import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Optional, Union

from freqtrade.strategy import (
    IStrategy,
    Trade,
    Order,
    PairLocks,
    informative,
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
)

import talib.abstract as ta
from technical import qtpylib

class SampleStrategy(IStrategy):
    INTERFACE_VERSION = 3
    can_short: bool = False
    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04,
    }
    stoploss = -0.10
    trailing_stop = False
    timeframe = "5m"
    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False
    buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
    short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
    exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
    startup_candle_count: int = 200
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }
    order_time_in_force = {"entry": "GTC", "exit": "GTC"}
    plot_config = {
        "main_plot": {
            "ema3": {},
            "ema5": {},
            "ema20": {},
            "ema50": {},
            "ema100": {},
            "ema200": {}
        },
        "subplots": {
            "MACD": {
                "macd": {"color": "blue"},
                "macdsignal": {"color": "orange"},
            },
            "RSI": {
                "rsi": {"color": "red"},
            },
        },
    }

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["ema3"] = ta.EMA(dataframe, timeperiod=3)
        dataframe["ema5"] = ta.EMA(dataframe, timeperiod=5)
        dataframe["ema20"] = ta.EMA(dataframe, timeperiod=20)
        dataframe["ema50"] = ta.EMA(dataframe, timeperiod=50)
        dataframe["ema100"] = ta.EMA(dataframe, timeperiod=100)
        dataframe["ema200"] = ta.EMA(dataframe, timeperiod=200)
        
        dataframe["rsi"] = ta.RSI(dataframe)
        dataframe["adx"] = ta.ADX(dataframe)
        
        stoch_fast = ta.STOCHF(dataframe)
        dataframe["fastd"] = stoch_fast["fastd"]
        dataframe["fastk"] = stoch_fast["fastk"]

        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        dataframe["mfi"] = ta.MFI(dataframe)

        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
            dataframe["bb_upperband"] - dataframe["bb_lowerband"]
        )
        dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]

        dataframe["sar"] = ta.SAR(dataframe)
        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)

        hilbert = ta.HT_SINE(dataframe)
        dataframe["htsine"] = hilbert["sine"]
        dataframe["htleadsine"] = hilbert["leadsine"]

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["ema3"] < dataframe["high"]) &
                (dataframe["ema3"] < dataframe["low"]) &
                (dataframe["rsi"] < self.buy_rsi.value)
            ),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["ema3"] > dataframe["high"]) &
                (dataframe["ema3"] > dataframe["low"]) &
                (dataframe["rsi"] > self.sell_rsi.value)
            ),
            "exit_long",
        ] = 1
        return dataframe