diff --git a/src/argus/analytics/metrics/trend_metrics.py b/src/argus/analytics/metrics/trend_metrics.py
index 5622acb..40e857c 100644
--- a/src/argus/analytics/metrics/trend_metrics.py
+++ b/src/argus/analytics/metrics/trend_metrics.py
@@ -73,3 +73,57 @@ def get_min_max_rates(df: pd.DataFrame) -> dict:
     min_max["max_date"].append(df.loc[max_id, "date"])
     min_max["max_rate"].append(df.loc[max_id, "rate"])
     return min_max
+
+
+def get_cumulative_return(df: pd.DataFrame) -> float:
+    if df.empty:
+        return 0.0
+
+    start_rate = float(df["rate"].iloc[0])
+    end_rate = float(df["rate"].iloc[-1])
+
+    if start_rate == 0.0:
+        return 0.0
+
+    return (end_rate - start_rate) / start_rate * 100
+
+
+def get_strongest_weakest_days(df: pd.DataFrame) -> dict:
+    if df.empty or len(df) < 2:
+        return {
+            "strongest_day": {"date": None, "pct_change": 0.0},
+            "weakest_day": {"date": None, "pct_change": 0.0},
+        }
+
+    pct_series = df.loc[:, "rate"].pct_change() * 100
+    valid_pct = pct_series.dropna()
+
+    if valid_pct.empty:
+        return {
+            "strongest_day": {"date": None, "pct_change": 0.0},
+            "weakest_day": {"date": None, "pct_change": 0.0},
+        }
+
+    max_idx = valid_pct.idxmax()
+    min_idx = valid_pct.idxmin()
+
+    return {
+        "strongest_day": {
+            "date": df.loc[max_idx, "date"],
+            "pct_change": round(float(pct_series.loc[max_idx]), 2),
+        },
+        "weakest_day": {
+            "date": df.loc[min_idx, "date"],
+            "pct_change": round(float(pct_series.loc[min_idx]), 2),
+        },
+    }
+
+
+def add_rolling_volatility(df: pd.DataFrame, window: int = 3) -> pd.DataFrame:
+    result = df.copy()
+    daily_returns = result["rate"].pct_change() * 100
+    result["rolling_volatility"] = daily_returns.rolling(
+        window=window, min_periods=1
+    ).std()
+    result["rolling_volatility"] = result["rolling_volatility"].fillna(0.0)
+    return result
diff --git a/tests/test_trend_metrics.py b/tests/test_trend_metrics.py
index fe175bd..5a3caac 100644
--- a/tests/test_trend_metrics.py
+++ b/tests/test_trend_metrics.py
@@ -1,10 +1,14 @@
 import pandas as pd
 import pandas.testing as pdt
 import numpy as np
+import pytest
 from argus.analytics.metrics.trend_metrics import (
     add_daily_percentage_change,
     add_rolling_average,
     get_min_max_rates,
+    get_cumulative_return,
+    get_strongest_weakest_days,
+    add_rolling_volatility,
 )
 
 
@@ -60,3 +64,63 @@ def test_get_min_max_():
     result_dict = get_min_max_rates(test_df)
 
     assert result_dict == min_max
+
+
+def test_get_cumulative_return():
+    test_timesseries = {
+        "date": ["2026-05-01", "2026-05-02", "2026-05-03"],
+        "rate": [1.00, 1.10, 1.21],
+    }
+    test_df = pd.DataFrame(test_timesseries)
+    resault = get_cumulative_return(test_df)
+    assert resault == pytest.approx(21.0)
+
+    # Egde case
+    empty_df = pd.DataFrame(columns=["date", "rate"])
+    result = get_cumulative_return(empty_df)
+    assert result == 0.0
+
+
+def test_get_strongest_weakest_days():
+    test_timeseries = {
+        "date": ["2026-05-01", "2026-05-02", "2026-05-03", "2026-05-04"],
+        "rate": [1.00, 1.20, 1.14, 2.00],
+    }
+    test_df = pd.DataFrame(test_timeseries)
+
+    result = get_strongest_weakest_days(test_df)
+
+    assert result == {
+        "strongest_day": {"date": "2026-05-04", "pct_change": 75.44},
+        "weakest_day": {"date": "2026-05-03", "pct_change": -5.0},
+    }
+
+    # Edge case
+    flat_timeseries = {
+        "date": ["2026-05-01", "2026-05-02", "2026-05-03"],
+        "rate": [1.15, 1.15, 1.15],
+    }
+    flat_df = pd.DataFrame(flat_timeseries)
+    result = get_strongest_weakest_days(flat_df)
+    assert result == {
+        "strongest_day": {"date": "2026-05-02", "pct_change": 0.0},
+        "weakest_day": {"date": "2026-05-02", "pct_change": 0.0},
+    }
+
+
+def test_is_rolling_volatility_added():
+    test_timeseries = {
+        "date": ["2026-05-01", "2026-05-02", "2026-05-03"],
+        "rate": [1.00, 2.00, 1.00],
+    }
+    test_df = pd.DataFrame(test_timeseries)
+
+    expect_result = {
+        "date": ["2026-05-01", "2026-05-02", "2026-05-03"],
+        "rate": [1.00, 2.00, 1.00],
+        "rolling_volatility": [0.0, 0.0, 106.06601717798213],
+    }
+    expect_df = pd.DataFrame(expect_result)
+    result_df = add_rolling_volatility(test_df, window=2)
+
+    pdt.assert_frame_equal(result_df, expect_df)