Skip to main content

​
What indicator parameters are

Indicator parameters control how an indicator reacts to market data. They define things like:
  • lookback length
  • smoothing method
  • sensitivity
  • thresholds
Changing parameters does not change what an indicator measures -
it changes how fast and how aggressively it reacts.

​
Common parameter types

Most indicators use one or more of the following parameter types.

Length / Period

Number of candles used for calculation.

Smoothing

How much noise is reduced in the output.

Multiplier / Factor

Scales volatility-based indicators.

Thresholds

Levels used for logic decisions.

​
Length (period)

The most common parameter. Examples: 
RSI(14)
EMA(50)
ATR(20)
Effects:
  • shorter length → faster, noisier signals
  • longer length → slower, smoother signals
Length changes often have the biggest impact on behavior.

​
Smoothing parameters

Some indicators apply smoothing internally. Examples:
  • EMA vs SMA
  • MACD signal smoothing
  • Stochastic smoothing
Smoothing:
  • reduces noise
  • increases lag
  • stabilizes signals

​
Multipliers and factors

Volatility-based indicators often use multipliers. Examples: 
Supertrend(10, 3)
Bollinger Bands(20, 2)
Multipliers:
  • widen or tighten ranges
  • affect stop distance
  • change trade frequency

​
Threshold values

Thresholds define decision boundaries. Examples: 
RSI > 70
RSI < 30
Trend strength > 25
Thresholds are logic-level parameters, but are tightly coupled to indicators.

​
Parameter behavior across timeframes

The same parameters behave differently on different timeframes. Example:
  • RSI(14) on 5m ≠ RSI(14) on 4h
Never assume parameter behavior transfers across timeframes.

​
Defaults and assumptions

If parameters are:
  • explicitly defined → used exactly
  • omitted but standard → ATI applies widely accepted defaults
  • non-standard or ambiguous → ATI asks for clarification

What Trinigence fills automatically

See how parameter defaults are chosen.

​
Over-optimization risks

Excessive parameter tuning can lead to:
  • curve fitting
  • poor out-of-sample performance
  • fragile strategies
A robust strategy tolerates small parameter changes.

​
Best practices

  • Start with standard parameters
  • Change one parameter at a time
  • Test across markets and timeframes
  • Prefer simplicity over precision

Iteration & optimization

Learn how to tune parameters safely.

Supported indicators

What indicators are available.

Operators & conditions

How parameters affect logic.

Crossovers & trend changes

Event sensitivity effects.

Backtesting metrics

See parameter impact in results.
Parameters tune behavior.
Robustness beats precision.