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https://github.com/mihakralj/QuanTAlib.git
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Miha Kralj
26280ce80b
- Implemented ChopIndicator for Quantower with configurable period and cold value display. - Created Chop class for calculating the Choppiness Index with detailed documentation. - Added comprehensive unit tests for Chop functionality, covering various market conditions and edge cases. - Developed markdown documentation for CHOP, detailing its historical context, mathematical foundation, and usage examples. - Established a remediation plan for channel indicators documentation, identifying gaps and prioritizing updates.
98 lines
2.9 KiB
Markdown
98 lines
2.9 KiB
Markdown
# [CODE: Full name of the indicator]
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> short witty quote or insight about the indicator
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One paragraph describing the indicator and its purpose to a trader.
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## Historical Context
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2-3 paragraphs about the origin of the indicator, who created it, and any relevant historical context. This should include the motivation behind its creation and how it fits into the broader landscape of technical analysis.
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## Architecture & Physics
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High-level description of calculation steps - both standard/naive and the optimized version. Use Mermaid diagram describing calculation pipeline if indicator is complex.
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### Calculation Step 1..n
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Mathematical formulas in LaTeX format, followed by with explanations of what each variable represents and how it contributes to the final output.
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## Performance Profile
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Describe the computational complexity of the indicator, including any optimizations that have been made. Explain if original is O(n) and how it was optimized to O(1) or O(log n) if applicable.
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### Operation Count - Single value
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| Operation | Count | Cost (cycles) | Subtotal |
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| :--- | :---: | :---: | :---: |
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| SUB (Sum - oldest) | 1 | 1 | 1 |
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| ADD (Sum + newest) | 1 | 1 | 1 |
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| DIV (Sum / N) | 1 | 15 | 15 |
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| **Total** | **3** | — | **~17 cycles** |
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### Operation Count - Batch processing
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Explain if/why vectorization accelerates calculations.
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| Operation | Scalar Ops | SIMD Ops (AVX-512) | Acceleration |
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| :--- | :---: | :---: | :---: |
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| Initial N-sum | N | N/8 | 8× |
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| Running update (per bar) | 3 | ~1 | ~3× |
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## Validation
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What are validation sources - if any. If no external sources, describe how the indicator was validated.
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| Library | Status | Notes |
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| :--- | :--- | :--- |
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| **TA-Lib** | ✅ | Matches `TA_FUNC` |
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| **Skender** | ✅ | Matches `Indicator` |
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| **Pandas-TA**| ✅ | Matches `ta.func` |
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## Usage & Pitfalls
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- List of practical tips for using the indicator effectively, including common pitfalls to avoid.
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## API
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Mermaid class diagram describing the public API of the indicator, including constructors, properties, and methods.
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```mermaid
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```
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### Class: `[ClassName]`
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| Parameter | Type | Default | Range | Description |
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| :--- | :--- | :--- | :--- | :--- |
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| `period` | `int` | `14` | `>0` | The window size for the calculation. |
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| `input` | `TValue` | — | `any` | Initial input source (optional). |
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### Properties
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- `Value` (`double`): The current value of the indicator.
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- `IsHot` (`bool`): Returns `true` if valid data is available (warmup complete).
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### Methods
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- `Calc(TValue input)`: Updates the indicator with a new data point and returns the result.
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## C# Example
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```csharp
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using QuanTAlib;
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// Initialize
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var indicator = new [ClassName](period: 14);
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// Update Loop
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foreach (var bar in quotes)
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{
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var result = indicator.Calc(bar.Close);
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// Use valid results
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if (indicator.IsHot)
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{
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Console.WriteLine($"{bar.Date}: {result.Value}");
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}
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}
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```
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