This guide explains what to record, the metrics to calculate, how to review a journal, and how to use one alongside automated strategies.
What to record for each trade
| Field | Example |
|---|---|
| Date and time | 9 Oct 2026, 9:47 am |
| Instrument | Nifty 25,000 call, weekly expiry |
| Strategy or setup | Opening range breakout |
| Direction and size | Buy, 1 lot |
| Planned entry, stop and target | ₹280, ₹196, ₹392 |
| Actual entry and exit | ₹281.50, ₹352.00 |
| Charges | Brokerage, taxes and fees |
| Net P&L | In rupees |
| R-multiple | Profit or loss divided by the planned risk |
| Rule followed? | Yes or no, and which rule if not |
| Notes | Market context, anything unusual |
For manual trades, add how you felt and whether you hesitated. For automated trades, note any manual intervention.
R-multiples
An R-multiple expresses each trade's result in units of the risk you planned to take. If you planned to risk ₹5,000 and made ₹7,500, the trade was +1.5R. If you lost ₹5,000, it was −1R; if a gap made you lose ₹7,000, it was −1.4R.
R-multiples make trades comparable across different sizes and instruments, and they show quickly when losses are exceeding plan.
Metrics to calculate
- Win rate: the share of trades that made money.
- Average win and average loss, in rupees and in R.
- Expectancy: win rate × average win − loss rate × average loss, after costs. It is the average result per trade.
- Profit factor: gross profits ÷ gross losses.
- Maximum drawdown: the largest fall from a peak in the running total. See the max drawdown calculator.
- Longest losing streak.
- Results by strategy, instrument, day of the week and time of day.
For risk-adjusted comparisons, see the Sharpe ratio calculator.
Reviewing your journal
Weekly
- Did you follow your rules on every trade?
- Were any losses larger than 1R? Why?
- Which trades were outside your plan?
Monthly
- Which strategies or setups made money, and which lost?
- Is expectancy positive after costs?
- How does the month compare with your backtest's typical month?
- What one change would you make, and how will you test it?
Make one change at a time, so you can tell what helped.
Journals for algo traders
Automated strategies need journals too, with a different focus:
- Live vs backtest: compare each strategy's live results with its backtest over the same type of period. A growing gap is a warning.
- Slippage: record the intended price and the actual fill of every order. See the slippage calculator.
- Interventions: note every time you paused, changed or overrode a strategy, and what happened.
- Errors: rejected orders, expired sessions and connection problems, with their cost.
QuantPower's order book and verified P&L records give the raw data for this review: every order with its time and price, and the realised P&L of your trades.
Common journaling mistakes
- Recording only losers or only winners.
- Leaving out costs, which hides strategies that lose after charges.
- Writing notes after the outcome is known, which colours the record with hindsight. Note the plan before the trade.
- Collecting data and never reviewing it.
From journal to better rules
The journal's job is to improve decisions. When a pattern appears, such as losses clustering in the first 15 minutes or on expiry day, turn it into a rule, test the rule in the backtesting engine and add it to the strategy in the strategy builder. Then keep journaling to see whether it helped.
Start your free trial
Connect your broker, build a strategy and test it on paper before a single live order goes out.
Frequently asked questions
Why should I keep a trading journal?
To find out which trades and setups actually make money, catch repeated mistakes and check whether you follow your rules.
What is the most important thing to record?
The planned risk, entry, stop and target before the trade, and whether you followed the plan.
How often should I review my journal?
Briefly every week and in more depth every month.
Do automated strategies need a journal?
Yes. Comparing live results with backtests and tracking slippage and interventions is essential for automated trading.
Sources
- Tharp, V. K. (2006). Trade Your Way to Financial Freedom. McGraw-Hill (R-multiples)
- Steenbarger, B. N. (2009). The Daily Trading Coach. Wiley