Develop Reference

Alpha Vantage: Iterate through list of stocks to get technical indicators

I have a list of 5 stocks for which I would like to obtain data using Alpha Vantage's TechIndicators. Below are what I have imported and defined:
from alpha_vantage.timeseries import TimeSeries
from alpha_vantage.foreignexchange import ForeignExchange
from alpha_vantage.cryptocurrencies import CryptoCurrencies
from alpha_vantage.techindicators import TechIndicators
from alpha_vantage.sectorperformance import SectorPerformances
import datetime
import numpy as np
import pandas as pd
top5 = ['CELH', 'MAXR', 'CD', 'WDC', 'IMAB']
Querying for technical indicators returns a dataframe (data) and a dictionary (meta_data)
data, meta_data = ti.get_sma(symbol=ticker, interval='weekly')
How do I run the query for my top5 list? I first thought it should look like this:
for ticker in top5:
ticker_sma[ticker], ticker_meta_sma[ticker] =
ti.get_sma(symbol=ticker, interval='weekly')
or like this:
sma = {}
for ticker in top5gainers:
gainers_sma[ticker], gainers_sma_data[ticker] =
ti.get_sma(symbol=ticker, interval='weekly')
gainers_sma.keys()
gainers_sma.values()
But I have had zero luck with that approach. I get a name error...
NameError: name 'ticker_sma' is not defined
...which is odd to me because I have success getting data for an individual stock, e.g., Microsoft, below:
msft_sma, msft_meta_sma = ti.get_sma(symbol='msft', interval='weekly')
If I remove [ticker], and just run this get request...
for ticker in top5:
data_sma, meta_sma = ti.get_sma(symbol=ticker, interval='weekly')
...then according to the meta data in meta_sma, I've only obtained data for the 4th ticker out of the 5 tickers:
{'1: Symbol': 'IMAB',
'2: Indicator': 'Simple Moving Average (SMA)',
'3: Last Refreshed': '2020-12-31',
'4: Interval': 'weekly',
'5: Time Period': 20,
'6: Series Type': 'close',
'7: Time Zone': 'US/Eastern'}
Thank you for reading this question and for your answer!

You are trying to dynamically create variables, which isn't exactly straightforward. The following shows you how to do that, but you may also want to consider just reading each symbol into its own dataframe and either concatenating them together or saving individually. That really just depends on what you are doing with the data (using live or saving for later).
This will create each variable in the loop and assign text to it, just so you can what's going on.
top5 = ['CELH', 'MAXR', 'CD', 'WDC', 'IMAB']
for ticker in top5:
globals()['{}_sma'.format(ticker)] = ticker + 'sma_value'
globals()['{}_meta_sma'.format(ticker)] = ticker + 'sma_meta_value'
Then print a few for proof:
In [6]: print(CELH_sma)
...: print(WDC_sma)
...: print(IMAB_meta_sma)
CELHsma_value
WDCsma_value
IMABsma_meta_value
I can't test but what I think will work for you is this:
for ticker in top5:
globals()['{}_sma'.format(ticker)], globals()['{}_meta_sma'.format(ticker)] =
ti.get_sma(symbol=ticker, interval='weekly')

How to start a for loop for this given DataFrame in Pandas for multiple same name rows?

I need some help, I am working on a .ipynb file to filter data and get certain things from that Dataframe.
This is DataFrame I'm working with.
From this dataframe, as you can see there are multiple rows of the same SYMBOL.
I need help to open a "for" loop which will get me the highest CHG_IN_OI for every symbol, take the row of that highest CHG_IN_OI for that row.
For example if there are 14 rows of ACC as a symbol, I need to find highest CHG_IN_OI for ACC from the CHG_IN_OI column and get that row of the highest change and Retain the remaining columns as well!.
I have made a list named, Multisymbols which has these symbols:
multisymbols = [
'ACC',
'ADANIENT',
'ADANIPORTS',
'AMARAJABAT',
'AMBUJACEM',
'APOLLOHOSP',
'APOLLOTYRE',
'ASHOKLEY',
'ASIANPAINT',
'AUROPHARMA',
'AXISBANK',
'BAJAJ-AUTO',
'BAJAJFINSV',
'BAJFINANCE',
'BALKRISIND',
'BANDHANBNK',
'BANKBARODA',
'BATAINDIA',
'BEL',
'BERGEPAINT',
'BHARATFORG',
'BHARTIARTL',
'BHEL',
'BIOCON',
'BOSCHLTD',
'BPCL',
'BRITANNIA',
'CADILAHC',
'CANBK',
'CENTURYTEX',
'CHOLAFIN',
'CIPLA',
'COALINDIA',
'COLPAL',
'CONCOR',
'CUMMINSIND',
'DABUR',
'DIVISLAB',
'DLF',
'DRREDDY',
'EICHERMOT',
'EQUITAS',
'ESCORTS',
'EXIDEIND',
'FEDERALBNK',
'GAIL',
'GLENMARK',
'GMRINFRA',
'GODREJCP',
'GODREJPROP',
'GRASIM',
'HAVELLS',
'HCLTECH',
'HDFC',
'HDFCBANK',
'HDFCLIFE',
'HEROMOTOCO',
'HINDALCO',
'HINDPETRO',
'HINDUNILVR',
'IBULHSGFIN',
'ICICIBANK',
'ICICIPRULI',
'IDEA',
'IDFCFIRSTB',
'IGL',
'INDIGO',
'INDUSINDBK',
'INFRATEL',
'INFY',
'IOC',
'ITC',
'JINDALSTEL',
'JSWSTEEL',
'JUBLFOOD',
'KOTAKBANK',
'L&TFH',
'LICHSGFIN',
'LT',
'LUPIN',
'M&M',
'M&MFIN',
'MANAPPURAM',
'MARICO',
'MARUTI',
'MCDOWELL-N',
'MFSL',
'MGL',
'MINDTREE',
'MOTHERSUMI',
'MRF',
'MUTHOOTFIN',
'NATIONALUM',
'NAUKRI',
'NESTLEIND',
'NIITTECH',
'NMDC',
'NTPC',
'ONGC',
'PAGEIND',
'PEL',
'PETRONET',
'PFC',
'PIDILITIND',
'PNB',
'POWERGRID',
'PVR',
'RAMCOCEM',
'RBLBANK',
'RECLTD',
'RELIANCE',
'SAIL',
'SBILIFE',
'SBIN',
'SHREECEM',
'SEIMENS',
'SRF',
'SRTRANSFIN',
'SUNPHARMA',
'SUNTV',
'TATACHEM',
'TATACONSUM',
'TATAMOTORS',
'TATAPOWER',
'TATASTEEL',
'TCS',
'TECHM',
'TITAN',
'TORNTPHARM',
'TORNTPOWER',
'TVSMOTOR',
'UBL',
'UJJIVAN',
'ULTRACEMCO',
'UPL',
'VEDL',
'VOLTAS',
'WIPRO',
'ZEEL'
]
df = df[df['SYMBOL'].isin(multisymbols)]
df
These are all the shares in the NSE. Hope you can understand and help me out. I used .groupby(),it successfully gave me the highest CHG_IN_OI and .agg() to retain the remaining columns but the data was not correct. I just simply want the row for every symbols "HIGHEST" CHG_IN_OI.
Thanks in Advance!

Although different from the data presented in the question, we have answered the same financial data using equity data as an example.
import pandas as pd
import pandas_datareader.data as web
import datetime
with open('./alpha_vantage_api_key.txt') as f:
api_key = f.read()
start = datetime.datetime(2019, 1, 1)
end = datetime.datetime(2020, 8,1)
df_all = pd.DataFrame()
symbol = ['AAPL','TSLA']
for i in symbol:
df = web.DataReader(i, 'av-daily', start, end, api_key=api_key)
df['symbol'] = i
df_all = pd.concat([df_all, df], axis=0)
df.index = pd.to_datetime(df.index)
Aggregating a single column
df_all.groupby('symbol')['volume'].agg('max').reset_index()
symbol volume
0 AAPL 106721200
1 TSLA 60938758
Multi-Column Aggregation
df_all.groupby('symbol')[['high','volume']].agg(high=('high','max'), volume=('volume','max'))
high volume
symbol
AAPL 425.66 106721200
TSLA 1794.99 60938758
Extract the target line
symbol_max = df_all.groupby('symbol').apply(lambda x: x.loc[x['volume'].idxmax()]).reset_index(drop=True)
symbol_max
open high low close volume symbol
0 257.26 278.4100 256.37 273.36 106721200 AAPL
1 882.96 968.9899 833.88 887.06 60938758 TSLA

How to View Several Excel Rows in Python

So, I am trying to create a Python program that reads a password protected excel file. The program is intended to report any names expiring between 90 and 105 days. The problem I am running into right now is getting the program to read multiple rows. I've been using import xlrd. I was hoping that 'counter' would change the row being read, but only the first row is being read.
Edit: Solved. I was able to use the code below to get my program to display entries that are expiring within my time field.
import pandas as pd
from datetime import date, timedelta
today = date.today()
ninety_Days = (date.today()+timedelta(days=90))
hundred_Days = (date.today()+timedelta(days=105))
hundred_Days = '%s-%s-%s' % (hundred_Days.month, hundred_Days.day,
hundred_Days.year)
ninety_Days = '%s-%s-%s' % (ninety_Days.month, ninety_Days.day,
ninety_Days.year)
wkbk = pd.read_excel('Practice Inventory.xlsx', 'Sheet1')
mask = (wkbk['Expiration'] >= ninety_Days) & (wkbk['Expiration'] <=
hundred_Days)
wkbk = wkbk.loc[mask]
print(wkbk)

Use Pandas!
import pandas as pd
df = pd.read_excel('Practice Inventory.xlsx')
new_df = df[df['days to expiration'] >= 90]
final_df = pd.concat([df[df['days to expiration'] <= 120], new_df]
The final_df will hold all the rows with days of expiration greater than 90 and less than 120.

Continuing during an exception in a try/except statement

I have read numerous StackOverflow threads about looping during try/except statements, using else and finally, if/else statements, and while statements, but none of them address what I want. That or I don't know how to utilise that information to get what I want done.
Basically, I am trying to get adjusted closing stock prices for various companies on a given date. I pasted some dummy data in the code block below to demonstrate (NOTE: you'll have to install pandas and pandas_datareader to get the dummy code to run). The get_stock_adj_close function returns the adj_close price given a ticker and date. The dummy_dataframe contains 4 companies with their tickers and random dates. And the add_days function takes a date and adds any number of days. I would like to append the adjusted close stock prices for each company in the dataframe on the listed date into the stock_prices list.
Because the yahoo stock price database isn't that reliable for older entries and because some dates fall on days when the market is closed, whenever a price isn't available it raises a KeyError: 'Date'. Thus, what I would like to do is keep adding days indefinitely until it finds a date where a price does exist. The problem is it only adds the day once and then raises the same KeyError. I want it to keep adding days until it finds a day where the database has a stock price available and then return back to the dataframe and keep going with the next row. Right now the whole thing breaks on the first GM date (fourth row), which raises the KeyError and the fifth row/second GM date is ignored. Any help is appreciated!
Dummy data:
from datetime import datetime, date, timedelta
import pandas as pd
import pandas_datareader as pdr
from dateutil.relativedelta import relativedelta
def add_days(d, num_days):
return d + timedelta(days=num_days)
def get_stock_adj_close(ticker, chosen_date):
stock_df = pdr.get_data_yahoo(ticker, start = chosen_date, end = chosen_date)
return stock_df.iloc[0]['Adj Close']
d = {'TICKER': ['AMD','AMD','CHTR','GM'], 'DATE': [datetime(2020,2,4), datetime(2019,2,8),datetime(2019,1,31), datetime(2010,4,7)]}
dummy_dataframe = pd.DataFrame(data=d)
stock_prices = []
for i, row in dummy_dataframe.iterrows():
given_date = row['DATE']
try:
stock_price = get_stock_adj_close(row['TICKER'], given_date)
print(stock_price)
stock_prices.append(stock_price)
except KeyError:
given_date = add_days(given_date,1)
stock_price = get_stock_adj_close(row['TICKER'], given_date)
stock_prices.append(stock_price)
print(stock_prices)

I think while loop will help you. For example:
for i, row in dummy_dataframe.iterrows():
given_date = row['DATE']
stock_price_found = False
while not stock_price_found:
try:
stock_price = get_stock_adj_close(row['TICKER'], given_date)
print(stock_price)
stock_prices.append(stock_price)
stock_price_found = False
except KeyError:
given_date = add_days(given_date,1)
Or you can also use while True together with break:
for i, row in dummy_dataframe.iterrows():
given_date = row['DATE']
while True:
try:
stock_price = get_stock_adj_close(row['TICKER'], given_date)
print(stock_price)
stock_prices.append(stock_price)
break
except KeyError:
given_date = add_days(given_date,1)
Don't forget to make sure that you are not stuck in indefinite loop, would be also helpful some other exit conditions from while loop, for example, after 10 failures.

Grabbing per minute stock data from a large time range Python

So I'm trying to grab per minute stock data over a one year time gap and I know the Google Finance API doesn't work anymore so I did some digging around I found some code from a old github thread that could find the range within 5 days from yahoo finance data; however, it does not do anymore than that even when I put a keyword like '1Y' which defaults to 1 day. Here is the code below:
import requests
import pandas as pd
import arrow
import datetime
import os
def get_quote_data(symbol='AAPL', data_range='5d', data_interval='1m'):
res = requests.get('https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?range={data_range}&interval={data_interval}'.format(**locals()))
data = res.json()
body = data['chart']['result'][0]
dt = datetime.datetime
dt = pd.Series(map(lambda x: arrow.get(x).datetime.replace(tzinfo=None), body['timestamp']), name='Datetime')
df = pd.DataFrame(body['indicators']['quote'][0], index=dt)
dg = pd.DataFrame(body['timestamp'])
df = df.loc[:, ('open', 'high', 'low', 'close', 'volume')]
df.dropna(inplace=True) #removing NaN rows
df.columns = ['OPEN', 'HIGH','LOW','CLOSE','VOLUME'] #Renaming columns in pandas
return df

body['meta']['validRanges'] tells you:
['1d', '5d', '1mo', '3mo', '6mo', '1y', '2y', '5y', '10y', 'ytd', 'max']
You are requesting 1Y instead of 1y. This difference is important.
By the way you can load the timestamps much more easily like this:
pd.to_datetime(body['timestamp'], unit='s')

print('stock ticker: {0}'.format(get_quote_data(symbol='AAPL', data_range='1d', data_interval='1m')))
works