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Top 42 Missing Indicators: Enhancing Mercury Bot's Technical Analysis

· 4 min read
Max Kaido
Max Kaido
Architect

Introduction​

As Mercury Bot continues to evolve, we've identified key technical indicators that could enhance its market analysis capabilities. While we've already implemented several core indicators (RSI, MACD, Bollinger Bands, Ichimoku Cloud, OBV), this guide ranks additional indicators by their value for crypto markets and implementation complexity.

Current Implementation Status​

Implemented Indicators ✅​

  1. RSI (Relative Strength Index)

    • Complete implementation with domain models
    • Proper enums for trend states and signal strength
    • Advanced divergence detection
    • Field names: value, state, strength, signal, signal_strength, divergence

  2. MACD (Moving Average Convergence Divergence)

    • Complete implementation with trend confirmation
    • Proper signal type and trend strength enums
    • Field names: macd, macd_signal, macd_hist, signal, signal_strength

  3. Bollinger Bands

    • Advanced implementation with position analysis and volatility states
    • Band position and width state enums
    • Field names: upper, middle, lower, distance_to_upper, distance_to_lower, position, width_state, signal, signal_strength

  4. Ichimoku Cloud

    • Complete cloud system with advanced breakout detection
    • Proper enums for cloud position, breakout types, and trend strength
    • Field names: tenkan_sen, kijun_sen, senkou_span_a, senkou_span_b, chikou_span, cloud_position, breakout, trend_strength, cloud_strength, signal, signal_strength, alignment_ratio, cloud_thickness

  5. OBV (On-Balance Volume)

    • Volume trend confirmation with direction analysis
    • Volume trend and momentum state enums
    • Field names: value, direction, trend_strength, signal, signal_strength

  6. EMA (Exponential Moving Average)

    • Multi-period EMA with crossover detection
    • Signal direction and trend strength enums
    • Field names: values, distances, trend_alignment, price_position, crossover, trend_strength, signal, signal_strength

  7. ADX (Average Directional Index)

    • Complete trend strength analysis
    • Direction state and trend strength enums
    • Field names: adx, plus_di, minus_di, strength, direction, signal, signal_strength

  8. ATR (Average True Range)

    • Volatility analysis with state detection
    • Volatility state enums
    • Field names: atr, atr_sma, volatility_ratio, is_volatile, volatility_state

  9. Stochastic Oscillator

    • Complete momentum analysis
    • Proper state and strength enums
    • Field names: k, d, is_overbought, is_oversold, signal, signal_strength

  10. Keltner Channels

    • Complete implementation with EMA and ATR-based channels
    • Channel position and signal strength analysis
    • Field names: upper, middle, lower, distance_to_upper, distance_to_lower, is_above_upper, is_below_lower, channel_width, signal, signal_strength

Ranking Methodology​

Each remaining indicator is evaluated on two key metrics:

  1. Value: How useful the indicator is for spotting high-probability moves in modern crypto/Bybit markets
  2. Difficulty: Implementation complexity (1 = easiest, 5 = hardest) based on:
    • Data availability
    • Algorithmic complexity
    • Integration challenges

Top 5 High-Value Indicators To Implement Next​

1. VWAP (Volume Weighted Average Price)​

  • Value: Critical (#1 remaining)
  • Difficulty: 2/5
  • Why: Essential for intraday traders to identify fair price
  • Implementation Notes: Can build on our existing volume analysis

2. Volume Profile​

  • Value: High (#2 remaining)
  • Difficulty: 4/5
  • Why: Critical for spotting supply/demand zones
  • Implementation Notes: Requires chunking raw data by price-level bins

3. SuperTrend​

  • Value: High (#3 remaining)
  • Difficulty: 2/5
  • Why: Popular stop & reverse overlay
  • Implementation Notes: Can build on our ATR implementation

4. Pivot Points​

  • Value: High (#4 remaining)
  • Difficulty: 2/5
  • Why: Key support/resistance levels
  • Implementation Notes: Standard, Fibonacci, Camarilla variants

5. Heikin-Ashi Bars​

  • Value: High (#5 remaining)
  • Difficulty: 3/5
  • Why: Smooths price action for trend identification
  • Implementation Notes: Requires custom OHLC calculation

Medium Priority Indicators (11-25)​

These indicators offer good value but may require more complex implementation:

  1. Renko/Range Bars (#13)

    • Filters noise
    • Difficulty: 4/5

  2. Elder's Force Index (#14)

    • Price change & volume momentum
    • Difficulty: 2/5

  3. Williams %R (#15)

    • Overbought/oversold oscillator
    • Difficulty: 1/5

Lower Priority Indicators (26-42)​

These indicators may be useful in specific scenarios:

  1. DMI (Directional Movement Index) (#26)

    • Trend strength confirmation
    • Difficulty: 1/5

  2. Hull Moving Average (#27)

    • Reduced lag MA
    • Difficulty: 2/5

  3. T3 Moving Average (#28)

    • Smooth MA variant
    • Difficulty: 3/5

  4. Ichimoku Multi-Timeframe (#29)

    • Advanced cloud analysis
    • Difficulty: 3-4/5

  5. RSI Divergence Detection (#30)

    • Automated divergence finding
    • Difficulty: 3/5

Implementation Strategy​

Phase 1: Quick Wins​

  1. Implement easy, high-value indicators (OBV, Keltner Channels)
  2. Add basic volume endpoints
  3. Integrate simple oscillators

Phase 2: Medium Complexity​

  1. Develop VWAP and derived indicators
  2. Add Heikin-Ashi and alternative chart types
  3. Implement momentum indicators

Phase 3: Advanced Features​

  1. Build Volume Profile functionality
  2. Add pattern recognition systems
  3. Develop multi-timeframe analysis tools

Integration with Mercury Bot​

These indicators will enhance Mercury Bot's capabilities in several ways:

interface TechnicalAnalysis {
  // Implemented indicators
  rsi?: {
    rsi: number;
    overbought: boolean;
    oversold: boolean;
  };
  macd?: {
    macd: number;
    macd_signal: number;
    macd_hist: number;
    macd_trend_confirm: boolean;
  };
  bollinger?: {
    bb_high: number;
    bb_mid: number;
    bb_low: number;
    near_upper_band: boolean;
    near_lower_band: boolean;
  };
  ichimoku?: {
    tenkan_sen: number;
    kijun_sen: number;
    senkou_span_a: number;
    senkou_span_b: number;
    chikou_span: number;
    cloud_direction: string;
    cloud_strength: string;
    cloud_breakout: number;
    price_rel_cloud: string;
  };
  obv?: {
    value: number;
    direction: string;
    trend_strength: number;
    change_percent: number;
  };

  // Future indicators
  vwap?: number;
  volumeProfile?: VolumeProfile;
  stochRsi?: StochRSI;
  // ... more indicators
}

interface AnalysisResult {
  symbol: string;
  timeframe: string;
  indicators: TechnicalAnalysis;
  confidence: number;
  signals: Signal[];
}

Future Considerations​

  1. Data Storage: Efficient storage for high-frequency data
  2. Calculation Optimization: Batch processing for resource-intensive indicators
  3. API Design: Clean interfaces for new indicator endpoints
  4. Testing: Comprehensive validation of indicator accuracy

Conclusion​

This roadmap of 42 indicators provides a clear path for enhancing Mercury Bot's technical analysis capabilities. By prioritizing high-value, low-difficulty indicators first, we can incrementally build a more comprehensive and powerful trading bot.

The next steps involve starting with the top 5 indicators, focusing on volume-based metrics and essential oscillators that will provide immediate value to our users.

Note: Implementation details and specific formulas will be documented in separate technical specifications.