Initial commit — Prism financial analysis dashboard

Streamlit app with market bar, price chart, financial statements,
DCF valuation engine, comparable companies, and news feed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

1 files changed, 82 insertions, 0 deletions

diff --git a/services/valuation_service.py b/services/valuation_service.py
new file mode 100644
index 0000000..f876f78
--- /dev/null
+++ b/services/valuation_service.py
@@ -0,0 +1,82 @@
+"""DCF valuation engine — Gordon Growth Model."""
+import numpy as np
+import pandas as pd
+
+
+def run_dcf(
+    fcf_series: pd.Series,
+    shares_outstanding: float,
+    wacc: float = 0.10,
+    terminal_growth: float = 0.03,
+    projection_years: int = 5,
+) -> dict:
+    """
+    Run a DCF model and return per-year breakdown plus intrinsic value per share.
+
+    Args:
+        fcf_series: Annual FCF values, most recent first (yfinance order).
+        shares_outstanding: Diluted shares outstanding.
+        wacc: Weighted average cost of capital (decimal, e.g. 0.10).
+        terminal_growth: Perpetuity growth rate (decimal, e.g. 0.03).
+        projection_years: Number of years to project FCFs.
+
+    Returns:
+        dict with keys:
+            intrinsic_value_per_share, total_pv, terminal_value_pv,
+            fcf_pv_sum, years, projected_fcfs, discounted_fcfs,
+            growth_rate_used
+    """
+    if fcf_series.empty or shares_outstanding <= 0:
+        return {}
+
+    # Use last N years of FCF (sorted oldest → newest)
+    historical = fcf_series.sort_index().dropna().values
+    if len(historical) < 2:
+        return {}
+
+    # Compute average YoY growth rate from historical FCF
+    growth_rates = []
+    for i in range(1, len(historical)):
+        if historical[i - 1] != 0:
+            g = (historical[i] - historical[i - 1]) / abs(historical[i - 1])
+            growth_rates.append(g)
+
+    # Cap growth rate to reasonable bounds [-0.5, 0.5]
+    raw_growth = float(np.median(growth_rates)) if growth_rates else 0.05
+    growth_rate = max(-0.50, min(0.50, raw_growth))
+
+    base_fcf = float(historical[-1])  # most recent FCF
+
+    # Project FCFs
+    projected_fcfs = []
+    for year in range(1, projection_years + 1):
+        fcf = base_fcf * ((1 + growth_rate) ** year)
+        projected_fcfs.append(fcf)
+
+    # Discount projected FCFs
+    discounted_fcfs = []
+    for i, fcf in enumerate(projected_fcfs, start=1):
+        pv = fcf / ((1 + wacc) ** i)
+        discounted_fcfs.append(pv)
+
+    fcf_pv_sum = sum(discounted_fcfs)
+
+    # Terminal value (Gordon Growth Model)
+    terminal_fcf = projected_fcfs[-1] * (1 + terminal_growth)
+    terminal_value = terminal_fcf / (wacc - terminal_growth)
+    terminal_value_pv = terminal_value / ((1 + wacc) ** projection_years)
+
+    total_pv = fcf_pv_sum + terminal_value_pv
+    intrinsic_value_per_share = total_pv / shares_outstanding
+
+    return {
+        "intrinsic_value_per_share": intrinsic_value_per_share,
+        "total_pv": total_pv,
+        "terminal_value_pv": terminal_value_pv,
+        "fcf_pv_sum": fcf_pv_sum,
+        "years": list(range(1, projection_years + 1)),
+        "projected_fcfs": projected_fcfs,
+        "discounted_fcfs": discounted_fcfs,
+        "growth_rate_used": growth_rate,
+        "base_fcf": base_fcf,
+    }