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Why Is EWO (Elliott Wave Oscillator) Highly Accurate for Bottom Fishing?

In financial markets, bottom fishing is a highly challenging operation. However, the EWO (Elliott Wave Oscillator) is considered highly effective in identifying bottom signals due to its sensitivity to trend changes. This article will analyze the calculation principles of the EWO indicator and explain why it can be effectively used for bottom fishing.

Calculation Principles of the EWO Indicator

EWO is a technical indicator based on two exponential moving averages (EMAs). Its calculation formula is as follows:

def ewo(dataframe, ema_length=5, ema2_length=35):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['low'] * 100
    return emadif
  • Short-term EMA (ema1): Captures rapid market changes, with a default length of 5.\n- Long-term EMA (ema2): Provides a stable reference for long-term trends, with a default length of 35.\n- Normalization: By normalizing the EMA difference (ema1 - ema2) with the lowest price (low), it generates a percentage value applicable across various price ranges.

Why Is EWO Highly Accurate for Bottom Fishing?

  1. Sensitivity of Short-term EMA
    The short-term EMA reflects rapid price changes. During sharp market declines, the short-term EMA drops quickly, while the long-term EMA decreases more slowly due to its smoothing effect. This rapid change causes the difference between the short-term and long-term EMAs (ema1 - ema2) to reach a significantly low negative value, indicating a possible oversold condition in the market.

  2. Sensitivity to Bottoms via Normalization
    By normalizing the difference as a percentage of the lowest price (ema1 - ema2) / df['low'] * 100, the indicator becomes more sensitive to extreme price changes. When prices approach a bottom, this normalization highlights EWO values, making the bottom signal more apparent.

  3. Deviation from Trend as a Signal
    When the short-term EMA drops sharply and falls significantly below the long-term EMA, the EWO value decreases drastically, signaling that the price has deviated significantly from the long-term trend. When this difference turns from negative to positive, it often indicates that the market has completed bottoming out and is starting to rebound.

  4. Dynamic Adaptability
    The dynamic adjustment feature of EMAs allows the EWO to maintain high sensitivity during rapid market changes. This characteristic enables it to respond quickly to price reversals, unlike simple moving average crossovers, which tend to lag.

Bottom Fishing Signals in Practice

  1. Identifying Oversold Zones
    When EWO values reach extremely low negative levels (often analyzed alongside historical data), it may signal an oversold zone.

  2. Catching Rebound Opportunities
    When EWO values recover sharply from a low point and approach positive territory, it often indicates that prices have bottomed out and are starting to rebound.

  3. Confirming with Other Indicators

  • RSI (Relative Strength Index): To validate whether the market is oversold.\n - Support Levels and Volume: To confirm whether there is strong bottom support in the market.

Precautions When Using EWO

  1. Verification Across Multiple Timeframes
    Validate EWO signals across different timeframes (e.g., 5-minute, 1-hour, or daily) to ensure consistency in bottom signals.

  2. Misjudgment Under Extreme Market Conditions
    In strong trend markets, EWO may issue premature bottom signals. Exercise caution to avoid failed bottom fishing attempts.

  3. Integrating Risk Management Strategies
    Even if EWO signals are strong, it is advisable to set stop-loss levels and target prices to minimize potential risks.

EWO and Divergence Mechanism

  1. Core Logic of the Indicator
  • EWO is based on the difference between short-term and long-term EMAs, reflecting the deviation between short-term prices and the long-term trend in the market.\n - When the difference (EWO value) between short-term and long-term EMAs contradicts price trends, divergence occurs.
  1. Typical Divergence Patterns
  • Bullish Divergence (Bottom Divergence):
    When prices make new lows but EWO values do not (or start to rise), it indicates weakening downward momentum and a potential rebound or reversal.\n - Bearish Divergence (Top Divergence):
    When prices make new highs but EWO values do not (or start to fall), it suggests weakening upward momentum and a potential pullback or decline.
  1. Practical Significance of Divergence
  • Divergence is often a market signal indicating that the current price trend is about to end or reverse. EWO captures such signals by analyzing the degree of short-term price deviation from the long-term trend.\n - This is especially important in choppy or overbought/oversold markets, where EWO's divergence signals are more critical.

Why Is EWO Divergence Effective?

  1. Momentum Decay Characteristics
  • When prices continue to rise or fall, a weakening EWO value suggests that the current trend's momentum is diminishing, signaling a potential trend reversal.
  1. Dynamic Adjustment Capability
  • Since EWO is based on EMAs, which are more sensitive to recent price changes, it can detect momentum changes early and capture divergence signals promptly.
  1. Combining Different Perspectives of Prices and Indicators
  • By combining price trends (short-term and long-term EMAs) and normalized low points in its calculations, EWO becomes highly sensitive to internal trend momentum changes.

How to Use EWO to Capture Divergence?

  1. Identifying Bullish Divergence (Bottom Fishing)
  • When prices keep falling to new lows but EWO values stop falling or start to rise, consider a bottom fishing opportunity.
  • Validate bottom signals with RSI or support levels.
  1. Identifying Bearish Divergence (Avoiding Tops)
  • When prices continue to rise to new highs but EWO values fail to do so or start falling, it suggests weakening upward momentum. Consider reducing positions or exiting.
  1. Multi-Timeframe Analysis
  • Use shorter timeframes (e.g., 5-minute) for quick divergence signals.\n - Use longer timeframes (e.g., daily) to confirm the stability of trend reversals.

Conclusion

EWO excels at capturing bottom rebound signals due to its analysis of the difference between short-term and long-term trends, its sensitivity through normalization, and the dynamic adaptability of EMAs. By observing price and EWO divergence, traders can effectively capture trend reversal signals, especially for bottom rebounds and top pullbacks. Combining it with other indicators and timeframe analyses can further enhance trading success rates.