Complete alpha-101 demo — 10 alphas, catalog stream, consolidated dashboards

[gangtao]

Closes #27.

Summary

Completes the alpha-101 demo with 10 WorldQuant 101 alphas, a catalog mutable stream describing each one, and two consolidated dashboards (Live + Backtest) with an Alpha dropdown that switches between all 10 signals.

What ships

1. Ten alphas — one per streaming-SQL pattern

#	Streaming-SQL pattern showcased
1	lags window + signed_power + array_first_index for ts_argmax + cross-sectional rank via group_array + array_sort + ARRAY JOIN array_enumerate
2	Cross-sectional rank of two derived features per bucket → per-stock 6-bucket Pearson correlation
3	Same pattern as #2 but with raw OHLC inputs and a 10-bucket window
4	Time-series rank within a rolling window — array_count(x -> x <= current, lags(_, 0, 8))
6	Raw-value rolling Pearson correlation (no rank step)
9	Rolling min/max via array_min/array_max over lags(_, 0, 4) + multi_if nested conditionals
12	Minimal alpha — single-step deltas via lags(_, 1, 1), sign, multiplication; tiny SQL footprint
22	Chained derived series: rolling correlation → delta of correlation × cross-sectional rank of rolling stddev
41	VWAP derivation in v_bars (sum(price*volume) / sum(volume)) + intra-bar nonlinear
54	Pure intra-bar nonlinear OHLC — no lags, no aggregation, all instantaneous

2. Shared pipeline

random_market_data  →  mv_market_data  →  market_data  →  v_bars
                                                    (time, stock_id,
                                                     open, close, high, low,
                                                     volume, vwap)

Each alpha branch:

v_bars
  →  v_features_alpha_N    (per-stock features + close-to-close returns)
  →  [v_ranks_alpha_N]     (optional: cross-sectional ranks per bucket)
  →  v_alpha_N             (the signal itself)
  →  v_backtest_alpha_N    (pairs lag(alpha) × returns, branched on `strategy`)

Simpler alphas (#12, #41, #54) skip the features and ranks layers.

3. alpha_catalog mutable stream

CREATE MUTABLE STREAM alpha_catalog (
  alpha_name  string,    -- PK
  description string,
  equation    string
) PRIMARY KEY alpha_name

Install-time seed via a single INSERT INTO ... VALUES (…) (registered as type: system in the manifest). After install, SELECT * FROM alpha_101.alpha_catalog returns one row per alpha with its name, short description, and original WorldQuant formula. Mutable-stream semantics mean re-inserting on the same alpha_name upserts in place — the catalog is editable without rebuilding.

4. Consolidated dashboards

Two dashboards, each with an Alpha dropdown (1, 2, 3, 4, 6, 9, 12, 22, 41, 54) that templates the suffix into panel queries — FROM alpha_101.v_alpha_{{filter_alpha}} resolves at render time:

Realtime Alpha 101 (6 panels)

• Stock + Alpha dropdowns
• Selected Alpha — markdown panel reading from alpha_catalog, shows the description and equation of the chosen alpha
• Live Prices + Live Volume (side-by-side, filtered by Stock dropdown)
• Alpha Leaderboard (latest, all stocks)
• Alpha Over Time (multi-stock line)

Alpha 101 Backtest (6 panels)

• Alpha dropdown
• t-stat tile + summary table
• PnL by Stock + Portfolio PnL per 30s
• Per-Stock PnL Over Time

Adding Alpha #N from here: a new DDL chain (v_features_alpha_N → … → v_backtest_alpha_N), append ,N to each dropdown's inlineValues, and INSERT a row into the catalog. No new dashboard files.

5. Naming convention

shared (unsuffixed):        per-alpha (suffixed):
random_market_data          v_features_alpha_N
market_data                 v_ts_argmax_5_alpha_1   (alpha-1 only)
mv_market_data              v_ranks_alpha_N         (where used)
v_bars                      v_alpha_N
                            v_backtest_alpha_N
alpha_catalog               (alpha-name keyed)

6. Synthetic-data caveat (documented in README)

Alphas operating on raw price levels (#3 rank(open), #4 rank(low), #6 correlation(open, …)) are degenerate on this synthetic feed because per-stock price bands never overlap — STOCK_0's low is always lowest, STOCK_2's is always highest, so cross-sectional ranks of price levels are constants. The math is correct, the alphas are honest about having no information to extract. Alphas operating on returns, deltas, or intra-bar quantities (#1, #2, #9, #12, #22, #41, #54) produce meaningful varying signals.

7. Bundled skill / docs improvements

skill/SKILL.md Common Errors gained four entries during this PR:

1. Multi-series line/area charts need color set
2. Dashboard WHERE filters must use time (not _tp_time) on tumble-derived views
3. YAML # truncates unquoted strings in manifest values
4. nullif is rejected; use null_if
5. Sprig templates in dashboard JSON get flattened to literals when re-saved via the UI

Verified end-to-end

Check	Result
.tpapp install	✓ 37 DDL resources + 2 dashboards register
All 10 alphas emit	✓ verified live
All 10 backtests emit pnl	✓ verified live
alpha_catalog populated on install	✓ 10 rows queryable via SELECT * FROM alpha_catalog
Markdown panel renders catalog row for selected alpha	✓ Alpha dropdown drives WHERE alpha_name = 'alpha_{{filter_alpha}}'
Sprig stock-selector template auto-scales	✓ install with config[num_stocks]=5 → STOCK_0..STOCK_4
Dashboard Alpha dropdown switches all panels	✓ leaderboard + over-time + t-stat + summary + PnL all update

Test plan

• make build && make install with default config
• make install with config[num_stocks]=5 config[bucket]=5s config[strategy]=sign
• SELECT alpha_name, description, equation FROM alpha_101.alpha_catalog returns all 10 rows
• Reviewer: on the Realtime Alpha 101 dashboard, cycle through all 10 Alpha values — the markdown banner updates with each alpha's description + formula; the leaderboard and over-time panels update accordingly
• Reviewer: switch Stock dropdown — Live Prices + Live Volume update
• Reviewer: on the Alpha 101 Backtest dashboard, cycle through alphas — t-stat tile, summary, per-stock PnL, portfolio PnL all update

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