Feature/1 machine learning feature pipeline app#2
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gangtao
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May 21, 2026
Closes #27. Extends the existing alpha-101 app to also implement WorldQuant Alpha #2: -1 * correlation(rank(delta(log(volume), 2)), rank((close - open) / open), 6) Shared pipeline changes (affecting Alpha #1 too, verified non-breaking): - random_market_data gains a `volume` field (uniform [1, 100] per tick). - market_data + mv_market_data plumb volume through. - v_bars now exposes `open` (earliest price in bucket), `close` (latest price), and `volume` (sum of tick volumes). Alpha #1's downstream chain works unchanged. New views for Alpha #2: - v_features_2: per-stock per-bucket intraday_ret = (close - open) / open, log_vol_delta_2 = log(volume_t) - log(volume_{t-2}), and bucket-to-bucket returns (plumbed for the backtest). - v_ranks_2: per-bucket cross-sectional rank of BOTH features (using the same mean-zero rank pattern as Alpha #1, computed once per bucket via group_array + array_sort + array_first_index for each feature). - v_alpha_2: per-stock 6-bucket rolling Pearson correlation between the two rank series, negated. Manual covariance / variance computation via array_reduce('avg', array_map(...)) over lags(_, 0, 5). - v_backtest_2: same shape as v_backtest, branched on `strategy` config (linear | sign). Two new dashboards: - "Alpha #2 Live" (volume per bucket, leaderboard, alpha over time) - "Alpha #2 Backtest" (t-stat tile, summary, per-stock, portfolio PnL) End-to-end live-verified: install of the rebuilt .tpapp produces both alphas; v_alpha_2 values bounded in [-1, 1]; v_backtest_2 emits sensible pnl on each (time, stock) row. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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gangtao
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May 21, 2026
With consistent _alpha_N naming, the dashboard SQL can template the
alpha suffix at runtime. Each dashboard now has a single "Alpha"
dropdown (values: 1, 2) writing to `filter_alpha`, and the panel
queries reference views as `alpha_101.v_alpha_{{filter_alpha}}` and
`alpha_101.v_backtest_alpha_{{filter_alpha}}`.
Before: 4 dashboards (Realtime Alpha 101, Alpha #1 Backtest, Alpha #2
Live, Alpha #2 Backtest).
After: 2 dashboards (Realtime Alpha 101, Alpha 101 Backtest), each
serving both alphas via the dropdown.
The Realtime dashboard also shows price AND volume side-by-side
(both filtered by the stock dropdown), since those are shared
market-data views that don't change between alphas.
Adding Alpha #N now requires only:
- a new DDL chain (v_features_alpha_N, ..., v_backtest_alpha_N)
- appending "N" to the Alpha dropdown's inlineValues
No new dashboard files. Verified end-to-end: 2 dashboards register
with 6 panels each; switching the Alpha dropdown updates the
leaderboard / alpha-over-time / all backtest panels in place.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
gangtao
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May 21, 2026
Alpha #2 + naming refactor + merged dashboards
gangtao
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The random source assigns each stock a fixed price band ([50, 80, 120, …] with only ±0.5% tick noise), so cross-sectional rank of raw price- level features is a constant per stock — bands never overlap. This makes alphas #3, #4, and #6 degenerate (alpha_4 always = -1 in steady state, etc.) — the math is correct, the data just has no rank information to extract. Documents which alphas DO produce meaningful varying signals on this synthetic feed (#1, #2, #9, #12, #22, #41, #54 — the ones operating on returns, deltas, or intra-bar quantities) versus which need real market data to demo properly. Caught when reviewer noticed alpha_4 = -1 everywhere. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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add ml feature demo