Direct answer
To assess RSI (Relative Strength Index), you need: (1) a price time series, (2) the RSI calculation settings (most importantly the lookback length and the timeframe), and (3) clear provenance and quality checks for the data used. You then compute RSI consistently and verify that your inputs match the methodology you intend to compare.
Mechanics and definition
RSI is an oscillator that transforms price changes into a bounded value, typically from 0 to 100. A standard RSI calculation starts from consecutive periods in a price series (often closing prices). It separates upward moves from downward moves over a lookback window, then converts the ratio of average gains to average losses into the RSI scale.
So, the core data needed is the ordered series of prices for each period in the chosen timeframe. At minimum, you need:
- The price field (e.g., closes) for each candle/period.
- The period length used to compute each step (the timeframe).
- The lookback length (commonly described as “RSI period”).
- Any rule for handling the first window where full history is not yet available.
Because RSI is derived from changes between periods, small differences in the underlying prices (or in which timestamps belong to which candle) can shift RSI values.
Evidence, example, and what to verify
An independent assessment typically looks like this:
- Fix assumptions: choose the timeframe and RSI lookback length, and specify the exact price field used (for example, closes).
- Collect provenance: record where the price data came from (market data provider, data export source, and the exact instrument definition).
- Perform quality checks on the time series:
- Continuity: ensure consecutive periods exist and are not accidentally missing.
- Alignment: confirm timestamps map correctly to the selected timeframe.
- Consistency: verify that corporate-action adjustments (if any) or data cleaning steps are applied consistently.
- Recalculate RSI using the same formula and settings.
- Compare results carefully: if RSI differs from a platform, the discrepancy may come from methodology differences (e.g., how averages are smoothed), data preprocessing, or different candle construction.
A practical example is choosing a specific timeframe and computing RSI from the corresponding sequence of closes. If you recompute RSI on the same instrument but accidentally use a different timeframe or a different lookback length, the RSI values will naturally diverge.
Limitations and risks (material failure modes)
Even with correct inputs, RSI assessment has important limitations:
- Methodological mismatch: different implementations can use distinct smoothing approaches or averaging rules, so “RSI” may not be numerically identical across sources.
- Data quality failure mode: missing candles, duplicate periods, or misaligned timestamps can distort the gain/loss sequence and therefore RSI.
- Configuration ambiguity: using a different lookback length or price field changes the indicator.
- Non-stationarity: the relationship between past RSI behavior and future price movement can shift as market conditions change.
Also, historical RSI patterns do not establish future outcomes. Costs, execution effects, and changing conditions can further reduce the relevance of any past indicator behavior.
Verification and next question
To verify RSI information, you can check whether the claimed RSI values are reproducible from the stated timeframe, lookback length, and the specific price series used. A good next step is to ask: “Which RSI calculation method and smoothing rule does the source use, and what exact price data (timestamps and price field) produced those values?” This separates stable RSI mechanics from variable platform or data-provider handling.