> ## Documentation Index
> Fetch the complete documentation index at: https://docs.trinigence.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Indicator Parameters
> How indicator parameters affect behavior, sensitivity, and strategy outcomes.
## 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.
Number of candles used for calculation.
How much noise is reduced in the output.
Scales volatility-based indicators.
Levels used for logic decisions.
***
## Length (period)
The most common parameter.
Examples:
```text theme={null}
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:
```text theme={null}
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:
```text theme={null}
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
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
Learn how to tune parameters safely.
***
## What to read next
What indicators are available.
How parameters affect logic.
Event sensitivity effects.
See parameter impact in results.
Parameters tune behavior.\
Robustness beats precision.