Creating a Day Trading AI with a Deep Neural Network in Python

Summary

This video script details the process of implementing a deep neural network for algorithmic trading to predict market movements. The aim is to classify market direction as up (1) or down (0/-1) and place corresponding long or short positions. Using historical currency data, the script explores data wrangling, model building with TensorFlow and Keras, and performance evaluation against a buy-and-hold benchmark. The initial model struggles, but after adding features like momentum and volatility, the strategy shows promise in-sample but underperforms out-of-sample, highlighting the importance of robust testing.

Takeaways

• 🤖 The video discusses implementing a deep neural network for algorithmic trading to predict market movements and place trades accordingly.
• 📈 The neural network aims to classify market direction as '1' for upward movement and '0' or '-1' for downward movement.
• 📊 Data wrangling involves using historical financial data, specifically looking at the Euro to US dollar exchange rate, and calculating returns and market direction.
• 🔢 The model includes lags to predict the next market value based on past five days of data, creating additional columns for this purpose.
• 🧠 The deep learning model is built using TensorFlow and Keras, with a sequential model consisting of two Dense layers and a Sigmoid output layer.
• ⚖️ The model's performance is evaluated using accuracy as the metric, with a focus on comparing it to a simple 'buy and hold' benchmark strategy.
• 📉 The initial model's accuracy is around 0.51 or 0.52, indicating a 50-50 chance of correctly predicting market movements, which is not ideal for trading.
• 📈 The video illustrates the process of transforming predictions into long or short positions and calculating the strategy's returns.
• 📉 The in-sample test set shows the strategy outperforming the benchmark, but the out-of-sample validation set shows underperformance, emphasizing the importance of out-of-sample testing.
• 🛠️ Additional features such as momentum, volatility, and distance are suggested to improve the model's predictive power.
• 📉 Despite improvements with additional features, the out-of-sample test still shows the strategy underperforming the benchmark, indicating the model's limitations.

Q & A

What is the main objective of implementing the deep neural network discussed in the script?

The main objective is to predict market movements (upward as 1 or downward as 0/negative 1) and place long or short positions in financial instruments accordingly, then compare the returns of this strategy against a benchmark buy-and-hold strategy.

Which financial instruments are initially considered for the deep neural network implementation?

Initially, the script mentions Bitcoin to US dollar and Euro to US dollar as the financial instruments for implementing the deep neural network.

What type of problem is this deep neural network setup as?

The deep neural network is set up as a classification problem, where the market movement direction is classified as either upward (1) or downward (0 or negative 1).

How is the market direction classified based on returns?

The market direction is classified as 1 if the return is greater than 0, indicating an upward movement, and as 0 if the return is less than 0, indicating a downward movement.

What additional columns are created to include lags in the data?

Five additional columns are created for lags 1 through 5 to use past values for predicting the next market movement.

Which machine learning libraries are mentioned for building the neural network?

TensorFlow and Keras are mentioned for building the neural network, with the model being a sequential model optimized with Adam.

What is the structure of the neural network described in the script?

The neural network has three layers: two Dense layers with 64 neurons each and a Sigmoid output layer, using accuracy as the metric for evaluation.

How is the time series data split for training and testing?

The data is split using a 70-30 ratio, with 70% for training and 30% for testing, ensuring the data remains in sequential order.

Why is feature normalization important in this context?

Feature normalization is important to bring all the feature values to a similar scale, which helps in faster and more efficient training of the neural network.

What additional features are considered to improve the model's performance?

Momentum, volatility, and distance are added as additional features to potentially improve the model's predictive performance.

How does the script suggest evaluating the robustness of the trading strategy?

The script suggests evaluating the robustness of the trading strategy by testing it on an out-of-sample data set to ensure it performs well in real-world, unseen data.

What caution does the script provide regarding the backtesting of trading strategies?

The script cautions that a strategy may appear to perform well on in-sample data but may underperform on out-of-sample data, emphasizing the importance of robust out-of-sample testing.

What is the final verdict on the performance of the deep neural network model based on the script?

The final verdict is that while the model shows promise in-sample, it underperforms the benchmark on out-of-sample testing, indicating that further improvements are needed before considering it for production.