# source: https://raw.githubusercontent.com/4tie/fortiesr/d5cc40760e3cbaff381058a0ea8733d2be469578/user_data/strategies/IntradayBasic_v1.py
# directory_url: https://github.com/4tie/fortiesr/blob/main/user_data/strategies/
# User: 4tie
# Repository: fortiesr
# --------------------# Auto-generated by Strategy Lab — Auto-Quant Factory
# Entry logic is controlled by the CategoricalParameter `entry_logic`.
# Optimise it with: freqtrade hyperopt --strategy Github_4tie_fortiesr__IntradayBasic_v1__20260709_213639 --spaces buy

from freqtrade.strategy import CategoricalParameter, IStrategy
from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class Github_4tie_fortiesr__IntradayBasic_v1__20260709_213639(IStrategy):
    INTERFACE_VERSION: int = 3

    # --- ROI / stoploss / timeframe defaults ----------------------------
    minimal_roi = {
        "0": 0.10,
        "30": 0.05,
        "60": 0.02,
        "120": 0,
    }

    stoploss = -0.05
    timeframe = "5m"

    trailing_stop = False

    # --- Categorical entry-logic selector --------------------------------
    # Hyperopt will search this space when --spaces buy is used.
    entry_logic = CategoricalParameter(
        ["macd_cross", "rsi_oversold", "bb_breakout"],
        default="macd_cross",
        space="buy",
        optimize=True,
    )

    # --- Indicator computation -------------------------------------------

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # MACD (fast=12, slow=26, signal=9)
        macd = ta.MACD(dataframe, fastperiod=12, slowperiod=26, signalperiod=9)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        # RSI (period=14)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)

        # Bollinger Bands (period=20, stddev=2)
        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=2
        )
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]

        return dataframe

    # --- Entry logic router ----------------------------------------------

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        logic = self.entry_logic.value

        if logic == "macd_cross":
            dataframe.loc[
                (
                    qtpylib.crossed_above(dataframe["macd"], dataframe["macdsignal"])
                    & (dataframe["volume"] > 0)
                ),
                "enter_long",
            ] = 1

        elif logic == "rsi_oversold":
            dataframe.loc[
                (
                    (dataframe["rsi"] < 30)
                    & (dataframe["volume"] > 0)
                ),
                "enter_long",
            ] = 1

        elif logic == "bb_breakout":
            dataframe.loc[
                (
                    (dataframe["close"] < dataframe["bb_lowerband"])
                    & (dataframe["volume"] > 0)
                ),
                "enter_long",
            ] = 1

        return dataframe

    # --- Exit logic stub (relies on ROI / stoploss) ----------------------

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