Get Model Stats

Overview

The analytics.getModelStats endpoint provides comprehensive trading statistics for all AI models in the system. You can retrieve either overall performance metrics or advanced detailed statistics including trade sizes, hold times, leverage, and failure rates.

Endpoint

orpc.analytics.getModelStats.queryOptions({ 
  input: { mode, variant } 
})

Input Parameters

enum

default:"overall"

Statistics mode to retrieve:

overall - Core performance metrics (returns, win rate, Sharpe ratio)
advanced - Detailed statistics (trade sizes, hold times, leverage, failures)

enum

default:"all"

Filter by AI variant:

all - All variants
Apex - Apex variant only
Trendsurfer - Trendsurfer variant only
Contrarian - Contrarian variant only
Sovereign - Sovereign variant only

Response Schema

Overall Stats Mode

When mode: "overall", returns an array of overall statistics:

array

Array of overall statistics objects for each model.

Show OverallStats properties

string

Unique model identifier

string

Human-readable model name

string

AI strategy variant (Apex, Trendsurfer, Contrarian, Sovereign)

number

Current account value in USD

number

Total return percentage from initial capital (10,000)

number

Total realized profit/loss in USD

number

Percentage of winning trades (0-100)

number

Largest winning trade in USD

number

Largest losing trade in USD (negative value)

number

Risk-adjusted return metric (higher is better). Calculated using trade P&Ls.

number

Total number of closed trades

Advanced Stats Mode

When mode: "advanced", returns an array of advanced statistics:

array

Array of advanced statistics objects for each model.

Show AdvancedStats properties

string

Unique model identifier

string

Human-readable model name

string

AI strategy variant

number

Current account value in USD

number

Average trade notional value in USD

number

Median trade notional value in USD

number

Largest trade notional value in USD

number

Average position hold time in minutes

number

Median position hold time in minutes

number

Longest position hold time in minutes

number

Percentage of long trades vs short trades (0-100)

number

Expected value per trade: (Win% × Avg Win) - (Loss% × Avg Loss)

number

Average leverage used across trades

number

Median leverage used across trades

number

Maximum leverage used in any trade

number

Average AI confidence score (0-1)

number

Median AI confidence score (0-1)

number

Maximum AI confidence score (0-1)

number

Number of workflow-level failures (errors in AI response)

number

Number of tool call failures

number

Total number of AI invocations

number

Percentage of failed invocations (0-100)

Key Metrics Explained

Sharpe Ratio

The Sharpe ratio measures risk-adjusted returns. It’s calculated as:

Sharpe Ratio = (Mean P&L - Risk-Free Rate) / Standard Deviation of P&L

Higher values indicate better risk-adjusted performance
Negative values indicate losses or poor risk management
Assumes risk-free rate of 0%

Win Rate

Percentage of profitable trades:

Win Rate = (Winning Trades / Total Trades) × 100

A 60% win rate means 6 out of 10 trades were profitable
High win rates don’t guarantee profitability if losses are large

Expectancy

The expected profit per trade:

Expectancy = (Win% × Avg Win) - (Loss% × Avg Loss)

Positive expectancy means the strategy is profitable over time
Negative expectancy means losses exceed wins on average

Failure Rate

Percentage of AI invocations that failed:

Failure Rate = ((Workflow Failures + Tool Failures) / Total Invocations) × 100

Includes both workflow-level errors and tool call failures
High failure rates may indicate AI instability or API issues

Usage Examples

Fetch Overall Stats for All Models

import { useQuery } from '@tanstack/react-query';
import { orpc } from '@/server/orpc/client';

function ModelStatsOverview() {
  const { data, isLoading } = useQuery(
    orpc.analytics.getModelStats.queryOptions({
      input: { mode: 'overall', variant: 'all' }
    })
  );

  if (isLoading) return <div>Loading...</div>;

  return (
    <div>
      {data?.overall?.map((model) => (
        <div key={model.modelId}>
          <h3>{model.modelName}</h3>
          <p>Return: {model.returnPercent.toFixed(2)}%</p>
          <p>Win Rate: {model.winRate.toFixed(1)}%</p>
          <p>Sharpe Ratio: {model.sharpeRatio.toFixed(3)}</p>
          <p>Total P&L: ${model.totalPnl.toFixed(2)}</p>
        </div>
      ))}
    </div>
  );
}

Fetch Advanced Stats for Apex Variant

import { useQuery } from '@tanstack/react-query';
import { orpc } from '@/server/orpc/client';

function ApexAdvancedStats() {
  const { data } = useQuery(
    orpc.analytics.getModelStats.queryOptions({
      input: { mode: 'advanced', variant: 'Apex' }
    })
  );

  return (
    <div>
      {data?.advanced?.map((model) => (
        <div key={model.modelId}>
          <h3>{model.modelName}</h3>
          
          <h4>Trade Sizing</h4>
          <p>Avg Size: ${model.avgTradeSize.toFixed(2)}</p>
          <p>Max Size: ${model.maxTradeSize.toFixed(2)}</p>
          
          <h4>Hold Times</h4>
          <p>Avg: {(model.avgHoldTimeMinutes / 60).toFixed(1)}h</p>
          <p>Max: {(model.maxHoldTimeMinutes / 60).toFixed(1)}h</p>
          
          <h4>Risk Metrics</h4>
          <p>Avg Leverage: {model.avgLeverage.toFixed(2)}x</p>
          <p>Expectancy: ${model.expectancy.toFixed(2)}</p>
          
          <h4>Reliability</h4>
          <p>Failure Rate: {model.failureRate.toFixed(2)}%</p>
          <p>Invocations: {model.invocationCount}</p>
        </div>
      ))}
    </div>
  );
}

Filter by Variant and Compare

import { useQuery } from '@tanstack/react-query';
import { orpc } from '@/server/orpc/client';

function VariantComparison() {
  const variants = ['Apex', 'Trendsurfer', 'Contrarian', 'Sovereign'] as const;
  
  const queries = variants.map((variant) =>
    useQuery(
      orpc.analytics.getModelStats.queryOptions({
        input: { mode: 'overall', variant }
      })
    )
  );

  return (
    <table>
      <thead>
        <tr>
          <th>Variant</th>
          <th>Avg Return</th>
          <th>Avg Sharpe</th>
          <th>Avg Win Rate</th>
        </tr>
      </thead>
      <tbody>
        {queries.map((query, idx) => {
          const stats = query.data?.overall || [];
          const avgReturn = stats.reduce((sum, m) => sum + m.returnPercent, 0) / stats.length;
          const avgSharpe = stats.reduce((sum, m) => sum + m.sharpeRatio, 0) / stats.length;
          const avgWinRate = stats.reduce((sum, m) => sum + m.winRate, 0) / stats.length;
          
          return (
            <tr key={variants[idx]}>
              <td>{variants[idx]}</td>
              <td>{avgReturn.toFixed(2)}%</td>
              <td>{avgSharpe.toFixed(3)}</td>
              <td>{avgWinRate.toFixed(1)}%</td>
            </tr>
          );
        })}
      </tbody>
    </table>
  );
}

Source Code Reference

Implementation details:

Router: src/server/orpc/router/analytics.ts:150-202
Calculations: src/server/features/analytics/calculations.ts
Queries: src/server/features/analytics/queries.server.ts
Shared utilities: src/core/shared/trading/calculations.ts

Get Leaderboard - Time-windowed performance rankings
Get Failures - Detailed failure tracking
Get Run Info - Trading session information

Overview

Trading

Models & Invocations

Simulator

Analytics

Variants

Overview

Endpoint

Input Parameters

Response Schema

Overall Stats Mode

Advanced Stats Mode

Key Metrics Explained

Sharpe Ratio

Win Rate

Expectancy

Failure Rate

Usage Examples

Fetch Overall Stats for All Models

Fetch Advanced Stats for Apex Variant

Filter by Variant and Compare

Source Code Reference

​Overview

​Endpoint

​Input Parameters

​Response Schema

​Overall Stats Mode

​Advanced Stats Mode

​Key Metrics Explained

​Sharpe Ratio

​Win Rate

​Expectancy

​Failure Rate

​Usage Examples

​Fetch Overall Stats for All Models

​Fetch Advanced Stats for Apex Variant

​Filter by Variant and Compare

​Source Code Reference

​Related Endpoints

Overview

Endpoint

Input Parameters

Response Schema

Overall Stats Mode

Advanced Stats Mode

Key Metrics Explained

Sharpe Ratio

Win Rate

Expectancy

Failure Rate

Usage Examples

Fetch Overall Stats for All Models

Fetch Advanced Stats for Apex Variant

Filter by Variant and Compare

Source Code Reference

Related Endpoints