This repository contains the code libraries and document for my dissertation, "Online Non-linear Prediction of Financial Time Series Patterns"
We consider a mechanistic non-linear machine learning approach to learning signals in financial time series data. A modularised and decoupled algorithm framework is established and is proven on daily sampled closing time-series data for JSE equity markets. The input patterns are based on input data vectors of data windows preprocessed into a sequence of daily, weekly and monthly or quarterly sampled feature measurement changes (log feature fluctuations). The data processing is split into a batch processed step where features are learnt using a Stacked AutoEncoder (SAE) via unsupervised learning, and then both batch and online supervised learning are carried out on Feedforward Neural Networks (FNN) using these features. The FNN output is a point prediction of measured time-series feature fluctuations (log differenced data) in the future (ex-post). Weight initializations for these networks are implemented with restricted Boltzmann machine pre-training, and variance based initializations. The validity of the FNN backtest results are shown under a rigorous assessment of backtest overfitting using both Combinatorially Symmetrical Cross Validation and Probabilistic and Deflated Sharpe Ratios. Results are further used to develop a view on the phenomenology of financial markets and the value of complex historical data under unstable dynamics.
Main document: https://github.com/joel11/Masters/blob/master/Thesis/JoeldaCosta_Thesis.pdf
The dataset used can be found at: https://zivahub.uct.ac.za/account/articles/11897628
DOI: 10.25375/uct.11897628
All libraries can be found in the following directory: https://github.com/joel11/Masters/tree/master/Code%20Libraries
The Main Tutorial module is made available to provide a step by step walkthrough for using the provided libraries. Configurations and methods are detailed for some naive training on an AGL dataset. It should be noted that these are not necessarily the optimal parameter choices possible for this exercise.
The first step is to create the database which will be used with the CreateDatabase function. Once created, the DatabaseOps module should be updated so that the db variable references the same database by name.
The JSE dataset can be read into memory using ReadJSETop40Data, and particular assets can then be filtered on (as is done for AGL). The data specifications such as horizons, prediction points and segregation points are also set in this step.
Once the database has been created, the SAE networks can be trained. This step uses the RunSAEExperiment method for training SAE networks, passing through the configurations chosen and noted in Main Tutorial. The configurations specify the parameters for the SAE networks and their SGD training, as detailed in Software Section 6 of the accompanying thesis document.
The selection of SAE networks is done semi-manually, and is dependent on the task at hand. In this case, the provided GetBestSAE function filters according to data horizons specified. This may differ according to what is being investigated. As the case may be, the selection of all `best' SAE networks should be specified by configuration id in the sae choices vector.
Much like the SAE networks, the FFN networks can be trained in a combinatorial manner using the noted configuration choices and the RunFFNExperiment function. These are available as specified in Section 6 of the accompanying thesis document.
The FFN training will include the recording of IS and OOS predictions for the specified data. This allows the running of the RunBatchTradeProcess and RunBatchAnalyticsProcess functions from the DatabaseBatchProcesses module. These processes record the per trade returns, as well as the aggregated performance metrics such as Sharpe ratios or strategy P&L.
Once the aggregated performance metrics have been calculated and recorded, they can be used for diagnostic visualizations. A set of example visualizations have been included in the Main Tutorial module, though any from the full set can be used, which are specified in Appendix 11.6.
Subsequently, the CSCV analysis can be run using ExperimentCSCVProcess. The set of configuration ids for the FFN networks are specified, with the split values to be run. The method will save the logit distribution graph in the GraphOutputDir directory and return the PBO score.
The DSR implementation is split across the Julia and Python code base. In Julia, the WriteCSVFileForDSR method will write out the required return data for the specified FFN configurations. Using the Python module DSR proc, the same CSV file can be specified and used to run the DSR analysis.