonly int _period; private readonly double _offset; private readonly double _sigma; - private readonly CircularBuffer? _buffer; - private readonly CircularBuffer? _weight; + private CircularBuffer? _buffer; + private CircularBuffer? _weight; private double _norm; /// The number of data points used in the ALMA calculation. /// Controls the smoothness and high-frequency filtering. Default is 0.85. /// Controls the shape of the Gaussian distribution. Default is 6. /// Thrown when period is less than 1. - public Alma(int period, double offset = 0.85, double sigma = 6) + public Alma(int period, double offset = 0.85, double sigma = 6) : base() { if (period < 1) { @@ -36,8 +36,6 @@ public class Alma : AbstractBase _sigma = sigma; WarmupPeriod = period; Name = "Alma"; - _buffer = new CircularBuffer(_period); - _weight = new CircularBuffer(_period); Init(); } @@ -54,6 +52,8 @@ public class Alma : AbstractBase public override void Init() { base.Init(); + _buffer = new CircularBuffer(_period); + _weight = new CircularBuffer(_period); _norm = 0; } diff --git a/lib/averages/Convolution.cs b/lib/averages/Convolution.cs index 60226d61..470f426c 100644 --- a/lib/averages/Convolution.cs +++ b/lib/averages/Convolution.cs @@ -4,8 +4,8 @@ public class Convolution : AbstractBase { private readonly double[] _kernel; private readonly int _kernelSize; - private readonly CircularBuffer _buffer; - private readonly double[] _normalizedKernel; + private CircularBuffer _buffer; + private double[] _normalizedKernel; public Convolution(double[] kernel) { diff --git a/lib/averages/Dema.cs b/lib/averages/Dema.cs index d3e37bfb..afd57c81 100644 --- a/lib/averages/Dema.cs +++ b/lib/averages/Dema.cs @@ -28,7 +28,7 @@ public class Dema : AbstractBase private double _lastEma2, _p_lastEma2; private double _k, _e, _p_e; - public Dema(int period) + public Dema(int period) : base() { if (period < 1) { diff --git a/lib/averages/Dsma.cs b/lib/averages/Dsma.cs index 18c0a765..93fbdede 100644 --- a/lib/averages/Dsma.cs +++ b/lib/averages/Dsma.cs @@ -50,8 +50,8 @@ public class Dsma : AbstractBase _buffer = new CircularBuffer(period); // SuperSmoother filter coefficients - double _a1 = Math.Exp(-1.414 * Math.PI / (0.5 * period)); - double _b1 = 2 * _a1 * Math.Cos(1.414 * Math.PI / (0.5 * period)); + _a1 = Math.Exp(-1.414 * Math.PI / (0.5 * period)); + _b1 = 2 * _a1 * Math.Cos(1.414 * Math.PI / (0.5 * period)); _c2 = _b1; _c3 = -_a1 * _a1; _c1 = 1 - _c2 - _c3; diff --git a/lib/averages/Ema.cs b/lib/averages/Ema.cs index 069d588f..056ec24a 100644 --- a/lib/averages/Ema.cs +++ b/lib/averages/Ema.cs @@ -25,14 +25,12 @@ public class Ema : AbstractBase // inherited _index // inherited _value private readonly int _period; - private readonly CircularBuffer _sma; + private CircularBuffer _sma; private double _lastEma, _p_lastEma; - private double _e, _p_e; - private readonly double _k; - private bool _isInit, _p_isInit; - private readonly bool _useSma; + private double _k, _e, _p_e; + private bool _isInit, _p_isInit, _useSma; - public Ema(int period, bool useSma = true) + public Ema(int period, bool useSma = true) : base() { if (period < 1) { @@ -47,14 +45,13 @@ public class Ema : AbstractBase Init(); } - public Ema(double alpha) + public Ema(double alpha) : base() { _k = alpha; _useSma = false; _sma = new(1); _period = 1; WarmupPeriod = (int)Math.Ceiling(Math.Log(0.05) / Math.Log(1 - _k)); //95th percentile - _sma = new(_period); Init(); } @@ -72,6 +69,7 @@ public class Ema : AbstractBase _lastEma = 0; _isInit = false; _p_isInit = false; + _sma = new(_period); } protected override void ManageState(bool isNew) diff --git a/lib/averages/Frama.cs b/lib/averages/Frama.cs index 4d47e76a..8a260cf3 100644 --- a/lib/averages/Frama.cs +++ b/lib/averages/Frama.cs @@ -6,20 +6,14 @@ namespace QuanTAlib { private readonly int _period; private readonly double _fc; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; private double _lastFrama; private double _prevLastFrama; - public Frama(int period, double fc = 0.5) + public Frama(int period, double fc = 0.5) : base() { if (period < 2) - { throw new ArgumentException("Period must be at least 2", nameof(period)); - } - if (fc <= 0 || fc >= 1) - { - throw new ArgumentException("Fc must be between 0 and 1", nameof(fc)); - } _period = period; _fc = fc; @@ -51,7 +45,7 @@ namespace QuanTAlib protected override double Calculation() { ManageState(Input.IsNew); - + _buffer.Add(Input.Value, Input.IsNew); if (_buffer.Count < _period) @@ -84,11 +78,11 @@ namespace QuanTAlib } double n1 = (hh - ll) / _period; - double n2 = (hh1 - ll1 + hh2 - ll2) / half; + double n2 = (hh1 - ll1 + hh2 - ll2) / (_period / 2); - double d = (Math.Log(n1 + double.Epsilon) - Math.Log(n2 + double.Epsilon)) / Math.Log(2); + double d = (Math.Log(n2 + double.Epsilon) - Math.Log(n1 + double.Epsilon)) / Math.Log(2); - double alpha = Math.Exp(-4.6 * (d - 1) * _fc); + double alpha = Math.Exp(-4.6 * (d - 1)); alpha = Math.Max(Math.Min(alpha, 1), 0.01); // Ensure alpha is between 0.01 and 1 _lastFrama = alpha * (Input.Value - _lastFrama) + _lastFrama; diff --git a/lib/averages/Htit.cs b/lib/averages/Htit.cs index 1dd57f21..08767763 100644 --- a/lib/averages/Htit.cs +++ b/lib/averages/Htit.cs @@ -1,7 +1,7 @@ //not working yet //TODO consistency test -namespace QuanTAlib; +using QuanTAlib; public class Htit : AbstractBase { @@ -18,10 +18,10 @@ public class Htit : AbstractBase private readonly CircularBuffer _sdBuffer = new(2); private readonly CircularBuffer _itBuffer = new(4); - private double _lastPd; - private double _p_lastPd; + private double _lastPd = 0; + private double _p_lastPd = 0; - public Htit() + public Htit() : base() { Name = "Htit"; WarmupPeriod = 12; diff --git a/lib/averages/Hwma.cs b/lib/averages/Hwma.cs index c2473349..b6e8d492 100644 --- a/lib/averages/Hwma.cs +++ b/lib/averages/Hwma.cs @@ -15,7 +15,7 @@ public class Hwma : AbstractBase { } - public Hwma(int period, double nA, double nB, double nC) + public Hwma(int period, double nA, double nB, double nC) : base() { if (period < 1) { diff --git a/lib/averages/Jma.cs b/lib/averages/Jma.cs index 898b0a2a..e5a58b11 100644 --- a/lib/averages/Jma.cs +++ b/lib/averages/Jma.cs @@ -1,34 +1,37 @@ -namespace QuanTAlib; +using QuanTAlib; //TODO consistency test public class Jma : AbstractBase { - private readonly int Period; + public readonly int Period; private readonly double _phase; - private readonly CircularBuffer _values; - private readonly CircularBuffer _voltyShort; - private readonly CircularBuffer _vsumBuff; + private readonly int _vshort, _vlong; + private CircularBuffer _values; + private CircularBuffer _voltyShort; + private CircularBuffer _vsumBuff; + private CircularBuffer _avoltyBuff; private double _beta, _len1, _pow1; private double _upperBand, _lowerBand, _prevMa1, _prevDet0, _prevDet1, _prevJma; private double _p_UpperBand, _p_LowerBand, _p_prevMa1, _p_prevDet0, _p_prevDet1, _p_prevJma; - public Jma(int period, double phase = 0, int vshort = 10) + public Jma(int period, double phase = 0, int vshort = 10) : base() { if (period < 1) { throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1."); } Period = period; - int _vshort = vshort; - int _vlong = 65; + _vshort = vshort; + _vlong = 65; _phase = Math.Clamp((phase * 0.01) + 1.5, 0.5, 2.5); _values = new CircularBuffer(period); - _voltyShort = new CircularBuffer(_vshort); + _voltyShort = new CircularBuffer(vshort); _vsumBuff = new CircularBuffer(_vlong); + _avoltyBuff = new CircularBuffer(2); Name = "JMA"; - WarmupPeriod = 65; + WarmupPeriod = period * 2; Init(); } @@ -39,6 +42,9 @@ public class Jma : AbstractBase _beta = 0.45 * (Period - 1) / (0.45 * (Period - 1) + 2); _len1 = Math.Max((Math.Log(Math.Sqrt(Period - 1)) / Math.Log(2.0)) + 2.0, 0); _pow1 = Math.Max(_len1 - 2.0, 0.5); + _avoltyBuff.Clear(); + _avoltyBuff.Add(0, true); + _avoltyBuff.Add(0, true); base.Init(); } @@ -91,9 +97,9 @@ public class Jma : AbstractBase double vsum = _vsumBuff.Newest() + 0.1 * (volty - _voltyShort.Oldest()); _vsumBuff.Add(vsum, Input.IsNew); - double avolty = 0; - for (int i = 0; i < _vsumBuff.Count; i++) { avolty += _vsumBuff[i]; } - avolty /= _vsumBuff.Count; + double prevAvolty = _avoltyBuff.Newest(); + double avolty = prevAvolty + 2.0 / (Math.Max(4.0 * Period, 30) + 1.0) * (vsum - prevAvolty); + _avoltyBuff.Add(avolty, Input.IsNew); double dVolty = (avolty > 0) ? volty / avolty : 0; dVolty = Math.Min(Math.Max(dVolty, 1.0), Math.Pow(_len1, 1.0 / _pow1)); diff --git a/lib/averages/Kama.cs b/lib/averages/Kama.cs index 1be50a04..81cda9cf 100644 --- a/lib/averages/Kama.cs +++ b/lib/averages/Kama.cs @@ -6,10 +6,10 @@ public class Kama : AbstractBase { private readonly int _period; private readonly double _scFast, _scSlow; - private readonly CircularBuffer? _buffer; + private CircularBuffer? _buffer; private double _lastKama, _p_lastKama; - public Kama(int period, int fast = 2, int slow = 30) + public Kama(int period, int fast = 2, int slow = 30) : base() { if (period < 1) { @@ -20,7 +20,6 @@ public class Kama : AbstractBase _scSlow = 2.0 / (slow + 1); WarmupPeriod = period; Name = $"Kama({_period}, {fast}, {slow})"; - _buffer = new CircularBuffer(_period + 1); Init(); } @@ -33,7 +32,7 @@ public class Kama : AbstractBase public override void Init() { base.Init(); - + _buffer = new CircularBuffer(_period + 1); _lastKama = 0; } diff --git a/lib/averages/Ltma.cs b/lib/averages/Ltma.cs index 87eeefb0..5d64d249 100644 --- a/lib/averages/Ltma.cs +++ b/lib/averages/Ltma.cs @@ -10,12 +10,10 @@ public class Ltma : AbstractBase public double Gamma => _gamma; - public Ltma(double gamma = 0.1) + public Ltma(double gamma = 0.1) : base() { if (gamma < 0 || gamma > 1) - { throw new ArgumentOutOfRangeException(nameof(gamma), "Gamma must be between 0 and 1."); - } _gamma = gamma; Name = $"Laguerre({gamma:F2})"; WarmupPeriod = 4; // Minimum number of samples needed diff --git a/lib/averages/Maaf.cs b/lib/averages/Maaf.cs index 6485d589..8230d4e8 100644 --- a/lib/averages/Maaf.cs +++ b/lib/averages/Maaf.cs @@ -8,13 +8,12 @@ public class Maaf : AbstractBase { private readonly CircularBuffer _priceBuffer; private readonly CircularBuffer _smoothBuffer; - private double _prevFilter, _prevValue2; - private readonly double _threshold; + private double _prevFilter, _prevValue2, _threshold; private double _p_prevFilter, _p_prevValue2; private readonly int _period; - public Maaf(int Period = 39, double Threshold = 0.002) + public Maaf(int Period = 39, double Threshold = 0.002) : base() { _period = Period; _threshold = Threshold; @@ -95,10 +94,7 @@ public class Maaf : AbstractBase length -= 2; } - if (length < 3) - { - length = 3; - } + if (length < 3) length = 3; double finalAlpha = 2.0 / (length + 1); double filter = finalAlpha * (smooth - _prevFilter) + _prevFilter; diff --git a/lib/averages/Mama.cs b/lib/averages/Mama.cs index b090e52d..de303cb4 100644 --- a/lib/averages/Mama.cs +++ b/lib/averages/Mama.cs @@ -1,17 +1,17 @@ -namespace QuanTAlib; - +using QuanTAlib; +using System; public class Mama : AbstractBase { private readonly double _fastLimit, _slowLimit; - private readonly CircularBuffer _pr, _sm, _dt, _i1, _q1, _i2, _q2, _re, _im, _pd, _ph; + private CircularBuffer _pr, _sm, _dt, _i1, _q1, _i2, _q2, _re, _im, _pd, _ph; private double _mama, _fama; private double _prevMama, _prevFama, _sumPr; private double _p_prevMama, _p_prevFama, _p_sumPr; public TValue Fama { get; private set; } - public Mama(double fastLimit = 0.5, double slowLimit = 0.05) + public Mama(double fastLimit = 0.5, double slowLimit = 0.05) : base() { Fama = new TValue(); Name = $"Mama({_fastLimit:F2}, {_slowLimit:F2})"; diff --git a/lib/averages/Mgdi.cs b/lib/averages/Mgdi.cs index f022a3ee..59970815 100644 --- a/lib/averages/Mgdi.cs +++ b/lib/averages/Mgdi.cs @@ -5,7 +5,7 @@ public class Mgdi : AbstractBase private readonly int _period; private readonly double _kFactor; private double _prevMd, _p_prevMd; - public Mgdi(int period, double kFactor = 0.6) + public Mgdi(int period, double kFactor = 0.6) : base() { if (period <= 0) { diff --git a/lib/averages/Mma.cs b/lib/averages/Mma.cs index d2fde801..0a7d27a6 100644 --- a/lib/averages/Mma.cs +++ b/lib/averages/Mma.cs @@ -9,7 +9,7 @@ namespace QuanTAlib private readonly CircularBuffer _buffer; private double _lastMma; - public Mma(int period) + public Mma(int period) : base() { if (period < 2) { diff --git a/lib/averages/Qema.cs b/lib/averages/Qema.cs index d1d49d90..f032cd07 100644 --- a/lib/averages/Qema.cs +++ b/lib/averages/Qema.cs @@ -2,25 +2,31 @@ namespace QuanTAlib; public class Qema : AbstractBase { + private readonly double _k1, _k2, _k3, _k4; private readonly Ema _ema1, _ema2, _ema3, _ema4; private double _lastQema, _p_lastQema; - public Qema(double k1 = 0.2, double k2 = 0.2, double k3 = 0.2, double k4 = 0.2) + public Qema(double k1=0.2, double k2=0.2, double k3=0.2, double k4=0.2) : base() { - if (k1 <= 0 || k2 <= 0 || k3 <= 0 || k4 <= 0) + if (k1 <= 0 || k2 <= 0 || k3 <= 0 || k4 <= 0 ) { - throw new ArgumentOutOfRangeException(nameof(k1), "All k values must be in the range (0, 1]."); + throw new ArgumentOutOfRangeException("All k values must be in the range (0, 1]."); } + _k1 = k1; + _k2 = k2; + _k3 = k3; + _k4 = k4; + _ema1 = new Ema(k1); _ema2 = new Ema(k2); _ema3 = new Ema(k3); _ema4 = new Ema(k4); Name = $"QEMA ({k1:F2},{k2:F2},{k3:F2},{k4:F2})"; - double smK = Math.Min(Math.Min(k1, k2), Math.Min(k3, k4)); + double smK = Math.Min(Math.Min(_k1, _k2), Math.Min(_k3, _k4)); - WarmupPeriod = (int)((2 - smK) / smK); + WarmupPeriod = (int) ((2 - smK) / smK); Init(); } diff --git a/lib/averages/Rema.cs b/lib/averages/Rema.cs index 2419b726..c89782c7 100644 --- a/lib/averages/Rema.cs +++ b/lib/averages/Rema.cs @@ -12,16 +12,12 @@ public class Rema : AbstractBase public int Period => _period; public double Lambda => _lambda; - public Rema(int period, double lambda = 0.5) + public Rema(int period, double lambda = 0.5) : base() { if (period < 1) - { throw new ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1."); - } if (lambda < 0) - { throw new ArgumentOutOfRangeException(nameof(lambda), "Lambda must be non-negative."); - } _period = period; _lambda = lambda; diff --git a/lib/averages/Rma.cs b/lib/averages/Rma.cs index 739c0733..3dadbba2 100644 --- a/lib/averages/Rma.cs +++ b/lib/averages/Rma.cs @@ -5,12 +5,11 @@ namespace QuanTAlib { public class Rma : AbstractBase { private readonly int _period; - private readonly double _alpha; + private double _alpha; private double _lastRMA; private double _savedLastRMA; - public Rma(int period) - { + public Rma(int period) : base() { if (period < 1) { throw new ArgumentException("Period must be greater than or equal to 1.", nameof(period)); } diff --git a/lib/averages/Sma.cs b/lib/averages/Sma.cs index 3d2fb71d..5cedd225 100644 --- a/lib/averages/Sma.cs +++ b/lib/averages/Sma.cs @@ -4,10 +4,10 @@ public class Sma : AbstractBase { // inherited _index // inherited _value - private readonly int Period; - private readonly CircularBuffer _buffer; + public readonly int Period; + private CircularBuffer _buffer; - public Sma(int period) + public Sma(int period) : base() { if (period < 1) { diff --git a/lib/averages/Smma.cs b/lib/averages/Smma.cs index ccf28efd..1e20301c 100644 --- a/lib/averages/Smma.cs +++ b/lib/averages/Smma.cs @@ -5,10 +5,10 @@ namespace QuanTAlib; public class Smma : AbstractBase { private readonly int _period; - private readonly CircularBuffer? _buffer; + private CircularBuffer? _buffer; private double _lastSmma, _p_lastSmma; - public Smma(int period) + public Smma(int period) : base() { if (period < 1) { @@ -17,7 +17,6 @@ public class Smma : AbstractBase _period = period; WarmupPeriod = period; Name = $"Smma({_period})"; - _buffer = new CircularBuffer(_period); Init(); } @@ -30,6 +29,7 @@ public class Smma : AbstractBase public override void Init() { base.Init(); + _buffer = new CircularBuffer(_period); _lastSmma = 0; } diff --git a/lib/averages/T3.cs b/lib/averages/T3.cs index c2b216d7..5e7a30df 100644 --- a/lib/averages/T3.cs +++ b/lib/averages/T3.cs @@ -3,27 +3,30 @@ namespace QuanTAlib; public class T3 : AbstractBase { private readonly int _period; + private readonly double _vfactor; private readonly bool _useSma; - private readonly double _k, _c1, _c2, _c3, _c4; + private readonly double _k, _k1m, _c1, _c2, _c3, _c4; private readonly CircularBuffer _buffer1, _buffer2, _buffer3, _buffer4, _buffer5, _buffer6; private double _lastEma1, _lastEma2, _lastEma3, _lastEma4, _lastEma5, _lastEma6; private double _p_lastEma1, _p_lastEma2, _p_lastEma3, _p_lastEma4, _p_lastEma5, _p_lastEma6; - public T3(int period, double vfactor = 0.7, bool useSma = true) + public T3(int period, double vfactor = 0.7, bool useSma = true) : base() { if (period < 1) { throw new ArgumentException("Period must be greater than or equal to 1.", nameof(period)); } _period = period; + _vfactor = vfactor; _useSma = useSma; WarmupPeriod = period; _k = 2.0 / (_period + 1); - _c1 = -vfactor * vfactor * vfactor; - _c2 = 3 * vfactor * vfactor + 3 * vfactor * vfactor * vfactor; - _c3 = -6 * vfactor * vfactor - 3 * vfactor - 3 * vfactor * vfactor * vfactor; - _c4 = 1 + 3 * vfactor + vfactor * vfactor * vfactor + 3 * vfactor * vfactor; + _k1m = 1.0 - _k; + _c1 = -_vfactor * _vfactor * _vfactor; + _c2 = 3 * _vfactor * _vfactor + 3 * _vfactor * _vfactor * _vfactor; + _c3 = -6 * _vfactor * _vfactor - 3 * _vfactor - 3 * _vfactor * _vfactor * _vfactor; + _c4 = 1 + 3 * _vfactor + _vfactor * _vfactor * _vfactor + 3 * _vfactor * _vfactor; _buffer1 = new(period); _buffer2 = new(period); @@ -33,7 +36,7 @@ public class T3 : AbstractBase _buffer6 = new(period); - Name = $"T3({_period}, {vfactor})"; + Name = $"T3({_period}, {_vfactor})"; Init(); } diff --git a/lib/averages/Tema.cs b/lib/averages/Tema.cs index a783cf54..2b12f6ee 100644 --- a/lib/averages/Tema.cs +++ b/lib/averages/Tema.cs @@ -8,7 +8,7 @@ public class Tema : AbstractBase private double _lastEma3, _p_lastEma3; private double _k, _e, _p_e; - public Tema(int period) + public Tema(int period) : base() { if (period < 1) { @@ -58,7 +58,7 @@ public class Tema : AbstractBase { double result, _ema1, _ema2, _ema3; ManageState(Input.IsNew); - + _e = (_e > 1e-10) ? (1 - _k) * _e : 0; double _invE = (_e > 1e-10) ? 1 / (1 - _e) : 1; diff --git a/lib/averages/Vidya.cs b/lib/averages/Vidya.cs index cbff1ee5..e421c16c 100644 --- a/lib/averages/Vidya.cs +++ b/lib/averages/Vidya.cs @@ -10,10 +10,10 @@ public class Vidya : AbstractBase private readonly int _longPeriod; private readonly double _alpha; private double _lastVIDYA, _p_lastVIDYA; - private readonly CircularBuffer? _shortBuffer; - private readonly CircularBuffer? _longBuffer; + private CircularBuffer? _shortBuffer; + private CircularBuffer? _longBuffer; - public Vidya(int shortPeriod, int longPeriod = 0, double alpha = 0.2) + public Vidya(int shortPeriod, int longPeriod = 0, double alpha = 0.2) : base() { if (shortPeriod < 1) { @@ -24,8 +24,6 @@ public class Vidya : AbstractBase _alpha = alpha; WarmupPeriod = _longPeriod; Name = $"Vidya({_shortPeriod},{_longPeriod})"; - _shortBuffer = new CircularBuffer(_shortPeriod); - _longBuffer = new CircularBuffer(_longPeriod); Init(); } @@ -40,6 +38,8 @@ public class Vidya : AbstractBase { base.Init(); _lastVIDYA = 0; + _shortBuffer = new CircularBuffer(_shortPeriod); + _longBuffer = new CircularBuffer(_longPeriod); } protected override void ManageState(bool isNew) @@ -59,7 +59,7 @@ public class Vidya : AbstractBase protected override double Calculation() { ManageState(Input.IsNew); - + _shortBuffer!.Add(Input.Value, Input.IsNew); _longBuffer!.Add(Input.Value, Input.IsNew); @@ -83,7 +83,7 @@ public class Vidya : AbstractBase } [MethodImpl(MethodImplOptions.AggressiveInlining)] - private static double CalculateStdDev(CircularBuffer buffer) + private double CalculateStdDev(CircularBuffer buffer) { double mean = buffer.Average(); double sumSquaredDiff = buffer.Sum(x => Math.Pow(x - mean, 2)); diff --git a/lib/averages/Wma.cs b/lib/averages/Wma.cs index 2fcefb53..9497ba25 100644 --- a/lib/averages/Wma.cs +++ b/lib/averages/Wma.cs @@ -1,9 +1,8 @@ namespace QuanTAlib; -//TODO fix WMA - passing Talib test - public class Wma : AbstractBase { + private readonly int _period; private readonly Convolution _convolution; public Wma(int period) @@ -12,9 +11,10 @@ public class Wma : AbstractBase { throw new ArgumentException("Period must be greater than or equal to 1.", nameof(period)); } - _convolution = new Convolution(GenerateWmaKernel(period)); + _period = period; + _convolution = new Convolution(GenerateWmaKernel(_period)); Name = "Wma"; - WarmupPeriod = period; + WarmupPeriod = _period; Init(); } diff --git a/lib/averages/Zlema.cs b/lib/averages/Zlema.cs index 4c5ba651..b7115d66 100644 --- a/lib/averages/Zlema.cs +++ b/lib/averages/Zlema.cs @@ -6,11 +6,12 @@ namespace QuanTAlib; public class Zlema : AbstractBase { private readonly int _period; - private readonly CircularBuffer? _buffer; - private readonly double _alpha; + private CircularBuffer? _buffer; + private double _alpha; + private int _lag; private double _lastZLEMA, _p_lastZLEMA; - public Zlema(int period) + public Zlema(int period) : base() { if (period < 1) { @@ -19,8 +20,8 @@ public class Zlema : AbstractBase _period = period; WarmupPeriod = period; _alpha = 2.0 / (_period + 1); + _lag = (_period - 1) / 2; Name = $"Zlema({_period})"; - _buffer = new CircularBuffer(_period); Init(); } @@ -33,6 +34,7 @@ public class Zlema : AbstractBase public override void Init() { base.Init(); + _buffer = new CircularBuffer(_period); _lastZLEMA = 0; } @@ -53,7 +55,7 @@ public class Zlema : AbstractBase protected override double Calculation() { ManageState(Input.IsNew); - + _buffer!.Add(Input.Value, Input.IsNew); int lag = Math.Max(Math.Min((int)((_period - 1) * 0.5), _buffer.Count - 1), 0) + 1; diff --git a/lib/core/circularbuffer.cs b/lib/core/circularbuffer.cs index 2730c56e..695c4da3 100644 --- a/lib/core/circularbuffer.cs +++ b/lib/core/circularbuffer.cs @@ -7,8 +7,8 @@ namespace QuanTAlib; public class CircularBuffer : IEnumerable { private readonly double[] _buffer; - private int _start; - private int _size; + private int _start = 0; + private int _size = 0; public int Capacity { get; } public int Count => _size; @@ -69,9 +69,7 @@ public class CircularBuffer : IEnumerable public double Newest() { if (_size == 0) - { return 0; - } return _buffer[(_start + _size - 1) % Capacity]; } @@ -79,9 +77,7 @@ public class CircularBuffer : IEnumerable public double Oldest() { if (_size == 0) - { ThrowInvalidOperationException(); - } return _buffer[_start]; } @@ -113,9 +109,7 @@ public class CircularBuffer : IEnumerable public bool MoveNext() { if (_index + 1 >= _buffer._size) - { return false; - } _index++; _current = _buffer[_index]; @@ -131,9 +125,7 @@ public class CircularBuffer : IEnumerable _current = default; } - public void Dispose() { - // not implemented - } + public void Dispose() { } } [MethodImpl(MethodImplOptions.AggressiveInlining)] diff --git a/lib/core/tbar.cs b/lib/core/tbar.cs index 0326b043..9f71e5e4 100644 --- a/lib/core/tbar.cs +++ b/lib/core/tbar.cs @@ -54,11 +54,11 @@ public class TBarSeries : List { private readonly TBar Default = new(DateTime.MinValue, double.NaN, double.NaN, double.NaN, double.NaN, double.NaN); - public TSeries Open { get; set; } - public TSeries High { get; set; } - public TSeries Low { get; set; } - public TSeries Close { get; set; } - public TSeries Volume { get; set; } + public TSeries Open; + public TSeries High; + public TSeries Low; + public TSeries Close; + public TSeries Volume; public TBar Last => Count > 0 ? this[^1] : Default; diff --git a/lib/core/tvalue.cs b/lib/core/tvalue.cs index fa06f0f5..30a67d29 100644 --- a/lib/core/tvalue.cs +++ b/lib/core/tvalue.cs @@ -52,6 +52,12 @@ public class TSeries : List var pubEvent = source.GetType().GetEvent("Pub"); if (pubEvent != null) { + /* + var nameProperty = source.GetType().GetProperty("Name"); + if (nameProperty != null) { + Name = nameProperty.GetValue(nameProperty)?.ToString()!; + } + */ pubEvent.AddEventHandler(source, new ValueSignal(Sub)); } } diff --git a/lib/feeds/GbmFeed.cs b/lib/feeds/GbmFeed.cs index 6b6d6991..dfcf2365 100644 --- a/lib/feeds/GbmFeed.cs +++ b/lib/feeds/GbmFeed.cs @@ -1,3 +1,5 @@ +using System.CommandLine.Rendering.Views; + namespace QuanTAlib; public class GbmFeed : TBarSeries @@ -6,7 +8,7 @@ public class GbmFeed : TBarSeries private readonly Random _random; private double _lastClose, _lastHigh, _lastLow; - public GbmFeed(double initialPrice = 100.0, double mu = 0.05, double sigma = 0.2) + public GbmFeed(double initialPrice = 100.0, double mu = 0.05, double sigma = 0.2) : base() { _lastClose = _lastHigh = _lastLow = initialPrice; _mu = mu; @@ -20,6 +22,7 @@ public class GbmFeed : TBarSeries public void Add(int count) { DateTime startTime = DateTime.UtcNow - TimeSpan.FromHours(count); + TBar lastBar = new(); for (int i = 0; i < count; i++) { Add(startTime, true); diff --git a/lib/quantalib.csproj b/lib/quantalib.csproj index 53b82781..4c228dec 100644 --- a/lib/quantalib.csproj +++ b/lib/quantalib.csproj @@ -1,38 +1,57 @@  - - QuanTAlib - Library of TA Calculations, Charts and Strategies for Quantower - Quantitative Technical Analysis Library in C# for Quantower - git - https://github.com/mihakralj/QuanTAlib - Miha Kralj - Miha Kralj - Apache-2.0 - readme.md - QuanTAlib - QuanTAlib - True - True - Indicators;Stock;Market;Technical;Analysis;Algorithmic;Trading;Trade;Trend;Momentum;Finance;Algorithm;Algo;AlgoTrading;Financial;Strategy;Chart;Charting;Oscillator;Overlay;Equity;Bitcoin;Crypto;Cryptocurrency;Forex;Quantitative;Historical;Quotes; - $(NoWarn);NU5104 - QuanTAlib2.png - https://raw.githubusercontent.com/mihakralj/QuanTAlib/main/.github/QuanTAlib2.png - True - + + QuanTAlib + Library of TA Calculations, Charts and Strategies for Quantower + Quantitative Technical Analysis Library in C# for Quantower + git + https://github.com/mihakralj/QuanTAlib + true + Miha Kralj + Miha Kralj + Apache-2.0 + readme.md + net8.0 + enable + preview + enable + false + en-US + QuanTAlib + QuanTAlib + True + AnyCPU + False + full + True + True + + Indicators;Stock;Market;Technical;Analysis;Algorithmic;Trading;Trade;Trend;Momentum;Finance;Algorithm;Algo; + AlgoTrading;Financial;Strategy;Chart;Charting;Oscillator;Overlay;Equity;Bitcoin;Crypto;Cryptocurrency;Forex; + Quantitative;Historical;Quotes; + + $(NoWarn);NU5104 + false + false + false + + + QuanTAlib2.png + https://raw.githubusercontent.com/mihakralj/QuanTAlib/main/.github/QuanTAlib2.png + True + - - + - - ..\.github\TradingPlatform.BusinessLayer.dll + ..\.github\TradingPlatform.BusinessLayer.dll TradingPlatform.BusinessLayer.xml + \ No newline at end of file diff --git a/lib/statistics/Entropy.cs b/lib/statistics/Entropy.cs index d1f2348b..67b86482 100644 --- a/lib/statistics/Entropy.cs +++ b/lib/statistics/Entropy.cs @@ -7,9 +7,9 @@ using System.Linq; public class Entropy : AbstractBase { public readonly int Period; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Entropy(int period) + public Entropy(int period) : base() { if (period < 2) { diff --git a/lib/statistics/Kurtosis.cs b/lib/statistics/Kurtosis.cs index 3b60f8ba..1a5be948 100644 --- a/lib/statistics/Kurtosis.cs +++ b/lib/statistics/Kurtosis.cs @@ -4,9 +4,9 @@ namespace QuanTAlib; public class Kurtosis : AbstractBase { public readonly int Period; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Kurtosis(int period) + public Kurtosis(int period) : base() { if (period < 4) { diff --git a/lib/statistics/Max.cs b/lib/statistics/Max.cs index 1257f63d..2eccaaaf 100644 --- a/lib/statistics/Max.cs +++ b/lib/statistics/Max.cs @@ -5,12 +5,12 @@ namespace QuanTAlib public class Max : AbstractBase { public readonly int Period; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; private readonly double _halfLife; private double _currentMax, _p_currentMax; private int _timeSinceNewMax, _p_timeSinceNewMax; - public Max(int period, double decay = 0) + public Max(int period, double decay = 0) : base() { if (period < 1) { diff --git a/lib/statistics/Median.cs b/lib/statistics/Median.cs index fe6955a8..9adb1ed5 100644 --- a/lib/statistics/Median.cs +++ b/lib/statistics/Median.cs @@ -6,9 +6,9 @@ namespace QuanTAlib public class Median : AbstractBase { public readonly int Period; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Median(int period) + public Median(int period) : base() { if (period < 1) { diff --git a/lib/statistics/Min.cs b/lib/statistics/Min.cs index 03e0e14f..eb842a16 100644 --- a/lib/statistics/Min.cs +++ b/lib/statistics/Min.cs @@ -5,12 +5,12 @@ namespace QuanTAlib public class Min : AbstractBase { public readonly int Period; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; private readonly double _halfLife; private double _currentMin, _p_currentMin; private int _timeSinceNewMin, _p_timeSinceNewMin; - public Min(int period, double decay = 0) + public Min(int period, double decay = 0) : base() { if (period < 1) { diff --git a/lib/statistics/Mode.cs b/lib/statistics/Mode.cs index 13b48465..3aacd879 100644 --- a/lib/statistics/Mode.cs +++ b/lib/statistics/Mode.cs @@ -3,9 +3,9 @@ namespace QuanTAlib; public class Mode : AbstractBase { public readonly int Period; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Mode(int period) + public Mode(int period) : base() { if (period < 1) { diff --git a/lib/statistics/Percentile.cs b/lib/statistics/Percentile.cs index 244bbbc4..d90afe0e 100644 --- a/lib/statistics/Percentile.cs +++ b/lib/statistics/Percentile.cs @@ -7,9 +7,9 @@ public class Percentile : AbstractBase { public readonly int Period; public readonly double Percent; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Percentile(int period, double percent) + public Percentile(int period, double percent) : base() { if (period < 2) { diff --git a/lib/statistics/Skew.cs b/lib/statistics/Skew.cs index 6101ae0d..f7aaeec8 100644 --- a/lib/statistics/Skew.cs +++ b/lib/statistics/Skew.cs @@ -6,9 +6,9 @@ using System.Linq; public class Skew : AbstractBase { public readonly int Period; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Skew(int period) + public Skew(int period) : base() { if (period < 3) { diff --git a/lib/statistics/Stddev.cs b/lib/statistics/Stddev.cs index c0463368..d1eff641 100644 --- a/lib/statistics/Stddev.cs +++ b/lib/statistics/Stddev.cs @@ -7,9 +7,9 @@ namespace QuanTAlib { public readonly int Period; public readonly bool IsPopulation; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Stddev(int period, bool isPopulation = false) + public Stddev(int period, bool isPopulation = false) : base() { if (period < 2) { diff --git a/lib/statistics/Variance.cs b/lib/statistics/Variance.cs index 18f71a9f..675958ca 100644 --- a/lib/statistics/Variance.cs +++ b/lib/statistics/Variance.cs @@ -7,9 +7,9 @@ namespace QuanTAlib { public readonly int Period; public readonly bool IsPopulation; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Variance(int period, bool isPopulation = false) + public Variance(int period, bool isPopulation = false) : base() { if (period < 2) { diff --git a/lib/statistics/Zscore.cs b/lib/statistics/Zscore.cs index 74d696f3..87f59b67 100644 --- a/lib/statistics/Zscore.cs +++ b/lib/statistics/Zscore.cs @@ -6,9 +6,9 @@ using System.Linq; public class Zscore : AbstractBase { public readonly int Period; - private readonly CircularBuffer _buffer; + private CircularBuffer _buffer; - public Zscore(int period) + public Zscore(int period) : base() { if (period < 2) { diff --git a/quantower/AbstractIndicatorBase.cs b/quantower/AbstractIndicatorBase.cs index 734535d5..0f650904 100644 --- a/quantower/AbstractIndicatorBase.cs +++ b/quantower/AbstractIndicatorBase.cs @@ -21,7 +21,7 @@ public abstract class AbstractIndicatorBase : Indicator public PriceType SourcePrice { get; set; } = PriceType.Close; [InputParameter(name: "Line smoothing", sortIndex: 19, minimum: 0.0, maximum: 1.0, increment: 0.1, decimalPlaces: 2)] - public double Tension { get; set; } = 0.2; + public double Tension = 0.2; [InputParameter("Show cold values", sortIndex: 20)] public bool ShowColdValues { get; set; } = true; @@ -31,12 +31,14 @@ public abstract class AbstractIndicatorBase : Indicator protected LineSeries? Series; protected abstract AbstractBase MovingAverage { get; } - protected AbstractIndicatorBase() + protected AbstractIndicatorBase() : base() { OnBackGround = true; SeparateWindow = false; Series = new(name: $"Name", color: Color.Orange, width: 2, style: LineStyle.Solid); AddLineSeries(Series); + + InitIndicator(); } protected virtual void InitIndicator() @@ -69,10 +71,7 @@ public abstract class AbstractIndicatorBase : Indicator { base.OnPaintChart(args); List allPoints = new List(); - if (CurrentChart == null) - { - return; - } + if (CurrentChart == null) return; Graphics gr = args.Graphics; var mainWindow = CurrentChart.MainWindow; @@ -103,7 +102,7 @@ public abstract class AbstractIndicatorBase : Indicator private void DrawSmoothCombinedCurve(Graphics gr, List allPoints, int hotCount) { - if (allPoints.Count < 2) { return; } + if (allPoints.Count < 2) return; using (Pen defaultPen = new(Series!.Color, Series.Width) { DashStyle = ConvertLineStyleToDashStyle(Series.Style) }) using (Pen coldPen = new(Series!.Color, Series.Width) { DashStyle = DashStyle.Dot }) diff --git a/quantower/Averages/AlmaIndicator.cs b/quantower/Averages/AlmaIndicator.cs index dd3aa81a..251ad0f1 100644 --- a/quantower/Averages/AlmaIndicator.cs +++ b/quantower/Averages/AlmaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class AlmaIndicator : IndicatorBase { @@ -7,21 +7,22 @@ public class AlmaIndicator : IndicatorBase public int Period { get; set; } = 10; [InputParameter("Offset", sortIndex: 5)] - public double Offset { get; set; } = 0.85; + public double Offset = 0.85; [InputParameter("Sigma", sortIndex: 6)] - public double Sigma { get; set; } = 6.0; + public double Sigma = 6.0; private Alma? ma; protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"ALMA {Period} : {Offset:F2} : {Sigma:F0} : {SourceName}"; - public AlmaIndicator() + public AlmaIndicator() : base() { Name = "ALMA - Arnaud Legoux Moving Average"; } protected override void InitIndicator() { + base.InitIndicator(); ma = new Alma(period: Period, offset: Offset, sigma: Sigma); } } diff --git a/quantower/Averages/DemaIndicator.cs b/quantower/Averages/DemaIndicator.cs index d8781e74..185cf6a5 100644 --- a/quantower/Averages/DemaIndicator.cs +++ b/quantower/Averages/DemaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class DemaIndicator : IndicatorBase { @@ -9,13 +9,14 @@ public class DemaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"DEMA {Period} : {SourceName}"; - public DemaIndicator() + public DemaIndicator() : base() { Name = "DEMA - Double Exponential Moving Average"; } protected override void InitIndicator() { + base.InitIndicator(); ma = new Dema(period: Period); } } diff --git a/quantower/Averages/DsmaIndicator.cs b/quantower/Averages/DsmaIndicator.cs index c301fce6..9bf1dc6d 100644 --- a/quantower/Averages/DsmaIndicator.cs +++ b/quantower/Averages/DsmaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class DsmaIndicator : IndicatorBase { @@ -12,7 +12,7 @@ public class DsmaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"DSMA {Period} : {Scale:F2} : {SourceName}"; - public DsmaIndicator() + public DsmaIndicator() : base() { Name = "DSMA - Deviation Scaled Moving Average"; } @@ -21,5 +21,6 @@ public class DsmaIndicator : IndicatorBase { ma = new Dsma(Period, Scale); MinHistoryDepths = ma.WarmupPeriod; + base.InitIndicator(); } } diff --git a/quantower/Averages/DwmaIndicator.cs b/quantower/Averages/DwmaIndicator.cs index d0f45dcf..5b80c398 100644 --- a/quantower/Averages/DwmaIndicator.cs +++ b/quantower/Averages/DwmaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class DwmaIndicator : IndicatorBase { @@ -11,7 +11,7 @@ public class DwmaIndicator : IndicatorBase public override string ShortName => $"DWMA {Period} : {SourceName}"; - public DwmaIndicator() + public DwmaIndicator() : base() { Name = "DWMA - Double Weighted Moving Average"; } @@ -19,5 +19,6 @@ public class DwmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Dwma(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/EmaIndicator.cs b/quantower/Averages/EmaIndicator.cs index 4ffe50a6..7adecd30 100644 --- a/quantower/Averages/EmaIndicator.cs +++ b/quantower/Averages/EmaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class EmaIndicator : IndicatorBase { @@ -13,7 +13,7 @@ public class EmaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"EMA {Period} : {SourceName}"; - public EmaIndicator() + public EmaIndicator() : base() { Name = "EMA - Exponential Moving Average"; Description = "Exponential Moving Average"; @@ -21,6 +21,7 @@ public class EmaIndicator : IndicatorBase protected override void InitIndicator() { + base.InitIndicator(); ma = new Ema(period: Period, useSma: UseSma); } } diff --git a/quantower/Averages/EpmaIndicator.cs b/quantower/Averages/EpmaIndicator.cs index 186e87ec..1a92d33c 100644 --- a/quantower/Averages/EpmaIndicator.cs +++ b/quantower/Averages/EpmaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class EpmaIndicator : IndicatorBase { @@ -10,13 +10,14 @@ public class EpmaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"EPMA {Period} : {SourceName}"; - public EpmaIndicator() + public EpmaIndicator() : base() { Name = "EPMA - Endpoint Moving Average"; } protected override void InitIndicator() { + base.InitIndicator(); ma = new Epma(period: Period); } } diff --git a/quantower/Averages/FramaIndicator.cs b/quantower/Averages/FramaIndicator.cs index 3dd036dd..865f4a53 100644 --- a/quantower/Averages/FramaIndicator.cs +++ b/quantower/Averages/FramaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class FramaIndicator : IndicatorBase { @@ -11,7 +11,7 @@ public class FramaIndicator : IndicatorBase public override string ShortName => $"FRAMA {Period} : {SourceName}"; - public FramaIndicator() + public FramaIndicator() : base() { Name = "FRAMA - Fractal Adaptive Moving Average"; } @@ -19,5 +19,6 @@ public class FramaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Frama(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/FwmaIndicator.cs b/quantower/Averages/FwmaIndicator.cs index 5a51f375..b4a9a389 100644 --- a/quantower/Averages/FwmaIndicator.cs +++ b/quantower/Averages/FwmaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class FwmaIndicator : IndicatorBase { @@ -11,7 +11,7 @@ public class FwmaIndicator : IndicatorBase public override string ShortName => $"FWMA {Period} : {SourceName}"; - public FwmaIndicator() + public FwmaIndicator() : base() { Name = "FWMA - Fibonacci-Weighted Moving Average"; } @@ -19,5 +19,6 @@ public class FwmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Fwma(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/GmaIndicator.cs b/quantower/Averages/GmaIndicator.cs index 32614ee4..5d85e499 100644 --- a/quantower/Averages/GmaIndicator.cs +++ b/quantower/Averages/GmaIndicator.cs @@ -1,5 +1,5 @@ using TradingPlatform.BusinessLayer; -namespace QuanTAlib; +using QuanTAlib; public class GmaIndicator : IndicatorBase { @@ -11,7 +11,7 @@ public class GmaIndicator : IndicatorBase public override string ShortName => $"GMA {Period} : {SourceName}"; - public GmaIndicator() + public GmaIndicator() : base() { Name = "GMA - Gaussian-Weighted Moving Average"; } @@ -19,5 +19,6 @@ public class GmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Gma(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/HmaIndicator.cs b/quantower/Averages/HmaIndicator.cs index 14ce5420..2dd2b472 100644 --- a/quantower/Averages/HmaIndicator.cs +++ b/quantower/Averages/HmaIndicator.cs @@ -11,7 +11,7 @@ public class HmaIndicator : IndicatorBase public override string ShortName => $"HMA {Period} : {SourceName}"; - public HmaIndicator() + public HmaIndicator() : base() { Name = "HMA - Hull Moving Average"; } @@ -19,5 +19,6 @@ public class HmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Hma(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/HtitIndicator.cs b/quantower/Averages/HtitIndicator.cs index 1368fd85..8d0f4009 100644 --- a/quantower/Averages/HtitIndicator.cs +++ b/quantower/Averages/HtitIndicator.cs @@ -7,7 +7,7 @@ public class HtitIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"HTIT : {SourceName}"; - public HtitIndicator() + public HtitIndicator() : base() { Name = "HTIT - Hilbert Transform Instantaneous Trendline"; } @@ -16,5 +16,6 @@ public class HtitIndicator : IndicatorBase { ma = new Htit(); MinHistoryDepths = ma.WarmupPeriod; + base.InitIndicator(); } } diff --git a/quantower/Averages/HwmaIndicator.cs b/quantower/Averages/HwmaIndicator.cs index ce51079b..e777af3c 100644 --- a/quantower/Averages/HwmaIndicator.cs +++ b/quantower/Averages/HwmaIndicator.cs @@ -17,13 +17,17 @@ public class HwmaIndicator : IndicatorBase public override string ShortName => $"HWMA {nA:F2} : {nB:F2} : {nC:F2} : {SourceName}"; - public HwmaIndicator() + public HwmaIndicator() : base() { Name = "HWMA - Holt-Winter Moving Average"; } protected override void InitIndicator() { + //nA = 2 / (1 + (double)Period); + //nB = 1 / (double)Period; + //nC = 1 / (double)Period; ma = new Hwma(nA: nA, nB: nB, nC: nC); + base.InitIndicator(); } } diff --git a/quantower/Averages/JmaIndicator.cs b/quantower/Averages/JmaIndicator.cs index c418d551..88a0a2ea 100644 --- a/quantower/Averages/JmaIndicator.cs +++ b/quantower/Averages/JmaIndicator.cs @@ -7,13 +7,13 @@ public class JmaIndicator : IndicatorBase public int Period { get; set; } = 10; [InputParameter("Phase", sortIndex: 2, -100, 100, 1, 0)] - public int Phase { get; set; } + public int Phase { get; set; } = 0; private Jma? ma; protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"JMA {Period} : {Phase} : {SourceName}"; - public JmaIndicator() + public JmaIndicator() : base() { Name = "JMA - Jurik Moving Average"; } @@ -21,5 +21,6 @@ public class JmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Jma(period: Period, phase: (double)Phase); + base.InitIndicator(); } } diff --git a/quantower/Averages/KamaIndicator.cs b/quantower/Averages/KamaIndicator.cs index 88d1a264..21587ab1 100644 --- a/quantower/Averages/KamaIndicator.cs +++ b/quantower/Averages/KamaIndicator.cs @@ -15,7 +15,7 @@ public class KamaIndicator : IndicatorBase public override string ShortName => $"KAMA {Period} : {Fast} : {Slow} : {SourceName}"; - public KamaIndicator() + public KamaIndicator() : base() { Name = "KAMA - Kaufman's Adaptive Moving Average"; } @@ -23,5 +23,6 @@ public class KamaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Kama(Period, Fast, Slow); + base.InitIndicator(); } } diff --git a/quantower/Averages/LtmaIndicator.cs b/quantower/Averages/LtmaIndicator.cs index 5c5b5f85..d786d0bb 100644 --- a/quantower/Averages/LtmaIndicator.cs +++ b/quantower/Averages/LtmaIndicator.cs @@ -10,7 +10,7 @@ public class LtmaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"Laguerre {Gamma:F2} : {SourceName}"; - public LtmaIndicator() + public LtmaIndicator() : base() { Name = "LTMA - Laguerre Transform Moving Average"; } @@ -18,5 +18,6 @@ public class LtmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Ltma(gamma: Gamma); + base.InitIndicator(); } } diff --git a/quantower/Averages/MaafIndicator.cs b/quantower/Averages/MaafIndicator.cs index 7dbe5950..6cb7128c 100644 --- a/quantower/Averages/MaafIndicator.cs +++ b/quantower/Averages/MaafIndicator.cs @@ -7,19 +7,20 @@ public class MaafIndicator : IndicatorBase public int Period { get; set; } = 39; [InputParameter("Threshold", sortIndex: 5, minimum: 0, maximum: 1, increment: 0.001, decimalPlaces:3)] - public double Threshold { get; set; } = 0.002; + public double Threshold = 0.002; private Maaf? ma; protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"MAAF {Period} : {Threshold:F2} : {SourceName}"; - public MaafIndicator() + public MaafIndicator() : base() { Name = "MAAF - Median-Average Adaptive Filter"; } protected override void InitIndicator() { + base.InitIndicator(); ma = new Maaf(Period: Period, Threshold: Threshold); } } diff --git a/quantower/Averages/MamaIndicator.cs b/quantower/Averages/MamaIndicator.cs index 2d1ca0b9..d3e14f30 100644 --- a/quantower/Averages/MamaIndicator.cs +++ b/quantower/Averages/MamaIndicator.cs @@ -12,7 +12,7 @@ public class MamaIndicator : IndicatorBase public override string ShortName => $"MAMA : {Fast} : {Slow} : {SourceName}"; - public MamaIndicator() + public MamaIndicator() : base() { Name = "MAMA - MESA Adaptive Moving Average"; } @@ -20,5 +20,6 @@ public class MamaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Mama(Fast, Slow); + base.InitIndicator(); } } diff --git a/quantower/Averages/MgdiIndicator.cs b/quantower/Averages/MgdiIndicator.cs index 528ea55b..a9ed0bb0 100644 --- a/quantower/Averages/MgdiIndicator.cs +++ b/quantower/Averages/MgdiIndicator.cs @@ -15,7 +15,7 @@ public class MgdiIndicator : IndicatorBase public override string ShortName => $"MGDI {Period} : {kfactor:F2} : {SourceName}"; - public MgdiIndicator() + public MgdiIndicator() : base() { Name = "MGDI - McGinley Dynamic Index"; } @@ -23,5 +23,6 @@ public class MgdiIndicator : IndicatorBase protected override void InitIndicator() { ma = new Mgdi(period: Period, kFactor: kfactor); + base.InitIndicator(); } } diff --git a/quantower/Averages/MmaIndicator.cs b/quantower/Averages/MmaIndicator.cs index 5a029c15..bf174265 100644 --- a/quantower/Averages/MmaIndicator.cs +++ b/quantower/Averages/MmaIndicator.cs @@ -10,13 +10,14 @@ public class MmaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"MMA {Period} : {SourceName}"; - public MmaIndicator() + public MmaIndicator() : base() { Name = "MMA - Modified Moving Average"; } protected override void InitIndicator() { + base.InitIndicator(); ma = new Mma(period: Period); } } diff --git a/quantower/Averages/QemaIndicator.cs b/quantower/Averages/QemaIndicator.cs index a1208a0e..3434249e 100644 --- a/quantower/Averages/QemaIndicator.cs +++ b/quantower/Averages/QemaIndicator.cs @@ -16,7 +16,7 @@ public class QemaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"QEMA {k1:F2} : {k2:F2} : {k3:F2} : {k4:F2} :{SourceName}"; - public QemaIndicator() + public QemaIndicator() : base() { Name = "QEMA - Quad Exponential Moving Average"; Description = "Quad Exponential Moving Average"; @@ -24,6 +24,7 @@ public class QemaIndicator : IndicatorBase protected override void InitIndicator() { + base.InitIndicator(); ma = new Qema(k1, k2, k3, k4); } } diff --git a/quantower/Averages/RemaIndicator.cs b/quantower/Averages/RemaIndicator.cs index cd66f1a8..26193da4 100644 --- a/quantower/Averages/RemaIndicator.cs +++ b/quantower/Averages/RemaIndicator.cs @@ -13,13 +13,14 @@ public class RemaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"REMA {Period} : {Lambda:F2} : {SourceName}"; - public RemaIndicator() + public RemaIndicator() : base() { Name = "REMA - Regularized Exponential Moving Average"; } protected override void InitIndicator() { + base.InitIndicator(); ma = new Rema(period: Period, lambda: Lambda); } } diff --git a/quantower/Averages/RmaIndicator.cs b/quantower/Averages/RmaIndicator.cs index 58f74fbe..a89e15a6 100644 --- a/quantower/Averages/RmaIndicator.cs +++ b/quantower/Averages/RmaIndicator.cs @@ -11,7 +11,7 @@ public class RmaIndicator : IndicatorBase public override string ShortName => $"RMA {Period} : {SourceName}"; - public RmaIndicator() + public RmaIndicator() : base() { Name = "RMA - wildeR Moving Average"; } @@ -19,5 +19,6 @@ public class RmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Rma(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/SinemaIndicator.cs b/quantower/Averages/SinemaIndicator.cs index 56be7972..2f3b231f 100644 --- a/quantower/Averages/SinemaIndicator.cs +++ b/quantower/Averages/SinemaIndicator.cs @@ -10,7 +10,7 @@ public class SinemaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"SINEMA {Period} : {SourceName}"; - public SinemaIndicator() + public SinemaIndicator() : base() { Name = "SINEMA - Sine-Weighted Moving Average"; } @@ -18,5 +18,6 @@ public class SinemaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Sinema(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/SmaIndicator.cs b/quantower/Averages/SmaIndicator.cs index 7ab77528..3cbf26fa 100644 --- a/quantower/Averages/SmaIndicator.cs +++ b/quantower/Averages/SmaIndicator.cs @@ -11,7 +11,7 @@ public class SmaIndicator : IndicatorBase public override string ShortName => $"SMA {Period} : {SourceName}"; - public SmaIndicator() + public SmaIndicator() : base() { Name = "SMA - Simple Moving Average"; } @@ -19,5 +19,6 @@ public class SmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Sma(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/SmmaIndicator.cs b/quantower/Averages/SmmaIndicator.cs index 5ac66cb7..a7516e22 100644 --- a/quantower/Averages/SmmaIndicator.cs +++ b/quantower/Averages/SmmaIndicator.cs @@ -11,7 +11,7 @@ public class SmmaIndicator : IndicatorBase public override string ShortName => $"SMMA {Period} : {SourceName}"; - public SmmaIndicator() + public SmmaIndicator() : base() { Name = "SMMA - Smoothed Moving Average"; } @@ -19,5 +19,6 @@ public class SmmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Smma(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/T3Indicator.cs b/quantower/Averages/T3Indicator.cs index 47adeac4..5b8f4766 100644 --- a/quantower/Averages/T3Indicator.cs +++ b/quantower/Averages/T3Indicator.cs @@ -10,13 +10,13 @@ public class T3Indicator : IndicatorBase public double Vfactor { get; set; } = 0.62; [InputParameter("Use SMA for warmup", sortIndex: 3)] - public bool UseSma { get; set; } + public bool UseSma { get; set; } = false; private T3? ma; protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"T3 {Period} : {Vfactor:F2} : {SourceName}"; - public T3Indicator() + public T3Indicator() : base() { Name = "T3 - Tillson T3 Moving Average"; } @@ -24,5 +24,6 @@ public class T3Indicator : IndicatorBase protected override void InitIndicator() { ma = new T3(period: Period, vfactor: Vfactor, useSma: UseSma); + base.InitIndicator(); } } diff --git a/quantower/Averages/TemaIndicator.cs b/quantower/Averages/TemaIndicator.cs index 4ad2b4cb..57d50da3 100644 --- a/quantower/Averages/TemaIndicator.cs +++ b/quantower/Averages/TemaIndicator.cs @@ -10,13 +10,14 @@ public class TemaIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"TEMA {Period} : {SourceName}"; - public TemaIndicator() + public TemaIndicator() : base() { Name = "TEMA - Triple Exponential Moving Average"; } protected override void InitIndicator() { + base.InitIndicator(); ma = new Tema(period: Period); } } diff --git a/quantower/Averages/TrimaIndicator.cs b/quantower/Averages/TrimaIndicator.cs index 2296ba37..b0612356 100644 --- a/quantower/Averages/TrimaIndicator.cs +++ b/quantower/Averages/TrimaIndicator.cs @@ -11,7 +11,7 @@ public class TrimaIndicator : IndicatorBase public override string ShortName => $"TRIMA {Period} : {SourceName}"; - public TrimaIndicator() + public TrimaIndicator() : base() { Name = "TRIMA - Triangular Moving Average"; } @@ -19,5 +19,6 @@ public class TrimaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Trima(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/VidyaIndicator.cs b/quantower/Averages/VidyaIndicator.cs index b46e29f3..a9ed10ad 100644 --- a/quantower/Averages/VidyaIndicator.cs +++ b/quantower/Averages/VidyaIndicator.cs @@ -15,7 +15,7 @@ public class VidyaIndicator : IndicatorBase public override string ShortName => $"VIDYA {Period} : {SourceName}"; - public VidyaIndicator() + public VidyaIndicator() : base() { Name = "VIDYA - Variable Index Dynamic Average"; } @@ -23,5 +23,6 @@ public class VidyaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Vidya(Period, LPeriod, Alpha); + base.InitIndicator(); } } diff --git a/quantower/Averages/WmaIndicator.cs b/quantower/Averages/WmaIndicator.cs index 3c61c8f9..5cc6a396 100644 --- a/quantower/Averages/WmaIndicator.cs +++ b/quantower/Averages/WmaIndicator.cs @@ -11,7 +11,7 @@ public class WmaIndicator : IndicatorBase public override string ShortName => $"WMA {Period} : {SourceName}"; - public WmaIndicator() + public WmaIndicator() : base() { Name = "WMA - Weighted Moving Average"; } @@ -19,5 +19,6 @@ public class WmaIndicator : IndicatorBase protected override void InitIndicator() { ma = new Wma(Period); + base.InitIndicator(); } } diff --git a/quantower/Averages/ZlemaIndicator.cs b/quantower/Averages/ZlemaIndicator.cs index fbfdaec2..c42b045c 100644 --- a/quantower/Averages/ZlemaIndicator.cs +++ b/quantower/Averages/ZlemaIndicator.cs @@ -11,13 +11,14 @@ public class ZlemaIndicator : IndicatorBase public override string ShortName => $"ZLEMA {Period} : {SourceName}"; - public ZlemaIndicator() + public ZlemaIndicator() : base() { Name = "ZLEMA - Weighted Moving Average"; } protected override void InitIndicator() { + base.InitIndicator(); ma = new Zlema(Period); } } diff --git a/quantower/Averages/_IndicatorBase.cs b/quantower/Averages/_IndicatorBase.cs index 76c628b9..043fd9e1 100644 --- a/quantower/Averages/_IndicatorBase.cs +++ b/quantower/Averages/_IndicatorBase.cs @@ -3,11 +3,10 @@ using TradingPlatform.BusinessLayer; using TradingPlatform.BusinessLayer.Chart; using System.Runtime.CompilerServices; using System.Drawing.Drawing2D; +using QuanTAlib; using System.Collections; using TradingPlatform.BusinessLayer.TimeSync; -namespace QuanTAlib; - #pragma warning disable CA1416 // Validate platform compatibility public abstract class IndicatorBase : Indicator, IWatchlistIndicator { @@ -28,7 +27,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator [InputParameter("Show cold values", sortIndex: 20)] public bool ShowColdValues { get; set; } = true; - public int MinHistoryDepths { get; set; } + public int MinHistoryDepths; // LineSeries.LineSeries(string, Color, int, LineStyle)' @@ -38,7 +37,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator int IWatchlistIndicator.MinHistoryDepths => 0; - protected IndicatorBase() + protected IndicatorBase() : base() { OnBackGround = true; SeparateWindow = false; @@ -46,9 +45,13 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator Series = new(name: $"{Name}", color: Color.Yellow, width: 2, style: LineStyle.Solid); AddLineSeries(Series); + InitIndicator(); } - protected abstract void InitIndicator(); + protected virtual void InitIndicator() + { + SourceName = GetName(Source); + } protected override void OnInit() { @@ -92,7 +95,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator { base.OnPaintChart(args); List allPoints = new List(); - if (CurrentChart == null) { return; } + if (CurrentChart == null) return; Graphics gr = args.Graphics; var mainWindow = this.CurrentChart.Windows[args.WindowIndex]; @@ -123,7 +126,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator private void DrawSmoothCombinedCurve(Graphics gr, List allPoints, int hotCount) { - if (allPoints.Count < 2) { return; } + if (allPoints.Count < 2) return; using (Pen defaultPen = new(Series!.Color, Series.Width) { DashStyle = ConvertLineStyleToDashStyle(Series.Style) }) using (Pen coldPen = new(Series!.Color, Series.Width) { DashStyle = DashStyle.Dot }) @@ -143,7 +146,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator } } } - private static DashStyle ConvertLineStyleToDashStyle(LineStyle lineStyle) + private DashStyle ConvertLineStyleToDashStyle(LineStyle lineStyle) { return lineStyle switch { @@ -154,7 +157,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator _ => DashStyle.Solid, }; } - protected static void DrawText(Graphics gr, string text, Rectangle clientRect) + protected void DrawText(Graphics gr, string text, Rectangle clientRect) { Font font = new Font("Inter", 8); SizeF textSize = gr.MeasureString(text, font); @@ -164,7 +167,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator gr.FillRectangle(SystemBrushes.ControlDarkDark, textRect); gr.DrawString(text, font, Brushes.White, new PointF(textRect.X + 6, textRect.Y + 5)); } - protected static string GetName(int pType) + protected string GetName(int pType) { return pType switch { diff --git a/quantower/Statistics/EntropyIndicator.cs b/quantower/Statistics/EntropyIndicator.cs index f56adee1..8e141c01 100644 --- a/quantower/Statistics/EntropyIndicator.cs +++ b/quantower/Statistics/EntropyIndicator.cs @@ -10,7 +10,7 @@ public class EntropyIndicator : IndicatorBase protected override AbstractBase QuanTAlib => entropy!; public override string ShortName => $"ENTROPY {Period} : {SourceName}"; - public EntropyIndicator() + public EntropyIndicator() : base() { Name = "ENTROPY - Entropy"; SeparateWindow = true; @@ -20,5 +20,6 @@ public class EntropyIndicator : IndicatorBase { entropy = new(Period); MinHistoryDepths = entropy.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/KurtosisIndicator.cs b/quantower/Statistics/KurtosisIndicator.cs index c3071712..c9d78068 100644 --- a/quantower/Statistics/KurtosisIndicator.cs +++ b/quantower/Statistics/KurtosisIndicator.cs @@ -10,7 +10,7 @@ public class KurtosisIndicator : IndicatorBase protected override AbstractBase QuanTAlib => kurtosis!; public override string ShortName => $"KURTOSIS {Period} : {SourceName}"; - public KurtosisIndicator() + public KurtosisIndicator() : base() { Name = "KURTOSIS - Relative Flatness"; SeparateWindow = true; @@ -20,5 +20,6 @@ public class KurtosisIndicator : IndicatorBase { kurtosis = new(Period); MinHistoryDepths = kurtosis.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/MaxIndicator.cs b/quantower/Statistics/MaxIndicator.cs index 994cf19c..e0b6200a 100644 --- a/quantower/Statistics/MaxIndicator.cs +++ b/quantower/Statistics/MaxIndicator.cs @@ -13,7 +13,7 @@ public class MaxIndicator : IndicatorBase protected override AbstractBase QuanTAlib => ma!; public override string ShortName => $"MAX {Period} : {Decay:F2} : {SourceName}"; - public MaxIndicator() + public MaxIndicator() : base() { Name = "MAX - Maximum value (with decay) "; } @@ -23,5 +23,6 @@ public class MaxIndicator : IndicatorBase ma = new Max(Period, Decay); MinHistoryDepths = ma.WarmupPeriod; Source = 2; + base.InitIndicator(); } } diff --git a/quantower/Statistics/MedianIndicator.cs b/quantower/Statistics/MedianIndicator.cs index ab217f49..e2e29d98 100644 --- a/quantower/Statistics/MedianIndicator.cs +++ b/quantower/Statistics/MedianIndicator.cs @@ -9,7 +9,7 @@ public class MedianIndicator : IndicatorBase private Median? med; protected override AbstractBase QuanTAlib => med!; public override string ShortName => $"MEDIAN {Period} : {SourceName}"; - public MedianIndicator() + public MedianIndicator() : base() { Name = "MEDIAN - Median historical value"; } @@ -18,5 +18,6 @@ public class MedianIndicator : IndicatorBase { med = new Median(Period); MinHistoryDepths = med.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/MinIndicator.cs b/quantower/Statistics/MinIndicator.cs index 50aaf0c6..a4c14e9e 100644 --- a/quantower/Statistics/MinIndicator.cs +++ b/quantower/Statistics/MinIndicator.cs @@ -12,7 +12,7 @@ public class MinIndicator : IndicatorBase private Min? mi; protected override AbstractBase QuanTAlib => mi!; public override string ShortName => $"MIN {Period} : {Decay:F2} : {SourceName}"; - public MinIndicator() + public MinIndicator() : base() { Name = "MIN - Minimum value (with decay)"; } @@ -22,5 +22,6 @@ public class MinIndicator : IndicatorBase mi = new Min(Period, Decay); MinHistoryDepths = mi.WarmupPeriod; Source = 3; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/ModeIndicator.cs b/quantower/Statistics/ModeIndicator.cs index 5cacbab4..c293b643 100644 --- a/quantower/Statistics/ModeIndicator.cs +++ b/quantower/Statistics/ModeIndicator.cs @@ -9,7 +9,7 @@ public class ModeIndicator : IndicatorBase private Mode? mode; protected override AbstractBase QuanTAlib => mode!; public override string ShortName => $"MODE {Period} : {SourceName}"; - public ModeIndicator() + public ModeIndicator() : base() { Name = "MODE - Most frequent historical value"; } @@ -18,5 +18,6 @@ public class ModeIndicator : IndicatorBase { mode = new Mode(Period); MinHistoryDepths = mode.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/PercentileIndicator.cs b/quantower/Statistics/PercentileIndicator.cs index 4341286c..dacb2fd9 100644 --- a/quantower/Statistics/PercentileIndicator.cs +++ b/quantower/Statistics/PercentileIndicator.cs @@ -12,7 +12,7 @@ public class PercentileIndicator : IndicatorBase protected override AbstractBase QuanTAlib => percentile!; public override string ShortName => $"PERCENTILE {Period} {Percent:F0}% : {SourceName}"; - public PercentileIndicator() + public PercentileIndicator() : base() { Name = "PERCENTILE - n-th Percentile "; SeparateWindow = false; @@ -22,6 +22,7 @@ public class PercentileIndicator : IndicatorBase { percentile = new(Period, Percent); MinHistoryDepths = percentile.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/SkewIndicator.cs b/quantower/Statistics/SkewIndicator.cs index 36ea301a..7cce283e 100644 --- a/quantower/Statistics/SkewIndicator.cs +++ b/quantower/Statistics/SkewIndicator.cs @@ -11,7 +11,7 @@ public class SkewIndicator : IndicatorBase protected override AbstractBase QuanTAlib => skew!; public override string ShortName => $"SKEW {Period} : {SourceName}"; - public SkewIndicator() + public SkewIndicator() : base() { Name = "SKEW - Skewness"; SeparateWindow = true; @@ -21,5 +21,6 @@ public class SkewIndicator : IndicatorBase { skew = new(Period); MinHistoryDepths = skew.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/StddevIndicator.cs b/quantower/Statistics/StddevIndicator.cs index 8cc639aa..6c9648c2 100644 --- a/quantower/Statistics/StddevIndicator.cs +++ b/quantower/Statistics/StddevIndicator.cs @@ -7,12 +7,12 @@ public class StddevIndicator : IndicatorBase public int Period { get; set; } = 20; [InputParameter("Population", sortIndex: 2)] - public bool IsPopulation { get; set; } + public bool IsPopulation { get; set; } = false; private Stddev? stddev; protected override AbstractBase QuanTAlib => stddev!; public override string ShortName => $"STDDEV {Period} : {SourceName}"; - public StddevIndicator() + public StddevIndicator() : base() { Name = "STDDEV - Standard Deviation"; SeparateWindow = true; @@ -22,5 +22,6 @@ public class StddevIndicator : IndicatorBase { stddev = new(Period, IsPopulation); MinHistoryDepths = stddev.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/VarianceIndictor.cs b/quantower/Statistics/VarianceIndictor.cs index 93e6ca17..de983873 100644 --- a/quantower/Statistics/VarianceIndictor.cs +++ b/quantower/Statistics/VarianceIndictor.cs @@ -7,12 +7,12 @@ public class VarianceIndicator : IndicatorBase public int Period { get; set; } = 20; [InputParameter("Population", sortIndex: 2)] - public bool IsPopulation { get; set; } + public bool IsPopulation { get; set; } = false; private Variance? variance; protected override AbstractBase QuanTAlib => variance!; public override string ShortName => $"VAR {Period} : {SourceName}"; - public VarianceIndicator() + public VarianceIndicator() : base() { Name = "VAR - Variance"; SeparateWindow = true; @@ -23,5 +23,6 @@ public class VarianceIndicator : IndicatorBase SeparateWindow = true; variance = new(Period, IsPopulation); MinHistoryDepths = variance.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/ZscoreIndicator.cs b/quantower/Statistics/ZscoreIndicator.cs index eb630db1..19727511 100644 --- a/quantower/Statistics/ZscoreIndicator.cs +++ b/quantower/Statistics/ZscoreIndicator.cs @@ -10,7 +10,7 @@ public class ZScoreIndicator : IndicatorBase protected override AbstractBase QuanTAlib => zScore!; public override string ShortName => $"ZSCORE {Period} : {SourceName}"; - public ZScoreIndicator() + public ZScoreIndicator() : base() { Name = "ZSCORE - Standard Score"; SeparateWindow = true; @@ -20,6 +20,7 @@ public class ZScoreIndicator : IndicatorBase { zScore = new(Period); MinHistoryDepths = zScore.WarmupPeriod; + base.InitIndicator(); } } \ No newline at end of file diff --git a/quantower/Statistics/_IndicatorBase.cs b/quantower/Statistics/_IndicatorBase.cs index d4d01fb7..aef60342 100644 --- a/quantower/Statistics/_IndicatorBase.cs +++ b/quantower/Statistics/_IndicatorBase.cs @@ -28,7 +28,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator [InputParameter("Show cold values", sortIndex: 20)] public bool ShowColdValues { get; set; } = true; - public int MinHistoryDepths { get; set; } + public int MinHistoryDepths; // LineSeries.LineSeries(string, Color, int, LineStyle)' @@ -38,7 +38,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator int IWatchlistIndicator.MinHistoryDepths => 0; - protected IndicatorBase() + protected IndicatorBase() : base() { OnBackGround = true; SeparateWindow = false; @@ -46,14 +46,17 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator Series = new(name: $"{Name}", color: Color.RoyalBlue, width: 2, style: LineStyle.Solid); AddLineSeries(Series); + InitIndicator(); } - protected abstract void InitIndicator(); + protected virtual void InitIndicator() + { + SourceName = GetName(Source); + } protected override void OnInit() { InitIndicator(); - SourceName = GetName(Source); base.OnInit(); } @@ -93,7 +96,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator { base.OnPaintChart(args); List allPoints = new List(); - if (CurrentChart == null) { return; } + if (CurrentChart == null) return; Graphics gr = args.Graphics; @@ -125,7 +128,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator private void DrawSmoothCombinedCurve(Graphics gr, List allPoints, int hotCount) { - if (allPoints.Count < 2) { return; } + if (allPoints.Count < 2) return; using (Pen defaultPen = new(Series!.Color, Series.Width) { DashStyle = ConvertLineStyleToDashStyle(Series.Style) }) using (Pen coldPen = new(Series!.Color, Series.Width) { DashStyle = DashStyle.Dot }) @@ -145,7 +148,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator } } } - private static DashStyle ConvertLineStyleToDashStyle(LineStyle lineStyle) + private DashStyle ConvertLineStyleToDashStyle(LineStyle lineStyle) { return lineStyle switch { @@ -156,7 +159,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator _ => DashStyle.Solid, }; } - protected static void DrawText(Graphics gr, string text, Rectangle clientRect) + protected void DrawText(Graphics gr, string text, Rectangle clientRect) { Font font = new Font("Inter", 8); SizeF textSize = gr.MeasureString(text, font); @@ -166,7 +169,7 @@ public abstract class IndicatorBase : Indicator, IWatchlistIndicator gr.FillRectangle(SystemBrushes.ControlDarkDark, textRect); gr.DrawString(text, font, Brushes.White, new PointF(textRect.X + 6, textRect.Y + 5)); } - protected static string GetName(int pType) + protected string GetName(int pType) { return pType switch {