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https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
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shawnkim1997andClaude Opus 4.6
ec2c5b37a2
Report page now auto-detects valuation tier based on company financials: - Tier 1 (FCF > 0): Traditional DCF analysis - Tier 2 (EBITDA > 0): EV/EBITDA relative valuation with Bear/Base/Bull scenarios - Tier 3 (Rev Growth > 10%): P/S revenue-based valuation - Tier 4 (all weak): P/B / NAV approach Includes RelativeValuationSection, PathToProfitability components, margin trajectory chart, and cash runway analysis. Also includes fixes across earnings, macro, screener, technical, filings pages and backend routers. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
567 lines
24 KiB
Python
567 lines
24 KiB
Python
"""Macro cycle service using FRED, World Bank, and market proxies."""
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from __future__ import annotations
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from dataclasses import dataclass
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from datetime import datetime, timedelta
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from io import StringIO
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from typing import Any, Dict, List, Optional
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import numpy as np
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from server.services.cache import cached
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_cached_snapshot = cached("macro_cycle_snapshot", ttl_seconds=3600)
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@dataclass(frozen=True)
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class MacroSeries:
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key: str
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label: str
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fred_code: str
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frequency: str = "monthly"
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unit: str = ""
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higher_is_better: bool = True
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is_index: bool = False
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US_SERIES: List[MacroSeries] = [
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MacroSeries("gdp_growth", "Real GDP Growth", "A191RL1Q225SBEA", "quarterly", "%", True),
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MacroSeries("inflation", "Inflation (CPI YoY)", "CPIAUCSL", "monthly", "%", False, is_index=True),
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MacroSeries("unemployment", "Unemployment Rate", "UNRATE", "monthly", "%", False),
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MacroSeries("fed_funds", "Fed Funds Rate", "FEDFUNDS", "monthly", "%", False),
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MacroSeries("yield_curve", "10Y-2Y Treasury Spread", "T10Y2Y", "daily", "bp", True),
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MacroSeries("industrial_production", "Industrial Production YoY", "INDPRO", "monthly", "%", True, is_index=True),
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]
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COUNTRY_META: Dict[str, Dict[str, str]] = {
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"United States": {"iso": "USA", "wb": "USA", "region": "Americas"},
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"Euro Area": {"iso": "EUU", "wb": "EUU", "region": "Europe"},
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"United Kingdom": {"iso": "GBR", "wb": "GBR", "region": "Europe"},
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"Germany": {"iso": "DEU", "wb": "DEU", "region": "Europe"},
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"France": {"iso": "FRA", "wb": "FRA", "region": "Europe"},
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"Japan": {"iso": "JPN", "wb": "JPN", "region": "Asia-Pacific"},
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"South Korea": {"iso": "KOR", "wb": "KOR", "region": "Asia-Pacific"},
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"China": {"iso": "CHN", "wb": "CHN", "region": "Asia-Pacific"},
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"India": {"iso": "IND", "wb": "IND", "region": "Asia-Pacific"},
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"Australia": {"iso": "AUS", "wb": "AUS", "region": "Asia-Pacific"},
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"Taiwan": {"iso": "TWN", "wb": "TWN", "region": "Asia-Pacific"},
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"Canada": {"iso": "CAN", "wb": "CAN", "region": "Americas"},
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"Brazil": {"iso": "BRA", "wb": "BRA", "region": "Americas"},
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"Mexico": {"iso": "MEX", "wb": "MEX", "region": "Americas"},
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"Indonesia": {"iso": "IDN", "wb": "IDN", "region": "Asia-Pacific"},
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}
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COUNTRY_SERIES: Dict[str, Dict[str, MacroSeries]] = {
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"United States": {
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"inflation": MacroSeries("inflation", "Inflation", "CPIAUCSL", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "UNRATE", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "FEDFUNDS", "monthly", "%", False),
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},
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"Euro Area": {
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"inflation": MacroSeries("inflation", "Inflation", "CP0000EZ19M086NEST", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRHUTTTTEZM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "ECBDFR", "daily", "%", False),
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},
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"United Kingdom": {
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"inflation": MacroSeries("inflation", "Inflation", "CPGRLE01GBM659N", "monthly", "%", False),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRHUTTTTGBM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "IR3TIB01GBM156N", "monthly", "%", False),
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},
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"Germany": {
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"inflation": MacroSeries("inflation", "Inflation", "DEUCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRHUTTTTDEM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "ECBDFR", "daily", "%", False),
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},
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"France": {
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"inflation": MacroSeries("inflation", "Inflation", "FRACPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRHUTTTTFRM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "ECBDFR", "daily", "%", False),
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},
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"Japan": {
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"inflation": MacroSeries("inflation", "Inflation", "JPNCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRHUTTTTJPM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "IR3TIB01JPM156N", "monthly", "%", False),
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},
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"South Korea": {
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"inflation": MacroSeries("inflation", "Inflation", "KORCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRUN64TTKRM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "IR3TIB01KRM156N", "monthly", "%", False),
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},
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"China": {
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"inflation": MacroSeries("inflation", "Inflation", "CHNCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRUN64TTCNQ156S", "quarterly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "INTDSRCNM193N", "monthly", "%", False),
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},
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"India": {
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"inflation": MacroSeries("inflation", "Inflation", "INDCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRUN64TTINQ156S", "quarterly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "INTDSRINM193N", "monthly", "%", False),
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},
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"Australia": {
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"inflation": MacroSeries("inflation", "Inflation", "AUSCPIALLQINMEI", "quarterly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRHUTTTTAUM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "IR3TIB01AUM156N", "monthly", "%", False),
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},
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"Taiwan": {
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"inflation": MacroSeries("inflation", "Inflation", "TWNCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRUN64TTTWQ156S", "quarterly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "INTDSRTWM193N", "monthly", "%", False),
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},
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"Canada": {
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"inflation": MacroSeries("inflation", "Inflation", "CANCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRHUTTTTCAM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "IR3TIB01CAM156N", "monthly", "%", False),
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},
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"Brazil": {
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"inflation": MacroSeries("inflation", "Inflation", "BRACPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRUN64TTBRQ156S", "quarterly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "INTDSRBRM193N", "monthly", "%", False),
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},
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"Mexico": {
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"inflation": MacroSeries("inflation", "Inflation", "MEXCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRUN64TTMXM156S", "monthly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "INTDSRMXM193N", "monthly", "%", False),
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},
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"Indonesia": {
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"inflation": MacroSeries("inflation", "Inflation", "IDNCPIALLMINMEI", "monthly", "%", False, is_index=True),
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"unemployment": MacroSeries("unemployment", "Unemployment", "LRUN64TTIDQ156S", "quarterly", "%", False),
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"policy_rate": MacroSeries("policy_rate", "Policy Rate", "INTDSRIDM193N", "monthly", "%", False),
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},
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}
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_WB_INDICATORS = {
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"inflation": "FP.CPI.TOTL.ZG",
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"unemployment": "SL.UEM.TOTL.ZS",
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"gdp_growth": "NY.GDP.MKTP.KD.ZG",
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"debt_gdp": "GC.DOD.TOTL.GD.ZS",
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}
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WB_FALLBACK: Dict[tuple[str, str], tuple[str, str]] = {}
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for _cname, _meta in COUNTRY_META.items():
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_wb = _meta["wb"]
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for _field, _indicator in _WB_INDICATORS.items():
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WB_FALLBACK[(_cname, _field)] = (_wb, _indicator)
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_STALE_MONTHS = 18
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ASSET_VALUATION_PROXIES = [
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{"asset": "US Equities", "symbol": "SPY", "lookback": "5y"},
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{"asset": "Tech Growth", "symbol": "QQQ", "lookback": "5y"},
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{"asset": "Gold", "symbol": "GLD", "lookback": "5y"},
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{"asset": "Oil", "symbol": "USO", "lookback": "5y"},
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{"asset": "Long Bonds", "symbol": "TLT", "lookback": "5y"},
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{"asset": "Bitcoin", "symbol": "BTC-USD", "lookback": "5y"},
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]
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def _safe_float(value: Any) -> Optional[float]:
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try:
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if value is None:
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return None
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number = float(value)
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if np.isnan(number) or np.isinf(number):
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return None
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return number
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except (TypeError, ValueError):
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return None
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def _zscore(values: List[float]) -> Optional[float]:
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if len(values) < 6:
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return None
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std = float(np.std(values))
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if std == 0:
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return 0.0
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return float((values[-1] - np.mean(values)) / std)
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def _signal_label(zscore: Optional[float], higher_is_better: bool) -> str:
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if zscore is None:
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return "neutral"
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effective = zscore if higher_is_better else -zscore
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if effective >= 0.75:
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return "bullish"
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if effective <= -0.75:
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return "bearish"
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return "neutral"
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def _series_to_pct_change(series) -> Any:
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if series is None or series.empty or len(series) < 13:
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return series
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return series.pct_change(12) * 100
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def _fetch_fred_series(fred_code: str):
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import pandas as pd
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import requests
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start = datetime(2014, 1, 1)
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try:
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from pandas_datareader import data as web
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return web.DataReader(fred_code, "fred", start=start).dropna()
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except Exception:
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url = f"https://fred.stlouisfed.org/graph/fredgraph.csv?id={fred_code}"
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response = requests.get(url, timeout=20)
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response.raise_for_status()
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df = pd.read_csv(StringIO(response.text))
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date_column = "DATE" if "DATE" in df.columns else "observation_date"
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df[date_column] = pd.to_datetime(df[date_column])
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df = df[df[date_column] >= pd.Timestamp(start)]
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df[fred_code] = pd.to_numeric(df[fred_code], errors="coerce")
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return df.set_index(date_column)[[fred_code]].dropna()
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def _fetch_worldbank_latest(country_code: str, indicator: str) -> Optional[float]:
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"""Fetch the most recent value from the World Bank API."""
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import requests
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try:
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url = (
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f"https://api.worldbank.org/v2/country/{country_code}"
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f"/indicator/{indicator}?format=json&per_page=5&mrv=3"
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)
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resp = requests.get(url, timeout=12)
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data = resp.json()
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if len(data) > 1 and data[1]:
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for item in data[1]:
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val = item.get("value")
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if val is not None:
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return round(float(val), 2)
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except Exception:
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pass
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return None
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def _is_stale(series) -> bool:
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"""Return True if the latest data point is older than _STALE_MONTHS."""
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try:
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import pandas as pd
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latest_date = pd.Timestamp(series.index[-1])
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cutoff = pd.Timestamp(datetime.utcnow() - timedelta(days=_STALE_MONTHS * 30))
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return latest_date < cutoff
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except Exception:
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return True
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def _fetch_series_payload(
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series_def: MacroSeries,
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country: Optional[str] = None,
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) -> Optional[Dict[str, Any]]:
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try:
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series = _fetch_fred_series(series_def.fred_code)
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fred_ok = series is not None and not series.empty
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stale = _is_stale(series) if fred_ok else True
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values = series.iloc[:, 0] if fred_ok else None
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if fred_ok and series_def.is_index:
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values = _series_to_pct_change(values)
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values = values.dropna() if values is not None else None
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latest: Optional[float] = None
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z: Optional[float] = None
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if values is not None and not values.empty:
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latest = _safe_float(values.iloc[-1])
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z = _zscore([float(v) for v in values.tail(60).tolist() if _safe_float(v) is not None])
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if stale and country:
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wb_key = (country, series_def.key)
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if wb_key in WB_FALLBACK:
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wb_country, wb_indicator = WB_FALLBACK[wb_key]
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wb_val = _fetch_worldbank_latest(wb_country, wb_indicator)
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if wb_val is not None:
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latest = wb_val
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if latest is None and z is None:
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return None
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direction = 0.0 if z is None else (z if series_def.higher_is_better else -z)
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return {
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"key": series_def.key,
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"label": series_def.label,
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"value": round(latest, 2) if latest is not None else None,
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"unit": series_def.unit,
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"zscore": round(z, 2) if z is not None else None,
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"signal": _signal_label(z, series_def.higher_is_better),
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"trend_score": round(direction, 2),
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}
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except Exception:
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return None
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def _build_cycle_heatmap() -> List[Dict[str, Any]]:
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items: List[Dict[str, Any]] = []
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for series_def in US_SERIES:
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payload = _fetch_series_payload(series_def)
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if payload:
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items.append(payload)
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return items
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def _build_country_heatmap() -> List[Dict[str, Any]]:
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rows: List[Dict[str, Any]] = []
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for country, mappings in COUNTRY_SERIES.items():
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meta = COUNTRY_META.get(country, {})
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metrics: Dict[str, Any] = {
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"country": country,
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"region": meta.get("region", "Other"),
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}
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scores: List[float] = []
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for field_name, series_def in mappings.items():
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payload = _fetch_series_payload(series_def, country=country)
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if payload is None:
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wb_key = (country, field_name)
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if wb_key in WB_FALLBACK:
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wb_country, wb_indicator = WB_FALLBACK[wb_key]
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wb_val = _fetch_worldbank_latest(wb_country, wb_indicator)
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if wb_val is not None:
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metrics[field_name] = wb_val
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continue
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metrics[field_name] = None
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continue
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metrics[field_name] = payload["value"]
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if payload["trend_score"] is not None:
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scores.append(float(payload["trend_score"]))
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for extra in ("gdp_growth", "debt_gdp"):
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wb_key = (country, extra)
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if wb_key in WB_FALLBACK:
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wb_country, wb_indicator = WB_FALLBACK[wb_key]
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metrics[extra] = _fetch_worldbank_latest(wb_country, wb_indicator)
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else:
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metrics[extra] = None
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metrics["score"] = round(float(np.mean(scores)), 2) if scores else None
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rows.append(metrics)
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return rows
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def _build_asset_valuation() -> List[Dict[str, Any]]:
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import yfinance as yf
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valuation_rows: List[Dict[str, Any]] = []
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for asset in ASSET_VALUATION_PROXIES:
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try:
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hist = yf.Ticker(asset["symbol"]).history(period=asset["lookback"])
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if hist is None or hist.empty or "Close" not in hist:
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continue
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closes = hist["Close"].dropna()
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if closes.empty:
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continue
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latest = float(closes.iloc[-1])
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mean_value = float(closes.mean())
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z = _zscore(closes.tail(252 * 3).astype(float).tolist())
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valuation_rows.append({
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"asset": asset["asset"],
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"symbol": asset["symbol"],
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"price": round(latest, 2),
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"history_mean": round(mean_value, 2),
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"zscore": round(z, 2) if z is not None else None,
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"status": "overvalued" if z is not None and z >= 0.75 else "undervalued" if z is not None and z <= -0.75 else "neutral",
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})
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except Exception:
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continue
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return valuation_rows
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@_cached_snapshot
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def get_macro_cycle_snapshot() -> Dict[str, Any]:
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"""Return macro cycle, country heatmap, and asset valuation snapshot."""
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cycle = _build_cycle_heatmap()
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country_heatmap = _build_country_heatmap()
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asset_valuation = _build_asset_valuation()
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scores = [item["trend_score"] for item in cycle if item.get("trend_score") is not None]
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cycle_score = round(float(np.mean(scores)), 2) if scores else 0.0
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if cycle_score >= 0.5:
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regime = "Expansion"
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elif cycle_score <= -0.5:
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regime = "Contraction"
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else:
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regime = "Transition"
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return {
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"updated_at": datetime.utcnow().isoformat() + "Z",
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"cycle_score": cycle_score,
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"regime": regime,
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"cycle_heatmap": cycle,
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"country_heatmap": country_heatmap,
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"asset_valuation": asset_valuation,
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}
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_SUBFACTOR_CATEGORIES: Dict[str, List[MacroSeries]] = {
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"Growth": [
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MacroSeries("gdp_growth", "Real GDP Growth", "A191RL1Q225SBEA", "quarterly", "%", True),
|
||
MacroSeries("ism_pmi", "ISM Manufacturing PMI", "MANEMP", "monthly", "idx", True, is_index=False),
|
||
MacroSeries("industrial_prod", "Industrial Production", "INDPRO", "monthly", "%", True, is_index=True),
|
||
MacroSeries("retail_sales", "Retail Sales", "RSXFS", "monthly", "%", True, is_index=True),
|
||
],
|
||
"Prices": [
|
||
MacroSeries("cpi_yoy", "CPI YoY", "CPIAUCSL", "monthly", "%", False, is_index=True),
|
||
MacroSeries("core_cpi", "Core CPI", "CPILFESL", "monthly", "%", False, is_index=True),
|
||
MacroSeries("ppi", "PPI", "PPIACO", "monthly", "%", False, is_index=True),
|
||
MacroSeries("pce", "PCE Price Index", "PCEPI", "monthly", "%", False, is_index=True),
|
||
],
|
||
"Labor": [
|
||
MacroSeries("unemployment", "Unemployment Rate", "UNRATE", "monthly", "%", False),
|
||
MacroSeries("nonfarm", "Nonfarm Payrolls", "PAYEMS", "monthly", "K", True, is_index=False),
|
||
MacroSeries("initial_claims", "Initial Claims", "ICSA", "weekly", "K", False),
|
||
MacroSeries("participation", "Participation Rate", "CIVPART", "monthly", "%", True),
|
||
],
|
||
"Financial": [
|
||
MacroSeries("yield_spread", "10Y-2Y Spread", "T10Y2Y", "daily", "bp", True),
|
||
MacroSeries("vix", "VIX", "VIXCLS", "daily", "idx", False),
|
||
MacroSeries("credit_spread", "BAA-AAA Spread", "BAAFFM", "monthly", "bp", False),
|
||
MacroSeries("fed_funds", "Fed Funds Rate", "FEDFUNDS", "monthly", "%", False),
|
||
],
|
||
}
|
||
|
||
|
||
def _subfactor_3m_change(series) -> Optional[float]:
|
||
"""Compute 3-month change from a pandas Series."""
|
||
if series is None or len(series) < 4:
|
||
return None
|
||
try:
|
||
recent = float(series.iloc[-1])
|
||
past = float(series.iloc[-4]) if len(series) >= 4 else float(series.iloc[0])
|
||
if past == 0:
|
||
return None
|
||
return round((recent - past) / abs(past) * 100, 2)
|
||
except Exception:
|
||
return None
|
||
|
||
|
||
def _subfactor_signal(zscore: Optional[float], change_3m: Optional[float], higher_is_better: bool) -> str:
|
||
"""Return improving / neutral / deteriorating."""
|
||
if change_3m is None and zscore is None:
|
||
return "neutral"
|
||
if change_3m is not None:
|
||
effective = change_3m if higher_is_better else -change_3m
|
||
if effective > 1.5:
|
||
return "improving"
|
||
if effective < -1.5:
|
||
return "deteriorating"
|
||
if zscore is not None:
|
||
effective_z = zscore if higher_is_better else -zscore
|
||
if effective_z > 0.5:
|
||
return "improving"
|
||
if effective_z < -0.5:
|
||
return "deteriorating"
|
||
return "neutral"
|
||
|
||
|
||
_cached_subfactors = cached("macro_subfactors", ttl_seconds=3600)
|
||
|
||
|
||
@_cached_subfactors
|
||
def get_subfactor_breakdown() -> Dict[str, Any]:
|
||
"""Return 4-category × 4-indicator subfactor breakdown with cycle stage."""
|
||
categories: Dict[str, Any] = {}
|
||
all_scores: List[float] = []
|
||
|
||
for cat_name, indicators in _SUBFACTOR_CATEGORIES.items():
|
||
items: List[Dict[str, Any]] = []
|
||
cat_scores: List[float] = []
|
||
|
||
for s in indicators:
|
||
try:
|
||
raw = _fetch_fred_series(s.fred_code)
|
||
if raw is None or raw.empty:
|
||
items.append({"key": s.key, "label": s.label, "value": None, "change_3m": None, "zscore": None, "signal": "neutral"})
|
||
continue
|
||
values = raw.iloc[:, 0]
|
||
if s.is_index:
|
||
values = _series_to_pct_change(values)
|
||
values = values.dropna() if values is not None else values
|
||
if values is None or values.empty:
|
||
items.append({"key": s.key, "label": s.label, "value": None, "change_3m": None, "zscore": None, "signal": "neutral"})
|
||
continue
|
||
|
||
latest = _safe_float(values.iloc[-1])
|
||
z = _zscore([float(v) for v in values.tail(60).tolist() if _safe_float(v) is not None])
|
||
change = _subfactor_3m_change(values)
|
||
signal = _subfactor_signal(z, change, s.higher_is_better)
|
||
|
||
if z is not None:
|
||
effective = z if s.higher_is_better else -z
|
||
cat_scores.append(effective)
|
||
all_scores.append(effective)
|
||
|
||
items.append({
|
||
"key": s.key,
|
||
"label": s.label,
|
||
"value": round(latest, 2) if latest is not None else None,
|
||
"unit": s.unit,
|
||
"change_3m": change,
|
||
"zscore": round(z, 2) if z is not None else None,
|
||
"signal": signal,
|
||
})
|
||
except Exception:
|
||
items.append({"key": s.key, "label": s.label, "value": None, "change_3m": None, "zscore": None, "signal": "neutral"})
|
||
|
||
cat_score = round(float(np.mean(cat_scores)), 2) if cat_scores else 0.0
|
||
categories[cat_name] = {"score": cat_score, "indicators": items}
|
||
|
||
# Determine cycle stage from composite score
|
||
composite = round(float(np.mean(all_scores)), 2) if all_scores else 0.0
|
||
growth_score = categories.get("Growth", {}).get("score", 0)
|
||
price_score = categories.get("Prices", {}).get("score", 0)
|
||
|
||
# 4-stage cycle: growth momentum + price momentum
|
||
if growth_score > 0 and price_score <= 0:
|
||
stage = "Early Expansion"
|
||
elif growth_score > 0 and price_score > 0:
|
||
stage = "Late Expansion"
|
||
elif growth_score <= 0 and price_score > 0:
|
||
stage = "Early Contraction"
|
||
else:
|
||
stage = "Late Contraction"
|
||
|
||
return {
|
||
"updated_at": datetime.utcnow().isoformat() + "Z",
|
||
"composite_score": composite,
|
||
"cycle_stage": stage,
|
||
"categories": categories,
|
||
}
|
||
|
||
|
||
def get_country_series(country: str, indicator: str, period: str = "5y") -> Dict[str, Any]:
|
||
"""Return a time series for a single country/indicator pair."""
|
||
mappings = COUNTRY_SERIES.get(country)
|
||
if not mappings or indicator not in mappings:
|
||
return {"country": country, "indicator": indicator, "series": [], "error": "Not found"}
|
||
|
||
series_def = mappings[indicator]
|
||
try:
|
||
raw = _fetch_fred_series(series_def.fred_code)
|
||
if raw is None or raw.empty:
|
||
return {"country": country, "indicator": indicator, "series": []}
|
||
|
||
values = raw.iloc[:, 0]
|
||
if series_def.is_index:
|
||
values = _series_to_pct_change(values)
|
||
values = values.dropna() if values is not None else values
|
||
|
||
period_map = {"3y": 36, "5y": 60, "10y": 120}
|
||
months = period_map.get(period, 60)
|
||
values = values.tail(months)
|
||
|
||
series_data = [
|
||
{"date": d.strftime("%Y-%m-%d"), "value": round(float(v), 4)}
|
||
for d, v in values.items()
|
||
if _safe_float(v) is not None
|
||
]
|
||
return {
|
||
"country": country,
|
||
"indicator": indicator,
|
||
"label": series_def.label,
|
||
"unit": series_def.unit,
|
||
"series": series_data,
|
||
}
|
||
except Exception as e:
|
||
return {"country": country, "indicator": indicator, "series": [], "error": str(e)}
|