# Signals diagnostic script

Does anyone know, how to calculate diagnostics properly for Signals?
I modified my scripts from the main tournament, but they give different results then what I get on the website.
What am I doing wrong? I can’t find the official scrips.
Here is mine:

``````def correlation(predictions, targets):
ranked_preds = predictions.rank(pct=True, method="first")
return np.corrcoef(ranked_preds, targets)[0, 1]

def score(df):
return correlation(df['signal'], df['target'])

def evaluation(df, plot=True):

scores = df.groupby(df.index).apply(score)

mean = scores.mean()
std = scores.std(ddof=0)
sharpe = mean / std

sortino = 0
if np.sum(np.minimum(0, scores))!=0:
sortino = scores.mean() / (np.sum(np.minimum(0, scores)**2)/(len(scores)))**.5

rolling_max = (scores + 1).cumprod().rolling(window=100, min_periods=1).max()
daily_value = (scores + 1).cumprod()
max_drawdown = -((rolling_max - daily_value) / rolling_max).max()

if plot==True:
cumsum = scores.cumsum()
plt.xticks(rotation='vertical')
plt.plot(val.index.unique(), cumsum)

print(f'Weeks: {len(scores)}')
print(f"Correlation sharpe: {sharpe:.4f}")
print(f'Mean Correlation: {mean:.4f}')
print(f'Correlation SD: {std:.4f}')
print(f'Profitability: {(np.power(mean+1, 52)-1)*100:.2f}%')
print(f'Drawdown: {max_drawdown:.4f}')

return mean, std, sharpe, sortino, max_drawdown``````

The Signals validation metrics given on the website are calculated after your predictions are neutralized to their features. It is not possible to replicate locally.

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