TRI - Multi-Timeframe BIAS

TRI - MULTI-TIMEFRAME BIAS INDICATOR

DESCRIPTION:
Advanced multi-timeframe bias indicator that analyzes market sentiment across
5 different timeframes (15m, 1h, 4h, 1d, 1w) using adaptive technical analysis.
Provides clear directional bias signals to help determine market momentum.

KEY FEATURES:

• ADAPTIVE PARAMETERS: Uses different EMA lengths and weights for each timeframe
  
• EMA TREND ANALYSIS: Fast/slow EMA crossovers with slope analysis for momentum
  
• RSI MOMENTUM: Adaptive overbought/oversold levels based on timeframe
  
• ADX STRENGTH: Directional movement confirmation with DI+/DI- analysis
  
• COMPOSITE SCORING: Weighted combination of trend, momentum, and strength
  

TIMEFRAME ANALYSIS:

• 15m: EMA9/21 + High momentum weight (45%) - Ultra-responsive for scalping
  
• 1h: EMA21/50 + Medium momentum weight (35%) - Balanced for day trading
  
• 4h: EMA50/200 + Lower momentum weight (25%) - Swing trading focus
  
• 1d: EMA50/200 + Trend focused (55%) - Position trading signals
  
• 1w: EMA50/200 + Maximum trend weight (60%) - Long-term bias
  

BIAS SIGNALS:
STRONG BULLISH/BEARISH: Score ≥ 0.5 - Very strong directional momentum
BULLISH/BEARISH: Score ≥ 0.25 - Clear directional signals
WEAK BULLISH/BEARISH: Score ≥ 0.1 - Mild directional bias
NEUTRAL: Score < 0.1 - No clear directional preference

ALERTS:

• Major Bullish/Bearish: When 4H and 1D timeframes align
  
• High confidence signals for strategic decision making
  

USAGE:

1. Higher timeframes (1d, 1w) show primary market direction
   
2. Lower timeframes (15m, 1h) provide entry timing
   
3. Look for alignment across multiple timeframes for stronger signals
   
4. Use confidence levels to assess signal reliability
   

TECHNICAL COMPONENTS:

• Exponential Moving Averages (EMA) for responsive trend detection
  
• Relative Strength Index (RSI) for momentum analysis
  
• Average Directional Index (ADX) with DI+/DI- for trend strength
  
• Volume ratio confirmation for signal validation
  
• Adaptive thresholds optimized for each timeframe's characteristics