ichiv1

import datetime
import os
from pandas import DataFrame
import pytz
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import pandas as pd
from strategies.istrategy import IStrategy

pd.options.mode.chained_assignment = None  # default='warn'
import technical.indicators as ftt
from functools import reduce


class ichiv1(IStrategy):
    # NOTE: settings as of the 25th july 21
    # Buy hyperspace params:
    buy_params = {
        "buy_trend_above_senkou_level": 1,
        "buy_trend_bullish_level": 6,
        "buy_fan_magnitude_shift_value": 3,
        "buy_min_fan_magnitude_gain": 1.001
        # "buy_min_fan_magnitude_gain": 1.002  # NOTE: Good value (Win% ~70%), alot of trades
        # "buy_min_fan_magnitude_gain": 1.008,  # NOTE: Very save value (Win% ~90%), only the biggest moves 1.008,
    }

    # Sell hyperspace params:
    # NOTE: was 15m but kept bailing out in dryrun
    sell_params = {
        "sell_trend_indicator": "trend_close_2h",
    }

    # ROI table:
    minimal_roi = {"0": 0.059, "10": 0.037, "41": 0.012, "114": 0}

    # Stoploss:
    stoploss = -0.275

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

    startup_candle_count = 96
    process_only_new_candles = False

    trailing_stop = False
    # trailing_stop_positive = 0.002
    # trailing_stop_positive_offset = 0.025
    # trailing_only_offset_is_reached = True

    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    plot_config = {
        "main_plot": {
            # fill area between senkou_a and senkou_b
            "senkou_a": {
                "color": "green",  # optional
                "fill_to": "senkou_b",
                "fill_label": "Ichimoku Cloud",  # optional
                "fill_color": "rgba(255,76,46,0.2)",  # optional
            },
            # plot senkou_b, too. Not only the area to it.
            "senkou_b": {},
            "trend_close_5m": {"color": "#FF5733"},
            "trend_close_15m": {"color": "#FF8333"},
            "trend_close_30m": {"color": "#FFB533"},
            "trend_close_1h": {"color": "#FFE633"},
            "trend_close_2h": {"color": "#E3FF33"},
            "trend_close_4h": {"color": "#C4FF33"},
            "trend_close_6h": {"color": "#61FF33"},
            "trend_close_8h": {"color": "#33FF7D"},
        },
        "subplots": {
            "fan_magnitude": {"fan_magnitude": {}},
            "fan_magnitude_gain": {"fan_magnitude_gain": {}},
        },
    }

    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
        dataframe = dataframe.reset_index()
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe["open"] = heikinashi["open"]
        # dataframe['close'] = heikinashi['close']
        dataframe["high"] = heikinashi["high"]
        dataframe["low"] = heikinashi["low"]
        dataframe = dataframe.set_index("date")

        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["chikou_span"] = ichimoku["chikou_span"]
        dataframe["tenkan_sen"] = ichimoku["tenkan_sen"]
        dataframe["kijun_sen"] = ichimoku["kijun_sen"]
        dataframe["senkou_a"] = ichimoku["senkou_span_a"]
        dataframe["senkou_b"] = ichimoku["senkou_span_b"]
        dataframe["leading_senkou_span_a"] = ichimoku["leading_senkou_span_a"]
        dataframe["leading_senkou_span_b"] = ichimoku["leading_senkou_span_b"]
        dataframe["cloud_green"] = ichimoku["cloud_green"]
        dataframe["cloud_red"] = ichimoku["cloud_red"]

        dataframe["atr"] = ta.ATR(dataframe)

        return dataframe

    def condition_early_close_seconds(self, df, conditions):
        condition_early_close_seconds = int(
            os.getenv("CONDITION_EARLY_CLOSE_SECONDS", 0)
        )

        timeframe_seconds = df.index.unique().to_series().diff().min().total_seconds()
        threshold = condition_early_close_seconds
        last_date = df.index[-1]
        next_date = last_date + datetime.timedelta(seconds=timeframe_seconds)
        now_date = datetime.datetime.now(tz=pytz.timezone("Asia/Shanghai"))
        # early close of last candle
        if next_date - now_date > datetime.timedelta(seconds=threshold):
            conditions.append(df.index < last_date)
            df.loc[df.index >= last_date, "signal_by"] = pd.Series(
                "removed_by_early_close",
                index=df.index[df.index >= last_date],
            )

    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
        conditions = []
        self.condition_early_close_seconds(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"])

        if self.buy_params["buy_trend_above_senkou_level"] >= 2:
            conditions.append(dataframe["trend_close_15m"] > dataframe["senkou_a"])
            conditions.append(dataframe["trend_close_15m"] > dataframe["senkou_b"])

        if self.buy_params["buy_trend_above_senkou_level"] >= 3:
            conditions.append(dataframe["trend_close_30m"] > dataframe["senkou_a"])
            conditions.append(dataframe["trend_close_30m"] > dataframe["senkou_b"])

        if self.buy_params["buy_trend_above_senkou_level"] >= 4:
            conditions.append(dataframe["trend_close_1h"] > dataframe["senkou_a"])
            conditions.append(dataframe["trend_close_1h"] > dataframe["senkou_b"])

        if self.buy_params["buy_trend_above_senkou_level"] >= 5:
            conditions.append(dataframe["trend_close_2h"] > dataframe["senkou_a"])
            conditions.append(dataframe["trend_close_2h"] > dataframe["senkou_b"])

        if self.buy_params["buy_trend_above_senkou_level"] >= 6:
            conditions.append(dataframe["trend_close_4h"] > dataframe["senkou_a"])
            conditions.append(dataframe["trend_close_4h"] > dataframe["senkou_b"])

        if self.buy_params["buy_trend_above_senkou_level"] >= 7:
            conditions.append(dataframe["trend_close_6h"] > dataframe["senkou_a"])
            conditions.append(dataframe["trend_close_6h"] > dataframe["senkou_b"])

        if self.buy_params["buy_trend_above_senkou_level"] >= 8:
            conditions.append(dataframe["trend_close_8h"] > dataframe["senkou_a"])
            conditions.append(dataframe["trend_close_8h"] > dataframe["senkou_b"])

        # Trends bullish
        if self.buy_params["buy_trend_bullish_level"] >= 1:
            conditions.append(dataframe["trend_close_5m"] > dataframe["trend_open_5m"])

        if self.buy_params["buy_trend_bullish_level"] >= 2:
            conditions.append(
                dataframe["trend_close_15m"] > dataframe["trend_open_15m"]
            )

        if self.buy_params["buy_trend_bullish_level"] >= 3:
            conditions.append(
                dataframe["trend_close_30m"] > dataframe["trend_open_30m"]
            )

        if self.buy_params["buy_trend_bullish_level"] >= 4:
            conditions.append(dataframe["trend_close_1h"] > dataframe["trend_open_1h"])

        if self.buy_params["buy_trend_bullish_level"] >= 5:
            conditions.append(dataframe["trend_close_2h"] > dataframe["trend_open_2h"])

        if self.buy_params["buy_trend_bullish_level"] >= 6:
            conditions.append(dataframe["trend_close_4h"] > dataframe["trend_open_4h"])

        if self.buy_params["buy_trend_bullish_level"] >= 7:
            conditions.append(dataframe["trend_close_6h"] > dataframe["trend_open_6h"])

        if self.buy_params["buy_trend_bullish_level"] >= 8:
            conditions.append(dataframe["trend_close_8h"] > dataframe["trend_open_8h"])

        # 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), "buy"] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
        conditions = []
        self.condition_early_close_seconds(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), "sell"] = 1

        return dataframe

    def populate_close_position(self, df: DataFrame) -> DataFrame:
        df["take_profit"] = pd.Series(dtype="str")
        df["stop_loss"] = pd.Series(dtype="str")
        return df

    def run(self, df, ex, args):
        df = self.populate_indicators(df)
        df = self.populate_buy_trend(df)
        df = self.populate_sell_trend(df)
        df = self.populate_close_position(df)
        self.trade(ex, df, args)
        return df