#!/usr/bin/env python
# coding: utf-8

# # Example using the data manager classes
# This notebook shows how to use the data manager framework for simpler API usage and for caching capabilities. 
# 
# Please note that in order to request bloomberg fields using property access, it must be CAPITALIZED. (sid.PX_AST NOT sid.px_last)

# In[1]:


import pandas as pd
import tia.bbg.datamgr as dm


# ## Single Security Accessor

# In[2]:


# create a DataManager for simpler api access
mgr = dm.BbgDataManager()
# retrieve a single security accessor from the manager
msft = mgr['MSFT US EQUITY']


# In[3]:


#  Can now access any Bloomberg field (as long as it is upper case)
msft.PX_LAST, msft.PX_OPEN


# In[4]:


# Access multiple fields at the same time
msft['PX_LAST', 'PX_OPEN']


# In[5]:


# OR pass an array
msft[['PX_LAST', 'PX_OPEN']]


# In[6]:


# Have the manager default to returning a frame instead of values
mgr.sid_result_mode = 'frame'
msft.PX_LAST


# In[7]:


# multiple fields returned as data frame
msft[['PX_LAST', 'PX_OPEN']]


# In[8]:


# Retrieve historical data
msft.get_historical(['PX_OPEN', 'PX_HIGH', 'PX_LOW', 'PX_LAST'], '1/1/2014', '1/12/2014').head()


# ## Multi-security accessor

# In[9]:


sids = mgr['MSFT US EQUITY', 'IBM US EQUITY', 'CSCO US EQUITY']
sids.PX_LAST


# In[10]:


sids.get_historical('PX_LAST', '1/1/2014', '11/12/2014').head()


# In[11]:


sids.get_historical(['PX_OPEN', 'PX_LAST'], '1/1/2014', '11/12/2014').head()


# ## Caching

# In[12]:


#
# ability to cache requests in memory or in h5 file
#
ms = dm.MemoryStorage()
cmgr = dm.CachedDataManager(mgr, ms, pd.datetime.now())


# In[13]:


cmsft = cmgr['MSFT US EQUITY']
cmsft.PX_LAST


# In[14]:


get_ipython().run_line_magic('timeit', 'msft.PX_LAST')


# In[15]:


get_ipython().run_line_magic('timeit', 'cmsft.PX_LAST')


# In[16]:


csids = cmgr['MSFT US EQUITY', 'IBM US EQUITY']
sids = mgr['MSFT US EQUITY', 'IBM US EQUITY']


# In[17]:


get_ipython().run_line_magic('timeit', "sids.get_historical('PX_LAST', start='1/3/2000', end='1/3/2014').head()")


# In[18]:


get_ipython().run_line_magic('timeit', "csids.get_historical('PX_LAST', start='1/3/2000', end='1/3/2014').head()")


# In[19]:


#
# HD Storage
# - note after executing the warning from hf api. I decided to leave blanks instead of replacing
#

import tempfile
fh, fp = tempfile.mkstemp()

h5storage = dm.HDFStorage(fp)  # Can set compression level for smaller files
h5mgr = dm.CachedDataManager(mgr, h5storage, pd.datetime.now())
h5msft = h5mgr['MSFT US EQUITY']
h5msft.PX_LAST


# In[20]:


# Notice no warning as it is taken from cache
h5msft.PX_LAST


# In[21]:


h5msft.get_historical('PX_LAST', start='1/2/2000', end='1/2/2014').head()


# In[22]:


get_ipython().run_line_magic('timeit', "h5msft.get_historical('PX_LAST', start='1/2/2000', end='1/2/2014')")


# In[23]:


# notice only IBM gets warning as MSFT is already cached, so it only retrieves IBM data
h5sids = h5mgr['MSFT US EQUITY', 'IBM US EQUITY']
h5sids.get_historical('PX_LAST', start='1/3/2000', end='1/2/2014').tail()


# In[24]:


# not perfect as it retrieves for each security and then concats BUT better than roundtrip to bloomberg plus consistency added for free
get_ipython().run_line_magic('timeit', "h5sids.get_historical('PX_LAST', start='1/3/2000', end='1/2/2014')")