What data is needed to assess Ichimoku?
To assess Ichimoku, you mainly need the price time series and the exact calculation settings used to generate its lines. Ichimoku is a multi-line indicator, so missing or inconsistent inputs can change the output even if the “formula” is correct.
At minimum, collect the following for the same instrument and timeframe:
- High prices for each period (high of the candle/bar).
- Low prices for each period (low of the candle/bar).
- Close prices for each period (closing price).
Additionally, to reproduce and interpret Ichimoku consistently, you need:
- Timeframe definition (e.g., 1 hour candles vs. daily candles). The indicator’s behavior changes across timeframes.
- Lookback parameters used by your implementation (the window lengths for the rolling calculations).
- Displacement / shifting rules (some Ichimoku versions plot lines forward or backward in time).
- Data source and feed details (which exchange or venue the OHLC data represents, and how the platform aggregates it).
How does the required data work in Ichimoku?
Ichimoku uses rolling computations that depend on historical highs and lows and also on a close-based reference. In plain terms, the process is:
- Compute midpoints from rolling ranges of highs and lows over specified lookback windows.
- Apply an additional line that is derived from closing prices (often involving an average).
- Plot or compare lines with a shift so that relationships are seen across different periods.
Because of this structure, Ichimoku assessment is not only about “prices,” but also about how those prices are measured and aligned:
- If your “high” and “low” come from different aggregation logic than your “close,” the derived lines can diverge.
- If candles are created with different time boundaries (server time vs. local time), the rolling windows will differ.
- If lookback parameters or the displacement differ from what you think you are using, you may misread the indicator.
A practical, independent verification approach is to recalculate at least one line from raw OHLC data using the stated settings, then compare the result to the indicator output.
Evidence or example: what to check before trusting outputs
With no real-time data assumed, focus on repeatable checks on historical data you already have:
Afvinkpunten (checklist)
- Provenance: Confirm the OHLC data comes from a single, documented source and represents one consistent market definition.
- Timeliness and alignment: Ensure the bars are complete (no gaps) and that timestamps match the chosen timeframe.
- Calculation settings: Record the window lengths and the displacement exactly as used.
- Quality: Inspect for obvious anomalies (missing bars, duplicated timestamps, or nonstandard session breaks).
- Consistency across platforms: If possible, compute Ichimoku from the same OHLC series in two tools and compare outputs.
Evidence of document / basis
For anything “current” like platform-specific parameter defaults or how a platform defines candle construction, you should rely on platform documentation or the vendor’s published indicator settings rather than assumptions.
Limitations and risks (material failure modes)
Even with correct inputs, Ichimoku interpretation can fail if assumptions break:
- Market condition mismatch: Relationships that appear coherent in one regime may look noisy or contradictory in another. Historical relationships do not establish future reliability.
- Data gaps and corporate events: Missing bars or changes in the underlying instrument (rollovers, adjustments) can distort rolling high/low ranges.
- Implementation differences: Different Ichimoku variants or charting implementations may use different default parameters or shifting behavior, producing visually similar but numerically different results.
- Costs and execution context: Indicator readings do not include trading costs (spreads, fees) or execution effects; those can change real-world outcomes.
A clear red flag is when two implementations that claim the same Ichimoku settings produce meaningfully different lines using the same OHLC series—this often indicates a setting mismatch or a candle construction difference.
Klaarcriterium (done criteria) for independent assessment
You can consider your assessment “verifiable” when you can:
- List the exact OHLC inputs used (high, low, close) and timeframe.
- State the calculation parameters (lookback windows and displacement).
- Recompute at least one line from the historical OHLC data and match the indicator output within an expected tolerance.
Verification or next question
If you want to go one step further, ask: how exactly does the platform construct candles for the chosen instrument and timeframe (session boundaries, missing bar handling, and time zone)?