#!/usr/bin/env python3
"""
Hyperopt 配置示例，用于优化新的趋势判定参数

使用方法:
1. 将此文件保存为 hyperopt_trend_params.py
2. 运行: freqtrade hyperopt --config config.json --strategy freqaiprimer --hyperopt hyperopt_trend_params
"""

from freqtrade.optimize.hyperopt_interface import IHyperOpt
from typing import Dict, Any
import logging

logger = logging.getLogger(__name__)

class TrendParamsHyperopt(IHyperOpt):
    """
    优化趋势判定参数的 Hyperopt 类
    """
    
    @staticmethod
    def generate_roi_table(params: Dict) -> Dict[int, float]:
        """生成ROI表格"""
        return {
            0: params['roi_p1'] + params['roi_p2'] + params['roi_p3'],
            params['roi_t3']: params['roi_p1'] + params['roi_p2'],
            params['roi_t3'] + params['roi_t2']: params['roi_p1'],
            params['roi_t3'] + params['roi_t2'] + params['roi_t1']: 0,
        }

    @staticmethod
    def roi_space():
        """ROI参数空间"""
        return {
            'roi_p1': [0.01, 0.08],
            'roi_p2': [0.01, 0.08],
            'roi_p3': [0.01, 0.08],
            'roi_t1': [10, 120],
            'roi_t2': [10, 60],
            'roi_t3': [10, 60],
        }

    @staticmethod
    def stoploss_space():
        """止损参数空间"""
        return {
            'stoploss': [-0.5, -0.05],
        }

    @staticmethod
    def trailing_space():
        """追踪止损参数空间"""
        return {
            'trailing_stop': [True, False],
            'trailing_stop_positive': [0.005, 0.05],
            'trailing_stop_positive_offset_p1': [0.005, 0.05],
            'trailing_only_offset_is_reached': [True, False],
        }

    def buy_params_space(self):
        """买入参数空间，包含新的趋势判定阈值"""
        return {
            # 原有参数
            'buy_threshold': [-0.05, -0.001],
            'volume_z_score_min': [0.1, 2.0],
            'rsi_max': [20, 60],
            
            # 新的趋势判定阈值
            'trend_final_bullish_threshold': [50, 90],  # 上涨趋势阈值
            'trend_final_bearish_threshold': [10, 50],   # 下跌趋势阈值
        }

    def sell_params_space(self):
        """卖出参数空间"""
        return {
            'sell_threshold': [0.001, 0.08],
            'rsi_min': [60, 90],
            'stochrsi_max': [60, 95],
        }

    def generate_estimator(self, dimensions: list, **kwargs) -> Any:
        """生成优化器"""
        from skopt import Optimizer
        from skopt.space import Real, Integer
        
        # 定义参数空间
        space = [
            Real(-0.5, -0.05, name='stoploss'),
            Real(0.001, 0.08, name='sell_threshold'),
            Real(-0.05, -0.001, name='buy_threshold'),
            Real(0.1, 2.0, name='volume_z_score_min'),
            Integer(20, 60, name='rsi_max'),
            Integer(60, 90, name='rsi_min'),
            Integer(60, 95, name='stochrsi_max'),
            Integer(50, 90, name='trend_final_bullish_threshold'),
            Integer(10, 50, name='trend_final_bearish_threshold'),
        ]
        
        return Optimizer(space, base_estimator='ET', acq_func='EI', n_initial_points=10)

# 使用示例配置
if __name__ == "__main__":
    print("趋势参数优化配置已加载")
    print("支持的优化参数:")
    print("- trend_final_bullish_threshold: 50-90")
    print("- trend_final_bearish_threshold: 10-50")