人工智能交易模型

python -m venv venvsource venv/bin/activate  # on windows use `venvscriptsctivate`

import pandas as pdfrom sklearn.preprocessing import minmaxscaler# load datadata = pd.read_csv('historical_data.csv')# handle missing valuesdata = data.dropna()# normalize datascaler = minmaxscaler()data[['open', 'high', 'low', 'close', 'volume']] = scaler.fit_transform(data[['open', 'high', 'low', 'close', 'volume']])# save preprocessed datadata.to_csv('preprocessed_data.csv', index=false)

import tensorflow as tffrom tensorflow.keras.models import sequentialfrom tensorflow.keras.layers import lstm, dense, dropoutmodel = sequential()model.add(lstm(units=50, return_sequences=true, input_shape=(x_train.shape[1], 1)))model.add(dropout(0.2))model.add(lstm(units=50, return_sequences=false))model.add(dropout(0.2))model.add(dense(units=1))model.compile(optimizer='adam', loss='mean_squared_error')

from sklearn.model_selection import train_test_splitx = data[['open', 'high', 'low', 'close', 'volume']].valuesy = data['close'].valuesx_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

model.fit(x_train, y_train, epochs=50, batch_size=32)

from sklearn.metrics import mean_squared_errorpredictions = model.predict(x_test)mse = mean_squared_error(y_test, predictions)print(f'mean squared error: {mse}')

new_data = pd.read_csv('new_data.csv')new_data_scaled = scaler.transform(new_data)predictions = model.predict(new_data_scaled)print(predictions)