RelaxedFibonacciStrategy

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple

from freqtrade.strategy import (
    IStrategy,
    Trade,
    Order,
    PairLocks,
    informative,  # @informative decorator
    # Hyperopt Parameters
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    # timeframe helpers
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    # Strategy helper functions
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
    AnnotationType,
)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
from technical import qtpylib


class RelaxedFibonacciStrategy(IStrategy):
    """
    宽松版斐波那契策略 - 增加交易机会
    
    策略特点：
    1. 大幅放宽交易条件
    2. 增加斐波那契容忍度
    3. 简化确认条件
    4. 提高交易频率
    """
    
    # Strategy interface version
    INTERFACE_VERSION = 3

    # 策略时间框架 - 15分钟
    timeframe = "15m"

    # 是否支持做空
    can_short: bool = False

    # 最小ROI设置
    minimal_roi = {
        "120": 0.01,  # 2小时后1%收益
        "60": 0.02,   # 1小时后2%收益
        "30": 0.03,   # 30分钟后3%收益
        "0": 0.05     # 立即5%收益
    }

    # 止损设置
    stoploss = -0.06  # 6%止损

    # 追踪止损
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    # 只处理新K线
    process_only_new_candles = True

    # 策略参数
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # 策略启动所需的K线数量
    startup_candle_count: int = 50

    # 宽松的参数设置
    fib_period = IntParameter(20, 80, default=30, space="buy")
    rsi_period = IntParameter(10, 20, default=14, space="buy")
    rsi_oversold = IntParameter(35, 55, default=45, space="buy")
    rsi_overbought = IntParameter(45, 65, default=55, space="sell")
    fib_tolerance = DecimalParameter(0.05, 0.20, default=0.10, space="buy")

    # 订单类型
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False
    }

    # 订单时间
    order_time_in_force = {
        "entry": "GTC",
        "exit": "GTC"
    }

    @property
    def plot_config(self):
        return {
            "main_plot": {
                "fib_0.236": {"color": "red", "type": "line"},
                "fib_0.382": {"color": "orange", "type": "line"},
                "fib_0.5": {"color": "yellow", "type": "line"},
                "fib_0.618": {"color": "green", "type": "line"},
                "fib_0.786": {"color": "blue", "type": "line"},
            },
            "subplots": {
                "RSI": {
                    "rsi": {"color": "purple"},
                }
            }
        }

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        计算技术指标
        """
        # 计算RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=self.rsi_period.value)
        
        # 计算斐波那契回归水平
        period = self.fib_period.value
        dataframe["highest"] = dataframe["high"].rolling(window=period).max()
        dataframe["lowest"] = dataframe["low"].rolling(window=period).min()
        dataframe["price_range"] = dataframe["highest"] - dataframe["lowest"]
        
        # 计算斐波那契回归水平
        dataframe["fib_0.236"] = dataframe["highest"] - 0.236 * dataframe["price_range"]
        dataframe["fib_0.382"] = dataframe["highest"] - 0.382 * dataframe["price_range"]
        dataframe["fib_0.5"] = dataframe["highest"] - 0.5 * dataframe["price_range"]
        dataframe["fib_0.618"] = dataframe["highest"] - 0.618 * dataframe["price_range"]
        dataframe["fib_0.786"] = dataframe["highest"] - 0.786 * dataframe["price_range"]
        
        # 计算EMA
        dataframe["ema_20"] = ta.EMA(dataframe, timeperiod=20)
        
        # 计算MACD
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        宽松的买入信号
        """
        tolerance = self.fib_tolerance.value
        
        # 基础条件
        base_conditions = (
            (dataframe["volume"] > 0) &
            (dataframe["price_range"] > 0) &
            (dataframe["highest"].notna()) &
            (dataframe["lowest"].notna())
        )
        
        # 斐波那契条件（大幅放宽）
        fib_conditions = (
            # 价格接近任何斐波那契支撑位
            (
                ((dataframe["close"] <= dataframe["fib_0.618"] * (1 + tolerance)) & 
                 (dataframe["close"] >= dataframe["fib_0.618"] * (1 - tolerance))) |
                ((dataframe["close"] <= dataframe["fib_0.786"] * (1 + tolerance)) & 
                 (dataframe["close"] >= dataframe["fib_0.786"] * (1 - tolerance))) |
                ((dataframe["close"] <= dataframe["fib_0.5"] * (1 + tolerance)) & 
                 (dataframe["close"] >= dataframe["fib_0.5"] * (1 - tolerance)))
            )
        )
        
        # RSI条件（放宽）
        rsi_conditions = (
            (dataframe["rsi"] <= self.rsi_oversold.value)
        )
        
        # 趋势条件（简化）
        trend_conditions = (
            (dataframe["close"] > dataframe["ema_20"])
        )
        
        # 综合买入条件
        dataframe.loc[
            base_conditions &
            fib_conditions &
            rsi_conditions &
            trend_conditions,
            "enter_long"
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        宽松的卖出信号
        """
        tolerance = self.fib_tolerance.value
        
        # 基础条件
        base_conditions = (
            (dataframe["volume"] > 0) &
            (dataframe["price_range"] > 0) &
            (dataframe["highest"].notna()) &
            (dataframe["lowest"].notna())
        )
        
        # 斐波那契条件（大幅放宽）
        fib_conditions = (
            # 价格接近任何斐波那契阻力位
            (
                ((dataframe["close"] >= dataframe["fib_0.382"] * (1 - tolerance)) & 
                 (dataframe["close"] <= dataframe["fib_0.382"] * (1 + tolerance))) |
                ((dataframe["close"] >= dataframe["fib_0.236"] * (1 - tolerance)) & 
                 (dataframe["close"] <= dataframe["fib_0.236"] * (1 + tolerance))) |
                ((dataframe["close"] >= dataframe["fib_0.5"] * (1 - tolerance)) & 
                 (dataframe["close"] <= dataframe["fib_0.5"] * (1 + tolerance)))
            )
        )
        
        # RSI条件（放宽）
        rsi_conditions = (
            (dataframe["rsi"] >= self.rsi_overbought.value)
        )
        
        # 综合卖出条件
        dataframe.loc[
            base_conditions &
            fib_conditions &
            rsi_conditions,
            "exit_long"
        ] = 1

        return dataframe