It is hard to review your own journal honestly. You remember the big trades and forget the strings of small losses.
The AI agent totals up the month from your ATAS journal and shows what works in your trading and what drags the result down. It can spot things that are hard to see yourself, for example that the whole month's profit rests on a single trade. If there are too few trades, the agent says so plainly.
Review my trades from the ATAS journal for the last 30 days: number of trades, net result, average win and average loss, best and worst day, longest losing streak. Then give me three observations based on my numbers. If there are too few trades, say so plainly and don't draw conclusions the numbers can't support. Don't change anything.
What happens
The agent reads the journal and does the math. The example uses a simulator journal: 10 trades over two trading days. Look at the three observations at the end of the answer. Each one is based on numbers from the journal.
Review my trades from the ATAS journal for the last 30 days: number of trades, net result, average win and average loss, best and worst day, longest losing streak. Then give me three observations based on my numbers. If there are too few trades, say so plainly and don't draw conclusions the numbers can't support. Don't change anything.
Over the last 30 days, Oct 2 to Nov 1, 2026, up to 21:49 London time, the journal has 10 closed trades. All of them are on ATAS Sim simulation accounts: 1002, 1003 and 1004.
| Metric | Result |
|---|---|
| Trades | 10: 1 winner, 9 losers |
| Net result | +$1,125.00 |
| Average winning trade | +$1,875.00 |
| Average losing trade | −$83.33 |
| Average result per trade | +$112.50 |
| Best trading day | Oct 9: +$1,712.50, 7 trades |
| Worst trading day | Oct 23: −$587.50, 3 trades |
| Longest losing streak | 9 trades in a row, Oct 9-23, −$750.00 in total |
Days are based on close time in the London time zone. The streak is counted across all three accounts together. Commission in the journal is $0, and the net result reflects exactly this data.
Three observations:
- The entire positive result came from one trade of +$1,875. The other nine lost $750. Without that single winner the period would have been a loss.
- Position size affects the comparison. The winning trade had a size of 5, each losing trade a size of 1. So the ratio of average win to average loss, 22.5 times, can't be read as a reward-to-risk ratio at equal size.
- Oct 23 accounts for 78.3% of all losses in the period: −$587.50 out of −$750, in just three trades. The losses are concentrated on one day, but there is not enough data to explain why.
The sample is small: 10 trades over two trading days, only one of them a winner. It is not enough to judge how stable the strategy is or what returns to expect. I didn't change anything.
Everything the agent reads becomes part of your conversation with the AI provider. More on the Security and data page.
This is a review of past trades. Trading decisions are yours.