Definition and stable mechanics of Tsi
Tsi is a momentum-style technical indicator that is computed from price history using configurable parameters. To assess it, start by separating two things: (1) the indicator’s stable mechanics (the mathematical steps and required inputs) and (2) variable conditions (market regime, data source, sampling frequency, and execution context). Your assessment is only as reliable as the data and the exact definition you implement, because different sources can use different parameter values or formula variants.
What data you need: inputs, provenance, and timeliness
To assess Tsi in a way you can independently verify, gather the following data categories.
1) The indicator definition and parameters
- The exact Tsi formula variant you will use.
- All parameter values used in the calculation (for example, lookback lengths or any smoothing/weighting factors).
- Any practical implementation rules (such as whether calculations are done on close-only data, how intermediate steps are handled, and whether the indicator is computed at bar close or intrabar).
Without this, you cannot confirm that your computed values correspond to the Tsi you are evaluating.
2) Price input series
- The specific price field(s) used by the formula (commonly an OHLC-derived series, such as close-to-close returns or similar transformations).
- The instrument identifier (the exact underlying market being measured).
- The trading session and timezone conventions used to define bars.
Even if the indicator definition is correct, using the wrong price field or inconsistent bar construction will change the resulting Tsi series.
3) Sampling frequency and alignment
- The bar size or data frequency (for example, 1-minute, 1-hour, or daily).
- Timestamp alignment rules: whether the indicator for a bar uses data up to the bar close, and how missing or partial bars are treated.
A common failure mode is comparing Tsi computed on one timeframe with another timeframe or mixing different timestamp conventions.
4) Data provenance and quality indicators
- The data source (data vendor, broker, platform) and the method of delivery (historical download vs. streamed updates).
- Coverage details: whether the series is continuous or contains gaps.
- Quality checks you can perform: missing values, duplicated bars, obvious outliers, and whether corporate actions could affect the instrument history (relevant for some markets, less for many FX datasets, but still worth noting in general terms).
Documenting provenance matters because the same “formula” can produce different results when the underlying data differs.
Evidence and example: how to validate your Tsi assessment
Use a verification approach based on repeatability rather than outcomes.
- Recompute from raw inputs: starting from the underlying price series, apply your chosen formula and parameters step-by-step. Record intermediate values if the implementation is complex.
- Cross-check with an independent computation: use an alternate implementation (for example, a different script or tool) while keeping definition, parameters, timeframe, and price inputs identical.
- Sensitivity checks: rerun Tsi while changing only one factor at a time (timeframe, sampling, or parameter values). If results swing dramatically, you have learned that your assessment is highly data- or parameter-dependent.
Assumption example (state it explicitly): if you compute Tsi on 1-hour bars using a close-to-close transformation and compute the indicator at bar close, then your evaluation should not compare those values to a Tsi series computed using a different bar closing convention.
Limitations and risks
At least one material limitation or failure mode should be part of your assessment.
- Data mismatch risk: inconsistent timeframe, timezone, or bar definition can make two Tsi series appear different even when the formula is the same.
- Provider/implementation variability: different platforms may implement “Tsi” with different parameter defaults or calculation details, so name matching alone is not sufficient.
- Quality and survivorship issues: gaps or corrupted historical data can distort smoothing steps and propagate errors forward.
- Market-dependency: historical indicator behavior does not establish how it will act in future regimes. Also, costs, execution constraints, and jurisdictional factors affect real-world results, even though the indicator itself is computed purely from price data.
Verification and next questions
To finish your assessment, create a checklist you can reuse:
- Did you lock the exact Tsi definition and parameter set? - Are the price inputs consistent in instrument, field, frequency, and timezone? - Can you reproduce the calculation from the raw series without relying on a black-box output?