ad45cb429c
REST API wrapping OpenBB SDK for stock data, sentiment analysis, technical indicators, macro data, and rule-based portfolio analysis. - Stock data via yfinance (quote, profile, metrics, financials, historical, news) - News sentiment via Alpha Vantage (per-article, per-ticker scores) - Analyst data via Finnhub (recommendations, insider trades, upgrades) - Macro data via FRED (Fed rate, CPI, GDP, unemployment, treasury yields) - Technical indicators via openbb-technical (RSI, MACD, SMA, EMA, Bollinger) - Rule-based portfolio analysis engine (BUY_MORE/HOLD/SELL) - Stock discovery (gainers, losers, active, undervalued, growth) - 102 tests, all passing
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
"""Shared mapping functions from raw dicts to response models."""
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from typing import Any
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from models import (
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DiscoverItem,
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MetricsResponse,
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ProfileResponse,
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QuoteResponse,
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)
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def quote_from_dict(symbol: str, data: dict[str, Any]) -> QuoteResponse:
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return QuoteResponse(
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symbol=symbol,
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name=data.get("name"),
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price=data.get("last_price") or data.get("close"),
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change=data.get("change"),
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change_percent=data.get("change_percent"),
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volume=data.get("volume"),
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market_cap=data.get("market_cap"),
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currency=data.get("currency"),
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)
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def profile_from_dict(symbol: str, data: dict[str, Any]) -> ProfileResponse:
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return ProfileResponse(
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symbol=symbol,
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name=data.get("name"),
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sector=data.get("sector"),
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industry=data.get("industry"),
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country=data.get("country"),
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description=data.get("long_description") or data.get("description"),
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website=data.get("website"),
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employees=data.get("full_time_employees") or data.get("employees"),
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)
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def metrics_from_dict(symbol: str, data: dict[str, Any]) -> MetricsResponse:
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return MetricsResponse(
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symbol=symbol,
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pe_ratio=data.get("pe_ratio"),
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pb_ratio=data.get("pb_ratio"),
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ps_ratio=data.get("ps_ratio"),
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peg_ratio=data.get("peg_ratio"),
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roe=data.get("roe") or data.get("return_on_equity"),
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roa=data.get("roa") or data.get("return_on_assets"),
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dividend_yield=data.get("dividend_yield"),
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beta=data.get("beta"),
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eps=data.get("eps_ttm") or data.get("eps"),
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revenue_growth=data.get("revenue_growth"),
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earnings_growth=data.get("earnings_growth"),
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)
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def discover_item_from_dict(data: dict[str, Any]) -> DiscoverItem:
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return DiscoverItem(
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symbol=data.get("symbol"),
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name=data.get("name"),
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price=data.get("price") or data.get("last_price"),
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change_percent=data.get("change_percent") or data.get("percent_change"),
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volume=data.get("volume"),
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market_cap=data.get("market_cap"),
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)
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def discover_items_from_list(items: list[dict[str, Any]]) -> list[dict[str, Any]]:
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return [discover_item_from_dict(item).model_dump() for item in items]
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