Oscillators and Divergences

Lessons in this module

1. 9.1

   RSI and Stochastic

   Use oscillators to assess overbought and oversold conditions

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2. 9.2

   WaveTrend: how it works and how it differs from RSI

   Understand the calculation through price deviation from its own average and see why it leads RSI and why it is noisier

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3. 9.3

   WaveTrend in practice: crosses, zones, noise filtering

   Tell a cross at an extreme apart from a cross in the middle of the range and filter noise by slope, divergence strength and a pause between signals

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4. 9.4

   Divergences: regular versus hidden

   Understand that a regular divergence works against the trend and a hidden one with the trend, and why they get confused

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5. 9.5

   Divergence quality: why they are not all equal

   Score a divergence by its size, the distance between pivots, the zone context and the integrity of the line, and know what history showed: none of these features, nor their sum, changed the outcome; on 15m, divergences with 3-4 of 4 features won 50% of the time versus 50% for a random entry

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6. 9.6

   Confirmation and divergence traps

   Do not enter on a divergence without confirmation, and recognize a series of false divergences in a strong trend, and know what history showed: confirmation by a break of the intermediate extreme did not raise the win rate; it only made the entry later

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7. 9.7

   Higher-timeframe divergences and confluence

   Combine divergences from different timeframes without counting the same signal twice

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8. 9.8

   Adaptive levels and an agreement heat map

   Replace the rigid 70/30 with mean ± σ and read a heat map that shows how many engines point the same way

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9. 9.9

   Stochastic of CCI: a double oscillator

   Build an oscillator on top of an oscillator and understand what it gives: the position of CCI within its own range reacts earlier than CCI itself, but is also noisier

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10. 9.10

    Momentum: an oscillator against its own average

    Use the gap between an oscillator and its own EMA as a direction filter, the same technique as MACD applied to any series

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11. 9.11

    Divergences on two sources at once

    Look for divergences on a smoothed oscillator and on the raw series beneath it at the same time: the smoothed one gives fewer signals, the raw one sees earlier

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12. 9.12

    Different filters for regular and hidden divergences

    Understand why reversal and continuation divergences need different admission rules: a trend filter cuts two thirds of regular divergences without improving the rest, and a third of hidden ones, separating out the worst of them only on 1h and 4h; it brought neither family to an edge over a random entry

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