# --- 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 chan.pipeline.ChanLun import ChanLun
from chan.core.ChanEnum import Chan_FX_TYPE, Chan_KLC_FX, Chan_BI_DIR, Chan_KLC_FX, Chan_BSP_TYPE
# --------------------------------
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 typing import Optional
import logging
logger = logging.getLogger(__name__)
### Now you can use logger.info('asfd') to log
# freqtrade plot-dataframe --strategy ChanLun_BTC_1m --datadir user_data/data/binance -c ./user_data/ChanLun_SOL_30.json --timerange=20250309-

# freqtrade trade -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies
# freqtrade backtesting -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange=20260501-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json -t 1m 1m 1h 1d 1M --pairs BTC/USDT:USDT --timerange=20250405-
# freqtrade download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json -t 1m 1h 1d 1M --pairs BTC/USDT --timerange=20170101-
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLossDaily --spaces roi --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies -c ./user_data/Chan/config/ChanLun_BTC_1m.json -e 200 --timerange=20250201-20250901
# freqtrade edge -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901
# freqtrade plot-dataframe -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange 20250721-20250901

# sudo docker compose run --rm chanlun_btc backtesting -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies --timerange=20250721-
# sudo docker compose run --rm chanlun_btc download-data -c ./user_data/Chan/config/ChanLun_BTC_1m.json --pairs BTC/USDT:USDT -t 1m --timerange 20240101-
# sudo docker compose run --rm chanlun_btc trade -c ./user_data/Chan/config/ChanLun_BTC_1m.json --strategy ChanLun_BTC_1m --strategy-path ./user_data/Chan/strategies

class ChanLun_BTC_1m_old(IStrategy):
	"""
	交易核心（缠论）：
	- 仅在缠论一/二/三类买卖点出现时交易。
	- 信号触发条件：前一笔被确认（bi.is_sure）时，该笔 end_klc 已被标记为 B1/B2/B3 或 S1/S2/S3。
	- 不使用未确认笔，不使用“状态猜测”列。
	"""
	INTERFACE_VERSION: int = 3
	timeframe = '1m'
	# Minimal ROI designed for the strategy.
	# This attribute will be overridden if the config file contains "minimal_roi"
	minimal_roi = {
		"0": 100
	}
	
	can_short = True
	enable_long = True
	enable_short = False
	lev = 1.0
	stoploss = -0.3  # 兜底止损，实际由 custom_stoploss 基于中枢 zg/zd 控制
	use_custom_stoploss = True
 
	trailing_stop = False
	trailing_stop_positive = 0.03
	trailing_stop_positive_offset = 0.06
	trailing_only_offset_is_reached = False
	use_exit_signal = True
	position_adjustment_enable = True
	startup_candle_count = 500
	# 以 1m 为基础周期时，1h = 60 根K线（用于读取 resample_60_* 列并做确认延迟）
	chan = ChanLun()
	def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe = self.add_indicators(dataframe)
		bsp_signal_data = self.chan.get_bsp_signal_data(dataframe)
		for column, values in bsp_signal_data.items():
			dataframe[column] = values
		return dataframe
	def add_indicators(self, df):
		df = self.add_base_indicators(df)
		base_interval = self.get_ticker_indicator()
		for interval in (5, 15, 60):
			if interval <= base_interval:
				df = self.copy_base_indicators_to_resample(df, interval)
				continue
			resampled = resample_to_interval(df, interval)
			resampled = self.add_base_indicators(resampled)
			df = resampled_merge(df, resampled)
		return df
	def copy_base_indicators_to_resample(self, df, interval):
		prefix = f'resample_{interval}_'
		for column in (
			'date', 'open', 'high', 'low', 'close', 'volume',
			'atr', 'macd', 'macdsignal', 'macdhist', 'ema24', 'ema52',
			'atr_ratio', 'resistance_240', 'support_240', 'trend'
		):
			if column in df.columns:
				df[f'{prefix}{column}'] = df[column]
		return df
	def add_base_indicators(self, df):
		fast = 12
		slow = 26
		period = 9
		macd = ta.MACD(df, fastperiod=fast, slowperiod=slow, signalperiod=period)
		df['atr'] = ta.ATR(df, timeperiod=14)
		df['macd'] = macd['macd']
		df['macdsignal'] = macd['macdsignal']
		df['macdhist'] = macd['macdhist']
		df['ema24'] = ta.EMA(df, timeperiod=24)
		df['ema52'] = ta.EMA(df, timeperiod=52)
		df['atr_ratio'] = df['atr'] / df['close']
		df['resistance_240'] = df['high'].rolling(240).max().shift(1)
		df['support_240'] = df['low'].rolling(240).min().shift(1)
		df['trend'] = 0
		df.loc[(df['close'] > df['ema52']) & (df['ema24'] >= df['ema52']), 'trend'] = 1
		df.loc[(df['close'] < df['ema52']) & (df['ema24'] <= df['ema52']), 'trend'] = -1
		return df

	def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		min_atr_ratio = 0.0005
		long_min_sr_distance_r = 1.0
		short_min_sr_distance_r = 0.8
		long_space_ratio = (dataframe['resistance_240'].shift(1) - dataframe['close'].shift(1)) / dataframe['close'].shift(1)
		short_space_ratio = (dataframe['close'].shift(1) - dataframe['support_240'].shift(1)) / dataframe['close'].shift(1)
		# 多周期趋势共振：3个周期中至少2个同向（而非全部3个）
		long_tf_aligned = (
			(dataframe['resample_5_trend'].shift(1) == 1).astype(int) +
			(dataframe['resample_15_trend'].shift(1) == 1).astype(int) +
			(dataframe['resample_60_trend'].shift(1) == 1).astype(int)
		) >= 2
		short_tf_aligned = (
			(dataframe['resample_5_trend'].shift(1) == -1).astype(int) +
			(dataframe['resample_15_trend'].shift(1) == -1).astype(int) +
			(dataframe['resample_60_trend'].shift(1) == -1).astype(int)
		) >= 2
		dataframe.loc[
			(
				self.enable_long &
				(dataframe['bsp_state'].shift(1) == -1) &
				(dataframe['bsp_risk_ratio'].shift(1) > 0) &
				(long_space_ratio >= dataframe['bsp_risk_ratio'].shift(1) * long_min_sr_distance_r) &
				(dataframe['macdhist'].shift(1) > 0) &
				(dataframe['atr_ratio'].shift(1) >= min_atr_ratio) &
				(dataframe['trend'].shift(1) == 1) &
				long_tf_aligned
			),
			['enter_long', 'enter_tag']] = (1, 'long_signal_chan')
		dataframe.loc[
			(
				self.enable_short &
				(dataframe['bsp_state'].shift(1) == 1) &
				(dataframe['bsp_risk_ratio'].shift(1) > 0) &
				(short_space_ratio >= dataframe['bsp_risk_ratio'].shift(1) * short_min_sr_distance_r) &
				(dataframe['macdhist'].shift(1) < 0) &
				(dataframe['atr_ratio'].shift(1) >= min_atr_ratio) &
				(dataframe['trend'].shift(1) == -1) &
				short_tf_aligned
			),
			['enter_short', 'enter_tag']] = (1, 'short_signal_chan')
		return dataframe
	def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
		dataframe['exit_long'] = 0
		dataframe['exit_short'] = 0
		return dataframe
	def get_trade_risk_ratio(self, pair: str, trade) -> float:
		risk_ratio = trade.get_custom_data('risk_ratio')
		if risk_ratio:
			return float(risk_ratio)

		risk_ratio = 0.001
		try:
			dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
			if len(dataframe) > 0:
				entry_rows = dataframe[dataframe['date'] <= trade.open_date_utc]
				entry_candle = entry_rows.iloc[-1] if len(entry_rows) > 0 else dataframe.iloc[-1]
				signal_rows = entry_rows.tail(3)
				signal_rows = signal_rows[signal_rows['bsp_risk_ratio'] > 0]
				if len(signal_rows) > 0:
					signal_candle = signal_rows.iloc[-1]
					risk_ratio = float(signal_candle['bsp_risk_ratio'])
					trade.set_custom_data('bsp_stop_price', float(signal_candle['bsp_stop_price']))
					trade.set_custom_data('bsp_zg', float(signal_candle['bsp_zg']))
					trade.set_custom_data('bsp_zd', float(signal_candle['bsp_zd']))
				else:
					risk_ratio = max(0.001, min(float(entry_candle['atr_ratio']), 0.005))
		except Exception:
			risk_ratio = 0.001

		trade.set_custom_data('risk_ratio', risk_ratio)
		return risk_ratio
	def adjust_trade_position(self, trade, current_time: datetime,
							  current_rate: float, current_profit: float,
							  min_stake: float | None, max_stake: float,
							  current_entry_rate: float, current_exit_rate: float,
							  current_entry_profit: float, current_exit_profit: float,
							  **kwargs):
		risk_ratio = self.get_trade_risk_ratio(trade.pair, trade)
		if current_profit >= risk_ratio and trade.nr_of_successful_exits == 0:
			return -(trade.stake_amount / 2), 'take_half_1r'
		return None
	def custom_exit(self, pair: str, trade, current_time: datetime, current_rate: float,
					current_profit: float, **kwargs):
		risk_ratio = self.get_trade_risk_ratio(pair, trade)
		if trade.nr_of_successful_exits > 0 and current_profit <= 0.001:
			return 'breakeven_after_1r'
		if current_profit >= risk_ratio * 2:
			return 'take_profit_2r'
		return None
	def custom_stoploss(self, pair: str, trade, current_time: datetime, current_rate: float,
						current_profit: float, after_fill: bool, **kwargs) -> float | None:
		bsp_stop_price = trade.get_custom_data('bsp_stop_price')
		if bsp_stop_price:
			sl = stoploss_from_absolute(float(bsp_stop_price), current_rate, is_short=trade.is_short)
			return min(sl, -0.05)
		return -0.05
	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
	def get_ticker_indicator(self):
		return int(self.timeframe[:-1])