feat: add portfolio optimization and congress tracking (TDD)
Portfolio optimization (3 endpoints): - POST /portfolio/optimize - HRP optimal weights via scipy clustering - POST /portfolio/correlation - pairwise correlation matrix - POST /portfolio/risk-parity - inverse-volatility risk parity weights Congress tracking (2 endpoints): - GET /regulators/congress/trades - congress member stock trades - GET /regulators/congress/bills?query= - search congress bills Implementation: - portfolio_service.py: HRP with scipy fallback to inverse-vol - congress_service.py: multi-provider fallback pattern - 51 new tests (14 portfolio unit, 20 portfolio route, 12 congress unit, 7 congress route) - All 312 tests passing
This commit is contained in:
Yaojia Wang
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portfolio_service.py
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portfolio_service.py
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"""Portfolio optimization: HRP, correlation matrix, risk parity."""
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import asyncio
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import logging
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from typing import Any
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import numpy as np
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import pandas as pd
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from obb_utils import fetch_historical
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logger = logging.getLogger(__name__)
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async def fetch_historical_prices(symbols: list[str], days: int = 365) -> pd.DataFrame:
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"""Fetch closing prices for multiple symbols and return as a DataFrame.
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Columns are symbol names; rows are dates. Symbols with no data are skipped.
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"""
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tasks = [fetch_historical(sym, days=days) for sym in symbols]
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results = await asyncio.gather(*tasks)
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price_series: dict[str, pd.Series] = {}
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for sym, result in zip(symbols, results):
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if result is None or result.results is None:
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logger.warning("No historical data for %s, skipping", sym)
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continue
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rows = result.results
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if not rows:
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continue
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dates = []
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closes = []
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for row in rows:
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d = getattr(row, "date", None)
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c = getattr(row, "close", None)
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if d is not None and c is not None:
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dates.append(str(d))
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closes.append(float(c))
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if dates:
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price_series[sym] = pd.Series(closes, index=dates)
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if not price_series:
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return pd.DataFrame()
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df = pd.DataFrame(price_series)
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df = df.dropna(how="all")
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return df
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def _compute_returns(prices: pd.DataFrame) -> pd.DataFrame:
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"""Compute daily log returns from a price DataFrame."""
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return prices.pct_change().dropna()
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def _inverse_volatility_weights(returns: pd.DataFrame) -> dict[str, float]:
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"""Compute inverse-volatility weights."""
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vols = returns.std()
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inv_vols = 1.0 / vols
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weights = inv_vols / inv_vols.sum()
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return {sym: float(w) for sym, w in weights.items()}
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def _hrp_weights(returns: pd.DataFrame) -> dict[str, float]:
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"""Compute Hierarchical Risk Parity weights via scipy clustering.
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Falls back to inverse-volatility if scipy is unavailable.
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"""
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symbols = list(returns.columns)
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n = len(symbols)
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if n == 1:
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return {symbols[0]: 1.0}
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try:
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from scipy.cluster.hierarchy import linkage, leaves_list
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from scipy.spatial.distance import squareform
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corr = returns.corr().fillna(0).values
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# Convert correlation to distance: d = sqrt(0.5 * (1 - corr))
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dist = np.sqrt(np.clip(0.5 * (1 - corr), 0, 1))
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np.fill_diagonal(dist, 0.0)
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condensed = squareform(dist)
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link = linkage(condensed, method="single")
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order = leaves_list(link)
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sorted_symbols = [symbols[i] for i in order]
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except ImportError:
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logger.warning("scipy not available; using inverse-volatility for HRP")
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return _inverse_volatility_weights(returns)
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cov = returns.cov().values
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def _bisect_weights(items: list[str]) -> dict[str, float]:
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if len(items) == 1:
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return {items[0]: 1.0}
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mid = len(items) // 2
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left_items = items[:mid]
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right_items = items[mid:]
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left_idx = [sorted_symbols.index(s) for s in left_items]
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right_idx = [sorted_symbols.index(s) for s in right_items]
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def _cluster_var(idx: list[int]) -> float:
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sub_cov = cov[np.ix_(idx, idx)]
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w = np.ones(len(idx)) / len(idx)
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return float(w @ sub_cov @ w)
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v_left = _cluster_var(left_idx)
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v_right = _cluster_var(right_idx)
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total = v_left + v_right
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alpha = 1.0 - v_left / total if total > 0 else 0.5
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w_left = _bisect_weights(left_items)
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w_right = _bisect_weights(right_items)
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result = {}
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for sym, w in w_left.items():
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result[sym] = w * (1.0 - alpha)
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for sym, w in w_right.items():
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result[sym] = w * alpha
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return result
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raw = _bisect_weights(sorted_symbols)
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total = sum(raw.values())
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return {sym: float(w / total) for sym, w in raw.items()}
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async def optimize_hrp(symbols: list[str], days: int = 365) -> dict[str, Any]:
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"""Compute Hierarchical Risk Parity portfolio weights.
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Args:
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symbols: List of ticker symbols (1-50).
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days: Number of historical days to use.
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Returns:
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Dict with keys ``weights`` (symbol -> float) and ``method``.
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Raises:
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ValueError: If symbols is empty or no price data is available.
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"""
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if not symbols:
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raise ValueError("symbols must not be empty")
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prices = await fetch_historical_prices(symbols, days=days)
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if prices.empty:
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raise ValueError("No price data available for the given symbols")
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returns = _compute_returns(prices)
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weights = _hrp_weights(returns)
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return {"weights": weights, "method": "hrp"}
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async def compute_correlation(
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symbols: list[str], days: int = 365
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) -> dict[str, Any]:
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"""Compute correlation matrix for a list of symbols.
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Args:
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symbols: List of ticker symbols (1-50).
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days: Number of historical days to use.
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Returns:
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Dict with keys ``symbols`` (list) and ``matrix`` (list of lists).
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Raises:
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ValueError: If symbols is empty or no price data is available.
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"""
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if not symbols:
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raise ValueError("symbols must not be empty")
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prices = await fetch_historical_prices(symbols, days=days)
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if prices.empty:
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raise ValueError("No price data available for the given symbols")
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returns = _compute_returns(prices)
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available = list(returns.columns)
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corr = returns.corr().fillna(0)
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matrix = corr.values.tolist()
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return {"symbols": available, "matrix": matrix}
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async def compute_risk_parity(
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symbols: list[str], days: int = 365
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) -> dict[str, Any]:
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"""Compute equal risk contribution (inverse-volatility) weights.
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Args:
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symbols: List of ticker symbols (1-50).
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days: Number of historical days to use.
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Returns:
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Dict with keys ``weights``, ``risk_contributions``, and ``method``.
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Raises:
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ValueError: If symbols is empty or no price data is available.
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"""
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if not symbols:
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raise ValueError("symbols must not be empty")
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prices = await fetch_historical_prices(symbols, days=days)
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if prices.empty:
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raise ValueError("No price data available for the given symbols")
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returns = _compute_returns(prices)
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weights = _inverse_volatility_weights(returns)
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# Risk contributions: w_i * sigma_i / sum(w_j * sigma_j)
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vols = returns.std()
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weighted_risk = {sym: weights[sym] * float(vols[sym]) for sym in weights}
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total_risk = sum(weighted_risk.values())
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if total_risk > 0:
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risk_contributions = {sym: v / total_risk for sym, v in weighted_risk.items()}
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else:
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n = len(weights)
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risk_contributions = {sym: 1.0 / n for sym in weights}
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return {
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"weights": weights,
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"risk_contributions": risk_contributions,
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"method": "risk_parity",
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}
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