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diff --git a/python/reto.py b/python/reto.py
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+import datetime
+import globeop_reports as go
+import pandas as pd
+import numpy as np
+import argparse
+
+from pandas.tseries.offsets import BDay, MonthEnd, BMonthEnd, CustomBusinessMonthEnd
+
+from risk.bonds import subprime_risk, crt_risk, clo_risk
+from risk.portfolio import build_portfolio, generate_vol_surface
+from pnl_explain import get_bond_pv
+
+import serenitas.analytics as ana
+from serenitas.analytics.index_data import load_all_curves
+from serenitas.analytics.scenarios import run_portfolio_scenarios
+from serenitas.analytics.basket_index import BasketIndex
+from serenitas.analytics.base import Trade
+from serenitas.utils.db2 import dbconn
+from serenitas.utils.pool import serenitas_pool, dawn_pool
+from serenitas.utils.db import serenitas_engine, dawn_engine
+from serenitas.analytics.dates import prev_business_day
+
+
+def parse_args():
+    """Parses command line arguments"""
+    parser = argparse.ArgumentParser(description="Shock data and insert into DB")
+    parser.add_argument(
+        "date",
+        nargs="?",
+        type=datetime.date.fromisoformat,
+        default=prev_business_day(datetime.date.today()),
+    )
+    parser.add_argument("-n", "--no-upload", action="store_true", help="do not upload")
+    return parser.parse_args()
+
+
+def gen_spreads(shock_date, fund):
+    Trade.init_ontr(shock_date)
+    ana._local = False
+    spread_shock = np.array([-100.0, -25.0, 1.0, +25.0, 100.0, 200.0, 500, 1000])
+    spread_shock /= Trade._ontr["HY"].spread
+    portf, _ = build_portfolio(shock_date, shock_date, fund)
+    vol_surface = generate_vol_surface(portf, 10, "BAML")
+    portf.reset_pv()
+    scens = run_portfolio_scenarios(
+        portf,
+        date_range=[pd.Timestamp(shock_date)],
+        params=["pnl", "hy_equiv"],
+        spread_shock=spread_shock,
+        vol_shock=[0.0],
+        corr_shock=[0.0],
+        vol_surface=vol_surface,
+    )
+
+    strategies = {}
+    strategies["options"] = [
+        "HYOPTDEL",
+        "HYPAYER",
+        "HYREC",
+        "IGOPTDEL",
+        "IGPAYER",
+        "IGREC",
+    ]
+    strategies["tranches"] = [
+        "HYSNR",
+        "HYMEZ",
+        "HYINX",
+        "HYEQY",
+        "IGSNR",
+        "IGMEZ",
+        "IGINX",
+        "IGEQY",
+        "EUSNR",
+        "EUMEZ",
+        "EUINX",
+        "EUEQY",
+        "XOSNR",
+        "XOMEZ",
+        "XOINX",
+        "XOEQY",
+        "BSPK",
+    ]
+    if fund == "BRINKER":
+        scens = scens.xs(0, level="corr_shock")
+    else:
+        scens = scens.xs((0.0, 0.0), level=["vol_shock", "corr_shock"])
+
+    scens.columns.names = ["strategy", "trade_id", "scen_type"]
+
+    results = {}
+    for i, g in scens.groupby(level="scen_type", axis=1):
+        temp = g.groupby(level="strategy", axis=1).sum()
+        for key, item in strategies.items():
+            exist_columns = set(temp.columns).intersection(item)
+            temp[key] = temp[exist_columns].sum(axis=1)
+            temp.drop(exist_columns, axis=1, inplace=True)
+        temp["total"] = temp.sum(axis=1)
+        results[i] = temp
+    results = pd.concat(results)
+    return results
+
+
+def transform_df(df):
+    df = df.rename(
+        columns={
+            "spread_shock": "spread_shock",
+            "CASH_BASIS": "CASH_BASIS",
+            "HEDGE_CLO": "HEDGE_CLO",
+            "TEST": "TEST",
+        }
+    )
+    df = df.reset_index().rename(columns={"level_0": "unit"})
+
+    # Get the list of strategy columns by slicing the `df.columns` array
+    strategy_columns = df.columns[3:]
+
+    # Create a new dataframe with the desired format, using the dynamic list of strategy columns
+    df = pd.melt(
+        df, id_vars=["unit", "date", "spread_shock"], value_vars=strategy_columns
+    )
+
+    # Rename the columns to the desired names
+    df = df.rename(
+        columns={"variable": "strategy", "value": "value", "unit": "output_type"}
+    )
+
+    # Print the new dataframe to check
+    return df
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    conn = dbconn("dawndb")
+    for fund in ("SERCGMAST", "BOWDST", "ISOSEL", "BRINKER"):
+        results = gen_spreads(args.date, fund)
+        with conn.cursor() as c:
+            c.execute(
+                "DELETE FROM shocks WHERE fund=%s and date=%s",
+                (
+                    fund,
+                    args.date,
+                ),
+            )
+        conn.commit()
+        df = transform_df(results)
+        df["fund"] = fund
+        df.to_sql("shocks", dawn_engine, if_exists="append", index=False)