diff --git a/notebooks/code_samples/capital_markets/quickstart_option_pricing_models.ipynb b/notebooks/code_samples/capital_markets/quickstart_option_pricing_models.ipynb
index b7446349d..39ed384b4 100644
--- a/notebooks/code_samples/capital_markets/quickstart_option_pricing_models.ipynb
+++ b/notebooks/code_samples/capital_markets/quickstart_option_pricing_models.ipynb
@@ -35,6 +35,8 @@
     "  - [Preview the documentation template](#toc4_1_)    \n",
     "- [Model development](#toc5_)   \n",
     "- [Data preparation](#toc6_)   \n",
+    "- [Model development](#toc5_)   \n",
+    "- [Data preparation](#toc6_)   \n",
     "  - [Synthetic data generation](#toc6_1_)\n",
     "  - [Data quality](#toc6_2_)\n",
     "  - [Model Calibration](#toc6_3_)    \n",
@@ -190,7 +192,7 @@
     "    # api_key=\"...\",\n",
     "    # api_secret=\"...\",\n",
     "    # model=\"...\",\n",
-    ")\n"
+    ")"
    ]
   },
   {
@@ -509,6 +511,83 @@
     "Let's check quality of the data using outliers and missing data tests."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8f52ac93",
+   "metadata": {},
+   "source": [
+    "#### Outliers detection using IQR method\n",
+    "Let's visualizes the distribution of outliers in the option_price feature using the Interquartile Range (IQR) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f1c1ab6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = run_test(\n",
+    "    \"validmind.data_validation.IQROutliersBarPlot:BlackScholes\",\n",
+    "    inputs={\n",
+    "        \"dataset\": vm_bs_market_data,\n",
+    "    },\n",
+    "    title=\"Outliers detection using IQR method for BlackScholes\",\n",
+    ")\n",
+    "result.log()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b5e8654",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = run_test(\n",
+    "    \"validmind.data_validation.IQROutliersTable:BlackScholes\",\n",
+    "    inputs={\n",
+    "        \"dataset\": vm_bs_market_data,\n",
+    "    },\n",
+    "    title=\"Outliers table using IQR method for BlackScholes\",\n",
+    ")\n",
+    "result.log()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d96f10c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = run_test(\n",
+    "    \"validmind.data_validation.IQROutliersBarPlot:StochasticVolatility\",\n",
+    "    inputs={\n",
+    "        \"dataset\": vm_sv_market_data,\n",
+    "    },\n",
+    "    title=\"Outliers detection using IQR method for StochasticVolatility\",\n",
+    ")\n",
+    "result.log()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5b821d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = run_test(\n",
+    "    \"validmind.data_validation.IQROutliersTable:StochasticVolatility\",\n",
+    "    inputs={\n",
+    "        \"dataset\": vm_sv_market_data,\n",
+    "    },\n",
+    "    title=\"Outliers table using IQR method for StochasticVolatility\",\n",
+    ")\n",
+    "result.log()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "8dfc7cc3",
@@ -575,7 +654,7 @@
     "    },\n",
     "    title=\"Missing Values detection for BlackScholes\",\n",
     ")\n",
-    "result.log()\n"
+    "result.log()"
    ]
   },
   {
@@ -609,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "id": "5e709e0e",
    "metadata": {},
    "outputs": [],
@@ -707,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 20,
    "id": "ac733262",
    "metadata": {},
    "outputs": [],
@@ -788,11 +867,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 22,
    "id": "80492c82",
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
     "@vm.test(\"my_custom_tests.Sensitivity\")\n",
     "def sensitivity_test(model_type, S0, T, r, N, M, strike=None, barrier=None, sigma=None, v0=None, kappa=None,theta=None, xi=None, rho=None):\n",
     "    \"\"\"\n",
@@ -819,7 +899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 23,
    "id": "b2115371",
    "metadata": {},
    "outputs": [],
@@ -838,25 +918,58 @@
    "id": "9a798ff8",
    "metadata": {},
    "source": [
-    "##### Common plot function"
+    "##### Common plot function\n",
+    "Let's create a line plot using the default result output data and log it by passing the function through the `post_process_fn` parameter in the `run_test()` method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "f6c98fbe",
+   "execution_count": 24,
+   "id": "d96969b7",
    "metadata": {},
    "outputs": [],
    "source": [
-    "def plot_results(df, params: dict = None):\n",
-    "        fig2 =  plt.figure(figsize=(10, 6))\n",
-    "        plt.plot(df[params[\"x\"]], df[params[\"y\"]], label=params[\"label\"])\n",
-    "        plt.xlabel(params[\"xlabel\"])\n",
-    "        plt.ylabel(params[\"ylabel\"])\n",
-    "        plt.title(params[\"title\"])\n",
-    "        plt.legend()\n",
-    "        plt.grid(True)\n",
-    "        plt.show() "
+    "from plotly_express import bar\n",
+    "from validmind.vm_models.figure import Figure\n",
+    "from validmind.vm_models.result import TestResult\n",
+    "import plotly.graph_objects as go\n",
+    "import random\n",
+    "\n",
+    "def process_results(result: TestResult):\n",
+    "    # Convert to DataFrame\n",
+    "    df = pd.DataFrame(result.tables[0].data)\n",
+    "    \n",
+    "    # Get the first two column names\n",
+    "    x_col = df.columns[0]\n",
+    "    y_col = df.columns[1]\n",
+    "    \n",
+    "    # Create figure\n",
+    "    fig = go.Figure()\n",
+    "    fig.add_trace(\n",
+    "        go.Scatter(\n",
+    "            x=df[x_col],\n",
+    "            y=df[y_col],\n",
+    "            mode='lines',\n",
+    "            name=y_col  # Use y-axis column name as trace name\n",
+    "        )\n",
+    "    )\n",
+    "    \n",
+    "    fig.update_layout(\n",
+    "        xaxis_title=x_col,\n",
+    "        yaxis_title=y_col,\n",
+    "        showlegend=True,\n",
+    "        template=\"plotly_white\"\n",
+    "    )\n",
+    "\n",
+    "    result.add_figure(\n",
+    "        Figure(\n",
+    "            figure=fig,\n",
+    "            key=\"sensitivity_plot_\" + str(random.randint(0, 1000000)),\n",
+    "            ref_id=result.ref_id,\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    return result"
    ]
   },
   {
@@ -893,19 +1006,9 @@
     "        \"xi\": [0.1],\n",
     "        \"rho\": [-0.5],\n",
     "    },\n",
+    "    post_process_fn= process_results\n",
     ")\n",
-    "result.log()\n",
-    "plot_results(\n",
-    "    pd.DataFrame(result.tables[0].data),\n",
-    "    params={\n",
-    "        \"x\": \"strike\",\n",
-    "        \"y\":\"Option price\",\n",
-    "        \"label\":\"Option price\",\n",
-    "        \"xlabel\":\"strike\",\n",
-    "        \"ylabel\":\"option price\",\n",
-    "        \"title\":\"Knockout Option Price - Strike Level sensitivity\",\n",
-    "    }\n",
-    ")"
+    "result.log()"
    ]
   },
   {
@@ -941,19 +1044,10 @@
     "        \"xi\": [0.1],\n",
     "        \"rho\": [-0.5],\n",
     "    },\n",
+    "    post_process_fn=process_results\n",
+    "\n",
     ")\n",
-    "result.log()\n",
-    "plot_results(\n",
-    "    pd.DataFrame(result.tables[0].data),\n",
-    "    params={\n",
-    "        \"x\": \"barrier\",\n",
-    "        \"y\":\"Option price\",\n",
-    "        \"label\":\"Option price\",\n",
-    "        \"xlabel\":\"barrier\",\n",
-    "        \"ylabel\":\"option price\",\n",
-    "        \"title\":\"Knockout Option Price - Barrier Level Sensitivity\",\n",
-    "    }\n",
-    ")"
+    "result.log()"
    ]
   },
   {
@@ -967,7 +1061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 27,
    "id": "7bdd02ed",
    "metadata": {},
    "outputs": [],
@@ -994,9 +1088,83 @@
    "metadata": {},
    "source": [
     "##### Rho (correlation) and Theta (long term vol) stress test\n",
+    "First, we create a surface plot to visualize the option price with respect to two variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "77d49b6a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def two_parameters_stress_surface_plot(result: TestResult):\n",
+    "    import plotly.graph_objects as go\n",
+    "    import numpy as np\n",
+    "    import pandas as pd\n",
+    "    # Convert to DataFrame\n",
+    "    data = pd.DataFrame(result.tables[0].data)\n",
+    "    \n",
+    "    # Get column names (assuming first column is x, next two are y1 and y2)\n",
+    "    z_col = data.columns[2]\n",
+    "    x_col = data.columns[0]\n",
+    "    y_col = data.columns[1]\n",
+    "    \n",
+    "    # Get unique values for x and y\n",
+    "    x_unique = np.sort(data[x_col].unique())\n",
+    "    y_unique = np.sort(data[y_col].unique())\n",
+    "    \n",
+    "    # Create meshgrid\n",
+    "    X, Y = np.meshgrid(x_unique, y_unique)\n",
+    "    \n",
+    "    # Create Z matrix\n",
+    "    Z = np.zeros_like(X)\n",
+    "    for i, x_val in enumerate(x_unique):\n",
+    "        for j, y_val in enumerate(y_unique):\n",
+    "            mask = (data[x_col] == x_val) & (data[y_col] == y_val)\n",
+    "            if mask.any():\n",
+    "                Z[j, i] = data.loc[mask, z_col].iloc[0]\n",
+    "    \n",
+    "    # Create the 3D surface plot\n",
+    "    fig = go.Figure(data=[go.Surface(x=X, y=Y, z=Z)])\n",
+    "    \n",
+    "    # Update the layout\n",
+    "    fig.update_layout(\n",
+    "        title=f'3D Surface Plot of {z_col}',\n",
+    "        scene=dict(\n",
+    "            xaxis_title=x_col,\n",
+    "            yaxis_title=y_col,\n",
+    "            zaxis_title=z_col,\n",
+    "            camera=dict(\n",
+    "                up=dict(x=0, y=0, z=1),\n",
+    "                center=dict(x=0, y=0, z=0),\n",
+    "                eye=dict(x=1.5, y=1.5, z=1.5)\n",
+    "            )\n",
+    "        ),\n",
+    "        width=900,\n",
+    "        height=700,\n",
+    "        margin=dict(l=65, r=50, b=65, t=90)\n",
+    "    )\n",
+    "\n",
+    "    result.add_figure(\n",
+    "        Figure(\n",
+    "            figure=fig,\n",
+    "            key=\"sensitivity_plot_\" + str(random.randint(0, 1000000)),\n",
+    "            ref_id=result.ref_id,\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b586190",
+   "metadata": {},
+   "source": [
     "Let's evaluates the sensitivity of a model's output to changes in the correlation parameter (rho) and the long-term variance parameter (theta) within a stochastic volatility framework.\n",
     "\n",
-    "This test is useful for understanding how variations in these parameters affect the model's valuation, which is crucial for risk management and model validation.\n"
+    "This test is useful for understanding how variations in these parameters affect the model's valuation, which is crucial for risk management and model validation."
    ]
   },
   {
@@ -1021,10 +1189,11 @@
     "        \"r\": [r],\n",
     "        \"v0\": [0.2],\n",
     "        \"kappa\": [2],\n",
-    "        \"theta\": [0, 0.2, 0.4],\n",
+    "        \"theta\": list(np.linspace(0,0.8, 10)),\n",
     "        \"xi\": [0.1],\n",
-    "        \"rho\": [-1, -0.5, 0, 0.5, 1],\n",
+    "        \"rho\": list(np.linspace(-1,0.8, 10)),\n",
     "    },\n",
+    "    post_process_fn=two_parameters_stress_surface_plot\n",
     ")\n",
     "result.log()\n"
    ]
@@ -1044,6 +1213,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
+    "\n",
     "result = run_test(\n",
     "    \"my_custom_tests.Stressing:TheRhoAndXiParameters\",\n",
     "    param_grid={\n",
@@ -1058,9 +1229,10 @@
     "        \"v0\": [0.2],\n",
     "        \"kappa\": [2],\n",
     "        \"theta\": [0.2],\n",
-    "        \"xi\": [0, 0.2, 0.4],\n",
-    "        \"rho\": [-1, -0.5, 0, 0.5, 1],\n",
+    "        \"xi\": list(np.linspace(0,0.8, 10)),\n",
+    "        \"rho\": list(np.linspace(-1,0.8, 10)),\n",
     "    },\n",
+    "    post_process_fn=two_parameters_stress_surface_plot\n",
     ")\n",
     "result.log()\n"
    ]
@@ -1096,19 +1268,9 @@
     "        \"r\": [r],\n",
     "        \"sigma\": list(np.linspace(0.2, 0.8, 10)),\n",
     "    },\n",
+    "    post_process_fn=process_results\n",
     ")\n",
-    "result.log()\n",
-    "plot_results(\n",
-    "    pd.DataFrame(result.tables[0].data),\n",
-    "    params={\n",
-    "        \"x\": \"sigma\",\n",
-    "        \"y\":\"Option price\",\n",
-    "        \"label\":\"Option price\",\n",
-    "        \"xlabel\":\"Sigma\",\n",
-    "        \"ylabel\":\"Option price\",\n",
-    "        \"title\":\"Knockout Option Price - Stress sigma\",\n",
-    "    }\n",
-    ")"
+    "result.log()\n"
    ]
   },
   {
@@ -1145,19 +1307,9 @@
     "        \"xi\": [0.1],\n",
     "        \"rho\": [-0.5],\n",
     "    },\n",
+    "    post_process_fn=process_results\n",
     ")\n",
-    "result.log()\n",
-    "plot_results(\n",
-    "    pd.DataFrame(result.tables[0].data),\n",
-    "    params={\n",
-    "        \"x\": \"kappa\",\n",
-    "        \"y\":\"Option price\",\n",
-    "        \"label\":\"Option price\",\n",
-    "        \"xlabel\":\"kappa\",\n",
-    "        \"ylabel\":\"option price\",\n",
-    "        \"title\":\"Knockout Option Price - stress kappa\",\n",
-    "    }\n",
-    ")"
+    "result.log()\n"
    ]
   },
   {
@@ -1193,19 +1345,9 @@
     "        \"xi\": [0.1],\n",
     "        \"rho\": [-0.5],\n",
     "    },\n",
+    "    post_process_fn=process_results\n",
     ")\n",
-    "result.log()\n",
-    "plot_results(\n",
-    "    pd.DataFrame(result.tables[0].data),\n",
-    "    params={\n",
-    "        \"x\": \"theta\",\n",
-    "        \"y\":\"Option price\",\n",
-    "        \"label\":\"Option price\",\n",
-    "        \"xlabel\":\"theta\",\n",
-    "        \"ylabel\":\"option price\",\n",
-    "        \"title\":\"Knockout Option Price - stress theta\",\n",
-    "    }\n",
-    ")"
+    "result.log()\n"
    ]
   },
   {
@@ -1241,19 +1383,9 @@
     "        \"xi\": list(np.linspace(0.05, 0.95, 10)),\n",
     "        \"rho\": [-0.5],\n",
     "    },\n",
+    "    post_process_fn=process_results\n",
     ")\n",
-    "result.log()\n",
-    "plot_results(\n",
-    "    pd.DataFrame(result.tables[0].data),\n",
-    "    params={\n",
-    "        \"x\": \"xi\",\n",
-    "        \"y\":\"Option price\",\n",
-    "        \"label\":\"Option price\",\n",
-    "        \"xlabel\":\"xi\",\n",
-    "        \"ylabel\":\"option price\",\n",
-    "        \"title\":\"Knockout Option Price - stress xi\",\n",
-    "    }\n",
-    ")"
+    "result.log()\n"
    ]
   },
   {
@@ -1289,19 +1421,9 @@
     "        \"xi\": [0.1],\n",
     "        \"rho\": list(np.linspace(-1.0, 1.0, 20)),\n",
     "    },\n",
+    "    post_process_fn=process_results\n",
     ")\n",
-    "result.log()\n",
-    "plot_results(\n",
-    "    pd.DataFrame(result.tables[0].data),\n",
-    "    params={\n",
-    "        \"x\": \"rho\",\n",
-    "        \"y\":\"Option price\",\n",
-    "        \"label\":\"Option price\",\n",
-    "        \"xlabel\":\"rho\",\n",
-    "        \"ylabel\":\"option price\",\n",
-    "        \"title\":\"Knockout Option Price - stress rho\",\n",
-    "    }\n",
-    ")"
+    "result.log()\n"
    ]
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index baeee3750..8b137eaec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,7 +10,7 @@ description = "ValidMind Library"
 license = "Commercial License"
 name = "validmind"
 readme = "README.pypi.md"
-version = "2.6.11"
+version = "2.7.0"
 
 [tool.poetry.dependencies]
 aiohttp = {extras = ["speedups"], version = "*"}
diff --git a/validmind/__version__.py b/validmind/__version__.py
index 396adecc4..2614ce9d9 100644
--- a/validmind/__version__.py
+++ b/validmind/__version__.py
@@ -1 +1 @@
-__version__ = "2.6.11"
+__version__ = "2.7.0"