Indicator Reference

All candles—synthetic and real—are enriched with a consistent pandas-ta indicator deck so the UI can toggle overlays without recomputing. The following studies are appended by engine/indicators.py.

Trend & Moving Averages

Indicator Columns Notes
Simple Moving Averages SMA_20, SMA_50, SMA_200 Baseline trend context from short to long horizon.
Exponential Moving Averages EMA_9, EMA_21, EMA_50 Emphasize recent data for faster reaction during momentum bursts.
Weighted & Hull MAs WMA_21, HMA_21 Alternative smoothing techniques for traders who prefer reduced lag.
VWAP VWAP_D (pandas-ta naming) Combines price and volume; requires a datetime index.
Supertrend SUPERT_7_3.0, SUPERTd_7_3.0, etc. Directional bias built from ATR bands.

Volatility & Channels

Indicator Columns Usage
Bollinger Bands BBL_20_2.0, BBM_20_2.0, BBU_20_2.0 20-period SMA envelope with 2σ offsets; used for mean reversion cues.
Keltner Channels KCL_20_2, KCM_20_2, KCU_20_2 ATR-based channel, tighter than Bollinger for breakout spotting.
Donchian Channels DCL_20_20, DCM_20_20, DCU_20_20 High/low breakout bands for range detection.
Parabolic SAR PSARl_0.02_0.2, PSARs_0.02_0.2 Trailing stop candidates; plotted as dots in the UI.

Momentum & Oscillators

Indicator Columns Notes
RSI RSI_14 14-period momentum oscillator for overbought/oversold signals.
MACD MACD_12_26_9, MACDh_12_26_9, MACDs_12_26_9 Trend-momentum combo; histogram highlights shifts.
Stochastic STOCHk_14_3_3, STOCHd_14_3_3 Fast oscillator measuring closes relative to recent ranges.
ADX & DI ADX_14, DMP_14, DMN_14 Directional strength via Average Directional Index.
CCI CCI_14_0.015 Commodity Channel Index for pullback detection.
ROC ROC_10 Percent rate of change over ten bars, useful for velocity screening.

Volume

Indicator Columns Notes
On-Balance Volume OBV Cumulative volume flow aligned with direction of closes.

Naming Conventions

  • pandas-ta appends parameter values to each column (RSI_14, BBL_20_2.0, etc.), so the UI can map user selections back to exact series.
  • Many channel-based studies emit multiple columns (lower/mid/upper); the Dash viewport picks the subset requested by the indicator selector.
  • If you extend add_technical_indicators(), keep names descriptive and unique so downstream components can reference them unambiguously.

This comprehensive catalog provides a technical breakdown of all 85 indicators organized by their mathematical complexity.

Indicator Groups

I. First-Order Primitives (Base Data Discretization)

Simplest transforms of raw Open, High, Low, Close, and Volume (OHLCV) data.

  1. Average Price (AP): An arithmetic mean of the High, Low, and Close for a single period.
    • Equation: \((H + L + C) / 3\)
    • Category: Trend/Benchmark
    • Use Case: Provides a simplified representation of a single bar's price action.
  2. Median Price (MP): The exact middle point between the High and Low of a specific time period.
    • Equation: \((H + L) / 2\)
    • Category: Trend/Benchmark
    • Use Case: Identifies the midpoint of a bar's range, often used as an input for oscillators.
  3. Typical Price (TP): A standard price calculation used as a more accurate representation of the average price than just the close.
    • Equation: \((H + L + C) / 3\)
    • Category: Trend/Benchmark
    • Use Case: Serves as the base input for more complex indicators like CCI and VWAP.
  4. Volume (V): The total number of shares or contracts traded during a specific time interval.
    • Equation: \(\sum \text{Trades}\)
    • Category: Volume
    • Use Case: Gauges market activity and validates the strength of price moves.
  5. Standard Deviation (StdDev): A statistical measure of how much price deviates from its mean.
    • Equation: \(\sqrt{\frac{1}{N} \sum_{i=1}^{N} (x_i - \bar{x})^2}\)
    • Category: Volatility
    • Use Case: Quantifies market risk and serves as the backbone for Bollinger Bands.
  6. Historical Volatility (HV): The realized variance of an asset based on past price changes.
    • Equation: \(\text{Annualized StdDev of Log Returns}\)
    • Category: Volatility
    • Use Case: Assesses the historical risk profile and prices options.
  7. Volatility Close-to-Close (C-C Vol): A volatility estimate calculated using only the closing prices of consecutive bars.
    • Equation: \(\sqrt{\frac{1}{n-1} \sum (\ln(\frac{C_t}{C_{t-1}}) - \mu)^2}\)
    • Category: Volatility
    • Use Case: Measures price dispersion specifically focused on settlement prices.
  8. Volatility O-H-L-C (OHLC Vol): A more efficient volatility estimator that incorporates the intrabar range.
    • Equation: Uses Parkinson or Garman-Klass formulas (e.g., \(0.511(H-L)^2 - \dots\))
    • Category: Volatility
    • Use Case: Provides a more granular risk assessment than close-only models.
  9. Volatility Zero-Trend C-C (Z-Trend Vol): A variance calculation that assumes the mean return (drift) is zero.
    • Equation: \(\sqrt{\frac{1}{n} \sum (\ln(\frac{C_t}{C_{t-1}}))^2}\)
    • Category: Volatility
    • Use Case: Useful for high-frequency data where drift is negligible.
  10. Rate of Change (ROC): Measures the percentage change in price between the current period and a period \(n\) days ago.
    • Equation: \((\frac{C_t - C_{t-n}}{C_{t-n}}) \times 100\)
    • Category: Momentum
    • Use Case: Identifies price velocity and potential trend exhaustion.
  11. Momentum (MOM): The absolute difference in price over a fixed lookback period.
    • Equation: \(C_t - C_{t-n}\)
    • Category: Momentum
    • Use Case: Simplest measure of trend strength and direction.
  12. Spread (SPD): The price difference between two distinct correlated assets.
    • Equation: \(Asset_A - Asset_B\)
    • Category: Relative Value
    • Use Case: Pairs trading and identifying inter-market arbitrage.
  13. Ratio (RAT): The relative value of one asset expressed in terms of another.
    • Equation: \(Asset_A / Asset_B\)
    • Category: Relative Value
    • Use Case: Inter-market analysis (e.g., Gold/Silver ratio) to find relative outperformance.
  14. Net Volume (NV): The sum of volume where up-volume is positive and down-volume is negative.
    • Equation: \(\sum (\text{if } C > C_{-1} \text{ then } V \text{ else } -V)\)
    • Category: Volume
    • Use Case: Visualizes net capital flow into or out of an asset.
  15. Williams Fractal (FRAC): Identifies local high and low points based on a 5-bar sequence.
    • Equation: High is Fractal if \(H_t > H_{t \pm 1, 2}\)
    • Category: Structure
    • Use Case: Defines support and resistance levels based on local price reversals.

II. Second-Order Smoothing (Moving Averages)

Temporal filters used to isolate the signal from price noise.

  1. Simple Moving Average (SMA): The unweighted average of price over a specific number of periods.
    • Equation: \(\frac{1}{n} \sum_{i=0}^{n-1} C_{t-i}\)
    • Category: Trend
    • Use Case: Baseline trend identification and institutional support/resistance.
  2. Exponential Moving Average (EMA): A moving average that applies more weight to recent prices for faster reaction.
    • Equation: \(EMA_t = C_t \times \alpha + EMA_{t-1} \times (1 - \alpha)\)
    • Category: Trend
    • Use Case: Reduces lag in trend following compared to SMA.
  3. Weighted Moving Average (WMA): A moving average where each data point is weighted by its position in the sequence.
    • Equation: \(\frac{\sum (C_{t-i} \times (n-i))}{\sum (n-i)}\)
    • Category: Trend
    • Use Case: Provides a faster response than SMA while being less volatile than EMA.
  4. Smoothed Moving Average (SMMA): A hybrid average similar to EMA but with a longer-term memory.
    • Equation: \(SMMA_t = \frac{\text{Sum}_{t-1} - SMMA_{t-1} + C_t}{n}\)
    • Category: Trend
    • Use Case: Long-term trend smoothing for position trading.
  5. Arnaud Legoux MA (ALMA): Uses a Gaussian distribution to provide extreme smoothness with minimal lag.
    • Equation: \(\sum (C_{t-i} \times w_i) \text{ where } w_i \text{ is Gaussian offset}\)
    • Category: Trend
    • Use Case: Superior trend tracking that filters "noise" without sacrificing timing.
  6. Hull Moving Average (HMA): A moving average designed to be extremely fast and eliminate lag almost entirely.
    • Equation: \(WMA(2 \times WMA_{n/2} - WMA_n, \sqrt{n})\)
    • Category: Trend
    • Use Case: Catching fast reversals and short-term trend changes.
  7. Least Squares MA (LSMA): Calculates a linear regression line over the period and plots the endpoint.
    • Equation: \(\text{End point of Linear Regression line}\)
    • Category: Trend
    • Use Case: Anticipating where price should be if the current trend continues linearly.
  8. Volume-Weighted MA (VWMA): A moving average that weighs price based on the volume of each period.
    • Equation: \(\sum (C_i \times V_i) / \sum V_i\)
    • Category: Trend/Volume
    • Use Case: Confirms price trends with high-volume conviction.
  9. Moving Average Hamming (MAH): Applies a Hamming window function to the moving average for signal processing.
    • Equation: \(\sum (C_{t-i} \times \text{Hamming Weight}_i)\)
    • Category: Trend
    • Use Case: Used in cycles and spectral analysis to minimize signal "leakage."
  10. MA Weighted by Volatility (V-WMA): An adaptive MA that slows down during low volatility and speeds up during high volatility.
    • Equation: \(MA \text{ where period } n \text{ is a function of } \sigma\)
    • Category: Trend/Volatility
    • Use Case: Stays closer to price during breakouts and ignores "chop."

III. Third-Order Oscillators & Bands (Derived from MAs)

Derived from the averages in Section II to create boundaries and relative signals.

  1. Moving Average Cross (MAX): A signal generated when a fast SMA crosses above or below a slow SMA.
    • Equation: \(SMA_{fast} - SMA_{slow}\)
    • Category: Trend
    • Use Case: Defining broad bullish or bearish regimes (e.g., Golden Cross).
  2. EMA Cross (EMAX): Uses exponential averages to signal trend changes with less lag.
    • Equation: \(EMA_{fast} - EMA_{slow}\)
    • Category: Trend
    • Use Case: Capturing trend shifts earlier in volatile markets.
  3. Price Oscillator (PO): The percentage difference between two moving averages.
    • Equation: \((\frac{MA_{fast} - MA_{slow}}{MA_{slow}}) \times 100\)
    • Category: Momentum
    • Use Case: Comparing momentum across different assets regardless of price level.
  4. Moving Average Convergence Divergence (MACD): A trend-following momentum indicator showing the relationship between two EMAs.
    • Equation: \(EMA_{12} - EMA_{26}\)
    • Category: Momentum
    • Use Case: Identifying momentum shifts and signal line crossovers.
  5. Bollinger Bands (BB): Volatility bands placed above and below a moving average.
    • Equation: \(SMA \pm (k \times StdDev)\)
    • Category: Volatility
    • Use Case: Identifying "overextended" prices and volatility squeezes.
  6. Bollinger %B (%B): Quantifies where the current price is relative to the Bollinger Bands.
    • Equation: \(\frac{C - LowerBand}{UpperBand - LowerBand}\)
    • Category: Momentum
    • Use Case: Identifying specific breakout or mean-reversion triggers.
  7. Bollinger Bandwidth (BBW): Measures the distance between the upper and lower Bollinger Bands.
    • Equation: \(\frac{UpperBand - LowerBand}{SMA}\)
    • Category: Volatility
    • Use Case: Detecting the beginning of volatility expansions (the "squeeze").
  8. Standard Error Bands (SEB): Similar to Bollinger Bands but uses Standard Error instead of Standard Deviation.
    • Equation: \(SMA \pm (k \times \text{Std Error})\)
    • Category: Volatility
    • Use Case: Creating tighter bands that focus on the average's reliability.
  9. Price Channel / Donchian (DC): Plots the highest high and lowest low over \(n\) periods.
    • Equation: \(Upper = \max(H_n), Lower = \min(L_n)\)
    • Category: Structure/Trend
    • Use Case: Classic breakout trading (e.g., Turtle Trading).
  10. Keltner Channels (KC): Volatility bands based on Average True Range instead of Standard Deviation.
    • Equation: \(EMA \pm (k \times ATR)\)
    • Category: Volatility
    • Use Case: Filtering price noise in trending markets more effectively than BB.
  11. Moving Average Channel (MAC): Uses the High and Low prices for the averages to create a price "envelope."
    • Equation: \(Upper = SMA(H, n), Lower = SMA(L, n)\)
    • Category: Trend
    • Use Case: Identifying the "trading range" of a specific trend.
  12. Envelopes (ENV): Fixed percentage bands placed above and below a moving average.
    • Equation: \(SMA \pm (SMA \times k\%)\)
    • Category: Volatility
    • Use Case: Mean reversion in stable, non-volatile instruments.
  13. Detrended Price Oscillator (DPO): Removes trend from price to make it easier to identify cycles.
    • Equation: \(C_{t} - SMA_{t-(n/2+1)}\)
    • Category: Momentum/Cycles
    • Use Case: Identifying cyclical highs and lows without trend interference.
  14. Average True Range (ATR): Measures market volatility by decomposing the entire range of an asset.
    • Equation: \(EMA(\max(H-L, |H-C_1|, |L-C_1|))\)
    • Category: Volatility
    • Use Case: Setting volatility-based stop losses.
  15. Commodity Channel Index (CCI): Measures the current price level relative to an average price level over a given time period.
    • Equation: \(\frac{TP - SMA(TP)}{0.015 \times \text{Mean Deviation}}\)
    • Category: Momentum
    • Use Case: Spotting new trends or extreme overbought/oversold conditions.
  16. Standard Error (SE): Estimates the precision of the mean price.
    • Equation: \(\sigma / \sqrt{n}\)
    • Category: Volatility/Statistics
    • Use Case: Gauging the consistency of a trend.
  17. Linear Regression Curve (LRC): A series of linear regression endpoints plotted as a curve.
    • Equation: \(\text{Series of LSMA values}\)
    • Category: Trend
    • Use Case: Identifying the "fair value" path of a trend.
  18. Linear Regression Slope (LRS): Measures the rate of change of the linear regression line.
    • Equation: \(\frac{n \sum (xy) - \sum x \sum y}{n \sum x^2 - (\sum x)^2}\)
    • Category: Momentum
    • Use Case: Identifying trend strength and potential exhaustion.

IV. Fourth-Order Complex Momentum & Volume

Highly processed indicators that often use other oscillators as inputs.

  1. Relative Strength Index (RSI): A speed and change of price movements oscillator.
    • Equation: \(100 - [100 / (1 + \text{Avg Gain} / \text{Avg Loss})]\)
    • Category: Momentum
    • Use Case: Identifying overbought (>70) and oversold (<30) conditions.
  2. Stochastic Oscillator (STOCH): Compares a specific closing price to a range of its prices over a certain period.
    • Equation: \(\frac{C - L_n}{H_n - L_n} \times 100\)
    • Category: Momentum
    • Use Case: Mean reversion and divergence trading.
  3. Stochastic RSI (StochRSI): Applies the Stochastic formula to RSI values instead of price.
    • Equation: \(\frac{RSI - RSI_{min}}{RSI_{max} - RSI_{min}}\)
    • Category: Momentum
    • Use Case: Identifying RSI reversals with high sensitivity.
  4. SMI Ergodic Indicator (SMI): A double-smoothed version of the Stochastic Oscillator.
    • Equation: Double EMA of price relative to range.
    • Category: Momentum
    • Use Case: Providing smoother, more reliable signals than standard Stochastics.
  5. True Strength Index (TSI): A double-smoothed momentum oscillator.
    • Equation: \(\frac{EMA(EMA(C-C_1))}{EMA(EMA|C-C_1|)} \times 100\)
    • Category: Momentum
    • Use Case: Identifying trend direction and overbought/oversold levels.
  6. Trend Strength Index (TRSI): Measures the persistence of a trend using smoothed price changes.
    • Equation: \(EMA(\Delta P) / EMA(|\Delta P|)\)
    • Category: Momentum/Trend
    • Use Case: Distinguishing between strong trends and "noise."
  7. Average Directional Index (ADX): Measures the overall strength of a trend.
    • Equation: \(EMA(\frac{|+DI - -DI|}{+DI + -DI}) \times 100\)
    • Category: Trend
    • Use Case: Determining if a market is trending or ranging (values > 25).
  8. Directional Movement (DM): The component used to calculate the ADX.
    • Equation: Difference between current and previous highs/lows.
    • Category: Momentum
    • Use Case: Building block for trend-strength indicators.
  9. Aroon (ARN): Measures the time between highs and lows over each period.
    • Equation: \(\frac{n - \text{periods since High}}{n} \times 100\)
    • Category: Trend
    • Use Case: Identifying the start of a new trend.
  10. Awesome Oscillator (AO): The difference between a 5-period and 34-period SMA of median price.
    • Equation: \(SMA_5(MP) - SMA_{34}(MP)\)
    • Category: Momentum
    • Use Case: Confirming trends and spotting "Twin Peaks" signals.
  11. Accelerator Oscillator (AC): Measures the acceleration/deceleration of the Awesome Oscillator.
    • Equation: \(AO - SMA_5(AO)\)
    • Category: Momentum
    • Use Case: Spotting momentum shifts before they appear in price.
  12. TRIX (TRX): The rate of change of a triple-exponentially smoothed moving average.
    • Equation: \(\%\) Change of \(EMA(EMA(EMA(C)))\)
    • Category: Trend/Momentum
    • Use Case: Filtering out insignificant cycles and noise.
  13. Coppock Curve (CC): A long-term price momentum indicator used to identify major market bottoms.
    • Equation: \(WMA_{10}(ROC_{14} + ROC_{11})\)
    • Category: Momentum
    • Use Case: Long-term investment timing in broad indices.
  14. Fisher Transform (FT): Transforms prices into a Gaussian normal distribution.
    • Equation: \(0.5 \times \ln(\frac{1+X}{1-X})\)
    • Category: Momentum
    • Use Case: Pinpointing turning points with extreme clarity.
  15. Relative Vigor Index (RVI): Measures the conviction of a trend by comparing close to open.
    • Equation: \(\frac{EMA(C-O)}{EMA(H-L)}\)
    • Category: Momentum
    • Use Case: Trend confirmation in volatile markets.
  16. Relative Volatility Index (RVI-V): An RSI-style oscillator that uses standard deviation of price changes.
    • Equation: RSI formula applied to StdDev.
    • Category: Volatility/Momentum
    • Use Case: Confirming breakout signals.
  17. Chande Momentum Oscillator (CMO): A momentum oscillator that uses price data in both the numerator and denominator.
    • Equation: \(100 \times \frac{SumUp - SumDn}{SumUp + SumDn}\)
    • Category: Momentum
    • Use Case: Capturing momentum changes without the smoothing lag of RSI.
  18. Ultimate Oscillator (UO): A momentum oscillator that uses three different timeframes.
    • Equation: Weighted average of three different cycles.
    • Category: Momentum
    • Use Case: Reducing false overbought/oversold signals.
  19. Vortex Indicator (VI): Two lines that capture positive and negative trend movement.
    • Equation: \(\sum |H - L_{-1}| / \sum TR\)
    • Category: Trend
    • Use Case: Spotting trend reversals and identifying current trend direction.

V. Microstructure & Flow Dynamics

Incorporates Volume to gauge the "force" or "liquidity" behind price.

  1. Volume Weighted Average Price (VWAP): The average price an asset has traded at throughout the day, based on both volume and price.
    • Equation: \(\sum (P \times V) / \sum V\)
    • Category: Volume/Benchmark
    • Use Case: Determining institutional fair value for the day.
  2. On-Balance Volume (OBV): A cumulative total of volume that adds volume on up days and subtracts it on down days.
    • Equation: \(OBV_{prev} \pm V\)
    • Category: Volume
    • Use Case: Identifying divergence between price and volume.
  3. Money Flow Index (MFI): A volume-weighted version of the RSI.
    • Equation: \(100 - [100 / (1 + \text{Money Ratio})]\)
    • Category: Volume/Momentum
    • Use Case: Spotting reversals when volume and price are out of sync.
  4. Chaikin Money Flow (CMF): Measures the amount of Money Flow Volume over a specific period.
    • Equation: \(\sum \text{Money Flow Volume} / \sum \text{Volume}\)
    • Category: Volume
    • Use Case: Gauging institutional accumulation or distribution.
  5. Accumulation/Distribution (A/D): Uses price and volume to show how strongly an asset is being accumulated.
    • Equation: \(\sum [(\frac{(C-L)-(H-C)}{H-L}) \times V]\)
    • Category: Volume
    • Use Case: Confirming a trend or spotting potential reversals.
  6. Chaikin Oscillator (CO): Applies the MACD formula to the Accumulation/Distribution line.
    • Equation: \(EMA_3(A/D) - EMA_{10}(A/D)\)
    • Category: Volume/Momentum
    • Use Case: Identifying momentum in volume flow.
  7. Price Volume Trend (PVT): Similar to OBV but adds only a percentage of volume based on price change.
    • Equation: \(PVT_{prev} + V \times (\frac{C - C_1}{C_1})\)
    • Category: Volume
    • Use Case: Provides a more granular view of volume flow than OBV.
  8. Elder’s Force Index (EFI): Uses price and volume to measure the power behind a move.
    • Equation: \((C - C_1) \times V\)
    • Category: Volume/Momentum
    • Use Case: Identifying the end of corrections within a trend.
  9. Ease of Movement (EOM): Relates price change to volume to show how easily a price can move.
    • Equation: \(\frac{\text{Midpoint Move}}{\text{Box Ratio}}\)
    • Category: Volume
    • Use Case: Identifying trends that require very little volume to persist.
  10. Klinger Oscillator (KO): High-volume force indicator that compares volume to price range.
    • Equation: \(EMA_{fast}(VF) - EMA_{slow}(VF)\)
    • Category: Volume
    • Use Case: Long-term trend confirmation and short-term timing.
  11. Volume Oscillator (VO): Measures the difference between two volume moving averages.
    • Equation: \(EMA_{fast}(V) - EMA_{slow}(V)\)
    • Category: Volume
    • Use Case: Identifying when volume is expanding or contracting.
  12. Volume Profile (VP): An advanced charting study that displays trading activity over a specified time period at specified price levels.
    • Equation: \(\sum V \text{ at each Price } P\)
    • Category: Volume/Structure
    • Use Case: Identifying High Volume Nodes (HVN) as support/resistance.

VI. Adaptive & Regime-Switching Systems

Sophisticated systems that adjust their sensitivity based on market conditions.

  1. Kaufman Adaptive MA (KAMA): A moving average that adjusts its speed based on the efficiency of price movement.
    • Equation: \(AMA_t = AMA_{t-1} + SC^2 \times (C_t - AMA_{t-1})\)
    • Category: Trend/Adaptive
    • Use Case: Following trends in noisy markets without getting whipsawed.
  2. McGinley Dynamic (MD): An adaptive average designed to follow price more accurately than traditional MAs.
    • Equation: \(MD_{prev} + \frac{C - MD_{prev}}{k \cdot n \cdot (C/MD_{prev})^4}\)
    • Category: Trend/Adaptive
    • Use Case: Avoiding the "separation" of price and average in fast moves.
  3. SuperTrend (ST): A trend-following indicator based on Average True Range.
    • Equation: \(\text{Median Price} \pm (k \times ATR)\)
    • Category: Trend/Regime
    • Use Case: Clear buy/sell signals and trailing stops.
  4. Parabolic SAR (PSAR): A trailing stop and reversal system.
    • Equation: \(SAR_{t+1} = SAR_t + \alpha(EP - SAR_t)\)
    • Category: Trend/Regime
    • Use Case: Trailing stops in strong trending markets.
  5. Choppiness Index (CHOP): Measures the market's tendency to trend or range.
    • Equation: \(100 \times \log_{10}(\frac{\sum ATR}{H_n - L_n}) / \log_{10}(n)\)
    • Category: Regime
    • Use Case: Avoiding trading in "sideways" or "choppy" markets (values > 61.8).
  6. Chaikin Volatility (CV): Measures the rate of change of the Average True Range.
    • Equation: \(\%\) Change in \(EMA(H-L)\)
    • Category: Volatility
    • Use Case: Identifying volatility peaks that lead to reversals.
  7. Chande Kroll Stop (CKS): A volatility-based exit indicator.
    • Equation: Uses \(n\)-period High/Low and ATR.
    • Category: Volatility/Regime
    • Use Case: Dynamic stop losses that don't get hit by random noise.
  8. Ichimoku Cloud (ICH): A comprehensive system identifying trend, support, and momentum.
    • Equation: Uses 5 different mid-point lines.
    • Category: Trend/Regime
    • Use Case: Full market-regime analysis.
  9. Guppy Multiple MA (GMMA): Uses two groups of moving averages to identify trend strength and investor behavior.
    • Equation: Clusters of 12 EMAs.
    • Category: Trend/Regime
    • Use Case: Spotting trend breakouts and exhaustion.
  10. Williams Alligator (ALLI): Uses three smoothed moving averages to identify trend formation.
    • Equation: 3 shifted SMAs (Jaw, Teeth, Lips).
    • Category: Trend/Regime
    • Use Case: Identifying "sleep" (range) and "hunger" (trend) phases.
  11. Mass Index (MI): Identifies trend reversals by measuring the widening and narrowing of the price range.
    • Equation: \(\sum (\frac{EMA(H-L)}{EMA(EMA(H-L))})\)
    • Category: Volatility/Momentum
    • Use Case: Predicting "reversal bulges" where a trend is about to turn.

Hierarchy Summary

  1. Level 1 (Data): Basic arithmetic on OHLCV (e.g., Average Price).
  2. Level 2 (Smoothing): Time-filters that remove noise (e.g., EMA).
  3. Level 3 (Derivatives): Using averages to find relative value (e.g., Bollinger Bands).
  4. Level 4 (Advanced): Recursive logic, multi-factor integration, and adaptive speed (e.g., KAMA, Ichimoku).