In light of micro-scale inefficiencies induced by the high degree of fragmentation of the Bitcoin trading landscape, we utilize a granular data set comprised of orderbook and trades data from the most liquid Bitcoin markets, in order to understand th
e price formation process at sub-1 second time scales. To achieve this goal, we construct a set of features that encapsulate relevant microstructural information over short lookback windows. These features are subsequently leveraged first to generate a leader-lagger network that quantifies how markets impact one another, and then to train linear models capable of explaining between 10% and 37% of total variation in $500$ms future returns (depending on which market is the prediction target). The results are then compared with those of various PnL calculations that take trading realities, such as transaction costs, into account. The PnL calculations are based on natural $textit{taker}$ strategies (meaning they employ market orders) that we associate to each model. Our findings emphasize the role of a markets fee regime in determining its propensity to being a leader or a lagger, as well as the profitability of our taker strategy. Taking our analysis further, we also derive a natural $textit{maker}$ strategy (i.e., one that uses only passive limit orders), which, due to the difficulties associated with backtesting maker strategies, we test in a real-world live trading experiment, in which we turned over 1.5 million USD in notional volume. Lending additional confidence to our models, and by extension to the features they are based on, the results indicate a significant improvement over a naive benchmark strategy, which we also deploy in a live trading environment with real capital, for the sake of comparison.
The transportation $mathrm{L}^p$ distance, denoted $mathrm{TL}^p$, has been proposed as a generalisation of Wasserstein $mathrm{W}^p$ distances motivated by the property that it can be applied directly to colour or multi-channelled images, as well as
multivariate time-series without normalisation or mass constraints. These distances, as with $mathrm{W}^p$, are powerful tools in modelling data with spatial or temporal perturbations. However, their computational cost can make them infeasible to apply to even moderate pattern recognition tasks. We propose line
We consider approaches to the classical problem of establishing a statistical ranking on a given set of items from incomplete and noisy pairwise comparisons, and propose spectral algorithms able to leverage available covariate information about the i
tems. We give a comprehensive study of several ways such side information can be useful in spectral ranking. We establish connections of the resulting algorithms to reproducing kernel Hilbert spaces and associated dependence measures, along with an extension to fair ranking using statistical parity. We present an extensive set of numerical experiments showcasing the competitiveness of the proposed algorithms with state-of-the-art methods.
