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docs: remove C# Implementation Considerations sections, clean up temp scripts, reorganize test files
- Remove 'C# Implementation Considerations' sections from 34 indicator .md files - Delete 29 temp PowerShell scripts (_fix_mojibake.ps1, _hex_scan.ps1, etc.) - Move test files into tests/ subdirectories for consistent project structure - Add trader-focused bullet points to indicator documentation
This commit is contained in:
Miha Kralj
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using TradingPlatform.BusinessLayer;
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using QuanTAlib;
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namespace QuanTAlib.Tests;
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public class BlmaIndicatorTests
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{
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[Fact]
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public void BlmaIndicator_Constructor_SetsDefaults()
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{
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var indicator = new BlmaIndicator();
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Assert.Equal(14, indicator.Period);
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Assert.True(indicator.ShowColdValues);
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Assert.Equal("BLMA - Blackman Window Moving Average", indicator.Name);
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Assert.False(indicator.SeparateWindow);
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Assert.Equal(SourceType.Close, indicator.Source);
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}
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[Fact]
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public void BlmaIndicator_MinHistoryDepths_EqualsPeriod()
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{
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var indicator = new BlmaIndicator { Period = 20 };
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Assert.Equal(0, BlmaIndicator.MinHistoryDepths);
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IWatchlistIndicator watchlistIndicator = indicator;
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Assert.Equal(0, watchlistIndicator.MinHistoryDepths);
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}
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[Fact]
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public void BlmaIndicator_ShortName_IncludesParameters()
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{
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var indicator = new BlmaIndicator { Period = 20 };
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indicator.Initialize();
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Assert.Contains("BLMA", indicator.ShortName, StringComparison.Ordinal);
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Assert.Contains("20", indicator.ShortName, StringComparison.Ordinal);
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}
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[Fact]
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public void BlmaIndicator_SourceCodeLink_IsValid()
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{
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var indicator = new BlmaIndicator();
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Assert.Contains("github.com", indicator.SourceCodeLink, StringComparison.Ordinal);
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Assert.Contains("Blma.Quantower.cs", indicator.SourceCodeLink, StringComparison.Ordinal);
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}
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[Fact]
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public void BlmaIndicator_Initialize_CreatesInternalBlma()
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{
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var indicator = new BlmaIndicator { Period = 14 };
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// Initialize should not throw
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indicator.Initialize();
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// After init, line series should exist
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Assert.Single(indicator.LinesSeries);
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}
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[Fact]
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public void BlmaIndicator_ProcessUpdate_HistoricalBar_ComputesValue()
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{
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var indicator = new BlmaIndicator { Period = 5 };
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indicator.Initialize();
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// Add historical data
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var now = DateTime.UtcNow;
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// Need enough bars for Period
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for (int i = 0; i < 20; i++)
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{
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indicator.HistoricalData.AddBar(now.AddMinutes(i), 100 + i, 110 + i, 90 + i, 105 + i);
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// Process update for each bar to simulate history loading
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var args = new UpdateArgs(UpdateReason.HistoricalBar);
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indicator.ProcessUpdate(args);
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}
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// Line series should have a value
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double blma = indicator.LinesSeries[0].GetValue(0);
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Assert.True(double.IsFinite(blma));
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}
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}
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namespace QuanTAlib.Tests;
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public class BlmaTests
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{
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private readonly GBM _gbm;
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public BlmaTests()
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{
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_gbm = new GBM();
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}
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[Fact]
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public void Constructor_ValidatesInput()
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{
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Assert.Throws<ArgumentOutOfRangeException>(() => new Blma(0));
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Assert.Throws<ArgumentOutOfRangeException>(() => new Blma(-1));
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}
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[Fact]
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public void Constructor_ValidatesSource()
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{
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Assert.Throws<ArgumentNullException>(() => new Blma(null!, 10));
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}
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[Fact]
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public void BasicCalculation_MatchesManual()
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{
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var blma = new Blma(3);
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var input = new[] { 10.0, 20.0, 30.0 };
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// Bar 1: Count=1. Weights for n=1: [1]. Result = 10.
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var r1 = blma.Update(new TValue(DateTime.UtcNow, input[0]));
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Assert.Equal(10.0, r1.Value);
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// Bar 2: Count=2. Weights for n=2 sum to 0. Fallback to average: (10+20)/2 = 15.
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var r2 = blma.Update(new TValue(DateTime.UtcNow, input[1]));
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Assert.Equal(15.0, r2.Value);
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// Bar 3: Count=3. Weights [0, 1, 0]. Sum=1. Result=20.
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var r3 = blma.Update(new TValue(DateTime.UtcNow, input[2]));
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Assert.Equal(20.0, r3.Value, 1e-6);
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}
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[Fact]
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public void AllModes_ProduceSameResult()
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{
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const int period = 10;
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var bars = _gbm.Fetch(100, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var series = bars.Close;
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// 1. Batch Mode
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var batchSeries = new Blma(period).Update(series);
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double expected = batchSeries.Last.Value;
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// 2. Span Mode
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var tValues = series.Values.ToArray();
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var spanInput = new ReadOnlySpan<double>(tValues);
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var spanOutput = new double[tValues.Length];
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Blma.Batch(spanInput, spanOutput, period);
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double spanResult = spanOutput[^1];
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// 3. Streaming Mode
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var streamingInd = new Blma(period);
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for (int i = 0; i < series.Count; i++)
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{
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streamingInd.Update(series[i]);
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}
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double streamingResult = streamingInd.Last.Value;
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// Assert
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Assert.Equal(expected, spanResult, 1e-9);
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Assert.Equal(expected, streamingResult, 1e-9);
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}
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[Fact]
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public void NaN_Handling()
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{
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var blma = new Blma(5);
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blma.Update(new TValue(DateTime.UtcNow, 10));
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blma.Update(new TValue(DateTime.UtcNow, 20));
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// For N=2, weights sum to 0. Fallback to average: (10+20)/2 = 15.
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var result = blma.Update(new TValue(DateTime.UtcNow, double.NaN));
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Assert.Equal(15.0, result.Value); // Should return last valid value
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Assert.Equal(15.0, blma.Last.Value); // Should retain last valid value
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}
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[Fact]
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public void IsNew_Behavior()
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{
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var blma = new Blma(3);
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// Bar 1
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blma.Update(new TValue(DateTime.UtcNow, 10), isNew: true);
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// Bar 2
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blma.Update(new TValue(DateTime.UtcNow, 20), isNew: true);
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// Bar 3 (Update)
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blma.Update(new TValue(DateTime.UtcNow, 30), isNew: true);
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var val1 = blma.Last.Value;
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// Bar 3 (Correction)
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blma.Update(new TValue(DateTime.UtcNow, 40), isNew: false);
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var val2 = blma.Last.Value;
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// For Blackman window, the newest value (index N-1) has weight 0.
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// So changing the newest value does NOT change the current result.
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Assert.Equal(val1, val2);
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// However, the internal buffer MUST be updated.
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// Case A: Bar 3 = 40 (current state)
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blma.Update(new TValue(DateTime.UtcNow, 100), isNew: true);
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var valWith40 = blma.Last.Value;
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// Case B: Reconstruct scenario with Bar 3 = 30
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var blma2 = new Blma(3);
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blma2.Update(new TValue(DateTime.UtcNow, 10), isNew: true);
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blma2.Update(new TValue(DateTime.UtcNow, 20), isNew: true);
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blma2.Update(new TValue(DateTime.UtcNow, 30), isNew: true);
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blma2.Update(new TValue(DateTime.UtcNow, 100), isNew: true);
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var valWith30 = blma2.Last.Value;
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Assert.NotEqual(valWith30, valWith40);
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}
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[Fact]
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public void Prime_PreservesTimestamps()
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{
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var blma = new Blma(5);
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double[] input = [1, 2, 3, 4, 5];
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var timestamps = new List<DateTime>();
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blma.Pub += (object? sender, in TValueEventArgs args) => timestamps.Add(args.Value.AsDateTime);
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blma.Prime(input);
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Assert.Equal(input.Length, timestamps.Count);
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// Verify timestamps are unique and increasing
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for (int i = 1; i < timestamps.Count; i++)
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{
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Assert.True(timestamps[i] > timestamps[i - 1], $"Timestamp at {i} ({timestamps[i].Ticks}) should be greater than {i - 1} ({timestamps[i - 1].Ticks})");
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}
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}
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[Fact]
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public void Prime_Overload_UsesProvidedTimestamps()
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{
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var blma = new Blma(5);
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var now = DateTime.UtcNow;
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TValue[] input =
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[
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new(now, 1),
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new(now.AddMinutes(1), 2),
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new(now.AddMinutes(2), 3)
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];
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var timestamps = new List<DateTime>();
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blma.Pub += (object? sender, in TValueEventArgs args) => timestamps.Add(args.Value.AsDateTime);
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blma.Prime(input);
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Assert.Equal(input.Length, timestamps.Count);
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Assert.Equal(input[0].AsDateTime, timestamps[0]);
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Assert.Equal(input[1].AsDateTime, timestamps[1]);
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Assert.Equal(input[2].AsDateTime, timestamps[2]);
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}
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}
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@@ -0,0 +1,130 @@
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namespace QuanTAlib.Tests;
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public class BlmaValidationTests
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{
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private readonly GBM _gbm;
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public BlmaValidationTests()
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{
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_gbm = new GBM();
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}
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[Fact]
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public void ValidateAgainstReferenceImplementation()
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{
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// Generate test data
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var bars = _gbm.Fetch(1000, DateTime.UtcNow.Ticks, TimeSpan.FromMinutes(1));
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var series = bars.Close;
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const int period = 14;
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// 1. QuanTAlib Implementation
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var blma = new Blma(period);
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var quantalibResult = new List<double>();
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foreach (var item in series)
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{
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quantalibResult.Add(blma.Update(item).Value);
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}
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// 2. Reference Implementation (PineScript logic)
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var referenceResult = CalculateReference(series, period);
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// Compare
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Assert.Equal(quantalibResult.Count, referenceResult.Count);
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for (int i = 0; i < quantalibResult.Count; i++)
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{
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// Allow small difference due to float precision
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Assert.Equal(referenceResult[i], quantalibResult[i], 1e-9);
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}
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}
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private static List<double> CalculateReference(TSeries source, int period)
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{
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var result = new List<double>();
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var buffer = new List<double>();
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for (int i = 0; i < source.Count; i++)
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{
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buffer.Add(source[i].Value);
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// PineScript logic:
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// int p = math.min(bar_index + 1, period)
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int p = Math.Min(buffer.Count, period);
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// Calculate weights
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var weights = new double[p];
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double totalWeight = 0;
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if (p == 1)
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{
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weights[0] = 1.0;
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totalWeight = 1.0;
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}
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else
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{
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double invPMinus1 = 1.0 / (p - 1);
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double pi2 = 2.0 * Math.PI;
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double pi4 = 4.0 * Math.PI;
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double a0 = 0.42;
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double a1 = 0.5;
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double a2 = 0.08;
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for (int j = 0; j < p; j++)
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{
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double ratio = j * invPMinus1;
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double w = a0 - (a1 * Math.Cos(pi2 * ratio)) + (a2 * Math.Cos(pi4 * ratio));
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weights[j] = w;
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totalWeight += w;
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}
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}
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// Calculate weighted sum
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double sum = 0;
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// PineScript: for i = 0 to p - 1
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// float price = source[i] (where source[0] is newest)
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// float w = array.get(weights, i)
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// So weights[0] * newest, weights[1] * 2nd newest...
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// My C# buffer is chronological (0 is oldest).
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// So buffer[buffer.Count - 1] is newest.
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// buffer[buffer.Count - 1 - j] is j-th lag.
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// Wait, in Blma.cs I implemented:
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// sum += buffer[i] * weights[i] (where buffer[0] is oldest)
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// So weights[0] * oldest.
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// PineScript: weights[0] * newest.
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// Since Blackman window is symmetric, weights[0] == weights[p-1].
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// So weights[0] * newest == weights[p-1] * newest (if symmetric).
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// But weights[0] is 0. weights[p-1] is 0.
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// weights[p/2] is peak.
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// So symmetric window applied forward or backward is the same.
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// Let's verify symmetry.
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// w(j) vs w(p-1-j).
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// ratio(j) = j/(p-1).
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// ratio(p-1-j) = (p-1-j)/(p-1) = 1 - j/(p-1) = 1 - ratio(j).
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// cos(2pi * (1-r)) = cos(2pi - 2pi*r) = cos(-2pi*r) = cos(2pi*r).
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// cos(4pi * (1-r)) = cos(4pi - 4pi*r) = cos(4pi*r).
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// So yes, w(j) == w(p-1-j).
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// So applying weights[0] to newest or oldest doesn't matter for the sum.
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// However, I should match my implementation in Blma.cs.
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// In Blma.cs: sum += buffer[i] * weights[i] (buffer[0] is oldest).
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// So weights[0] * oldest.
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// In this reference implementation, let's do the same.
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// Use the last p elements of buffer.
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int start = buffer.Count - p;
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for (int j = 0; j < p; j++)
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{
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// buffer[start + j] is the value.
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// weights[j] is the weight.
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sum += buffer[start + j] * weights[j];
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}
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result.Add(sum / totalWeight);
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}
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return result;
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}
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}
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Block a user