QuanTAlib/lib/oscillators/crsi/Crsi.md

CRSI: Connors RSI

Connors RSI blends classic RSI with streak length and percentile rank, creating a multi-dimensional momentum snapshot.

Property	Value
Category	Oscillator
Inputs	Source (close)
Parameters	rsiPeriod (default 3), streakPeriod (default 2), rankPeriod (default 100)
Outputs	Single series (Crsi)
Output range	Varies (see docs)
Warmup	1 bar
PineScript	crsi.pine

• Connors RSI is a composite momentum oscillator that combines three independent measurements of price behavior into a single bounded (0-100) output:...
• Similar: RSI, StochRSI | Complementary: Volume | Trading note: Connors RSI; combines RSI, streak RSI, and percentile rank. Short-term mean-reversion signal.
• Validated against TA-Lib, Skender, and Tulip reference implementations where available.

Connors RSI is a composite momentum oscillator that combines three independent measurements of price behavior into a single bounded (0-100) output: a short-term RSI of price, an RSI of the consecutive up/down streak length, and a percentile rank of the current rate of change within its recent history. The equal-weighted average of these three components produces a mean-reverting oscillator where extreme readings (above 90 or below 10) identify statistically overbought or oversold conditions with higher reliability than single-component RSI alone.

Historical Context

Larry Connors and Cesar Alvarez introduced Connors RSI in their 2012 publication, building on Connors' earlier research into short-term mean reversion strategies. The indicator addressed a recognized weakness of standard RSI: its tendency to remain in overbought or oversold territory during strong trends without providing actionable reversal signals. By combining three orthogonal measurements of price behavior, each capturing a different aspect of momentum, CRSI reduces the false signal rate inherent in any single oscillator. The streak RSI component was particularly novel, converting the categorical information of consecutive up/down days into a continuous oscillator via a second RSI application. The percent rank component adds a non-parametric statistical dimension that is robust to distribution assumptions. Connors' backtesting showed the composite outperformed standard RSI for mean-reversion entry timing on equity indices and ETFs.

Architecture & Physics

Three-Component Pipeline

CRSI combines three independent calculations with equal weighting:

1. Price RSI (Component 1): Standard Wilder RSI with exponential smoothing (\alpha = 1/\text{rsiPeriod}) applied to the source series. Uses warmup compensation via the decaying exponential e = \beta^n to correct for initial bias, producing valid output from bar 1.

2. Streak RSI (Component 2): First computes a consecutive streak counter (positive for up-closes, negative for down-closes, zero for unchanged), then applies the same Wilder RSI to the streak series. This converts run-length information into a bounded oscillator.

3. Percent Rank (Component 3): Computes 1-bar ROC, stores in a circular buffer, then counts what percentage of historical ROC values are less than or equal to the current ROC. This is a non-parametric ranking that is distribution-free.

Warmup Compensation

Both RSI stages use the "section 2" warmup pattern: track e = \beta^n and apply correction factor c = 1/(1 - e) to the raw exponential averages until e drops below 10^{-10}. This eliminates the startup bias that plagues naive EMA initialization.

Final Composition

The three components are averaged and clamped to [0, 100]:

\text{CRSI} = \text{clamp}\!\left(\frac{\text{PriceRSI} + \text{StreakRSI} + \text{PctRank}}{3}, 0, 100\right)

Mathematical Foundation

Component 1: Price RSI with Wilder smoothing (\alpha = 1/p_1):

\overline{G}_t = \alpha \cdot \max(\Delta x_t, 0) + (1-\alpha) \cdot \overline{G}_{t-1} \overline{L}_t = \alpha \cdot \max(-\Delta x_t, 0) + (1-\alpha) \cdot \overline{L}_{t-1} RSI_1 = \frac{100 \cdot \overline{G}_t}{\overline{G}_t + \overline{L}_t}

Component 2: Streak counter then RSI:

\text{streak}_t = \begin{cases} \text{streak}_{t-1} + 1 & \text{if } x_t > x_{t-1} \text{ and streak}_{t-1} \geq 0 \\ 1 & \text{if } x_t > x_{t-1} \text{ and streak}_{t-1} < 0 \\ \text{streak}_{t-1} - 1 & \text{if } x_t < x_{t-1} \text{ and streak}_{t-1} \leq 0 \\ -1 & \text{if } x_t < x_{t-1} \text{ and streak}_{t-1} > 0 \\ 0 & \text{otherwise} \end{cases} RSI_2 = \text{Wilder\_RSI}(\text{streak}_t, p_2)

Component 3: Percent Rank of 1-bar ROC over window p_3:

ROC_t = \frac{x_t - x_{t-1}}{x_{t-1}} \times 100 PctRank_t = \frac{|\{ROC_i : ROC_i \leq ROC_t,\; i \in \text{window}\}|}{|\text{window}|} \times 100

Composite:

CRSI_t = \frac{RSI_1 + RSI_2 + PctRank}{3}

Default parameters: rsiPeriod = 3, streakPeriod = 2, rankPeriod = 100.

Performance Profile

Operation Count (Streaming Mode)

ConnorsRSI = average of RSI(3), StreakRSI(2), PercentRank(100). Three sub-indicators.

Operation	Count	Cost (cycles)	Subtotal
RSI(3) update (2 EMA + ratio)	6	4	24
Streak count (up/down/flat)	2	1	2
StreakRSI(2) update (2 EMA + ratio)	6	4	24
PercentRank scan (O(N), N=100)	100	1	100
ADD × 2 + MUL ÷3 (average)	3	3	9
Total	117	—	~159 cycles

The O(100) PercentRank linear scan dominates. For N=100: ~159 cycles per bar.

Batch Mode (SIMD Analysis)

Operation	Vectorizable?	Notes
RSI(3) / StreakRSI(2) EMA passes	No	Recursive IIR — sequential
PercentRank scan	Yes	SIMD comparison count: VCMPPD + VPCNT per window
Final averaging	Yes	VADDPD + VMULPD

PercentRank scan is the only sub-step with meaningful SIMD acceleration potential.

Quality Metrics

Metric	Score	Notes
Accuracy	9/10	Three independently calibrated sub-signals
Timeliness	5/10	100-bar PercentRank window dominates warmup
Smoothness	7/10	Averaging three signals reduces individual signal noise
Noise Rejection	7/10	Multi-component design reduces false signals

Resources

• Connors, L. & Alvarez, C. (2012). An Introduction to ConnorsRSI. TradingMarkets
• Connors, L. (2009). Short-Term Trading Strategies That Work. TradingMarkets
• PineScript reference: crsi.pine