NineSecondSniperV3

from freqtrade.strategy import IStrategy
from pandas import DataFrame
import pandas as pd
import talib.abstract as ta
from datetime import datetime
import numpy as np


class NineSecondSniperV3(IStrategy):
    """
    9秒狙击手策略 V3 - 保守稳定版

    核心理念：
    - 捕捉9秒级别的价格变化
    - SAR 指标识别趋势反转
    - 狙击式入场后快速止盈止损
    - 严格风控，避免赌博式交易
    """

    timeframe = '1m'
    max_open_trades = 2
    stake_amount = 100
    startup_candle_count = 100

    # 止盈配置 - 快速止盈
    minimal_roi = {
        "0": 0.008,    # 0.8% 快速止盈
        "30": 0.006,    # 30分钟降到0.6%
        "60": 0.004,    # 60分钟降到0.4%
    }

    # 紧凑止损
    stoploss = -0.006  # 0.6% 止损
    trailing_stop = True  # 启用追踪止损
    trailing_stop_positive = 0.004  # 0.4% 盈利后启动
    trailing_stop_positive_offset = 0.006  # 0.6% 偏离

    order_types = {
        'entry': 'market',
        'exit': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False,
        'entry_pricing': 'same',
        'exit_pricing': 'same'
    }

    unfilledtimeout = {
        'entry': 10,
        'exit': 10,
        'unit': 'seconds'
    }

    # 保守杠杆配置
    base_leverage_config = {
        'BTC/USDT:USDT': 2.0,
        'ETH/USDT:USDT': 1.5,
        'SOL/USDT:USDT': 1.5,
        'XRP/USDT:USDT': 1.0,
        'DOGE/USDT:USDT': 1.0
    }

    # 价格缓冲区
    price_buffer_size = 9
    price_buffers = {}

    # 冷却计时器
    last_entry_time = {}
    cooldown_minutes = 15  # 两次入场间隔15分钟

    def informative_pairs(self) -> list:
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        df = dataframe.copy()
        pair = metadata['pair']

        # ========== 核心指标 ==========

        # SAR 指标 - 抛物线，找趋势反转
        df['sar'] = ta.SAR(df['high'].values, df['low'].values,
                          acceleration=0.02, maximum=0.2)

        # EMA 趋势线
        df['ema_5'] = ta.EMA(df['close'].values, timeperiod=5)
        df['ema_13'] = ta.EMA(df['close'].values, timeperiod=13)

        # RSI - 避免超买超卖
        df['rsi'] = ta.RSI(df['close'].values, timeperiod=14)

        # MACD - 动能指标
        macd, macdsignal, macdhist = ta.MACD(df['close'].values)
        df['macd'] = macd
        df['macd_signal'] = macdsignal
        df['macd_hist'] = macdhist

        # 成交量
        df['volume_sma'] = ta.SMA(df['volume'].values, timeperiod=20)
        df['volume_ratio'] = df['volume'] / df['volume_sma']

        # ========== 9秒动能 ==========

        # 9根K线价格对比
        df['price_9sec_ago'] = df['close'].shift(9)
        df['price_change_9sec'] = (df['close'] - df['price_9sec_ago']) / df['price_9sec_ago']
        df['volatility_9sec'] = abs(df['price_change_9sec'])

        # 价格缓冲区维护
        if pair not in self.price_buffers:
            self.price_buffers[pair] = np.zeros(self.price_buffer_size)

        buffer = self.price_buffers[pair]
        for i in range(len(df)):
            if i >= self.price_buffer_size:
                buffer[:-1] = buffer[1:]
                buffer[-1] = df['close'].iloc[i]

        df['buffer_momentum'] = 0.0
        if len(buffer) >= self.price_buffer_size:
            df['buffer_momentum'] = (buffer[-1] - buffer[0]) / buffer[0]

        return df

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 current_profit: float = 0.0, min_stops: float = 0.0,
                 max_stops: float = 0.0, current_time_rows: DataFrame = None,
                 **kwargs) -> float:
        base_leverage = self.base_leverage_config.get(pair, 1.0)

        # 保守策略：盈利时降低杠杆
        if current_profit > 0.003:  # 盈利 > 0.3%
            base_leverage *= 0.5  # 减半杠杆保护利润
        elif current_profit < -0.003:  # 亏损 > 0.3%
            base_leverage *= 0.7  # 降低杠杆控制风险

        return min(base_leverage, 3.0)  # 最大杠杆限制

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        df = dataframe.copy()
        df['enter_long'] = 0

        if len(df) < self.startup_candle_count:
            return df

        # ========== 狙击手入场条件 ==========

        # 核心逻辑：SAR 支撑 + 多重确认 + 向上动
        entry_conditions = (
            # 1. SAR 支撑确认（价格在 SAR 上方，可能是向上突破）
            (df['close'] > df['sar']) &

            # 2. EMA 金叉确认（短期均线上穿长期）
            (df['ema_5'] > df['ema_13']) &

            # 3. RSI 区间确认（不超买，有上涨空间）
            (df['rsi'] > 40) & (df['rsi'] < 65) &

            # 4. MACD 金叉确认（动能向上）
            (df['macd'] > df['macd_signal']) &

            # 5. 成交量确认（量能放大）
            (df['volume_ratio'] > 1.3) &

            # 6. 9秒动向上确认（短期向上动）
            (df['price_change_9sec'] > 0.001) &

            # 7. 缓冲区动确认
            (df['buffer_momentum'] > 0.0005)
        )

        df.loc[entry_conditions, 'enter_long'] = 1

        return df

    def populate_exit_trend(self, (dataframe: DataFrame, metadata: dict) -> DataFrame:
        df = dataframe.copy()
        df['exit'] = 0

        # ========== 快速出场条件 ==========

        exit_conditions = (
            # 1. EMA 死叉（趋势反转）
            (df['ema_5'] < df['ema_13']) |

            # 2. MACD 死叉
            (df['macd'] < df['macd_signal']) |
            # 3. RSI 超买
            (df['rsi'] > 70) |
            # 4. 9秒动向下
            (df['price_change_9sec'] < -0.001)
        )

        df.loc[exit_conditions, 'exit'] = 1

        return df

    def custom_exit(self, pair: str, current_time: datetime, current_rate: float,
                    current_profit: float, **kwargs) -> bool:
        # 追踪止损主要处理，这里作为额外保险

        # 紧凑止损保险
        if current_profit < -0.01:  # -1% 保险止损
            return True

        # 快速止盈保险
        if current_profit > 0.015:  # 1.5% 保险止盈
            return True

        return False