FiT: from market language to market decisions

Financial time-series foundation models have recently enabled language-like modeling of financial markets through discrete representations of candle stick dynamics. However, existing models remain limited in faithfully representing market structure, anticipating future dynamics, and adapting these representations to downstream decision-oriented tasks. We propose **FiT**, **Fi**nancial **T**ransformer, which builds a unified pipeline from market representation to future prediction and downstream adaptation. On the data side, we introduce the FiT Dataset, expanding market coverage to more than 100 exchanges and temporal resolution down to 1-second K-lines, thereby enabling broader generalization across sampling frequencies. With more than 200 billion K-line records—17 times the size of the Kronos corpus—making it the largest financial K-line corpus to date, by the best of our knowledge. For pretraining, we propose Rotated Leech Spherical Quantization (RLSQ) for K-line tokenization designed to better capture the geometry of candlestick dynamics. Compared with the Kronos tokenizer, it substantially improves reconstruction performance, reducing reconstruction error by 62% while improving directional accuracy and K-line structural validity by 21% and 22%, respectively. For the predictor, we augment next-token prediction (NTP) with Future Summary Prediction (FSP), yielding 52% and 95% improvements in zero-shot IC and RankIC, respectively, over the best-performing baselines. On the post-training period, we perform BiRank GRPO to adapt FiT to downstream tasks, producing task-specific experts that are subsequently consolidated into the base model through multi-teacher on-policy distillation (MOPD). This procedure improves downstream performance while largely preserving the capabilities and generalization of the pre-trained model. We further develop a hybrid optimization strategy tailored to large-scale financial time-series pretraining, combining Muon, Lion, and AdamW across parameter groups, which reduces validation loss by 5.6% relative to an AdamW-only baseline. Together, these advances trace a complete path from market language to market dynamics to market decisions: RLSQ learns a high-fidelity discrete representation of K-line behavior, FSP learns to anticipate its future evolution, and BiRank GRPO with MOPD translates these capabilities into downstream tasks. FiT thus provides a unified foundation for financial K-line modeling that moves beyond representing the market toward anticipating its dynamics and adapting them to downstream decisions.