# source: https://raw.githubusercontent.com/Willemstijn/user_data/758a23f80ce7a5dca176e939c4f3f8562d11a304/stratscorer/all_strategies/kumo_breakout.py
# ==============================================================================================
# Kumo Breakout strategy
#
# Made by:
# ______         _         _      _____                      _         ______            _
# |  _  \       | |       | |    /  __ \                    | |        |  _  \          | |
# | | | | _   _ | |_  ___ | |__  | /  \/ _ __  _   _  _ __  | |_  ___  | | | | __ _   __| |
# | | | || | | || __|/ __|| '_ \ | |    | '__|| | | || '_ \ | __|/ _ \ | | | |/ _` | / _` |
# | |/ / | |_| || |_| (__ | | | || \__/\| |   | |_| || |_) || |_| (_) || |/ /| (_| || (_| |
# |___/   \__,_| \__|\___||_| |_| \____/|_|    \__, || .__/  \__|\___/ |___/  \__,_| \__,_|
#                                               __/ || |
#                                              |___/ |_|
# Version : 1.0 Final
# Date    : 2022-10-15
# Remarks :
#    As published, explained and tested in my Youtube video's:
#    - https://youtu.be/XA7Za-mtVBc
#    - https://youtu.be/KBAGa01TUkA
# ==============================================================================================


# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame  # noqa
from datetime import datetime  # noqa
from typing import Optional, Union  # noqa

from freqtrade.strategy import (
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IStrategy,
    IntParameter,
)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class github_Willemstijn_user_data__kumo_breakout__20230822_152729(IStrategy):
    INTERFACE_VERSION = 3
    timeframe = "1d"

    # Can this strategy go short?
    can_short: bool = False

    minimal_roi = {"0": 1.0}

    stoploss = -0.25
    trailing_stop = False
    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 15

    # Optional order type mapping.
    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    # Optional order time in force.
    order_time_in_force = {"entry": "gtc", "exit": "gtc"}

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Crypto trading bot strategy indicators

        # Variables
        TS = 9
        KS = 26
        SS = 52
        CS = 26
        OS = 0

        # Ichimoku indicator
        dataframe["tenkan"] = pta.ichimoku(
            high=dataframe["high"],
            low=dataframe["low"],
            close=dataframe["close"],
            tenkan=TS,
            kijun=KS,
            senkou=SS,
            offset=OS,
        )[0][f"ITS_{TS}"]
        dataframe["kijun"] = pta.ichimoku(
            high=dataframe["high"],
            low=dataframe["low"],
            close=dataframe["close"],
            tenkan=TS,
            kijun=KS,
            senkou=SS,
            offset=OS,
        )[0][f"IKS_{KS}"]
        dataframe["senkou_a"] = pta.ichimoku(
            high=dataframe["high"],
            low=dataframe["low"],
            close=dataframe["close"],
            tenkan=TS,
            kijun=KS,
            senkou=SS,
            offset=OS,
        )[0][f"ISA_{TS}"]
        dataframe["senkou_b"] = pta.ichimoku(
            high=dataframe["high"],
            low=dataframe["low"],
            close=dataframe["close"],
            tenkan=TS,
            kijun=KS,
            senkou=SS,
            offset=OS,
        )[0][f"ISB_{KS}"]
        dataframe["chikou"] = pta.ichimoku(
            high=dataframe["high"],
            low=dataframe["low"],
            close=dataframe["close"],
            tenkan=TS,
            kijun=KS,
            senkou=SS,
            offset=OS,
        )[0][f"ICS_{KS}"]

        # No LONG entries when price is below Senkou A, Senkou B and Kijun sen
        dataframe["long_signal"] = (
            (dataframe["close"] > dataframe["senkou_a"])
            & (dataframe["close"] > dataframe["senkou_b"])
            & (dataframe["close"] > dataframe["kijun"])
        )

        # No SHORT entries when price is above Senkou A, Senkou B and Kijun sen
        dataframe["short_signal"] = (
            (dataframe["close"] < dataframe["senkou_a"])
            & (dataframe["close"] < dataframe["senkou_b"])
            & (dataframe["close"] < dataframe["kijun"])
        )

        # Exit indicators
        dataframe["long_exit"] = (dataframe["close"] < dataframe["kijun"]) & (
            dataframe["long_signal"] == True
        )
        dataframe["short_exit"] = (dataframe["close"] > dataframe["kijun"]) & (
            dataframe["short_signal"] == True
        )

        # Uncomment this if you use this strategy for real/dummy trading
        # #print(dataframe)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            ((dataframe["long_signal"] == True)), ["enter_long", "enter_tag"]
        ] = (1, "github_Willemstijn_user_data__kumo_breakout__20230822_152729_long")
        dataframe.loc[
            ((dataframe["short_signal"] == True)), ["enter_short", "enter_tag"]
        ] = (1, "github_Willemstijn_user_data__kumo_breakout__20230822_152729_short")

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[((dataframe["long_exit"] == True)), ["exit_long", "exit_tag"]] = (
            1,
            "closeprice_below_ks",
        )
        dataframe.loc[
            ((dataframe["short_exit"] == True)), ["exit_short", "exit_tag"]
        ] = (1, "closeprice_above_ks")
        return dataframe