# source: https://raw.githubusercontent.com/typhonun/DcaTpStrategy/9b58b38ee03919aec237956be13562284438e610/user_data/strategies/DcaTp.py
from freqtrade.strategy.interface import IStrategy
from freqtrade.persistence import Trade, Order
from pandas import DataFrame, Series, Timestamp
import pandas as pd
import talib.abstract as ta
from datetime import datetime, timedelta
import logging
import math

RED = "\033[31m"
GREEN = "\033[32m"
BLUE = "\033[34m"
YELLOW = "\033[33m"
RESET = "\033[0m"
logger = logging.getLogger(__name__)


class Github_typhonun_DcaTpStrategy__DcaTp__20250821_074750(IStrategy):
    timeframe = '30m'  # 时间周期
    stoploss = -7
    can_short = False
    can_long = True
    use_exit_signal = False
    trailing_stop = False
    position_adjustment_enable = True

    minimal_roi = {"0": 777.0}
    minimal_roi_user_defined = {  # 止盈参数
        "300": 0.010, "290": 0.020, "280": 0.030, "270": 0.040, "260": 0.050,"250": 0.060, "240": 0.080, "230": 0.090, "220": 0.100, "210": 0.110,"200": 0.120, "190": 0.130,
        "180": 0.140, "160": 0.145, "140": 0.150,"120": 0.155, "100": 0.160, "80": 0.165,
        "60": 0.170, "50": 0.175,"40": 0.180, "30": 0.185, "20": 0.190, "10": 0.195, "0": 0.200,
    }

    def leverage(self, pair: str, **kwargs) -> float:
        return 20

    def on_trade_open(self, trade: Trade, **kwargs) -> None:
        flags = {
            'dca_count': 0, 'tp_count': 0, 'dca_done': False,
            'last_dca_candle': None, 'last_dca_time': None,
            'dca_reduce_done': False, 'open_reduce_done': False,
            'need_rebuy': False, 'last_tp_time': None,
            'low_margin_start': None, 'trend_level': 0,
            'bottom_added': False, 'top_reduced': False,
            'bb_added': False, 'pullback_ready': True,
            'reset_needed': False, 'last_trend_side': None,
            'last_fallback_price': None, 'fallback_repull_done': False,
            'tp_repull_done': False
        }
        for k, v in flags.items():
            trade.set_custom_data(k, v)
        trade.set_custom_data('dynamic_avg_entry', trade.open_rate)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        upper, mid, lower = ta.BBANDS(dataframe['close'], timeperiod=20)
        dataframe['bb_upperband'] = upper
        dataframe['bb_midband'] = mid
        dataframe['bb_lowerband'] = lower
        dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)

        df30 = self.dp.get_pair_dataframe(metadata['pair'], '30m')
        if not df30.empty:
            # MACD 参数
            macd, macdsignal, macdhist = ta.MACD(
                df30['close'], fastperiod=8, slowperiod=21, signalperiod=5
            )
            # KDJ 参数
            k, d = ta.STOCH(
                df30['high'], df30['low'], df30['close'],
                fastk_period=5,
                slowk_period=3, slowk_matype=0,
                slowd_period=3, slowd_matype=0
            )
            j = 3 * k - 2 * d
            # EMA 参数
            ema9 = ta.EMA(df30['close'], timeperiod=9)
            ema21 = ta.EMA(df30['close'], timeperiod=21)
            ema99 = ta.EMA(df30['close'], timeperiod=99)
            adx = ta.ADX(df30['high'], df30['low'], df30['close'])

            series_map = {
                'macd_30': macd, 'macdsig_30': macdsignal,
                'macdhist_30': macdhist,
                'k_30': k, 'd_30': d, 'j_30': j,
                'ema9_30': ema9, 'ema21_30': ema21, 'ema99_30': ema99,
                'adx_30': adx
            }
            for name, series in series_map.items():
                dataframe[name] = pd.Series(series, index=df30.index) \
                    .reindex(dataframe.index).ffill()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # 趋势入场
        long_cond1 = (
                (dataframe['macd_30'] > dataframe['macdsig_30']) &
                (dataframe['k_30'] > dataframe['d_30']) &
                (dataframe['adx_30'] > 25) &
                (dataframe['ema9_30'] > dataframe['ema21_30']) &
                (dataframe['ema21_30'] > dataframe['ema99_30'])
        )
        # 抄底入场
        long_cond2 = (
                (dataframe['close'] < dataframe['bb_lowerband']) &
                (dataframe['rsi'] < 35)
        )
        dataframe['enter_long'] = (long_cond1 | long_cond2).astype(int)
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['exit_long'] = 0
        return dataframe

    def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float, current_profit: float,
                              **kwargs) -> tuple[float, str] | None:
        if trade.has_open_orders:
            return None
        df, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
        if df.empty:
            return None
        last = df.iloc[-1]
        margin = float(trade.stake_amount)
        if current_time.tzinfo:
            current_time = current_time.replace(tzinfo=None)
        open_time = trade.open_date_utc
        if open_time.tzinfo:
            open_time = open_time.replace(tzinfo=None)
        candle_ts = pd.Timestamp(df.index[-1]).tz_localize(None).floor('min')
        df30, _ = self.dp.get_analyzed_dataframe(trade.pair, '30m')
        if df30.empty:
            return None
        last30_ts = pd.Timestamp(df30.index[-1]).tz_localize(None).floor('T')
        if candle_ts != last30_ts:
            return None

        if not self.wallets:
            return None
        collateral = self.wallets.get_total('USDT')

        def collateral_add(frac: float) -> float:
            return collateral * frac

        price = last['close']
        lower = last['bb_lowerband']
        upper = last['bb_upperband']
        mid = last['bb_midband']

        # # -- 趋势加多 --
        # level = int(trade.get_custom_data('trend_level') or 0)
        # reset_needed = bool(trade.get_custom_data('trend_reset'))
        # # 多头信号
        # is_bullish_trend = (
        #         last['macd_30'] > last['macdsig_30'] and
        #         last['k_30'] > last['d_30'] and
        #         last['adx_30'] > 25 and
        #         last['ema9_30'] > last['ema21_30'] > last['ema99_30']
        # )
        # if level == 0 and not reset_needed and is_bullish_trend:
        #     trade.set_custom_data('trend_level', 2)
        #     trade.set_custom_data('last_trend_side', 'long')
        #     amt = collateral_add(0.03)  # 趋势加仓参数
        #     logger.info(f"{GREEN}[{trade.pair}] 多头趋势加仓总资金 3%{RESET} 保证金={margin:.4f}, 加仓={abs(amt):.4f} USDT")
        #     return amt, 'trend_add30_bull'
        # # KDJ 衰弱减仓
        # if level == 2 and last['k_30'] < last['d_30']:
        #     trade.set_custom_data('trend_level', 0)
        #     trade.set_custom_data('last_trend_side', 'long')
        #     amt = -0.4 * margin  # KDJ死叉减仓参数
        #     logger.info(f"{RED}[{trade.pair}] KDJ 衰弱减仓{RESET} 保证金={margin:.4f}, 减仓={abs(amt):.4f} USDT")
        #     return amt, 'kdj_reduce40_long'

        # # -- 空头信号止损 --
        # last_side = trade.get_custom_data('last_trend_side') or 'none'
        # is_bearish_trend = (
        #         last['macd_30'] < last['macdsig_30'] and
        #         last['k_30'] < last['d_30'] and
        #         last['adx_30'] > 25 and
        #         last['ema9_30'] < last['ema21_30'] < last['ema99_30']
        # )
        # if last_side == 'long' and is_bearish_trend:
        #     trade.set_custom_data('last_trend_side', 'short')
        #     amt = -0.5 * margin
        #     logger.info(
        #         f"{BLUE}[{trade.pair}] 空头信号止损{RESET} 保证金={margin:.4f}, 减仓={abs(amt):.4f} USDT"
        #     )
        #     return amt, 'trend_stop50_long'

        # # -- 趋势回撤加仓 --
        # high14 = df['close'].rolling(14).max().iat[-1]
        # if (last['ema9_30'] > last['ema21_30'] > last['ema99_30']
        #         and last['close'] == high14):
        #     trade.set_custom_data('ref_high', float(high14))
        #     trade.set_custom_data('pullback_done', False)
        # ref = trade.get_custom_data('ref_high')
        # pb_done = bool(trade.get_custom_data('pullback_done'))
        # ready = bool(trade.get_custom_data('pullback_ready'))
        # if (ref is not None and ready and not pb_done
        #         and current_rate <= ref * 0.99
        #         and last['ema9_30'] > last['ema21_30']):
        #     # 回撤到高点 99% 且 EMA9 仍在 EMA21 之上时，加仓
        #     amt = collateral_add(0.02)  # 回撤加仓参数
        #     trade.set_custom_data('pullback_done', True)
        #     trade.set_custom_data('pullback_ready', False)
        #     logger.info(
        #         f"{BLUE}[{trade.pair}] 趋势回撤加仓总资金 2%"
        #         f"高点={ref:.4f}, 当前价={current_rate:.4f} "
        #         f"保证金={margin:.4f}, 加仓={amt:.4f} USDT{RESET}"
        #     )
        #     return amt, 'pullback_dca20'

        # -- 浮亏 DCA 加仓 --
        if df.empty:
            return None
        last_idx = df.index[-1]
        candle_ts = pd.Timestamp(last_idx).tz_localize(None).floor('min')
        last_rsi = df['rsi'].iat[-1]
        u = int(trade.get_custom_data('dca_count') or 0)
        last_dca = trade.get_custom_data('last_dca_candle')
        last_dca_ts = Timestamp(last_dca, unit='s') if last_dca else None
        if last_dca_ts is None or last_dca_ts != candle_ts:
            trade.set_custom_data('dca_done', False)
        dca_done = bool(trade.get_custom_data('dca_done'))
        avg_entry = float(trade.get_custom_data('dynamic_avg_entry') or trade.open_rate)
        threshold = avg_entry * (1 - 0.01 - 0.01 * u)  # Dca加仓价格参数
        # 触发加仓
        rsi_thresh = max(0, 35)  # RSI参数
        if not dca_done and current_rate <= threshold and last_rsi < rsi_thresh:
            buy_amt = collateral_add(0.02)  # DCA加仓参数
            leverage = self.leverage(trade.pair)
            prev_qty = float(trade.amount)
            prev_cost = prev_qty * avg_entry
            added_qty = (buy_amt * leverage) / current_rate
            new_avg_entry = (prev_cost + buy_amt * leverage) / (prev_qty + added_qty)
            trade.set_custom_data('dca_count', u + 1)
            trade.set_custom_data('dca_done', True)
            trade.set_custom_data('last_dca_candle', int(candle_ts.timestamp()))
            trade.set_custom_data('last_dca_time', int(current_time.timestamp()))
            trade.set_custom_data('dca_reduce_done', False)
            trade.set_custom_data('open_reduce_done', False)
            trade.set_custom_data('tp_count', 0)
            trade.set_custom_data('dynamic_avg_entry', new_avg_entry)
            logger.info(
                f"[{trade.pair}][浮亏 DCA 加仓] {RED}u=({u}→{u + 1}){RESET},RSI<{rsi_thresh} "
                f"{YELLOW}保证金={trade.stake_amount:.8f}{RESET}, {RED}加仓={buy_amt:.8f}{RESET}, "
                f"{BLUE}成交价={threshold:.8f}, 新均价={new_avg_entry:.8f}{RESET}"
            )
            return buy_amt, f"dca_u={u + 1}"

        # -- 浮盈加仓 --
        ema20_arr = ta.EMA(df['close'], timeperiod=20)
        ema20 = float(ema20_arr[-1])
        need_rebuy = bool(trade.get_custom_data('need_rebuy'))
        n = int(trade.get_custom_data('tp_count') or 0)
        if need_rebuy:
            base_frac = 0.02  # 浮盈加仓参数
            # 如果价格偏离 20EMA 超过 5%，加仓量减半
            if price > ema20 * 1.05:  # ema偏离参数
                base_frac /= 2
                tag = 'rebuy10_ema'
                logger.info(f"价格过于偏离，可能触顶，浮盈加仓量减半")
            else:
                tag = 'rebuy20'
            buy_amt = collateral_add(base_frac)
            logger.info(
                f"[{trade.pair}][分批止盈加仓] base={base_frac * 100:.1f}% "
                f"(ema20={ema20:.4f}), 当前价={price:.4f},"
                f"{YELLOW}保证金={margin:.4f}{RESET}, {GREEN}加仓={buy_amt:.4f}{RESET}"
            )
            trade.set_custom_data('need_rebuy', False)
            trade.set_custom_data('dca_done', False)
            return buy_amt, tag

        # -- 止盈后回撤减仓 --
        if n > 0 and current_profit < 0.01:
            pct = min(1.0, 0.5 + 0.05 * n)
            sell_amt = -pct * margin
            trade.set_custom_data('last_fallback_price', current_rate)
            trade.set_custom_data('fallback_ready', True)
            trade.set_custom_data('fallback_repull_done', False)
            logger.info(
                f"[{trade.pair}][止盈后回撤1%] u={u}, n={n},"
                f"回撤价={price:.4f}, 当前价={price:.4f},"
                f"{YELLOW}保证金={margin:.2f}{RESET},{GREEN}减仓={abs(sell_amt):.2f}{RESET}"
            )
            trade.set_custom_data('dca_count', 0)
            trade.set_custom_data('tp_count', 0)
            trade.set_custom_data('dca_done', False)
            trade.set_custom_data('last_tp_time', int(current_time.timestamp()))
            return sell_amt, f"tp_fallback1%_{int(pct * 100)}%"

        # -- 止盈回落加仓 --
        last_tp_price = trade.get_custom_data('last_tp_price')
        repull_done = bool(trade.get_custom_data('tp_repull_done'))
        if last_tp_price and not repull_done and price <= last_tp_price * 0.99:
            amt = collateral_add(0.02)
            trade.set_custom_data('tp_repull_done', True)
            logger.info(
                f"{GREEN}[{trade.pair}] 止盈回落加仓总资金 2%: "
                f"止盈价={last_tp_price:.4f}, 当前价={price:.4f}, "
                f"保证金={margin:.4f}, 加仓={abs(amt):.4f} USDT{RESET}"
            )
            return amt, 'tp_repull20_tp'

        # -- 回撤加仓 --
        last_fb_price = trade.get_custom_data('last_fallback_price')
        ready = bool(trade.get_custom_data('fallback_ready'))
        done = bool(trade.get_custom_data('fallback_repull_done'))
        # 当价格回落到回撤价的 0.99 时加仓
        if last_fb_price and ready and not done and price <= last_fb_price * 0.99:  # 回落价格参数
            amt = collateral_add(0.02)  # 回落加仓参数
            trade.set_custom_data('fallback_repull_done', True)
            trade.set_custom_data('fallback_ready', False)
            logger.info(
                f"{GREEN}[{trade.pair}] 回撤价回落加仓总资金 2%: "
                f"回撤价={last_fb_price:.4f}, 当前价={price:.4f}, "
                f"保证金={margin:.4f}, 加仓={amt:.4f} USDT{RESET}"
            )
            return amt, 'tp_repull20'

        # -- 分批止盈 --
        last_tp = trade.get_custom_data('last_tp_time')
        base_time = datetime.fromtimestamp(last_tp) if last_tp else open_time
        elapsed = (current_time - base_time).total_seconds() / 60
        roi_target = 0.0
        for k, v in sorted(self.minimal_roi_user_defined.items(), key=lambda x: int(x[0]), reverse=True):
            if elapsed >= int(k):
                roi_target = v
                break
        if current_profit >= roi_target:
            if u > 0:
                pct = min(1.0, 0.5 + 0.05 * u)  # 浮亏止盈卖出参数
                sell_amt = -pct * margin
                logger.info(
                    f"[{trade.pair}][浮亏 DCA 后止盈] u={u}, n={n}, {YELLOW}保证金={margin:.2f}{RESET},"
                    f"{GREEN}减仓={abs(sell_amt):.2f}{RESET}"
                )
                trade.set_custom_data('dca_count', 0)
                trade.set_custom_data('tp_count', 0)
                trade.set_custom_data('dca_done', False)
                trade.set_custom_data('last_tp_time', int(current_time.timestamp()))
                return sell_amt, f"tp_afterDCA_{int(pct * 100)}%"
            else:
                if not last_tp or Timestamp(last_tp, unit='s').floor('T') != candle_ts:
                    sell_amt = -0.30 * margin  # 浮盈止盈卖出参数
                    logger.info(
                        f"[{trade.pair}][浮盈减仓 卖30%→后续加仓总资金 3%] u=0, n={n}->{n + 1}, "
                        f"{YELLOW}保证金={margin:.2f}{RESET}, {GREEN}减仓={abs(sell_amt):.2f}{RESET}"
                    )
                    trade.set_custom_data('tp_count', n + 1)
                    trade.set_custom_data('dca_count', 0)
                    trade.set_custom_data('dca_done', False)
                    trade.set_custom_data('last_tp_time', int(current_time.timestamp()))
                    return sell_amt, "tp30"

        # -- 抄底逃顶 --
        if trade.get_custom_data('bottom_added') and price > upper:
            trade.set_custom_data('bottom_added', False)
        if trade.get_custom_data('top_reduced') and price < lower:
            trade.set_custom_data('top_reduced', False)
        # 抄底
        if not trade.get_custom_data('bottom_added') and last['j_30'] < 0 and last['rsi'] < 35:  # KDJ_J&Rsi参数
            trade.set_custom_data('bottom_added', True)
            amt = collateral_add(0.02)  # 抄底加仓参数
            logger.info(
                f"{BLUE}[{trade.pair}] 抄底加仓总资金 2%: J={last['j_30']:.2f}, RSI={last['rsi']:.1f}, "
                f"保证金={margin:.4f}, 加仓={amt:.4f} USDT{RESET}"
            )
            return amt, 'bottom_add20'
        # 逃顶
        if not trade.get_custom_data('top_reduced') and current_profit > 0 and last['j_30'] > 100 and last['rsi'] > 65:  # KDJ_J&Rsi参数
            trade.set_custom_data('top_reduced', True)
            amt = -0.7 * margin  # 逃顶卖出参数
            logger.info(
                f"{RED}[{trade.pair}] 逃顶减仓70%: J={last['j_30']:.2f}, RSI={last['rsi']:.1f}, "
                f"保证金={margin:.4f}, 减仓={abs(amt):.4f} USDT{RESET}"
            )
            return amt, 'top_reduce70'
        # 逃顶后回落至布林中轨加仓
        if trade.get_custom_data('top_reduced') and price <= mid:
            trade.set_custom_data('top_reduced', False)
            amt = collateral_add(0.02)  # 回落加仓参数
            logger.info(
                f"{GREEN}[{trade.pair}] 逃顶回落加仓总资金 2%: 当前价={price:.4f}, 布林中轨={mid:.4f}, "
                f"保证金={margin:.4f}, 加仓={amt:.4f} USDT{RESET}"
            )
            return amt, 'rebound_add20'

        # # -- 24hDCA减仓 --
        # u = int(trade.get_custom_data('dca_count') or 0)
        # last_dca_time = trade.get_custom_data('last_dca_time')
        # reduce6_done = bool(trade.get_custom_data('dca_reduce_done'))
        # if u > 0 and last_dca_time and not reduce6_done:
        #     dca_dt = datetime.fromtimestamp(int(last_dca_time))
        #     if current_time >= dca_dt + timedelta(hours=24):  # Dca持续时间参数
        #         # 价格突破布林带上轨
        #         if price > upper:
        #             amt = -0.30 * margin  # 布林上轨卖出参数
        #             logger.info(
        #                 f"{YELLOW}[{trade.pair}][24h DCA后 · 突破上轨减仓30%] "
        #                 f"当前价={price:.4f}, 上轨={upper:.4f}, 保证金={margin:.2f}, 减仓={abs(amt):.2f} USDT{RESET}"
        #             )
        #             trade.set_custom_data('dca_reduce_done', True)
        #             return amt, 'reduce300%_postDCA_long'
        #
        # # -- 仓位过小加仓 --
        # if collateral > 0 and margin < collateral * 0.01:  # 仓位下限
        #     buy_amt = 1.0 * margin  # 小仓位加仓参数
        #     logger.info(
        #         f"{GREEN}[{trade.pair}] 保证金过低，当前保证金={margin:.4f} USDT, "
        #         f"总资产={collateral:.4f} USDT，加仓→{buy_amt:.4f} USDT{RESET}"
        #     )
        #     return buy_amt, 'add50_low_margin'
        # # -- 仓位过大减仓 --
        # if collateral > 0 and margin > collateral * 0.30:  # 仓位上限
        #     sell_amt = -0.30 * margin  # 大仓位减仓参数
        #     logger.info(
        #         f"{YELLOW}[{trade.pair}] 保证金过大，当前保证金={margin:.4f} USDT, "
        #         f"总资产={collateral:.4f} USDT，减仓→{abs(sell_amt):.4f} USDT{RESET}"
        #     )
        #     return sell_amt, 'reduce20_over_collateral'
        return None

    def order_filled(self, pair: str, trade: Trade, order: Order, current_time: datetime, **kwargs) -> None:
        if getattr(order, 'ft_order_tag', None) == "tp30" and order.side == "sell":
            trade.set_custom_data('need_rebuy', True)
            trade.set_custom_data('last_tp_price', order.price)
            trade.set_custom_data('tp_repull_done', False)
            trade.set_custom_data('fallback_repull_done', False),
            trade.set_custom_data('pullback_ready', True)

    def custom_stoploss(self, *args, **kwargs) -> float | None:
        return None