1 files changed, 24 insertions, 0 deletions
diff --git a/python/globeop_reports.py b/python/globeop_reports.py
index 1d798677..3e7eb3df 100644
--- a/python/globeop_reports.py
+++ b/python/globeop_reports.py
@@ -66,3 +66,27 @@ def get_net_navs():
     df = pd.read_csv('/home/serenitas/edwin/Python/subscription_fee_data.csv', parse_dates=['date'], index_col =['date'])
     df.index = df.index.to_period('M').to_timestamp('M')
     return df.join(nav)
+
+def calc_trade_performance_stats():
+    df = trade_performance().set_index('trade_date')
+    df.days_held = df.days_held.dt.days
+    df['winners'] = df.apply(lambda df: True if df.percent_gain > 0 else False, axis = 1)
+    df['curr_face'] = df.principal_payment/(df.price/100)
+
+    index = ['All','2017','2016','2015','2014','2013']
+    results = pd.DataFrame(index = index)
+
+    win_per = len(df[df.winners].index)/len(df)
+    loss_per = 1- win_per
+    temp = {}
+    temp1 = {}
+    for x, df1 in df.groupby('winners'):
+        for y, df2 in df1.groupby(pd.TimeGrouper(freq='A')):
+            import pdb; pdb.set_trace()
+            y = y.date().year
+            results.loc[y] = df2[df2.days_held.notnull()].mean()[['curr_face','initialinvestment', 'days_held']]
+            results.loc[] = len(df2[df2.winners == x].index)/len(df)
+
+
+        df[df.days_held.notnull()]['days_held'].groupby(pd.TimeGrouper(freq='A')).mean()
+