LiFT: Neurosymbolic Conditioning for Portfolio Allocation

Deep learning portfolio allocators achieve strong in-sample performance but degrade in out-of-sample performance during regime shifts. To mitigate this, recent deep portfolio allocators add market knowledge and investment rules at three levels: the input, where regime labels are added to the features; the representation, where a conditioning signal modulates each stock's encoding; or the output, as constrained decision layers or differentiable logic penalties on portfolio limits. Each is demonstrated on a single architecture, so the levels cannot be compared, and no gain can be attributed to the underlying knowledge. Supplying all three to any encoder raises three challenges. A regime describes the current market state, not a stock, so it has no natural place in a stock's input series. Trading rules are discrete, giving no usable gradient, and their relative weights are undetermined. Risk rules cap holdings but never require a minimum, so holding only cash satisfies them all. To address all three, we present $\text{LiFT}$, a neurosymbolic framework that operates at the input, representation, and output level of any time-series encoder. At input, market-state features join the per-stock inputs, At the representation level, binary regime indicators modulate each stock's encoding via FiLM. At output, trading rules become differentiable penalties, with each rule's weight learned by dual ascent. A minimum-investment rule and a cash ceiling prevent the all-cash solution. Being encoder-agnostic, the three additions run unchanged on five encoders, two stock universes, and four walk-forward windows at matched capacity. Together they condition allocation on market state and penalize rule violations during training. LiFT improves out-of-sample Sharpe over the baseline by 50% on the DJIA, on all five encoders, and by 25% on the S&P100, with Sortino and Calmar also improving. Separating the levels reveals which knowledge drives these gains.