# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple

from freqtrade.strategy import (
    IStrategy,
    Trade,
    Order,
    PairLocks,
    informative,  # @informative decorator
    # Hyperopt Parameters
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    # timeframe helpers
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    # Strategy helper functions
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
    AnnotationType,
)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
from technical import qtpylib


class FibonacciStrategy(IStrategy):
    """
    基于斐波那契回归的15分钟交易策略 - 简化版本
    
    策略逻辑：
    1. 计算最近N个周期的最高价和最低价
    2. 基于价格区间计算斐波那契回归水平
    3. 在关键回归位附近寻找买入和卖出机会
    4. 结合RSI确认信号
    """
    
    # Strategy interface version
    INTERFACE_VERSION = 3

    # 策略时间框架 - 15分钟
    timeframe = "15m"

    # 是否支持做空
    can_short: bool = False

    # 最小ROI设置
    minimal_roi = {
        "120": 0.01,  # 2小时后1%收益
        "60": 0.02,   # 1小时后2%收益
        "30": 0.03,   # 30分钟后3%收益
        "0": 0.05     # 立即5%收益
    }

    # 止损设置
    stoploss = -0.06  # 6%止损

    # 追踪止损
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    # 只处理新K线
    process_only_new_candles = True

    # 策略参数
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # 策略启动所需的K线数量
    startup_candle_count: int = 50

    # 斐波那契策略参数
    fib_period = IntParameter(20, 100, default=30, space="buy")  # 计算斐波那契的周期数
    rsi_period = IntParameter(10, 30, default=14, space="buy")   # RSI周期
    rsi_oversold = IntParameter(25, 45, default=35, space="buy") # RSI超卖线
    rsi_overbought = IntParameter(55, 75, default=65, space="sell") # RSI超买线

    # 订单类型
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False
    }

    # 订单时间
    order_time_in_force = {
        "entry": "GTC",
        "exit": "GTC"
    }

    @property
    def plot_config(self):
        return {
            "main_plot": {
                "fib_0.236": {"color": "red", "type": "line"},
                "fib_0.382": {"color": "orange", "type": "line"},
                "fib_0.5": {"color": "yellow", "type": "line"},
                "fib_0.618": {"color": "green", "type": "line"},
                "fib_0.786": {"color": "blue", "type": "line"},
            },
            "subplots": {
                "RSI": {
                    "rsi": {"color": "purple"},
                }
            }
        }

    def informative_pairs(self):
        """
        定义额外的信息性交易对
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        计算技术指标
        """
        # 计算RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=self.rsi_period.value)
        
        # 计算斐波那契回归水平
        # 使用滚动窗口计算最高价和最低价
        period = self.fib_period.value
        dataframe["highest"] = dataframe["high"].rolling(window=period).max()
        dataframe["lowest"] = dataframe["low"].rolling(window=period).min()
        
        # 计算价格区间
        dataframe["price_range"] = dataframe["highest"] - dataframe["lowest"]
        
        # 计算斐波那契回归水平
        dataframe["fib_0.236"] = dataframe["highest"] - 0.236 * dataframe["price_range"]
        dataframe["fib_0.382"] = dataframe["highest"] - 0.382 * dataframe["price_range"]
        dataframe["fib_0.5"] = dataframe["highest"] - 0.5 * dataframe["price_range"]
        dataframe["fib_0.618"] = dataframe["highest"] - 0.618 * dataframe["price_range"]
        dataframe["fib_0.786"] = dataframe["highest"] - 0.786 * dataframe["price_range"]
        
        # 计算EMA作为趋势确认
        dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
        
        # 计算MACD
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        基于斐波那契回归的买入信号 - 简化版本
        """
        dataframe.loc[
            (
                # 价格在0.618斐波那契回归位附近（关键支撑位）
                (dataframe["close"] <= dataframe["fib_0.618"] * 1.02) &  # 允许2%的误差
                (dataframe["close"] >= dataframe["fib_0.618"] * 0.98) &
                
                # RSI显示超卖状态
                (dataframe["rsi"] <= self.rsi_oversold.value) &
                
                # 价格在EMA20之上（趋势确认）
                (dataframe["close"] > dataframe["ema_20"]) &
                
                # 确保有足够的数据
                (dataframe["volume"] > 0) &
                (dataframe["price_range"] > 0) &
                (dataframe["highest"].notna()) &
                (dataframe["lowest"].notna())
            ),
            "enter_long"
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        基于斐波那契回归的卖出信号 - 简化版本
        """
        dataframe.loc[
            (
                # 价格在0.382斐波那契回归位附近（关键阻力位）
                (dataframe["close"] >= dataframe["fib_0.382"] * 0.98) &  # 允许2%的误差
                (dataframe["close"] <= dataframe["fib_0.382"] * 1.02) &
                
                # RSI显示超买状态
                (dataframe["rsi"] >= self.rsi_overbought.value) &
                
                # 确保有足够的数据
                (dataframe["volume"] > 0) &
                (dataframe["price_range"] > 0) &
                (dataframe["highest"].notna()) &
                (dataframe["lowest"].notna())
            ),
            "exit_long"
        ] = 1

        return dataframe

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
                       current_rate: float, current_profit: float, **kwargs) -> float:
        """
        自定义止损逻辑
        当价格突破关键斐波那契水平时调整止损
        """
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        if len(dataframe) == 0:
            return self.stoploss
            
        last_candle = dataframe.iloc[-1].squeeze()
        
        # 如果价格跌破0.786斐波那契水平，收紧止损
        if current_rate <= last_candle["fib_0.786"]:
            return -0.04  # 4%止损
        
        # 如果价格突破0.5斐波那契水平，设置保本止损
        if current_rate >= last_candle["fib_0.5"]:
            return -0.01  # 1%止损（接近保本）
        
        # 默认止损
        return self.stoploss