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TradingAgents/tradingagents/dataflows/polaris.py

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"""
Polaris Knowledge API data vendor for TradingAgents.
Polaris provides sentiment-scored intelligence briefs, composite trading signals,
technical indicators, financial data, and news impact analysis. Unlike raw data
feeds, every Polaris response includes confidence scores, bias analysis, and
NLP-derived metadata that enriches agent decision-making.
Setup:
pip install polaris-news cachetools
export POLARIS_API_KEY=pr_live_xxx # Free: 1,000 credits/month at thepolarisreport.com
API docs: https://thepolarisreport.com/api-reference
"""
import os
import threading
from typing import Annotated
from datetime import datetime
from cachetools import TTLCache
# ---------------------------------------------------------------------------
# Configuration
# ---------------------------------------------------------------------------
_CACHE_TTL = 300 # 5 minutes
_CACHE_MAX = 500
_cache = TTLCache(maxsize=_CACHE_MAX, ttl=_CACHE_TTL)
_cache_lock = threading.Lock()
_client_instance = None
_client_lock = threading.Lock()
def _get_client():
"""Lazy-initialize Polaris client (thread-safe singleton)."""
global _client_instance
if _client_instance is not None:
return _client_instance
with _client_lock:
if _client_instance is not None:
return _client_instance
try:
from polaris_news import PolarisClient
except ImportError:
raise ImportError(
"polaris-news is required for the Polaris data vendor. "
"Install it with: pip install polaris-news"
)
api_key = os.environ.get("POLARIS_API_KEY")
if not api_key:
raise EnvironmentError(
"POLARIS_API_KEY environment variable is required. "
"Get a free key at https://thepolarisreport.com/pricing"
)
_client_instance = PolarisClient(api_key=api_key)
return _client_instance
def _cached(key: str):
"""Check cache for a key. Returns cached value or None (thread-safe)."""
with _cache_lock:
return _cache.get(key)
def _set_cache(key: str, data):
"""Store data in cache (thread-safe)."""
with _cache_lock:
_cache[key] = data
# ---------------------------------------------------------------------------
# Shared helpers
# ---------------------------------------------------------------------------
def _safe_get(obj, key, default='N/A'):
"""Get attribute from dict or object, returning default if missing or None."""
if isinstance(obj, dict):
val = obj.get(key, default)
return default if val is None else val
val = getattr(obj, key, default)
return default if val is None else val
def _days_to_range(days: int) -> str:
"""Convert a day count to a Polaris range string."""
if days <= 30:
return "1mo"
elif days <= 90:
return "3mo"
elif days <= 180:
return "6mo"
elif days <= 365:
return "1y"
elif days <= 730:
return "2y"
else:
return "5y"
def _extract_briefs(data) -> list:
"""Extract briefs list from API response (handles both dict and typed objects)."""
if not isinstance(data, dict):
data = vars(data) if hasattr(data, '__dict__') else {}
return data.get("briefs", [])
# ---------------------------------------------------------------------------
# Core Stock APIs
# ---------------------------------------------------------------------------
def get_stock_data(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""Fetch OHLCV stock data from Polaris (via multi-provider: Yahoo/TwelveData/FMP)."""
cache_key = f"stock:{symbol}:{start_date}:{end_date}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
start = datetime.strptime(start_date, "%Y-%m-%d")
end = datetime.strptime(end_date, "%Y-%m-%d")
days = (end - start).days
if days <= 0:
return f"Invalid date range: start_date ({start_date}) must be before end_date ({end_date})"
range_param = _days_to_range(days)
try:
data = client.candles(symbol, interval="1d", range=range_param)
except Exception as e:
return f"Error fetching stock data for {symbol}: {e}"
candles = data.get("candles", [])
if not candles:
return f"No data found for symbol '{symbol}' between {start_date} and {end_date}"
# Filter to requested date range
candles = [c for c in candles if start_date <= c["date"] <= end_date]
lines = [
f"# Stock data for {symbol.upper()} from {start_date} to {end_date}",
f"# Source: Polaris Knowledge API (multi-provider: Yahoo/TwelveData/FMP)",
f"# Total records: {len(candles)}",
"",
"Date,Open,High,Low,Close,Volume",
]
lines.extend(
f"{c['date']},{c['open']},{c['high']},{c['low']},{c['close']},{c['volume']}"
for c in candles
)
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
# ---------------------------------------------------------------------------
# Technical Indicators
# ---------------------------------------------------------------------------
def get_indicators(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get"],
curr_date: Annotated[str, "Current trading date, YYYY-mm-dd"],
look_back_days: Annotated[int, "how many days to look back"],
) -> str:
"""Fetch technical indicators from Polaris (20 indicators + signal summary).
Uses curr_date and look_back_days to determine the data range.
"""
cache_key = f"indicators:{symbol}:{indicator}:{curr_date}:{look_back_days}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
# Use curr_date to determine if we need historical vs current data
today = datetime.now().strftime("%Y-%m-%d")
is_historical = curr_date < today if curr_date else False
# Map common indicator names to Polaris types
indicator_map = {
"close_50_sma": "sma", "close_20_sma": "sma", "close_200_sma": "sma",
"rsi_14": "rsi", "rsi": "rsi",
"macd": "macd", "macds": "macd", "macdh": "macd",
"boll": "bollinger", "boll_ub": "bollinger", "boll_lb": "bollinger",
"atr": "atr", "atr_14": "atr",
"stoch": "stochastic", "stochrsi": "stochastic",
"adx": "adx", "williams_r": "williams_r",
"cci": "cci", "mfi": "mfi", "roc": "roc",
"obv": "obv", "vwap": "vwap",
}
polaris_type = indicator_map.get(indicator.lower(), indicator.lower())
# If historical, we need enough range to cover curr_date - look_back_days
if is_historical:
days_from_now = (datetime.strptime(today, "%Y-%m-%d") - datetime.strptime(curr_date, "%Y-%m-%d")).days
range_param = _days_to_range(days_from_now + look_back_days)
else:
range_param = _days_to_range(look_back_days)
known_types = {
"sma", "ema", "rsi", "macd", "bollinger", "atr",
"stochastic", "adx", "obv", "vwap", "williams_r",
"cci", "mfi", "roc", "ppo", "trix", "donchian",
"parabolic_sar", "ichimoku", "fibonacci",
}
try:
if polaris_type in known_types:
data = client.indicators(symbol, type=polaris_type, range=range_param)
else:
# Unknown indicator — return an error rather than silently falling back
# to client.technicals() which returns a different structure
return (
f"Unknown indicator '{indicator}' for {symbol}. "
f"Supported: {', '.join(sorted(known_types))}"
)
except Exception as e:
return f"Error fetching indicators for {symbol}: {e}"
values = data.get("values", [])
lines = [
f"# Technical Indicator: {indicator} for {symbol.upper()}",
f"# Source: Polaris Knowledge API",
f"# Period: {range_param} | Data points: {len(values) if isinstance(values, list) else 'N/A'}",
"",
]
if isinstance(values, list) and values:
first = values[0]
if "value" in first:
lines.append("Date,Value")
lines.extend(f"{v['date']},{v.get('value', '')}" for v in values)
elif "macd" in first:
lines.append("Date,MACD,Signal,Histogram")
lines.extend(f"{v['date']},{v.get('macd', '')},{v.get('signal', '')},{v.get('histogram', '')}" for v in values)
elif "upper" in first:
lines.append("Date,Upper,Middle,Lower")
lines.extend(f"{v['date']},{v.get('upper', '')},{v.get('middle', '')},{v.get('lower', '')}" for v in values)
elif "k" in first:
lines.append("Date,K,D")
lines.extend(f"{v['date']},{v.get('k', '')},{v.get('d', '')}" for v in values)
else:
keys = list(first.keys())
lines.append(",".join(keys))
lines.extend(",".join(str(v.get(k, '')) for k in keys) for v in values)
elif isinstance(values, dict):
for k, v in values.items():
lines.append(f"{k}: {v}")
else:
lines.append("No indicator data available")
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
# ---------------------------------------------------------------------------
# Fundamental Data
# ---------------------------------------------------------------------------
def _get_financials_cached(symbol: str) -> dict:
"""Shared cached financials fetch — used by fundamentals, balance_sheet, cashflow, income_statement."""
cache_key = f"financials_raw:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
data = client.financials(symbol)
_set_cache(cache_key, data)
return data
def get_fundamentals(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Fetch company fundamentals from Polaris (via Yahoo Finance quoteSummary)."""
cache_key = f"fundamentals:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
try:
data = _get_financials_cached(symbol)
except Exception as e:
return f"Error fetching fundamentals for {symbol}: {e}"
lines = [
f"# Company Fundamentals: {data.get('company_name', symbol)}",
f"# Source: Polaris Knowledge API",
"",
f"Sector: {_safe_get(data, 'sector')}",
f"Industry: {_safe_get(data, 'industry')}",
f"Market Cap: {_safe_get(data, 'market_cap_formatted')}",
f"P/E Ratio: {_safe_get(data, 'pe_ratio')}",
f"Forward P/E: {_safe_get(data, 'forward_pe')}",
f"EPS: {_safe_get(data, 'eps')}",
f"Revenue: {_safe_get(data, 'revenue_formatted')}",
f"EBITDA: {_safe_get(data, 'ebitda_formatted')}",
f"Profit Margin: {_safe_get(data, 'profit_margin')}",
f"Debt/Equity: {_safe_get(data, 'debt_to_equity')}",
f"ROE: {_safe_get(data, 'return_on_equity')}",
f"Beta: {_safe_get(data, 'beta')}",
f"52-Week High: {_safe_get(data, 'fifty_two_week_high')}",
f"52-Week Low: {_safe_get(data, 'fifty_two_week_low')}",
]
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
def get_balance_sheet(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Fetch balance sheet from Polaris."""
cache_key = f"balance_sheet:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
try:
data = _get_financials_cached(symbol)
except Exception as e:
return f"Error fetching balance sheet for {symbol}: {e}"
sheets = data.get("balance_sheets", [])
lines = [
f"# Balance Sheet: {symbol.upper()}",
f"# Source: Polaris Knowledge API",
"",
"Date,Total Assets,Total Liabilities,Total Equity",
]
lines.extend(f"{s['date']},{s['total_assets']},{s['total_liabilities']},{s['total_equity']}" for s in sheets)
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
def get_cashflow(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Fetch cash flow data from Polaris."""
cache_key = f"cashflow:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
try:
data = _get_financials_cached(symbol)
except Exception as e:
return f"Error fetching cashflow for {symbol}: {e}"
statements = data.get("cash_flow_statements", [])
lines = [
f"# Cash Flow: {symbol.upper()}",
f"# Source: Polaris Knowledge API",
"",
]
if statements:
lines.append("Date,Operating Cash Flow,Capital Expenditure,Free Cash Flow")
lines.extend(
f"{s.get('date', '')},{s.get('operating_cash_flow', '')},{s.get('capital_expenditure', '')},{s.get('free_cash_flow', '')}"
for s in statements
)
else:
lines.append(f"Free Cash Flow: {_safe_get(data, 'free_cash_flow')}")
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
def get_income_statement(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Fetch income statement from Polaris."""
cache_key = f"income_stmt:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
try:
data = _get_financials_cached(symbol)
except Exception as e:
return f"Error fetching income statement for {symbol}: {e}"
stmts = data.get("income_statements", [])
lines = [
f"# Income Statement: {symbol.upper()}",
f"# Source: Polaris Knowledge API",
"",
"Date,Revenue,Net Income,Gross Profit",
]
lines.extend(f"{s['date']},{s['revenue']},{s['net_income']},{s['gross_profit']}" for s in stmts)
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
# ---------------------------------------------------------------------------
# News & Intelligence (Polaris advantage — sentiment-scored, not raw headlines)
# ---------------------------------------------------------------------------
def _format_brief_detail(b, lines: list) -> None:
"""Format a single brief into output lines (shared by get_news)."""
prov = _safe_get(b, "provenance", {})
if not isinstance(prov, dict):
prov = {}
lines.append(f"--- Brief: {_safe_get(b, 'id', '')} ---")
lines.append(f"Date: {_safe_get(b, 'published_at', '')}")
lines.append(f"Headline: {_safe_get(b, 'headline', '')}")
lines.append(f"Summary: {_safe_get(b, 'summary', '')}")
lines.append(f"Category: {_safe_get(b, 'category', '')}")
lines.append(f"Confidence: {_safe_get(prov, 'confidence_score')}")
lines.append(f"Bias Score: {_safe_get(prov, 'bias_score')}")
lines.append(f"Review Status: {_safe_get(prov, 'review_status')}")
lines.append(f"Sentiment: {_safe_get(b, 'sentiment')}")
lines.append(f"Impact Score: {_safe_get(b, 'impact_score')}")
entities = _safe_get(b, "entities_enriched", [])
if isinstance(entities, list) and entities:
ent_str = ", ".join(
f"{_safe_get(e, 'name', '?')}({_safe_get(e, 'sentiment_score', '?')})"
for e in entities[:5]
)
lines.append(f"Entities: {ent_str}")
ca = _safe_get(b, "counter_argument", None)
if ca and ca != 'N/A':
lines.append(f"Counter-Argument: {str(ca)[:200]}...")
lines.append("")
def get_news(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""Fetch sentiment-scored intelligence briefs from Polaris.
Unlike raw news feeds, each brief includes:
- Confidence score (0-1)
- Bias score and direction
- Counter-arguments
- Entity-level sentiment (-1.0 to +1.0)
"""
cache_key = f"news:{symbol}:{start_date}:{end_date}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
try:
data = client.search(symbol, per_page=20, from_date=start_date, to_date=end_date)
briefs = _extract_briefs(data)
except Exception as e:
return f"Error fetching news for {symbol}: {e}"
if not briefs:
return f"No intelligence briefs found for {symbol} between {start_date} and {end_date}"
lines = [
f"# Intelligence Briefs for {symbol.upper()} ({start_date} to {end_date})",
f"# Source: Polaris Knowledge API (sentiment-scored, bias-analyzed)",
f"# Total: {len(briefs)} briefs",
"",
]
for b in briefs:
_format_brief_detail(b, lines)
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
def get_global_news(
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
) -> str:
"""Fetch global intelligence feed from Polaris with sentiment and bias scoring."""
cache_key = f"global_news:{start_date}:{end_date}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
try:
data = client.feed(per_page=20, from_date=start_date, to_date=end_date)
briefs = _extract_briefs(data)
except Exception as e:
return f"Error fetching global news: {e}"
if not briefs:
return f"No intelligence briefs found between {start_date} and {end_date}"
lines = [
f"# Global Intelligence Feed ({start_date} to {end_date})",
f"# Source: Polaris Knowledge API",
f"# Briefs: {len(briefs)}",
"",
]
for b in briefs:
prov = _safe_get(b, "provenance", {})
if not isinstance(prov, dict):
prov = {}
pub = str(_safe_get(b, 'published_at', ''))[:10]
lines.append(
f"[{pub}] [{_safe_get(b, 'category', '')}] "
f"{_safe_get(b, 'headline', '')} "
f"(confidence={_safe_get(prov, 'confidence_score')}, "
f"bias={_safe_get(prov, 'bias_score')}, "
f"sentiment={_safe_get(b, 'sentiment')})"
)
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
def get_sec_filings(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Fetch SEC EDGAR earnings filings (8-K, 10-Q, 10-K) via Polaris."""
cache_key = f"sec_filings:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
try:
data = client.transcripts(symbol, days=365)
except Exception as e:
return f"Error fetching filings for {symbol}: {e}"
filings = data.get("filings", [])
lines = [
f"# SEC Filings for {symbol.upper()}",
f"# Source: Polaris Knowledge API (SEC EDGAR)",
"",
"Date,Form,Description,URL",
]
lines.extend(
f"{_safe_get(f, 'date', '')},{_safe_get(f, 'form', '')},{_safe_get(f, 'description', '')},{_safe_get(f, 'filing_url', '')}"
for f in filings[:20]
)
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
# ---------------------------------------------------------------------------
# Polaris-Exclusive: Sentiment & Trading Signals
# (Complements price/fundamental data from yfinance and Alpha Vantage)
# ---------------------------------------------------------------------------
def get_sentiment_score(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Get composite trading signal from Polaris.
Returns a multi-factor score combining:
- Sentiment (40% weight)
- Momentum (25% weight)
- Coverage velocity (20% weight)
- Event proximity (15% weight)
Polaris-exclusive: complements price data from other vendors with intelligence signals.
"""
cache_key = f"sentiment:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
try:
data = client.ticker_score(symbol)
except Exception as e:
return f"Error fetching sentiment score for {symbol}: {e}"
components = _safe_get(data, "components", {})
sent = _safe_get(components, "sentiment", {})
mom = _safe_get(components, "momentum", {})
vol = _safe_get(components, "volume", {})
evt = _safe_get(components, "events", {})
lines = [
f"# Composite Trading Signal: {symbol.upper()}",
f"# Source: Polaris Knowledge API",
"",
f"Signal: {_safe_get(data, 'signal')}",
f"Composite Score: {_safe_get(data, 'composite_score')}",
"",
f"Sentiment (40%): current_24h={_safe_get(sent, 'current_24h')}, week_avg={_safe_get(sent, 'week_avg')}",
f"Momentum (25%): {_safe_get(mom, 'direction')} (value={_safe_get(mom, 'value')})",
f"Volume (20%): {_safe_get(vol, 'briefs_24h')} briefs/24h, velocity={_safe_get(vol, 'velocity_change_pct')}%",
f"Events (15%): {_safe_get(evt, 'count_7d')} events, latest={_safe_get(evt, 'latest_type')}",
]
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
def get_sector_analysis(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Get competitor intelligence — same-sector peers with live price, RSI, sentiment, and news coverage.
Polaris-exclusive: complements price data from other vendors with intelligence signals.
"""
cache_key = f"sector_analysis:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
try:
data = client.competitors(symbol)
except Exception as e:
return f"Error fetching sector analysis for {symbol}: {e}"
peers = data.get("competitors", [])
lines = [
f"# Sector & Peer Analysis: {symbol.upper()} ({_safe_get(data, 'sector')})",
f"# Source: Polaris Knowledge API",
f"# Peers: {len(peers)}",
"",
"Ticker,Name,Price,Change%,RSI(14),Sentiment_7d,Briefs_7d,Signal",
]
for c in peers:
lines.append(
f"{_safe_get(c, 'ticker')},{_safe_get(c, 'entity_name')},"
f"{_safe_get(c, 'price')},{_safe_get(c, 'change_pct', '')},"
f"{_safe_get(c, 'rsi_14')},{_safe_get(c, 'sentiment_7d')},"
f"{_safe_get(c, 'briefs_7d')},{_safe_get(c, 'signal', 'N/A')}"
)
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
def get_news_impact(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Measure how news moved the stock price — brief-to-price causation analysis.
Polaris-exclusive: complements price data from other vendors with intelligence signals.
"""
cache_key = f"impact:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
try:
data = client.news_impact(symbol)
except Exception as e:
return f"Error fetching news impact for {symbol}: {e}"
best = data.get("best_impact", {}) or {}
worst = data.get("worst_impact", {}) or {}
lines = [
f"# News Impact Analysis: {symbol.upper()}",
f"# Source: Polaris Knowledge API",
"",
f"Briefs Analyzed: {_safe_get(data, 'briefs_analyzed', 0)}",
f"Avg 1-Day Impact: {_safe_get(data, 'avg_impact_1d_pct')}%",
f"Avg 3-Day Impact: {_safe_get(data, 'avg_impact_3d_pct')}%",
"",
]
if best:
lines.append(f"Best Impact: {_safe_get(best, 'headline', '')[:60]} (+{_safe_get(best, 'impact_1d_pct')}%)")
if worst:
lines.append(f"Worst Impact: {_safe_get(worst, 'headline', '')[:60]} ({_safe_get(worst, 'impact_1d_pct')}%)")
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
# ---------------------------------------------------------------------------
# Polaris-Exclusive: Technical Analysis
# (Complements price/fundamental data from yfinance and Alpha Vantage)
# ---------------------------------------------------------------------------
def get_technicals(
symbol: Annotated[str, "ticker symbol of the company"],
) -> str:
"""Get full technical analysis with 20 indicators and buy/sell/neutral signal.
Returns all indicators at once: SMA, EMA, RSI, MACD, Bollinger, ATR,
Stochastic, ADX, OBV, VWAP, Williams %R, CCI, MFI, ROC, and more.
Includes a composite signal summary with buy/sell/neutral recommendation.
Polaris-exclusive: complements price data from other vendors with intelligence signals.
"""
cache_key = f"technicals:{symbol}"
cached = _cached(cache_key)
if cached:
return cached
client = _get_client()
try:
data = client.technicals(symbol, range="6mo")
except Exception as e:
return f"Error fetching technicals for {symbol}: {e}"
latest = data.get("latest", {}) or {}
signal = data.get("signal_summary", {}) or {}
macd = latest.get("macd", {}) or {}
boll = latest.get("bollinger", {}) or {}
stoch = latest.get("stochastic", {}) or {}
lines = [
f"# Technical Analysis: {symbol.upper()}",
f"# Source: Polaris Knowledge API (20 indicators)",
"",
f"Signal: {_safe_get(signal, 'overall', 'N/A').upper()}",
f"Buy signals: {_safe_get(signal, 'buy_count', 0)} | Sell signals: {_safe_get(signal, 'sell_count', 0)} | Neutral: {_safe_get(signal, 'neutral_count', 0)}",
"",
f"Price: {_safe_get(latest, 'price')}",
f"RSI(14): {_safe_get(latest, 'rsi_14')}",
f"MACD: {_safe_get(macd, 'macd')} (signal={_safe_get(macd, 'signal')}, hist={_safe_get(macd, 'histogram')})",
f"SMA(20): {_safe_get(latest, 'sma_20')} | SMA(50): {_safe_get(latest, 'sma_50')}",
f"EMA(12): {_safe_get(latest, 'ema_12')} | EMA(26): {_safe_get(latest, 'ema_26')}",
f"Bollinger: upper={_safe_get(boll, 'upper')}, middle={_safe_get(boll, 'middle')}, lower={_safe_get(boll, 'lower')}",
f"ATR(14): {_safe_get(latest, 'atr_14')}",
f"Stochastic: K={_safe_get(stoch, 'k')}, D={_safe_get(stoch, 'd')}",
f"ADX(14): {_safe_get(latest, 'adx_14')}",
f"Williams %R(14): {_safe_get(latest, 'williams_r_14')}",
f"CCI(20): {_safe_get(latest, 'cci_20')}",
f"MFI(14): {_safe_get(latest, 'mfi_14')}",
f"ROC(12): {_safe_get(latest, 'roc_12')}",
f"OBV: {_safe_get(latest, 'obv')}",
f"VWAP: {_safe_get(latest, 'vwap')}",
]
result = "\n".join(lines) + "\n"
_set_cache(cache_key, result)
return result
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