diff --git a/optimization/sharpe_opt.py b/optimization/sharpe_opt.py
new file mode 100644
index 0000000..1792eec
--- /dev/null
+++ b/optimization/sharpe_opt.py
@@ -0,0 +1,38 @@
+"""An optimizer for Sharpe Ratio"""
+
+import numpy as np
+import scipy.optimize
+# from cvxopt import solvers, matrix
+# solvers.options["show_progress"] = False
+# solvers.options['maxiters'] = 500
+
+
+def optimize_sharpe(returns, r_f=1):
+    """
+    Optimize the Sharpe Ratio of a portfolio,
+    short_sales not working yet...
+    """
+    returns = np.asmatrix(returns)
+    num_stocks, _ = returns.shape
+    covar = np.cov(returns)
+    means = np.mean(returns, axis=1)
+
+    def transform_input(decision_vars):
+        """Make sure the decision vars sum to 1"""
+        mod_vars = list(decision_vars)
+        mod_vars.append(1 - sum(mod_vars))
+        return mod_vars
+
+    def neg_sharpe(dec_vars):
+        """Compute -1*sharpe_ratio"""
+        dec_vars = np.asmatrix(transform_input(dec_vars)).T
+        sharpe = (r_f - means.T * dec_vars) \
+            / np.sqrt(dec_vars.T * covar * dec_vars)
+        return sharpe
+
+    sol = scipy.optimize.fmin(
+        neg_sharpe,
+        scipy.ones(num_stocks - 1, dtype=float) * 1. / num_stocks,
+        disp=False,
+        full_output=False)
+    return transform_input(sol)
diff --git a/tests/test_optimization.py b/tests/test_optimization.py
index 9e8f2e0..9bbdbae 100644
--- a/tests/test_optimization.py
+++ b/tests/test_optimization.py
@@ -3,7 +3,7 @@
 import csv
 from collections import defaultdict
 import numpy as np
-from optimization import mad, mvo, downside_var
+from optimization import mad, mvo, downside_var, sharpe_opt
 
 # Might want to consider breaking this function out
 
@@ -57,9 +57,27 @@ def smoke_test(optimizers):
                 assert not all(shorting_test)
 
 
+def smoke_test2(optimizers):
+    """A different smoke test for other models"""
+    rets = load_data()
+    gross_returns = []
+    for k in sorted(rets.keys()):
+        gross_returns.append([1.0 + i / 100 for i in rets[k]])
+    means = np.mean(gross_returns, axis=1)
+    steps = np.linspace(0.0, min(means), num=100)
+    for opt_model in optimizers:
+        allocations = [np.transpose(opt_model(gross_returns, x))
+                       for x in steps]
+        shorting_test = [all([x >= -np.finfo(np.float32).eps for x in row])
+                         for row in allocations]
+        assert not all(shorting_test)
+
+
 def test_models():
     """Run over all the models"""
     smoke_test([
         mvo.optimize_mv,
         mad.optimize_mad,
         downside_var.optimize_downside_variance])
+    # Sharpe doesn't support turning on and off short sales
+    smoke_test2([sharpe_opt.optimize_sharpe])