# --- 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 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__)
### 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_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange=20251008-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m 1m 1h 1d 1M --pairs BTC/USDT:USDT --timerange=20250405-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_30.json -e 200 --timerange=20250201-20250901
# freqtrade edge -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901

# sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies --timerange=20250721-
# sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_30.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
# sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/ChanLun_BTC_30.json --strategy ChanLun_BTC --strategy-path ./user_data/Chan/strategies

class ChanLun_BTC(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 = True  # 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
	time3m = 3
	time5m = 5
	time10m = 10
	time15m = 15
	time30m = 30
	time_m = [3, 5, 10, 15, 30]
	
	time1h = 60
	time2h = 2
	time4h = 4
	time6h = 6
	time8h = 8
	time12h = 12
	time16h = 16
	time_h = [2, 4, 6, 8, 12, 16]
 
	time2d = 2
	time3d = 3
	time1w = 7
	time_d = [2, 3, 7]
 
	time2M = 2
	time3M = 3
	time_M = [2, 3]
 
	last_time = datetime.now()
	chan = ChanLun()
	last_order = None
	last_trade = None
	pair = 'BTC/USDT:USDT'
	def informative_pairs(self):
		timeframes = ['1h', '1d', '1M']
		informative_pairs = [(self.pair, tf) for tf in timeframes]
		return informative_pairs
	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		self.init_dataframes(dataframe)
		return dataframe
	def init_dataframes(self, dataframe_1m):
		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_1M = self.dp.get_pair_dataframe(pair=self.pair, timeframe='1M')
		self.chan.init_dataframes(dataframe_1m, dataframe_1h, dataframe_1d, dataframe_1M)
		current_price = dataframe_1m.iloc[-1]['close']
		print("Current Price: ", current_price)
		self.print_all_current_klc()
	def print_all_ema52(self):
		for key, value in self.chan.get_ema52_dict().items():
			print(key, value)
	def print_all_ema24(self):
		for key, value in self.chan.get_ema24_dict().items():
			print(key, value)
	def print_all_current_klc(self):
		for key, value in self.chan.get_current_klc_dict().items():
			print(key, value.to_string())
	def add_indicators(self, df):
		fast = 12
		slow = 26
		period = 9
		macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
		bb365 = ta.BBANDS(df, timeperiod=365, nbdevup=3.0, nbdevdn=3.0, matype=0)
		bb120 = ta.BBANDS(df, timeperiod=120, nbdevup=3.0, nbdevdn=3.0, matype=0)
		bb30 = ta.BBANDS(df, timeperiod=41, nbdevup=2.3, nbdevdn=2.3, matype=0)
		bb302 = ta.BBANDS(df, timeperiod=41, nbdevup=2.0, nbdevdn=2.0, matype=0)
		bb30 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
		bb302 = ta.BBANDS(df, timeperiod=20, nbdevup=2.0, nbdevdn=2.0, matype=0)
		# 计算布林带中轨（移动平均线）
		bb30_middle = ta.SMA(df, timeperiod=90)
		
		# 手动计算布林带 %B 指标 (BBP)
		# %B = (Price - Lower Band) / (Upper Band - Lower Band)
		bbp365 = (df['close'] - bb365['lowerband']) / (bb365['upperband'] - bb365['lowerband'])
		bbp120 = (df['close'] - bb120['lowerband']) / (bb120['upperband'] - bb120['lowerband'])
		bbp30 = (df['close'] - bb30['lowerband']) / (bb30['upperband'] - bb30['lowerband'])
		bbp302 = (df['close'] - bb302['lowerband']) / (bb302['upperband'] - bb302['lowerband'])
		df['atr'] = ta.ATR(df, timeperiod=14)
		df['bbup365'] = bb365['upperband']
		df['bblow365'] = bb365['lowerband']
		df['bbp365'] = bbp365
		df['bbup120'] = bb120['upperband']
		df['bblow120'] = bb120['lowerband']
		df['bbp120'] = bbp120
		df['bbup30'] = bb30['upperband']
		df['bblow30'] = bb30['lowerband']
		df['bbmiddle30'] = bb30_middle  # 添加bb30中轨
		df['bbp30'] = bbp30
		df['bbup302'] = bb302['upperband']
		df['bblow302'] = bb302['lowerband']
		df['bbp302'] = bbp302
		df['macd'] = macd['macd']
		df['macdsignal'] = macd['macdsignal']
		df['macdhist'] = macd['macdhist']
		df['ema5'] = ta.EMA(df, timeperiod=5)
		df['ema10'] = ta.EMA(df, timeperiod=10)
		df['ema24'] = ta.EMA(df, timeperiod=24)
		df['ema26'] = ta.EMA(df, timeperiod=26)
		df['ema52'] = ta.EMA(df, timeperiod=52)
		df['rsi'] = ta.RSI(df, timeperiod=14)
		df['volume_ratio'] = self.cal_volume_ratio(df)
		return df
	def cal_volume_ratio(self, dataframe, window=10):
		df = dataframe.copy()
		# 计算过去N根K线的平均成交量
		df['avg_volume'] = df['volume'].rolling(window=window).mean()
		# 计算量比
		df['volume_ratio'] = df['volume'] / df['avg_volume']
		# 填充缺失值（前N根K线）
		df['volume_ratio'] = df['volume_ratio'].fillna(1.0)
		return df['volume_ratio']
	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_stoploss(self, pair: str, trade: Trade, current_time: datetime,
						current_rate: float, current_profit: float, after_fill: bool,
						**kwargs) -> float | None:
		"""
		止损 = 开仓价 ± 1 * ATR（开仓时的ATR）。
		多单: 开仓价 - ATR；空单: 开仓价 + ATR。
		"""
		# 保本止损：当浮盈达到或超过 1% 时，将止损提至开仓价
		#if current_profit is not None and current_profit >= 0.14:
			#return stoploss_from_absolute(trade.open_rate, current_rate, is_short=trade.is_short)

		entry_atr = trade.get_custom_data(key="entry_atr")
		if entry_atr is None:
			# 回退：取当前数据的 ATR 估算
			dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
			if dataframe is not None and len(dataframe) > 0 and 'atr' in dataframe.columns:
				entry_atr = float(dataframe.iloc[-1]['atr'])
			else:
				# 最保守的回退：5%
				return -0.05
		dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
		last_candle = dataframe.iloc[-1].squeeze()
		ema52_str = 'resample_{}_ema52'.format(self.time15m)
		ema52_val = float(last_candle.get(ema52_str, 0) or 0)
		close_str = 'resample_{}_close'.format(self.time15m)
		close_val = float(last_candle.get(close_str, 0) or 0)
		if close_val < ema52_val:
			return -0.01
		if trade.is_short:
			stop_price = trade.open_rate + float(entry_atr)
		else:
			stop_price = trade.open_rate - float(entry_atr)
		return stoploss_from_absolute(stop_price, current_rate, is_short=trade.is_short)
	
	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 confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
							time_in_force: str, current_time: datetime, entry_tag: str | None,
							side: str, **kwargs) -> bool:
		"""
		ATR 过滤：atr < 100 不开单。
		"""
		try:
			dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
			if dataframe is None or len(dataframe) == 0:
				return False
			last = dataframe.iloc[-1]
			atr_str = 'resample_{}_atr'.format(self.time1h)
			atr_val = float(last.get(atr_str, 0) or 0)
			if atr_val < 0.001:
				#logger.info(f"ATR过滤：atr={atr_val:.2f} < 100, 拒绝进场 {pair}")
				return False
			return True
		except Exception as e:
			logger.warning(f"confirm_trade_entry 异常: {e}")
			return True

	def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
		"""
		Called right after an order fills. 
		Will be called for all order types (entry, exit, stoploss, position adjustment).
		:param pair: Pair for trade
		:param trade: trade object.
		:param order: Order object.
		:param current_time: datetime object, containing the current datetime
		:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
		"""
		# Obtain pair dataframe (just to show how to access it)
		dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
		last_candle = dataframe.iloc[-1].squeeze()
		atr_str = 'resample_{}_atr'.format(elf.time15)
		# 保存开仓时的ATR值用于止损计算
		if (trade.nr_of_successful_entries == 1) and (order.ft_order_side == trade.entry_side):
			entry_atr = last_candle[atr_str] * 4
			trade.set_custom_data(key="entry_atr", value=entry_atr)
			#logger.info(f"保存开仓时ATR值: {entry_atr}")
		return None
	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		shift15 = self.time15m
		shift60 = self.time1h
		bsp_col = 'resample_{}_bsp_mtf'.format(shift15)
		score_col = 'resample_{}_mtf_score'.format(shift15)
		macdh_col = 'resample_{}_macdhist'.format(shift15)
		c60_col = 'resample_{}_close'.format(shift60)
		e60_col = 'resample_{}_ema52'.format(shift60)
		# 强化过滤：15m BSP + 分数阈值 + 60m 趋势同向 + 15m MACD柱同向
		if all(col in dataframe.columns for col in [bsp_col, score_col, macdh_col, c60_col, e60_col]):
			dataframe.loc[
				(
					(dataframe[bsp_col].shift(shift15) == 1) &
					(dataframe[score_col].shift(shift15) >= 1.2) &
					(dataframe[c60_col].shift(shift60) >= dataframe[e60_col].shift(shift60)) &
					(dataframe[macdh_col].shift(shift15) > 0)
				),
				['enter_long', 'enter_tag']] = (1, 'long_bsp15_v2')
			dataframe.loc[
				(
					(dataframe[bsp_col].shift(shift15) == -1) &
					(dataframe[score_col].shift(shift15) <= -1.2) &
					(dataframe[c60_col].shift(shift60) <= dataframe[e60_col].shift(shift60)) &
					(dataframe[macdh_col].shift(shift15) < 0)
				),
				['enter_short', 'enter_tag']] = (1, 'short_bsp15_v2')
		return dataframe
	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		shift15 = self.time15m
		shift60 = self.time1h
		bsp_col = 'resample_{}_bsp_mtf'.format(shift15)
		score_col = 'resample_{}_mtf_score'.format(shift15)
		c60_col = 'resample_{}_close'.format(shift60)
		e60_col = 'resample_{}_ema52'.format(shift60)
		# 反向强信号或60m趋势反向时平仓
		if all(col in dataframe.columns for col in [bsp_col, score_col, c60_col, e60_col]):
			dataframe.loc[
				(
					((dataframe[bsp_col].shift(shift15) == -1) & (dataframe[score_col].shift(shift15) <= -0.8)) |
					(dataframe[c60_col].shift(shift60) < dataframe[e60_col].shift(shift60))
				),
				['exit_long', 'exit_tag']] = (1, 'long_close_bsp15')
			dataframe.loc[
				(
					((dataframe[bsp_col].shift(shift15) == 1) & (dataframe[score_col].shift(shift15) >= 0.8)) |
					(dataframe[c60_col].shift(shift60) > dataframe[e60_col].shift(shift60))
				),
				['exit_short', 'exit_tag']] = (1, 'short_close_bsp15')
		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