Direct answer: the inputs Candlestick Reversal uses
Candlestick Reversal is a pattern-based concept that uses price-derived candlestick inputs—most importantly Open, High, Low, Close (OHLC) for each candle—plus rule parameters that define what “reversal” means in your specific definition. In practice, it may also use optional context inputs such as prior trend direction or volume, and confirmation timing inputs (for example, “the next candle must do X”).
Mechanism: definition and the typical input set
A candlestick summarizes trading activity over a fixed time interval (the timeframe). The core “inputs” are therefore:
- OHLC data per candle:
- Open (starting price), High (maximum), Low (minimum), Close (ending).
- Candle geometry rules (pattern parameters):
- Examples of geometry-based inputs are body size, wick size (upper/lower shadows), and relative positioning (e.g., a long lower wick compared with the body).
- These rules define whether a candle qualifies as part of a reversal pattern.
Candlestick Reversal conceptually separates stable mechanics from variable conditions:
- Stable mechanics: the method turns OHLC history into pattern classifications using deterministic rules you define (what qualifies, what invalidates, and what comes next).
- Variable conditions: the market’s behavior, trading costs, execution quality, and the timeframe can all change how those classifications translate into outcomes.
In addition, many implementations introduce optional inputs that are not strictly required by the candle itself but are used to reduce ambiguity. Typical examples include:
- Prior context: a requirement that the market was moving in a certain direction before the pattern (trend context).
- Confirmation input: a rule that waits for a subsequent candle to support the reversal idea (timing dependency).
- Volume input (optional): using volume to qualify the pattern, if you have reliable volume data for your market.
A simple example of inputs (no live data)
Assume you choose a single timeframe (for example, one fixed interval) and define a reversal candidate based on candle shape using OHLC. Your inputs are then:
- OHLC values for candle A (the candidate), and optionally candle B (confirmation).
- Your thresholds for body/wick relationships.
- Your confirmation rule (e.g., candle B must close beyond a reference level derived from candle A).
The “data” used is still just OHLC; the differences between versions of Candlestick Reversal are mainly the rule parameters and the required context/confirmation inputs.
Evidence and verification: what you can check independently
Because there are no fixed, universal inputs that all users must follow, independent verification comes down to checking whether your rules are applied consistently. You can verify:
- Input computation: confirm that the candles you feed into the definition are built from the same OHLC source and timeframe you intend.
- Rule implementation: test that the pattern criteria (body/wick thresholds, relative comparisons, and invalidation rules) behave as expected on historical examples.
- Dependency on assumptions: compare results when you change only one input parameter at a time (such as adding or removing confirmation, or changing timeframe).
A practical evidence approach is to record, for each historical instance:
- whether the candle meets your OHLC-based pattern criteria,
- whether your confirmation condition triggered,
- and how often outcomes differ across timeframes or market conditions.
Limitations and risks (material failure modes)
Candlestick Reversal can fail in several common ways that relate directly to its inputs and dependencies:
- Ambiguity without context: the same candle shape can occur in multiple market regimes; without trend context, a “reversal-looking” candle may reflect noise.
- Overfitting thresholds: if your geometry thresholds and confirmation rules are tuned to past data, they may not generalize.
- Timeframe sensitivity: pattern appearance depends on the timeframe. Changing the timeframe changes OHLC inputs and can change classifications.
- Execution and trading frictions: even when the candle pattern appears to signal a change, real outcomes are affected by spread, slippage, and order handling. This is especially relevant when reversals are expected to happen quickly after confirmation.
- Event-driven gaps and volatility spikes: sudden price jumps can produce candles that visually resemble reversals or distort wick/body relationships, making rule-based classification less reliable.
Verification or next question to clarify your definition
To explain Candlestick Reversal accurately, you need to state exactly which inputs you are using:
- What timeframe supplies the OHLC candles? - Which candle geometry thresholds define a reversal candle (and how are wicks measured)?