"Phase 3 — Labels & walk-forward dataset (Alpaca crypto)"

Status: DONE (2026-08-15) · Suite: 260 passed (9 new tests in tests/test_alpaca_dataset.py)

#What was built

#Labels (labels.py)

The bar-close signal must be executable: a signal read at bar t close enters at the next open and exits h bars later at the open — the only honest leg (this matches the repo's PANEL open-entry book).

• Primary label: y[t] = open[t+1+h] / open[t+1] - 1 (executable, long/flat).
• Research label: y_cc[t] = close[t+h] / close[t] - 1 — not the executable leg; kept only for IC diagnostics side-by-side with y.
• h must be ≥ 1 (raising otherwise); NaN only at tail rows that lack the future t+1+h bar — never imputed.
• add_labels() merges labels onto a Phase-2 feature frame on the bar-close timestamp grid; if feature/bars lengths diverge (dropped/duplicate bars) it reindexes labels onto the feature timestamps instead of assuming alignment.

#Dataset assembly (dataset.py)

• assemble_dataset(symbol, timeframe, h) — features + labels, then:
  1. drop tail rows with NaN y (never impute);
  2. optional warmup drop (rolling-feature warmup rows);
  3. enforce fixed schema [timestamp, symbol, y, y_cc] + FEATURE_SCHEMA[2:].
• Persistence: data/processed/alpaca/datasets/{SYM}/{TF}/h{h}.parquet with a strict pyarrow schema (dataset_schema(), ns timestamps), safe reload.
• build_all_datasets(...) bulk-builds (symbol × timeframe × horizon).

#Walk-forward splits (leakage-free by construction)

• wf_splits(ts, n_splits, gap_bars) — reference-based, expandable splits over the sorted timestamp axis: split k tests interval k with train = all rows before it minus an embargo of gap_bars rows. Earlier splits carry less history (the honest walk-forward property); no randomized/CV splitting.
• leakage_check(train_ts, test_ts, gap_bars) — asserts the actual timestamps of train/test respect the embargo before training starts (ground-truth check, converts to ns for exact timedelta math).

#Tests (tests/test_alpaca_dataset.py, isolated dirs → tmp)

• forward_open_return exact math (and forward_close_return);
• add_labels alignment, incl. reindex path when bars are dropped;
• tail rows without future label dropped; warmup trim;
• dataset save/load round-trip with strict schema;
• wf_splits invariants + embargo respected (gap_bars honored in timestamps);
• leakage_check raises on overlap.

#Integration notes

• Datasets are inputs to Phase 4 (per-symbol walk-forward trainer): config defaults gap_bars = config.ml.embargo_bars (50 for the PANEL-style monthly cadence; crypto runs use same default), horizons (1, 5, 12, 24).
• Real datasets get built after the historical backfill completes (Phase 1/2 raw/feature stores are currently being repopulated 2021→now).