| --extra-index-url https://download.pytorch.org/whl/cpu |
|
|
| # --- core data / numerics --- |
| # floor is 2.1, not just "2.0": the code's resample/date_range rules use the |
| # lowercase offset aliases ("10min", "4h", "min") -- confirmed these work on |
| # 2.1.4 through the current 3.0.x, and confirmed pandas 3.0 now HARD-ERRORS |
| # on the old uppercase forms ("4H", "10T") rather than just warning, so |
| # lowercase was the right choice; 2.1 is the safe, verified floor for it. |
| pandas>=2.1 |
| numpy>=1.24 |
|
|
| # --- market data (free, no API key) --- |
| yfinance>=0.2.40 |
|
|
| # --- UI & charts --- |
| gradio>=4.36 |
| plotly>=5.20 |
|
|
| # --- ARIMA / Auto-ARIMA / ARIMA-GARCH --- |
| statsmodels>=0.14 |
| arch>=6.3 |
|
|
| # --- Moirai (Salesforce) — https://github.com/SalesforceAIResearch/uni2ts --- |
| uni2ts |
| gluonts |
|
|
| # --- TimesFM 2.5 (Google) — https://github.com/google-research/timesfm --- |
| # [xreg] is only needed for the 10-feature covariate path (models/timesfm_model.py's |
| # forecast_with_covariates()) -- it pulls in scikit-learn + JAX/jaxlib on top of |
| # [torch]. Plain Close-only TimesFM forecasting never touches xreg and would work |
| # fine with just timesfm[torch], but since this project's `features=` support is |
| # meant to work out of the box, both extras are requested together here. |
| timesfm[torch,xreg] |
|
|
| # --- shared deep-learning backend --- |
| torch |
| einops |
| huggingface_hub |
|
|
