DCF Valuation with Valuein SDK

Overview

A Discounted Cash Flow (DCF) model estimates a company's intrinsic value by projecting future free cash flows and discounting them back to present value. Valuein provides the WACC, FCF base, and growth rate assumptions via the get_valuation_metrics MCP tool, or directly via the Python SDK.

Install and Authenticate

Install the Valuein SDK and set your API token.

pip install valuein-sdk

from valuein_sdk import ValueinClient
client = ValueinClient()

Fetch Valuation Inputs

Use the valuation table to get pipeline-computed WACC, FCF base, and growth rate assumptions.

from valuein_sdk import ValueinClient, ValueinError

try:
    with ValueinClient() as client:
        df = client.query("""
          SELECT 
            entity_id, period_end, knowledge_at,
            wacc, fcf_base, stage1_growth_rate, terminal_growth_rate,
            stage1_years, shares_outstanding
          FROM valuation
          WHERE entity_id = '0000320193'  -- AAPL CIK
          ORDER BY period_end DESC
          LIMIT 1
        """)
        row = df.iloc[0]
except ValueinError as e:
    print(f"Error: {e}")

Compute Intrinsic Value

Implement the two-stage DCF: high-growth phase followed by terminal value.

def dcf_two_stage(fcf, growth, wacc, terminal_growth, years, shares):
    # Stage 1: explicit growth period
    pv_fcfs = 0
    for t in range(1, years + 1):
        fcf_t = fcf * (1 + growth) ** t
        pv_fcfs += fcf_t / (1 + wacc) ** t
    
    # Stage 2: terminal value (Gordon Growth)
    terminal_fcf = fcf * (1 + growth) ** years * (1 + terminal_growth)
    terminal_value = terminal_fcf / (wacc - terminal_growth)
    pv_terminal = terminal_value / (1 + wacc) ** years
    
    total_equity = pv_fcfs + pv_terminal
    return total_equity / shares

fcf, wacc, growth, terminal, years, shares = (
    row[4], row[3], row[5], row[6], row[7], row[8]
)
intrinsic_value = dcf_two_stage(fcf, growth, wacc, terminal, years, shares)
print(f"Intrinsic value per share: ${intrinsic_value:.2f}")

Point-in-Time Backtest

To avoid look-ahead bias, filter by knowledge_at when using DCF for historical analysis.

# Only use fundamentals known before 2023-01-01
from valuein_sdk import ValueinClient, ValueinError

try:
    with ValueinClient() as client:
        df = client.query("""
          SELECT wacc, fcf_base, stage1_growth_rate, terminal_growth_rate,
                 stage1_years, shares_outstanding
          FROM valuation
          WHERE entity_id = '0000320193'
            AND knowledge_at <= TIMESTAMP '2023-01-01'
          ORDER BY knowledge_at DESC
          LIMIT 1
        """)
        row = df.iloc[0]
except ValueinError as e:
    print(f"Error: {e}")