# source: https://raw.githubusercontent.com/camster91/crypto-bots/5fd45118d5be1eaadd338e71ca26072dd5437831/strategies/SampleStrategy.py
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
import numpy as np
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy import IStrategy, IntParameter
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib


class Github_camster91_crypto_bots__SampleStrategy__20260511_032334(IStrategy):
    """
    Basic RSI Strategy - Beginner starting point.

    Buy when RSI < 30 (oversold), sell when RSI > 70 (overbought).
    Results: 72.2% win rate but negative overall due to large losses.
    See ImprovedStrategy.py for enhancements.
    """

    INTERFACE_VERSION = 3

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

    minimal_roi = {"60": 0.01, "30": 0.02, "0": 0.04}
    stoploss = -0.10
    trailing_stop = False
    timeframe = "5m"
    process_only_new_candles = True
    startup_candle_count: int = 200

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe["rsi"] = ta.RSI(dataframe)
        dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
        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"]
        dataframe["volume_mean"] = dataframe["volume"].rolling(window=20).mean()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["rsi"] < self.buy_rsi.value) &
                (dataframe["close"] > dataframe["ema_200"]) &
                (dataframe["volume"] > 0)
            ),
            "enter_long",
        ] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
                (dataframe["rsi"] > self.sell_rsi.value) &
                (dataframe["volume"] > 0)
            ),
            "exit_long",
        ] = 1
        return dataframe