  We consider the viability of a modularised mechanistic online machine
learning framework to learn signals in low-frequency financial time series
data. The framework is proved on daily sampled closing time-series data from
JSE equity markets. The input patterns are vectors of pre-processed sequences
of daily, weekly and monthly or quarterly sampled feature changes. The data
processing is split into a batch processed step where features are learnt using
a stacked autoencoder via unsupervised learning, and then both batch and online
supervised learning are carried out using these learnt features, with the
output being a point prediction of measured time-series feature fluctuations.
Weight initializations are implemented with restricted Boltzmann machine
pre-training, and variance based initializations. Historical simulations are
then run using an online feedforward neural network initialised with the
weights from the batch training and validation step. The validity of results
are considered under a rigorous assessment of backtest overfitting using both
combinatorially symmetrical cross validation and probabilistic and deflated
Sharpe ratios. Results are used to develop a view on the phenomenology of
financial markets and the value of complex historical data-analysis for trading
under the unstable adaptive dynamics that characterise financial markets.
