# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
#默认库freq框架已经导入，只需要在脚本调配置就行了。
# --- 基础库导入 ---
import numpy as np
import pandas as pd
from datetime import datetime
from pandas import DataFrame
from typing import Optional

from freqtrade.strategy import (
    IStrategy,
    informative,
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    timeframe_to_minutes,
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
)
import talib.abstract as ta
from technical import qtpylib

class OptimizedETHStrategy(IStrategy):
    # ============ 杠杆与资金费用设置 ============
    
    can_short = False   # 只做多方向
    timeframe = '15m'
    process_only_new_candles = True
    startup_candle_count = 100

    # ============ 风险管理参数 ============
    stoploss = -0.50  # 单笔最大亏损50%（考虑杠杆后实际为0.5/6 1/12%本金）
    use_custom_stoploss = True
    position_adjustment_enable = True
    max_entry_position_adjustment = 4

    # ============ 动态止盈参数 ============
    minimal_roi = {
        "0": 0.15, 
        "10": 0.10, 
        "30": 0.05, 
        "60": 0
    }

    # ============ 追踪止损参数 ============
    trailing_stop = True
    trailing_stop_positive = 0.03  # 盈利3%后激活
    trailing_stop_positive_offset = 0.08  # 从8%利润开始追踪
    trailing_only_offset_is_reached = True

    # ============ 订单类型 ============
    order_types = {
        'entry': 'market',
        'exit': 'market',
        'emergency_exit': 'market',
        'force_entry': "market",
        'stoploss': 'market',
        'stoploss_on_exchange': False,
        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_limit_ratio': 0.99
    }

    # 训练过的yperopt参数 （不要修改）
    buy_rsi = IntParameter(low=1, high=50, default=31.685, space="buy", optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=72.581, space="sell", optimize=True, load=True)
    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)

    plot_config = {
        "main_plot": {
            "tema": {},
            "sar": {"color": "white"},
        },
        "subplots": {
            "MACD": {
                "macd": {"color": "blue"},
                "macdsignal": {"color": "orange"},
            },
            "RSI": {
                "rsi": {"color": "red"},
            },
        },
    }

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Momentum Indicators
        dataframe["adx"] = ta.ADX(dataframe)
        dataframe["rsi"] = ta.RSI(dataframe)
        
        # Stochastic Fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe["fastd"] = stoch_fast["fastd"]
        dataframe["fastk"] = stoch_fast["fastk"]

        # MACD
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
            dataframe["bb_upperband"] - dataframe["bb_lowerband"]
        )
        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]

        # TEMA
        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
                & (dataframe["tema"] <= dataframe["bb_middleband"])
                & (dataframe["tema"] > dataframe["tema"].shift(1))
                & (dataframe["volume"] > 0)
            ),
            "enter_long",
        ] = 1

        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value))
                & (dataframe["tema"] > dataframe["bb_middleband"])
                & (dataframe["tema"] < dataframe["tema"].shift(1))
                & (dataframe["volume"] > 0)
            ),
            "enter_short",
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
                & (dataframe["tema"] > dataframe["bb_middleband"])
                & (dataframe["tema"] < dataframe["tema"].shift(1))
                & (dataframe["volume"] > 0)
            ),
            "exit_long",
        ] = 1

        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value))
                & (dataframe["tema"] <= dataframe["bb_middleband"])
                & (dataframe["tema"] > dataframe["tema"].shift(1))
                & (dataframe["volume"] > 0)
            ),
            "exit_short",
        ] = 1

        return dataframe