PriceVolume1MNoticeStrategy

# 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,
)

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


class PriceVolume1MNoticeStrategy(IStrategy):
    """
    This is a strategy template to get you started.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 3

    # Optimal timeframe for the strategy.
    timeframe = "1m"

    # Can this strategy go short?
    can_short: bool = True

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "0": 100000
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -100000

    # Trailing stoploss
    # trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Strategy parameters
    # buy_rsi = IntParameter(10, 40, default=30, space="buy")
    # sell_rsi = IntParameter(60, 90, default=70, space="sell")# Optional order type mapping.
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False
    }

    # Optional order time in force.
    order_time_in_force = {
        "entry": "GTC",
        "exit": "GTC"
    }
    @property
    def plot_config(self):
        return {
            # Main plot indicators (Moving averages, ...)
            "main_plot": {
                "tema": {},
                "sar": {"color": "white"},
            },
            "subplots": {
                # Subplots - each dict defines one additional plot
                "MACD": {
                    "macd": {"color": "blue"},
                    "macdsignal": {"color": "orange"},
                },
                "RSI": {
                    "rsi": {"color": "red"},
                }
            }
        }

    def informative_pairs(self):
        """
        Define additional, informative pair/interval combinations to be cached from the exchange.
        These pair/interval combinations are non-tradeable, unless they are part
        of the whitelist as well.
        For more information, please consult the documentation
        :return: List of tuples in the format (pair, interval)
            Sample: return [("ETH/USDT", "5m"),
                            ("BTC/USDT", "15m"),
                            ]
        """
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict):
        dataframe = dataframe[-100:]
        # 计算5分钟涨跌幅
        recent = dataframe.iloc[-5:]  # 最近5分钟
        open_price = recent.iloc[0]['open']
        close_price = recent.iloc[-1]['close']
        pct_change = (close_price - open_price) / open_price
        dataframe.loc[dataframe.index[-1], "price_change"] = pct_change

        # 计算5分钟成交量
        dataframe["5m_volume"] = dataframe["volume"].rolling(window=5).sum()
        windows = 30
        mean_vol = dataframe['5m_volume'].rolling(window=windows).mean()
        std_vol = dataframe['5m_volume'].rolling(window=windows).std()
        dataframe["z_score"] = (dataframe['5m_volume'] - mean_vol) / std_vol

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict):
        dataframe = dataframe[-100:]
        dataframe.loc[
            (
                (dataframe['price_change'] > 0.01) &  # 价格上涨
                (dataframe['z_score'] > 2)  # Z-score大于1
            ),
            'enter_long'] = 1
        dataframe.loc[
            (
                (dataframe['price_change'] < -0.01) &  # 价格下跌
                (dataframe['z_score'] > 2)  # Z-score小于-1
            ),
            'enter_short'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict):
        dataframe = dataframe[-100:]
        return dataframe