EURUSD
Short- Expectancy per trade
- +0.54R
- Profit factor
- 3.17
- Sharpe (annualized)
- 3.17
- Return @ 1% risk
- +19%
- Max drawdown
- 1.00R
- Longest losing streak
- 1
- Trades per month
- 3
- Sample size
- 16
Live Track Record
AI Traders powered by the top AI models: Claude and GPT, one per market. Live forward trading. Every trade tracked, graded, and evaluated automatically. Updated every five minutes.
The live track record of our AI Traders: a Buy book and a Short book per instrument, powered by Claude and GPT. Each side is its own agent with its own record, so a market can be worth trading one way and not the other. The agents below are included in the Pro plan, and some or all of them in the Lite plan; retired ones keep their record but are no longer offered. Combined across the fleet and updated every 5 minutes from the connected broker account. YTD R is our published year-to-date record and also counts traders we have since retired, so it runs ahead of the cards below rather than matching their sum. For the full history, see our case studies.
August 2026 update: we split every AI Trader into an independent Buy agent and Short agent. We now track separately how the AI performs going long and going short, so each side is its own trading agent, with its own track record, that can be managed on its own. That is why some percentages went up and others went down: a single blended number was hiding two different results. We manage our agents on performance: the ones that stop working get retired and their record stays on this page, while others move to higher subscription tiers, where seasoned traders often run them as idea generators or to help time the markets rather than as a signal to follow outright.
No longer trading live. We keep their full-year results on the page, and counted in the fleet tally above, so nothing is cherry-picked out of the record.
Methodology
Every metric on this page comes from per-trade records on a live broker account. Each trade collapses to one realized R value using a committed primary-target rule, and all aggregate statistics are computed from the resulting R series.
annual_return = expectancy_R × 0.01 × monthly_trades × 12Compounding implicitly assumes 100% reinvestment with continuous sizing, a strong assumption that inflates the headline. The linear version is conservative.FAQ
Everything you might want to ask before trusting these numbers.
Live forward trading. Every metric on this page is computed from real trades executed on a connected broker account. We do not publish backtested performance.
Per-trade expected return at 1% account risk per trade, multiplied by trades per year. Formula: (expectancy in R) × 0.01 × (monthly trades average) × 12. Linear projection, not compounded, which is more conservative and easier to defend.
Each system commits to a take-profit level (TP1, TP2, or TP3) in advance. Every trade's realized R is measured against that committed target. No cherry-picking the best leg in hindsight.
(mean R / stddev R) × √(trades per year), where trades-per-year comes from the sample's actual time span. Reference scale: 1.0 = decent, 2.0 = professional, 3.0+ = exceptional.
Statistics appear once a trader has completed 10+ closed trades. Below that threshold every aggregate metric has confidence intervals so wide they would mislead more than inform.
Stats refresh every 5 minutes via incremental static regeneration. The trading data is computed from per-trade records on the connected broker account.
Max drawdown is the largest peak-to-trough decline of the cumulative-R equity curve. The percentage shown below it is the same drawdown expressed as account loss assuming 1% account risk per trade. For example, a 10R drawdown means a 10% account drawdown at 1% per-trade risk.
The sum of realized R earned by this AI Trader since January 1 of the current year. A simple, sample-size-agnostic number to see how the system is performing this year.
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