12b34929d7
- new energy_irpc collector parsing IRPC performance-highlights table (net profit/EBITDA/ROE margins, latest period 3M26: +10.27%) - factor energy_irpc_net_margin (sign +1) wired into refining_energy/ exploration/utilities, extending the energy theme beyond TOP - scheduler job + dashboard fetch + sources table row (now 9 sources) - tests: parse (incl paren-negatives), value-key resolution, direction; suite 368 OK. Independent review passed: true - Phase B feasibility: REIC/EPPO/NBTC/PTTEP are JS-rendered or anti-bot (recorded deferred in plan); IRPC was the clean server-rendered win
365 lines
13 KiB
Python
365 lines
13 KiB
Python
"""Declarative FACTORS registry — the single source of truth for every factor.
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Each factor is a plain-data unit describing:
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- where the data comes from (source + fetch module + which key holds the value)
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- its data frequency (for frequency alignment, never naive mixing)
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- its SIGN (+1 = higher value is bullish for a theme, -1 = bearish)
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- a default weight (themes override per-theme)
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ADDING A NEW DATA SOURCE/FACTOR = append one entry here + (optionally) a theme
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factor line. It requires NO change to any scoring function. This is what makes the
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analysis engine data-driven and auditable.
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"""
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from __future__ import annotations
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import math
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import statistics
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from typing import Any, Callable, Optional
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# fetch key -> module that exposes a fetch_<...>() callable returning .to_dict()
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_FETCH_MODULE: dict[str, str] = {
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"tourism": "bot_tourism",
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"auto_credit": "auto_credit",
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"auto_npl": "auto_npl",
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"bank_npl": "bank_npl",
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"energy_thai": "energy_thai",
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"energy_irpc": "energy_irpc",
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"macro_thai": "macro_thai",
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"thai_trade": "thai_trade",
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"te_thailand": "te_thailand",
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}
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# Factor -> value key. Sign: +1 higher-is-bullish, -1 lower-is-bullish.
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# weight: default global weight; themes may override.
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FACTORS: dict[str, dict[str, Any]] = {
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# ---- real Thai collectors ----
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"tourism_arrivals_ytd": {
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"name_th": "นักท่องเที่ยวสะสมปี",
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"source": "BOT",
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"frequency": "monthly",
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"fetch": "macro_thai",
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"value_key": "tourists_ytd_mn",
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"sign": 1,
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"weight": 1.0,
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"center": 20.0, "span": 15.0, # cumulative arrivals in millions, ~20mn neutral
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},
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"auto_sales_yoy": {
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"name_th": "ยอดขายรถยนต์ (YoY)",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "auto_credit",
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"value_key": "new_car_sales_yoy",
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"sign": 1,
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"weight": 1.0,
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"center": 5.0, "span": 10.0, # YoY %, ~5% long-run growth
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},
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"auto_production": {
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"name_th": "การผลิตรถยนต์",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "auto_credit",
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"value_key": "vehicle_production",
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"sign": 1,
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"weight": 0.4,
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"center": 0.0, "span": 200000.0, # units/month (~117K), scale captured as level
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},
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"auto_exports": {
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"name_th": "ส่งออกรถยนต์",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "auto_credit",
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"value_key": "auto_exports",
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"sign": 1,
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"weight": 0.3,
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"center": 0.0, "span": 200000.0, # units (~82K), scale captured as level
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},
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"auto_npl": {
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"name_th": "NPL รถยนต์",
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"source": "BOT",
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"frequency": "quarterly",
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"fetch": "auto_npl",
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"value_key": "pct_of_npls",
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"sign": -1,
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"weight": 1.0,
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"center": 3.0, "span": 5.0, # NPL as % of loans, ~3% neutral
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},
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"bank_npl": {
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"name_th": "NPL ภาคการเงิน",
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"source": "BOT",
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"frequency": "quarterly",
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"fetch": "bank_npl",
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"value_key": "pct_of_npls",
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"sign": -1,
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"weight": 1.0,
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"center": 0.5, "span": 3.0, # financial-sector NPL share (~0.5-5%)
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},
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"energy_net_margin": {
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"name_th": "กำไรสุทธิโรงกลั่น",
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"source": "TOP",
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"frequency": "quarterly",
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"fetch": "energy_thai",
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"value_key": "net_margin_quarter",
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"sign": 1,
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"weight": 1.0,
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"center": 5.0, "span": 10.0, # net margin % (derived from quarterly)
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},
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# ---- second Thai refiner: IRPC net margin (extends energy beyond TOP) ----
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"energy_irpc_net_margin": {
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"name_th": "กำไรสุทธิ IRPC",
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"source": "IRPC",
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"frequency": "quarterly",
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"fetch": "energy_irpc",
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"value_key": "net_margin_pct",
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"sign": 1,
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"weight": 0.6,
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"center": 3.0, "span": 8.0, # net margin %, ~0-3% neutral, refiners volatile
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},
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# ---- macro backdrop (proxy for expanded SET50 themes) ----
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"macro_consumption": {
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"name_th": "การบริโภคภาคเอกชน (YoY)",
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"source": "BOT",
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"frequency": "monthly",
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"fetch": "macro_thai",
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"value_key": "private_consumption_yoy",
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"sign": 1,
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"weight": 1.0,
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"center": 3.0, "span": 10.0, # YoY %, ~3% trend
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},
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"macro_investment": {
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"name_th": "การลงทุนภาคเอกชน (YoY)",
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"source": "BOT",
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"frequency": "monthly",
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"fetch": "macro_thai",
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"value_key": "private_investment_yoy",
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"sign": 1,
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"weight": 1.0,
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"center": 5.0, "span": 10.0, # YoY %, ~5% trend
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},
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"macro_mfg": {
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"name_th": "ผลผลิตภาคอุตสาหกรรม (MPI)",
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"source": "BOT",
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"frequency": "monthly",
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"fetch": "macro_thai",
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"value_key": "manufacturing_yoy",
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"sign": 1,
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"weight": 1.0,
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"center": 0.0, "span": 10.0, # YoY %, ~0 neutral
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},
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"macro_inflation": {
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"name_th": "เงินเฟ้อ",
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"source": "BOT",
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"frequency": "monthly",
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"fetch": "macro_thai",
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"value_key": "headline_inflation_yoy",
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"sign": -1,
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"weight": 0.5,
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"center": 2.0, "span": 10.0, # ~2% target; higher is worse (sign -1)
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},
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# ---- BOT Thai Economy fields that were already fetched but never used ----
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# Wired into the registry so every scraped value actually feeds analysis
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# (the user's rule: a fetched data point must be used, not just displayed).
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"macro_core_inflation": {
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"name_th": "เงินเฟ้อพื้นฐาน (Core)",
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"source": "BOT",
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"frequency": "monthly",
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"fetch": "macro_thai",
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"value_key": "core_inflation_yoy",
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"sign": -1,
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"weight": 0.4,
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"center": 1.0, "span": 6.0, # core ~1% target; higher is worse
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},
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"macro_unemployment": {
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"name_th": "อัตราการว่างงาน",
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"source": "BOT",
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"frequency": "monthly",
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"fetch": "macro_thai",
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"value_key": "unemployment_pct",
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"sign": -1,
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"weight": 0.5,
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"center": 1.0, "span": 3.0, # ~1% Thailand; higher unemployment is worse
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},
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# ---- Thailand external sector (TradingEconomics, current through 2026) ----
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"external_current_account": {
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"name_th": "ดุลบัญชีเดินสะพัด (USD ล้าน)",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "thai_trade",
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"value_key": "current_account_usdm",
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"sign": 1,
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"weight": 1.0,
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"center": 0.0, "span": 3000.0, # USD mn; surplus positive, deficit negative
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},
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"external_exports": {
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"name_th": "มูลค่าส่งออก (USD ล้าน)",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "thai_trade",
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"value_key": "exports_usdm",
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"sign": 1,
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"weight": 1.0,
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"center": 30000.0, "span": 8000.0, # ~$34k USD mn/month
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},
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"external_imports": {
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"name_th": "มูลค่านำเข้า (USD ล้าน)",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "thai_trade",
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"value_key": "imports_usdm",
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"sign": 1,
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"weight": 1.0,
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"center": 34000.0, "span": 8000.0, # ~$38k USD mn/month (domestic demand proxy)
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},
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# ---- Thailand rates / credit / retail / confidence (TradingEconomics) ----
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# Single-page snapshot factors deepening the macro-proxy themes. Sign +1 =
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# higher value is bullish/helpful; sign -1 = the opposite. Theme weights are
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# always positive magnitude (direction lives in `sign`, per the sign fix).
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"te_interest_rate": {
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"name_th": "อัตราดอกเบี้ย (rate)",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "te_thailand",
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"value_key": "interest_rate_pct",
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"sign": 1,
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"weight": 0.5,
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"center": 1.5, "span": 1.5, # ~1.0-2.0%; higher rate widens bank margin
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},
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"te_loan_growth": {
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"name_th": "สินเชื่อภาคธุรกิจ",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "te_thailand",
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"value_key": "loans_to_fin_corp",
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"sign": 1,
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"weight": 0.5,
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"center": 10000000.0, "span": 1500000.0, # THB mn (~10.5M); credit demand proxy
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},
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"te_consumer_credit": {
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"name_th": "สินเชื่อผู้บริโภค",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "te_thailand",
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"value_key": "consumer_credit_thbmn",
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"sign": 1,
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"weight": 0.5,
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"center": 5000000.0, "span": 800000.0, # THB mn consumer credit book
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},
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"te_household_debt_gdp": {
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"name_th": "หนี้ครัวเรือนต่อ GDP",
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"source": "TradingEconomics",
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"frequency": "quarterly",
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"fetch": "te_thailand",
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"value_key": "household_debt_gdp_pct",
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"sign": -1,
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"weight": 0.5,
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"center": 85.0, "span": 8.0, # ~87.5% of GDP; higher = leverage risk
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},
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"te_retail_sales_yoy": {
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"name_th": "ยอดขายปลีก (YoY)",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "te_thailand",
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"value_key": "retail_sales_yoy",
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"sign": 1,
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"weight": 0.8,
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"center": 0.0, "span": 10.0, # % YoY, ~0 neutral
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},
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"te_consumer_confidence": {
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"name_th": "ความเชื่อมั่นผู้บริโภค",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "te_thailand",
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"value_key": "consumer_confidence",
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"sign": 1,
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"weight": 0.5,
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"center": 50.0, "span": 12.0, # points (~51.8); above 50 = optimistic
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},
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"te_property_prices": {
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"name_th": "ราคาอสังหาริมทรัพย์ (YoY)",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "te_thailand",
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"value_key": "property_prices_yoy",
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"sign": 1,
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"weight": 0.8,
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"center": 0.0, "span": 6.0, # residential price % YoY, ~0-2% neutral
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},
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"te_business_confidence": {
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"name_th": "ความเชื่อมั่นภาคธุรกิจ",
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"source": "TradingEconomics",
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"frequency": "monthly",
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"fetch": "te_thailand",
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"value_key": "business_confidence",
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"sign": 1,
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"weight": 0.4,
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"center": 50.0, "span": 10.0, # points (~46.7); above 50 = optimistic
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},
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}
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class FactorError(ValueError):
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pass
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def factor_value(fact: dict, fetched: Optional[dict]) -> Optional[float]:
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"""Pull the numeric value out of a fetched collector dict for a factor."""
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key = fact.get("value_key")
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if fetched is None:
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return None
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if fact.get("fetch") == "energy_thai":
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# derive a single metric from the quarterly dict
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q = fetched.get("quarterly") or {}
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if isinstance(q, dict):
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row = next((v for v in q.values() if isinstance(v, dict)), {})
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np_ = row.get("net_profit")
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rev = row.get("sales")
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if np_ is not None and rev:
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try:
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margin = float(np_) / float(rev) * 100.0 # net margin %
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except (TypeError, ValueError, ZeroDivisionError):
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return None
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return margin if math.isfinite(margin) else None
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return None
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if key is None:
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return None
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val = fetched.get(key)
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try:
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out = float(val) if val is not None else None
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except (TypeError, ValueError):
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return None
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if out is None or not math.isfinite(out):
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return None
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return out
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def normalize(value: Optional[float], sign: int = 1,
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center: float = 0.0, span: float = 10.0) -> Optional[float]:
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"""Deterministic bounded normalization: sign-aware, clamped to [-1, +1].
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value == center -> 0. positive beyond center (for sign=+1) -> positive.
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Non-finite values (NaN/inf) are rejected rather than propagated.
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"""
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if value is None:
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return None
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try:
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value = float(value)
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except (TypeError, ValueError):
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return None
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if not math.isfinite(value):
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return None
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if span <= 0:
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span = 1.0
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num = (value - center) / span * float(sign)
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return round(min(max(num, -1.0), 1.0), 4)
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def z_score(value: float, population: list[float]) -> float:
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"""Population z-score with tiny-stdev guard (deterministic)."""
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if not population:
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return 0.0
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mean = statistics.fmean(population)
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stdev = statistics.pstdev(population)
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if stdev < 1e-9:
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return 0.0
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return round((float(value) - mean) / stdev * 10.0, 4) # scale to decile-ish
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