# source: https://raw.githubusercontent.com/DerSalvador/freqtrade-helm-chart/a669dc11b640b0eb63aa8f8b51e9f181fd7ee43c/chart/deployed_strategies/binance-futures-k8s-namespace/NowoIchimoku1hV1.py
# This version of the strategy is broken!
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import math
from typing import Callable
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
# --------------------------------
# Add your lib to import here
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
from finta import TA

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})

class Github_DerSalvador_freqtrade_helm_chart__NowoIchimoku1hV1__20260115_122204(IStrategy):
    INTERFACE_VERSION = 3
    # Optimal timeframe for the strategy
    timeframe = '1h'
    startup_candle_count = 100
    use_exit_signal = False
    use_custom_stoploss = True
    minimal_roi = {'0': 999}
    stoploss = -0.08
    # 'lead_1': {
    #     'color': 'green',
    #     'fill_to': 'lead_2',
    #     'fill_label': 'Ichimoku Cloud',
    #     'fill_color': 'rgba(0,0,0,0.2)',
    # },
    # 'lead_2': {
    #     'color': 'red',
    # },
    # 'conversion_line': {'color': 'blue'},
    # 'base_line': {'color': 'orange'},
    plot_config = {'main_plot': {}}

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()
        # 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 > 1.1:
                return 'srsi_k above 80 with profit above 10%'
            if current_rate > last_candle['upper'] & current_profit > 1.01:
                return 'current rate above upper band with profit above 1%'
            limit = trade_candle['close'] + (trade_candle['close'] - trade_candle['shifted_lower_cloud']) * 2
            if current_rate > limit:
                return 'current rate above limit'
            if current_rate < trade_candle['shifted_lower_cloud']:
                return 'current rate below stop'

    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
        return 1
    # def informative_pairs(self):
    #     return []

    def populate_indicators(self, df: DataFrame, metadata: dict) -> DataFrame:
        df['upper'] = bollinger_bands(df['close'], moving_average='hma', length=20, mult=2.5)['upper']
        ichi = indicators.ichimoku(df)
        df['conversion_line'] = ichi['tenkan_sen']
        df['base_line'] = ichi['kijun_sen']
        df['lead_1'] = ichi['leading_senkou_span_a']
        df['lead_2'] = ichi['leading_senkou_span_b']
        df['cloud_green'] = ichi['cloud_green']
        df['upper_cloud'] = df['lead_1'].where(df['lead_1'] > df['lead_2'], df['lead_2'])
        df['lower_cloud'] = df['lead_1'].where(df['lead_1'] < df['lead_2'], df['lead_2'])
        df['shifted_upper_cloud'] = df['upper_cloud'].shift(25)
        df['shifted_lower_cloud'] = df['lower_cloud'].shift(25)
        smoothK = 3
        smoothD = 3
        lengthRSI = 14
        lengthStoch = 14
        df['rsi'] = ta.RSI(df, timeperiod=lengthRSI)
        stochrsi = (df['rsi'] - df['rsi'].rolling(lengthStoch).min()) / (df['rsi'].rolling(lengthStoch).max() - df['rsi'].rolling(lengthStoch).min())
        df['srsi_k'] = stochrsi.rolling(smoothK).mean() * 100
        df['srsi_d'] = df['srsi_k'].rolling(smoothD).mean()
        # df['srsi_top'] = 80
        # df['srsi_bottom'] = 20
        return df

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df['is_cloud_green'] = df['lead_1'] > df['lead_2']
        double_shifted_upper_cloud = df['upper_cloud'].shift(50)
        close_above_shifted_upper_cloud = df['close'] > df['shifted_upper_cloud'] * 1.04
        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)
        df['should_entry'] = (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['entry_allowed'] = -df['is_cloud_green']
        for i in range(101, len(df)):
            df.loc[i, 'entry_allowed'] = df.loc[i - 1, 'entry_allowed']
            if df.loc[i - 1, 'entry']:
                df.loc[i, 'entry_allowed'] = False
            elif not df.loc[i, 'is_cloud_green']:
                df[i, 'entry_allowed'] = True
        df.loc[df['entry_allowed'] & df['should_entry'], 'entry'] = 1
        return df

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