diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000..7131b09
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1 @@
+notebooks/** -linguist-detectable
\ No newline at end of file
diff --git a/Cargo.lock b/Cargo.lock
index 2e7af32..08b4261 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -259,7 +259,7 @@ checksum = "37909eebbb50d72f9059c3b6d82c0463f2ff062c9e95845c43a6c9c0355411be"
 
 [[package]]
 name = "finlib"
-version = "0.0.2"
+version = "0.0.3"
 dependencies = [
  "getrandom 0.2.15",
  "log",
@@ -274,7 +274,7 @@ dependencies = [
 
 [[package]]
 name = "finlib-ffi"
-version = "0.0.2"
+version = "0.0.3"
 dependencies = [
  "cbindgen",
  "csbindgen",
@@ -283,7 +283,7 @@ dependencies = [
 
 [[package]]
 name = "finlib-wasm"
-version = "0.0.2"
+version = "0.0.3"
 dependencies = [
  "console_error_panic_hook",
  "console_log",
@@ -606,7 +606,7 @@ dependencies = [
 
 [[package]]
 name = "pyfinlib"
-version = "0.0.2"
+version = "0.0.3"
 dependencies = [
  "finlib",
  "log",
diff --git a/Cargo.toml b/Cargo.toml
index 6b2dbbb..5c81626 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -14,7 +14,7 @@ default-members = [
 ]
 
 [workspace.package]
-version = "0.0.2"
+version = "0.0.3"
 authors = ["sarsoo <andy@sarsoo.xyz>"]
 description = "Quant finance functions implemented in Rust"
 edition = "2021"
diff --git a/finlib-wasm/js-local/index.js b/finlib-wasm/js-local/index.js
index 9a0ccb5..404a962 100644
--- a/finlib-wasm/js-local/index.js
+++ b/finlib-wasm/js-local/index.js
@@ -1,8 +1,10 @@
-import { ValueAtRisk, Portfolio, PortfolioAsset } from "finlib";
+import { ValueAtRisk, Portfolio, PortfolioAsset, init_logging } from "finlib";
+
+init_logging();
 
 console.log(ValueAtRisk.varcovar([1, 2, 3, 4], 0.1));
 console.log(ValueAtRisk.varcovar([1, 2, 3, 4], 0.05));
 
 let portfolio = new Portfolio([new PortfolioAsset(1.0, "test", [1.0, 2.0, 3.0])]);
 console.log(portfolio.isValid());
-console.log(portfolio.valueAtRisk(0.1));
+console.log(portfolio.valueAtRisk(0.1, 1000000));
diff --git a/finlib-wasm/src/lib.rs b/finlib-wasm/src/lib.rs
index 84e8ddd..109bffb 100644
--- a/finlib-wasm/src/lib.rs
+++ b/finlib-wasm/src/lib.rs
@@ -2,8 +2,13 @@ use wasm_bindgen::prelude::wasm_bindgen;
 use console_log;
 use log::Level;
 
-#[wasm_bindgen(start)]
-fn start() {
+// #[wasm_bindgen(start)]
+// fn start() {
+//
+// }
+
+#[wasm_bindgen]
+pub fn init_logging() {
     if let Err(_) = console_log::init_with_level(Level::Debug) {
 
     }
diff --git a/finlib/src/risk/forecast/mod.rs b/finlib/src/risk/forecast/mod.rs
index e484c0d..25fdf49 100644
--- a/finlib/src/risk/forecast/mod.rs
+++ b/finlib/src/risk/forecast/mod.rs
@@ -1,5 +1,3 @@
-use statrs::distribution::{ContinuousCDF, Normal};
-
 pub fn mean_investment(portfolio_mean_change: f64, initial_investment: f64) -> f64 {
     (1. + portfolio_mean_change) * initial_investment
 }
diff --git a/finlib/src/risk/portfolio.rs b/finlib/src/risk/portfolio.rs
index 3adade4..cd79a01 100644
--- a/finlib/src/risk/portfolio.rs
+++ b/finlib/src/risk/portfolio.rs
@@ -1,17 +1,20 @@
+use log::{debug, error, info};
 use ndarray::prelude::*;
 use ndarray_stats::CorrelationExt;
 #[cfg(feature = "wasm")]
 use wasm_bindgen::prelude::*;
 #[cfg(feature = "py")]
 use pyo3::prelude::*;
-use crate::risk::var::varcovar::{portfolio_value_at_risk, portfolio_value_at_risk_percent};
+use statrs::distribution::{ContinuousCDF, Normal};
+use crate::risk::forecast::{mean_investment, std_dev_investment};
+use crate::risk::var::varcovar::{investment_value_at_risk};
 use crate::stats;
 use crate::util::roc::rates_of_change;
 
 #[cfg_attr(feature = "wasm", wasm_bindgen)]
 #[cfg_attr(feature = "py", pyclass)]
 #[cfg_attr(feature = "ffi", repr(C))]
-#[derive(Clone)]
+#[derive(Clone, Debug, PartialEq, PartialOrd)]
 pub struct Portfolio {
     assets: Vec<PortfolioAsset>
 }
@@ -28,7 +31,7 @@ pub enum ValueType {
 #[cfg_attr(feature = "wasm", wasm_bindgen)]
 #[cfg_attr(feature = "py", pyclass)]
 #[cfg_attr(feature = "ffi", repr(C))]
-#[derive(Clone)]
+#[derive(Clone, Debug, PartialEq, PartialOrd)]
 pub struct PortfolioAsset {
     portfolio_weight: f64,
     name: String,
@@ -56,6 +59,7 @@ impl PortfolioAsset {
     pub fn get_mean_and_std(&self) -> Option<(f64, f64)> {
         match self.value_type {
             ValueType::Absolute => {
+                info!("[{}] Asset's values are currently absolute, calculating rates of change first", self.name);
                 let roc = rates_of_change(&self.values).collect::<Vec<f64>>();
                 Some((stats::mean(&roc), stats::sample_std_dev(&roc)))
             }
@@ -106,7 +110,7 @@ impl Portfolio {
     }
 
     pub fn valid_weights(&self) -> bool {
-        let mut weight = 1 as f64;
+        let mut weight = 1f64;
 
         for asset in &self.assets {
             weight -= asset.portfolio_weight;
@@ -140,23 +144,27 @@ impl Portfolio {
 
     pub fn get_mean_and_std(&mut self) -> Option<(f64, f64)> {
         if !self.valid_sizes() {
+            error!("Can't get portfolio mean and std dev because asset value counts arent't the same");
             return None;
         }
 
         self.apply_rates_of_change();
         let m = self.get_matrix();
         if m.is_none() {
+            error!("Couldn't format portfolio as matrix");
             return None;
         }
         let m = m.unwrap();
 
         let cov = m.cov(1.);
         if cov.is_err() {
+            error!("Failed to calculate portfolio covariance");
             return None;
         }
         let cov = cov.unwrap();
         let mean_return = m.mean_axis(Axis(1));
         if mean_return.is_none() {
+            error!("Failed to calculate portfolio mean");
             return None;
         }
         let mean_return = mean_return.unwrap();
@@ -170,12 +178,36 @@ impl Portfolio {
         Some((porfolio_mean_return, portfolio_stddev))
     }
 
+    // https://www.interviewqs.com/blog/value-at-risk
+
     pub fn value_at_risk(&mut self, confidence: f64, initial_investment: f64) -> Option<f64> {
-        portfolio_value_at_risk(self, confidence, initial_investment)
+        match self.get_mean_and_std() {
+            None => None,
+            Some((mean, std_dev)) => {
+                debug!("Portfolio percent movement mean[{}], std dev[{}]", mean, std_dev);
+                let investment_mean = mean_investment(mean, initial_investment);
+                let investment_std_dev = std_dev_investment(std_dev, initial_investment);
+                debug!("Investment[{}] mean[{}], std dev[{}]", initial_investment, mean, std_dev);
+
+                let investment_var = investment_value_at_risk(confidence, investment_mean, investment_std_dev);
+
+                debug!("Investment[{}] value at risk [{}]", initial_investment, investment_var);
+
+                Some(initial_investment - investment_var)
+            }
+        }
     }
 
+    // https://www.interviewqs.com/blog/value-at-risk
+
     pub fn value_at_risk_percent(&mut self, confidence: f64) -> Option<f64> {
-        portfolio_value_at_risk_percent(self, confidence)
+        match self.get_mean_and_std() {
+            None => None,
+            Some((mean, std_dev)) => {
+                let n = Normal::new(mean, std_dev).unwrap();
+                Some(n.inverse_cdf(confidence))
+            }
+        }
     }
 }
 
diff --git a/finlib/src/risk/var/varcovar.rs b/finlib/src/risk/var/varcovar.rs
index cb74953..c121692 100644
--- a/finlib/src/risk/var/varcovar.rs
+++ b/finlib/src/risk/var/varcovar.rs
@@ -1,12 +1,9 @@
-use log::info;
 use crate::stats;
 use crate::util::roc::rates_of_change;
 
-use crate::risk::portfolio::Portfolio;
 #[cfg(feature = "parallel")]
 use rayon::prelude::*;
 use statrs::distribution::{ContinuousCDF, Normal};
-use crate::risk::forecast::{mean_investment, std_dev_investment};
 // https://medium.com/@serdarilarslan/value-at-risk-var-and-its-implementation-in-python-5c9150f73b0e
 
 pub fn value_at_risk_percent(values: &[f64], confidence: f64) -> f64 {
@@ -20,34 +17,6 @@ pub fn value_at_risk_percent(values: &[f64], confidence: f64) -> f64 {
     n.inverse_cdf(confidence)
 }
 
-pub fn portfolio_value_at_risk_percent(portfolio: &mut Portfolio, confidence: f64) -> Option<f64> {
-    match portfolio.get_mean_and_std() {
-        None => None,
-        Some((mean, std_dev)) => {
-            let n = Normal::new(mean, std_dev).unwrap();
-            Some(n.inverse_cdf(confidence))
-        }
-    }
-}
-
-pub fn portfolio_value_at_risk(portfolio: &mut Portfolio, confidence: f64, initial_investment: f64) -> Option<f64> {
-    match portfolio.get_mean_and_std() {
-        None => None,
-        Some((mean, std_dev)) => {
-            let investment_mean = mean_investment(mean, initial_investment);
-            let investment_std_dev = std_dev_investment(std_dev, std_dev);
-
-            info!("{:?}, {:?}", investment_mean, investment_std_dev);
-
-            let investment_var = investment_value_at_risk(confidence, investment_mean, investment_std_dev);
-
-            println!("{:?}", investment_var);
-
-            Some(initial_investment - investment_var)
-        }
-    }
-}
-
 pub fn investment_value_at_risk(confidence: f64, investment_mean: f64, investment_std_dev: f64) -> f64 {
     let n = Normal::new(investment_mean, investment_std_dev).unwrap();
 
@@ -61,6 +30,7 @@ pub fn scale_value_at_risk(initial_value: f64, time_cycles: isize) -> f64 {
 #[cfg(test)]
 mod tests {
     use super::*;
+    use crate::risk::portfolio::Portfolio;
     use crate::risk::portfolio::PortfolioAsset;
 
     #[test]
@@ -72,7 +42,7 @@ mod tests {
 
         let mut portfolio = Portfolio::from(assets);
 
-        portfolio_value_at_risk_percent(&mut portfolio, 0.1);
+        portfolio.value_at_risk_percent(0.1);
 
     }
 
@@ -84,7 +54,7 @@ mod tests {
 
         let mut portfolio = Portfolio::from(assets);
 
-        portfolio_value_at_risk_percent(&mut portfolio, 0.1);
+        portfolio.value_at_risk_percent(0.1);
     }
 
     #[test]
@@ -96,8 +66,8 @@ mod tests {
 
         let mut portfolio = Portfolio::from(assets);
 
-        println!("{:?}", portfolio_value_at_risk(&mut portfolio, 0.01, 1_000_000.));
-        println!("{:?}", portfolio_value_at_risk(&mut portfolio, 0.1, 1_000_000.));
-        println!("{:?}", portfolio_value_at_risk(&mut portfolio, 0.5, 1_000_000.));
+        println!("{:?}", portfolio.value_at_risk(0.01, 1_000_000.));
+        println!("{:?}", portfolio.value_at_risk(0.1, 1_000_000.));
+        println!("{:?}", portfolio.value_at_risk(0.5, 1_000_000.));
     }
 }
\ No newline at end of file
diff --git a/finlib/src/util/vector.rs b/finlib/src/util/vector.rs
index 18df3d3..31bbcea 100644
--- a/finlib/src/util/vector.rs
+++ b/finlib/src/util/vector.rs
@@ -1,5 +1,9 @@
+use log::error;
 
 pub fn dot_product(a: &[f64], b: &[f64]) -> f64 {
+    if a.len() != b.len() {
+        error!("Can't dot product two vectors of different lengths, a = {}, b = {}", a.len(), b.len());
+    }
     assert_eq!(a.len(), b.len());
 
     a.iter()
diff --git a/notebooks/interviewqs_sample.ipynb b/notebooks/interviewqs_sample.ipynb
deleted file mode 100644
index 96c93fb..0000000
--- a/notebooks/interviewqs_sample.ipynb
+++ /dev/null
@@ -1,347 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "id": "initial_id",
-   "metadata": {
-    "collapsed": true,
-    "ExecuteTime": {
-     "end_time": "2025-02-15T23:03:06.288017Z",
-     "start_time": "2025-02-15T23:03:00.795664Z"
-    }
-   },
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from openbb import obb\n",
-    "import datetime as dt\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "# Create our portfolio of equities\n",
-    "tickers = ['AAPL','META', 'C', 'DIS']\n",
-    "\n",
-    "# Set the investment weights (I arbitrarily picked for example)\n",
-    "weights = np.array([.25, .3, .15, .3])\n",
-    "\n",
-    "# Set an initial investment level\n",
-    "initial_investment = 1000000\n",
-    "\n",
-    "# Download closing prices\n",
-    "data = (obb.equity.price.historical(symbol=tickers, provider='yfinance')\n",
-    "        .to_df()\n",
-    "        .drop(columns=['open', 'high', 'low', 'volume', 'dividend'])\n",
-    "        .pivot(columns='symbol'))\n",
-    "\n",
-    "#From the closing prices, calculate periodic returns\n",
-    "returns = data.pct_change()\n",
-    "\n",
-    "# returns.tail()"
-   ],
-   "outputs": [],
-   "execution_count": 1
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-15T22:25:07.699602Z",
-     "start_time": "2025-02-15T22:25:07.694945Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# Generate Var-Cov matrix\n",
-    "cov_matrix = returns.cov()\n",
-    "cov_matrix"
-   ],
-   "id": "146c7f1b5a34b41a",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "                 close                              \n",
-       "symbol            AAPL         C       DIS      META\n",
-       "      symbol                                        \n",
-       "close AAPL    0.000227  0.000039  0.000015  0.000080\n",
-       "      C       0.000039  0.000291  0.000079  0.000063\n",
-       "      DIS     0.000015  0.000079  0.000202  0.000023\n",
-       "      META    0.000080  0.000063  0.000023  0.000363"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr:last-of-type th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"4\" halign=\"left\">close</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>symbol</th>\n",
-       "      <th>AAPL</th>\n",
-       "      <th>C</th>\n",
-       "      <th>DIS</th>\n",
-       "      <th>META</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th>symbol</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"4\" valign=\"top\">close</th>\n",
-       "      <th>AAPL</th>\n",
-       "      <td>0.000227</td>\n",
-       "      <td>0.000039</td>\n",
-       "      <td>0.000015</td>\n",
-       "      <td>0.000080</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>C</th>\n",
-       "      <td>0.000039</td>\n",
-       "      <td>0.000291</td>\n",
-       "      <td>0.000079</td>\n",
-       "      <td>0.000063</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DIS</th>\n",
-       "      <td>0.000015</td>\n",
-       "      <td>0.000079</td>\n",
-       "      <td>0.000202</td>\n",
-       "      <td>0.000023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>META</th>\n",
-       "      <td>0.000080</td>\n",
-       "      <td>0.000063</td>\n",
-       "      <td>0.000023</td>\n",
-       "      <td>0.000363</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 31
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-15T22:25:07.744917Z",
-     "start_time": "2025-02-15T22:25:07.741519Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# Calculate mean returns for each stock\n",
-    "avg_rets = returns.mean()\n",
-    "\n",
-    "# Calculate mean returns for portfolio overall,\n",
-    "# using dot product to\n",
-    "# normalize individual means against investment weights\n",
-    "# https://en.wikipedia.org/wiki/Dot_product#:~:targetText=In%20mathematics%2C%20the%20dot%20product,and%20returns%20a%20single%20number.\n",
-    "port_mean = avg_rets.dot(weights)\n",
-    "\n",
-    "# Calculate portfolio standard deviation\n",
-    "port_stdev = np.sqrt(weights.T.dot(cov_matrix).dot(weights))\n",
-    "\n",
-    "# Calculate mean of investment\n",
-    "mean_investment = (1+port_mean) * initial_investment\n",
-    "\n",
-    "# Calculate standard deviation of investmnet\n",
-    "stdev_investment = initial_investment * port_stdev"
-   ],
-   "id": "a157b600c98f83fd",
-   "outputs": [],
-   "execution_count": 32
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-15T22:28:35.064271Z",
-     "start_time": "2025-02-15T22:28:35.046813Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# Select our confidence interval (I'll choose 95% here)\n",
-    "conf_level1 = 0.05\n",
-    "\n",
-    "# Using SciPy ppf method to generate values for the\n",
-    "# inverse cumulative distribution function to a normal distribution\n",
-    "# Plugging in the mean, standard deviation of our portfolio\n",
-    "# as calculated above\n",
-    "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.norm.html\n",
-    "from scipy.stats import norm\n",
-    "cutoff1 = norm.ppf(conf_level1, mean_investment, stdev_investment)\n",
-    "cutoff1"
-   ],
-   "id": "7cf30fad1b4a37f7",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(983640.6453882146)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 36
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-15T22:28:39.289345Z",
-     "start_time": "2025-02-15T22:28:39.287143Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "#Finally, we can calculate the VaR at our confidence interval\n",
-    "var_1d1 = initial_investment - cutoff1\n",
-    "var_1d1"
-   ],
-   "id": "5c5c2a6f22e44002",
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(16359.354611785384)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "execution_count": 37
-  },
-  {
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2025-02-15T22:25:09.199604Z",
-     "start_time": "2025-02-15T22:25:09.093065Z"
-    }
-   },
-   "cell_type": "code",
-   "source": [
-    "# Calculate n Day VaR\n",
-    "var_array = []\n",
-    "num_days = int(15)\n",
-    "for x in range(1, num_days+1):\n",
-    "        var_array.append(np.round(var_1d1 * np.sqrt(x),2))\n",
-    "        print(str(x) + \" day VaR @ 95% confidence: \" + str(np.round(var_1d1 * np.sqrt(x),2)))\n",
-    "\n",
-    "# Build plot\n",
-    "plt.xlabel(\"Day #\")\n",
-    "plt.ylabel(\"Max portfolio loss (USD)\")\n",
-    "plt.title(\"Max portfolio loss (VaR) over 15-day period\")\n",
-    "plt.plot(var_array, \"r\")"
-   ],
-   "id": "641bda40d9fa37f",
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 day VaR @ 95% confidence: 16359.35\n",
-      "2 day VaR @ 95% confidence: 23135.62\n",
-      "3 day VaR @ 95% confidence: 28335.23\n",
-      "4 day VaR @ 95% confidence: 32718.71\n",
-      "5 day VaR @ 95% confidence: 36580.63\n",
-      "6 day VaR @ 95% confidence: 40072.07\n",
-      "7 day VaR @ 95% confidence: 43282.78\n",
-      "8 day VaR @ 95% confidence: 46271.24\n",
-      "9 day VaR @ 95% confidence: 49078.06\n",
-      "10 day VaR @ 95% confidence: 51732.82\n",
-      "11 day VaR @ 95% confidence: 54257.84\n",
-      "12 day VaR @ 95% confidence: 56670.47\n",
-      "13 day VaR @ 95% confidence: 58984.49\n",
-      "14 day VaR @ 95% confidence: 61211.1\n",
-      "15 day VaR @ 95% confidence: 63359.51\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15cf1e630>]"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "execution_count": 35
-  },
-  {
-   "metadata": {},
-   "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
-   "source": "",
-   "id": "16442df6e6372aa5"
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/notebooks/varcovar_var_portfolio.ipynb b/notebooks/varcovar_var_portfolio.ipynb
index 965a664..65a5cf5 100644
--- a/notebooks/varcovar_var_portfolio.ipynb
+++ b/notebooks/varcovar_var_portfolio.ipynb
@@ -6,8 +6,8 @@
    "metadata": {
     "collapsed": true,
     "ExecuteTime": {
-     "end_time": "2025-02-15T23:38:23.437304Z",
-     "start_time": "2025-02-15T23:38:18.283134Z"
+     "end_time": "2025-02-16T14:38:29.802570Z",
+     "start_time": "2025-02-16T14:38:24.600692Z"
     }
    },
    "source": [
@@ -18,12 +18,13 @@
     "import logging\n",
     "\n",
     "# logging.basicConfig(level=logging.DEBUG)\n",
+    "logging.basicConfig(level=logging.INFO)\n",
     "\n",
     "# Create our portfolio of equities\n",
-    "tickers = ['AAPL','META', 'C', 'DIS']\n",
+    "tickers = ['AAPL','META', 'C', 'DIS', 'CL=F']\n",
     "\n",
     "# Set the investment weights (I arbitrarily picked for example)\n",
-    "weights = np.array([.25, .3, .15, .3])\n",
+    "weights = np.array([.1, .1, .1, .1, .6])\n",
     "\n",
     "# Set an initial investment level\n",
     "initial_investment = 1000000\n"
@@ -34,13 +35,13 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-02-15T23:38:25.013640Z",
-     "start_time": "2025-02-15T23:38:24.301243Z"
+     "end_time": "2025-02-16T14:38:30.804442Z",
+     "start_time": "2025-02-16T14:38:29.810045Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "data = obb.equity.price.historical(symbol=tickers, provider='yfinance')\n",
+    "data = obb.equity.price.historical(symbol=tickers, start_date=\"2022-01-01\", provider='yfinance')\n",
     "data.results[0].date, data.results[-1].date"
    ],
    "id": "e5573366e39b2962",
@@ -48,7 +49,7 @@
     {
      "data": {
       "text/plain": [
-       "(datetime.date(2024, 2, 15), datetime.date(2025, 2, 14))"
+       "(datetime.date(2022, 1, 3), datetime.date(2025, 2, 14))"
       ]
      },
      "execution_count": 2,
@@ -61,22 +62,36 @@
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-02-15T23:38:25.026860Z",
-     "start_time": "2025-02-15T23:38:25.018102Z"
+     "end_time": "2025-02-16T14:40:52.243456Z",
+     "start_time": "2025-02-16T14:40:52.223672Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "portfolio = pyfinlib.Portfolio(\n",
+    "    [\n",
+    "        pyfinlib.PortfolioAsset(.1, \"AAPL\", [i.close for i in data.results if i.symbol == 'AAPL']),\n",
+    "        pyfinlib.PortfolioAsset(.1, \"META\", [i.close for i in data.results if i.symbol == 'META']),\n",
+    "        pyfinlib.PortfolioAsset(.1, \"C\", [i.close for i in data.results if i.symbol == 'C']),\n",
+    "        pyfinlib.PortfolioAsset(.1, \"DIS\", [i.close for i in data.results if i.symbol == 'DIS']),\n",
+    "        pyfinlib.PortfolioAsset(.6, \"CL=F\", [i.close for i in data.results if i.symbol == 'CL=F']),\n",
+    "     ]\n",
+    ")"
+   ],
+   "id": "28a68dea99911874",
+   "outputs": [],
+   "execution_count": 8
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-16T14:40:52.968790Z",
+     "start_time": "2025-02-16T14:40:52.955560Z"
     }
    },
    "cell_type": "code",
    "source": [
     "returns = pyfinlib.util.rates_of_change([i.close for i in data.results if i.symbol == 'AAPL'])\n",
-    "portfolio = pyfinlib.Portfolio(\n",
-    "    [\n",
-    "        pyfinlib.PortfolioAsset(.25, \"AAPL\", [i.close for i in data.results if i.symbol == 'AAPL']),\n",
-    "        pyfinlib.PortfolioAsset(.3, \"META\", [i.close for i in data.results if i.symbol == 'META']),\n",
-    "        pyfinlib.PortfolioAsset(.15, \"C\", [i.close for i in data.results if i.symbol == 'C']),\n",
-    "        pyfinlib.PortfolioAsset(.3, \"DIS\", [i.close for i in data.results if i.symbol == 'DIS']),\n",
-    "     ]\n",
-    ")\n",
-    "\n",
     "aapl_portfolio = pyfinlib.Portfolio(\n",
     "    [\n",
     "        pyfinlib.PortfolioAsset(1., \"AAPL\", [i.close for i in data.results if i.symbol == 'AAPL'])\n",
@@ -86,26 +101,26 @@
     "VaR_historical_10 = aapl_portfolio.value_at_risk_percent(0.1)\n",
     "VaR_historical, VaR_historical_10"
    ],
-   "id": "28a68dea99911874",
+   "id": "d6bf8e256ebb7ac1",
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(-0.023538456317296196, -0.018062230402611013)"
+       "(-0.027671346350936935, -0.021443681951589365)"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 3
+   "execution_count": 9
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-02-15T23:38:25.157066Z",
-     "start_time": "2025-02-15T23:38:25.040525Z"
+     "end_time": "2025-02-16T14:40:53.898760Z",
+     "start_time": "2025-02-16T14:40:53.787791Z"
     }
    },
    "cell_type": "code",
@@ -128,53 +143,46 @@
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ],
-      "image/png": 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"
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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 4
+   "execution_count": 10
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-02-15T23:38:25.798987Z",
-     "start_time": "2025-02-15T23:38:25.794344Z"
+     "end_time": "2025-02-16T14:41:05.352334Z",
+     "start_time": "2025-02-16T14:41:05.346646Z"
     }
    },
    "cell_type": "code",
    "source": [
-    "value = portfolio.value_at_risk(0.01, 1_000_000.)\n",
+    "value = aapl_portfolio.value_at_risk(0.1, 1_000_000.)\n",
     "value"
    ],
    "id": "aa1bd5ee47522592",
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1001159.1538127009\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "-1159.1538127008826"
+       "21443.681951589417"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "execution_count": 5
+   "execution_count": 11
   },
   {
    "metadata": {
     "ExecuteTime": {
-     "end_time": "2025-02-15T23:38:26.675900Z",
-     "start_time": "2025-02-15T23:38:26.593243Z"
+     "end_time": "2025-02-16T14:41:07.301479Z",
+     "start_time": "2025-02-16T14:41:07.224601Z"
     }
    },
    "cell_type": "code",
@@ -183,13 +191,13 @@
     "var_array = []\n",
     "num_days = int(15)\n",
     "for x in range(1, num_days+1):\n",
-    "    var_array.append(np.round(value * np.sqrt(x),2))\n",
-    "    print(str(x) + \" day VaR @ 95% confidence: \" + str(np.round(value * np.sqrt(x),2)))\n",
+    "    var_array.append(np.round(pyfinlib.risk.value_at_risk.scale_value_at_risk(value, x),2))\n",
+    "    print(str(x) + \" day VaR @ 95% confidence: \" + str(np.round(pyfinlib.risk.value_at_risk.scale_value_at_risk(value, x))))\n",
     "\n",
     "# Build plot\n",
     "plt.xlabel(\"Day #\")\n",
     "plt.ylabel(\"Max portfolio loss (USD)\")\n",
-    "plt.title(\"Max portfolio loss (VaR) over 15-day period\")\n",
+    "plt.title(f\"Max portfolio loss (VaR) over {num_days}-day period\")\n",
     "plt.plot(var_array, \"r\")"
    ],
    "id": "ddeb6ada3ab526b5",
@@ -198,30 +206,30 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1 day VaR @ 95% confidence: -1159.15\n",
-      "2 day VaR @ 95% confidence: -1639.29\n",
-      "3 day VaR @ 95% confidence: -2007.71\n",
-      "4 day VaR @ 95% confidence: -2318.31\n",
-      "5 day VaR @ 95% confidence: -2591.95\n",
-      "6 day VaR @ 95% confidence: -2839.34\n",
-      "7 day VaR @ 95% confidence: -3066.83\n",
-      "8 day VaR @ 95% confidence: -3278.58\n",
-      "9 day VaR @ 95% confidence: -3477.46\n",
-      "10 day VaR @ 95% confidence: -3665.57\n",
-      "11 day VaR @ 95% confidence: -3844.48\n",
-      "12 day VaR @ 95% confidence: -4015.43\n",
-      "13 day VaR @ 95% confidence: -4179.39\n",
-      "14 day VaR @ 95% confidence: -4337.16\n",
-      "15 day VaR @ 95% confidence: -4489.38\n"
+      "1 day VaR @ 95% confidence: 21444.0\n",
+      "2 day VaR @ 95% confidence: 30326.0\n",
+      "3 day VaR @ 95% confidence: 37142.0\n",
+      "4 day VaR @ 95% confidence: 42887.0\n",
+      "5 day VaR @ 95% confidence: 47950.0\n",
+      "6 day VaR @ 95% confidence: 52526.0\n",
+      "7 day VaR @ 95% confidence: 56735.0\n",
+      "8 day VaR @ 95% confidence: 60652.0\n",
+      "9 day VaR @ 95% confidence: 64331.0\n",
+      "10 day VaR @ 95% confidence: 67811.0\n",
+      "11 day VaR @ 95% confidence: 71121.0\n",
+      "12 day VaR @ 95% confidence: 74283.0\n",
+      "13 day VaR @ 95% confidence: 77316.0\n",
+      "14 day VaR @ 95% confidence: 80235.0\n",
+      "15 day VaR @ 95% confidence: 83051.0\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x15345cd10>]"
+       "[<matplotlib.lines.Line2D at 0x115896f60>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -230,21 +238,26 @@
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ],
-      "image/png": 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"
+      "image/png": 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"
      },
      "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "execution_count": 6
+   "execution_count": 12
   },
   {
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2025-02-16T14:38:31.324523Z",
+     "start_time": "2025-02-16T14:38:31.323194Z"
+    }
+   },
    "cell_type": "code",
-   "outputs": [],
-   "execution_count": null,
    "source": "",
-   "id": "17c360afc5728ddc"
+   "id": "17c360afc5728ddc",
+   "outputs": [],
+   "execution_count": null
   }
  ],
  "metadata": {
diff --git a/pyfinlib/src/lib.rs b/pyfinlib/src/lib.rs
index 3f2346c..173cf4c 100644
--- a/pyfinlib/src/lib.rs
+++ b/pyfinlib/src/lib.rs
@@ -10,7 +10,7 @@ mod pyfinlib {
     use finlib::risk::portfolio::PortfolioAsset;
 
     #[pymodule_init]
-    fn init(m: &Bound<'_, PyModule>) -> PyResult<()> {
+    fn init(_m: &Bound<'_, PyModule>) -> PyResult<()> {
         pyo3_log::init();
         Ok(())
     }
@@ -30,7 +30,7 @@ mod pyfinlib {
         use super::*;
 
         #[pymodule]
-        mod var {
+        mod value_at_risk {
             use super::*;
 
             #[pyfunction]
@@ -42,6 +42,11 @@ mod pyfinlib {
             fn varcovar(values: Vec<f64>, confidence: f64) -> PyResult<f64> {
                 Ok(finlib::risk::var::varcovar::value_at_risk_percent(&values, confidence))
             }
+
+            #[pyfunction]
+            fn scale_value_at_risk(initial_value: f64, time_cycles: isize) -> PyResult<f64> {
+                Ok(finlib::risk::var::varcovar::scale_value_at_risk(initial_value, time_cycles))
+            }
         }
     }