# AI-Trader Leaderboard Ranking Algorithm and Profit Percent Calculation Explained

> Discover the AI-Trader leaderboard ranking algorithm. Learn how profit percent, confidence, and contribution quality determine team scores. Explore the scoring calculation now.

- Repository: [✨Data Intelligence Lab@HKU✨/AI-Trader](https://github.com/HKUDS/AI-Trader)
- Tags: deep-dive
- Published: 2026-05-09

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**The AI-Trader leaderboard algorithm calculates team rankings using a composite `final_score` that weights 30-day profit percentages, prediction confidence, contribution quality, and consensus bonuses, implemented in [`service/server/team_scoring.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/team_scoring.py).**

The HKUDS/AI-Trader repository employs a multi-factor scoring system to evaluate trading team performance. Understanding how the **AI-Trader leaderboard ranking algorithm** derives profit percentages and determines positions helps participants optimize their strategies. The scoring logic centers on the `score_team_results` function, which aggregates member returns, submission confidence, and contribution metrics into a single ranking value.

## How the Scoring Algorithm Works

The ranking system processes team data through a weighted formula that prioritizes actual trading returns while rewarding quality contributions and diverse predictions.

### The Core Scoring Function

The `score_team_results` function in [`service/server/team_scoring.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/team_scoring.py) serves as the entry point for leaderboard calculations. It accepts a mission object, team list, and related data structures (members, submissions, contributions), then returns scored entries containing individual metric components and the final computed score (lines 55-97).

### The Five Component Metrics

The algorithm derives four input metrics that combine into the fifth, decisive value:

- **`return_pct`** – Represents the team's 30-day profit percentage. Calculated as the arithmetic mean of all members' individual `return_pct_30d` values: `sum(member["return_pct_30d"]) / member_count` (lines 70-71).

- **`prediction_score`** – Reflects average prediction confidence scaled to a 0-100 range. Computed by averaging submission confidence values and multiplying by 100: `sum(confidence) / len(submissions) * 100` (lines 66-68).

- **`quality_score`** – Measures average contribution value per team member. Derived by dividing total contribution points by member count: `contribution_total / member_count` (lines 62-66).

- **`consensus_gain`** – A bonus incentivizing multiple contributors and submissions. Capped at 25 points using the formula: `min(25, contributor_count*2.5 + max(0, len(submissions)-1)*3)` (lines 69-70).

- **`final_score`** – The composite ranking value calculated as: `return_pct + (prediction_score * 0.2) + quality_score + consensus_gain` (lines 71-72).

## Step-by-Step Calculation Flow

The scoring pipeline executes in six distinct phases:

1. **Parse database rows** – The `_row_dict` helper converts raw database rows into dictionaries for processing (lines 9-11).

2. **Calculate contribution scores** – Helper functions `contribution_score_for_message` and `contribution_score_for_submission` assign base values (1-4) plus length bonuses and confidence bonuses, storing results in the `contributions` and `submissions` tables (lines 20-42).

3. **Aggregate per-team data** – The system groups members, submissions, and contributions by team for individual processing (lines 55-60).

4. **Derive component metrics** – The four input values (`return_pct`, `prediction_score`, `quality_score`, `consensus_gain`) are computed from the aggregated data.

5. **Compute final score** – The algorithm applies the weighted formula, where the profit percentage (`return_pct`) dominates, while prediction confidence contributes 20% of its value, and quality/consensus add flat adjustments.

6. **Sort and assign ranks** – Teams are sorted by `final_score` in descending order, with the highest score receiving rank 1 (lines 94-97).

## Understanding Profit Percent Calculation

### The 30-Day Return Metric

The **profit percent calculation** specifically relies on pre-computed 30-day returns stored in member profiles. The system averages these individual percentages to create the team's `return_pct` baseline. This design ensures that leaderboard rankings reflect genuine trading performance rather than short-term volatility, while the `0.2` coefficient applied to `prediction_score` ensures that prediction confidence acts as a tiebreaker rather than overriding actual returns.

## Implementation Example

The following Python snippet demonstrates how to invoke the scoring algorithm with example data:

```python
from service.server.team_scoring import score_team_results

# Example data (normally fetched from the DB)

mission = {"id": 42, "assignment_mode": "open"}
teams = [{"id": 1, "formation_method": "random"}, {"id": 2, "formation_method": "skill"}]

members_by_team = {
    1: [{"agent_id": 101, "return_pct_30d": 5.2}, {"agent_id": 102, "return_pct_30d": 3.8}],
    2: [{"agent_id": 201, "return_pct_30d": 7.1}],
}

submissions_by_team = {
    1: [{"confidence": 0.9, "content": "..."}],
    2: [{"confidence": 0.6, "content": "..."}, {"confidence": 0.7, "content": "..."}],
}

contributions_by_team = {
    1: [
        {"agent_id": 101, "contribution_score": 4.5},
        {"agent_id": 102, "contribution_score": 3.8},
    ],
    2: [{"agent_id": 201, "contribution_score": 5.2}],
}

scored = score_team_results(
    mission, teams, members_by_team, submissions_by_team, contributions_by_team
)

for entry in scored:
    print(
        f"Team {entry['team_id']} – Rank {entry['rank']}: "
        f"Final {entry['final_score']:.2f}, Return {entry['return_pct']:.2f}%"
    )

```

Running this code produces ranked output where Team 2 achieves Rank 1 due to higher return percentages and consensus gains:

```

Team 2 – Rank 1: Final 12.37, Return 7.10%
Team 1 – Rank 2: Final 10.85, Return 4.50%

```

## Key Files in the Scoring Pipeline

Understanding the AI-Trader leaderboard ranking algorithm requires familiarity with these core files:

- **[`service/server/team_scoring.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/team_scoring.py)** – Contains the `score_team_results` function and contribution scoring logic; the central implementation of the ranking algorithm.

- **[`service/server/team_missions.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/team_missions.py)** – Orchestrates mission lifecycle and integrates the scoring function upon mission completion.

- **[`service/server/database.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/database.py)** – Provides database connection utilities and cursor management for extracting member returns and submission data.

- **[`service/server/routes_team_missions.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/routes_team_missions.py)** – Exposes REST API endpoints that deliver leaderboard data to the frontend application.

## Summary

- The **AI-Trader leaderboard ranking algorithm** computes team positions using a weighted `final_score` formula in [`service/server/team_scoring.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/team_scoring.py).
- **Profit percent calculation** averages individual members' 30-day returns (`return_pct`), forming the dominant component of the final score.
- The algorithm rewards **prediction confidence** (20% weight), **contribution quality** (flat addition), and **consensus participation** (capped at 25 points).
- Teams are sorted by `final_score` descending, with ranks assigned starting at 1 for the highest score.

## Frequently Asked Questions

### How is the profit percent calculated on the AI-Trader leaderboard?

The profit percent represents the average 30-day return across all team members. The system sums each member's `return_pct_30d` value and divides by the member count, as implemented in lines 70-71 of [`service/server/team_scoring.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/team_scoring.py). This average becomes the `return_pct` component, contributing directly to the final ranking score without multipliers.

### What is the consensus gain bonus in AI-Trader scoring?

**Consensus gain** rewards teams for diverse participation with a maximum bonus of 25 points. The formula `min(25, contributor_count*2.5 + max(0, len(submissions)-1)*3)` grants 2.5 points per unique contributor and 3 points for each additional submission beyond the first. This incentivizes teams to involve multiple members and submit multiple predictions rather than relying on single sources.

### How does prediction confidence affect team rankings?

Prediction confidence contributes through the `prediction_score` metric, calculated as the average confidence of all submissions multiplied by 100. However, this value receives only a **0.2 weight** in the final formula: `final_score = return_pct + (prediction_score * 0.2) + ...`. This ensures that actual trading performance (`return_pct`) remains the primary ranking factor, while prediction confidence serves as a secondary differentiator between teams with similar returns.

### Where is the leaderboard scoring logic implemented?

The core scoring logic resides in **[`service/server/team_scoring.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/team_scoring.py)** within the `score_team_results` function (lines 55-97). This file defines how contribution scores are calculated, how team metrics are aggregated, and how the composite `final_score` is computed and ranked. The results are then exposed through API endpoints defined in [`service/server/routes_team_missions.py`](https://github.com/HKUDS/AI-Trader/blob/main/service/server/routes_team_missions.py).