mirror of
https://github.com/shawnkim1997/All-in-one-Financial-Analysis.git
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feat: add 4-tier auto-valuation system for negative FCF companies + platform-wide improvements
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>
This commit is contained in:
shawnkim1997
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
8fe3aaf771
commit
ec2c5b37a2
@@ -110,6 +110,157 @@ def _run_backtest_impl(
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}
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def _run_portfolio_backtest_impl(
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tickers: list[str],
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weights: list[float],
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start_date: str,
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end_date: str,
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rebalance_months: int = 3,
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benchmark_ticker: str = "SPY",
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) -> Dict[str, Any]:
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"""Multi-asset portfolio backtest with periodic rebalancing."""
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import numpy as np
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import pandas as pd
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import yfinance as yf
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if len(tickers) != len(weights) or not tickers:
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return {"error": "Tickers and weights must be non-empty and same length"}
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# Normalize weights
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total_w = sum(weights)
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if total_w <= 0:
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return {"error": "Weights must sum to a positive number"}
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norm_weights = [w / total_w for w in weights]
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# Fetch price data
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price_frames = {}
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for t in tickers:
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hist = yf.Ticker(t.upper()).history(start=start_date, end=end_date, auto_adjust=True)
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if hist is not None and not hist.empty and "Close" in hist:
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price_frames[t.upper()] = hist["Close"]
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if not price_frames:
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return {"error": "No price data for any ticker"}
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prices = pd.DataFrame(price_frames).dropna()
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if len(prices) < 5:
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return {"error": "Insufficient overlapping price data"}
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# Benchmark
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bm_sym = (benchmark_ticker or "SPY").upper()
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bm_hist = yf.Ticker(bm_sym).history(start=start_date, end=end_date, auto_adjust=True)
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if bm_hist is None or bm_hist.empty:
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return {"error": f"No benchmark data for {bm_sym}"}
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common = prices.index.intersection(bm_hist.index)
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if len(common) < 5:
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return {"error": "Insufficient overlap with benchmark"}
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prices = prices.loc[common]
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bm_close = bm_hist.loc[common, "Close"]
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returns = prices.pct_change().fillna(0)
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bm_returns = bm_close.pct_change().fillna(0)
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# Map tickers to weights (use only tickers that have data)
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avail_tickers = list(prices.columns)
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ticker_weight = {}
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for t, w in zip(tickers, norm_weights):
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tu = t.upper()
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if tu in avail_tickers:
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ticker_weight[tu] = w
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# Re-normalize
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tw_sum = sum(ticker_weight.values())
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if tw_sum <= 0:
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return {"error": "No valid tickers with data"}
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for k in ticker_weight:
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ticker_weight[k] /= tw_sum
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# Rebalancing: compute portfolio returns
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current_weights = {t: ticker_weight[t] for t in ticker_weight}
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portfolio_returns = []
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last_rebal = None
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for i, dt in enumerate(prices.index):
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if i == 0:
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portfolio_returns.append(0.0)
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last_rebal = dt
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continue
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# Daily portfolio return = sum of weight * return
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daily_ret = sum(current_weights.get(t, 0) * returns.loc[dt, t] for t in avail_tickers if t in current_weights)
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portfolio_returns.append(daily_ret)
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# Drift weights
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for t in current_weights:
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current_weights[t] *= (1 + returns.loc[dt, t])
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w_sum = sum(current_weights.values())
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if w_sum > 0:
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for t in current_weights:
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current_weights[t] /= w_sum
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# Rebalance check
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if last_rebal is not None and _months_between(last_rebal, dt) >= rebalance_months:
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current_weights = {t: ticker_weight[t] for t in ticker_weight}
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last_rebal = dt
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port_ret = pd.Series(portfolio_returns, index=prices.index)
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cumulative = (1 + port_ret).cumprod()
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benchmark_cum = (1 + bm_returns).cumprod()
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# Metrics
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total_ret = round((float(cumulative.iloc[-1]) - 1) * 100, 2)
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bm_ret = round((float(benchmark_cum.iloc[-1]) - 1) * 100, 2)
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mdd = round(float(((cumulative / cumulative.cummax()) - 1).min()) * 100, 2)
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sharpe = round(float(port_ret.mean() / (port_ret.std() + 1e-10) * (252**0.5)), 2)
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# Sortino
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downside = port_ret[port_ret < 0]
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sortino = round(float(port_ret.mean() / (downside.std() + 1e-10) * (252**0.5)), 2) if len(downside) > 0 else 0.0
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# Contribution per ticker
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contributions = {}
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for t in ticker_weight:
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t_ret = returns[t]
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contrib = float((t_ret * ticker_weight[t]).sum()) * 100
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contributions[t] = round(contrib, 2)
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return {
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"tickers": list(ticker_weight.keys()),
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"weights": {t: round(w, 4) for t, w in ticker_weight.items()},
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"benchmark_ticker": bm_sym,
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"total_return_pct": total_ret,
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"benchmark_return_pct": bm_ret,
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"alpha": round(total_ret - bm_ret, 2),
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"max_drawdown_pct": mdd,
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"sharpe_ratio": sharpe,
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"sortino_ratio": sortino,
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"rebalance_months": rebalance_months,
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"contributions": contributions,
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"equity_curve": [round(float(x), 4) for x in cumulative.tolist()],
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"benchmark_curve": [round(float(x), 4) for x in benchmark_cum.tolist()],
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"dates": prices.index.strftime("%Y-%m-%d").tolist(),
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}
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async def run_portfolio_backtest(
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tickers: list[str],
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weights: list[float],
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start_date: str,
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end_date: str,
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rebalance_months: int = 3,
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benchmark_ticker: str = "SPY",
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) -> dict:
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"""Run a multi-asset portfolio backtest with periodic rebalancing."""
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return await asyncio.to_thread(
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_run_portfolio_backtest_impl,
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tickers,
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weights,
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start_date,
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end_date,
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rebalance_months,
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benchmark_ticker,
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)
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async def run_backtest(
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ticker: str,
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strategy: str,
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@@ -393,6 +393,142 @@ def get_macro_cycle_snapshot() -> Dict[str, Any]:
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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),
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MacroSeries("ism_pmi", "ISM Manufacturing PMI", "MANEMP", "monthly", "idx", True, is_index=False),
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MacroSeries("industrial_prod", "Industrial Production", "INDPRO", "monthly", "%", True, is_index=True),
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MacroSeries("retail_sales", "Retail Sales", "RSXFS", "monthly", "%", True, is_index=True),
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],
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"Prices": [
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MacroSeries("cpi_yoy", "CPI YoY", "CPIAUCSL", "monthly", "%", False, is_index=True),
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MacroSeries("core_cpi", "Core CPI", "CPILFESL", "monthly", "%", False, is_index=True),
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MacroSeries("ppi", "PPI", "PPIACO", "monthly", "%", False, is_index=True),
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MacroSeries("pce", "PCE Price Index", "PCEPI", "monthly", "%", False, is_index=True),
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],
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"Labor": [
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MacroSeries("unemployment", "Unemployment Rate", "UNRATE", "monthly", "%", False),
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MacroSeries("nonfarm", "Nonfarm Payrolls", "PAYEMS", "monthly", "K", True, is_index=False),
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MacroSeries("initial_claims", "Initial Claims", "ICSA", "weekly", "K", False),
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MacroSeries("participation", "Participation Rate", "CIVPART", "monthly", "%", True),
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],
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"Financial": [
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MacroSeries("yield_spread", "10Y-2Y Spread", "T10Y2Y", "daily", "bp", True),
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MacroSeries("vix", "VIX", "VIXCLS", "daily", "idx", False),
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MacroSeries("credit_spread", "BAA-AAA Spread", "BAAFFM", "monthly", "bp", False),
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MacroSeries("fed_funds", "Fed Funds Rate", "FEDFUNDS", "monthly", "%", False),
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],
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}
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def _subfactor_3m_change(series) -> Optional[float]:
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"""Compute 3-month change from a pandas Series."""
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if series is None or len(series) < 4:
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return None
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try:
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recent = float(series.iloc[-1])
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past = float(series.iloc[-4]) if len(series) >= 4 else float(series.iloc[0])
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if past == 0:
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return None
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return round((recent - past) / abs(past) * 100, 2)
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except Exception:
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return None
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def _subfactor_signal(zscore: Optional[float], change_3m: Optional[float], higher_is_better: bool) -> str:
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"""Return improving / neutral / deteriorating."""
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if change_3m is None and zscore is None:
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return "neutral"
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if change_3m is not None:
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effective = change_3m if higher_is_better else -change_3m
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if effective > 1.5:
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return "improving"
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if effective < -1.5:
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return "deteriorating"
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if zscore is not None:
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effective_z = zscore if higher_is_better else -zscore
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if effective_z > 0.5:
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return "improving"
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if effective_z < -0.5:
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return "deteriorating"
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return "neutral"
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_cached_subfactors = cached("macro_subfactors", ttl_seconds=3600)
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@_cached_subfactors
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def get_subfactor_breakdown() -> Dict[str, Any]:
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"""Return 4-category × 4-indicator subfactor breakdown with cycle stage."""
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categories: Dict[str, Any] = {}
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all_scores: List[float] = []
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for cat_name, indicators in _SUBFACTOR_CATEGORIES.items():
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items: List[Dict[str, Any]] = []
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cat_scores: List[float] = []
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for s in indicators:
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try:
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raw = _fetch_fred_series(s.fred_code)
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if raw is None or raw.empty:
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items.append({"key": s.key, "label": s.label, "value": None, "change_3m": None, "zscore": None, "signal": "neutral"})
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continue
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values = raw.iloc[:, 0]
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if s.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 values
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if values is None or values.empty:
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items.append({"key": s.key, "label": s.label, "value": None, "change_3m": None, "zscore": None, "signal": "neutral"})
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continue
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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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change = _subfactor_3m_change(values)
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signal = _subfactor_signal(z, change, s.higher_is_better)
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if z is not None:
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effective = z if s.higher_is_better else -z
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cat_scores.append(effective)
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all_scores.append(effective)
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items.append({
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"key": s.key,
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"label": s.label,
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"value": round(latest, 2) if latest is not None else None,
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"unit": s.unit,
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"change_3m": change,
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"zscore": round(z, 2) if z is not None else None,
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"signal": signal,
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})
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except Exception:
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items.append({"key": s.key, "label": s.label, "value": None, "change_3m": None, "zscore": None, "signal": "neutral"})
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cat_score = round(float(np.mean(cat_scores)), 2) if cat_scores else 0.0
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categories[cat_name] = {"score": cat_score, "indicators": items}
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# Determine cycle stage from composite score
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composite = round(float(np.mean(all_scores)), 2) if all_scores else 0.0
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growth_score = categories.get("Growth", {}).get("score", 0)
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price_score = categories.get("Prices", {}).get("score", 0)
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# 4-stage cycle: growth momentum + price momentum
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if growth_score > 0 and price_score <= 0:
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stage = "Early Expansion"
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elif growth_score > 0 and price_score > 0:
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stage = "Late Expansion"
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elif growth_score <= 0 and price_score > 0:
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stage = "Early Contraction"
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else:
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stage = "Late Contraction"
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return {
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"updated_at": datetime.utcnow().isoformat() + "Z",
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"composite_score": composite,
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"cycle_stage": stage,
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"categories": categories,
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}
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def get_country_series(country: str, indicator: str, period: str = "5y") -> Dict[str, Any]:
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"""Return a time series for a single country/indicator pair."""
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mappings = COUNTRY_SERIES.get(country)
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