Cosine Self Similarity

Cosine-Self-Similarity: Identify Trends & Reversals

What is Cosine Similarity?
Cosine similarity measures how alike two sequences are by looking at the angle between them. A value of +1 means they move perfectly together, -1 means they move perfectly opposite, and 0 means there's no directional relationship. This indicator applies this concept to price returns.

Is the market trending strongly, or is it choppy and reversing? Gain insight with the Cosine-Self-Similarity indicator!

This powerful tool analyzes the directional consistency of recent price movements compared to slightly older movements using a sophisticated weighted cosine self-similarity metric. It helps you distinguish between trending, mean-reverting, and potentially reversing market conditions by focusing purely on the alignment of price return patterns, ignoring their magnitude.


Key Features & Interpretation:

Cosine Similarity Histogram:

  • Displays the core weighted self-similarity value.
  • What Higher/Rising Self-Similarity Indicates: High positive values (approaching +1, blue bars) mean consecutive return patterns are consistently aligned. This reflects steadiness and suggests TREND FORMATION or momentum persistence, as seen in trending markets where price continues moving in the same direction.
  • What Lower/Falling Self-Similarity Indicates: Low or negative values (approaching 0 or -1, yellow bars) mean return patterns are less aligned, oscillating, or opposing. This suggests MEAN REVERSION or cyclical behavior, where prices oscillate without a clear directional trend, or potential REVERSALS.
Smoothed Similarity Line:
  • A SuperSmoother filter clarifies the underlying similarity trend.
  • Rising Line (blue): Increasing consistency – potential trend strengthening.
  • Falling Line (yelloq): Decreasing consistency – potential trend weakening, chop, or reversal setup.
Statistical Bands (Mean, +/- 2 Standard Deviations):
  • Show the average similarity and its recent volatility.
  • Values hitting or exceeding the bands indicate statistically extreme levels of consistency or inconsistency, potentially highlighting exhaustion points.

Zero Line:

  • A quick reference for positive (trending similarity) vs. negative (reversing similarity) regimes.



How to Use:
  • Trend Confirmation: Look for sustained high positive histogram values (steadiness) and a rising (blue) smoothed line (momentum).
  • Mean Reversion / Chop Detection: Look for histogram values oscillating around zero or negative (inconsistency), often with a falling (orange) smoothed line. This suggests returns are alternating direction.
  • Potential Reversals: Extremely low/negative readings (opposition), or sharp moves from positive extremes back towards/below zero, can signal potential turning points when confirmed by price action.
  • Regime Filter: Use the indicator's state (high/rising similarity vs. low/falling similarity) to adapt your strategy (e.g., favour trend-following in high similarity; favour mean-reversion in low similarity).


Input Parameters:
  • Sample Size: (Default: 100) The main lookback period for comparing return sequences. Adjust based on the market cycle length you want to analyze.
  • Smoothing Sample Size: (Default: 8) The period for the SuperSmoother filter. Adjust for desired responsiveness vs. smoothness.

Experiment with the settings to optimize for your trading style and timeframe!


Disclaimer: The Cosine-Self-Similarity indicator provides analytical insights but should be used in conjunction with other analysis methods and sound risk management practices. Past performance is not indicative of future results. Indicator is provided for statistical analysis and showing probabilities only and should not be construed as financial advice.



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Percentile of Historical Volatility and Correlation Coefficient shows if the asset is cheap or expensive based on the volatility. It is used to determine a good entry point. It has two indicators built in: Historical Volatility is ranked percentile wise and its correlation to price action which gives an indication of the direction of a possible future move. Together the both indicators can give good entries and direction.   Historical Volatility is a statistical measure of the dispersion of retu
This z-score indicator shows the correct z-score of an asset, as it uses the normalized price data for calculation, which is the only correct way. Z-score is only applicable for normal distributed data, therefore not the actual price is considered, but the normalised returns, which were assumed to follow a normal distribution. Returns are mean reverting and assumed to follow a normal distribution, therefore z-score calculation of returns is more reliable than z-score on price, as price is NOT m
The RSI2.0 indicator uses normalized price data and signal processing steps to get a normal distributed oscillator with no skew (mean is zero). Therefore, it can give much better reads than the traditional RSI.   Areas/Levels of reversal: Overbought or oversold levels from traditional RSI have no statistical significance, therefore the standard deviation bands are implemented here, which can be used in similar way as possible reversal points.   Divergence: As the indicator is nearly normal dist
The Returns Momentum Oscillator (RMO) shows the difference of exponentially weighted volatility. It is used to find market tops and bottoms. Volatility comes in waves, and as the Returns often front run price action it gives directional prediction of market movement.   The Oscillator signal is RMSed (root mean squared) to make the distribution closer to Gaussian distribution. While the traditional RSI indicators are often stuck in overbought or oversold areas for a long time, RMSing of the sign
Bollinger Bands based on Returns   This indicator characterizes the price and volatility by providing a channel/band of standard deviations like the Bollinger Bands. In contrary to standard Bollinger Bands which uses price directly, this indicator uses returns due to normalization.   The standard Bollinger Bands based on price directly, were one of the first quant or statistical methods for retail traders available. The issue with these bands, standard deviations can only be calculated, if the u
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