How to Import AvaTrade Trades into PipJournal

Importing your AvaTrade history into a dedicated journal turns raw trade data into analyzable performance records. This guide walks you through the full export-to-import process — from locating your closed positions in the AvaTrade platform to verifying your trades are correctly loaded in PipJournal. No prior experience with CSV imports is required.

Step 1: Log In to AvaTradeGO or WebTrader

Open your AvaTrade account at the web platform (WebTrader) or launch the desktop client. If you trade on MetaTrader 4 or MT5 through AvaTrade, you can also export directly from those terminals — the process is covered in the MT4 import guide and MT5 import guide.

For the native AvaTrade platform, log in and confirm you’re viewing a live or demo account that contains closed trades. Pending or open positions are not included in history exports.

Step 2: Export Your Trade History as CSV

Navigate to Reports or Account History in the platform menu. Set the date range to cover the period you want to import — start with the last 90 days if you’re testing the process, then expand to your full history once it works.

Select Closed Positions and choose CSV as the export format. Click Download or Export. The file will typically be named something like TradeHistory_[AccountID]_[Date].csv.

If you are using AvaTrade’s MT4 or MT5 platform, go to Account History in the terminal, right-click the history panel, set the custom period, then select Save as Report — choose the Detailed format for the most complete data.

Step 3: Format the CSV for PipJournal

Open the CSV in a spreadsheet application and check that these columns are present:

Required Field	Typical AvaTrade Column Name
Symbol / Pair	Instrument, Symbol
Open time	Open Date, Open Time
Close time	Close Date, Close Time
Direction	Action, Type (Buy/Sell)
Lot size	Volume, Quantity
Open price	Open Rate
Close price	Close Rate
Net P&L	Net P&L, Profit/Loss

Remove any summary rows at the top or bottom of the file — these are totals rows that are not individual trades and will cause import errors. Save the cleaned file as CSV (UTF-8 encoding).

Step 4: Import the CSV into PipJournal

In PipJournal, navigate to Import Trades and select CSV Upload. Choose your cleaned AvaTrade export file.

PipJournal’s import wizard will attempt to auto-detect column mappings. Review each mapping carefully:

• Symbol: should map to the instrument column (e.g., EUR/USD, GBP/JPY)
• Open/Close DateTime: ensure date and time are combined or mapped correctly — AvaTrade sometimes splits them into separate columns
• Side: map Buy/Long to “buy” and Sell/Short to “sell”
• Volume: confirm this is in lots, not units — AvaTrade standard accounts use lots (1 lot = 100,000 units)
• P&L: map to the net profit/loss column, not gross

Click Preview Import to see a sample of 5-10 trades before confirming. If the pip values or P&L figures look off by a factor of 10 or 100, the lot size column is likely mapped incorrectly. Fix the mapping and preview again.

Click Confirm Import once the preview looks accurate.

Step 5: Verify and Tag Your Trades

After import, open the trades list and spot-check 5-10 trades against your AvaTrade platform history. Confirm that open/close prices, P&L in pips, and trade duration match. A EUR/USD trade with a 30-pip gain on a 0.5 lot should show approximately $150 profit — if the numbers are significantly off, revisit column mapping.

Once verified, add tags to your trades: session (London, New York, Asian), strategy name, or setup type. These tags unlock PipJournal’s filtering and performance analytics, letting you break down win rate and average R by setup or session rather than viewing all trades as a single undifferentiated batch.

Pro Tips

• Export in smaller date ranges (3 months at a time) if you have hundreds of trades — smaller files import faster and are easier to verify.
• Keep raw exports archived in a local folder with the export date in the filename. If you ever need to re-import, you have the source data.
• Map swap/rollover costs if AvaTrade includes them as a separate column — importing swap separately gives you a more accurate cost-of-carry picture on held positions.
• Use the MT4/MT5 detailed report if you trade on MetaTrader through AvaTrade — it includes more granular data (including comment fields) than the native platform export.
• Tag imported trades in bulk using PipJournal’s multi-select after import rather than tagging one by one — saves significant time on large imports.

Common Mistakes to Avoid

1. Importing open positions — AvaTrade history views sometimes default to “All Positions” rather than closed-only. Always filter to closed trades before exporting, or you’ll get import errors on incomplete records.
2. Ignoring the lot size unit — AvaTrade uses standard lot sizes (1.0 = 100,000 units). If you accidentally map a units column instead of lots, all position sizes will be inflated by 100,000x. Always verify P&L on a known trade.
3. Skipping the preview step — Confirming an import without previewing can load hundreds of malformed trades. Use the preview every time, even on repeated imports.
4. Not archiving source files — Deleting the CSV after import leaves no recovery path if trades need to be re-imported with corrected mappings.
5. Treating import as a one-time task — Set a monthly reminder to export and import the previous month’s trades. Staying current makes your analytics meaningful; stale data produces misleading trend reports.

How PipJournal Helps

PipJournal’s CSV import wizard is built to handle the column naming variations between brokers, including AvaTrade’s native export format and its MT4/MT5 derivatives. Once trades are loaded, the analytics dashboard automatically calculates expectancy, win rate, average R, and drawdown metrics without any manual entry. You can filter every metric by the tags you apply post-import — breaking down performance by session, setup, or currency pair. For traders running multiple AvaTrade accounts, PipJournal supports separate account workspaces so prop firm and personal account data never cross-contaminate your analysis.