d87a1ada39
A. Cross-theme comparability: - compute_theme_surprises now weight-normalizes by total |weight| (weighted average), so every theme surprise on same [-1,1] scale regardless of factor count/weight (retail 0.189->0.145; auto_credit 1.0->0.64). B. Historical factor store (enables learning macro/demographic factors): - New factor_history.py: append-only per-factor JSONL, dedupes unchanged values, rejects non-finite, records every FACTORS value each scheduler run. - scheduler.py: jobs carry fetch_module; refresh_all records factor history (non-fatal); added bank_npl job. - GET /api/v1/learning/factors?min_points= reports n_points/learnable per factor so users see when P4 learning unlocks (validated query parsing). - weight_learning: generic learn_factor_series() aggregator (momentum reuses). Independent review deleg_5dd358e3 passed=true (empty security/logic arrays); its two robustness suggestions applied (finite guard in record(), clean 400 on bad min_points). 234 tests pass; Vite build passes.
509 lines
20 KiB
Python
509 lines
20 KiB
Python
"""Multi-theme registry, symbol->theme exposure mapping, and combined scoring.
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Aggregates the three Thai alternative-factor themes (tourism, auto_credit,
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refining_energy) plus the Siamchart fundamental provider into a per-symbol
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combined score, per the user's confirmed weighting:
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combined = 0.6 * theme_score + 0.4 * siamchart_score
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where `theme_score` is the mean of the enabled theme scores that cover a symbol
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(so a symbol in several themes averages its theme scores), and `siamchart_score`
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is a normalized fundamental score.
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Frequency handling: theme scores carry an explicit `frequency` (daily/monthly/
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quarterly/annual). Consumers must not mix different-frequency factors as if they
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were the same timestamp — see `frequency` on each scored theme.
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"""
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from __future__ import annotations
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import statistics
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from typing import Optional
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# ---------------------------------------------------------------------------
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# Theme registry + symbol->theme exposure mapping
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# ---------------------------------------------------------------------------
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# Symbols known to belong to each theme. This is a *curated* partial mapping of
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# the SET50 universe; symbols not listed here get no direct theme exposure for
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# that theme (theme_score contribution = those themes' factor value applied via
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# a generic market exposure fallback, see scoring).
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#
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# Expanded to cover the FULL SET50 universe (2026, 49 names): every symbol is
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# assigned to at least one industry/theme so the board + per-symbol detail can
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# always report a theme. Deterministic, curated by industry sector.
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THEME_SYMBOLS: dict[str, set[str]] = {
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"tourism": {
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"AOT", "CENTEL", "MINT", "AWC", "CPN", "CRC", "BEM", "BTS", "ERW",
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"DHOUSE", "MAJOR", "SNNP",
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},
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"auto_credit": {
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"KKP", "TISCO", "TCAP", "THANI", "MTC", "SAWAD", "NSI", "AEONTS", "TK",
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"THG", "GLAND", "ASI", "TGPRO",
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},
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"refining_energy": {
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"PTT", "PTTGC", "TOP", "IRPC", "SPRC", "BCP", "ESSO", "BANPU", "GPSC",
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},
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"banks": {"BBL", "KBANK", "KTB", "SCB", "TTB"},
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"retail": {"CPALL", "COM7", "GLOBAL", "HMPRO", "OSP", "OR", "CPN", "CRC"},
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"telecom_it": {"ADVANC", "TRUE", "DELTA", "COM7"},
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"property": {"LH", "AWC", "CPN", "CRC", "GLOBAL", "HMPRO"},
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"healthcare": {"BDMS", "BH"},
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"petrochem_materials": {"IVL", "SCC", "SCGP", "PTTGC", "BANPU"},
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"consumer_staples": {"CPF", "TU", "OSP", "CBG"},
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"utilities": {"BGRIM", "EGCO", "RATCH", "GPSC", "GULF", "BANPU", "EA"},
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"nonbank_finance": {"JMT", "JMART", "KTC", "TIDLOR", "MTC", "SAWAD", "KTC", "AEONTS"},
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"exploration": {"PTTEP", "PTT"},
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}
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# Theme frequency (data cadence of the underlying factor). Used to keep
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# multi-frequency factors from being mixed as same-timestamp.
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THEME_FREQUENCY: dict[str, str] = {
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"tourism": "monthly",
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"auto_credit": "monthly",
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"refining_energy": "quarterly",
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"banks": "quarterly",
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"retail": "monthly",
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"telecom_it": "quarterly",
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"property": "quarterly",
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"healthcare": "quarterly",
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"petrochem_materials": "quarterly",
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"consumer_staples": "quarterly",
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"utilities": "quarterly",
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"nonbank_finance": "quarterly",
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"exploration": "quarterly",
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}
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# Thai labels for every theme (used in the board theme column + per-symbol view)
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THEME_LABELS_TH: dict[str, str] = {
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"tourism": "ท่องเที่ยว",
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"auto_credit": "รถยนต์/สินเชื่อ",
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"refining_energy": "พลังงาน/โรงกลั่น",
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"banks": "ธนาคาร",
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"retail": "ค้าปลีก",
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"telecom_it": "สื่อสาร/ไอที",
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"property": "อสังหาริมทรัพย์",
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"healthcare": "โรงพยาบาล",
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"petrochem_materials": "ปิโตรเคมี/วัสดุ",
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"consumer_staples": "อาหาร/อุปโภค",
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"utilities": "สาธารณูปโภค",
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"nonbank_finance": "การเงินนอกธนาคาร",
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"exploration": "สำรวจ/ผลิตพลังงาน",
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}
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# Declarative THEMES definition — which FACTORS drive each theme, with per-theme
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# weight (flexible: how much that factor plausibly impacts stock valuation in
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# this theme). A theme's surprise = weighted blend of its factors. ADDING a
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# factor to a theme = edit this dict; no scoring-function change.
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THEMES: dict[str, dict] = {
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"tourism": {
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"label_th": "ท่องเที่ยว",
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"factors": [
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{"key": "tourism_arrivals_ytd", "weight": 1.0},
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{"key": "macro_consumption", "weight": 0.4},
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],
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},
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"auto_credit": {
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"label_th": "รถยนต์/สินเชื่อ",
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"factors": [
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{"key": "auto_sales_yoy", "weight": 1.0},
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{"key": "auto_production", "weight": 0.4},
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{"key": "auto_exports", "weight": 0.3},
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{"key": "auto_npl", "weight": -0.6},
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],
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},
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"refining_energy": {
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"label_th": "พลังงาน/โรงกลั่น",
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"factors": [
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{"key": "energy_net_margin", "weight": 1.0},
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{"key": "macro_mfg", "weight": 0.3},
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],
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},
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"banks": {
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"label_th": "ธนาคาร",
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"factors": [
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{"key": "macro_investment", "weight": 1.0},
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{"key": "macro_inflation", "weight": -0.4},
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{"key": "bank_npl", "weight": -0.6},
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],
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},
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"retail": {
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"label_th": "ค้าปลีก",
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"factors": [
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{"key": "macro_consumption", "weight": 1.0},
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{"key": "macro_inflation", "weight": -0.3},
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],
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},
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"consumer_staples": {
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"label_th": "อาหาร/อุปโภค",
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"factors": [
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{"key": "macro_consumption", "weight": 1.0},
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{"key": "macro_inflation", "weight": -0.2},
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],
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},
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"telecom_it": {
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"label_th": "สื่อสาร/ไอที",
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"factors": [
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{"key": "macro_consumption", "weight": 0.8},
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{"key": "macro_investment", "weight": 0.4},
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],
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},
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"property": {
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"label_th": "อสังหาริมทรัพย์",
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"factors": [
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{"key": "macro_investment", "weight": 1.0},
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{"key": "macro_consumption", "weight": 0.5},
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{"key": "macro_inflation", "weight": -0.3},
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],
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},
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"healthcare": {
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"label_th": "โรงพยาบาล",
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"factors": [
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{"key": "macro_consumption", "weight": 0.5},
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],
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},
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"petrochem_materials": {
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"label_th": "ปิโตรเคมี/วัสดุ",
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"factors": [
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{"key": "macro_mfg", "weight": 1.0},
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{"key": "macro_inflation", "weight": -0.3},
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],
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},
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"utilities": {
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"label_th": "สาธารณูปโภค",
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"factors": [
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{"key": "macro_mfg", "weight": 1.0},
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{"key": "energy_net_margin", "weight": 0.3},
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],
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},
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"nonbank_finance": {
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"label_th": "การเงินนอกธนาคาร",
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"factors": [
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{"key": "macro_consumption", "weight": 1.0},
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{"key": "auto_npl", "weight": -0.3},
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],
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},
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"exploration": {
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"label_th": "สำรวจ/ผลิตพลังงาน",
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"factors": [
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{"key": "energy_net_margin", "weight": 1.0},
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{"key": "macro_inflation", "weight": -0.2},
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],
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},
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}
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# ---------------------------------------------------------------------------
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# Scoring helpers
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# ---------------------------------------------------------------------------
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def _zscore(values: list) -> dict:
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"""Normalize a list of floats to z-scores, clamped to [-3, 3]."""
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n = len(values)
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if n == 0:
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return {}
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mean = sum(values) / n
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var = sum((v - mean) ** 2 for v in values) / n
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std = var ** 0.5 or 1.0
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out = {}
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for i, v in enumerate(values):
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z = (v - mean) / std
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out[i] = max(-3.0, min(3.0, z))
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return out
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def compute_theme_surprises(fetched: dict, tourism_surprise: Optional[float] = None) -> dict:
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"""Registry-driven per-theme surprise — THE single source of truth.
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`fetched` maps fetch-module name -> collector dict (e.g.
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{"macro_thai": {...}, "auto_credit": {...}, "energy_thai": {...}}).
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For each theme in `THEMES`, the surprise is the **weighted average** of its
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declared FACTORS (each normalized by its own center/span/sign and extracted
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via `factor_value`), normalised by the total absolute weight of the factors
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that actually contributed. Normalising by |weight| keeps every theme's
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surprise on the same [-1, 1] scale regardless of how many (or how heavy)
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its factors are, so a surprise of +0.2 means the same thing for retail and
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for banks (cross-theme comparable — important for P4 weight learning).
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`tourism_surprise` (optional) overrides the tourism theme so the richer
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bot-tourism observation z-score can win when available; otherwise tourism
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falls back to its registry factors.
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"""
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from . import factors as factors_mod
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out: dict[str, Optional[float]] = {}
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for tid, tdef in THEMES.items():
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weighted = 0.0
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w_sum = 0.0
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for ref in tdef.get("factors", []):
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fkey = ref.get("key")
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fact = factors_mod.FACTORS.get(fkey)
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if not fact:
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continue
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val = factors_mod.factor_value(fact, fetched.get(fact.get("fetch")))
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if val is None:
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continue
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norm = factors_mod.normalize(val, sign=fact.get("sign", 1),
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center=fact.get("center", 0.0),
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span=fact.get("span", 10.0))
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if norm is None:
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continue
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w = float(ref.get("weight", 1.0))
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weighted += w * norm
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w_sum += abs(w)
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if w_sum == 0:
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out[tid] = None
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else:
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s = max(-1.0, min(1.0, weighted / w_sum))
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out[tid] = round(s, 3)
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# tourism override: prefer the observation-derived surprise when provided.
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if tourism_surprise is not None and "tourism" in out:
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out["tourism"] = round(max(-1.0, min(1.0, float(tourism_surprise))), 3)
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return out
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_SIAMCHART_GROWTH_W = 1.5 # R1 (PEAD): EPS-growth dominates value; literature (Bernard-Thomas 1990,
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# Livnat-Mendenhall 2006) shows drift follows earnings, not just yield.
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_SIAMCHART_YIELD_W = 2.0 # dividend floor for value names
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_SIAMCHART_MOMENTUM_W = 0.5 # R2: EM momentum exists but is noisy -> keep it a small trend boost.
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def _load_momentum(lookback_days: int = 252) -> dict[str, float]:
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"""12-1 momentum per symbol from the latest price snapshot (deterministic).
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R2 (Jegadeesh-Titman 1993; EM evidence: weaker but positive). Returns
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{symbol: (close_today / close_{-12m}) - 1}. Lookback uses trading days so it
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aligns to ~12 calendar months.
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"""
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try:
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from . import simulation
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series = simulation.load_price_snapshot()
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except Exception:
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return {}
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out: dict[str, float] = {}
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for sym, s in series.items():
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bars = s.get("bars", [])
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if len(bars) < lookback_days + 1:
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continue
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try:
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today = float(bars[-1]["adjusted_close"])
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base = float(bars[-1 - lookback_days]["adjusted_close"])
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except (KeyError, TypeError, ValueError, IndexError):
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continue
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if today <= 0 or base <= 0:
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continue
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out[sym] = round((today / base) - 1.0, 4)
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return out
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def build_siamchart_score(factors: dict,
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momentum: Optional[dict[str, float]] = None) -> dict[str, float]:
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"""Derive a normalized fundamental score from the Siamchart factor view.
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Uses EPS growth YoY (weighted above yield per PEAD literature) and dividend
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yield. Optional momentum (12-1, from price snapshot) adds a low-weight
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trend component; EM momentum is noisier, so it stays small.
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"""
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out: dict[str, float] = {}
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for f in factors.get("factors", []):
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sym = f.get("symbol")
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if not sym:
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continue
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g = f.get("eps_growth_yoy")
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d = f.get("dividend_yield") or 0.0
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g = float(g) if g is not None else 0.0
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m = (momentum or {}).get(sym, 0.0)
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# R1+R2: growth dominates (PEAD), yield floors, momentum adds trend.
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out[sym] = g * _SIAMCHART_GROWTH_W + d * _SIAMCHART_YIELD_W + _SIAMCHART_MOMENTUM_W * m
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syms = list(out.keys())
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z = _zscore([out[s] for s in syms])
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return {s: z.get(i, 0.0) for i, s in enumerate(syms)}
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def combine_score(theme_scores: list[dict[str, float]], siamchart_score: dict[str, float],
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weight_theme: float = 0.6, weight_siamchart: float = 0.4) -> dict[str, dict]:
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"""Combine per-symbol theme (averaged) and siamchart scores into final.
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Returns {symbol: {'theme_score', 'siamchart_score', 'combined', 'themes':[...]}}.
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"""
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all_syms: dict[str, list[float]] = {}
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theme_membership: dict[str, list[str]] = {}
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for ts in theme_scores:
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for sym, val in ts.items():
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all_syms.setdefault(sym, []).append(val)
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theme_membership.setdefault(sym, []).append(sym)
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merged: dict[str, dict] = {}
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# every symbol that has a siamchart score OR a theme score
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universe = set(all_syms) | set(siamchart_score)
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for sym in universe:
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tl = all_syms.get(sym, [])
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tavg = (sum(tl) / len(tl)) if tl else 0.0
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sc = siamchart_score.get(sym, 0.0)
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combined = weight_theme * tavg + weight_siamchart * sc
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merged[sym] = {
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"theme_score": tavg,
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"siamchart_score": sc,
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"combined": combined,
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"themes": theme_membership.get(sym, []),
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}
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return merged
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def quality_within_theme(symbol: str, theme_id: str, factor_view: dict) -> float:
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"""Relative firm quality of a symbol within its theme cohort (stock picking).
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Deterministic, bounded to [0.5, 1.5] around 1.0:
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- compute the theme cohort = all THEME_SYMBOLS[theme_id] that have a factor row
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- quality = 1.0 + 0.5 * z(ROE, cohort) (above cohort = >1, below = <1)
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- damped by 0.3 * z(EPS growth, cohort)
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Stronger names in a hot theme rank higher -> the theme actually "picks" stocks
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instead of giving every member the same flat surprise.
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"""
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cohort = [s for s in THEME_SYMBOLS.get(theme_id, set()) if s != symbol]
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fmap = {f.get("symbol"): f for f in factor_view.get("factors", [])}
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fac = fmap.get(symbol)
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if not fac:
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return 1.0
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roe = fac.get("roe")
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epsg = fac.get("eps_growth_yoy")
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def _cohort_z(val, getter):
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vals = []
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for c in cohort:
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cf = fmap.get(c)
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v = getter(cf)
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if v is not None:
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vals.append(float(v))
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if not vals or val is None:
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return 0.0
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mean = statistics.fmean(vals)
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stdev = statistics.pstdev(vals)
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if stdev < 1e-9:
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return 0.0
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return (float(val) - mean) / stdev
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z_roe = _cohort_z(roe, lambda f: f.get("roe") if f else None)
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z_epsg = _cohort_z(epsg, lambda f: f.get("eps_growth_yoy") if f else None)
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quality = 1.0 + 0.5 * max(min(z_roe, 2.0), -2.0) + 0.3 * max(min(z_epsg, 2.0), -2.0)
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return round(max(min(quality, 1.5), 0.5), 3)
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def symbol_breakdown(
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symbol: str,
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*,
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factor_view: dict,
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theme_surprises: dict[str, float],
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latest_price: Optional[float] = None,
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price_date: str = "",
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weight_theme: float = 0.6,
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weight_siamchart: float = 0.4,
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momentum: Optional[dict[str, float]] = None,
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) -> dict:
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"""Transparent per-symbol scoring breakdown.
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Shows exactly how `combined` was derived:
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theme_score = mean of the theme surprise scores covering this symbol
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siamchart_score = z-scored (EPS growth + dividend_yield*2)
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combined = weight_theme*theme_score + weight_siamchart*siamchart_score
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Returns a dict suitable for the /api/v1/symbols/<symbol> view. Deterministic
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and reuses the same formula as `combine_score` so the board and the detail
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always agree.
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"""
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# the themes this symbol belongs to (from the curated exposure map)
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member_themes = [tid for tid, syms in THEME_SYMBOLS.items() if symbol in syms]
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theme_lines = []
|
||
theme_values = []
|
||
for tid in member_themes:
|
||
s = theme_surprises.get(tid)
|
||
q = quality_within_theme(symbol, tid, factor_view)
|
||
if s is not None:
|
||
ts = round(float(s) * q, 3)
|
||
theme_values.append(ts)
|
||
theme_lines.append({
|
||
"theme": tid,
|
||
"label_th": THEME_LABELS_TH.get(tid, tid),
|
||
"surprise": round(float(s), 3),
|
||
"quality": q,
|
||
"theme_score": ts,
|
||
})
|
||
else:
|
||
theme_lines.append({
|
||
"theme": tid,
|
||
"label_th": THEME_LABELS_TH.get(tid, tid),
|
||
"surprise": None,
|
||
"quality": q,
|
||
"theme_score": None,
|
||
})
|
||
theme_score = (sum(theme_values) / len(theme_values)) if theme_values else 0.0
|
||
|
||
# find the factor row for this symbol
|
||
fac = next((f for f in factor_view.get("factors", []) if f.get("symbol") == symbol), {})
|
||
g = fac.get("eps_growth_yoy")
|
||
d = fac.get("dividend_yield") or 0.0
|
||
g = float(g) if g is not None else 0.0
|
||
m = (momentum or {}).get(symbol, 0.0)
|
||
raw_siamchart = g * _SIAMCHART_GROWTH_W + d * _SIAMCHART_YIELD_W + _SIAMCHART_MOMENTUM_W * m
|
||
|
||
# z-score against the full universe (same as build_siamchart_score); capture
|
||
# the population stats so the view can show HOW -2.8 became -0.588.
|
||
siamchart_map = build_siamchart_score(factor_view, momentum=momentum)
|
||
siamchart_score = siamchart_map.get(symbol, 0.0)
|
||
# recompute the population of raw scores to expose mean / stdev
|
||
raw_values = []
|
||
for f in factor_view.get("factors", []):
|
||
if not f.get("symbol"):
|
||
continue
|
||
gg = f.get("eps_growth_yoy")
|
||
dd = f.get("dividend_yield") or 0.0
|
||
gg = float(gg) if gg is not None else 0.0
|
||
mm = (momentum or {}).get(f.get("symbol"), 0.0)
|
||
raw_values.append(gg * _SIAMCHART_GROWTH_W + dd * _SIAMCHART_YIELD_W + _SIAMCHART_MOMENTUM_W * mm)
|
||
pop_mean = statistics.mean(raw_values) if raw_values else 0.0
|
||
pop_stdev = statistics.pstdev(raw_values) if raw_values else 0.0
|
||
|
||
combined = round(weight_theme * theme_score + weight_siamchart * siamchart_score, 3)
|
||
|
||
return {
|
||
"symbol": symbol,
|
||
"company_name": fac.get("company_name", ""),
|
||
"themes": member_themes,
|
||
"theme_contributions": theme_lines,
|
||
"theme_score": round(theme_score, 3),
|
||
"siamchart_score": round(siamchart_score, 3),
|
||
"siamchart_components": {
|
||
"eps_growth_yoy": fac.get("eps_growth_yoy"),
|
||
"dividend_yield": fac.get("dividend_yield"),
|
||
"raw_growth_plus_yield_2x": round(raw_siamchart, 3),
|
||
},
|
||
"siamchart_z_note": {
|
||
"formula": "z = (raw_i - mean) / stdev",
|
||
"raw_i": round(raw_siamchart, 3),
|
||
"population_mean": round(pop_mean, 3),
|
||
"population_stdev": round(pop_stdev, 3),
|
||
"universe_size": len(raw_values),
|
||
},
|
||
"combined_calc": [
|
||
{"label": "คะแนนธีม", "value": round(theme_score, 3), "weight": weight_theme,
|
||
"note": "ค่าเฉลี่ยของ theme surprise ที่หุ้นนี้อยู่ใน", "term": f"{theme_score:.3f} × {weight_theme}"},
|
||
{"label": "คะแนนพื้นฐาน", "value": round(siamchart_score, 3), "weight": weight_siamchart,
|
||
"note": f"z-score ของ ((EPS growth {g:+.1f}%) + ปันผล {d:.1f}%×2) เมื่อเทียบทั้ง universe",
|
||
"term": f"{siamchart_score:.3f} × {weight_siamchart}"},
|
||
],
|
||
"combined_score": combined,
|
||
"combined_formula": f"({theme_score:.3f} × {weight_theme}) + ({siamchart_score:.3f} × {weight_siamchart}) = {combined}",
|
||
"weights": {"theme": weight_theme, "siamchart": weight_siamchart},
|
||
"fundamentals": {
|
||
"pe": fac.get("pe"),
|
||
"eps": fac.get("eps"),
|
||
"pbv": fac.get("pbv"),
|
||
"roe": fac.get("roe"),
|
||
"dps": fac.get("dps"),
|
||
"is_dividend": fac.get("is_dividend"),
|
||
},
|
||
"price": {"latest": latest_price, "date": price_date},
|
||
}
|