The strategy below is simply to understand the signal implementation, and not to trade live...

The _SMA Crossover_ strategy uses two SMAs (simple moving averages) of different lengths, `fast_sma` and `slow_sma`, to determine entry and exit signals. The faster SMA reflects short-term price trends, whereas the slower SMA reflects longer-term price trends. 

```python
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy

class SMACrossover(IStrategy):
    """
        Enter and exit based on crossover of fast and slow Simple Moving Averages.
    """
    INTERFACE_VERSION: int = 3
    minimal_roi = {"0": 10}   # Close immediately at 1000% ROI.
    stoploss = -1             # SL at -100%.
    timeframe = '4h'
    fast_sma = 50
    slow_sma = 200

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['fast_sma'] = dataframe['close'].rolling(window=self.fast_sma).mean()
        dataframe['slow_sma'] = dataframe['close'].rolling(window=self.slow_sma).mean()
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(
            (dataframe['fast_sma'] > dataframe['slow_sma']) &
            (dataframe['fast_sma'].shift(1) < dataframe['slow_sma'].shift(1))
        ), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[(
            (dataframe['fast_sma'] < dataframe['slow_sma']) &
            (dataframe['fast_sma'].shift(1) > dataframe['slow_sma'].shift(1))
        ), 'exit_long'] = 1
        return dataframe
```

Explanation of the code:

In the `populate_entry_trend` method, `dataframe.loc[(...), 'enter_long'] = 1` sets the `enter_long` column to `1` for candles where:
- The fast SMA is above the slow SMA, indicating a bullish trend.
- The previous candle had the fast SMA below the slow SMA, indicating a crossover.

In the `populate_exit_trend` method, `dataframe.loc[(...), 'exit_long'] = 1` sets the `exit_long` column to `1` for candles where:
- The fast SMA is below the slow SMA, indicating a bearish trend.
- The previous candle had the fast SMA above the slow SMA, indicating a crossover.

So the `.loc` method simply modifies the values of specific rows in the dataframe.

The strategy determines _entry_ signals based on two conditions:
1. `dataframe['fast_sma'] > dataframe['slow_sma']`
   The fast SMA must be above the slow SMA.
2. `dataframe['fast_sma'].shift(1) < dataframe['slow_sma'].shift(1)`
   The previous candle must have the fast SMA below the slow SMA.

The strategy determines _exit_ signals based on two conditions:
1. `dataframe['fast_sma'] < dataframe['slow_sma']`
   The fast SMA must be below the slow SMA.
2. `dataframe['fast_sma'].shift(1) > dataframe['slow_sma'].shift(1)`
   The previous candle must have the fast SMA above the slow SMA.