# source: https://raw.githubusercontent.com/flessner/freqtrade/89d04067a9bdd588029770281eecd7b837fcbad0/user_data/strategies/nowoi2.py
import math
from typing import Callable
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.strategy import IStrategy
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import IntParameter
from pandas import Series
from numpy.typing import ArrayLike
from datetime import datetime, timedelta
import technical.indicators as indicators
from freqtrade.exchange import timeframe_to_prev_date

# NowoIchimoku5mV2
# source: https://github.com/davidzr/freqtrade-strategies/blob/9623c1f3d8c7f60c8b411010fa26377e6ca99ab9/strategies/NowoIchimoku5mV2/NowoIchimoku5mV2.py


class Github_flessner_freqtrade__nowoi2__20250222_025553(IStrategy):
    # Optimal timeframe for the strategy
    timeframe = "5m"
    informative_timeframe = "1h"

    time_factor = int(60 / 5)
    startup_candle_count = int(100 * time_factor)

    use_exit_signal = False
    use_custom_stoploss = True
    trailing_stop = True

    minimal_roi = {"0": 999}

    stoploss = -0.1

    srsi_k_min_profit = DecimalParameter(
        0.01, 0.99, decimals=3, default=0.036, space="sell", optimize=True
    )
    above_upper_min_profit = DecimalParameter(
        0.001, 0.5, decimals=3, default=0.011, space="sell"
    )
    limit_factor = DecimalParameter(
        0.5, 5, decimals=3, default=1.918, space="sell", optimize=True
    )
    lower_cloud_factor = DecimalParameter(
        0.5, 1.5, decimals=3, default=0.971, space="sell", optimize=True
    )
    close_above_shifted_upper_cloud = DecimalParameter(
        0.5, 2, decimals=3, default=0.603, space="buy", optimize=True
    )

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
        return informative_pairs

    def populate_indicators(self, df_5m: DataFrame, metadata: dict) -> DataFrame:
        # Get the informative pair
        df_1h = self.dp.get_pair_dataframe(
            pair=metadata["pair"], timeframe=self.informative_timeframe
        )

        df_1h["upper"] = bollinger_bands(
            df_1h["close"], moving_average="hma", length=20, mult=2.5
        )["upper"]

        ichi_1h = indicators.ichimoku(df_1h)

        df_1h["conversion_line"] = ichi_1h["tenkan_sen"]
        df_1h["base_line"] = ichi_1h["kijun_sen"]

        df_1h["lead_1"] = ichi_1h["leading_senkou_span_a"]
        df_1h["lead_2"] = ichi_1h["leading_senkou_span_b"]

        df_1h["is_cloud_green"] = ichi_1h["cloud_green"]

        df_1h["upper_cloud"] = df_1h["lead_1"].where(
            df_1h["lead_1"] > df_1h["lead_2"], df_1h["lead_2"]
        )
        df_1h["lower_cloud"] = df_1h["lead_1"].where(
            df_1h["lead_1"] < df_1h["lead_2"], df_1h["lead_2"]
        )

        df_1h["shifted_upper_cloud"] = df_1h["upper_cloud"].shift(25)
        df_1h["shifted_lower_cloud"] = df_1h["lower_cloud"].shift(25)

        smoothK = 3
        smoothD = 3
        lengthRSI = 14
        lengthStoch = 14

        df_1h["rsi"] = ta.RSI(df_1h, timeperiod=lengthRSI)

        stochrsi = (df_1h["rsi"] - df_1h["rsi"].rolling(lengthStoch).min()) / (
            df_1h["rsi"].rolling(lengthStoch).max()
            - df_1h["rsi"].rolling(lengthStoch).min()
        )

        df_1h["srsi_k"] = stochrsi.rolling(smoothK).mean() * 100
        df_1h["srsi_d"] = df_1h["srsi_k"].rolling(smoothD).mean()

        df = merge_informative_pair(
            df_5m, df_1h, self.timeframe, self.informative_timeframe, ffill=True
        )
        # don't overwrite the base dataframe's OHLCV information
        skip_columns = [
            (s + "_" + self.informative_timeframe)
            for s in ["date", "open", "high", "low", "close", "volume"]
        ]

        df.rename(
            columns=lambda s: (
                s.replace("_{}".format(self.informative_timeframe), "")
                if (not s in skip_columns)
                else s
            ),
            inplace=True,
        )

        return df

    def custom_stoploss(
        self,
        pair: str,
        trade: "Trade",
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ) -> float:

        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1 * self.time_factor].squeeze()
        previous_candle = dataframe.iloc[-2 * self.time_factor].squeeze()

        if (last_candle is not None) & (previous_candle is not None):
            # In dry/live runs trade open date will not match candle open date therefore it must be
            # rounded.
            trade_date = timeframe_to_prev_date(self.timeframe, trade.open_date_utc)
            # Look up trade candle.
            trade_candle = dataframe.loc[dataframe["date"] == trade_date]

            # trade_candle may be empty for trades that just opened as it is still incomplete.
            if not trade_candle.empty:
                trade_candle = trade_candle.squeeze()

                if (last_candle["srsi_k"] > 80) & (
                    current_profit > self.srsi_k_min_profit.value
                ):
                    return -0.0001

                if (
                    (previous_candle["close"] < previous_candle["upper"])
                    & (current_rate > last_candle["upper"])
                    & (current_profit > self.above_upper_min_profit.value)
                ):
                    return -0.0001

                limit = trade.open_rate + (
                    (trade.open_rate - trade_candle["shifted_lower_cloud"])
                    * self.limit_factor.value
                )

                if current_rate > limit:
                    return -0.0001

                if current_rate < (
                    trade_candle["shifted_lower_cloud"] * self.lower_cloud_factor.value
                ):
                    return -0.0001

        return -0.99

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df.info(verbose=True)

        double_shifted_upper_cloud = df["upper_cloud"].shift(50 * self.time_factor)

        close_above_shifted_upper_cloud = (
            df["close"]
            > df["shifted_upper_cloud"] * self.close_above_shifted_upper_cloud.value
        )
        close_above_shifted_lower_cloud = df["close"] > df["shifted_lower_cloud"]
        close_above_double_shifted_upper_cloud = (
            df["close"] > double_shifted_upper_cloud
        )

        conversion_line_above_base_line = df["conversion_line"] > df["base_line"]
        close_above_shifted_conversion_line = df["close"] > df["conversion_line"].shift(
            25 * self.time_factor
        )

        df["should_buy"] = (
            (df["close"] > df["open"])
            & close_above_shifted_upper_cloud
            & close_above_shifted_lower_cloud
            & df["is_cloud_green"]
            & conversion_line_above_base_line
            & close_above_shifted_conversion_line
            & close_above_double_shifted_upper_cloud
        )

        df["buy"] = False
        df["buy_allowed"] = True

        for i in range(1, len(df)):
            df.loc[i, "buy_allowed"] = df.at[i - 1, "buy_allowed"]

            if df.at[i - 1, "buy"]:
                df.loc[i, "buy_allowed"] = False

            if not df.at[i, "is_cloud_green"]:
                df.loc[i, "buy_allowed"] = True

            df.loc[i, "buy"] = df.at[i, "buy_allowed"] & df.at[i, "should_buy"]

        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df["sell"] = 0
        return df


def merge_informative_pair(
    dataframe: pd.DataFrame,
    informative: pd.DataFrame,
    timeframe: str,
    timeframe_inf: str,
    ffill: bool = True,
) -> pd.DataFrame:

    minutes_inf = timeframe_to_minutes(timeframe_inf)
    minutes = timeframe_to_minutes(timeframe)
    if minutes == minutes_inf:
        # No need to forwardshift if the timeframes are identical
        informative["date_merge"] = informative["date"]
    elif minutes < minutes_inf:
        # Subtract "small" timeframe so merging is not delayed by 1 small candle
        # Detailed explanation in https://github.com/freqtrade/freqtrade/issues/4073
        informative["date_merge"] = (
            informative["date"]
            + pd.to_timedelta(minutes_inf, "m")
            - pd.to_timedelta(minutes, "m")
        )
    else:
        raise ValueError(
            "Tried to merge a faster timeframe to a slower timeframe."
            "This would create new rows, and can throw off your regular indicators."
        )

    # Rename columns to be unique
    informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]

    # Combine the 2 dataframes
    # all indicators on the informative sample MUST be calculated before this point
    dataframe = pd.merge(
        dataframe,
        informative,
        left_on="date",
        right_on=f"date_merge_{timeframe_inf}",
        how="left",
    )
    dataframe = dataframe.drop(f"date_merge_{timeframe_inf}", axis=1)

    if ffill:
        dataframe = dataframe.ffill()

    return dataframe


def wma(series: Series, length: int) -> Series:
    norm = 0
    sum = 0

    for i in range(1, length - 1):
        weight = (length - i) * length
        norm = norm + weight
        sum = sum + series.shift(i) * weight

    return sum / norm


def hma(series: Series, length: int) -> Series:
    h = 2 * wma(series, math.floor(length / 2)) - wma(series, length)
    hma = wma(h, math.floor(math.sqrt(length)))
    return hma


def bollinger_bands(
    series: Series, moving_average="sma", length=20, mult=2.0
) -> DataFrame:
    basis = None
    if moving_average == "sma":
        basis = ta.SMA(series, length)
    elif moving_average == "hma":
        basis = hma(series, length)
    else:
        raise Exception("moving_average has to be sma or hma")

    dev = mult * ta.STDDEV(series, length)

    return DataFrame({"upper": basis + dev})