<jupyter_start><jupyter_text>EEM and SLV has extreme price movement before 2009 need to find a better source? TSLA is not split adjusted<jupyter_code>df = download_alpaca_data('TSLA',)
df['Open'].plot()
from arctic import CHUNK_STORE, Arctic
a = Arctic('localhost')
a.initialize_library('Sample', lib_type=CHUNK_STORE)
Sample = a['Sample']
df1 = download_alpaca_data('AAPL')
Sample.update('AAPL', df1, chunk_range=df1.index, upsert=True)
Sample.list_symbols()
Sample.read('AAPL').set_index('date').columns
a.delete_library('Sample')
US_Equity.read('GLD').set_index('date')['Open'].plot()
US_Equity.read('EEM').set_index('date')['Open'].plot()
selected_symbols=[ u for u in US_Futures.list_symbols() if u[-1] == '1']
selected_symbols
US_Futures.read('CHRIS/CME_FV1')['Close'].plot()
US_Equity2.read('V')
df1 = US_Equity.read('GS').set_index('date')
df2 = US_Equity.read('AAPL').set_index('date')
df1.merge(df2,how='outer',on='date')
merged_df = df1.merge(df2,how='outer',on='date').merge(df2,how='outer',on='date').sort_values(by='date').fillna(0)
price_tensor = np.zeros(shape=(merged_df.shape[0],5,int(merged_df.shape[1]/5)))
price_tensor.shape
for count in range(int(merged_df.shape[1]/5)):
    price_tensor[:,:,count] = merged_df.values[:,5*count:5*count+5]<jupyter_output><empty_output>
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