Marks Head Bobbers Hand Jobbers Serina May 2026

import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
# Assume 'data' is a DataFrame with historical trading volumes
data = pd.read_csv('trading_data.csv')
# Preprocess
scaler = MinMaxScaler(feature_range=(0,1))
scaled_data = scaler.fit_transform(data)
# Split into training and testing sets
train_size = int(len(scaled_data) * 0.8)
train_data = scaled_data[0:train_size]
test_data = scaled_data[train_size:]
# Define the model
model = Sequential()
model.add(LSTM(units=50, return_sequences=True, input_shape=(scaled_data.shape[1], 1)))
model.add(LSTM(units=50))
model.add(Dense(1))
# Compile and train
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(train_data, epochs=50)
# Make predictions
predictions = model.predict(test_data)

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