Which inputs does Divergence Reversal use?

Direct answer

Divergence Reversal uses two main types of inputs: (1) price information from a chosen timeframe (such as highs/lows or closing values), and (2) a second data series that represents “momentum” or “trend strength” (often an oscillator). The exact behavior then depends on the settings of that second series and on the rules used to define what counts as divergence and when a reversal is considered “confirmed.”

Mechanism and definition (what inputs are used)

Divergence generally means: price makes one sequence of moves, while the other data series makes a different sequence. In a typical Divergence Reversal approach, you can separate inputs into stable mechanics versus variable conditions.

1. Price inputs

• Price series: commonly the instrument’s price (e.g., closing price) from a chosen timeframe.
• The reference points used for comparison: for example, swing highs and swing lows, or a sequence of local extrema.
• Data dependency: the same rules applied to different timeframes can produce different divergence points because swing structure changes.

2. “Divergence companion” inputs

• A second series: frequently a momentum-style measure computed from price (an oscillator or similar construct).
• Indicator settings: the lookback length(s) and any smoothing parameters used to compute that series.
• Calculation dependency: because the second series is derived from the same underlying price data, its “signal” is sensitive to parameter choices.

3. Rule inputs (the operational definition) A Divergence Reversal method only becomes actionable (as a definition) once you specify rules such as:

• How you classify divergence: for example, whether you require price to make a higher high while the companion series makes a lower high (bearish divergence), or the opposite.
• How you match swings: whether divergence is measured between the same type of swing (high-to-high or low-to-low) and with what tolerance.
• Confirmation or rejection logic: what change, if any, must occur after divergence (for example, a break of a recent level, or a move back toward the series’ midrange). Different rule choices change the inputs required for confirmation.

Evidence or example (a simple, checkable input list)

A self-contained way to list the inputs for a typical Divergence Reversal definition is:

• Assumptions: you will not rely on live quotes; you will use historical candles for one currency pair at a single timeframe.
• Inputs you choose:
  1. Timeframe (e.g., 1-hour vs 4-hour) for generating swing highs/lows.
  2. Price definition (e.g., candle close) and the method to detect swing points.
  3. Companion indicator choice and parameters (e.g., its lookback length and smoothing).
  4. Divergence rule: which direction qualifies as divergence (higher-high vs lower-high logic, or lower-low vs higher-low logic).
  5. Confirmation rule: what “after” condition must happen (and how many candles are allowed before you invalidate the setup).
• Verification path: you can replay these inputs on the same historical window multiple times, changing one parameter at a time (timeframe, companion settings, or tolerance) to observe how often the defined divergence appears.

This approach separates stable mechanics (price-versus-companion comparison and rule-based classification) from variable conditions (timeframe, parameter values, and the exact tolerance/confirmation criteria you set).

Limitations and risks (material failure modes)

Several limitations follow directly from the inputs themselves:

• Subjectivity in swing matching and tolerance: if the rules for “which highs/lows” count are not precise, different people can label the same chart differently, changing the outputs.
• False positives during strong trend regimes: divergence can appear while the dominant trend continues, so the confirmation rule becomes crucial.
• Parameter sensitivity: because the companion series is derived from price using lookbacks and smoothing, small parameter changes can materially shift divergence timing.
• Execution and cost dependence (even in historical checks): if you later connect divergence definitions to real trading, spreads, slippage, and order execution quality can change realized outcomes.
• Jurisdiction and platform differences: any later attempt to implement rules can be affected by local brokerage/platform constraints (for example, data availability or order handling), so historical agreement does not guarantee comparable behavior.

Verification or next question (what you can independently check)

To verify that you understand “which inputs” Divergence Reversal uses, you can do three checks:

1. Write your exact input list (timeframe, price field, companion series settings, divergence rule, confirmation rule).