为什么我的逻辑回归分数总是 1.0？

Question

I am using sklearn in python and the idea of the implementation is to predict SPX 500 by using the Logistic Regression.

I got the SPX historical prices from yfinance and calculated 5 features (x) based on daily returns (which I calculated as well).

The dependent variable (y) is 1 for positive retunrs and 0 for negative returns.

When I predicted the model and check the model.score(), the value is always 1. But Why?

the code is:

# Import the data
df= yf.download('^GSPC', start="2018-1-1", end="2020-10-20")
df = df.dropna()

df['Return'] = np.log(df['Adj Close']/df['Adj Close'].shift(1))

# Create Indicators
df['Ret_1'] = df['Return'].shift(1)
df['Ret_2'] = df['Return'].shift(2)
df['Adj Close-Adj Close 1day'] = df['Adj Close'] - df['Adj Close'].shift(1)
df['Adj Close-Adj Close 5days'] = df['Adj Close'] - df['Adj Close'].shift(5)
df['S_10'] = df['Adj Close'].rolling(window=5).mean()

df = df.dropna()
X = df.iloc[:,-5:]

y =np.where(df['Return'] > 0 ,1,0)


# Split the Dataset and Instantiate Logistic Regression
split = int(0.7*len(df))
X_train, X_test, y_train, y_test = X[:split], X[split:], y[:split], y[split:]

model = LogisticRegression()
model = model.fit (X_train,y_train)
predicted = model.predict(X_test)

print(model.score(X_test,y_test))  

Answer 1

Among the 5 features that you are inputing in your logistic regression, the variable

df['Adj Close-Adj Close 1day'] = df['Adj Close'] - df['Adj Close'].shift(1)

has the same sign as the underlying variable df['Return'] that you are using in the target

y =np.where(df['Return'] > 0 ,1,0)

hence the Logistic regression is going to match very well.

Moreover both use df['Adj Close'] , so you are trying to predict a target value using something that in fact enables you to precisely calculate the target value.