How to Import eToro Trades into PipJournal

Importing your eToro trade history into PipJournal gives you performance data that eToro’s built-in stats cannot provide — breakdowns by session, setup type, risk-to-reward, and drawdown curve. This guide is written for beginners who have never exported from eToro before. By the end, you will have your closed forex trades loaded into PipJournal and ready for analysis.

Step 1: Export Your eToro Trade History

Log into your eToro account and navigate to Portfolio > History. Set the date range to cover all trades you want to analyse — eToro supports up to 365 days per export. Click the Download icon (a spreadsheet arrow) in the top-right corner of the history panel. eToro generates a CSV file called something like Closed_Positions_01-01-2026_31-12-2026.csv.

If you need history older than 12 months, submit a support ticket to eToro requesting a full account statement. Turnaround is typically 3-5 business days.

Step 2: Prepare the CSV for Import

Open the CSV in any spreadsheet application. eToro’s export includes all asset classes — stocks, ETFs, crypto, and forex pairs. PipJournal is a forex journal, so delete every row where the Action column does not correspond to a currency pair (EUR/USD, GBP/JPY, etc.).

Also check these columns before importing:

Column	What to confirm
Open Date	Format is YYYY-MM-DD HH:MM:SS
Close Date	Same format as Open Date
Amount	Units (USD) — not pips
Profit	Signed number, negative for losses
Open Rate / Close Rate	Decimal prices, not percentages

Remove any header rows that eToro adds above the main column row — PipJournal expects a single header line followed immediately by data rows.

Step 3: Import the CSV into PipJournal

In PipJournal, go to Trades > Import and select eToro from the broker dropdown. Upload your cleaned CSV file. PipJournal will display a column mapping preview showing how each eToro column maps to its internal fields (pair, direction, open price, close price, profit in USD, lot size).

Confirm the mappings look correct — pay particular attention to lot size. eToro expresses position size in units rather than standard lots. PipJournal converts automatically: 100,000 units = 1.0 lot, 10,000 units = 0.10 lot. If you traded micro sizes, a 1,000-unit position will appear as 0.01 lot, which is correct.

Click Import to load the trades. A progress indicator shows how many rows are processed. Typical import of 200 trades takes under 10 seconds.

Step 4: Verify Imported Trades

After import, open the Trades list and spot-check 5-10 trades against your eToro account statement. Verify:

• Pair and direction match (long EUR/USD vs short EUR/USD)
• Open and close prices are identical to eToro’s records
• P&L in USD matches within a few cents (minor rounding differences are normal)
• Pip count is calculated correctly — a 50-pip move on EUR/USD at 0.10 lot should show $50 profit

If pip counts look off, confirm the instrument name uses standard notation. eToro sometimes uses EURUSD without the slash — PipJournal handles both formats, but check the import log for any unrecognised symbols.

Step 5: Tag and Categorise Your Trades

Raw imported data gives you baseline stats, but tagging unlocks the deeper analysis. For each trade (or in bulk using PipJournal’s multi-select), add:

• Setup tag — the entry pattern you used (e.g., breakout, pullback, news-fade)
• Session — London, New York, or Asian session when the trade was opened
• Risk note — your planned risk in pips or USD before entry

With 50 or more tagged trades, PipJournal’s analytics can show you which setups produce the best expectancy and which sessions drag your profit factor down.

Pro Tips

• Export quarterly rather than yearly if you trade frequently — smaller files are faster to clean and easier to spot-check.
• eToro’s “Profit” column is net of spread but may exclude overnight rollover fees. Cross-reference your eToro account statement for swap-adjusted figures if you hold positions overnight.
• If you use eToro’s CopyTrader feature, exclude copied positions from your import — those trades do not reflect your own decision-making and will skew your analytics.
• After your first import, set a calendar reminder to export monthly so you maintain a continuous, gap-free trade log in PipJournal.
• Use PipJournal’s weekly review workflow the same week you import — fresh context means more accurate trade notes.

Common Mistakes to Avoid

1. Importing non-forex rows. Leaving stock or crypto rows in the CSV causes import errors or corrupts pip calculations. Always filter to currency pairs only before uploading.

2. Skipping the column mapping check. Accepting the default mapping without reviewing it can assign the wrong field to profit or lot size. Spend 30 seconds confirming each column before clicking Import.

3. Importing without tagging. Untagged trades produce only surface-level stats. Without setup tags, you cannot identify which patterns are profitable — the core reason to journal in the first place. Tag as you go rather than leaving it for later.

4. Ignoring the import log. PipJournal flags rows that could not be parsed. Dismissing the log means missing trades in your dataset, which distorts win rate and average R. Read every warning before closing the import dialog.

5. Double-importing overlapping date ranges. If you export January-June and then export March-December, you will have duplicate trades for March through June. Use non-overlapping date ranges or use PipJournal’s duplicate detection tool to clean up.

How PipJournal Helps

PipJournal’s native eToro importer handles the unit-to-lot conversion automatically, so you do not need to manually reformat position sizes before uploading. Once your trades are in, the analytics dashboard breaks performance down by pair, session, and setup tag — analysis that eToro’s portfolio view does not offer. The equity curve view visualises your account growth trade-by-trade, making drawdown periods immediately visible. For traders building consistency before scaling, PipJournal tracks risk per trade over time so you can confirm your position sizing is disciplined across the full sample.