IchiV1

# --- Do not remove these libs ---
from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas as pd  # noqa
pd.options.mode.chained_assignment = None  # default='warn'
import technical.indicators as ftt
from functools import reduce
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair
import numpy as np
from freqtrade.strategy import stoploss_from_open

class IchiV1(IStrategy):
    """
    IchiV1 Strategy ported to V3
    """

    INTERFACE_VERSION = 3

    # NOTE: Optimized for faster trading pace (~24 trades/day)
    # Buy hyperspace params:
    buy_params = {
        "buy_trend_above_senkou_level": 1,
        "buy_trend_bullish_level": 4,  # Lowered from 6 to allow more entries
        "buy_fan_magnitude_shift_value": 1,  # Reduced from 3 to allow faster entries
        "buy_min_fan_magnitude_gain": 1.0005  # Lowered from 1.002 for more frequent signals
    }

    # Sell hyperspace params:
    sell_params = {
        "sell_trend_indicator": "trend_close_15m",  # Changed from 2h to 15m for faster exits
    }

    # ROI table: Tighter targets for faster closes
    minimal_roi = {
        "0": 0.02,    # Take 2% profit immediately if available
        "5": 0.015,   # 1.5% after 5 minutes
        "15": 0.01,   # 1% after 15 minutes
        "30": 0.005,  # 0.5% after 30 minutes
        "60": 0       # Close at breakeven after 1 hour
    }

    # Stoploss: Tighter for less margin risk
    stoploss = -0.05  # Changed from -27.5% to -5%

    # Optimal timeframe for the strategy
    timeframe = '5m'

    startup_candle_count = 96
    process_only_new_candles = False

    trailing_stop = True
    trailing_stop_positive = 0.005  # Trail at 0.5% profit
    trailing_stop_positive_offset = 0.01  # Start trailing after 1% profit
    trailing_only_offset_is_reached = True

    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Plot config
    plot_config = {
        'main_plot': {
            'senkou_a': {
                'color': 'green',
                'fill_to': 'senkou_b',
                'fill_label': 'Ichimoku Cloud',
                'fill_color': 'rgba(255,76,46,0.2)',
            },
            'senkou_b': {},
            'trend_close_5m': {'color': '#FF5733'},
            'trend_close_2h': {'color': '#E3FF33'},
        },
        'subplots': {
            'fan_magnitude': {
                'fan_magnitude': {}
            },
        }
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['open'] = heikinashi['open']
        dataframe['high'] = heikinashi['high']
        dataframe['low'] = heikinashi['low']
        # Do not overwrite close with HA close for typical price action strategies unless strictly intended
        # The original strategy commented out HA close, so we will use regular close for indicators?
        # Revisiting logic: The original populated `trend_close_5m` = `close`.
        # If we use HA open/high/low, but regular close, it's a mix.
        # However, looking at the code: `dataframe['open'] = heikinashi['open']`.
        # It modifies the main dataframe "open" column. This is risky for backtesting if not careful.
        # But this is what the user provided. We will trust the logic "as provided" but ensure V3 compatibility.

        dataframe['trend_close_5m'] = dataframe['close']
        dataframe['trend_close_15m'] = ta.EMA(dataframe['close'], timeperiod=3)
        dataframe['trend_close_30m'] = ta.EMA(dataframe['close'], timeperiod=6)
        dataframe['trend_close_1h'] = ta.EMA(dataframe['close'], timeperiod=12)
        dataframe['trend_close_2h'] = ta.EMA(dataframe['close'], timeperiod=24)
        dataframe['trend_close_4h'] = ta.EMA(dataframe['close'], timeperiod=48)
        dataframe['trend_close_6h'] = ta.EMA(dataframe['close'], timeperiod=72)
        dataframe['trend_close_8h'] = ta.EMA(dataframe['close'], timeperiod=96)

        dataframe['trend_open_5m'] = dataframe['open']
        dataframe['trend_open_15m'] = ta.EMA(dataframe['open'], timeperiod=3)
        dataframe['trend_open_30m'] = ta.EMA(dataframe['open'], timeperiod=6)
        dataframe['trend_open_1h'] = ta.EMA(dataframe['open'], timeperiod=12)
        dataframe['trend_open_2h'] = ta.EMA(dataframe['open'], timeperiod=24)
        dataframe['trend_open_4h'] = ta.EMA(dataframe['open'], timeperiod=48)
        dataframe['trend_open_6h'] = ta.EMA(dataframe['open'], timeperiod=72)
        dataframe['trend_open_8h'] = ta.EMA(dataframe['open'], timeperiod=96)

        dataframe['fan_magnitude'] = (dataframe['trend_close_1h'] / dataframe['trend_close_8h'])
        dataframe['fan_magnitude_gain'] = dataframe['fan_magnitude'] / dataframe['fan_magnitude'].shift(1)

        ichimoku = ftt.ichimoku(dataframe, conversion_line_period=20, base_line_periods=60, laggin_span=120, displacement=30)
        dataframe['senkou_a'] = ichimoku['senkou_span_a']
        dataframe['senkou_b'] = ichimoku['senkou_span_b']

        return dataframe


    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []

        # Trending market
        if self.buy_params['buy_trend_above_senkou_level'] >= 1:
            conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_a'])
            conditions.append(dataframe['trend_close_5m'] > dataframe['senkou_b'])

        # Trends bullish (relaxed for more signals)
        if self.buy_params['buy_trend_bullish_level'] >= 4:
            conditions.append(dataframe['trend_close_1h'] > dataframe['trend_open_1h'])

        # Trends magnitude
        conditions.append(dataframe['fan_magnitude_gain'] >= self.buy_params['buy_min_fan_magnitude_gain'])
        conditions.append(dataframe['fan_magnitude'] > 1)

        for x in range(self.buy_params['buy_fan_magnitude_shift_value']):
            conditions.append(dataframe['fan_magnitude'].shift(x+1) < dataframe['fan_magnitude'])

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'enter_long'] = 1

        return dataframe


    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        conditions = []

        conditions.append(qtpylib.crossed_below(dataframe['trend_close_5m'], dataframe[self.sell_params['sell_trend_indicator']]))

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'exit_long'] = 1

        return dataframe