Table of Contents / Indicators

Indicators

6. Indicators

This document provides a comprehensive reference for technical indicators used in trading, organized by category. Each indicator includes its calculation, interpretation, and practical application guidelines.

Signal Type Classifications

MangroveAI uses two signal types to categorize trading signals:

TRIGGER: Discrete events that occur at specific moments (e.g., crossovers, breakouts). These signals fire once when conditions transition from false to true.
FILTER: Continuous state conditions that remain true while conditions persist (e.g., price above MA, RSI overbought). These signals evaluate to true or false at each bar.

Note: Terms like "Entry" and "Exit" are context-dependent. Any signal can be used for entries, exits, or filters depending on your strategy design.

6.1 Trend Indicators

Trend indicators are mathematical calculations applied to price data to identify the direction and strength of market trends. They smooth price action to filter noise and reveal underlying directional bias, helping traders align with prevailing market momentum.

Core Principles:

Lag vs. Smoothing Trade-off: More smoothing means more lag but cleaner signals
Trend Confirmation: Indicators confirm trend direction, not predict reversals
Multiple Timeframe Agreement: Trend indicators across timeframes should align
Trend Following: Best used in trending markets, not ranges
Price Always Leads: Indicators are derivatives of price; price changes first

Common Use Cases:

Identifying trend direction for trade bias
Dynamic support/resistance levels
Entry triggers on pullbacks to moving averages
Exit signals when trend indicators turn
Filtering out counter-trend trades

6.1.1 Simple Moving Average (SMA)

Definition

The arithmetic mean of price over a specified number of periods.

Formula

SMA(n) = (P_1 + P_2 + ... + P_n) / n

Interpretation

Price above SMA = Bullish bias
Price below SMA = Bearish bias
Slope indicates trend strength
Common periods: 20 (short-term), 50 (medium), 200 (long-term)

Trading Applications

200 SMA as major trend filter
Price crossing SMA as trend change signal
Multiple SMA crossovers (Golden Cross: 50 crosses above 200; Death Cross: 50 crosses below 200)

MangroveAI API Reference

Indicator Class: SMA
Category: Trend
Required Data: close
Parameters:
window: Parameter for SMA calculation
Outputs: sma
Usage Example: SMA.compute(data={'close': df['Close']}, params={'window': value})

<summary>is_above_sma — FILTER — Check if current price is above Simple Moving Average

Description

Check if current price is above Simple Moving Average

Parameters

window (int, 1-1000, required): SMA window in bars

Usage

{"name": "is_above_sma", "params": {"window": value}}

sma_cross_up — TRIGGER — Detect when fast SMA crosses above slow SMA (bullish entry signal)

Description

Detect when fast SMA crosses above slow SMA (bullish entry signal)

Parameters

window_fast (int, 1-500, required): Fast window SMA window in bars
window_slow (int, 1-1000, required): Slow window SMA window in bars

Usage

{"name": "sma_cross_up", "params": {"window_fast": value, "window_slow": value}}

sma_cross_down — TRIGGER — Detect when fast SMA crosses below slow SMA (bearish exit signal)

Description

Detect when fast SMA crosses below slow SMA (bearish exit signal)

Parameters

window_fast (int, 1-500, required): Fast window SMA window in bars
window_slow (int, 1-1000, required): Slow window SMA window in bars

Usage

{"name": "sma_cross_down", "params": {"window_fast": value, "window_slow": value}}

sma_crossover — TRIGGER — Detect SMA crossover signal with configurable direction (bullish [or](/doc/9-glossary#opening-range) bearish)

Description

Detect SMA crossover signal with configurable direction (bullish or bearish)

Parameters

window_fast (int, 1-500, required): Fast window SMA window in bars
window_slow (int, 1-1000, required): Slow window SMA window in bars
direction (str, optional, default="bullish"): Crossover direction: 'bullish' or 'bearish'

Usage

{"name": "sma_crossover", "params": {"window_fast": value, "window_slow": value, "direction": value}}

6.1.2 Exponential Moving Average (EMA)

Definition

A weighted moving average that gives more weight to recent prices, making it more responsive to new information.

Formula

EMA_t = (Price_t * k) + (EMA_(t-1) * (1-k))
Where k = 2 / (n + 1)

Interpretation

More responsive than SMA to recent price changes
Less lag but more sensitive to noise
Popular periods: 8, 12, 21, 50, 200

Trading Applications

Faster trend identification than SMA
Short-term EMAs (8, 12, 21) for momentum
EMA ribbons (multiple EMAs) for trend strength visualization

MangroveAI API Reference

Indicator Class: EMA
Category: Trend
Required Data: close
Parameters:
window: Parameter for EMA calculation
Outputs: ema
Usage Example: EMA.compute(data={'close': df['Close']}, params={'window': value})

ema_cross_down — TRIGGER — Detect bearish EMA crossover (fast EMA crosses below slow EMA)

Description

Detect bearish EMA crossover (fast EMA crosses below slow EMA)

Parameters

window_fast (int, 2-100, optional, default=9): Fast window EMA window
window_slow (int, 5-200, optional, default=21): Slow window EMA window

Usage

{"name": "ema_cross_down", "params": {"window_fast": value, "window_slow": value}}

ema_cross_up — TRIGGER — Detect bullish EMA crossover (fast EMA crosses above slow EMA)

Description

Detect bullish EMA crossover (fast EMA crosses above slow EMA)

Parameters

window_fast (int, 2-100, optional, default=9): Fast window EMA window
window_slow (int, 5-200, optional, default=21): Slow window EMA window

Usage

{"name": "ema_cross_up", "params": {"window_fast": value, "window_slow": value}}

ema_crossover — TRIGGER — Detect EMA crossover signal with configurable direction (bullish or bearish) Common periods: 9/21 (short), 50/200 (long). Adjust for crypto's 24/7 markets.

Description

Detect EMA crossover signal with configurable direction (bullish or bearish) Common periods: 9/21 (short), 50/200 (long). Adjust for crypto's 24/7 markets.

Parameters

window_fast (int, 1-500, required): Fast window EMA window in bars
window_slow (int, 1-1000, required): Slow window EMA window in bars
direction (str, optional, default="bullish"): Crossover direction: 'bullish' or 'bearish'

Usage

{"name": "ema_crossover", "params": {"window_fast": value, "window_slow": value, "direction": value}}

price_above_ema — FILTER — Check if price is above the EMA

Description

Check if price is above the EMA

Parameters

window (int, 2-200, optional, default=20): EMA window

Usage

{"name": "price_above_ema", "params": {"window": value}}

6.1.3 Double Exponential Moving Average (DEMA)

Definition

A smoothing indicator that attempts to eliminate lag associated with traditional moving averages by applying EMA calculation twice.

Formula

DEMA = 2 * EMA(n) - EMA(EMA(n))

Interpretation

Less lag than standard EMA
More responsive to price changes
Smoother than single EMA

6.1.4 Triple Exponential Moving Average (TEMA)

Definition

Further reduces lag by applying EMA calculation three times.

Formula

TEMA = 3 * EMA - 3 * EMA(EMA) + EMA(EMA(EMA))

Interpretation

Minimal lag among moving average variants
Highly responsive but may generate more false signals
Best for shorter-term trend identification

6.1.5 Average Directional Index (ADX)

Definition

Measures trend strength regardless of direction, derived from the Directional Movement Index (DMI) components +DI and -DI.

Formula

+DI = 100 * EMA(+DM) / ATR
-DI = 100 * EMA(-DM) / ATR
DX = 100 * |+DI - -DI| / (+DI + -DI)
ADX = EMA(DX, n periods, typically 14)

Interpretation

ADX > 25: Strong trend
ADX < 20: Weak trend / ranging
ADX rising: Trend strengthening
ADX falling: Trend weakening
+DI > -DI: Uptrend; -DI > +DI: Downtrend

Trading Applications

Filter trades based on trend strength (only trade when ADX > 25)
Avoid trend strategies when ADX < 20
Use DI crossovers for directional signals

MangroveAI API Reference

Indicator Class: ADX
Category: Trend
Required Data: high, low, close
Parameters:
window: Parameter for ADX calculation
Outputs: adx, adx_pos, adx_neg
Usage Example: ADX.compute(data={'high': df['High'], 'low': df['Low'], 'close': df['Close']}, params={'window': value})

adx_bullish_di — FILTER — Check if +DI is greater than -DI (bullish directional movement)

Description

Check if +DI is greater than -DI (bullish directional movement)

Parameters

window (int, 5-50, optional, default=14): ADX period

Usage

{"name": "adx_bullish_di", "params": {"window": value}}

adx_strong_trend — FILTER — Check if ADX indicates a strong trend (above 25)

Description

Check if ADX indicates a strong trend (>25)

Parameters

window (int, 5-50, optional, default=14): ADX period
threshold (float, 15-50, optional, default=25.0): Trend strength threshold

Usage

{"name": "adx_strong_trend", "params": {"window": value, "threshold": value}}

6.1.6 Parabolic SAR

Definition

Stop and Reverse system that provides potential entry/exit points by plotting dots above or below price, indicating trend direction and potential reversal points.

Formula

SAR_tomorrow = SAR_today + AF * (EP - SAR_today)

Where:
  AF = Acceleration Factor (starts at 0.02, increments by 0.02, max 0.20)
  EP = Extreme Point (highest high in uptrend, lowest low in downtrend)

Interpretation

Dots below price: Uptrend (long)
Dots above price: Downtrend (short)
Dot flip signals potential trend reversal

Trading Applications

Trailing stop-loss placement
Trend direction confirmation
Entry signals when dots flip
Best in trending markets; poor in ranges (whipsaws)

psar_bullish — FILTER — Check if [PSAR](/doc/9-glossary#parabolic-sar) indicates bullish trend (PSAR below price)

Description

Check if PSAR indicates bullish trend (PSAR below price)

Parameters

step (float, 0.01-0.1, optional, default=0.02): PSAR acceleration factor step
max_step (float, 0.1-0.5, optional, default=0.2): PSAR max acceleration factor

Usage

{"name": "psar_bullish", "params": {"step": value, "max_step": value}}

psar_bearish — FILTER — Check if PSAR indicates bearish trend (PSAR above price)

Description

Check if PSAR indicates bearish trend (PSAR above price)

Parameters

step (float, 0.01-0.1, optional, default=0.02): PSAR acceleration factor step
max_step (float, 0.1-0.5, optional, default=0.2): PSAR max acceleration factor

Usage

{"name": "psar_bearish", "params": {"step": value, "max_step": value}}

psar_reversal — TRIGGER — Check if PSAR flips sides (potential reversal)

Description

Check if PSAR flips sides (potential reversal)

Parameters

step (float, 0.01-0.1, optional, default=0.02): PSAR acceleration factor step
max_step (float, 0.1-0.5, optional, default=0.2): PSAR max acceleration factor
direction (str, optional, default="bullish"): Direction: 'bullish' or 'bearish'

Usage

{"name": "psar_reversal", "params": {"step": value, "max_step": value, "direction": value}}

6.1.7 Ichimoku Cloud

Definition

A comprehensive indicator system providing support/resistance, trend direction, and momentum through five components.

Components:

Tenkan-sen (Conversion Line) = (Highest High + Lowest Low) / 2 over 9 periods
Kijun-sen (Base Line) = (Highest High + Lowest Low) / 2 over 26 periods
Senkou Span A (Leading Span A) = (Tenkan + Kijun) / 2, plotted 26 periods ahead
Senkou Span B (Leading Span B) = (Highest High + Lowest Low) / 2 over 52 periods, plotted 26 periods ahead
Chikou Span (Lagging Span) = Current close, plotted 26 periods back

Interpretation

Price above cloud: Bullish
Price below cloud: Bearish
Price in cloud: Consolidation
Cloud color change: Trend shift
Thick cloud: Strong support/resistance
Thin cloud: Weak support/resistance

Trading Applications

Cloud as dynamic support/resistance
Tenkan/Kijun cross as entry signal
Chikou Span for momentum confirmation
All-time-frame analysis system

MangroveAI API Reference

Indicator Class: Ichimoku
Category: Trend
Required Data: high, low
Parameters:
window_tenkan: Tenkan-sen (conversion line) window
window_kijun: Kijun-sen (base line) window
window_senkou: Senkou Span B (leading span B) window
visual: Displacement for cloud projection
Outputs: conversion_line, base_line, span_a, span_b
Usage Example: Ichimoku.compute(data={'high': df['High'], 'low': df['Low']}, params={'window_tenkan': value, 'window_kijun': value, 'window_senkou': value, 'visual': value})

ichimoku_bearish — FILTER — Check if Ichimoku indicates bearish signal (price below cloud)

Description

Check if Ichimoku indicates bearish signal (price below cloud)

Parameters

window_tenkan (int, 5-20, optional, default=9): Tenkan-sen (conversion line) window
window_kijun (int, 15-40, optional, default=26): Kijun-sen (base line) window
window_senkou (int, 30-70, optional, default=52): Senkou Span B (leading span B) window

Usage

{"name": "ichimoku_bearish", "params": {"window_tenkan": value, "window_kijun": value, "window_senkou": value}}

ichimoku_bullish — FILTER — Check if Ichimoku indicates bullish signal (price above cloud)

Description

Check if Ichimoku indicates bullish signal (price above cloud)

Parameters

window_tenkan (int, 5-20, optional, default=9): Tenkan-sen (conversion line) window
window_kijun (int, 15-40, optional, default=26): Kijun-sen (base line) window
window_senkou (int, 30-70, optional, default=52): Senkou Span B (leading span B) window

Usage

{"name": "ichimoku_bullish", "params": {"window_tenkan": value, "window_kijun": value, "window_senkou": value}}

ichimoku_tk_cross — TRIGGER — Check if Tenkan-sen crosses Kijun-sen (TK cross)

Description

Check if Tenkan-sen crosses Kijun-sen (TK cross)

Parameters

window_tenkan (int, 5-20, optional, default=9): Tenkan-sen (conversion line) window
window_kijun (int, 15-40, optional, default=26): Kijun-sen (base line) window
window_senkou (int, 30-70, optional, default=52): Senkou Span B (leading span B) window
direction (str, optional, default="bullish"): Direction: 'bullish' or 'bearish'

Usage

{"name": "ichimoku_tk_cross", "params": {"window_tenkan": value, "window_kijun": value, "window_senkou": value, "direction": value}}

6.1.8 Aroon

Definition

The Aroon indicator measures the time elapsed since the highest high and lowest low over a given period, helping identify trend strength and potential reversals.

Formula

Aroon Up = ((Period - Days Since Highest High) / Period) * 100
Aroon Down = ((Period - Days Since Lowest Low) / Period) * 100
Standard period: 25

Interpretation

Aroon Up > 70 and Aroon Down < 30: Strong uptrend
Aroon Down > 70 and Aroon Up < 30: Strong downtrend
Both below 50: Consolidation/no clear trend
Crossovers signal potential trend changes

Trading Applications

Trend identification and confirmation
Timing entries on trend strength readings
Detecting consolidation periods

aroon_up_trend — FILTER — Check if Aroon Up indicates strong uptrend

Description

Check if Aroon Up indicates strong uptrend

Parameters

window (int, 10-50, optional, default=25): Lookback period
threshold (float, 50-100, optional, default=70.0): Strong trend threshold

Usage

{"name": "aroon_up_trend", "params": {"window": value, "threshold": value}}

aroon_down_trend — FILTER — Check if Aroon Down indicates strong downtrend

Description

Check if Aroon Down indicates strong downtrend

Parameters

window (int, 10-50, optional, default=25): Lookback period
threshold (float, 50-100, optional, default=70.0): Strong trend threshold

Usage

{"name": "aroon_down_trend", "params": {"window": value, "threshold": value}}

aroon_crossover — TRIGGER — Check if Aroon lines cross (trend change signal)

Description

Check if Aroon lines cross (trend change signal)

Parameters

window (int, 10-50, optional, default=25): Lookback period
direction (str, optional, default="bullish"): Direction: 'bullish' or 'bearish'

Usage

{"name": "aroon_crossover", "params": {"window": value, "direction": value}}

6.1.9 Weighted Moving Average (WMA)

Definition

A moving average that assigns more weight to recent prices using a linear weighting scheme.

Formula

WMA = (P_n * n + P_(n-1) * (n-1) + ... + P_1 * 1) / (n + (n-1) + ... + 1)
Where weights decrease linearly from most recent to oldest

Interpretation

More responsive than SMA but smoother than EMA
Price above WMA suggests bullish bias
Crossovers between fast and slow WMA signal trend changes

wma_cross_up — TRIGGER — Check if fast WMA crosses above slow WMA (bullish)

Description

Check if fast WMA crosses above slow WMA (bullish)

Parameters

window_fast (int, 2-50, optional, default=9): Fast window WMA window
window_slow (int, 10-100, optional, default=21): Slow window WMA window

Usage

{"name": "wma_cross_up", "params": {"window_fast": value, "window_slow": value}}

wma_cross_down — TRIGGER — Check if fast WMA crosses below slow WMA (bearish)

Description

Check if fast WMA crosses below slow WMA (bearish)

Parameters

window_fast (int, 2-50, optional, default=9): Fast window WMA window
window_slow (int, 10-100, optional, default=21): Slow window WMA window

Usage

{"name": "wma_cross_down", "params": {"window_fast": value, "window_slow": value}}

6.1.10 KAMA (Kaufman Adaptive Moving Average)

Definition

An adaptive moving average that adjusts its smoothing based on market volatility—faster during trends, slower during consolidation.

Formula

ER = Change / Volatility (Efficiency Ratio)
SC = (ER * (Fast - Slow) + Slow)^2 (Smoothing Constant)
KAMA = Previous KAMA + SC * (Price - Previous KAMA)

Interpretation

Adapts to market conditions automatically
Less whipsaw than traditional MAs in ranging markets
More responsive during trending periods

kama_cross_up — TRIGGER — Check if price crosses above KAMA (bullish signal)

Description

Check if price crosses above KAMA (bullish signal)

Parameters

window (int, 5-30, optional, default=10): Efficiency ratio period
pow1 (int, 1-10, optional, default=2): Fast smoothing constant
pow2 (int, 10-50, optional, default=30): Slow smoothing constant

Usage

{"name": "kama_cross_up", "params": {"window": value, "pow1": value, "pow2": value}}

kama_cross_down — TRIGGER — Check if price crosses below KAMA (bearish signal)

Description

Check if price crosses below KAMA (bearish signal)

Parameters

window (int, 5-30, optional, default=10): Efficiency ratio period
pow1 (int, 1-10, optional, default=2): Fast smoothing constant
pow2 (int, 10-50, optional, default=30): Slow smoothing constant

Usage

{"name": "kama_cross_down", "params": {"window": value, "pow1": value, "pow2": value}}

6.1.11 TRIX

Definition

Triple exponential moving average oscillator that filters out noise and shows the rate of change of a triple-smoothed EMA.

Formula

EMA1 = EMA(Close, period)
EMA2 = EMA(EMA1, period)
EMA3 = EMA(EMA2, period)
TRIX = (EMA3 - Previous EMA3) / Previous EMA3 * 100

Interpretation

Positive TRIX: Bullish momentum
Negative TRIX: Bearish momentum
Zero line crossovers signal trend changes
Very smooth—filters out most noise

trix_bullish — FILTER — Check if TRIX indicates bullish momentum

Description

Check if TRIX indicates bullish momentum

Parameters

window (int, 5-30, optional, default=15): TRIX period
threshold (float, 0.0-100.0, optional, default=0.0): Bullish threshold

Usage

{"name": "trix_bullish", "params": {"window": value, "threshold": value}}

trix_bearish — FILTER — Check if TRIX indicates bearish momentum

Description

Check if TRIX indicates bearish momentum

Parameters

window (int, 5-30, optional, default=15): TRIX period
threshold (float, 0.0-100.0, optional, default=0.0): Bearish threshold

Usage

{"name": "trix_bearish", "params": {"window": value, "threshold": value}}

6.1.12 Vortex Indicator

Definition

Identifies trend direction and strength by comparing positive and negative trend movements.

Formula

+VM = |High - Previous Low|
-VM = |Low - Previous High|
TR = True Range
+VI = Sum(+VM, n) / Sum(TR, n)
-VI = Sum(-VM, n) / Sum(TR, n)

Interpretation

+VI > -VI: Bullish trend
-VI > +VI: Bearish trend
Crossovers signal trend reversals
Wider spread = stronger trend

vortex_bullish — FILTER — Check if Vortex Indicator shows bullish trend (+VI above -VI)

Description

Check if Vortex Indicator shows bullish trend (+VI > -VI)

Parameters

window (int, 5-30, optional, default=14): Vortex period

Usage

{"name": "vortex_bullish", "params": {"window": value}}

vortex_bearish — FILTER — Check if Vortex Indicator shows bearish trend (-VI above +VI)

Description

Check if Vortex Indicator shows bearish trend (-VI > +VI)

Parameters

window (int, 5-30, optional, default=14): Vortex period

Usage

{"name": "vortex_bearish", "params": {"window": value}}

vortex_crossover — TRIGGER — Check if Vortex lines cross (trend change)

Description

Check if Vortex lines cross (trend change)

Parameters

window (int, 5-30, optional, default=14): Vortex period
direction (str, optional, default="bullish"): Direction: 'bullish' or 'bearish'

Usage

{"name": "vortex_crossover", "params": {"window": value, "direction": value}}

6.1.13 DPO (Detrended Price Oscillator)

Definition

Removes trend from price to identify cycles and overbought/oversold levels relative to a displaced moving average.

Formula

DPO = Close - SMA(Close, period) shifted back (period/2 + 1) days

Interpretation

Positive DPO: Price above detrended average
Negative DPO: Price below detrended average
Helps identify cycle highs and lows
Not affected by long-term trends

dpo_positive — FILTER — Check if DPO is positive (price above detrended average)

Description

Check if DPO is positive (price above detrended average)

Parameters

window (int, 10-50, optional, default=20): DPO period

Usage

{"name": "dpo_positive", "params": {"window": value}}

dpo_negative — FILTER — Check if DPO is negative (price below detrended average)

Description

Check if DPO is negative (price below detrended average)

Parameters

window (int, 10-50, optional, default=20): DPO period

Usage

{"name": "dpo_negative", "params": {"window": value}}

Best Practices for Trend Indicators

Use longer-period indicators for trend direction, shorter for entry timing
Don't fight the trend shown by major moving averages (50, 200)
Combine trend indicators with oscillators for complete picture
Adjust periods based on asset volatility and trading timeframe
Remember that all trend indicators lag; price leads

6.2 Momentum Indicators

Definition

Momentum indicators measure the speed or velocity of price changes, helping identify overbought/oversold conditions, divergences, and potential trend reversals. They oscillate around a centerline or within a bounded range.

Core Principles

Overbought/Oversold: Extreme readings suggest potential reversal but not certainty
Divergence: Momentum diverging from price warns of potential trend change
Trend Confirmation: Momentum should confirm price moves for validity
Mean Reversion: Momentum tends to return to neutral over time
Failure Swings: Momentum failing to reach previous extreme can signal reversal

6.2.1 Relative Strength Index (RSI)

Definition

Oscillator measuring the speed and magnitude of recent price changes to evaluate overbought or oversold conditions, bounded between 0 and 100.

Formula

RS = Average Gain over n periods / Average Loss over n periods
RSI = 100 - (100 / (1 + RS))

Standard period: 14

Interpretation

RSI > 70: Overbought (potential pullback)
RSI < 30: Oversold (potential bounce)
RSI 40-60: Neutral zone
Bullish divergence: Price makes lower low, RSI makes higher low
Bearish divergence: Price makes higher high, RSI makes lower high

Trading Applications

Overbought/oversold signals in ranging markets
Divergence signals for reversal warnings
RSI trend lines and pattern analysis
Failure swings as reversal confirmation
Centerline (50) crossovers as trend signals

MangroveAI API Reference

Indicator Class: RSI
Category: Momentum
Required Data: close
Parameters:
window: Parameter for RSI calculation
Outputs: rsi
Usage Example: RSI.compute(data={'close': df['Close']}, params={'window': value})
Reference: https://www.investopedia.com/terms/r/rsi.asp

<summary>rsi_overbought — FILTER — Check if RSI is above the overbought threshold (default 70)

Description

Check if RSI is above the overbought threshold (default 70)

Parameters

window (int, 2-100, optional, default=14): RSI calculation window
threshold (float, 50-100, optional, default=70.0): Overbought threshold

Usage

{"name": "rsi_overbought", "params": {"window": value, "threshold": value}}

rsi_oversold — FILTER — Check if RSI is below the oversold threshold (default 30)

Description

Check if RSI is below the oversold threshold (default 30)

Parameters

window (int, 2-100, optional, default=14): RSI calculation window
threshold (float, 0-50, optional, default=30.0): Oversold threshold

Usage

{"name": "rsi_oversold", "params": {"window": value, "threshold": value}}

rsi_cross_up — TRIGGER — Check if RSI crosses above a threshold level In crypto markets, consider higher thresholds (80/20) during strong trends.

Description

Check if RSI crosses above a threshold level In crypto markets, consider higher thresholds (80/20) during strong trends.

Parameters

window (int, 2-100, optional, default=14): RSI calculation window
threshold (float, 0-100, optional, default=50.0): Threshold level to cross above

Usage

{"name": "rsi_cross_up", "params": {"window": value, "threshold": value}}

rsi_cross_down — TRIGGER — Check if RSI crosses below a threshold level In crypto markets, consider higher thresholds (80/20) during strong trends.

Description

Check if RSI crosses below a threshold level In crypto markets, consider higher thresholds (80/20) during strong trends.

Parameters

window (int, 2-100, optional, default=14): RSI calculation window
threshold (float, 0-100, optional, default=50.0): Threshold level to cross below

Usage

{"name": "rsi_cross_down", "params": {"window": value, "threshold": value}}

stochrsi_overbought — FILTER — Check if Stochastic RSI indicates overbought condition

Description

Check if Stochastic RSI indicates overbought condition

Parameters

window (int, 5-30, optional, default=14): RSI period
smooth1 (int, 1-10, optional, default=3): Stochastic %K smoothing
smooth2 (int, 1-10, optional, default=3): Stochastic %D smoothing
threshold (float, 0.6-1.0, optional, default=0.8): Overbought threshold (0-1 scale)

Usage

{"name": "stochrsi_overbought", "params": {"window": value, "smooth1": value, "smooth2": value, "threshold": value}}

stochrsi_oversold — FILTER — Check if Stochastic RSI indicates oversold condition

Description

Check if Stochastic RSI indicates oversold condition

Parameters

window (int, 5-30, optional, default=14): RSI period
smooth1 (int, 1-10, optional, default=3): Stochastic %K smoothing
smooth2 (int, 1-10, optional, default=3): Stochastic %D smoothing
threshold (float, 0.0-0.4, optional, default=0.2): Oversold threshold (0-1 scale)

Usage

{"name": "stochrsi_oversold", "params": {"window": value, "smooth1": value, "smooth2": value, "threshold": value}}

6.2.2 Moving Average Convergence Divergence (MACD)

Definition

Trend-following momentum indicator showing the relationship between two EMAs of price, consisting of MACD line, Signal line, and Histogram.

Formula

MACD Line = EMA(12) - EMA(26)
Signal Line = EMA(9) of MACD Line
Histogram = MACD Line - Signal Line

Interpretation

MACD above zero: Bullish momentum
MACD below zero: Bearish momentum
MACD crossing Signal Line up: Buy signal
MACD crossing Signal Line down: Sell signal
Histogram shows momentum strength and direction

Trading Applications

Signal line crossovers for entries
Zero line crossovers for trend confirmation
Histogram divergences for reversal warnings
MACD in conjunction with price patterns

MangroveAI API Reference

Indicator Class: MACD
Category: Trend
Required Data: close
Parameters:
window_slow: Parameter for MACD calculation
window_fast: Parameter for MACD calculation
window_sign: Parameter for MACD calculation
Outputs: macd, signal, histogram
Usage Example: MACD.compute(data={'close': df['Close']}, params={'window_slow': value, 'window_fast': value, 'window_sign': value})

macd_bearish_cross — TRIGGER — Detect MACD bearish crossover (MACD line crosses below signal line)

Description

Detect MACD bearish crossover (MACD line crosses below signal line)

Parameters

window_fast (int, 2-50, optional, default=12): Fast window EMA window
window_slow (int, 10-100, optional, default=26): Slow window EMA window
window_sign (int, 2-50, optional, default=9): Signal line EMA window

Usage

{"name": "macd_bearish_cross", "params": {"window_fast": value, "window_slow": value, "window_sign": value}}

macd_bullish_cross — TRIGGER — Detect MACD bullish crossover (MACD line crosses above signal line)

Description

Detect MACD bullish crossover (MACD line crosses above signal line)

Parameters

window_fast (int, 2-50, optional, default=12): Fast window EMA window
window_slow (int, 10-100, optional, default=26): Slow window EMA window
window_sign (int, 2-50, optional, default=9): Signal line EMA window

Usage

{"name": "macd_bullish_cross", "params": {"window_fast": value, "window_slow": value, "window_sign": value}}

macd_positive — FILTER — Check if MACD histogram is positive (bullish momentum)

Description

Check if MACD histogram is positive (bullish momentum)

Parameters

window_fast (int, 2-50, optional, default=12): Fast window EMA window
window_slow (int, 10-100, optional, default=26): Slow window EMA window
window_sign (int, 2-50, optional, default=9): Signal line EMA window

Usage

{"name": "macd_positive", "params": {"window_fast": value, "window_slow": value, "window_sign": value}}

6.2.3 Stochastic Oscillator

stoch_overbought — FILTER — Check if Stochastic %K is above the overbought threshold

Description

Check if Stochastic %K is above the overbought threshold

Parameters

window (int, 5-50, optional, default=14): %K period
smooth_window (int, 1-10, optional, default=3): %K smoothing period
threshold (float, 70-100, optional, default=80.0): Overbought threshold

Usage

{"name": "stoch_overbought", "params": {"window": value, "smooth_window": value, "threshold": value}}

stoch_oversold — FILTER — Check if Stochastic %K is below the oversold threshold

Description

Check if Stochastic %K is below the oversold threshold

Parameters

window (int, 5-50, optional, default=14): %K period
smooth_window (int, 1-10, optional, default=3): %K smoothing period
threshold (float, 0-30, optional, default=20.0): Oversold threshold

Usage

{"name": "stoch_oversold", "params": {"window": value, "smooth_window": value, "threshold": value}}

Definition

Momentum indicator comparing closing price to price range over a specified period, showing where current price stands relative to recent high-low range.

Formula

%K = 100 * (Close - Lowest Low) / (Highest High - Lowest Low)
%D = SMA(%K, 3)

Typical period: 14 for %K
Fast Stochastic: Raw %K and %D
Slow Stochastic: %K smoothed, %D of smoothed %K

Interpretation

Above 80: Overbought
Below 20: Oversold
%K crossing above %D: Buy signal
%K crossing below %D: Sell signal
Divergences with price signal potential reversals

Trading Applications

Overbought/oversold signals
Crossover signals in trend direction
Divergence analysis
Slow stochastic for smoother signals

6.2.4 Commodity Channel Index (CCI)

Definition

Oscillator measuring current price level relative to average price over a given period, designed to identify cyclical trends.

Formula

Typical Price (TP) = (High + Low + Close) / 3
CCI = (TP - SMA(TP, n)) / (0.015 * Mean Deviation)
Mean Deviation = Average of |TP - SMA(TP)|

Typical period: 20

Interpretation

CCI > +100: Strong uptrend / overbought
CCI < -100: Strong downtrend / oversold
Zero line crossovers: Trend changes
Divergences: Potential reversals

Trading Applications

Trend identification and strength
Overbought/oversold signals
Breakout confirmation
Divergence trading

MangroveAI API Reference

Indicator Class: CCI
Category: Trend
Required Data: high, low, close
Parameters:
window: Parameter for CCI calculation
constant: Parameter for CCI calculation
Outputs: cci
Usage Example: CCI.compute(data={'high': df['High'], 'low': df['Low'], 'close': df['Close']}, params={'window': value, 'constant': value})

cci_overbought — FILTER — Check if CCI indicates overbought condition

Description

Check if CCI indicates overbought condition

Parameters

window (int, 10-50, optional, default=20): CCI period
constant (float, 0.0-100.0, optional, default=0.015): CCI constant
threshold (float, 50-200, optional, default=100.0): Overbought threshold

Usage

{"name": "cci_overbought", "params": {"window": value, "constant": value, "threshold": value}}

cci_oversold — FILTER — Check if CCI indicates oversold condition

Description

Check if CCI indicates oversold condition

Parameters

window (int, 10-50, optional, default=20): CCI period
constant (float, 0.0-100.0, optional, default=0.015): CCI constant
threshold (float, -200--50, optional, default=-100.0): Oversold threshold

Usage

{"name": "cci_oversold", "params": {"window": value, "constant": value, "threshold": value}}

6.2.5 Rate of Change (ROC)

Definition

Measures the percentage change in price from one period to another, showing momentum as a percentage.

Formula

ROC = ((Close - Close_n) / Close_n) * 100

Typical periods: 9, 12, 25

Interpretation

ROC > 0: Upward momentum
ROC < 0: Downward momentum
Rising ROC: Accelerating momentum
Falling ROC: Decelerating momentum

MangroveAI API Reference

Indicator Class: ROC
Category: Momentum
Required Data: close
Parameters:
window: Parameter for ROC calculation
Outputs: roc
Usage Example: ROC.compute(data={'close': df['Close']}, params={'window': value})

roc_momentum_shift — TRIGGER — Check if ROC crosses zero (momentum shift)

Description

Check if ROC crosses zero (momentum shift)

Parameters

window (int, 1-50, optional, default=12): ROC period
direction (str, optional, default="bullish"): Direction: 'bullish' or 'bearish'

Usage

{"name": "roc_momentum_shift", "params": {"window": value, "direction": value}}

roc_negative — FILTER — Check if Rate of Change indicates negative momentum

Description

Check if Rate of Change indicates negative momentum

Parameters

window (int, 1-50, optional, default=12): ROC period
threshold (float, -10-10, optional, default=0.0): Negative momentum threshold

Usage

{"name": "roc_negative", "params": {"window": value, "threshold": value}}

roc_positive — FILTER — Check if Rate of Change indicates positive momentum

Description

Check if Rate of Change indicates positive momentum

Parameters

window (int, 1-50, optional, default=12): ROC period
threshold (float, -10-10, optional, default=0.0): Positive momentum threshold

Usage

{"name": "roc_positive", "params": {"window": value, "threshold": value}}

6.2.6 Williams %R

williams_r_overbought — FILTER — Check if Williams %R is above the overbought threshold (above -20)

Description

Check if Williams %R is above the overbought threshold (> -20)

Parameters

window (int, 5-50, optional, default=14): Lookback window
threshold (float, -30-0, optional, default=-20.0): Overbought threshold

Usage

{"name": "williams_r_overbought", "params": {"window": value, "threshold": value}}

williams_r_oversold — FILTER — Check if Williams %R is below the oversold threshold (below -80)

Description

Check if Williams %R is below the oversold threshold (< -80)

Parameters

window (int, 5-50, optional, default=14): Lookback window
threshold (float, -100--70, optional, default=-80.0): Oversold threshold

Usage

{"name": "williams_r_oversold", "params": {"window": value, "threshold": value}}

Definition

Momentum oscillator measuring overbought/oversold levels, similar to stochastic but inverted scale.

Formula

%R = (Highest High - Close) / (Highest High - Lowest Low) * -100

Typical period: 14

Interpretation

-20 to 0: Overbought
-80 to -100: Oversold
Note: Scale is inverted (0 at top, -100 at bottom)

6.2.7 Money Flow Index (MFI)

Definition

Volume-weighted RSI that incorporates volume to measure buying and selling pressure.

Formula

Typical Price = (High + Low + Close) / 3
Raw Money Flow = Typical Price * Volume
Money Flow Ratio = Positive Money Flow / Negative Money Flow
MFI = 100 - (100 / (1 + Money Flow Ratio))

Typical period: 14

Interpretation

MFI > 80: Overbought
MFI < 20: Oversold
Divergences with price signal reversals
Volume confirmation of price moves

MangroveAI API Reference

Indicator Class: MFI
Category: Volume
Required Data: high, low, close, volume
Parameters:
window: Parameter for MFI calculation
Outputs: mfi
Usage Example: MFI.compute(data={'high': df['High'], 'low': df['Low'], 'close': df['Close'], 'volume': df['Volume']}, params={'window': value})

mfi_overbought — FILTER — Check if MFI (Money Flow Index) indicates overbought condition

Description

Check if MFI (Money Flow Index) indicates overbought condition

Parameters

window (int, 5-30, optional, default=14): MFI period
threshold (float, 70-95, optional, default=80.0): Overbought threshold

Usage

{"name": "mfi_overbought", "params": {"window": value, "threshold": value}}

mfi_oversold — FILTER — Check if MFI (Money Flow Index) indicates oversold condition

Description

Check if MFI (Money Flow Index) indicates oversold condition

Parameters

window (int, 5-30, optional, default=14): MFI period
threshold (float, 5-30, optional, default=20.0): Oversold threshold

Usage

{"name": "mfi_oversold", "params": {"window": value, "threshold": value}}

6.2.8 Awesome Oscillator (AO)

Definition

Momentum indicator that measures market momentum using the difference between a 5-period and 34-period simple moving average of the bar's midpoints.

Formula

Midpoint = (High + Low) / 2
AO = SMA(Midpoint, 5) - SMA(Midpoint, 34)

Interpretation

AO > 0: Bullish momentum
AO < 0: Bearish momentum
Zero line crossovers signal momentum shifts
Twin peaks pattern for divergence analysis

ao_bullish — FILTER — Check if Awesome Oscillator indicates bullish momentum

Description

Check if Awesome Oscillator indicates bullish momentum

Parameters

window_fast (int, 2-15, optional, default=5): Fast SMA window
window_slow (int, 20-60, optional, default=34): Slow SMA window
threshold (float, 0.0-100.0, optional, default=0.0): Bullish threshold

Usage

{"name": "ao_bullish", "params": {"window_fast": value, "window_slow": value, "threshold": value}}

ao_bearish — FILTER — Check if Awesome Oscillator indicates bearish momentum

Description

Check if Awesome Oscillator indicates bearish momentum

Parameters

window_fast (int, 2-15, optional, default=5): Fast SMA window
window_slow (int, 20-60, optional, default=34): Slow SMA window
threshold (float, 0.0-100.0, optional, default=0.0): Bearish threshold

Usage

{"name": "ao_bearish", "params": {"window_fast": value, "window_slow": value, "threshold": value}}

ao_zero_cross — TRIGGER — Check if Awesome Oscillator crosses zero line

Description

Check if Awesome Oscillator crosses zero line

Parameters

window_fast (int, 2-15, optional, default=5): Fast SMA window
window_slow (int, 20-60, optional, default=34): Slow SMA window
direction (str, optional, default="bullish"): Direction: 'bullish' or 'bearish'

Usage

{"name": "ao_zero_cross", "params": {"window_fast": value, "window_slow": value, "direction": value}}

6.2.9 Force Index

Definition

Combines price change and volume to measure the strength behind price moves.

Formula

Force Index = (Close - Previous Close) * Volume
Smoothed Force Index = EMA(Force Index, period)

Interpretation

Positive Force Index: Bulls in control
Negative Force Index: Bears in control
Rising Force Index: Increasing buying pressure
Falling Force Index: Increasing selling pressure

force_bullish — FILTER — Check if Force Index indicates bullish momentum

Description

Check if Force Index indicates bullish momentum

Parameters

window (int, 5-30, optional, default=13): EMA period for smoothing
threshold (float, 0.0-100.0, optional, default=0.0): Bullish threshold

Usage

{"name": "force_bullish", "params": {"window": value, "threshold": value}}

force_bearish — FILTER — Check if Force Index indicates bearish momentum

Description

Check if Force Index indicates bearish momentum

Parameters

window (int, 5-30, optional, default=13): EMA period for smoothing
threshold (float, 0.0-100.0, optional, default=0.0): Bearish threshold

Usage

{"name": "force_bearish", "params": {"window": value, "threshold": value}}

6.2.10 KST (Know Sure Thing)

Definition

Momentum oscillator based on the smoothed rate-of-change for four different timeframes, combined with weights.

Formula

ROC1 = SMA(ROC(10), 10)
ROC2 = SMA(ROC(15), 10)
ROC3 = SMA(ROC(20), 10)
ROC4 = SMA(ROC(30), 15)
KST = (ROC1 * 1) + (ROC2 * 2) + (ROC3 * 3) + (ROC4 * 4)
Signal = SMA(KST, 9)

Interpretation

KST above Signal: Bullish momentum
KST below Signal: Bearish momentum
Crossovers generate trading signals
Smoothed version reduces whipsaws

kst_bullish_cross — TRIGGER — Check if KST crosses above signal line (bullish)

Description

Check if KST crosses above signal line (bullish)

Parameters

roc1 (int, 1-1000, optional, default=10): ROC1 period
roc2 (int, 1-1000, optional, default=15): ROC2 period
roc3 (int, 1-1000, optional, default=20): ROC3 period
roc4 (int, 1-1000, optional, default=30): ROC4 period
window_sma1 (int, 2-500, optional, default=10): SMA1 smoothing window (for ROC1)
window_sma2 (int, 2-500, optional, default=10): SMA2 smoothing window (for ROC2)
window_sma3 (int, 2-500, optional, default=10): SMA3 smoothing window (for ROC3)
window_sma4 (int, 2-500, optional, default=15): SMA4 smoothing window (for ROC4)
nsig (int, 1-1000, optional, default=9): Signal line period

Usage

{"name": "kst_bullish_cross", "params": {"roc1": value, "roc2": value, "roc3": value, "roc4": value, "window_sma1": value, "window_sma2": value, "window_sma3": value, "window_sma4": value, "nsig": value}}

kst_bearish_cross — TRIGGER — Check if KST crosses below signal line (bearish)

Description

Check if KST crosses below signal line (bearish)

Parameters

roc1 (int, 1-1000, optional, default=10): ROC1 period
roc2 (int, 1-1000, optional, default=15): ROC2 period
roc3 (int, 1-1000, optional, default=20): ROC3 period
roc4 (int, 1-1000, optional, default=30): ROC4 period
window_sma1 (int, 2-500, optional, default=10): SMA1 smoothing window (for ROC1)
window_sma2 (int, 2-500, optional, default=10): SMA2 smoothing window (for ROC2)
window_sma3 (int, 2-500, optional, default=10): SMA3 smoothing window (for ROC3)
window_sma4 (int, 2-500, optional, default=15): SMA4 smoothing window (for ROC4)
nsig (int, 1-1000, optional, default=9): Signal line period

Usage

{"name": "kst_bearish_cross", "params": {"roc1": value, "roc2": value, "roc3": value, "roc4": value, "window_sma1": value, "window_sma2": value, "window_sma3": value, "window_sma4": value, "nsig": value}}

6.2.11 PPO (Percentage Price Oscillator)

Definition

Momentum oscillator showing the percentage difference between two EMAs, similar to MACD but normalized.

Formula

PPO = ((EMA(12) - EMA(26)) / EMA(26)) * 100
Signal Line = EMA(PPO, 9)

Interpretation

Positive PPO: Short-term momentum above long-term
Negative PPO: Short-term momentum below long-term
Normalized for comparison across different price levels
Signal line crossovers for timing

ppo_bullish_cross — TRIGGER — Check if PPO crosses above signal line (bullish)

Description

Check if PPO crosses above signal line (bullish)

Parameters

window_slow (int, 15-50, optional, default=26): Slow EMA period
window_fast (int, 5-20, optional, default=12): Fast EMA period
window_sign (int, 3-15, optional, default=9): Signal line period

Usage

{"name": "ppo_bullish_cross", "params": {"window_slow": value, "window_fast": value, "window_sign": value}}

ppo_bearish_cross — TRIGGER — Check if PPO crosses below signal line (bearish)

Description

Check if PPO crosses below signal line (bearish)

Parameters

window_slow (int, 15-50, optional, default=26): Slow EMA period
window_fast (int, 5-20, optional, default=12): Fast EMA period
window_sign (int, 3-15, optional, default=9): Signal line period

Usage

{"name": "ppo_bearish_cross", "params": {"window_slow": value, "window_fast": value, "window_sign": value}}

6.2.12 STC (Schaff Trend Cycle)

Definition

Combines MACD with a stochastic calculation to create a faster, smoother oscillator.

Formula

MACD Line = EMA(23) - EMA(50)
%K of MACD = Stochastic of MACD Line
STC = Double smoothed %K

Interpretation

STC > 75: Overbought
STC < 25: Oversold
Faster signals than traditional MACD
Good for identifying trend changes early

stc_oversold — FILTER — Check if STC indicates oversold condition

Description

Check if STC indicates oversold condition

Parameters

window_slow (int, 2-500, optional, default=50): Slow EMA period
window_fast (int, 2-500, optional, default=23): Fast EMA period
cycle (int, 1-1000, optional, default=10): Cycle period
smooth1 (int, 1-1000, optional, default=3): Smoothing 1
smooth2 (int, 1-1000, optional, default=3): Smoothing 2
threshold (float, 0.0-100.0, optional, default=25.0): Oversold threshold

Usage

{"name": "stc_oversold", "params": {"window_slow": value, "window_fast": value, "cycle": value, "smooth1": value, "smooth2": value, "threshold": value}}

stc_overbought — FILTER — Check if STC indicates overbought condition

Description

Check if STC indicates overbought condition

Parameters

window_slow (int, 2-500, optional, default=50): Slow EMA period
window_fast (int, 2-500, optional, default=23): Fast EMA period
cycle (int, 1-1000, optional, default=10): Cycle period
smooth1 (int, 1-1000, optional, default=3): Smoothing 1
smooth2 (int, 1-1000, optional, default=3): Smoothing 2
threshold (float, 0.0-100.0, optional, default=75.0): Overbought threshold

Usage

{"name": "stc_overbought", "params": {"window_slow": value, "window_fast": value, "cycle": value, "smooth1": value, "smooth2": value, "threshold": value}}

6.2.13 TSI (True Strength Index)

Definition

Double-smoothed momentum indicator that shows both trend direction and overbought/oversold conditions.

Formula

PC = Close - Previous Close
Double Smoothed PC = EMA(EMA(PC, 25), 13)
Double Smoothed Absolute PC = EMA(EMA(|PC|, 25), 13)
TSI = (Double Smoothed PC / Double Smoothed Absolute PC) * 100

Interpretation

TSI > 0: Bullish momentum
TSI < 0: Bearish momentum
Extreme readings suggest overbought/oversold
Less noise than RSI due to double smoothing

tsi_bullish — FILTER — Check if True Strength Index indicates bullish momentum (TSI above threshold)

Description

Check if True Strength Index indicates bullish momentum (TSI > threshold)

Parameters

window_slow (int, 10-50, optional, default=25): Slow EMA period
window_fast (int, 5-25, optional, default=13): Fast EMA period
threshold (float, -50-50, optional, default=0.0): Bullish threshold

Usage

{"name": "tsi_bullish", "params": {"window_slow": value, "window_fast": value, "threshold": value}}

tsi_bearish — FILTER — Check if True Strength Index indicates bearish momentum (TSI below threshold)

Description

Check if True Strength Index indicates bearish momentum (TSI < threshold)

Parameters

window_slow (int, 10-50, optional, default=25): Slow EMA period
window_fast (int, 5-25, optional, default=13): Fast EMA period
threshold (float, -50-50, optional, default=0.0): Bearish threshold

Usage

{"name": "tsi_bearish", "params": {"window_slow": value, "window_fast": value, "threshold": value}}

6.2.14 Ultimate Oscillator

Definition

Momentum oscillator that combines short, intermediate, and long timeframes to reduce false signals.

Formula

BP = Close - Min(Low, Previous Close)
TR = Max(High, Previous Close) - Min(Low, Previous Close)
Average7 = Sum(BP, 7) / Sum(TR, 7)
Average14 = Sum(BP, 14) / Sum(TR, 14)
Average28 = Sum(BP, 28) / Sum(TR, 28)
UO = 100 * ((4 * Average7) + (2 * Average14) + Average28) / 7

Interpretation

UO > 70: Overbought
UO < 30: Oversold
Multi-timeframe reduces whipsaws
Divergences with price signal reversals

uo_oversold — FILTER — Check if Ultimate Oscillator indicates oversold condition

Description

Check if Ultimate Oscillator indicates oversold condition

Parameters

window_short (int, 3-20, optional, default=7): Short window
window_medium (int, 7-30, optional, default=14): Medium window
window_long (int, 14-50, optional, default=28): Long window
threshold (float, 10-40, optional, default=30.0): Oversold threshold

Usage

{"name": "uo_oversold", "params": {"window_short": value, "window_medium": value, "window_long": value, "threshold": value}}

uo_overbought — FILTER — Check if Ultimate Oscillator indicates overbought condition

Description

Check if Ultimate Oscillator indicates overbought condition

Parameters

window_short (int, 3-20, optional, default=7): Short window
window_medium (int, 7-30, optional, default=14): Medium window
window_long (int, 14-50, optional, default=28): Long window
threshold (float, 60-90, optional, default=70.0): Overbought threshold

Usage

{"name": "uo_overbought", "params": {"window_short": value, "window_medium": value, "window_long": value, "threshold": value}}

Best Practices for Momentum Indicators

Don't rely solely on overbought/oversold signals in strong trends
Look for divergences as early warning, not immediate signals
Combine momentum with trend indicators for confirmation
Adjust overbought/oversold thresholds based on market conditions
Use multiple momentum indicators for confluence

6.3 Volatility Indicators

Definition

Volatility indicators measure the degree of price variation over time, helping traders assess risk, set stop-losses, determine position sizes, and identify potential breakout conditions.

Core Principles

Volatility Cycles: Markets alternate between high and low volatility periods
Volatility Clustering: High volatility tends to follow high volatility
Mean Reversion: Extreme volatility tends to revert to average
Breakout Potential: Low volatility often precedes significant moves
Risk Adjustment: Volatility should influence position sizing and stops

6.3.1 Bollinger Bands

Definition

Volatility bands placed above and below a moving average, with width determined by standard deviation.

Formula

Middle Band = SMA(Close, 20)
Upper Band = SMA + (2 * Standard Deviation)
Lower Band = SMA - (2 * Standard Deviation)

Bandwidth = (Upper - Lower) / Middle * 100
%B = (Close - Lower) / (Upper - Lower)

Interpretation

Price at upper band: Potentially overbought / strong trend
Price at lower band: Potentially oversold / strong trend
Band squeeze (narrow bands): Low volatility, breakout potential
Band expansion: High volatility, trend in progress
%B > 1: Above upper band; %B < 0: Below lower band

Trading Applications

Mean reversion trades at bands in ranges
Trend trades with band walking in trends
Squeeze detection for breakout anticipation
Volatility assessment for position sizing

MangroveAI API Reference

Indicator Class: BollingerBands
Category: Volatility
Required Data: close
Parameters:
window: Parameter for BollingerBands calculation
window_dev: Parameter for BollingerBands calculation
Outputs: mavg, hband, lband, wband, pband, hband_indicator, lband_indicator
Usage Example: BollingerBands.compute(data={'close': df['Close']}, params={'window': value, 'window_dev': value})

<summary>bb_lower_breakout — TRIGGER — Check if price closes below the lower Bollinger Band

Description

Check if price closes below the lower Bollinger Band

Parameters

window (int, 5-100, optional, default=20): MA period for center band
window_dev (int, 1-5, optional, default=2): Standard deviation multiplier

Usage

{"name": "bb_lower_breakout", "params": {"window": value, "window_dev": value}}

bb_squeeze — TRIGGER — Detect Bollinger Band squeeze (low volatility, potential breakout)

Description

Detect Bollinger Band squeeze (low volatility, potential breakout)

Parameters

window (int, 5-100, optional, default=20): MA period for center band
window_dev (int, 1-5, optional, default=2): Standard deviation multiplier
threshold (float, 1-20, optional, default=5.0): Band width percentage threshold

Usage

{"name": "bb_squeeze", "params": {"window": value, "window_dev": value, "threshold": value}}

bb_upper_breakout — TRIGGER — Check if price closes above the upper Bollinger Band

Description

Check if price closes above the upper Bollinger Band

Parameters

window (int, 5-100, optional, default=20): MA period for center band
window_dev (int, 1-5, optional, default=2): Standard deviation multiplier

Usage

{"name": "bb_upper_breakout", "params": {"window": value, "window_dev": value}}

6.3.2 Average True Range (ATR)

Definition

Measures market volatility by calculating the average range of price movement, accounting for gaps.

Formula

True Range = max(
  High - Low,
  |High - Previous Close|,
  |Low - Previous Close|
)
ATR = EMA(True Range, n periods)

Typical period: 14

Interpretation

Higher ATR: More volatility
Lower ATR: Less volatility
Rising ATR: Volatility expanding
Falling ATR: Volatility contracting

Trading Applications

Stop-loss placement (e.g., 2 * ATR from entry)
Position sizing (smaller positions when ATR high)
Profit target setting
Volatility breakout detection
Normalizing across different assets

MangroveAI API Reference

Indicator Class: ATR
Category: Volatility
Required Data: high, low, close
Parameters:
window: Parameter for ATR calculation
Outputs: atr
Usage Example: ATR.compute(data={'high': df['High'], 'low': df['Low'], 'close': df['Close']}, params={'window': value})

atr_high_volatility — FILTER — Check if ATR indicates high volatility relative to price

Description

Check if ATR indicates high volatility relative to price

Parameters

window (int, 5-50, optional, default=14): ATR period
threshold_pct (float, 0.5-10, optional, default=3.0): ATR as percentage of close threshold

Usage

{"name": "atr_high_volatility", "params": {"window": value, "threshold_pct": value}}

6.3.3 Keltner Channels

kc_upper_breakout — TRIGGER — Check if price breaks above upper Keltner Channel band

Description

Check if price breaks above upper Keltner Channel band

Parameters

window (int, 10-50, optional, default=20): EMA period
window_atr (int, 5-30, optional, default=10): ATR period

Usage

{"name": "kc_upper_breakout", "params": {"window": value, "window_atr": value}}

kc_lower_breakout — TRIGGER — Check if price breaks below lower Keltner Channel band

Description

Check if price breaks below lower Keltner Channel band

Parameters

window (int, 10-50, optional, default=20): EMA period
window_atr (int, 5-30, optional, default=10): ATR period

Usage

{"name": "kc_lower_breakout", "params": {"window": value, "window_atr": value}}

Definition

Volatility-based bands using ATR instead of standard deviation, typically around an EMA.

Formula

Middle Line = EMA(Close, 20)
Upper Channel = EMA + (2 * ATR)
Lower Channel = EMA - (2 * ATR)

Interpretation

Similar to Bollinger Bands but uses ATR for width
Less sensitive to sudden price spikes
Squeeze occurs when Bollinger Bands move inside Keltner Channels

Trading Applications

Combined with Bollinger Bands for squeeze detection
Trend direction and strength
Dynamic support/resistance

6.3.4 Standard Deviation

Definition

Statistical measure of the dispersion of returns around the mean.

Formula

Variance = Sum((x_i - mean)^2) / (n-1)
Standard Deviation = sqrt(Variance)

Annualized Vol = Daily Std Dev * sqrt(252)

Interpretation

Higher std dev: More volatile
Lower std dev: Less volatile
Used to normalize comparisons across assets

6.3.5 Volatility Index (VIX)

Definition

Market expectation of near-term volatility implied by S&P 500 index option prices.

Interpretation

VIX < 15: Low volatility / complacency
VIX 15-20: Normal volatility
VIX 20-30: Elevated volatility / uncertainty
VIX > 30: High volatility / fear
VIX spikes often coincide with market bottoms

Trading Applications

Market sentiment gauge
Regime classification
Volatility trading strategies
Portfolio hedging timing

6.3.6 Donchian Channels

Definition

Price channels formed by the highest high and lowest low over a specified period, used in the famous Turtle Trading system.

Formula

Upper Channel = Highest High over n periods
Lower Channel = Lowest Low over n periods
Middle Line = (Upper + Lower) / 2
Standard period: 20

Interpretation

Price at upper channel: Strong uptrend / potential overbought
Price at lower channel: Strong downtrend / potential oversold
Breakouts above upper channel signal long entries
Breakouts below lower channel signal short entries

dc_upper_breakout — TRIGGER — Check if price breaks above upper Donchian Channel (new high)

Description

Check if price breaks above upper Donchian Channel (new high)

Parameters

window (int, 5-100, optional, default=20): Lookback period

Usage

{"name": "dc_upper_breakout", "params": {"window": value}}

dc_lower_breakout — TRIGGER — Check if price breaks below lower Donchian Channel (new low)

Description

Check if price breaks below lower Donchian Channel (new low)

Parameters

window (int, 5-100, optional, default=20): Lookback period

Usage

{"name": "dc_lower_breakout", "params": {"window": value}}

6.3.7 Mass Index

Definition

Identifies trend reversals by measuring the range between high and low prices, looking for range expansion followed by contraction.

Formula

Single EMA = EMA(High - Low, 9)
Double EMA = EMA(Single EMA, 9)
Mass Index = Sum(Single EMA / Double EMA, 25)

Interpretation

Mass Index > 27 then < 26.5: "Reversal bulge" pattern
Signals that current trend may be exhausted
Best combined with trend indicators for direction
Identifies volatility cycle changes

mass_reversal_signal — TRIGGER — Check if Mass Index signals potential reversal (reversal bulge)

Description

Check if Mass Index signals potential reversal (reversal bulge)

Parameters

window_fast (int, 5-15, optional, default=9): Fast EMA period
window_slow (int, 15-40, optional, default=25): Sum period
threshold_high (float, 25-30, optional, default=27.0): Upper threshold
threshold_low (float, 24-27, optional, default=26.5): Lower threshold

Usage

{"name": "mass_reversal_signal", "params": {"window_fast": value, "window_slow": value, "threshold_high": value, "threshold_low": value}}

6.3.8 Ulcer Index

Definition

Measures downside risk and volatility by focusing only on drawdowns from recent highs.

Formula

Percentage Drawdown = ((Close - 14-period High Close) / 14-period High Close) * 100
Ulcer Index = Square Root of Mean of Squared Drawdowns

Interpretation

Low Ulcer Index: Low downside risk
High Ulcer Index: High downside risk
Focus on downside volatility only
Useful for risk-adjusted performance (Martin Ratio)

ulcer_low_risk — FILTER — Check if Ulcer Index indicates low downside risk

Description

Check if Ulcer Index indicates low downside risk

Parameters

window (int, 5-50, optional, default=14): Lookback period
threshold (float, 1-15, optional, default=5.0): Low risk threshold

Usage

{"name": "ulcer_low_risk", "params": {"window": value, "threshold": value}}

ulcer_high_risk — FILTER — Check if Ulcer Index indicates high downside risk

Description

Check if Ulcer Index indicates high downside risk

Parameters

window (int, 5-50, optional, default=14): Lookback period
threshold (float, 5-30, optional, default=10.0): High risk threshold

Usage

{"name": "ulcer_high_risk", "params": {"window": value, "threshold": value}}

Best Practices for Volatility Indicators

Use ATR for stop-loss and position sizing calculations
Watch for volatility compression as setup for breakouts
Reduce position size during high volatility periods
Normalize volatility when comparing different assets
Remember that volatility is mean-reverting over time

6.4 Volume & Order Flow Indicators

Definition

Volume indicators analyze trading volume to confirm price movements, identify accumulation/distribution, and provide insight into the strength of market participation.

Core Principles

Volume Confirms Price: Strong moves should be accompanied by strong volume
Volume Precedes Price: Volume changes often lead price changes
Climactic Volume: Extreme volume often occurs at turning points
Dry Up Warning: Decreasing volume in a move suggests weakening
Effort vs. Result: Compare volume (effort) to price movement (result)

6.4.1 On-Balance Volume (OBV)

Definition

Cumulative volume indicator that adds volume on up days and subtracts on down days.

Formula

If Close > Previous Close: OBV = Previous OBV + Volume
If Close < Previous Close: OBV = Previous OBV - Volume
If Close = Previous Close: OBV = Previous OBV

Interpretation

Rising OBV: Accumulation (buying pressure)
Falling OBV: Distribution (selling pressure)
OBV divergence from price: Potential reversal warning
OBV breakouts can precede price breakouts

MangroveAI API Reference

Indicator Class: OBV
Category: Volume
Required Data: close, volume
Outputs: obv
Usage Example: OBV.compute(data={'close': df['Close'], 'volume': df['Volume']}, params={})

<summary>obv_bearish — FILTER — Check if OBV is falling (bearish volume confirmation)

Description

Check if OBV is falling (bearish volume confirmation)

Parameters

window (int, 5-50, optional, default=20): Lookback for trend

Usage

{"name": "obv_bearish", "params": {"window": value}}

obv_bullish — FILTER — Check if OBV is rising (bullish volume confirmation)

Description

Check if OBV is rising (bullish volume confirmation)

Parameters

window (int, 5-50, optional, default=20): Lookback for trend

Usage

{"name": "obv_bullish", "params": {"window": value}}

6.4.2 Accumulation/Distribution Line (ADL)

adi_bullish — FILTER — Check if ADI (Accumulation/Distribution) is rising

Description

Check if ADI (Accumulation/Distribution) is rising

Parameters

window (int, 5-50, optional, default=20): Lookback for trend

Usage

{"name": "adi_bullish", "params": {"window": value}}

adi_bearish — FILTER — Check if ADI (Accumulation/Distribution) is falling

Description

Check if ADI (Accumulation/Distribution) is falling

Parameters

window (int, 5-50, optional, default=20): Lookback for trend

Usage

{"name": "adi_bearish", "params": {"window": value}}

Definition

Volume-based indicator measuring the cumulative flow of money into and out of a security.

Formula

Money Flow Multiplier = ((Close - Low) - (High - Close)) / (High - Low)
Money Flow Volume = Money Flow Multiplier * Volume
ADL = Previous ADL + Money Flow Volume

Interpretation

Rising ADL: Accumulation
Falling ADL: Distribution
ADL confirming price: Trend likely to continue
ADL diverging from price: Potential reversal

6.4.3 Volume Weighted Average Price (VWAP)

Definition

The average price weighted by volume, representing the average price a security has traded at throughout the day.

Formula

VWAP = Cumulative(Typical Price * Volume) / Cumulative(Volume)
Typical Price = (High + Low + Close) / 3

Interpretation

Price above VWAP: Buyers in control
Price below VWAP: Sellers in control
VWAP as dynamic support/resistance
Deviation from VWAP indicates overextension

Trading Applications

Institutional benchmark for execution quality
Intraday support/resistance
Entry/exit timing
Mean reversion trading

MangroveAI API Reference

Indicator Class: VWAP
Category: Volume
Required Data: high, low, close, volume
Parameters:
window: Parameter for VWAP calculation
Outputs: vwap
Usage Example: VWAP.compute(data={'high': df['High'], 'low': df['Low'], 'close': df['Close'], 'volume': df['Volume']}, params={'window': value})

vwap_above — FILTER — Check if price is above VWAP (bullish bias)

Description

Check if price is above VWAP (bullish bias)

Parameters

window (int, 5-50, optional, default=14): VWAP period

Usage

{"name": "vwap_above", "params": {"window": value}}

vwap_below — FILTER — Check if price is below VWAP (bearish bias)

Description

Check if price is below VWAP (bearish bias)

Parameters

window (int, 5-50, optional, default=14): VWAP period

Usage

{"name": "vwap_below", "params": {"window": value}}

6.4.4 Chaikin Money Flow (CMF)

Definition

Measures the amount of Money Flow Volume over a specific period.

Formula

CMF = Sum(Money Flow Volume, n) / Sum(Volume, n)

Typical period: 20

Interpretation

CMF > 0: Buying pressure
CMF < 0: Selling pressure
Rising CMF: Increasing buying pressure
Falling CMF: Increasing selling pressure

MangroveAI API Reference

Indicator Class: CMF
Category: Volume
Required Data: high, low, close, volume
Parameters:
window: Parameter for CMF calculation
Outputs: cmf
Usage Example: CMF.compute(data={'high': df['High'], 'low': df['Low'], 'close': df['Close'], 'volume': df['Volume']}, params={'window': value})

cmf_bearish — FILTER — Check if CMF (Chaikin Money Flow) indicates selling pressure

Description

Check if CMF (Chaikin Money Flow) indicates selling pressure

Parameters

window (int, 10-50, optional, default=20): CMF period
threshold (float, 0.0-100.0, optional, default=0.0): Bearish threshold

Usage

{"name": "cmf_bearish", "params": {"window": value, "threshold": value}}

cmf_bullish — FILTER — Check if CMF (Chaikin Money Flow) indicates buying pressure

Description

Check if CMF (Chaikin Money Flow) indicates buying pressure

Parameters

window (int, 10-50, optional, default=20): CMF period
threshold (float, 0.0-100.0, optional, default=0.0): Bullish threshold

Usage

{"name": "cmf_bullish", "params": {"window": value, "threshold": value}}

6.4.5 Klinger Volume Oscillator

Definition

Volume-based oscillator designed to identify long-term money flow trends.

Formula

Volume Force = Volume * |2 * (dm/cm) - 1| * trend * 100
KVO = EMA(Volume Force, 34) - EMA(Volume Force, 55)
Signal = EMA(KVO, 13)

Interpretation

KVO above zero: Bullish volume pressure
KVO below zero: Bearish volume pressure
KVO crossing signal line: Potential trade signal
Divergences with price indicate weakening trends

6.4.6 Ease of Movement (EMV)

Definition

Volume-based oscillator that relates price change to volume, showing how easily price moves.

Formula

Distance Moved = ((High + Low) / 2) - ((Previous High + Previous Low) / 2)
Box Ratio = (Volume / 10000) / (High - Low)
EMV = Distance Moved / Box Ratio
EMV SMA = SMA(EMV, 14)

Interpretation

Positive EMV: Price rising easily with volume
Negative EMV: Price falling easily with volume
High values: Easy price movement
Low values: Difficult price movement

eom_bullish — FILTER — Check if Ease of Movement indicates bullish

Description

Check if Ease of Movement indicates bullish

Parameters

window (int, 5-30, optional, default=14): EOM period
threshold (float, 0.0-100.0, optional, default=0.0): Bullish threshold

Usage

{"name": "eom_bullish", "params": {"window": value, "threshold": value}}

eom_bearish — FILTER — Check if Ease of Movement indicates bearish

Description

Check if Ease of Movement indicates bearish

Parameters

window (int, 5-30, optional, default=14): EOM period
threshold (float, 0.0-100.0, optional, default=0.0): Bearish threshold

Usage

{"name": "eom_bearish", "params": {"window": value, "threshold": value}}

6.4.7 NVI (Negative Volume Index)

Definition

Tracks price changes on days when volume decreases from the previous day, thought to track "smart money" activity.

Formula

If Volume < Previous Volume:
  NVI = Previous NVI + ((Close - Previous Close) / Previous Close) * Previous NVI
Else:
  NVI = Previous NVI

Interpretation

Rising NVI on down volume: Smart money accumulating
Falling NVI on down volume: Smart money distributing
Compare to 255-day EMA for trend
Less noise than volume-inclusive indicators

nvi_bullish — FILTER — Check if NVI (Negative Volume Index) indicates smart money buying

Description

Check if NVI (Negative Volume Index) indicates smart money buying

Parameters

window (int, 100-500, optional, default=255): EMA period for signal

Usage

{"name": "nvi_bullish", "params": {"window": value}}

nvi_bearish — FILTER — Check if NVI (Negative Volume Index) indicates smart money selling

Description

Check if NVI (Negative Volume Index) indicates smart money selling

Parameters

window (int, 100-500, optional, default=255): EMA period for signal

Usage

{"name": "nvi_bearish", "params": {"window": value}}

6.4.8 PVO (Percentage Volume Oscillator)

Definition

Momentum oscillator for volume, showing the percentage difference between two volume EMAs.

Formula

PVO = ((EMA(Volume, 12) - EMA(Volume, 26)) / EMA(Volume, 26)) * 100
Signal Line = EMA(PVO, 9)

Interpretation

Positive PVO: Short-term volume above average
Negative PVO: Short-term volume below average
Crossovers indicate volume momentum shifts
Confirms price breakouts with volume expansion

pvo_bullish_cross — TRIGGER — Check if PVO crosses above signal line (bullish volume)

Description

Check if PVO crosses above signal line (bullish volume)

Parameters

window_slow (int, 15-50, optional, default=26): Slow EMA period
window_fast (int, 5-20, optional, default=12): Fast EMA period
window_sign (int, 3-15, optional, default=9): Signal line period

Usage

{"name": "pvo_bullish_cross", "params": {"window_slow": value, "window_fast": value, "window_sign": value}}

pvo_bearish_cross — TRIGGER — Check if PVO crosses below signal line (bearish volume)

Description

Check if PVO crosses below signal line (bearish volume)

Parameters

window_slow (int, 15-50, optional, default=26): Slow EMA period
window_fast (int, 5-20, optional, default=12): Fast EMA period
window_sign (int, 3-15, optional, default=9): Signal line period

Usage

{"name": "pvo_bearish_cross", "params": {"window_slow": value, "window_fast": value, "window_sign": value}}

6.4.9 VPT (Volume Price Trend)

Definition

Cumulative indicator that adds or subtracts a portion of volume based on the percentage change in price.

Formula

VPT = Previous VPT + Volume * ((Close - Previous Close) / Previous Close)

Interpretation

Rising VPT: Accumulation / bullish volume-price relationship
Falling VPT: Distribution / bearish volume-price relationship
Divergences with price signal potential reversals
More sensitive than OBV to price magnitude

vpt_bullish — FILTER — Check if VPT (Volume Price Trend) is rising

Description

Check if VPT (Volume Price Trend) is rising

Parameters

window (int, 5-50, optional, default=20): Lookback for trend

Usage

{"name": "vpt_bullish", "params": {"window": value}}

vpt_bearish — FILTER — Check if VPT (Volume Price Trend) is falling

Description

Check if VPT (Volume Price Trend) is falling

Parameters

window (int, 5-50, optional, default=20): Lookback for trend

Usage

{"name": "vpt_bearish", "params": {"window": value}}

Best Practices for Volume Indicators

Always confirm price breakouts with volume
Watch for volume divergences as early warnings
Use VWAP for intraday trading reference
Note that volume patterns differ by asset class
Consider relative volume (vs. average) not just absolute

6.5 Breadth & Market Health

Definition

Market breadth indicators measure the participation and health of the overall market by analyzing the number of advancing vs. declining stocks, new highs/lows, and other internal market metrics.

Core Principles

Confirmation: Healthy rallies should have broad participation
Divergence Warning: Narrow rallies (few stocks leading) are suspect
Extremes: Extreme breadth readings often precede reversals
Internal vs. External: Breadth reveals what indices may hide
Cumulative Analysis: Track breadth trends over time

6.5.1 Advance-Decline Line

Definition

Cumulative measure of the difference between advancing and declining stocks.

Formula

Daily A/D = Advancing Stocks - Declining Stocks
A/D Line = Cumulative sum of Daily A/D

Interpretation

Rising A/D Line: Broad participation, healthy market
Falling A/D Line: Narrow participation, weak market
A/D divergence from index: Warning signal
A/D making new highs with index: Confirms strength

6.5.2 McClellan Oscillator

Definition

Market breadth oscillator based on the difference between 19-day and 39-day EMAs of daily advance-decline data.

Formula

McClellan Oscillator = EMA(A-D, 19) - EMA(A-D, 39)

Interpretation

Oscillator > 0: Bullish breadth
Oscillator < 0: Bearish breadth
Extreme positive readings: Overbought
Extreme negative readings: Oversold
Divergences with price signal potential reversals

6.5.3 McClellan Summation Index

Definition

Cumulative sum of the McClellan Oscillator, providing a longer-term view of market breadth.

Formula

Summation Index = Cumulative sum of McClellan Oscillator

Interpretation

Rising Summation Index: Bull market environment
Falling Summation Index: Bear market environment
Extreme readings indicate potential reversals

6.5.4 Arms Index (TRIN)

Definition

Measures the relationship between advancing/declining stocks and advancing/declining volume.

Formula

TRIN = (Advancing Issues / Declining Issues) / (Advancing Volume / Declining Volume)

Interpretation

TRIN < 1: Bullish (more volume in advancers)
TRIN > 1: Bearish (more volume in decliners)
TRIN < 0.5: Extremely bullish
TRIN > 2.0: Extremely bearish (often contrarian buy signal)

Best Practices for Breadth Indicators

Use breadth to confirm index moves
Watch for divergences between breadth and price
Extreme breadth readings often precede reversals
Breadth is most useful for equity indices
Combine multiple breadth indicators for confirmation

6.6 Oscillators Deep Dive

Definition

Oscillators are indicators that fluctuate within a bounded range, typically used to identify overbought/oversold conditions and momentum changes. This section provides advanced concepts for oscillator usage.

Core Principles

Bounded Range: Most oscillators move between fixed values (0-100 or centered on zero)
Overbought/Oversold: Extreme readings don't guarantee reversals
Divergence Analysis: Comparing oscillator to price action
Centerline Significance: Crossing zero or 50 often significant
Trend Context: Interpret oscillators differently in trends vs. ranges

Advanced Oscillator Concepts

Hidden Divergence: - Bullish Hidden: Price makes higher low, oscillator makes lower low (trend continuation) - Bearish Hidden: Price makes lower high, oscillator makes higher high (trend continuation)

Failure Swings: - Bullish Failure Swing: Oscillator fails to reach previous low, then breaks above intervening high - Bearish Failure Swing: Oscillator fails to reach previous high, then breaks below intervening low

Oscillator Trend Lines: - Draw trend lines on oscillator, not just price - Oscillator trend line breaks can precede price breaks

Range Shift: - In uptrends: RSI tends to oscillate 40-80 instead of 30-70 - In downtrends: RSI tends to oscillate 20-60 instead of 30-70 - Adjust overbought/oversold thresholds based on trend

Best Practices for Oscillators

Don't fade strong trends based solely on overbought/oversold
Use divergence as a warning, not an entry trigger
Consider trend context when interpreting readings
Combine oscillator signals with price action confirmation
Look for failure swings for higher probability signals

6.7 Moving Average Variants

Definition

Beyond basic SMA and EMA, numerous moving average variants exist, each with different smoothing characteristics and applications.

Moving Average Types

Weighted Moving Average (WMA):

WMA = Sum(Price_i * Weight_i) / Sum(Weight_i)
Where Weight_i = (n - i + 1) for linear weighting

Gives more weight to recent prices linearly
Less lag than SMA, more than EMA

Hull Moving Average (HMA):

HMA = WMA(2 * WMA(n/2) - WMA(n), sqrt(n))

Significantly reduces lag while maintaining smoothness
Faster response than EMA

Kaufman Adaptive Moving Average (KAMA):

Efficiency Ratio (ER) = Change / Volatility
Smoothing Constant (SC) = (ER * (Fast - Slow) + Slow)^2
KAMA = Previous KAMA + SC * (Price - Previous KAMA)

Adapts to market volatility
Slow in ranges, fast in trends

Variable Index Dynamic Average (VIDYA): - Volatility-adjusted smoothing - More responsive during high volatility

Arnaud Legoux Moving Average (ALMA):

ALMA applies Gaussian distribution weighting

Smooth with minimal lag
Reduces noise while maintaining responsiveness

Best Practices for Moving Average Selection

Use SMA for simplicity and wide recognition (200 SMA)
Use EMA for faster response to price changes
Consider HMA or KAMA for reduced lag
Match MA type to strategy requirements
Test different variants on historical data

6.8 Return Indicators

Return indicators measure price changes over time, providing metrics for momentum, performance, and risk-adjusted returns. Unlike oscillators that normalize values, return indicators show actual percentage or absolute changes.

6.8.1 Rate of Change (ROC)

Definition

ROC measures the percentage price change over a specified lookback period.

Formula

ROC(n) = ((Close - Close[n]) / Close[n]) * 100

Where n is the lookback period (typically 12 bars).

Interpretation

Positive ROC: Price is rising over the period (bullish momentum)
Negative ROC: Price is falling over the period (bearish momentum)
Zero line cross: Momentum shift from positive to negative or vice versa
Extreme values: Can indicate overbought (high positive) or oversold (high negative) conditions

Trading Applications

Momentum confirmation: Positive ROC confirms uptrend, negative ROC confirms downtrend
Divergence detection: ROC making lower highs while price makes higher highs (bearish divergence)
Threshold signals: ROC crossing above/below specific levels (e.g., +10%/-10%)
Zero line crosses: ROC crossing zero indicates momentum direction change

Crypto-Specific Considerations

Crypto markets exhibit higher ROC volatility than traditional assets
Adjust thresholds higher for crypto (e.g., ±15-20% vs ±5-10% for stocks)
Shorter periods (6-9 bars) may work better on lower timeframes (5m, 15m)
Use in conjunction with volume to confirm momentum strength

MangroveAI API Reference

Indicator Class: ROC
Category: Momentum/Return
Required Data: close
Parameters:
period: Lookback period for ROC calculation (default: 12)
Outputs: roc (percentage change)
Usage Example: ROC.compute(data={'close': df['Close']}, params={'period': 12})

6.8.2 Daily and Cumulative Returns

Daily Return

Measures the percentage change from the previous bar's close.

Formula:

Daily Return = ((Close - Close[1]) / Close[1]) * 100

Use Cases: - Track short-term performance - Identify volatility patterns - Risk management (daily drawdown limits)

Cumulative Return

Measures the total percentage change from a starting point.

Formula:

Cumulative Return = ((Close - Close[start]) / Close[start]) * 100

Use Cases: - Strategy performance tracking - Benchmark comparison - Target-based exits (e.g., exit at +20% cumulative return)

<summary>daily_return_positive — FILTER — Check if daily return is positive

Description

Evaluates to true when the current bar shows a positive return compared to the previous bar

Parameters

threshold (float, 0.0-100.0, optional, default=0.0): Minimum return threshold in percent

Usage

{"name": "daily_return_positive", "params": {}}

daily_return_negative — FILTER — Check if daily return is negative

Description

Evaluates to true when the current bar shows a negative return compared to the previous bar

Parameters

threshold (float, 0.0-100.0, optional, default=0.0): Maximum return threshold in percent

Usage

{"name": "daily_return_negative", "params": {}}

cumulative_return_positive — FILTER — Check if cumulative return from start is positive

Description

Evaluates to true when the total return from the strategy start point is positive

Parameters

threshold (float, 0.0-100.0, optional, default=0.0): Minimum cumulative return in percent

Usage

{"name": "cumulative_return_positive", "params": {}}

cumulative_return_target — FILTER — Check if cumulative return has reached target

Description

Evaluates to true when cumulative return exceeds a specified target threshold

Parameters

target (float, 1-100, optional, default=10.0): Target cumulative return in percent

Usage

{"name": "cumulative_return_target", "params": {"target": 20.0}}

Best Practices for Return Indicators

Combine with trend indicators: ROC confirms trend direction
Use appropriate periods: Shorter for day trading (6-9), longer for swing trading (12-25)
Adjust for asset volatility: Crypto needs wider thresholds than stocks
Watch for divergences: Price and ROC disagreement can signal reversals
Consider timeframe: Daily returns on 5m bars vs 1d bars have different meanings

Summary

Technical indicators are tools for interpreting market data, not crystal balls for prediction. Effective use requires:

Understanding the math: Know what each indicator actually measures
Context awareness: Interpret indicators based on market conditions
Confirmation: Use multiple indicators from different categories
Price primacy: Indicators follow price; always consider price action first
Simplicity: More indicators don't equal better decisions

Select indicators that complement each other (trend + momentum + volume) and match your trading style and timeframe.

6. Indicators

Signal Type Classifications

6.1 Trend Indicators

6.1.1 Simple Moving Average (SMA)

Definition

Formula

Interpretation

Trading Applications

MangroveAI API Reference

Related Trading Signals

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6.1.2 Exponential Moving Average (EMA)

Definition

Formula

Interpretation

Trading Applications

MangroveAI API Reference

Related Trading Signals

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6.1.3 Double Exponential Moving Average (DEMA)

Definition

Formula

Interpretation

6.1.4 Triple Exponential Moving Average (TEMA)

Definition

Formula

Interpretation

6.1.5 Average Directional Index (ADX)

Definition

Formula

Interpretation

Trading Applications

MangroveAI API Reference

Related Trading Signals

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Description

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Usage

6.1.6 Parabolic SAR

Definition

Formula

Interpretation

Trading Applications

Related Trading Signals

Description

Parameters

Usage

Description

Parameters

Usage

Description

Parameters

Usage

6.1.7 Ichimoku Cloud

Definition

Interpretation