def get_mktsymbol_list():
"""get a list of symbols to lookup on AlphaVantage"""
tbl = 'stockindex_app_stock'
stock = SqlConnection(tbl)
stmt = select([stock.table.c.marketsymbol]).where(stock.table.c.inactive == 0)
df = stock.select_query(stmt)
symbol_list = df['marketsymbol'].tolist()
return symbol_list
def get_stock_fk(self):
"""get the foreign keys for each symbol in the observations DataFrame"""
tbl = 'stockindex_app_stock'
stock = SqlConnection(tbl)
stmt = select([stock.table.c.marketsymbol, stock.table.c.id]).where(stock.table.c.inactive == 0)
df_symbol = stock.select_query(stmt)
self.df = pd.merge(df_symbol, self.df, on='marketsymbol')
self.df.drop(columns='marketsymbol', inplace=True)
self.df.rename(columns={'id': 'stock_id'}, inplace=True)
def update_52_week_highlow(self):
"""compute the 52 week high/low price of a stock"""
tbl = 'stockindex_app_observations'
observation = SqlConnection(tbl)
highest_price = func.max(observation.table.c.high_price).label('high_price_52_weeks')
lowest_price = func.min(observation.table.c.low_price).label('low_price_52_weeks')
stmt = select([observation.table.c.stock_id, highest_price, lowest_price])
stmt = stmt.where(observation.table.c.observation_date >= datetime.datetime.now()+datetime.timedelta(weeks=-52))
stmt = stmt.group_by(observation.table.c.stock_id)
df_highlow = observation.select_query(stmt)
self.df.drop(['high_price_52_weeks', 'low_price_52_weeks'], axis=1, inplace=True)
self.df = pd.merge(self.df, df_highlow, how='inner', left_on='id', right_on='stock_id')
self.df.drop('stock_id', axis=1, inplace=True)
def update_52_week_highlow(self):
"""compute the 52 week high/low value of an index"""
tbl = 'stockindex_app_indexobservations'
index_obs = SqlConnection(tbl)
highest_value = func.max(index_obs.table.c.high_value).label('high_value_52_weeks')
lowest_value = func.min(index_obs.table.c.low_value).label('low_value_52_weeks')
stmt = select([index_obs.table.c.index_id, highest_value, lowest_value])
stmt = stmt.where(index_obs.table.c.observation_date >= datetime.datetime.now()+datetime.timedelta(weeks=-52))
df_index_obs = index_obs.select_query(stmt)
self.df.drop(['high_value_52_weeks', 'low_value_52_weeks'], axis=1, inplace=True)
self.df = pd.merge(self.df, df_index_obs, how='inner', left_on='id', right_on='index_id')
self.df.rename(columns={'close_value': 'current_value'}, inplace=True)
self.df = self.df[['id', 'current_value', 'prior_close_value', 'change_value', 'high_value', 'low_value',
'high_value_52_weeks', 'low_value_52_weeks']]
def get_observation(self):
"""update the current, prior, high, low and change value of the index"""
tbl = 'stockindex_app_stocksindexed'
stocksindexed = SqlConnection(tbl)
stmt = select([stocksindexed.table])
df_stocksindexed = stocksindexed.select_query(stmt)
df_update = pd.merge(self.df, df_stocksindexed, how='inner', left_on='id', right_on='index_id')
df_update = df_update[['index_id', 'stock_id']]
tbl = 'stockindex_app_stock'
stock = SqlConnection(tbl)
stmt = select([stock.table.c.id,
stock.table.c.marketsymbol,
stock.table.c.market_cap,
stock.table.c.high_market_cap,
stock.table.c.low_market_cap,
]).where(stock.table.c.inactive == 0)
df_stock = stock.select_query(stmt)
df_update = pd.merge(df_update, df_stock, how='inner', left_on='stock_id', right_on='id')
df_update.drop(['stock_id', 'id'], axis=1, inplace=True)
df_update['market_cap'] = (df_update['market_cap']/1000000).astype('int64')
df_update['high_market_cap'] = (df_update['high_market_cap'] / 1000000).astype('int64')
df_update['low_market_cap'] = (df_update['low_market_cap'] / 1000000).astype('int64')
df_update = df_update.groupby('index_id')['market_cap', 'high_market_cap', 'low_market_cap'].sum()
df_update.rename(columns={'market_cap': 'current_value', 'high_market_cap': 'high_value',
'low_market_cap': 'low_value'}, inplace=True)
self.df['open_value'] = self.df['current_value']
self.df.drop(['current_value', 'high_value', 'low_value'], axis=1, inplace=True)
self.df = pd.merge(self.df, df_update, how='inner', left_on='id', right_on='index_id')
self.df['observation_date'] = datetime.datetime.strptime(
(datetime.datetime.now() + datetime.timedelta(days=-1)).strftime('%Y-%m-%d'), '%Y-%m-%d')
self.df.rename(columns={'id': 'index_id', 'current_value': 'close_value'}, inplace=True)
self.df = self.df[['observation_date', 'open_value', 'high_value', 'low_value', 'close_value', 'index_id']]
def __init__(self):
tbl = 'stockindex_app_stock'
stock = SqlConnection(tbl)
stmt = select([stock.table]).where(stock.table.c.inactive == 0)
self.df = stock.select_query(stmt)
def __init__(self):
tbl = 'stockindex_app_index'
index = SqlConnection(tbl)
stmt = select([index.table]).where(index.table.c.inactive == 0)
self.df = index.select_query(stmt)