1 files changed, 37 insertions, 1 deletions
diff --git a/python/option_trades.py b/python/option_trades.py
index 1510f583..f0282a85 100644
--- a/python/option_trades.py
+++ b/python/option_trades.py
@@ -1,3 +1,4 @@
+import cvxpy
 import datetime
 import math
 import numpy as np
@@ -238,5 +239,40 @@ def sell_vol_strategy(index="IG", months=3):
     conn.commit()
     return d
 
+def aggregate_trades(d):
+    r = pd.Series()
+    for v in d.values():
+        r = r.add(-v.sum(1).diff().dropna(), fill_value=0)
+    return r
+
+
+def compute_allocation(df):
+    Sigma = df.cov().values
+    gamma = cvxpy.Parameter(sign='positive')
+    mu = df.mean().values
+    w = cvxpy.Variable(3)
+    ret = mu.T*w
+    risk = cvxpy.quad_form(w, Sigma)
+    prob = cvxpy.Problem(cvxpy.Maximize(ret - gamma * risk),
+                         [cvxpy.sum_entries(w) == 1,
+                          w >= -2,
+                           w <= 2])
+
+    gamma_x = np.linspace(0, 0.02, 500)
+    W = np.empty((3, gamma_x.size))
+    for i, val in enumerate(gamma_x):
+        gamma.value = val
+        prob.solve()
+        W[:,i] = np.asarray(w.value).squeeze()
+
+    fund_return = mu @ W
+    fund_vol= np.array([math.sqrt(W[:,i] @ Sigma @W[:,i]) for i in range(gamma_x.size)])
+    return (W, fund_return, fund_vol)
+
+
 if __name__ == "__main__":
-    d = sell_vol_strategy()
+    d1 = sell_vol_strategy(months=1)
+    d2 = sell_vol_strategy(months=2)
+    d3 = sell_vol_strategy(months=3)
+    all_tenors = pd.concat([aggregate_trades(d) for d in [d1, d2, d3]], axis=1)
+    all_tenors.columns = ['1m', '2m', '3m']