Momentum Confluence

Warmup Window

Minimum bars needed: max(money_flow_length, trend_wave_length) bars (default params: 14 bars (money_flow_length=14))

The warmup is determined by the slowest sub-indicator (RSI, Stochastic, or money flow). After warmup, the composite score updates in real-time.

✅ After the warmup window is filled, this indicator produces a new value on every incoming bar in real-time.

Lagging Indicator

Confluence score and reversal signals lag behind the actual momentum shift.

Event	Lag	Detail
Confluence crosses zero (trend change)	≈ max(money_flow_length, trend_wave_length) bars	Composite of RSI, Stochastic, and EMA components; dominated by the slowest
Strong reversal signal fires	≈ max(money_flow_length, trend_wave_length) bars	Requires multiple sub-indicators to agree
Money flow crosses threshold	≈ money_flow_length bars	Money flow uses its own smoothing period

Formula for custom params: lag ≈ max(money_flow_length, trend_wave_length)

Momentum Confluence is a comprehensive multi-component oscillator that combines multiple technical analysis components to provide a powerful trend following and reversal detection system.

Components:

1. Money Flow: Measures buying/selling liquidity entering the market (-100 to +100)
2. Thresholds: Dynamic levels showing significant buying/selling activity
3. Overflow: Detects excess buying/selling that predicts reversals
4. Trend Wave: A highly reactive trend-following oscillator (0-100)
5. Real-Time Divergences: Price vs oscillator divergence detection
6. Reversal Signals: High-frequency (small dots) and strong (arrows) reversal signals
7. Confluence: Combined signal strength from all components (-100 to +100)

def momentum_confluence(
    data: Union[PdDataFrame, PlDataFrame],
    money_flow_length: int = 14,
    trend_wave_length: int = 10,
    threshold_mult: float = 1.5,
    overflow_threshold: float = 0.8,
    divergence_lookback: int = 5,
    high_column: str = 'High',
    low_column: str = 'Low',
    close_column: str = 'Close',
    volume_column: str = 'Volume',
    ...
) -> Union[PdDataFrame, PlDataFrame]:

Example

from pyindicators import (
    momentum_confluence,
    momentum_confluence_signal,
    get_momentum_confluence_stats
)

# Calculate Momentum Confluence
df = momentum_confluence(df)

# Generate trading signals
df = momentum_confluence_signal(df)

# Get statistics
stats = get_momentum_confluence_stats(df)
print(f"Strong bullish reversals: {stats['strong_reversal_bullish_count']}")
print(f"Divergences detected: {stats['divergence_bullish_count']}")

Output Columns:

• money_flow: Money flow oscillator (-100 to +100)
• mf_upper_threshold / mf_lower_threshold: Dynamic threshold levels
• overflow_bullish / overflow_bearish: Excess buying/selling (0 or 1)
• trend_wave: Trend oscillator (0-100)
• trend_wave_signal: Trend direction (1=bullish, -1=bearish, 0=neutral)
• divergence_bullish / divergence_bearish: Divergence detection (0 or 1)
• reversal_bullish / reversal_bearish: High-frequency reversal signals (0 or 1)
• reversal_strong_bullish / reversal_strong_bearish: Strong reversal signals (0 or 1)
• confluence: Combined signal strength (-100 to +100)
• mc_trend: Overall trend direction (1=bullish, -1=bearish, 0=neutral)

Signal Values (from momentum_confluence_signal):

• 2: Strong bullish reversal signal
• 1: Bullish confluence
• 0: Neutral
• -1: Bearish confluence
• -2: Strong bearish reversal signal

Volatility indicators

Indicators that measure the rate of price movement, regardless of direction. They help to identify periods of high and low volatility in the market.