# pragma pylint: disable=missing-docstring, invalid-name, too-few-public-methods
"""
SampleStrategy — stratégie de base MidasBot (Phase 1).

Croisement de moyennes mobiles exponentielles (EMA) avec filtre RSI.
Sert à valider le pipeline Freqtrade (dry-run, backtesting, FreqUI) avant
d'ajouter la couche IA (cf. AiBiasStrategy, Phase 3).
"""
from __future__ import annotations

import talib.abstract as ta
from pandas import DataFrame

from freqtrade.strategy import IStrategy


class SampleStrategy(IStrategy):
    INTERFACE_VERSION = 3

    # Timeframe d'analyse
    timeframe = "1h"

    # Take-profit échelonné (ROI minimal par durée, en minutes)
    minimal_roi = {
        "0": 0.05,
        "120": 0.03,
        "360": 0.01,
        "720": 0,
    }

    # Stop-loss dur
    stoploss = -0.10

    # Trailing stop
    trailing_stop = True
    trailing_stop_positive = 0.02
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    # Nombre de bougies nécessaires avant de produire un signal
    startup_candle_count: int = 50

    # Ordres
    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["ema_fast"] = ta.EMA(dataframe, timeperiod=9)
        dataframe["ema_slow"] = ta.EMA(dataframe, timeperiod=21)
        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["ema_fast"].shift(1) <= dataframe["ema_slow"].shift(1))
                & (dataframe["rsi"] < 70)
                & (dataframe["volume"] > 0)
            ),
            "enter_long",
        ] = 1
        return dataframe

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