Updated 2025-06-04·Editorial policy·Trading system

What is Multi-Timeframe Analysis?

Multi-timeframe analysis uses higher timeframe for direction and lower for timing entries and stops.

Formula

Stop: $513.00 (below the 1H swing low) — risk = $2.20/share

Indian market context (NSE)

Reference levels: Nifty 50 at 24,300, Reliance Industries at ₹1,300, Bank Nifty futures at 55,000 (lot size 30). Examples below show how Multi-Timeframe Analysis shows up on Indian index, equity, and futures books — update to live quotes in your journal.

Nifty 50 perspective

Multi-Timeframe Analysis on Nifty (24,300): on the 15-minute chart, combine with session VWAP and 9:15–10:00 liquidity — index multi-timeframe analysis signals misfire on expiry Tuesdays without volume confirmation.

Reliance Industries perspective

Multi-Timeframe Analysis on Reliance at ₹1,300: daily vs hourly settings diverge around results and ex-dividend dates; note corporate events in journal when multi-timeframe analysis readings spike.

Bank Nifty futures perspective

Multi-Timeframe Analysis on Bank Nifty futures (55,000): first-hour signals differ from post-14:30 behaviour; avoid standalone entries when banking names lead the move.

How to validate

Forward-test Multi-Timeframe Analysis on paper or sim for two weeks after rule changes.
Validate only on trades where Multi-Timeframe Analysis settings matched the written playbook.
Split results by trending vs range weeks on Nifty before trusting the signal.
Require higher-timeframe bias agreement if that is part of your rule.

How to track in TradeLyser

Add Multi-Timeframe Analysis reading to trade entry notes (value + timeframe).
Create tags: “Multi-Timeframe Analysis aligned” / “Multi-Timeframe Analysis ignored”.
Monthly: filter trades by alignment tag and compare win rate and avg R.
Screenshot chart context for mentor review on disputed trades.

Best practices

Combine Multi-Timeframe Analysis with higher-timeframe bias — not as a lone trigger.
Avoid curve-fitting settings on less than three months of tagged data.
Refresh playbook screenshots when changing Multi-Timeframe Analysis parameters.
Skip trading when Multi-Timeframe Analysis conflicts with written risk limits.

Common pitfalls

Treating Multi-Timeframe Analysis as a guaranteed reversal signal.
Optimising parameters on one bullish month only.
Trading against higher-timeframe bias because Multi-Timeframe Analysis “said so”.
Failing to log when you overrode Multi-Timeframe Analysis discretionally.