Creating an FX Price Prediction Model with ChatGPT: A Machine Learning (Deep Learning) Approach

FX price prediction using deep learning is a technique that aims to predict future currency prices based on various information such as past exchange rate data, economic indicators, and news. Deep learning utilizes multi-layer neural networks to extract complex features and patterns from data to build predictive models.

For FX price prediction, deep learning models such as the following are commonly used:

1. LSTM (Long Short-Term Memory): A type of recurrent neural network (RNN) suitable for time series data, capable of retaining past information while learning new information.
2. CNN (Convolutional Neural Network): Originally used for image recognition, this type of neural network can also be applied to time series data to extract local features.
3. GRU (Gated Recurrent Unit): Like LSTM, it is a type of recurrent neural network suitable for time series data learning. Compared to LSTM, it has fewer parameters and lower computational load.

Using these models, future currency prices can be predicted by inputting past exchange rate data and other economic indicators. However, the market is influenced by many factors, and there are elements, such as sudden events and political factors, that cannot be fully incorporated into the model. Therefore, there are limitations to FX price predictions using deep learning models, and absolute predictions are difficult.

When using deep learning for FX price prediction, it is important to understand its limitations, avoid excessive expectations for the prediction results, and properly manage risk. While adjusting the model’s parameters and structure can improve prediction accuracy, it is fundamentally impossible to eliminate market uncertainty. Nevertheless, deep learning-based FX price prediction can be effectively utilized in combination with other prediction methods. Here are some points to consider when using deep learning:

1. Data preprocessing: Perform preprocessing, such as normalization or standardization, on exchange rate data and economic indicators to convert them into a form that is easier for the model to learn.
2. Feature selection: Select important features and exclude noisy, unnecessary features to improve the learning efficiency of the model.
3. Hyperparameter tuning: Adjust hyperparameters such as the learning rate, batch size, and number of epochs to improve the model’s prediction accuracy.
4. Ensemble learning: Combine multiple models and methods to capitalize on the strengths of each model and potentially improve prediction accuracy.
5. Preventing overfitting: Properly control the complexity of the model and introduce regularization or dropout to prevent overfitting and improve prediction accuracy for unknown data.

In conclusion, deep learning-based FX price prediction should be considered a supplementary tool, and perfect predictions should not be expected. Using deep learning as part of an investment strategy while properly managing risk and funds is a more important point. Understanding market uncertainty and making cautious judgments while utilizing deep learning models is desirable.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
from keras.optimizers import Adam

# Load the data
data = pd.read_csv("your_data_file.csv")
Select necessary columns (e.g., USD/JPY Close price)
data = data['USD_JPY_Close']

Normalize the data
scaler = MinMaxScaler(feature_range=(0, 1))
data = scaler.fit_transform(np.array(data).reshape(-1, 1))

Split the data into training and testing sets
train_size = int(len(data) * 0.8)
train_data = data[:train_size]
test_data = data[train_size:]

def create_dataset(data, look_back=1):
    x_data, y_data = [], []
    for i in range(len(data) - look_back - 1):
        x_data.append(data[i:(i + look_back), 0])
        y_data.append(data[i + look_back, 0])
    return np.array(x_data), np.array(y_data)
look_back = 60
x_train, y_train = create_dataset(train_data, look_back)
x_test, y_test = create_dataset(test_data, look_back)

Convert to the input format for LSTM
x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(look_back, 1)))
model.add(Dropout(0.2))
model.add(LSTM(50, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(50))
model.add(Dense(1))

Compile the model
model.compile(optimizer=Adam(lr=0.001), loss='mean_squared_error')

Train the model
history = model.fit(x_train, y_train, epochs=100, batch_size=64, validation_data=(x_test, y_test))

train_predict = model.predict(x_train)
test_predict = model.predict(x_test)
Inverse transform the normalization
train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform([y_train])
test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])

Evaluation (RMSE)
train_rmse = np.sqrt(mean_squared_error(y_train[0], train_predict[:, 0]))
test_rmse = np.sqrt(mean_squared_error(y_test[0], test_predict[:, 0]))

print("Training data RMSE: ", train_rmse)
print("Testing data RMSE: ", test_rmse)

6. Visualize the results

plt.figure(figsize=(14, 5))
plt.plot(scaler.inverse_transform(data), label='Actual price')
train_predicted = np.empty_like(data)
train_predicted[:, :] = np.nan
train_predicted[look_back:len(train_predict) + look_back, :] = train_predict
plt.plot(train_predicted, label='Predicted price on training data')
test_predicted = np.empty_like(data)
test_predicted[:, :] = np.nan
test_predicted[len(train_predict) + (look_back * 2) + 1:len(data) - 1, :] = test_predict
plt.plot(test_predicted, label='Predicted price on testing data')

plt.xlabel('Time')
plt.ylabel('Price')
plt.legend()
plt.show()

By using the code above, you can visualize the predicted results for training and test data. This model is just an example, and its accuracy may change depending on the adjustment of hyperparameters and model structure. Additionally, trying other deep learning models or methods may help improve accuracy further. However, it’s important to keep in mind that absolute predictions are difficult for any model, as market fluctuations are influenced by many factors.

As the results show, the RMSE (Root Mean Square Error) for the training data is approximately 12.68, while the RMSE for the test data is about 21.30. The RMSE represents the magnitude of the prediction error, with smaller values indicating higher prediction accuracy.From these results, it is clear that while the model can make relatively accurate predictions for the training data, its prediction accuracy for the test data is lower. This could be due to the model overfitting the training data.

To prevent overfitting, the following measures can be considered:

1. Reduce the complexity of the model: By decreasing the number of layers or units, you can simplify the model and potentially reduce overfitting.
2. Introduce regularization: Using L1 or L2 regularization, you can apply constraints to the model’s parameters to prevent them from taking on large values.
3. Adjust dropout: By changing the dropout rate, you can expect the model to better control overfitting.
4. Increase the amount of data: If there is insufficient data, overfitting is more likely to occur. If possible, preparing more data and training the model with it can help prevent overfitting.

Additionally, tuning hyperparameters and trying other models or techniques may improve prediction accuracy. However, it is important to keep in mind that making absolute predictions is difficult for any model.

From the results of the improved code, the RMSE of the training data is about 14.73, and the RMSE of the test data is about 22.70. These results do not clearly indicate whether overfitting has been improved. The difference between the RMSEs of the training and test data has decreased slightly, but there is still room for improvement.For further improvements, consider the following methods:

1. Increase the dropout ratio: Increasing the dropout from 0.2 to 0.4 or 0.5 can help suppress overfitting.
2. Change the data window size: By adjusting the value of look_back, you can change the period of data the model learns from. An appropriate window size may improve the model’s generalization performance.
3. Change the way training and validation data are split: For time-series data, using techniques such as time-series cross-validation to split the data can improve the model’s generalization performance.
4. Try other models or algorithms: You can try models using GRU or CNN instead of LSTM or try algorithms like ensemble learning to potentially improve accuracy.

To find the optimal model, it’s important to conduct repeated experiments while trying these methods. However, keep in mind that it is difficult to make absolute predictions with any model, as market fluctuations are influenced by many factors.

Apologies for that. I have added the inverse transformation of normalization part. With the following code, you can perform the inverse transformation of normalization and calculate the RMSE.

# Inverse transformation of normalization
train_predict = scaler.inverse_transform(train_predict)
y_train = scaler.inverse_transform([y_train])
test_predict = scaler.inverse_transform(test_predict)
y_test = scaler.inverse_transform([y_test])
Calculation of RMSE
train_rmse = np.sqrt(mean_squared_error(y_train[0], train_predict[:, 0]))
test_rmse = np.sqrt(mean_squared_error(y_test[0], test_predict[:, 0]))

print(f"Training data RMSE: {train_rmse}")
print(f"Test data RMSE: {test_rmse}")

From the results, the RMSE of the training data is approximately 13.70, and the RMSE of the test data is approximately 20.40. When compared to the results before the improvements were applied, the difference between the RMSEs of the training and test data has slightly decreased. This may indicate that overfitting has been somewhat mitigated.However, it is not possible to say that overfitting has been completely resolved. Further improvements are necessary. Additionally, predicting FX prices is a very difficult task, with many factors influencing the outcome. It is important to keep in mind that absolute predictions are challenging for any model.

Possible further improvements include:

1. Hyperparameter tuning: By adjusting various hyperparameters such as the learning rate, batch size, number of epochs, and regularization coefficient, the performance of the model may be improved.
2. Feature engineering: Adding more technical indicators and features based on fundamental analysis will enable the model to learn more information.
3. Ensemble learning: Combining multiple models can compensate for the weaknesses of each model and improve overall performance.

To find the optimal model, it is important to conduct iterative experiments while trying these methods.