diff --git a/experiments/moving_midpoint.ipynb b/experiments/moving_midpoint.ipynb
index cd24b4c..b3d4aa3 100644
--- a/experiments/moving_midpoint.ipynb
+++ b/experiments/moving_midpoint.ipynb
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -132,6 +132,9 @@
       "bust_sort: 1\n",
       "\n",
       "Portfolio exited early at 2020-03-16 00:00:00\n",
+      "bust_sort: 1\n",
+      "\n",
+      "Portfolio exited early at 2021-10-12 00:00:00\n",
       "bust_sort: 1\n"
      ]
     }
@@ -158,128 +161,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 712/712 [00:28<00:00, 25.12it/s]\n"
+      "  0%|          | 0/712 [00:00<?, ?it/s]"
      ]
-    }
-   ],
-   "source": [
-    "stat_arb_metrics = metrics(portfolios_after_cost, results)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Average number of assets per SA: 5.3\n",
-      "Median number of assets per SA: 5.0\n",
-      "Number of unique SAs: 712\n",
-      "Mean profit: 1.10\n",
-      "Median profit: 0.80\n",
-      "Std profit: 3.03\n",
-      "75th percentile: 2.07\n",
-      "25th percentile: -0.24\n",
-      "Fraction profitable: 0.70\n",
-      "Average minimum cumulative profit: -1.76\n",
-      "\n",
-      "Mean return\n",
-      "Mean return: 0.15\n",
-      "Median return: 0.12\n",
-      "75th percentile return: 0.24\n",
-      "25th percentile return: 0.03\n",
-      "Fraction positive: 0.81\n",
-      "\n",
-      "Risk\n",
-      "Mean risk: 0.20\n",
-      "Median risk: 0.15\n",
-      "75th percentile risk: 0.25\n",
-      "25th percentile risk: 0.09\n",
-      "\n",
-      "Sharpe\n",
-      "Average Sharpe: 0.84\n",
-      "Median Sharpe: 0.88\n",
-      "75th percentile Sharpe: 1.52\n",
-      "25th percentile Sharpe: 0.21\n",
-      "Fraction positive: 0.81\n",
-      "\n",
-      "Drawdown\n",
-      "Mean drawdown: 0.12\n",
-      "Median drawdown: 0.09\n",
-      "75th percentile drawdown: 0.15\n",
-      "25th percentile drawdown: 0.05\n"
+      "100%|██████████| 712/712 [00:28<00:00, 24.56it/s]\n"
      ]
     }
    ],
    "source": [
-    "avg_lens = np.mean(lens)\n",
-    "median_len = np.median(lens)\n",
-    "n_unique_stat_arbs = n_stat_arbs\n",
-    "\n",
-    "means = stat_arb_metrics.means\n",
-    "stdevs = stat_arb_metrics.stdevs\n",
-    "sharpes = stat_arb_metrics.sharpes\n",
-    "profits = stat_arb_metrics.profits\n",
-    "min_navs = stat_arb_metrics.min_navs\n",
-    "min_cum_prof = stat_arb_metrics.min_cum_prof\n",
-    "drawdowns = stat_arb_metrics.drawdowns\n",
-    "\n",
-    "print(f\"Average number of assets per SA: {avg_lens:.1f}\")\n",
-    "print(f\"Median number of assets per SA: {median_len:.1f}\")\n",
-    "print(f\"Number of unique SAs: {n_unique_stat_arbs}\")\n",
-    "print(f\"Mean profit: {profits.mean():.2f}\")\n",
-    "print(f\"Median profit: {profits.median():.2f}\")\n",
-    "print(f\"Std profit: {profits.std():.2f}\")\n",
-    "print(f\"75th percentile: {profits.quantile(0.75):.2f}\")\n",
-    "print(f\"25th percentile: {profits.quantile(0.25):.2f}\")\n",
-    "print(f\"Fraction profitable: {(profits > 0).mean():.2f}\")\n",
-    "print(f\"Average minimum cumulative profit: {min_cum_prof.mean():.2f}\")\n",
-    "\n",
-    "print(\"\\nMean return\")\n",
-    "print(f\"Mean return: {means.mean():.2f}\")\n",
-    "print(f\"Median return: {means.median():.2f}\")\n",
-    "# print(f\"Std return: {means.std():.2f}\")\n",
-    "print(f\"75th percentile return: {means.quantile(0.75):.2f}\")\n",
-    "print(f\"25th percentile return: {means.quantile(0.25):.2f}\")\n",
-    "print(f\"Fraction positive: {(means > 0).mean():.2f}\")\n",
-    "\n",
-    "\n",
-    "print(\"\\nRisk\")\n",
-    "print(f\"Mean risk: {stdevs.mean():.2f}\")\n",
-    "print(f\"Median risk: {stdevs.median():.2f}\")\n",
-    "# print(f\"Std risk: {stdevs.std():.2f}\")\n",
-    "print(f\"75th percentile risk: {stdevs.quantile(0.75):.2f}\")\n",
-    "print(f\"25th percentile risk: {stdevs.quantile(0.25):.2f}\")\n",
-    "\n",
-    "print(\"\\nSharpe\")\n",
-    "print(f\"Average Sharpe: {sharpes.mean():.2f}\")\n",
-    "print(f\"Median Sharpe: {sharpes.median():.2f}\")\n",
-    "# print(f\"Std Sharpe: {sharpes.std():.2f}\")\n",
-    "print(f\"75th percentile Sharpe: {sharpes.quantile(0.75):.2f}\")\n",
-    "print(f\"25th percentile Sharpe: {sharpes.quantile(0.25):.2f}\")\n",
-    "print(f\"Fraction positive: {(sharpes > 0).mean():.2f}\")\n",
-    "\n",
-    "print(\"\\nDrawdown\")\n",
-    "print(f\"Mean drawdown: {drawdowns.mean():.2f}\")\n",
-    "print(f\"Median drawdown: {drawdowns.median():.2f}\")\n",
-    "# print(f\"Std drawdown: {drawdowns.std():.2f}\")\n",
-    "print(f\"75th percentile drawdown: {drawdowns.quantile(0.75):.2f}\")\n",
-    "print(f\"25th percentile drawdown: {drawdowns.quantile(0.25):.2f}\")\n"
+    "stat_arb_metrics = metrics(portfolios_after_cost, results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -383,52 +289,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Average number of assets per SA: 5.3\n",
-      "Median number of assets per SA: 5.0\n",
-      "Number of unique SAs: 712\n",
-      "Mean profit: 1.10\n",
-      "Median profit: 0.80\n",
-      "Std profit: 3.01\n",
-      "75th percentile: 2.05\n",
-      "25th percentile: -0.24\n",
-      "Fraction profitable: 0.70\n",
-      "Average minimum cumulative profit: -1.76\n",
-      "\n",
-      "Mean return\n",
-      "Mean return: 0.15\n",
-      "Median return: 0.12\n",
-      "75th percentile return: 0.24\n",
-      "25th percentile return: 0.03\n",
-      "Fraction positive: 0.81\n",
-      "\n",
-      "Risk\n",
-      "Mean risk: 0.20\n",
-      "Median risk: 0.15\n",
-      "75th percentile risk: 0.25\n",
-      "25th percentile risk: 0.09\n",
-      "\n",
-      "Sharpe\n",
-      "Average Sharpe: 0.84\n",
-      "Median Sharpe: 0.88\n",
-      "75th percentile Sharpe: 1.52\n",
-      "25th percentile Sharpe: 0.21\n",
-      "Fraction positive: 0.81\n",
-      "\n",
-      "Drawdown\n",
-      "Mean drawdown: 0.11\n",
-      "Median drawdown: 0.09\n",
-      "75th percentile drawdown: 0.15\n",
-      "25th percentile drawdown: 0.05\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "avg_lens = np.mean(lens)\n",
     "median_len = np.median(lens)\n",
@@ -494,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
diff --git a/experiments/utils.py b/experiments/utils.py
index 0994d16..ca96df7 100644
--- a/experiments/utils.py
+++ b/experiments/utils.py
@@ -127,7 +127,7 @@ def metrics(portfolios_after_cost, results):
         profit -= portfolio.trading_costs.sum(axis=1)
 
         ### Shorting cost
-        short_rate = 3 * 0.01**2
+        short_rate = 0.5 / 100 / 252  # half a percent per year
         short_cost = (
             portfolio.stocks[portfolio.stocks < 0].abs() * portfolio.prices
         ).sum(axis=1) * short_rate
@@ -179,7 +179,15 @@ def simulate(res, portfolio, trading_cost_model, lev_fraction):
         initial_cash=lev_fraction * lev0,
     )
 
-    navs = portfolio_temp.nav
+    ### Shorting cost
+    short_rate = 0.5 / 100 / 252  # half a percent per year
+    short_cost = (portfolio.stocks[portfolio.stocks < 0].abs() * portfolio.prices).sum(
+        axis=1
+    ) * short_rate
+
+    navs = portfolio_temp.nav - short_cost.cumsum()
+
+    # navs = portfolio_temp.nav
     positions = portfolio_temp.stocks
     absolute_notionals = positions.abs() * portfolio_temp.prices
     long_positions = absolute_notionals[positions > 0].sum(axis=1)
@@ -629,7 +637,7 @@ def plot_stat_arb(
     profit -= portfolio.trading_costs.sum(axis=1)
 
     ### Shorting cost
-    short_rate = 3 * 0.01**2
+    short_rate = 0.5 / 100 / 252  # half a percent per year
     short_cost = (portfolio.stocks[portfolio.stocks < 0].abs() * portfolio.prices).sum(
         axis=1
     ) * short_rate