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Miha Kralj
90d5638008
- LTMA (Linear Trend Moving Average): Introduces a predictive moving average using dual cascaded EMAs for trend estimation. - MCNMA (McNicholl EMA): Implements a zero-lag TEMA using a cascaded EMA structure for enhanced responsiveness. - NLMA (Non-Lag Moving Average): Utilizes a damped cosine kernel to achieve reduced lag in moving averages. - NMA (Natural Moving Average): Adapts smoothing based on volatility profiles using a square-root kernel. - NYQMA (Nyquist Moving Average): Applies the Nyquist-Shannon theorem to prevent aliasing in cascaded moving averages. - RAIN (Rainbow Moving Average): Combines multiple SMA layers with weighted averages for multi-scale smoothing. - TRAMA (Trend Regularity Adaptive Moving Average): Adapts smoothing based on the frequency of new highs and lows in price data.
93 lines
3.6 KiB
Markdown
93 lines
3.6 KiB
Markdown
# QSTICK: Qstick Indicator
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> "The average candlestick body reveals the market's true conviction."
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The Qstick indicator, developed by Tushar Chande, computes a moving average of the close-minus-open difference over a lookback period, quantifying whether bars are predominantly bullish or bearish. Positive values indicate closes above opens (buying pressure); negative values indicate closes below opens (selling pressure). It supports both SMA (O(N) space via ring buffer) and EMA (O(1) space) smoothing modes and requires TBar input for open/close access.
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## Historical Context
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Tushar Chande introduced Qstick as part of his candlestick quantification work in *The New Technical Trader* (1994, co-authored with Stanley Kroll). Traditional candlestick analysis relies on visual pattern recognition; Qstick reduces bar body direction and magnitude to a single continuous number suitable for systematic tracking. The indicator addresses a specific gap: close-to-close momentum indicators miss intrabar dynamics captured by the open-to-close differential. The name "Qstick" reflects the "quick stick" reading of candlestick conviction.
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## Architecture & Physics
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### 1. Body Difference
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$$d_t = \text{Close}_t - \text{Open}_t$$
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Positive $d_t$ represents a bullish bar (close above open), negative represents bearish, zero represents a doji.
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### 2. Moving Average Smoothing
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**SMA mode:** Maintains a ring buffer of $N$ differences and a running sum for O(1) incremental updates:
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$$\text{Qstick}_t = \frac{1}{N} \sum_{i=0}^{N-1} d_{t-i}$$
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**EMA mode:** Standard recursive filter with decay $\alpha = 2/(N+1)$:
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$$\text{Qstick}_t = \alpha \cdot d_t + (1 - \alpha) \cdot \text{Qstick}_{t-1}$$
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EMA mode uses O(1) space but weights recent bars more heavily than SMA.
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### 3. Complexity
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| Metric | SMA Mode | EMA Mode |
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|:-------|:---------|:---------|
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| Time | O(1) per bar | O(1) per bar |
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| Space | O(N) ring buffer | O(1) |
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| Ops | 1 add, 1 sub, 1 div | 1 sub, 1 mul, 1 FMA |
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## Mathematical Foundation
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### Parameters
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| Parameter | Type | Default | Constraint | Description |
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|:----------|:-----|:--------|:-----------|:------------|
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| period | int | 14 | > 0 | Lookback period for moving average |
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| useEma | bool | false | — | Use EMA (true) or SMA (false) |
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### Pseudo-code
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```
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QSTICK(bar, period=14, useEma=false):
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diff = bar.Close - bar.Open
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if useEma:
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// EMA mode
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alpha = 2.0 / (period + 1)
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if count == 0:
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ema_val = diff
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else:
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ema_val = FMA(alpha, diff - ema_val, ema_val) // alpha*(diff-ema)+ema
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result = ema_val
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else:
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// SMA mode with ring buffer
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if buffer is full:
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running_sum -= buffer.oldest
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buffer.add(diff)
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running_sum += diff
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result = running_sum / min(count, period)
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return result
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```
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### Zero-Crossing Interpretation
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| Condition | Meaning |
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|:----------|:--------|
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| Qstick > 0 | Closes above opens dominate (net buying pressure) |
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| Qstick < 0 | Closes below opens dominate (net selling pressure) |
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| Qstick crosses zero | Shift in intrabar momentum direction |
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| Qstick rising | Increasing bullish pressure regardless of sign |
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| Qstick falling | Increasing bearish pressure regardless of sign |
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### Scale Dependence
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Qstick values are in absolute price units, not normalized. Cross-instrument comparison requires normalization (e.g., divide by ATR or price level). Short periods (5-8) suit trading signals; longer periods (20+) suit trend identification.
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## Resources
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- Chande, T. S. & Kroll, S. (1994). *The New Technical Trader*. John Wiley and Sons.
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- Kirkpatrick, C. D. & Dahlquist, J. R. (2015). *Technical Analysis: The Complete Resource for Financial Market Technicians*. FT Press.
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