Multi-Timeframe Strategy Design

Multi-timeframe PyPScript means one script can be scoped to more than one timeframe in its config.

That does not mean the script should become vague.

Good multi-timeframe scripts keep their logic readable and their assumptions explicit.

Start With Config

The simplest form is:

config {
    pairs: ["EURUSD"]
    timeframes: ["1m", "5m", "15m"]
}

This tells PyP which timeframes the script is intended for.

When Multi-Timeframe Makes Sense

Use more than one timeframe when the same idea is valid across nearby regimes.

Examples:

• a momentum script that works on 1m and 5m
• a trend-following script that works on 15m and 1h
• a swing structure script that works on 1h and 4h

It makes less sense when:

• the script is highly tuned to one very specific rhythm
• the stop/target logic depends on one market speed only
• the same indicator window means very different things across the listed timeframes

Keep The Strategy Timeframe-Aware In Naming

If a script is multi-timeframe, write it in a way that survives that broader scope.

Weak:

config {
    timeframes: ["1m", "1h"]
}

with a script that was clearly invented only for 1m.

Better:

config {
    timeframes: ["5m", "15m"]
}

for a momentum or trend script whose logic is still recognizable in both.

Parameter Discipline

The biggest multi-timeframe mistake is using windows that are only sensible on one timeframe.

For example:

fast_ma = sma.evaluate(close, 3)
slow_ma = sma.evaluate(close, 5)

That may be a very noisy scalp construct.

If you then list 1h in config as well, the script is no longer clearly designed.

Prefer windows that still mean something across the selected set:

fast_ma = sma.evaluate(close, 20)
slow_ma = sma.evaluate(close, 50)
momentum = rsi.evaluate(close, 14)

Build Around Market Structure, Not Tiny Timing Artifacts

Multi-timeframe logic survives better when it expresses concepts like:

• trend alignment
• pullback quality
• momentum confirmation
• volatility filter

Example:

#!pyp/1.0
engine: pyp-1.0

import indicators.sma
import indicators.rsi
import indicators.atr
import functions.above
import functions.below
import functions.cross

strategy "multi_timeframe_trend_pullback" {
    fast_ma = sma.evaluate(close, 20)
    slow_ma = sma.evaluate(close, 50)
    rsi_value = rsi.evaluate(close, 14)
    atr_value = atr.evaluate(high, low, close, 14)

    trend_up = above(fast_ma, slow_ma)
    trend_down = below(fast_ma, slow_ma)
    long_momentum = above(rsi_value, 55)
    short_momentum = below(rsi_value, 45)
    volatility_ok = above(atr_value, 0.0)
    long_trigger = cross(close, fast_ma)
    short_trigger = cross(fast_ma, close)

    patterns {
        trend_up and long_momentum and long_trigger and volatility_ok -> UP(confidence: 0.84)
        trend_down and short_momentum and short_trigger and volatility_ok -> DOWN(confidence: 0.84)
        default -> HOLD(confidence: 0.96)
    }
}

meta {
    author: "PyP"
    version: "1.0.0"
    description: "Trend pullback logic intended for intraday momentum regimes"
}

config {
    pairs: ["EURUSD"]
    timeframes: ["5m", "15m"]
}

Good Multi-Timeframe Pairings

These pairings are usually more coherent than extreme jumps:

• 1m and 5m
• 5m and 15m
• 15m and 1h
• 1h and 4h

These are harder to justify in one script:

• 1m and 1d
• 5m and 4h

You can still do it, but the script should have a strong reason for spanning that gap.

Choose One Primary Use Case

Even in a multi-timeframe script, there should still be one primary market behavior:

• trend continuation
• pullback entry
• breakout
• mean reversion

Do not try to make one file be:

• a scalp script on 1m
• a swing script on 4h
• a reversal script on 1d

That usually turns into vague logic and bad explanations.

Signs Your Multi-Timeframe Scope Is Too Broad

• the same confidence values no longer make intuitive sense across all timeframes
• one timeframe generates far too many candidate setups and another generates almost none
• your comments keep saying “on lower timeframes” or “on higher timeframes” without the logic reflecting that clearly
• you cannot explain why each listed timeframe belongs in the same script

Practical Rule

If you can describe the script in one sentence and that sentence still sounds true across every listed timeframe, the scope is probably fine.

If not, split it into separate scripts.

Multi-Timeframe Checklist

• keep timeframes close enough to share the same market idea
• use indicator windows that stay meaningful across the listed set
• avoid overfitting to one timeframe while claiming many
• write comments that explain the market structure, not just the indicator names

Related Pages

• Basics
• Best Practices
• Troubleshooting