# source: https://raw.githubusercontent.com/zbchi/strategies/258a5f95c233f7b9d885c4fbbf6fad75a3a6b901/final.py

import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Optional, Union

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,
)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


# This class is a sample. Feel free to customize it.
class Github_zbchi_strategies__final__20250530_032102(IStrategy):

    INTERFACE_VERSION = 3

    # Can this strategy go short?
    can_short: bool = False

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        # "120": 0.0,  # exit after 120 minutes at break even
        "0": 0.99,
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.015

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Optimal timeframe for the strategy.
    timeframe = "15m"

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Hyperoptable parameters
    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=70, 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)

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 200

    # Optional order type mapping.
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    # Optional order time in force.
    order_time_in_force = {"entry": "GTC", "exit": "GTC"}

    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:
        dataframe['ema_5']=ta.EMA(dataframe,timeperiod=5)
        dataframe['ema_10']=ta.EMA(dataframe,timeperiod=10)



        return dataframe



    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe['ema_5']>dataframe['ema_10'])&
                (dataframe['ema_5'].shift(1)<dataframe['ema_10'].shift(1))
            ),
            'enter_long'
        ] = 1

        return dataframe
    
    def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        
        trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
        trade_index = dataframe.index[dataframe["date"] == trade_date].tolist()

        if trade_index:  # 确保找到了对应 K 线
            prev_candle = dataframe.iloc[trade_index[0] - 1]  # 获取前一根 K 线
            prev_open = prev_candle["open"]  # 获取前一根 K 线的开盘价

        
        return stoploss_from_absolute(prev_open,current_rate=current_rate,is_short=trade.is_short,leverage=trade.leverage)


    def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        current_candle = dataframe.iloc[-1].squeeze()

        trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
        trade_index = dataframe.index[dataframe["date"] == trade_date].tolist()

        if trade_index:  # 确保找到了对应 K 线
            prev_candle = dataframe.iloc[trade_index[0] - 1]  # 获取前一根 K 线
            prev_open = prev_candle["open"]  # 获取前一根 K 线的开盘价
            prev_close=prev_candle["close"]
        
        delta=prev_close-prev_open

        if current_rate>=prev_close+3*delta
            return "profit"




    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        return dataframe