Indicator Library
170+ features powering AlgoZilla's ensemble model. Each indicator is expanded into delta, divergence, and multi-timeframe variants.
All 74
Acceleration 2
Bounce Detection 1
Harmonics 1
Ichimoku 2
Liquidity 5
Machine Learning 5
Market Structure 2
Microstructure 2
Momentum 10
Multi-Timeframe 7
Pi Cycle 1
Price Action 1
Seasonality 5
Sentiment 3
Shock Analysis 3
Statistical 5
Support & Resistance 2
Trend 3
Volatility 3
Volume 6
Wyckoff Analysis 5
★ Top Rated Indicators
Market Structure
ATH Distance
How far current price is from its all-time high. Expressed as a percentage drawdown from ATH.
VolatilityATR Z-Score
Average True Range normalized as a z-score against recent history. High z = expanding volatility, low z = compressed volatility.
VolatilityBollinger Band Position
Normalized position within Bollinger Bands (0 = lower band, 1 = upper band). Captures where price sits relative to its volatility envelope.
StatisticalCUSUM Change Detection
Cumulative Sum control chart applied to price returns. Detects structural breaks and regime changes by tracking cumulative deviations from the mean.
TrendEMA Spread
Distance between fast and slow EMAs, normalized by ATR. Wide spread = strong trend, narrow = consolidation.
TrendEMA Stack
Measures the ordering of multiple EMAs (8/21/55/200). Perfect stack = all EMAs aligned bullish (+1) or bearish (-1). Partial alignment = mixed signal.
Machine LearningLorentzian Classification
K-Nearest Neighbors classifier using Lorentzian distance metric instead of Euclidean. Adapts to the non-normal distribution of financial returns.
MomentumMACD Histogram
Difference between the MACD line and signal line. Positive = bullish momentum, negative = bearish. The histogram captures momentum acceleration.
MomentumMFI-14 (Money Flow Index)
Volume-weighted RSI that incorporates both price and volume data. Detects buying/selling pressure more accurately than price-only indicators.
Multi-TimeframeMulti-TF RSI
RSI computed at 2H, 4H, 8H, 12H, and 24H horizons. Each timeframe reveals different aspects of momentum.
Multi-TimeframeMulti-TF Trend Alignment
Trend direction computed across 5 timeframes (2H, 4H, 8H, 12H, 24H). When all timeframes agree, trend is strongest.
VolumeOBV Z-Score
On-Balance Volume normalized as a z-score. Positive = cumulative buying pressure above average, negative = selling pressure.
Machine LearningOracle DP Regime Probabilities
Probability estimates for Bull, Bear, and Sideways regimes from the Oracle Dynamic Programming engine. Data-driven regime classification.
Machine LearningOracle Trade Optimizer
Proprietary optimization engine that identifies optimal trade entry and exit points using dynamic programming. Generates the training labels for the ensemble classifier.
Machine LearningRegime Detection Engine
Proprietary multi-factor regime classifier that categorizes market conditions into distinct states. Drives threshold adjustment across the entire model.
MomentumRSI 14-bar
The classic 14-bar RSI — the industry standard for momentum measurement. Measures the ratio of recent gains to losses.
MomentumRSI 21-bar
Slower RSI with 21-bar lookback. Filters out noise and captures the underlying trend momentum.
MomentumRSI 5-bar
Ultra-fast Relative Strength Index with a 5-bar lookback. Captures rapid overbought/oversold reversals within hours.
MomentumSqueeze Momentum
Measures momentum during Bollinger Band squeezes — periods of low volatility that often precede explosive moves. Combines Keltner Channels with Bollinger Bands.
VolumeVolume Imbalance
Ratio of buy volume to sell volume estimated from price action. Persistent imbalance indicates institutional positioning.
VolumeVolume Spike
Detects abnormally high volume relative to recent history. Volume spikes often mark the beginning or end of significant moves.
VolumeVWAP Position
Price position relative to the Volume Weighted Average Price. Above VWAP = bullish intraday bias, below = bearish.
Wyckoff AnalysisWyckoff Absorption
Detects high volume with minimal price movement — the hallmark of institutional accumulation. Smart money is buying without moving the price.
Wyckoff AnalysisWyckoff SOS/SOW
Sign of Strength (SOS) and Sign of Weakness (SOW) confirm accumulation or distribution phases with volume-price analysis.
Ichimoku
Ichimoku Cloud Analysis
Complete Ichimoku Kinko Hyo system with 8 parameter presets. Computes cloud position, TK cross, Kijun momentum, cloud thickness, and TK slope.
IchimokuIchimoku Cloud Signals
Buy/sell signals derived from Ichimoku Cloud components — Tenkan-Sen, Kijun-Sen, Senkou Span crossovers, and Chikou Span confirmation.
Liquidity
Adverse Selection Risk
Measures the risk of trading against informed counterparties by analyzing the relationship between volume and subsequent price moves.
LiquidityAmihud Illiquidity
Measures market liquidity by dividing absolute return by volume. High values indicate thin markets where large orders move price significantly.
LiquidityBid-Ask Spread Estimate
Estimates the effective bid-ask spread from OHLC data using the Corwin-Schultz method. Wider spread = less liquid = higher trading costs.
LiquidityVPIN (Volume-Synchronized PIN)
Estimates the probability of informed trading by analyzing volume-synchronized order imbalance. High VPIN = likely insider activity.
LiquidityVWAP Flow Direction
Direction and intensity of volume flow relative to VWAP. Persistent buying above VWAP = institutional accumulation.
Machine Learning
Lorentzian Classification
K-Nearest Neighbors classifier using Lorentzian distance metric instead of Euclidean. Adapts to the non-normal distribution of financial returns.
Machine LearningOracle DP Regime Probabilities
Probability estimates for Bull, Bear, and Sideways regimes from the Oracle Dynamic Programming engine. Data-driven regime classification.
Machine LearningOracle Trade Optimizer
Proprietary optimization engine that identifies optimal trade entry and exit points using dynamic programming. Generates the training labels for the ensemble classifier.
Machine LearningPCA Reconstruction Error
Principal Component Analysis reconstruction error — measures how well current market conditions fit historical patterns. High error = regime change.
Machine LearningRegime Detection Engine
Proprietary multi-factor regime classifier that categorizes market conditions into distinct states. Drives threshold adjustment across the entire model.
Microstructure
Candlestick Patterns
Composite score of 8 candlestick patterns (hammer, engulfing, doji, morning star, etc.) weighted by historical reliability per coin.
MicrostructureLower Wick Ratio
Size of the lower wick relative to the total bar range. Long lower wick = buying pressure absorbed the dip.
Momentum
CCI-20
Commodity Channel Index with 20-bar lookback. Measures price deviation from its statistical mean, identifying cyclical turns.
MomentumMACD Histogram
Difference between the MACD line and signal line. Positive = bullish momentum, negative = bearish. The histogram captures momentum acceleration.
MomentumMFI-14 (Money Flow Index)
Volume-weighted RSI that incorporates both price and volume data. Detects buying/selling pressure more accurately than price-only indicators.
MomentumRate of Change (ROC-10)
Percentage price change over 10 bars. Simple but effective measure of price velocity.
MomentumRelative Strength Index (RSI)
The Relative Strength Index measures the speed and magnitude of recent price changes to evaluate overbought or oversold conditions. AlgoZilla uses RSI at three horizons (5, 14, and 21 bars) to capture both fast reversals and sustained momentum shifts.
MomentumRSI 14-bar
The classic 14-bar RSI — the industry standard for momentum measurement. Measures the ratio of recent gains to losses.
MomentumRSI 21-bar
Slower RSI with 21-bar lookback. Filters out noise and captures the underlying trend momentum.
MomentumRSI 5-bar
Ultra-fast Relative Strength Index with a 5-bar lookback. Captures rapid overbought/oversold reversals within hours.
MomentumSqueeze Momentum
Measures momentum during Bollinger Band squeezes — periods of low volatility that often precede explosive moves. Combines Keltner Channels with Bollinger Bands.
MomentumWilliams %R
Inverted stochastic oscillator measuring where price closes relative to its high-low range. Ranges from -100 (oversold) to 0 (overbought).
Multi-Timeframe
Multi-TF ADX
Average Directional Index at 5 timeframes. Measures trend strength without regard to direction.
Multi-TimeframeMulti-TF ATR
Average True Range at 5 timeframes. Normalized ATR reveals whether volatility is expanding or contracting at different scales.
Multi-TimeframeMulti-TF MACD
MACD histogram computed across 5 timeframes. Multi-TF MACD divergences are among the most reliable reversal signals.
Multi-TimeframeMulti-TF RSI
RSI computed at 2H, 4H, 8H, 12H, and 24H horizons. Each timeframe reveals different aspects of momentum.
Multi-TimeframeMulti-TF Stochastic
Stochastic oscillator at 5 timeframes. Stochastic crossovers at multiple TFs simultaneously generate high-probability signals.
Multi-TimeframeMulti-TF Trend Alignment
Trend direction computed across 5 timeframes (2H, 4H, 8H, 12H, 24H). When all timeframes agree, trend is strongest.
Multi-TimeframeMulti-TF Volume Direction
Volume flow direction at multiple timeframes. Positive = net buying, negative = net selling. Agreement across TFs = high conviction.
Seasonality
Dominant Cycle Analysis
Spectral analysis of price data to identify the dominant cycle length. Uses FFT and autocorrelation to find recurring periodic patterns.
SeasonalityHalving Cycle Position
Position within the Bitcoin halving cycle (0 to 1). Captures the well-documented post-halving bull cycle pattern.
SeasonalityLunar Cycle
Sine and cosine encoding of the lunar phase. A contrarian feature that captures behavioral patterns linked to lunar cycles.
SeasonalityQuarterly Cycle
Sine and cosine encoding of position within the quarter. Captures end-of-quarter rebalancing and seasonal patterns.
SeasonalityWeekly Cycle
Sine and cosine encoding of the day-of-week position. Captures recurring weekly patterns (e.g., Monday dips, weekend rallies).
Sentiment
Capitulation Score
Composite score measuring extreme selling conditions: high volume + large negative returns + fear gauge spike. Identifies potential market bottoms.
SentimentFear and Greed Index
The Crypto Fear and Greed Index aggregates social media sentiment, volatility, volume, and market dominance into a single 0-100 score. AlgoZilla uses this as the primary regime classifier — it determines whether the market is in BEAR (0-20), SIDEWAYS (20-55), BULL (55-70), or BUBBLE (70-100) mode.
SentimentFOMO Score
Detects Fear Of Missing Out conditions: rapidly rising prices + increasing volume + greed gauge. Identifies potential tops and overextended rallies.
Shock Analysis
News Combo Signal
Composite signal from price velocity, volume spike, and spread expansion that detects news-driven events before they fully resolve.
Shock AnalysisPost-Shock Reversal
Measures the reversal tendency after a shock event. Combines mean reversion distance, run exhaustion, and volatility normalization.
Shock AnalysisShock Magnitude
Detects abnormally large price moves (measured in rolling standard deviations). Identifies flash crashes and pumps in real-time.
Statistical
Autocorrelation
Measures how correlated price returns are with their own lagged values. High autocorrelation = trending, low = mean-reverting.
StatisticalCUSUM Change Detection
Cumulative Sum control chart applied to price returns. Detects structural breaks and regime changes by tracking cumulative deviations from the mean.
StatisticalHurst Exponent
Measures the long-term memory of a time series. H > 0.5 = trending, H < 0.5 = mean-reverting, H = 0.5 = random walk.
StatisticalMarket Efficiency
Measures how efficiently price incorporates information by comparing directional movement to total movement.
StatisticalVariance Ratio
Tests whether returns follow a random walk by comparing variance at different horizons. Deviation from 1.0 indicates predictability.
Trend
EMA Spread
Distance between fast and slow EMAs, normalized by ATR. Wide spread = strong trend, narrow = consolidation.
TrendEMA Stack
Measures the ordering of multiple EMAs (8/21/55/200). Perfect stack = all EMAs aligned bullish (+1) or bearish (-1). Partial alignment = mixed signal.
TrendMicro Trend Classifier
Short-horizon trend classification using a combination of price action, volume, and momentum. Classifies the immediate trend as up, down, or sideways.
Volatility
ATR Z-Score
Average True Range normalized as a z-score against recent history. High z = expanding volatility, low z = compressed volatility.
VolatilityBollinger Band Position
Normalized position within Bollinger Bands (0 = lower band, 1 = upper band). Captures where price sits relative to its volatility envelope.
VolatilityBollinger Bands
Bollinger Bands measure price volatility by plotting standard deviation bands around a moving average. AlgoZilla uses the BB Position (where price sits within the bands, normalized 0-1) as a volatility-adjusted momentum gauge.
Volume
OBV Z-Score
On-Balance Volume normalized as a z-score. Positive = cumulative buying pressure above average, negative = selling pressure.
VolumeOn-Balance Volume (OBV)
On-Balance Volume tracks cumulative buying and selling pressure by adding volume on up-bars and subtracting on down-bars. The z-score normalized version reveals when volume flow diverges from price — a leading indicator of trend reversals.
VolumeVolume Congestion
Identifies periods of decreasing volume during consolidation — the calm before the storm. Low congestion = imminent breakout.
VolumeVolume Imbalance
Ratio of buy volume to sell volume estimated from price action. Persistent imbalance indicates institutional positioning.
VolumeVolume Spike
Detects abnormally high volume relative to recent history. Volume spikes often mark the beginning or end of significant moves.
VolumeVWAP Position
Price position relative to the Volume Weighted Average Price. Above VWAP = bullish intraday bias, below = bearish.
Wyckoff Analysis
Effort vs Result
Compares volume effort to price result. High effort + low result = exhaustion. Low effort + high result = path of least resistance.
Wyckoff AnalysisWyckoff Absorption
Detects high volume with minimal price movement — the hallmark of institutional accumulation. Smart money is buying without moving the price.
Wyckoff AnalysisWyckoff Analysis
Wyckoff methodology identifies institutional accumulation and distribution through volume-price analysis. AlgoZilla implements four Wyckoff features that detect smart money activity in real-time.
Wyckoff AnalysisWyckoff SOS/SOW
Sign of Strength (SOS) and Sign of Weakness (SOW) confirm accumulation or distribution phases with volume-price analysis.
Wyckoff AnalysisWyckoff Spring
Identifies the classic Wyckoff Spring — price briefly breaks below support on low volume, then reverses sharply. A manipulation trap for weak hands.
More Indicators
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Bounce Detection
Bear Bounce Detector
Identifies potential bounce setups in downtrends by combining oversold momentum, support proximity, and volume characteristics.
HarmonicsHarmonic Pattern Scanner
Detects Fibonacci-based harmonic patterns (Gartley, Butterfly, Bat, Crab) in price structure. Patterns suggest high-probability reversal zones.
Pi CyclePi Cycle Top Indicator
Ratio of the 111-day SMA to 2x the 350-day SMA. When these cross, it has historically marked Bitcoin cycle tops with remarkable accuracy.
Price ActionFair Value Gap Detection
Identifies imbalances in price action where gaps between candle wicks suggest unfilled orders. FVGs often act as magnets for price reversion.
