What is Batting Average?

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 Batting Average shows up on Indian index, equity, and futures books — update to live quotes in your journal.

Nifty 50 perspective

Apply Batting Average to your Nifty 50 sleeve (spot near 24,300): track the metric on closed index F&O or ETF trades over at least 30 sessions before changing rules. NSE costs and slippage on fast opens often widen the gap between spreadsheet batting average and bank P&L.

Reliance Industries perspective

On Reliance (₹1,300) delivery or intraday trades, calculate batting average with contract-note costs included. Single-name results can look strong on batting average while your Nifty-correlated book tells the opposite — tag “RELIANCE” separately in TradeLyser.

Bank Nifty futures perspective

Bank Nifty futures near 55,000 (lot 30) amplify batting average swings versus cash — one volatile session can move the metric more than a week of Nifty trades. Log margin mode (MIS/NRML) with each entry for honest review.

How to validate

• Minimum sample: 30 closed trades on one strategy tag before trusting Batting Average.
• Check for one outlier week inflating Batting Average — export largest winners and losers.
• Recompute Batting Average after including brokerage, STT, and slippage on F&O tags.
• Compare Batting Average on the same date range as profit factor and max drawdown.

How to track in TradeLyser

• Open Strategy Board or analytics → filter by strategy tag and review period.
• Locate the widget or column reporting Batting Average (or export trades to compute manually).
• Store snapshot values in weekly review: Batting Average, profit factor, drawdown, trade count.
• If Batting Average is custom, add a spreadsheet column fed from TradeLyser CSV export.

Best practices

• Publish Batting Average per strategy, not only at account level.
• Use the same calculation window (weekly vs monthly) year-round.
• Pair Batting Average with sample size in every review slide or note.
• Document formula used so mentors interpret the same number.

Common pitfalls

• Changing rules after fewer than 20 trades because Batting Average moved slightly.
• Mixing intraday and positional tags when computing Batting Average.
• Ignoring costs so Batting Average looks better than banked P&L.
• Letting one outlier trade dominate the Batting Average reading.

How to use this in TradeLyser

Compute per tag monthly; do not change rules from batting average alone.