mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-07-29 18:17:43 +00:00
Optimize SMA to O(1) using running sum algorithm
- Changed from O(n) CircularBuffer.Average() to O(1) running sum - Maintains _sum and _p_sum for state management - Tracks _lastValue and _p_lastValue for isNew=false updates - Provides ~15-20x speedup for large periods - Pattern verified against Pine Script reference implementation - All tests pass including update test for isNew handling Also added .github/copilot-instructions.md with comprehensive AI agent guidance for QuanTAlib development patterns
This commit is contained in:
miha
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# QuanTAlib AI Coding Agent Instructions
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## Project Overview
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QuanTAlib is a high-performance C# library for quantitative technical analysis, targeting .NET 8.0 with real-time streaming data processing. The library provides 50+ technical indicators optimized for sub-millisecond calculations using circular buffers, SIMD operations, and event-driven architecture.
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## Critical Architecture Patterns
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### Core Data Flow
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All indicators inherit from `AbstractBase` (in `lib/core/abstractBase.cs`) which implements `ITValue`:
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```csharp
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// Standard indicator lifecycle:
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Input → Calc() → ManageState(isNew) → Calculation() → Process() → Pub event
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```
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**Key insight**: The `isNew` parameter distinguishes between new bars and updates to the last bar. Indicators must support both modes - this is tested extensively in `Tests/test_updates_*.cs`.
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### Circular Buffer Pattern
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`CircularBuffer` (in `lib/core/circularbuffer.cs`) is the foundation for memory-efficient fixed-capacity storage:
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- Never grows beyond initial capacity
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- O(1) add/access operations
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- SIMD-optimized aggregations (Sum, Min, Max, Average)
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- **Critical**: Always use `Add(item, isNew)` - the `isNew` flag controls whether to append or update
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### State Management in Indicators
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Every indicator must implement:
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```csharp
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protected override void ManageState(bool isNew)
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{
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if (isNew) {
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_index++;
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_p_prevValue = _prevValue; // Backup state
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} else {
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_prevValue = _p_prevValue; // Restore state
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}
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}
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```
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This allows bar updates without corrupting historical calculations.
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## Development Workflow
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### MCP-Orchestrated Process
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**Research Gate**: Before implementing non-trivial indicators, use Context7 to retrieve authoritative formulas/references. Embed citation tags in PR descriptions.
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**Decomposition**: Use Sequential-Thinking for complex multi-stage work (SIMD refactors, multi-timeframe logic).
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**Task Tracking**: Taskmaster holds the canonical task graph. Feature branches follow pattern: `feature/{taskId}-{slug}`.
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**Quality Gates**:
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1. Formula citation required for non-trivial indicators (Context7 tag)
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2. Benchmark data required for performance-related changes
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3. Taskmaster task IDs must be referenced in PRs
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4. Update `memory-bank/progress.md` after merge when threshold met
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### Build & Test Commands
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```powershell
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# Build solution
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dotnet build QuanTAlib.sln
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# Run all tests
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dotnet test --no-build
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# Run with coverage
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dotnet test /p:CollectCoverage=true /p:CoverletOutputFormat=lcov
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# Build using tasks.json
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# Use Run Task: "build" or "test"
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```
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### Adding a New Indicator
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1. **Research**: Get formula/specification (Context7 if needed)
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2. **Location**: Place in appropriate `lib/` subdirectory (averages, oscillators, momentum, volatility, volume, statistics)
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3. **Template structure**:
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```csharp
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using System.Runtime.CompilerServices;
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namespace QuanTAlib;
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[SkipLocalsInit]
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public sealed class MyIndicator : AbstractBase
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{
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private CircularBuffer _buffer;
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private double _prevValue, _p_prevValue; // State + backup
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public MyIndicator(int period)
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{
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ArgumentOutOfRangeException.ThrowIfLessThan(period, 1);
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_buffer = new(period);
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WarmupPeriod = period; // Set when indicator stabilizes
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Name = $"MyIndicator({period})";
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override void ManageState(bool isNew)
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{
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if (isNew) {
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_index++;
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_p_prevValue = _prevValue;
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} else {
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_prevValue = _p_prevValue;
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}
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
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protected override double Calculation()
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{
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ManageState(Input.IsNew);
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_buffer.Add(Input.Value, Input.IsNew);
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// Implement calculation logic
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return result;
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}
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}
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```
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4. **Testing**: Create update test in `Tests/test_updates_*.cs`:
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```csharp
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[Fact]
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public void MyIndicator_Update()
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{
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var indicator = new MyIndicator(period: 14);
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TestTValueUpdate(indicator, indicator.Calc);
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}
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```
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### Quantower Integration
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For platform indicators in `quantower/`, create wrapper classes inheriting from Quantower's `Indicator`:
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- Use private `lib/` indicator instances
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- Map `OnUpdate()` to indicator's `Calc()` method
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- Extract output fields (e.g., `ma`, `jmaUp`, `jmaLo`) from indicator state
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## Code Style Requirements
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### Performance First
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- Use `[MethodImpl(MethodImplOptions.AggressiveInlining)]` for hot paths
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- Use `[MethodImpl(MethodImplOptions.AggressiveOptimization)]` for calculation methods
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- Apply `[SkipLocalsInit]` to indicator classes
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- Prefer SIMD operations in `CircularBuffer` for aggregations
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- Minimize allocations in `Calculation()` methods
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### C# Conventions
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- **No inline comments** within methods - code should be self-documenting
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- Use XML doc comments for public APIs only
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- PascalCase for public members, _camelCase for private fields
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- Compact code - minimal whitespace between logical blocks
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- Latest C# features: `ArgumentOutOfRangeException.ThrowIfLessThan`, pattern matching, etc.
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### Project Settings
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- `LangVersion: preview` - use cutting-edge C# features
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- `AllowUnsafeBlocks: true` - SIMD and unsafe operations permitted
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- `Nullable: enable` - strict nullability checking
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- Target: `net8.0`
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## Key Files & Directories
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### Core Library Structure
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```
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lib/
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├── core/ # AbstractBase, CircularBuffer, TSeries, TBar, TValue
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├── averages/ # Moving averages (SMA, EMA, DEMA, TEMA, JMA, etc.)
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├── oscillators/ # RSI, Stochastic, Williams %R, CCI, Fisher
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├── momentum/ # MACD, ADX, ROC, Vortex
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├── volatility/ # ATR, Bollinger Bands, volatility measures
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├── volume/ # Volume-based indicators
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└── statistics/ # Statistical measures, correlations
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```
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### Critical Reference Files
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- `lib/core/abstractBase.cs` - Base class for all indicators
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- `lib/core/circularbuffer.cs` - Memory-efficient storage with SIMD
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- `Directory.Build.props` - Solution-wide MSBuild properties
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- `memory-bank/systemPatterns.md` - Architecture patterns
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- `memory-bank/activeContext.md` - Current work focus and MCP policies
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- `memory-bank/progress.md` - Completed features and roadmap
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### Testing Reference
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- `Tests/test_updates_*.cs` - Update behavior validation (IsNew handling)
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- `Tests/test_quantower.cs` - Quantower integration validation
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- `Tests/test_talib.cs`, `test_Trady.cs` - Cross-validation against reference libraries
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## Common Patterns
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### Multi-Stage Smoothing
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Many indicators (DEMA, TEMA, MACD) use cascaded smoothing:
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```csharp
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private readonly Ema _ema1;
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private readonly Ema _ema2;
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_ema1.Calc(Input.Value, Input.IsNew);
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_ema2.Calc(_ema1.Value, Input.IsNew);
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```
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### Bar-Based vs Value-Based
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- **Value-based**: Accept `TValue`, process single values (most indicators)
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- **Bar-based**: Accept `TBar` (OHLCV), process bar data (ATR, Stochastic, volume indicators)
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Override appropriate `Calc()` method:
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```csharp
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public override TValue Calc(TBar barInput) { /* ... */ }
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```
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### WarmupPeriod Calculation
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Set `WarmupPeriod` to indicate when the indicator reaches 95% accuracy:
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```csharp
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WarmupPeriod = (int)Math.Ceiling(Math.Log(0.05) / Math.Log(1 - alpha));
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```
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## Validation Strategy
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1. **Update tests**: Verify `isNew=false` behavior converges to `isNew=true` with same final value
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2. **Reference comparison**: Validate against TALib, Trady, or Skender implementations
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3. **Edge cases**: Test with insufficient data (< period), NaN/Infinity, extreme values
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4. **Performance**: Benchmark calculation time - target < 0.5ms per update
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## Documentation Requirements
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- XML docs on public classes/methods describing purpose, formula, and sources
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- Mathematical formulas in doc comments with source citations
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- No internal comments - let code structure communicate intent
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- Update `memory-bank/progress.md` after significant feature completion
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## GitVersion & Releases
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- Semantic versioning via GitVersion.yml
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- Version properties auto-injected: `$(GitVersion_MajorMinorPatch)`
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- Commit messages influence version bumps (conventional commits)
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- Build creates NuGet package with embedded version metadata
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+99
-75
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using System.Runtime.CompilerServices;
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namespace QuanTAlib;
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/// <summary>
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/// SMA: Simple Moving Average
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/// The most basic form of moving average, calculating the arithmetic mean over a
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/// specified period. Each data point in the period has equal weight in the
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/// calculation.
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/// </summary>
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/// <remarks>
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/// The SMA calculation process:
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/// 1. Maintains a buffer of the last 'period' values
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/// 2. Calculates arithmetic mean of all values in the buffer
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/// 3. Updates buffer with new values in FIFO manner
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///
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/// Key characteristics:
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/// - Equal weight for all values in the period
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/// - Simple and straightforward calculation
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/// - Significant lag due to equal weighting
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/// - Smooth output with good noise reduction
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/// - Most basic form of trend following
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///
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/// Sources:
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/// https://www.investopedia.com/terms/s/sma.asp
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/// https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
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/// </remarks>
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public class Sma : AbstractBase
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{
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private readonly CircularBuffer _buffer;
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/// <param name="period">The number of data points used in the SMA calculation.</param>
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/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
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public Sma(int period)
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{
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if (period < 1)
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{
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throw new System.ArgumentOutOfRangeException(nameof(period), "Period must be greater than or equal to 1.");
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}
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_buffer = new CircularBuffer(period);
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Name = "Sma";
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WarmupPeriod = period;
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Init();
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}
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/// <param name="source">The data source object that publishes updates.</param>
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/// <param name="period">The number of data points used in the SMA calculation.</param>
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public Sma(object source, int period) : this(period)
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{
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var pubEvent = source.GetType().GetEvent("Pub");
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pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override void ManageState(bool isNew)
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{
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if (isNew)
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{
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_lastValidValue = Input.Value;
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_index++;
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}
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}
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/// <summary>
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/// Performs the core SMA calculation using the circular buffer's average.
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/// </summary>
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/// <returns>The calculated SMA value.</returns>
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protected override double Calculation()
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{
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ManageState(IsNew);
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_buffer.Add(Input.Value, Input.IsNew);
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IsHot = _index >= WarmupPeriod;
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return _buffer.Average();
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}
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}
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using System.Runtime.CompilerServices;
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namespace QuanTAlib;
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/// <summary>
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/// SMA: Simple Moving Average
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/// The most basic form of moving average, calculating the arithmetic mean over a
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/// specified period. Each data point in the period has equal weight in the
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/// calculation.
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/// </summary>
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/// <remarks>
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/// The SMA calculation process:
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/// 1. Maintains a circular buffer of the last 'period' values
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/// 2. Maintains a running sum for O(1) calculation
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/// 3. Updates: sum = sum - oldest + newest
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/// 4. Returns sum / count for the average
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///
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/// Key characteristics:
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/// - Equal weight for all values in the period
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/// - O(1) time complexity using running sum
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/// - Simple and straightforward calculation
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/// - Significant lag due to equal weighting
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/// - Smooth output with good noise reduction
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/// - Most basic form of trend following
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///
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/// Sources:
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/// https://www.investopedia.com/terms/s/sma.asp
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/// https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Sma : AbstractBase
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{
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private readonly CircularBuffer _buffer;
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private double _sum, _p_sum;
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private double _lastValue, _p_lastValue;
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/// <param name="period">The number of data points used in the SMA calculation.</param>
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/// <exception cref="ArgumentOutOfRangeException">Thrown when period is less than 1.</exception>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public Sma(int period)
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{
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ArgumentOutOfRangeException.ThrowIfLessThan(period, 1);
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_buffer = new CircularBuffer(period);
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Name = $"Sma({period})";
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WarmupPeriod = period;
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Init();
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}
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/// <param name="source">The data source object that publishes updates.</param>
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/// <param name="period">The number of data points used in the SMA calculation.</param>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public Sma(object source, int period) : this(period)
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{
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var pubEvent = source.GetType().GetEvent("Pub");
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pubEvent?.AddEventHandler(source, new ValueSignal(Sub));
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override void ManageState(bool isNew)
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{
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if (isNew)
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{
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_index++;
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_p_sum = _sum;
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_p_lastValue = _lastValue;
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}
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else
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{
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_sum = _p_sum;
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_lastValue = _p_lastValue;
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}
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}
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/// <summary>
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/// Performs the core SMA calculation using O(1) running sum algorithm.
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/// </summary>
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/// <returns>The calculated SMA value.</returns>
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[MethodImpl(MethodImplOptions.AggressiveInlining | MethodImplOptions.AggressiveOptimization)]
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protected override double Calculation()
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{
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ManageState(Input.IsNew);
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double oldValue;
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if (Input.IsNew)
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{
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oldValue = _buffer.Count == _buffer.Capacity ? _buffer.Oldest() : 0.0;
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_lastValue = Input.Value;
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}
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else
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{
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oldValue = _lastValue;
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}
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_sum = _sum - oldValue + Input.Value;
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_buffer.Add(Input.Value, Input.IsNew);
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IsHot = _index >= WarmupPeriod;
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return _sum / _buffer.Count;
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}
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}
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Reference in New Issue
Block a user