mirror of
https://github.com/mihakralj/QuanTAlib.git
synced 2026-08-15 00:58:04 +00:00
New indicators: - HWC (Holt-Winters Channel) — channels, 27 tests - VWMACD (Volume-Weighted MACD) — momentum, 38 tests - Squeeze Pro — oscillators, 69 tests - BW_MFI (Bill Williams MFI) — oscillators - DSTOCH (Double Stochastic) — oscillators - ATRSTOP (ATR Trailing Stop) — reversals - VSTOP (Volatility Stop) — reversals - Convexity (Beta Convexity) — statistics, 23 tests Integration: - Python bridge: Exports.cs, _bridge.py, wrapper modules - Documentation: _sidebar.md, _index.md pages, SPEC.md - All analyzer warnings fixed (MA0074, xUnit2013, S2699) Build: 0 warnings, 0 errors | Tests: 15,933 passed, 0 failed
124 lines
3.6 KiB
Markdown
124 lines
3.6 KiB
Markdown
# DSTOCH — Double Stochastic (Bressert DSS)
|
||
|
||
## Overview
|
||
|
||
**DSTOCH** (Double Stochastic / DSS Bressert) applies the Stochastic oscillator formula twice with EMA smoothing between stages, producing a momentum indicator bounded between 0 and 100. Developed by Walter Bressert, it is more responsive than standard Stochastic while remaining bounded.
|
||
|
||
| Property | Value |
|
||
| :--------- | :-------------- |
|
||
| Category | Oscillator |
|
||
| Output | Single (DSS) |
|
||
| Range | [0, 100] |
|
||
| Default | period = 21 |
|
||
| Input | TBar (HLC) |
|
||
| Hot after | period bars |
|
||
|
||
**Source:** [Dstoch.cs](Dstoch.cs) · [PineScript](dstoch.pine)
|
||
|
||
---
|
||
|
||
## Formula
|
||
|
||
### Stage 1: Raw %K
|
||
|
||
$$
|
||
\text{rawK}_t = \begin{cases}
|
||
100 \cdot \frac{C_t - LL_t}{HH_t - LL_t} & \text{if } HH_t \neq LL_t \\
|
||
0 & \text{otherwise}
|
||
\end{cases}
|
||
$$
|
||
|
||
where $HH_t$ and $LL_t$ are the highest high and lowest low over the last $n$ bars.
|
||
|
||
### Stage 1: EMA Smoothing
|
||
|
||
$$
|
||
\text{smoothK}_t = \alpha \cdot \text{rawK}_t + (1 - \alpha) \cdot \text{smoothK}_{t-1}
|
||
$$
|
||
|
||
where $\alpha = \frac{2}{n + 1}$.
|
||
|
||
### Stage 2: Stochastic of smoothK
|
||
|
||
$$
|
||
\text{dsRaw}_t = \begin{cases}
|
||
100 \cdot \frac{\text{smoothK}_t - \min(\text{smoothK}, n)}{\max(\text{smoothK}, n) - \min(\text{smoothK}, n)} & \text{if range} > 0 \\
|
||
0 & \text{otherwise}
|
||
\end{cases}
|
||
$$
|
||
|
||
### Stage 2: EMA Smoothing (Final Output)
|
||
|
||
$$
|
||
\text{DSS}_t = \alpha \cdot \text{dsRaw}_t + (1 - \alpha) \cdot \text{DSS}_{t-1}
|
||
$$
|
||
|
||
---
|
||
|
||
## Interpretation
|
||
|
||
| Zone | Meaning |
|
||
| :-------- | :------------------------------------- |
|
||
| DSS > 80 | Overbought — potential bearish reversal|
|
||
| DSS < 20 | Oversold — potential bullish reversal |
|
||
| Cross 50↑ | Bullish momentum shift |
|
||
| Cross 50↓ | Bearish momentum shift |
|
||
|
||
The double application of the Stochastic formula makes DSTOCH more sensitive to short-term price changes than the standard Stochastic oscillator.
|
||
|
||
---
|
||
|
||
## Implementation Details
|
||
|
||
### 1. MonotonicDeque Streaming (Stage 1)
|
||
|
||
Two `MonotonicDeque` instances provide O(1) amortized min/max tracking for HH/LL:
|
||
|
||
- **Max deque**: decreasing order of highs; front is always the window maximum.
|
||
- **Min deque**: increasing order of lows; front is always the window minimum.
|
||
- **Circular buffers** (`_hBuf`, `_lBuf`): store raw H/L values for deque rebuild on bar correction.
|
||
|
||
### 2. MonotonicDeque Streaming (Stage 2)
|
||
|
||
A second pair of `MonotonicDeque` instances tracks `smoothK` values:
|
||
|
||
- **`_skMaxDeque`**: highest smoothK over the window.
|
||
- **`_skMinDeque`**: lowest smoothK over the window.
|
||
- **`_skBuf`**: circular buffer for smoothK values.
|
||
|
||
### 3. EMA Smoothing
|
||
|
||
Both EMA stages use `Math.FusedMultiplyAdd` for optimal precision:
|
||
|
||
```csharp
|
||
smoothK = Math.FusedMultiplyAdd(prev_smoothK, decay, alpha * rawK);
|
||
```
|
||
|
||
### 4. Bar Correction
|
||
|
||
On `isNew=false`, all four deques are rebuilt from their circular buffers via `RebuildMax`/`RebuildMin`, and the scalar state is restored from `_ps`.
|
||
|
||
### 5. Batch Path
|
||
|
||
The batch implementation uses `Highest.Batch` / `Lowest.Batch` for both stages, with `stackalloc` for ≤ 256 elements and `ArrayPool` beyond.
|
||
|
||
---
|
||
|
||
## Complexity Analysis
|
||
|
||
| Operation | Complexity |
|
||
| :--------------------- | :------------- |
|
||
| Per-update (amortized) | O(1) |
|
||
| Per-update (worst) | O(n) |
|
||
| Bar correction | O(n) × 4 deques|
|
||
| Batch (N bars) | O(N) |
|
||
| Memory (streaming) | O(n) × 3 buffers + 4 deques |
|
||
|
||
---
|
||
|
||
## References
|
||
|
||
- Bressert, W. (1998). *The Power of Oscillator/Cycle Combinations*
|
||
- TradingView: DSS Bressert indicator
|
||
- Investopedia: Double Smoothed Stochastic
|