What the journal is
A ledger of every open and closed trade you've ever made, with:
- Entry price, exit price (if closed), quantity, holding days
- Realised P&L in rupees and as percent of capital
- R-multiple (P&L divided by initial risk)
- Conviction snapshot at entry
- Auto-linked filing or corporate action within ±2 days of entry
The page above the ledger surfaces aggregate stats: win rate, expectancy, max drawdown, profit factor, equity curve.
Where trades come from
Three ways trades enter the journal:
- Manual: fill the trade form on the journal page
- CSV/Excel tradebook upload: see Uploading holdings via CSV
- Broker sync: daily holdings snapshots are diffed; deltas infer trades automatically
Method 3 is the default for Pro broker-sync users. The platform writes a daily holdings snapshot at 17:00 IST every weekday and computes deltas. Corporate-action days (splits, bonuses) are skipped to avoid false inferences.
Reading the KPI strip
Eight metrics, top of the page:
- Total Trades: count of closed trades in the selected window
- Win Rate: percent of closed trades that ended profitable
- Avg Win / Avg Loss: average return on winners vs losers (in percent)
- Win/Loss Ratio: avg win divided by avg loss (in absolute value)
- Expectancy: average P&L per trade in rupees
- Profit Factor: gross profit divided by gross loss
- Max Drawdown: largest peak-to-trough decline in cumulative P&L
The single most important read: expectancy. A positive expectancy with reasonable trade count means the system is working; a negative expectancy means something needs to change in setup selection, position sizing, or exit discipline.
The equity curve
A line chart of cumulative P&L over time. Toggle between:
- Realized: only closed trades, the truth of what you've actually banked
- +MTM: adds unrealised P&L on open positions, gives current "if everything were closed today" value
Optional benchmark overlay (Nifty 50 or Nifty Midcap 150). If your equity curve underperforms the benchmark on a year window, the bar to beat is "buy and hold an index". Your trading is paying you to take more risk for less return.
R-multiple distribution
A histogram of how each trade did, expressed in R units (multiples of initial risk).
- A normal-looking distribution centred around 0–0.5R means inconsistent execution
- A right-skewed distribution (many small losses, fewer big wins) is the trend-following pattern, desirable for momentum traders
- A left-skewed distribution (many small wins, occasional big losses) is the mean-reversion pattern. Common, but the big losses can erase years of gains if unmanaged
Conviction-at-entry attribution
The most distinctive feature. The platform snapshots the conviction score at the moment of every entry. The attribution table then groups trades by their entry-conviction:
- Trades entered at Strong Bullish: X trades, Y win rate, Z avg R-multiple
- Trades entered at Moderate Bullish: …
- Trades entered at Weak / Bearish: …
Over enough trades, you can see whether your trades work better when you wait for high conviction. If your Strong-Bullish entries average 1.8R and your Weak-entry trades average 0.2R, you have a quantitative argument for being more selective.
Tags
Every trade can be tagged across three categories:
- Setup: what kind of trade this was (breakout, mean-reversion, news catalyst)
- Mistake: what went wrong (held too long, sized too small, exited too early)
- Proficiency: what went right (followed plan, took partial profits, scaled in correctly)
System default tags exist, but the value comes from creating your own. Over months of tagging, the highest-expectancy setups and the most-common mistakes become quantitatively visible.
Open the journal