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Parameters are not portable Intermediate

Timeframe-specific optimization

A setting that shines on the daily chart can be useless on the 5-minute, and the reverse is just as true. Timeframe changes the number of trades, the size of the moves, and the weight of costs, so the best parameters move with it. Optimize for the timeframe you will actually trade.

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Parameters do not transfer between timeframes

A 20-period moving average means twenty days on a daily chart and one hundred minutes on a 5-minute chart. Those measure completely different market behaviour. Lengths, stop distances, and thresholds that were tuned on one timeframe are not just suboptimal on another, they are measuring a different thing. Re-optimize whenever you change the chart's timeframe.

As the timeframe drops, trades multiply and the per-trade move shrinks, so costs eat a larger share of each winner.

Costs scale against lower timeframes

Lower timeframes produce more, smaller moves. A fixed cost per trade is trivial against a 5 percent daily swing and crushing against a 0.2 percent intraday move. This is why intraday strategies live or die on costs: the same commission and slippage that barely register on the daily chart can erase the entire edge on the 5-minute. Read modeling slippage and commission before optimizing anything intraday.

Warning

The intraday trap

A lower timeframe looks attractive because it produces more trades and a smoother equity curve in a costless backtest. Add realistic costs and many of those edges vanish.

Trade count moves with timeframe

Higher timeframes give you fewer trades but more history per bar; lower timeframes give you many trades but less calendar history. Both matter for sample size. On a daily chart you may need several years to reach a few hundred trades; on a 15-minute chart you can reach that in months, but you also see fewer distinct market regimes. Balance the two when you choose your window.

Per-timeframe range presets (JSON)

JSON

Keep a separate parameter-range preset per timeframe and import the right one before each run, rather than reusing one set everywhere.

{
  "1D": {
    "Fast MA Length": { "min": 10, "max": 60, "step": 5, "enabled": true },
    "ATR Stop Mult":   { "min": 2.0, "max": 5.0, "step": 0.5, "enabled": true }
  },
  "15": {
    "Fast MA Length": { "min": 20, "max": 200, "step": 10, "enabled": true },
    "ATR Stop Mult":   { "min": 1.0, "max": 3.0, "step": 0.25, "enabled": true }
  }
}

Pick the timeframe you will trade

Optimize on the timeframe you will actually hold positions on, with the costs you will actually pay. Do not tune on the daily chart because the curve is prettier and then trade the 15-minute because it is more exciting. The cleanest result comes from matching the backtest timeframe, the cost model, and your real trading behaviour, then re-optimizing if any of those change.

Key idea

The one rule

Re-optimize for the exact timeframe you will trade, with realistic costs, and never reuse parameters tuned on a different timeframe.

Key takeaways

  • Parameters tuned on one timeframe measure a different thing on another.
  • Lower timeframes magnify the impact of commission and slippage.
  • Higher timeframes mean fewer trades but more regime coverage.
  • Optimize on the timeframe and costs you will actually trade.

Frequently asked questions

Can I reuse optimized parameters across timeframes?

No. A length or stop distance tuned on the daily chart measures a different amount of price action on the 5-minute chart. Re-optimize whenever you change timeframe.

Why do my intraday strategies fail once I add costs?

Lower timeframes produce smaller per-trade moves, so a fixed commission and slippage eat a much larger share of each winner. Many intraday edges that look good costless disappear under realistic costs.

Which timeframe should I optimize on?

The one you will actually trade, with the costs you will actually pay. Matching the backtest timeframe, the cost model, and your real holding behaviour gives the most trustworthy result.

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