"""
Minimal sample strategy for the 428 / Integral backtester scaffold.

Replace this file (or add your own under user_data/strategies/) and set
STRATEGY=<ClassName> when running scripts. Defaults in config / compose
point here so a fresh clone backtests without proprietary logic.
"""
from __future__ import annotations

from pandas import DataFrame

from freqtrade.strategy import IStrategy, IntParameter
import talib.abstract as ta


class SampleStrategy(IStrategy):
    """EMA crossover + RSI filter — placeholder only, not a production system."""

    INTERFACE_VERSION = 3
    timeframe = "15m"
    can_short = True

    minimal_roi = {"0": 0.04, "60": 0.02, "180": 0.01, "360": 0}
    stoploss = -0.03
    trailing_stop = False
    process_only_new_candles = True
    startup_candle_count = 50

    buy_rsi = IntParameter(20, 40, default=30, space="buy", optimize=True)
    sell_rsi = IntParameter(60, 80, default=70, space="sell", optimize=True)

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=12)
        dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=26)
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["ema_fast"] > dataframe["ema_slow"])
            & (dataframe["rsi"] < self.buy_rsi.value)
            & (dataframe["volume"] > 0),
            "enter_long",
        ] = 1

        dataframe.loc[
            (dataframe["ema_fast"] < dataframe["ema_slow"])
            & (dataframe["rsi"] > self.sell_rsi.value)
            & (dataframe["volume"] > 0),
            "enter_short",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe["ema_fast"] < dataframe["ema_slow"]) & (dataframe["volume"] > 0),
            "exit_long",
        ] = 1
        dataframe.loc[
            (dataframe["ema_fast"] > dataframe["ema_slow"]) & (dataframe["volume"] > 0),
            "exit_short",
        ] = 1
        return dataframe