updated tsmom

GARCH_code.ipynb

@@ -3448,7 +3448,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,

MomentumIndia.ipynb

@@ -5109,7 +5109,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.2"
+   "version": "3.6.3"
   }
  },
  "nbformat": 4,

YahooFinance.py

@@ -1,14 +1,14 @@
 import pandas as pd
 class YahooDailyReader():
     def __init__(self, symbol=None, start=None, end=None):
-        import datetime, time
+        import datetime as dt, time
         self.symbol = symbol
 
         # initialize start/end dates if not provided
         if end is None:
-            end = datetime.datetime.today()
+            end = dt.datetime.today()
         if start is None:
-            start = datetime.datetime(2010,1,1)
+            start = dt.datetime(2010,1,1)
 
         self.start = start
         self.end = end
@@ -20,19 +20,20 @@ def __init__(self, symbol=None, start=None, end=None):
 
         url = 'https://finance.yahoo.com/quote/{}/history?'
         url += 'period1={}&period2={}'
-        url += '&filter=history'
-        url += '&interval=1d'
-        url += '&frequency=1d'
+        url += '&filter=div'
         self.url = url.format(self.symbol, unix_start, unix_end)
 
-    def read(self):
+    def base(self):
         import requests, re, json
-
         r = requests.get(self.url)
 
         ptrn = r'root\.App\.main = (.*?);\n}\(this\)\);'
         txt = re.search(ptrn, r.text, re.DOTALL).group(1)
         jsn = json.loads(txt)
+        return jsn
+
+    def read(self):
+        jsn = self.base()
         df = pd.DataFrame(
                 jsn['context']['dispatcher']['stores']
                 ['HistoricalPriceStore']['prices']
@@ -49,3 +50,17 @@ def read(self):
         df.sort_index(ascending = True, inplace = True)
         return df
 
+    def read_div(self):
+        jsn = self.base()
+        df = pd.DataFrame(jsn['context']
+                     ['dispatcher']
+                     ['stores']
+                     ['HistoricalPriceStore']
+                     ['eventsData'])
+        df.insert(0, 'symbol', self.symbol)
+        df['date'] = pd.to_datetime(df.date, unit= 's').dt.date
+        df.set_index('date', inplace = True)
+        df.sort_index(inplace = True)
+        df = df[['amount']]
+        return df
+

momentum.ipynb

@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -501,6 +501,54 @@
     "df_ts_df = get_ts(bonds)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x12ee7b240>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x12eb8d748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ausie = bonds.iloc[:, 5].dropna()\n",
+    "t = 1\n",
+    "x = ausie[t:]\n",
+    "y = ausie[:-t]\n",
+    "plt.scatter(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

tsmom.py

@@ -1,6 +1,6 @@
 import pandas as pd
 import numpy as np
-#from pandas_datareader import data as web
+from pandas_datareader import data as web
 import plotly.plotly as py
 import plotly.tools as tls
 from plotly.graph_objs import *
@@ -577,10 +577,8 @@ def tsmom(series, mnth_vol, mnth_cum, tolerance = 0, vol_flag = False, scale = 0
     """Function to calculate Time Series Momentum returns on a time series.
     params:
         series: used for name purpose only, provide a series with the name of the ticker
-        mnth_vol: ex-ante volatility which is a monthly timeseries
-        mnth_cum: cumulative monthly returns.
         tolerance: (optional) -1 < x < 1, for signal processing, x < 0 is loss thus short the asst and vice-versa
-        vol_flag: (optional) Boolean default is False. Wherether we are using vol scaling or not
+        vol_flag: (optional) Boolean default is False,
         scale: (optional) volatility scaling parameter
         lookback: (optional) int, lookback months
 
@@ -627,7 +625,6 @@ def tsmom(series, mnth_vol, mnth_cum, tolerance = 0, vol_flag = False, scale = 0
     return new_longs, new_shorts, new_lev
 
 def get_long_short(mnth_cum, lookback = 12):
-    """Function to calculate number of longs and shorts in a particular month"""
     lback_ret = mnth_cum.pct_change(lookback)
     lback_ret = lback_ret.dropna(how = 'all')
     nlongs = lback_ret[lback_ret > 0].count(axis = 1)
@@ -676,20 +673,6 @@ def get_ts(df):
     return df_ts_df
 
 def get_tsmom(mnth_vol, mnth_cum, flag = False, scale = 0.20, lookback = 12):
-    """Calculates the tsmom returns for longside of the portfolio and short side of
-    the portfolio
-
-
-    params:
-        mnth_vol: volatility, monthly timeseries
-        mnth_cum: cumulative monthly returns
-        flag: Volatility flag, whether or not we are using leverage by scaling returns
-        scale: scaling factor
-        lookback: return look, in this case 12 mo returns
-
-    returns:
-        long port returns, short portfolio returns, and leverage
-        """
     total = mnth_cum.apply(lambda x: tsmom(x, mnth_vol, mnth_cum, scale = scale, vol_flag= flag, lookback= lookback))
     pnl_long = pd.concat([i[0] for i in total], axis = 1)
     pnl_short = pd.concat([i[1] for i in total], axis = 1)