Fixed market data following google API change, revised README.md

README.md

@@ -1,4 +1,4 @@
-# Robinhood
+# Robinhood Portfolio
 Python client to access and analyze the Robinhood portfolio.
 Based on unofficial [robinhood-api](https://github.com/Jamonek/Robinhood) and several python libraries for financial analysis, such as:
 - [empyrical](https://github.com/quantopian/empyrical)
@@ -26,26 +26,34 @@ Based on unofficial [robinhood-api](https://github.com/Jamonek/Robinhood) and se
 
 ### How To Install:
 It is recommended to use virtualenv:
-	git clone https://github.com/omdv/robinhood-portfolio && cd robinhood-portfolio
-	virtualenv robinhood && source robinhood/bin/activate && pip3 install -r requirements.txt
+```
+git clone https://github.com/omdv/robinhood-portfolio && cd robinhood-portfolio
+virtualenv robinhood && source robinhood/bin/activate && pip3 install -r requirements.txt
+```
 
 ### How to Use
-	python3 app.py
+```
+python3 app.py
+```
 
 ### Docker container
 Docker container based on Ubuntu is [available](https://hub.docker.com/r/omdv/robinhood-portfolio/). To launch it in a background mode:
-	docker run -d -p 8080:8080 --name robinhood omdv/robinhood-portfolio:ubuntu
+```
+docker run -d -p 8080:8080 --name robinhood omdv/robinhood-portfolio:ubuntu
+```
 
 Once up and running connect to [http://localhost:8080](http://localhost:8080). If using the older versions of docker you will need to use the ip of the docker-machine.
 
-### Jupyter notebook
-Jupyter notebook using the backtrader library with pyfolio in in "notebooks" folder. It is work in progress.
+### Jupyter notebook (WORK IN PROGRESS)
+You can find the Jupyter notebook using the backtrader library with pyfolio in "notebooks" folder.
 
 
+### Disclaimer
+This tool uses the unofficial Robinhood API to access your account. This code and the corresponding tools are provided on "as is" basis and the user is responsible for the safety of his/her own account.
+
 ------------------
 
 # Related
-
 * [empyrical](https://github.com/quantopian/empyrical)
 * [portfolioopt](https://github.com/czielinski/portfolioopt)
 * [robinhood-api](https://github.com/Jamonek/Robinhood)

app.py

@@ -16,7 +16,8 @@
 
 app = Flask(__name__)
 app.config['SECRET_KEY'] = 'My_l0ng_very_secure_secret_k3y'
-app.debug = True
+app.config['DEBUG'] = False
+app.config['TESTING'] = False
 
 
 def plot_returns(data):

backend/backend.py

@@ -69,7 +69,13 @@ def _validate_user_dict(self):
         ):
             print('Market data is not consistent with Robinhood data')
             self.update_market_data(fresh_start=True)
-            self.user
+        # check if dates match the actual data
+        elif (
+            (self.user['mkt_dates'][0] != self._market.major_axis.min()) or
+            (self.user['mkt_dates'][1] != self._market.major_axis.max())
+        ):
+            print('Dates are not consistent with dataset')
+            self.update_market_data(fresh_start=True)
         return self
 
     def _pickle_user_dict(self):
@@ -141,7 +147,7 @@ def _get_latest_portfolio_snapshot(self):
 
         columns_to_names = OrderedDict([
             ('cum_size', ['Shares', '{:,.0f}']),
-            ('current_weight', ['Portfolio weight', '{:.2f}']),
+            ('current_weight', ['Portfolio weight', '{:.2f}%']),
             ('cum_cost_basis', ['Current cost basis', '{:,.2f}']),
             ('cum_value_close', ['Current value', '{:,.2f}']),
             ('cum_realized_gain', ['Realized gain', '{:,.2f}']),

backend/market_data.py

@@ -1,10 +1,20 @@
 import pandas as pd
 import numpy as np
 import requests as rq
-from pandas_datareader import data
+import pandas_datareader as pdr
+from pandas_datareader.google.daily import GoogleDailyReader
 from io import StringIO
 
 
+# hack until pandas_datareader is fixed to a new google API
+@property
+def url(self):
+    return 'http://finance.google.com/finance/historical'
+
+
+GoogleDailyReader.url = url
+
+
 # download market benchmark data
 # indices: ^dji, ^spx
 class MarketData:
@@ -35,27 +45,28 @@ def _get_market_index(self, start_date, end_date):
 
     # returns panel using goodle finance server, pause is required to avoid ban
     def _get_historical_prices(self, tickers, start_date, end_date):
-        pf = data.DataReader(tickers, "google", start_date, end_date, pause=10)
+        # pf = data.DataReader(
+            # tickers, "google", start_date, end_date, pause=10)
+        pf = pdr.get_data_google(
+            tickers, start_date, end_date, pause=10)
         pf = pf.astype(np.float32)
         return pf
 
-    # # download treasury bills yields for different periods for capm model
-    # def _get_treasury_yields(self, start_date, end_date):
-    #     tickers = ["TB4WK", "TB3MS", "TB1YR"]
-    #     pf = data.DataReader(tickers, "fred", start_date, end_date)
-    #     return pf
-
     # return all stocks and index in one panel
     def download_save_market_data(self, tickers, start_date, end_date,
                                   update_existing=False):
-        start_date = self._date_fmt.format(start_date)
-        end_date = self._date_fmt.format(end_date)
-        print("Downloading market data for {}-{}".format(start_date, end_date))
+        start_date_str = self._date_fmt.format(start_date)
+        end_date_str = self._date_fmt.format(end_date)
+        print("Downloading market data for {}-{}".format(
+            start_date_str, end_date_str))
 
         # add market index
-        pf = self._get_historical_prices(tickers, start_date, end_date)
-        pf.loc[:, :, 'market'] = self._get_market_index(start_date, end_date)
-        # tb = self._get_treasury_yields(start_date, end_date)
+        pf = self._get_historical_prices(
+            tickers,
+            start_date.date(),
+            end_date.date())
+        pf.loc[:, :, 'market'] = self._get_market_index(
+            start_date_str, end_date_str)
 
         if update_existing:
             new_dict = {}
@@ -66,7 +77,6 @@ def download_save_market_data(self, tickers, start_date, end_date,
             pf = pd.Panel(new_dict)
         else:
             pf.to_hdf(self.datafile, 'market')
-            # tb.to_hdf(self.datafile, 'treasury_bills')
         return pf
 
 

backend/portfolio_model.py

@@ -187,6 +187,10 @@ def daily_portfolio_changes(self):
         pf['cum_unrealized_gain'] =\
             pf['cum_value_close'] - pf['cum_cost_basis']
 
+        # investment return without dividends
+        pf['investment_return'] = pf['cum_unrealized_gain'] + \
+            pf['cum_realized_gain']
+
         # total return
         pf['cum_total_return'] = pf['cum_unrealized_gain'] +\
             pf['cum_dividends'] + pf['cum_realized_gain']
@@ -281,7 +285,7 @@ def actual_portfolio_stats(self):
         pf = self.panelframe
 
         # can choose either a total return or capital gain only
-        return_to_use = 'cum_total_return'
+        return_to_use = 'investment_return'
 
         cum_return_D1 = pf[return_to_use].sum(1).shift(1)
         cum_return_D2 = pf[return_to_use].sum(1)