app_models.py

from typing import List, Optional

from models.results import ProjectStat
from models.scores import ScoreModel
from loguru import logger


"""
App leaderboard score model, launched since S4
40% - UAW
20% - Median TX count per user
5%  - non-premium off-chain
15%  - premium off-chain
20% - Stickiness Factor (off-chain) (avg.DAU / Season Active Users)
"""
class AppLeaderboardModelV2(ScoreModel):

    def calculate(self, metrics: List[ProjectStat]):
        for project in metrics:
            logger.info(f"Calculating score for {project}")
            project.score = 40 * self.normalized_max(project, ProjectStat.APP_ONCHAIN_UAW, metrics) + \
                    20 * self.rank_index(project, ProjectStat.APP_ONCHAIN_MEDIAN_TX, metrics) + \
                    5 * self.normalized_max(project, ProjectStat.APP_OFFCHAIN_NON_PREMIUM_USERS, metrics) + \
                    15 * self.normalized_max(project, ProjectStat.APP_OFFCHAIN_PREMIUM_USERS, metrics) + \
                    20 * self.normalized_max(project, ProjectStat.APP_STICKINESS, metrics)

        return sorted(metrics, key=lambda m: m.score, reverse=True)

"""
App leaderboard score model, launched since S5
40% - UAW
20% - Token new holder ranks
5%  - non-premium off-chain
15%  - premium off-chain
20% - Stickiness Factor (off-chain) (avg.DAU / Season Active Users)
"""
class AppLeaderboardModelV3(ScoreModel):
    def __init__(self, reward_list: Optional[List[int]] = None):
        super().__init__()
        self.params[ScoreModel.PARAM_TOKEN_MIN_VALUE_FOR_NEW_HOLDER] = 1.0 # 1 TON
        self.reward_list = reward_list

    def calculate(self, metrics: List[ProjectStat]):
        for project in metrics:
            logger.info(f"Calculating score for {project}")
            project.score = 40 * self.normalized_max(project, ProjectStat.APP_ONCHAIN_UAW, metrics) + \
                            20 * self.rank_index(project, ProjectStat.TOKEN_NEW_USERS_WITH_MIN_AMOUNT, metrics) + \
                            5 * self.normalized_max(project, ProjectStat.APP_OFFCHAIN_NON_PREMIUM_USERS, metrics) + \
                            15 * self.normalized_max(project, ProjectStat.APP_OFFCHAIN_PREMIUM_USERS, metrics) + \
                            20 * self.normalized_max(project, ProjectStat.APP_STICKINESS, metrics)

        return self.calculate_rewards(sorted(metrics, key=lambda m: m.score, reverse=True))
    
    
class AppLeaderboardModelS6(ScoreModel):
    def __init__(self):
        super().__init__()

    def calculate(self, metrics: List[ProjectStat]):
        return sorted(metrics, key=lambda m: m.metrics[ProjectStat.APP_TOTAL_POINTS], reverse=True)