  Systematic financial trading strategies account for over 80% of trade volume
in equities and a large chunk of the foreign exchange market. In spite of the
availability of data from multiple markets, current approaches in trading rely
mainly on learning trading strategies per individual market. In this paper, we
take a step towards developing fully end-to-end global trading strategies that
leverage systematic trends to produce superior market-specific trading
strategies. We introduce QuantNet: an architecture that learns market-agnostic
trends and use these to learn superior market-specific trading strategies. Each
market-specific model is composed of an encoder-decoder pair. The encoder
transforms market-specific data into an abstract latent representation that is
processed by a global model shared by all markets, while the decoder learns a
market-specific trading strategy based on both local and global information
from the market-specific encoder and the global model. QuantNet uses recent
advances in transfer and meta-learning, where market-specific parameters are
free to specialize on the problem at hand, whilst market-agnostic parameters
are driven to capture signals from all markets. By integrating over
idiosyncratic market data we can learn general transferable dynamics, avoiding
the problem of overfitting to produce strategies with superior returns. We
evaluate QuantNet on historical data across 3103 assets in 58 global equity
markets. Against the top performing baseline, QuantNet yielded 51% higher
Sharpe and 69% Calmar ratios. In addition we show the benefits of our approach
over the non-transfer learning variant, with improvements of 15% and 41% in
Sharpe and Calmar ratios. Code available in appendix.
