human
I am working on the file ".ipynb_checkpoints/jpx-tokyo-simple-lstm-network-scuec-checkpoint.ipynb".
The first task is:
define metric: sharp rate and compute for the training data with known Target
Can you write Python code for it?

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What is a Python code to compute for the training data with known Target?
assistant
#Here is Python code to compute for the training data with known Target
.

def calc_spread_return_sharpe(df: pd.DataFrame, portfolio_size: int = 200, toprank_weight_ratio: float = 2, rank='Rank') -> float:
    """
    Args:
        df (pd.DataFrame): predicted results
        portfolio_size (int): # of equities to buy/sell
        toprank_weight_ratio (float): the relative weight of the most highly ranked stock compared to the least.
    Returns:
        (float): sharpe ratio
    """
    def _calc_spread_return_per_day(df, portfolio_size, toprank_weight_ratio, rank='Rank'):
        """
        Args:
            df (pd.DataFrame): predicted results
            portfolio_size (int): # of equities to buy/sell
            toprank_weight_ratio (float): the relative weight of the most highly ranked stock compared to the least.
        Returns:
            (float): spread return
        """
        assert df[rank].min() == 0
        assert df[rank].max() == len(df[rank]) - 1
        weights = np.linspace(start=toprank_weight_ratio, stop=1, num=portfolio_size)
        purchase = (df.sort_values(by=rank)['Target'][:portfolio_size] * weights).sum() / weights.mean()
        short = (df.sort_values(by=rank, ascending=False)['Target'][:portfolio_size] * weights).sum() / weights.mean()
        return purchase - short

    buf = df.groupby('Date').apply(_calc_spread_return_per_day, portfolio_size, toprank_weight_ratio, rank)
    sharpe_ratio = buf.mean() / buf.std()
    return sharpe_ratio

# add rank according to Target for train

train_df['Rank'] = train_df.groupby("Date")['Target'].transform('rank', ascending=False, method="first") - 1
train_df['Rank'] = train_df['Rank'].astype(int)
# print(train_df['Rank'].min())
df_astock = train_df[train_df['Date'] == '2021-12-03']

# make sure it's correct
print(df_astock['Rank'].min(), df_astock['Rank'].max())
df_astock.sort_values(by=['Target'], ascending=False)
# sharp = calc_spread_return_sharpe(train_df)

tmpdf = test_df.copy()
tmpdf["Close_shift1"] = tmpdf["Close"].shift(-1)
tmpdf["Close_shift2"] = tmpdf["Close"].shift(-2)

tmpdf["rate"] =...