# --- Do not remove these libs ---
from statistics import median
from freqtrade.strategy import IStrategy, stoploss_from_absolute
import sys 
import os
# 添加父目录到系统路径
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from chanlun import ChanLun
from chanlun.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX
# --------------------------------
from technical.util import resample_to_interval, resampled_merge
import talib.abstract as ta
from pandas import DataFrame
import pandas as pd
from datetime import datetime, timedelta
from freqtrade.persistence import Trade, Order
from typing import Optional
import logging
logger = logging.getLogger(__name__)
"""
大周期：1h
小周期：15m，30m
大周期EMA156以下找做空机会
找到最近的中枢，中枢下跌以后穿过EMA156，EMA52均线，形成死叉，macd黄白线穿越0轴
EMA24，EMA52，EMA104，EMA156成下跌趋势依次排列（EMA156 > EMA104 > EMA52 > EMA24）
做空
1. 做空开始点位条件：
确定下跌周期，价格在大于大周期的时间周期找到MACD归零轴+EMA52阻力线，按照K线动能理论，小周期确认是否背驰，背驰则开仓并且MACD穿零轴
止损放到最近的顶分型高点或者价格突破EMA156
2. 开始点位止盈策略
计算盈亏比方式：至少1:2，到达1:2后平仓一半，移动止损到开仓价，1:3再平仓剩下的一半仓位，依次类推
如果大周期遇到底背离可以平完所有仓位
3. 加仓点位
小周期顶分型+价格接近或突破大周期EMA24但是不突破EMA52后下跌可以加仓到最大仓位+大周期黄白线归零轴/小周期顶分型+小周期EMA52归零轴
大周期顶分型+大周期macd归零轴可以加仓到最大仓位
大周期顶分型或顶分型后，macd穿零轴后价格和macd红绿柱背驰可以加仓到最大仓位
小周期顶分型+大周期macd归零轴
"""

### Now you can use logger.info('asfd') to log
# freqtrade plot-dataframe --strategy ChanLun_BTC --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-

# freqtrade trade -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies --timerange=20260101-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_EMA52.json -t 1m 1m 1h 1d 1w 1M --pairs BTC/USDT:USDT --timerange=20240101-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_EMA52.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_EMA52.json -e 200 --timerange=20250201-20250901
# freqtrade edge -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901

# sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies --timerange=20250721-
# sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_EMA52.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
# sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/ChanLun_EMA52.json --strategy ChanLun_EMA52 --strategy-path ./user_data/Chan/strategies

class ChanLun_EMA52(IStrategy):
	INTERFACE_VERSION: int = 3
	# Minimal ROI designed for the strategy.
	# This attribute will be overridden if the config file contains "minimal_roi"
	# 30m and 1h
 
	minimal_roi = {
		"0": 0.15,
		"360": 0.2,
		"640": 0.1,
		"1200": 0
	}
	# 5m and 15m
	minimal_roi_1 = {
		"0": 0.1,
		"60": 0.05,
		"120": 0.02,
		"240": 0
	}
	# 15m and 30m
	minimal_roi_1 = {
		"0": 0.1,
		"240": 0.05,
		"480": 0.03,
		"600": 0
	}
	minimal_roi_1 = {
		"0": 1.50,
		"120": 0.05,
		"240": 0.025,
		"360": 0
	}
	
	can_short = True
	lev = 1.0
	stoploss = -0.3  # 设置为很大的负值，让custom_stoploss来控制
	use_custom_stoploss = False  # 启用自定义止损
 
	trailing_stop = False
	trailing_stop_positive = 0.03
	trailing_stop_positive_offset = 0.06
	trailing_only_offset_is_reached = False

	# 关闭分批止盈/仓位调整
	position_adjustment_enable = False
	# startup_candle_count = 1600
	big_tf = '1h'
	small_tf = '15m'
	last_time = None
	chan = ChanLun()
	last_order = None
	last_trade = None
	pair = 'BTC/USDT:USDT'
	long_tf = '1h'
	short_tf = '15m'
	long_time = 60
	short_time = 15
	def informative_pairs(self):
		return [(self.pair, "1h"),
				(self.pair, "1d"),
				#(self.pair, "1M"),
				(self.pair, "15m"),
				#(self.pair, "1w"),
				]
	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe = self.add_indicators(dataframe)
		long_df = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1h')
		long_df = self.add_indicators(long_df)
		dataframe['rsi'] = ta.RSI(long_df, timeperiod=14)
		if self.last_time is None or self.last_time + timedelta(seconds=10) < datetime.now():
			self.last_time = datetime.now()
			logger.info("init_dataframes----------------------------")
			last_price = dataframe.iloc[-1]['close']
			date = dataframe.iloc[-1]['date']
			tf_ema52_list = self.chan.check_price_ema52(last_price)
			self.init_dataframes(dataframe)
			logger.info("Date: " + date.strftime('%Y-%m-%d %H:%M:%S') + " Price: " + str(last_price) + " EMA52_list: " + str(tf_ema52_list))
			#print(long_df.iloc[-1])
		dataframe = resampled_merge(dataframe, long_df)
		return dataframe
	def ema_dir(self, dataframe):
		"""
		趋势方向综合判断，分为三个维度：
		
		1. ema_dir: 主趋势方向 (基于MACD零轴 + 价格与EMA52/EMA156关系)
		   - 3: 强多（价格在EMA156上方 + MACD在零轴上方 + 价格在EMA24上方）
		   - 2: 中多（价格在EMA156上方 + MACD在零轴上方）
		   - 1: 弱多（价格在EMA52上方 或 MACD在零轴上方，满足其一）
		   - -1: 弱空（价格在EMA52下方 或 MACD在零轴下方，满足其一）
		   - -2: 中空（价格在EMA156下方 + MACD在零轴下方）
		   - -3: 强空（价格在EMA156下方 + MACD在零轴下方 + 价格在EMA24下方）
		   - 0: 盘整（无明确方向）
		
		2. ema_align: EMA排列状态（辅助确认趋势强度）
		   - 1: 多头排列 (EMA24 > EMA52 > EMA104 > EMA156)
		   - -1: 空头排列 (EMA24 < EMA52 < EMA104 < EMA156)
		   - 0: 交叉/纠缠
		
		3. ema_slope: EMA52斜率方向（趋势加速/减速判断）
		   - 正值: EMA52向上倾斜，趋势加速
		   - 负值: EMA52向下倾斜，趋势减速
		"""
		close = dataframe['close']
		ema24 = dataframe['ema24']
		ema52 = dataframe['ema52']
		ema104 = dataframe['ema104']
		ema156 = dataframe['ema156']
		macd_signal = dataframe['macdsignal']  # 黄线（慢线），用于判断零轴

		# === 1. 主趋势方向 ===
		# 核心条件：价格与EMA156的关系（大趋势）+ MACD黄线与零轴的关系
		above_ema156 = close > ema156
		below_ema156 = close < ema156
		above_ema52 = close > ema52
		below_ema52 = close < ema52
		above_ema24 = close > ema24
		below_ema24 = close < ema24
		macd_above_zero = macd_signal > 0
		macd_below_zero = macd_signal < 0

		dataframe['ema_dir'] = 0
		# 强多：价格在EMA156上方 + MACD零轴上方 + 价格在EMA24上方（超强势结构）
		dataframe.loc[above_ema156 & macd_above_zero & above_ema24, 'ema_dir'] = 3
		# 中多：价格在EMA156上方 + MACD零轴上方
		dataframe.loc[above_ema156 & macd_above_zero & ~above_ema24, 'ema_dir'] = 2
		# 弱多：满足其一（价格在EMA52上方 或 MACD零轴上方）
		dataframe.loc[(above_ema52 & ~macd_above_zero) | (macd_above_zero & ~above_ema156), 'ema_dir'] = 1
		# 弱空：满足其一（价格在EMA52下方 或 MACD零轴下方）
		dataframe.loc[(below_ema52 & ~macd_below_zero) | (macd_below_zero & ~below_ema156), 'ema_dir'] = -1
		# 中空：价格在EMA156下方 + MACD零轴下方
		dataframe.loc[below_ema156 & macd_below_zero & ~below_ema24, 'ema_dir'] = -2
		# 强空：价格在EMA156下方 + MACD零轴下方 + 价格在EMA24下方（超强空势结构）
		dataframe.loc[below_ema156 & macd_below_zero & below_ema24, 'ema_dir'] = -3

		# === 2. EMA排列状态（辅助参考）===
		bull_align = (ema24 > ema52) & (ema52 > ema104) & (ema104 > ema156)
		bear_align = (ema24 < ema52) & (ema52 < ema104) & (ema104 < ema156)
		dataframe['ema_align'] = 0
		dataframe.loc[bull_align, 'ema_align'] = 1
		dataframe.loc[bear_align, 'ema_align'] = -1

		# === 3. EMA52斜率（趋势加速/减速）===
		# 用EMA52的变化率判断趋势是否在加速
		dataframe['ema_slope'] = (ema52 - ema52.shift(3)) / ema52.shift(3) * 100

		return dataframe
	def add_indicators(self, dataframe):
		dataframe['ema24'] = ta.EMA(dataframe, timeperiod=24)
		dataframe['dir24'] = dataframe['close'] - dataframe['ema24']
		dataframe['ema52'] = ta.EMA(dataframe, timeperiod=52)
		dataframe['dir52'] = dataframe['close'] - dataframe['ema52']
		dataframe['ema104'] = ta.EMA(dataframe, timeperiod=104)
		dataframe['dir104'] = dataframe['close'] - dataframe['ema104']
		dataframe['ema156'] = ta.EMA(dataframe, timeperiod=156)
		dataframe['dir156'] = dataframe['close'] - dataframe['ema156']
		dataframe['dir52_156'] = dataframe['dir52'] - dataframe['dir156']
		dataframe_macd = ta.MACD(dataframe, fast=12, slow=26, signal=9)
		dataframe['macdsignal'] = dataframe_macd['macdsignal']
		dataframe['macd'] = dataframe_macd['macd']
		dataframe['macdhist'] = dataframe_macd['macdhist']
		return dataframe
	def init_dataframes(self, dataframe_1m):
		dataframe_15m = self.dp.get_pair_dataframe(pair=self.pair, timeframe='15m')
		dataframe_1h = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1h')
		dataframe_1d = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1d')
		#dataframe_1w = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1w')
		#dataframe_1M = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1M')
		self.chan.init_dataframes(dataframe_1m, dataframe_15m,dataframe_1h, dataframe_1d)
	def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
													 entry_tag: str | None, side: str, **kwargs) -> float:
		new_entryprice = proposed_rate
		if trade:
			if trade.is_short:
				new_entryprice = proposed_rate - 50
			else:
				new_entryprice = proposed_rate + 50
		return new_entryprice

	def custom_exit_price(self, pair: str, trade: Trade,
													current_time: datetime, proposed_rate: float,
													current_profit: float, exit_tag: str | None, **kwargs) -> float:
		new_exitprice = proposed_rate
		if trade:
			if trade.is_short:
				new_exitprice = proposed_rate + 50
			else:
				new_exitprice = proposed_rate - 50
		return new_exitprice	
	
	def adjust_trade_position(self, trade: Trade, current_time: datetime,
							  current_rate: float, current_profit: float,
							  min_stake: Optional[float], max_stake: float,
							  current_entry_rate: float, current_exit_rate: float,
							  current_entry_profit: float, current_exit_profit: float,
							  **kwargs) -> Optional[float]:
		# 关闭分批止盈，始终不调整仓位
		return None
	
	def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
					current_profit: float, **kwargs):
		# 不做分批止盈/最终止盈处理，退出由策略信号/ROI/止损决定
		return None

	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe.loc[
            (dataframe['dir156'] > 0) &
            (dataframe['dir52_156'] > 0) &
            (dataframe['macdhist'] > 0),
            'enter_long'] = 1
		return dataframe
	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe.loc[
            (dataframe['dir156'] < 0) &
            (dataframe['dir52_156'] < 0) &
            (dataframe['macdhist'] < 0),
            'exit_long'] = 1
		return dataframe
	def leverage(self, pair: str, current_time: datetime, current_rate: float,
				 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str], side: str,
				 **kwargs) -> float:
		return self.lev