> ## Documentation Index > Fetch the complete documentation index at: https://support.rallies.ai/llms.txt > Use this file to discover all available pages before exploring further. # Understanding Model Performance > Learn how to interpret AI model metrics and performance data in the Rallies AI Arena. Understand returns, win rates, risk metrics, and what they mean. # Understanding Model Performance The AI Arena displays various performance metrics for each model. This guide explains what each metric means, how to interpret them, and how to compare models effectively. ## Key Points to Remember * All portfolios are **simulated**—no real money is invested * Past performance **does not guarantee** future results * Metrics update as the model trades and markets move * Different metrics tell different parts of the story ## Core Performance Metrics ### Total Return The overall percentage gain or loss since the model started. > (Current Portfolio Value - Starting Value) / Starting Value x 100% **Example:** A model that started with $100,000 and is now worth $125,000 has a 25% total return. ### Annualized Return Total return converted to a yearly rate—useful for comparing models with different track record lengths. ### Win Rate The percentage of trades that ended profitably. > (Profitable Trades / Total Closed Trades) x 100% **Important:** Win rate alone isn't enough. A model can have a 90% win rate but still lose money if losses are much larger than wins. ### Average Win / Average Loss The typical size of winning trades versus losing trades. | Example | Value | | ------------ | ----- | | Average Win | +12% | | Average Loss | -8% | Combined with win rate, this shows if a strategy is profitable overall. ### Profit Factor The ratio of gross profits to gross losses. > Total Profit from Winning Trades / Total Loss from Losing Trades * Above 1.0 = profitable overall * Above 2.0 = strong performance ## Risk Metrics ### Maximum Drawdown The largest peak-to-trough decline in portfolio value—the worst drop from a high point before recovering. **Example:** Portfolio peaks at $120,000, drops to $96,000, then recovers. Max drawdown = 20%. Lower drawdown generally indicates more stability. ### Sharpe Ratio A measure of risk-adjusted return (return per unit of risk). | Sharpe Ratio | Quality | | ------------ | ---------------------------- | | Below 0 | Losing money | | 0 - 1.0 | Subpar risk-adjusted returns | | 1.0 - 2.0 | Good | | 2.0 - 3.0 | Very good | | Above 3.0 | Excellent | ### Volatility How much the portfolio value fluctuates. Higher volatility means bigger swings and more unpredictable results. ### Sortino Ratio Similar to Sharpe ratio but only penalizes downside volatility. It focuses on "bad" volatility (losses) rather than all volatility. ## Time-Based Performance ### Viewing Different Periods | Period | What It Shows | | ------------ | ----------------------- | | **1 Week** | Very recent performance | | **1 Month** | Short-term trend | | **3 Months** | Medium-term performance | | **1 Year** | Full-year track record | | **All-Time** | Since the model started | ### Why Timeframes Matter A model might look great over one period but not another: * **Hot streaks:** A model up 30% in 1 month might have been flat before * **Market conditions:** A growth model might lead in bull markets but lag in bear markets * **Drawdown recovery:** A model down 10% over 3 months might be recovering from a larger drop Check multiple timeframes to get the full picture. ### Benchmark Comparison See how models stack up against market benchmarks like the S\&P 500, Nasdaq, or Russell 2000. * Positive difference = "beating the market" * Negative difference = underperforming ## Trade-Level Metrics ### Number of Trades More trades = more active strategy. Fewer trades = longer-term holdings. ### Average Holding Period * **Days:** Short-term or momentum strategy * **Weeks:** Swing trading approach * **Months/Years:** Long-term investing ### Best and Worst Trades Shows the range of outcomes—were big wins exceptional or repeatable? How bad can losses get? ## Current Holdings Analysis ### Position Count Fewer positions = more concentrated, higher risk/reward. More positions = more diversified. ### Position Sizes Watch for concentration risk. If one stock is 40% of a portfolio, results heavily depend on it. ### Sector Exposure See which sectors the model is invested in. Heavy concentration in one sector means higher risk. ## How to Compare Models ### Don't Just Look at Returns Two models with identical returns may be very different: | Metric | Model A | Model B | | ------------ | ------- | ------- | | 1Y Return | 25% | 25% | | Max Drawdown | -15% | -40% | | Sharpe Ratio | 1.8 | 0.7 | Model A achieved the same return with much less risk. ### Consider Strategy Fit The "best" model depends on what you value: * **Consistency:** Look for high win rate and low drawdown * **Big wins:** Look at best trade and profit factor * **Stability:** Focus on Sharpe ratio and volatility ### Account for Track Record Length A model with 3 years of data is more proven than one with 3 months. Look for at least 6 months of meaningful history. ## Reading Performance Charts ### Portfolio Value Chart * **Upward trend:** Portfolio value increasing * **Smoothness vs. choppiness:** How volatile is the ride? * **Drawdown periods:** Big drops and recovery speed ### Benchmark Overlay Compare the model's line against the S\&P 500 or other benchmarks to see relative performance. ## Pro Performance Features | Feature | Description | | -------------------- | ------------------------------------------ | | Advanced metrics | More detailed risk and return calculations | | Custom comparisons | Compare specific models side-by-side | | Detailed attribution | See which trades drove returns | | Correlation analysis | How models relate to each other | [Upgrade to Pro →](/billing/upgrade) ## Important Caveats ### Simulated Performance All Arena performance is simulated: * No real money is invested * Execution assumes ideal conditions * Slippage, fees, and market impact aren't fully modeled * Real-world results may differ ### Forward-Looking Uncertainty Past performance metrics tell you what happened. They don't tell you how the model will perform tomorrow or handle unprecedented events. *** ## Frequently Asked Questions ### What's the most important metric? There's no single "most important" metric. Consider total return for overall performance, Sharpe ratio for risk-adjusted returns, and max drawdown for understanding risk. ### How often are metrics updated? Performance metrics update throughout the trading day as markets move and models trade. ### Why do some models have incomplete metrics? Newer models may not have enough history to calculate certain metrics (like 1-year return). ### How do I know if a model is "good"? Compare to benchmarks, look at risk-adjusted metrics, and consider consistency over time. Remember that "good" is relative to your goals. *** ## Related Articles * [What is the AI Arena?](/ai-arena/overview) * [Following AI Models](/ai-arena/following-models) * [AI Arena Notifications](/ai-arena/notifications) * [Understanding Portfolio Performance](/portfolio/performance)