# source: https://raw.githubusercontent.com/camster91/crypto-bots/501b6cfa18d2c1c1e4f0cad91cc70614fbabc5bb/bot-instance/user_data/strategies/ShortOnlyBearStrategy.py
"""
Github_camster91_crypto_bots__ShortOnlyBearStrategy__20260531_214717 — Short-only for bear market (2026 YTD: ETH -22.5%)

The simplest thing that could work: in a downtrend, short every bounce to resistance.
No complicated indicators. Just:
- Price below 200 EMA = bear regime
- Short when RSI goes above 60 (overbought in bear = short entry)
- Short when price bounces to 20 EMA in a downtrend
- Exit at ROI or trailing stop
"""

import talib.abstract as ta
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy import IStrategy


class Github_camster91_crypto_bots__ShortOnlyBearStrategy__20260531_214717(IStrategy):
    INTERFACE_VERSION = 3

    timeframe = '1h'
    startup_candle_count = 100
    can_short = True
    process_only_new_candles = True

    # --- Risk ---
    stoploss = -0.02  # 2% stop
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.02
    trailing_only_offset_is_reached = True
    use_custom_stoploss = False

    # --- ROI ---
    minimal_roi = {
        "0": 0.04,     # 4% immediate
        "360": 0.02,   # 2% after 15 days
        "720": 0,
    }

    protections = [
        {"method": "CooldownPeriod", "stop_duration": 12},
        {"method": "StoplossGuard", "lookback_period": 48, "trade_limit": 2, "stop_duration": 24, "only_per_pair": False},
    ]

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ema_20'] = ta.EMA(dataframe, timeperiod=20)
        dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['volume_ema'] = ta.EMA(dataframe['volume'], timeperiod=20)
        dataframe['volume_ratio'] = dataframe['volume'] / (dataframe['volume_ema'] + 1e-10)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Only short in bear market: price below 200 EMA
        bear = (
            (dataframe['close'] < dataframe['ema_200']) &
            (dataframe['ema_50'] < dataframe['ema_200'])
        )

        # Short entry 1: bounce to EMA resistance
        ema_bounce_short = (
            (dataframe['high'] >= dataframe['ema_20'] * 0.995) &
            (dataframe['high'] >= dataframe['ema_50'] * 0.99) &
            (dataframe['rsi'] > 50) &
            (dataframe['close'] < dataframe['open']) &
            (dataframe['volume_ratio'] > 0.8)
        )

        # Short entry 2: overbought in bear market
        overbought_short = (
            (dataframe['rsi'] > 60) &
            (dataframe['rsi'].shift(1) < dataframe['rsi']) &  # RSI rising
            (dataframe['close'] < dataframe['open']) &
            (dataframe['volume_ratio'] > 1.0)
        )

        # Short entry 3: BB upper band rejection
        bb = ta.BBANDS(dataframe, timeperiod=20, nbdevup=2.0, nbdevdn=2.0)
        bb_upper = bb['upperband']

        bb_rejection_short = (
            (dataframe['high'] >= bb_upper * 0.995) &
            (dataframe['close'] < bb_upper) &
            (dataframe['rsi'] > 55) &
            (dataframe['close'] < dataframe['open'])
        )

        dataframe.loc[
            bear & (ema_bounce_short | overbought_short | bb_rejection_short),
            'enter_short'] = 1

        return dataframe

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