# source: https://raw.githubusercontent.com/ythere-y/QuantLab/4c16942a9bfed55206e58cee087641540e38c1fc/user_data/strategies/SmaCrossStrategy.py
"""
SMA Crossover Strategy

Buy when short SMA crosses above long SMA (golden cross).
Sell when short SMA crosses below long SMA (death cross).

Timeframe: 1h
Pairs: BTC/USDT, ETH/USDT, SOL/USDT, BNB/USDT
"""
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Optional, Union

from freqtrade.strategy import (
    IStrategy,
    Trade,
    Order,
    PairLocks,
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
)

import talib.abstract as ta
from technical import qtpylib


class Github_ythere_y_QuantLab__SmaCrossStrategy__20260502_162050(IStrategy):
    """
    Simple SMA Crossover strategy.
    Buy: SMA short crosses above SMA long.
    Sell: SMA short crosses below SMA long.
    """

    INTERFACE_VERSION = 3
    can_short: bool = False

    # ROI table: exit at these profit levels after N minutes
    minimal_roi = {
        "0": 0.05,     # 5% profit at any time
        "60": 0.03,    # 3% after 60 minutes
        "120": 0.01,   # 1% after 120 minutes
        "240": 0.0,    # break-even after 240 minutes
    }

    stoploss = -0.07  # 7% stop loss

    # Trailing stop
    trailing_stop = True
    trailing_stop_positive = 0.01
    trailing_stop_positive_offset = 0.03
    trailing_only_offset_is_reached = True

    timeframe = "1h"
    process_only_new_candles = True
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Hyperoptable parameters
    buy_sma_short = IntParameter(5, 30, default=10, space="buy", optimize=True)
    buy_sma_long = IntParameter(20, 100, default=50, space="buy", optimize=True)

    # Number of candles needed before producing valid signals
    startup_candle_count: int = 100

    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    order_time_in_force = {"entry": "GTC", "exit": "GTC"}

    plot_config = {
        "main_plot": {
            "sma_short": {"color": "blue"},
            "sma_long": {"color": "orange"},
        },
        "subplots": {
            "Volume": {
                "volume": {"color": "green"},
            },
        },
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """Calculate SMA indicators."""
        # Calculate SMAs for the full range to support hyperopt
        for period in range(5, 101):
            dataframe[f"sma_{period}"] = ta.SMA(dataframe, timeperiod=period)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """Buy when short SMA crosses above long SMA."""
        sma_short = dataframe[f"sma_{self.buy_sma_short.value}"]
        sma_long = dataframe[f"sma_{self.buy_sma_long.value}"]

        dataframe.loc[
            (
                (qtpylib.crossed_above(sma_short, sma_long))
                & (dataframe["volume"] > 0)
            ),
            "enter_long",
        ] = 1

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """Sell when short SMA crosses below long SMA."""
        sma_short = dataframe[f"sma_{self.buy_sma_short.value}"]
        sma_long = dataframe[f"sma_{self.buy_sma_long.value}"]

        dataframe.loc[
            (
                (qtpylib.crossed_below(sma_short, sma_long))
                & (dataframe["volume"] > 0)
            ),
            "exit_long",
        ] = 1

        return dataframe