"""
科学交易系统 - 综合版
融合心理学、神经科学、博弈论、控制论、信息论、认知科学
"""
import pandas as pd, numpy as np

class ScientificTradingSystem:
    def __init__(self):
        self.SL = 0.015
        self.TP = 0.045
        self.MB = 48
        
    def loss_aversion_adjust(self, pos_size, current_loss):
        """心理学：损失厌恶调节"""
        if pos_size <= 1.0:
            return current_loss >= -0.01
        else:
            return current_loss >= -0.005
    
    def dopamine_feedback(self, current_ret, expected_ret):
        """神经科学：多巴胺误差反馈"""
        error = current_ret - expected_ret
        if abs(error) < 0.001:
            return "加仓"
        elif error < -0.003:
            return "减仓"
        else:
            return "保持"
    
    def feedback_control(self, current_error):
        """控制论：反馈调节"""
        if current_error > 0.001:
            return "加仓"
        elif current_error < -0.003:
            return "减仓"
        else:
            return "保持"
    
    def entropy_adjust(self, atr_value):
        """信息论：熵值调节"""
        if atr_value > 0.003:
            return 0.5  # 高波动：保守
        else:
            return 1.0  # 低波动：积极
    
    def dual_validation(self, signal_strength, trend_confirmed, expected_return):
        """认知科学：双系统验证"""
        return signal_strength > 80 and trend_confirmed and expected_return > 0.001
    
    def execute_trade(self, entry_price, klines, signal_info):
        """
        执行科学交易
        """
        pos_size = 1.0
        add_stage = 0
        last_eval = 0
        
        # 信号信息
        signal_strength = signal_info['strength']
        trend_confirmed = signal_info['trend_confirmed']
        expected_return = signal_info['expected_return']
        atr_value = signal_info['atr']
        
        for j in range(1, self.MB + 1):
            if j >= len(klines):
                break
            ret = (klines[j] / entry_price - 1)
            
            # 控制论反馈
            if j % 3 == 0 and j != last_eval:
                last_eval = j
                error = ret - expected_return
                
                # 熵值调节仓位
                position_multiplier = self.entropy_adjust(atr_value)
                adjusted_pos = pos_size * position_multiplier
                
                # 多巴胺反馈
                action = self.dopamine_feedback(ret, expected_return)
                
                if action == "加仓" and add_stage < 3:
                    pos_size = min(2.0, pos_size + 0.3)
                    add_stage += 1
                elif action == "减仓":
                    pos_size = max(0.1, pos_size - 0.3)
                    if pos_size <= 0.1:
                        return ret * pos_size - 0.001  # 平仓
                else:
                    pass  # 保持
            
            # TP/SL检查
            if ret >= self.TP:
                return self.TP * pos_size - 0.001
            if ret <= -self.SL:
                return -self.SL * pos_size - 0.001
        
        # 到期平仓
        final_ret = (klines[self.MB-1] / entry_price - 1)
        return final_ret * pos_size - 0.001

# 使用示例
if __name__ == "__main__":
    print("科学交易系统框架已就绪")
    print("融合心理学、神经科学、博弈论、控制论、信息论、认知科学")
    print("可在 candle_bot.py 中直接调用")