How to Use DeepFolio for Real-Time Portfolio Optimization

Feb 17, 2021 | Data Science

Welcome to the world of DeepFolio, a powerful Python library built on Google’s TensorFlow platform, designed specifically for real-time portfolio optimization. This guide will help you set up and start using DeepFolio, making your investment strategies more robust and adaptive to market changes.

Understanding DeepFolio

At its core, DeepFolio utilizes a framework called the Portfolio Transformer (PT) which is inspired by successful natural language processing techniques. Imagine arranging a seating plan for a dinner where people’s preferences are taken into account. Each guest (portfolio asset) has specific likes and dislikes (performance trends), and the seating arrangement (portfolio allocation) needs to adapt to their preferences for an enjoyable evening (optimized returns). Similarly, DeepFolio intelligently adapts investment allocations based on historical performance and changing market conditions.

Features of DeepFolio

  • Portfolio Transformer: Attention-Based Asset Allocation Framework
  • Differentiable portfolio optimization
  • Real-time optimization capabilities
  • Robust and multi-period optimization
  • Support for multiple asset classes
  • Backtesting system for historical performance evaluation
  • Risk management tools
  • Factor model integration
  • Automated hyperparameter tuning supported by Optuna
  • Trade execution simulation
  • Event-driven rebalancing
  • Comprehensive reporting
  • Integration of sentiment analysis
  • Tax-aware optimization
  • Interactive visualization dashboard

Installation

To get started, you need to install DeepFolio. Simply run the following command in your terminal:

pip install -U deepfolio

Quick Start Guide

Let’s dive straight into using DeepFolio with a simple quick start example. Begin by importing the necessary components:

from deepfolio.models import DiffOptPortfolio
from deepfolio.optimizers import CustomOptimizer
from deepfolio import Backtester

Next, initialize the model:

model = DiffOptPortfolio(input_dim=50, n_assets=10, hidden_dim=64)

Create an optimizer and load your financial data:

optimizer = CustomOptimizer(model.parameters())
features, returns = load_your_data()

Now, create a backtester and run the backtesting:

backtester = Backtester(model, features=features, returns=returns)
backtester.run()

Finally, get your results:

results = backtester.get_results()
print(f'Sharpe Ratio: {results["sharpe_ratio"]}')
print(f'Max Drawdown: {results["max_drawdown"]}') 

Troubleshooting Tips

If you encounter any issues while using DeepFolio, consider the following troubleshooting steps:

  • Ensure you have installed all required dependencies mentioned in the documentation.
  • Check the format of your input data; it should conform to the expected structure.
  • When tweaking hyperparameters, ensure they are appropriate for your specific dataset.
  • For real-time optimization issues, verify your data source API credentials.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Deep Dive into Advanced Usage

Once you’re comfortable with the basics, explore more advanced features of DeepFolio:

Using the Portfolio Transformer

from deepfolio.optimizers import portfolio_transformer
input_shape = (30, 10)  # 30 time steps, 10 assets
d_model = 64
num_heads = 4
dff = 128
num_layers = 4
model = portfolio_transformer(input_shape, d_model, num_heads, dff, num_layers)
model.compile(optimizer='adam', loss='mse')
model.summary()

Real-Time Optimization Setup

from deepfolio.models import RealtimeOptimizer
from deepfolio.data import DataSource
data_source = DataSource(api_key='your_api_key')
optimizer = RealtimeOptimizer(model, data_source)
optimizer.start()

Interactive Dashboard

from deepfolio.utils import PortfolioDashboard
dashboard = PortfolioDashboard(portfolio_data, benchmark_data)
dashboard.run()

Documentation and Contribution

For detailed documentation, visit our documentation site. If you are interested in contributing to this project, please refer to the contributing guidelines provided.

Conclusion

DeepFolio provides an innovative and comprehensive approach to portfolio optimization, leveraging deep learning techniques for better financial decision-making. Start exploring the features and craft your financial strategies today!

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Stay Informed with the Newest F(x) Insights and Blogs

Tech News and Blog Highlights, Straight to Your Inbox