Photon Machine Learning Framework
A Machine Learning framework that extends the functionality of other frameworks such as TensorFlow and Keras. Photon ML is built to apply neural network and ensemble modeling techniques for deep learning financial algorithms. The framework supports the entire lifecycle of a Machine Learning project including data preparation, model development, training, monitoring, evaluation and deployment.
Project Repo: github.com/sequenzia/photon
Technologies & Methods: Python, Neural Networks, Deep Learning, Distributed Training
Frameworks & Libraries: TensorFlow, Keras, NumPy, Pandas, Apache Arrow, Apache Parquet, Scikit-learn
Key Features of Photon ML:
- Custom object-oriented API with built-in subclassing of Keras and TensorFlow APIs.
- Built-in custom modules such as models, layers, optimizers and loss functions.
- Highly customizable interface to extend built-in modules for specific algorithms/networks.
- Detailed logging and analysis of model parameters to increase interpretability and optimization.
- Works natively with TensorFlow distributed strategies.
- Real-time data preprocessing; dataset splitting, normalization, scaling, aggregation and time series resampling.
- Custom batching, padding and masking of data.
- Designed to be model/algorithm agnostic and to work natively with container services.
- Natively shares input and output between multiple networks to streamline deep ensemble learning.
- Simple interface for saving, serializing and loading entire networks including learned and hyper parameters.
- Custom dynamic learning rate scheduling.
Modeling Research & Development
An evolving collection of data models and algorithms used to model financial markets including equities, options, futures and crypto assets. The project has grown from only traditional statistical modeling to Machine Learning based modeling which includes deep neural networks and probabilistic reasoning networks.
Project Repo: github.com/sequenzia/dyson
Technologies & Methods: Deep Learning, Neural Networks, Regression, Classification, Probabilistic Reasoning, Deep Ensemble Learning, Back-Propagation, Statistical Analysis, Financial Modeling
Frameworks & Libraries: TensorFlow, Keras, Pandas, NumPy, Seaborn, Matplotlib, Statsmodels, Scikit-Learn
Algorithms & Models: CNNs, RNNs, Transformers, Attention Mechanisms, Temporal Conv Networks, Autoencoders, Bayesian Neural Networks, ARIMA, Structural Time Series
- Performed predictions and quantitative analysis of asset prices with a focus on making predictive inferences on time series data.
- Utilized advanced generalization and regularization techniques to increase the performance of models outside training data sets.
- Developed custom optimizers and loss functions to address the unique complexities of time series market data.
- Conducted extensive exploratory data analysis (EDA), feature engineering, data scaling/normalization, resampling, and principal component analysis (PCA).
Machine Learning Preprocessing & Pipelines
Developed a set of custom libraries to handle the unique characteristics of acquisition, preparation and storage of financial market data. These libraries include WebSockets/RESTful API data connectors to access data, detect anomalies in hundreds of millions of data points, then cleanse and pre-process to provide high-integrity data modeling. Also included is a set of tools for domain specific feature engineering and labeling of financial market data.
Project Repo: github.com/sequenzia/maxwell
Technologies & Methods: Data Preprocessing and Cleaning, Feature Engineering, Machine Learning Pipelines, Data Labeling, WebSockets/RESTful APIs, Nvidia CUDA GPUs, Python Data Structures and Storage
Frameworks: Pandas, NumPy, Numba, Rapids AI, DASK, Apache Arrow, Apache Parquet, Scikit-learn, Seaborn, Matplotlib
- Utilized domain knowledge to engineer features based on technical indicators and statistical markers.
- Processed over 20 years of both trading and book market data with multiple time resolutions.
- Increased the speed and efficiency of data storage and retrieval, utilizing Apache Arrow for in-memory columnar storage and Apache Parquet for persistent on-disk storage.
- Produced a wealth of useful tools for managing the flow of data for various modeling and algorithmic trading, orchestrating Machine Learning pipelines, providing a high level of fluidity to feature extraction and helping to increase the integrity of the data.
- Designed custom procedures to label time series data with algorithmic trading attributes such as stop losses, long/short price targets, ATR and VWAP values.
- Processed and stored data from high-volume real-time market data APIs.
- Automated data labeling, binning, one-hot encoding, scaling and normalization of time series market data.
Algorithmic Trading System
Developed set of tools and libraries designed to interface with market brokers and execute algorithmic trading. The algorithmic trading systems executes both buy and sell orders in equities, options, futures, and crypto markets. The tools also support back testing of trading algorithms, applying predictive models to historical data to evaluate their accuracy.
Technologies & Methods: Python, C++, Algorithmic Trading, Financial Models
Frameworks: NumPy, SciPy, Pandas, Numba, Seaborn, Matplotlib, Statsmodels
- Developed utilizing object-oriented programming in Python and C++ for increased performance.
- Designed a set of libraries that work with both rules-based and Machine Learning based models and algorithms.
- These same libraries can be used for both live execution of algorithmic trading systems and back testing.
- Produced a highly functional and efficient algorithmic trading and back testing system; able to trade various assets in various markets and provide increased exposure to key trading metrics.