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DNN
Denison Mines Corp
stock NYSEAMERICAN

At Close

May 16, 2025 3:59:30 PM EDT

1.44USD-4.333%(-0.07)57,929,242

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May 16, 2025 9:18:30 AM EDT

1.52USD+1.327%(+0.02)89,018

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May 16, 2025 4:52:30 PM EDT

1.44USD+0.348%(+0.01)14,949

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DNN Specific Mentions

As of May 17, 2025 4:52:25 AM EDT (<1 min. ago)

Includes all comments and posts. Mentions per user per ticker capped at one per hour.

4 days ago • u/Wild-Dependent4500 • r/algotrading • longtime_professional_software_engineer_and • C

I benchmarked three architectures: deep neural networks (DNNs), support-vector regression (SVR), and transformers. The DNN consistently delivered the better results. You can explore the feature matrix here (refreshed every 5 minutes): [https://ai2x.co/data\_1d\_update.csv](https://ai2x.co/data_1d_update.csv)
build\_matrix() code is as follows.
def build_matrix():
scaled_features = df_features.values
scaled_target = df_target.values
print("scaled_features.shape", scaled_features.shape)
print("scaled_target.shape", scaled_target.shape)

# Split into sequences (X) and targets (y)
X, y = [], []
for i in range(len(scaled_features) - SEQ_LENGTH):
X.append(scaled_features[i:i + SEQ_LENGTH])
y.append(scaled_target[i + SEQ_LENGTH])
X, y = np.array(X), np.array(y)
print("X.shape", X.shape)
print("y.shape", y.shape)
X_flat = X.reshape(X.shape[0], -1)
print("X_flat.shape", X_flat.shape)

# Train-test split (last 100 samples for testing)
split = len(X_flat) - m_test_size
X_train, X_test = X_flat[:split], X_flat[split:]
y_train, y_test = y[:split], y[split:]

# Flatten y to 1D arrays if needed for SVR
y_train = y_train.flatten()
y_test = y_test.flatten()
return X_train, X_test, y_train, y_test

4 days ago • u/Wild-Dependent4500 • r/algotrading • longtime_professional_software_engineer_and • C

I benchmarked three architectures: deep neural networks (DNNs), support-vector regression (SVR), and transformers. The DNN consistently delivered the better results. You can explore the feature matrix here (refreshed every 5 minutes): [https://ai2x.co/data\_1d\_update.csv](https://ai2x.co/data_1d_update.csv)
build\_matrix() code is as follows.
def build_matrix():
scaled_features = df_features.values
scaled_target = df_target.values
print("scaled_features.shape", scaled_features.shape)
print("scaled_target.shape", scaled_target.shape)

# Split into sequences (X) and targets (y)
X, y = [], []
for i in range(len(scaled_features) - SEQ_LENGTH):
X.append(scaled_features[i:i + SEQ_LENGTH])
y.append(scaled_target[i + SEQ_LENGTH])
X, y = np.array(X), np.array(y)
print("X.shape", X.shape)
print("y.shape", y.shape)
X_flat = X.reshape(X.shape[0], -1)
print("X_flat.shape", X_flat.shape)

# Train-test split (last 100 samples for testing)
split = len(X_flat) - m_test_size
X_train, X_test = X_flat[:split], X_flat[split:]
y_train, y_test = y[:split], y[split:]

# Flatten y to 1D arrays if needed for SVR
y_train = y_train.flatten()
y_test = y_test.flatten()
return X_train, X_test, y_train, y_test

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