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From raw text streams to market trading signals
Text preprocessing in quantitative trading acts as an active signal-shaping operator rather than a passive data-cleaning routine. Splitting tokens, stripping punctuation, or deleting words directly alters the geometry of the feature space, changing the conditional expectation of forward returns and impacting strategy execution.
What’s inside:
Evaluating preprocessing as a signal operator: Preprocessing logic directly modifies input vectors, meaning every string transformation parameterizes the feature space and dictates what the classifier can observe.
Preventing semantic and structural failure modes: Unaligned text rules introduce structural risks, including semantic inversion from deleted negations, unstable feature representations across vendor feeds, and collapsed entity definitions.
Preserving entity meaning through canonicalization: Universal lowercasing acts as a destructive projection operator, merging distinct ticker symbols, corporate entities, and ordinary nouns into identical feature coordinates.
Defining typed token boundaries: Protected regular expressions preserve specialized financial spans, keeping currency pairs, percentages, and execution log values intact before standard tokenizers shatter them.
Protecting logical sign with targeted stopword filters: Retaining direction-governing terms like “not,” “under,” and “without” prevents semantic inversion in sentiment classification and stops false trade signals.
Validating morphological reduction under economic loss: Naive stemming merges distinct temporal and probabilistic risk states, making it necessary to distill contextual language model insights into deterministic lookup tables.
Combining multi-channel sparse and dense representations: Ensembling deterministic sparse vectors with dense contextual embeddings balances explicit, auditable feature attribution against abstract semantic interpretation.
