update#22
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gangtao
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Closes the alpha-101 demo's main goal: 10 alphas from the WorldQuant 101 paper, each chosen to demonstrate a different streaming-SQL pattern in Timeplus. Shared pipeline upgrade (additive, non-breaking): - v_bars now exposes high (max price/bucket), low (min price/bucket), vwap (sum(price*volume)/sum(volume)), in addition to the existing open, close, volume. - Reshaped v_bars's aggregation so vwap is computed in an outer SELECT after the GROUP BY — Timeplus 3.3.1 rejects `null_if(sum(volume),0)` in the same SELECT as `sum(volume)` with a "nested aggregate" error. Renamed the inner sum-of-volume alias to `vol_sum` to avoid alias collision with the column name. New alpha chains and the pattern each demonstrates: #3 rank-correlation(open, volume, 10) — cross-sectional rank of raw OHLC inputs + 10-bucket Pearson corr #4 ts_rank(rank(low), 9) — time-series rank within rolling window via array_count(x -> x <= current, lags(_, 0, 8)) #6 correlation(open, volume, 10) — raw-value rolling Pearson correlation (no rank step) for contrast #9 nested-if(ts_min/ts_max of delta-close) — array_min/array_max over lags(_, 0, 4) + multi_if conditionals #12 sign(Δvolume) × −Δclose — minimal alpha: single-step deltas via lags(_, 1, 1), sign, multiplication; tiny SQL footprint to contrast with the others #22 delta(corr(high, volume, 5), 5) × rank(stddev(close, 20)) — chained derived series: rolling corr → delta of derived signal × cross-sectional rank of rolling stddev #41 √(high × low) − vwap — VWAP derivation in v_bars + intra-bar nonlinear math #54 -1 × (low − close) × open⁵ / ((low − high) × close⁵) — pure intra-bar nonlinear OHLC, no lags or aggregation Naming follows the consistent _alpha_N suffix convention. Files numbered 013–035 in /ddl. Each alpha is a 1-4 file chain (features → ranks → alpha → backtest, with simpler alphas skipping intermediate views). Dashboards: extended the Alpha dropdown's inlineValues from "1,2" to "1,2,3,4,6,9,12,22,41,54". No new dashboard files needed — the {{filter_alpha}} substitution handles all 10 alphas. README rewritten with a per-alpha pattern table and updated pipeline diagram. End-to-end verified: fresh .tpapp install brings up all 35 DDL resources + 2 dashboards. Every v_alpha_N emits non-null values in streaming queries; v_backtest_alpha_N emits pnl. (Some longer- lookback alphas like #3 and #22 take more warmup before the correlation arrays fully populate — same pattern as Alpha #1.) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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gangtao
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May 21, 2026
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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