The code in this repository is the result of secondment work at Lightbox Technologies SA as part of the SMARTHEP European Training Network (ETN).
Lightbox provides consultancy, research and development of hardware and software infrastructures, algorithms, mathematical and statistical models, aimed at the acquisition, maintenance, processing and analysis of massive volumes of data, and the optimization of decision-making and production processes of third-party entities. The work contained in this repository focuses on the application of machine learning (ML) tools to financial time-series data through the development of systems for predictive analysis using historical market data time series for a multitude of financial instruments.
Algorithmic biases were prevented by dividing the time series in training and testing periods. A Transfer Learning technique was employed in order to improve the training of the model by combining data from different securities to compensate for the limited availability of historical data. The training sample was identified by selecting datasets showing similar statistical features. The various models provide future predictions in unseen time-series data along with associated uncertainties.
This repository contains the codebase for a number of ML models ranging from the simple to the complex. We utilise and show the training and inference from classical boosted decision trees to Bayesian and recurrent neural networks. Each network architecture informed subsequent designs. An important part of the problem was the model adapting to changing and often turbulent time series data. In comparison to other ML applications the models are susceptible to going “out-of-date” (out-of-sample) very rapidly following training - thus presenting challenges to our choice of training/validation data.
This repository contains part of the results produced during this project.
In order to start working with the code contained in this repo use the install.sh (first time) and setup.sh scripts found in the setup directory. This will create a conda/miniconda environment with the necessary python libraries installed to reproduce our base results.
The code contained in the src directory can be used to train and save particular models before inferring on unseen test data using the pre-trained model weights. A simplified workflow is described below.
In the lstm-scripts directory we build a custom model using the tensorflow ML library. The training can be initiated by giving several command line arguments to the training script for example
python lstm-train.py --name AAPL --end_date 2024-01-01 --seq_length 10 --epochs 150
where the required arguments are --name the name of the underlying stock or asset following yahoo finance naming, --end_date the final date that will be included in your overall dataset, --seq_length the sequence length that the LSTM model considers and --epochs the number of epochs to train.
Similarly the inference script can be run with the following example:
python lstm-infer.py --name AAPL --end_date 2024-01-01 --seq_length 10
with the same command line arguments, excluding --epochs. The name of the saved model weights is consistent across train.py and infer.py.
In the darts-framework directory we use the Darts python library to aid with the analysis and processing of time series data natively in python. Much of the model and architecture initialisation is done in the Darts backend therefore to train and infer it is simply enough to call the python scripts:
python commodity_*.py
Which explicitly specify the ticker symbol, dataset beginning and end inside the script. Then a variety of the currently implemented models curated by Darts are trained and saved.
The rnn_train.py and transformer_train.py specifically look at the Darts implementation of a simple LSTM or Transformer based model, with the same usage as the commodity_*.py scripts.
Finally, the notebooks in the notebooks directory contain bitesize examples of various ML models on publically available financial data. There we utilise a number of different frameworks and approaches to explore the large space of models and data preparation techniques available.
We acknowledge funding by the European Union’s Horizon 2020 research and innovation programme, call H2020-MSCA-ITN-2020, under Grant Agreement n. 956086 (SMARTHEP).