Point-in-Time Backtesting

from valuein_sdk import ValueinClient, ValueinError rebalance_date = "2022-01-01" try:    with ValueinClient() as client:         # PIT-safe: each company's latest annual revenue the SEC had        # accepted by the rebalance date        revenue_pit = client.run_query(f"""          SELECT entity_id, period_end, numeric_value AS revenue, accepted_at          FROM fact          WHERE standard_concept = 'TotalRevenue'            AND fiscal_period = 'FY'            AND accepted_at <= TIMESTAMP '{rebalance_date}'          QUALIFY ROW_NUMBER() OVER (            PARTITION BY entity_id ORDER BY period_end DESC, accepted_at DESC          ) = 1        """)        print(revenue_pit.head()) except ValueinError as e:    print(f"Error: {e}")

from valuein_sdk import ValueinClient, ValueinError # MCP: get the S&P 500 members on Jan 1, 2020# get_pit_universe(as_of_date="2020-01-01", index="SP500") # Python SDK: pit_universe() reads index_membership (keyed on cik) with the# half-open effective_date / removal_date window, delisted members included.try:    with ValueinClient() as client:        universe_2020 = client.pit_universe("2020-01-01", index="SP500")        print(f"S&P 500 universe on 2020-01-01: {len(universe_2020)} companies")        print(universe_2020[["cik", "company_name", "ticker_at_date"]].head()) except ValueinError as e:    print(f"Error: {e}")

import pandas as pdfrom valuein_sdk import ValueinClient, ValueinError rebalance_dates = pd.date_range("2018-01-01", "2023-12-01", freq="QS")portfolio_returns = [] try:    with ValueinClient() as client:        for date in rebalance_dates:            date_str = date.strftime("%Y-%m-%d")             # Step 1: PIT universe on this date (delisted members included)            universe = client.pit_universe(date_str)            if universe.empty:                continue             # Step 2: latest annual revenue known on this date — fact.entity_id == cik            ids = ",".join(f"'{cik}'" for cik in universe["cik"])            signals = client.run_query(f"""              SELECT entity_id,                numeric_value AS revenue,                accepted_at              FROM fact              WHERE standard_concept = 'TotalRevenue'                AND fiscal_period = 'FY'                AND accepted_at <= TIMESTAMP '{date_str}'                AND entity_id IN ({ids})              QUALIFY ROW_NUMBER() OVER (                PARTITION BY entity_id ORDER BY period_end DESC, accepted_at DESC              ) = 1            """)             # Step 3: rank by revenue (simplified)            signals["rank"] = signals["revenue"].rank(pct=True)            top_quintile = signals[signals["rank"] > 0.8]            portfolio_returns.append({"date": date, "n": len(top_quintile)})         print(pd.DataFrame(portfolio_returns).tail()) except ValueinError as e:    print(f"Error: {e}")