def btnSearchScript(self):
try:
ts = TimeSeries(self.key, output_format='pandas')
self.searchTuple = ts.get_symbol_search(
self.search_symbol_combo.get())
#print(searchTuple[0].columns)
#print(searchTuple[0].values)
search_values_list = list()
self.search_symbol_combo['values'] = search_values_list
for i in range(len(self.searchTuple[0].values)):
search_values_list.append(self.searchTuple[0].values[i][0] +
"--" +
self.searchTuple[0].values[i][1])
self.search_symbol_combo['values'] = search_values_list
self.search_symbol_combo.focus_force()
self.search_symbol_combo.event_generate('<Down>')
except Exception as e:
msgbx.showerror("Search Symbol Error", str(e))
self.search_symbol_combo.focus_force()
return
def quote_val_by_symbol(symba):
#import pandas as bear
#from alpha_vantage.timeseries import TimeSeries #https://alpha-vantage.readthedocs.io/en/latest/genindex.html
#keys = 'BGDF14R9D6NKLQ19' #get free API Key here: https://www.alphavantage.co/support/#api-key
output_format = TimeSeries(key=keys, output_format='pandas')
x = output_format.get_symbol_search(keywords=symba)
x[0].reset_index(inplace=True)
x[0].columns = ['date','symbol','name','type','region','marketOpen','marketClose','timezone','currency','matchScore']
base_currency = x[0][x[0]['symbol']==symba]['currency'].to_string(index=False)
base_currency = base_currency.replace(" ","")
rate = convert_currency(base_currency)
output_format = TimeSeries(key=keys, output_format='pandas')
y = output_format.get_quote_endpoint(symbol=symba)
y[0].reset_index(inplace=True)
y[0].columns = ['index','symbol','open','high','low','price','volume','latest trading day','previous close','change','change percent']
y[0]
quote_in_usd=y[0]
quote_in_usd['open'] = (float(quote_in_usd['open']) * rate )
quote_in_usd['high'] = (float(quote_in_usd['high']) * rate )
quote_in_usd['low'] = (float(quote_in_usd['low']) * rate )
quote_in_usd['price'] = (float(quote_in_usd['price']) * rate)
return quote_in_usd
def company_search(query):
found = []
ts = TimeSeries(key=settings.ALPHA_VANTAGE_API_KEY)
companies, _ = ts.get_symbol_search(query)
for company in companies:
found.append(save_company(company))
return found
def StockSearch(searchQuery):
# try to search for stock
try:
# initiate timeseries alpha vantage api
ts = TimeSeries(key=apiKey, output_format='json')
# get data from search query
data = ts.get_symbol_search(searchQuery)
# take the first item from data
data = data[0]
# create list for search results
searchResults = []
# for each item in data
for item in data:
# add item ticker and item name to the search results
searchResults.append([item['1. symbol'], item['2. name']])
# sort results based on stock ticker
searchResults = sorted(searchResults, key=lambda x: x[0])
# if search results are empty
if searchResults == []:
# return error message
return '> Sorry, there were no results for this search term!'
# create string for filtered search results
filteredSearchResults = '**Search Results** \n'
# for each item in search results
for item in searchResults:
# open text file of listings
with open('StockListings.txt', 'r') as file:
# read each line from file
for line in file:
# check if line contains stock ticker + '|'
if (str(item[0]) + '|') in line:
# if line contains stock ticker + '|' add to result string
filteredSearchResults += '> ' + str(
item[0]) + ' - ' + str(item[1]) + '\n'
break
# close file
file.close()
# return search results string
return filteredSearchResults
# if an error occurs
except:
# print error message
print('An error occurred in AlphaVantage.StockSearch')
# return error string
return '> Sorry, an error occurred while getting the search results!'
def get_fin_quotes(ticker_symbol):
ts = TimeSeries(key="QG9CQILAD7ASQXK0")
data = ts.get_quote_endpoint(ticker_symbol)
symbol_search = ts.get_symbol_search(ticker_symbol)
try:
return str(symbol_search[0][0]['2. name']) \
+ ": Цена - " + str(data[0]['05. price']) \
+ " Объем - " + str(data[0]['06. volume']) \
+ " Процент изменений с последнего торгового дня - " + str(data[0]['10. change percent'])
except ValueError:
print("Неверный тикет")
finally:
print("Конец обработки исключения")
def look_it_up(keyword):
#import pandas as bear
#from alpha_vantage.timeseries import TimeSeries #https://alpha-vantage.readthedocs.io/en/latest/genindex.html
#keys = 'BGDF14R9D6NKLQ19' #get free API Key here: https://www.alphavantage.co/support/#api-key
output_format = TimeSeries(key=keys, output_format='pandas')
x = output_format.get_symbol_search(keywords=keyword)
x[0].reset_index(inplace=True)
x[0].columns = [
'date', 'symbol', 'name', 'type', 'region', 'marketOpen',
'marketClose', 'timezone', 'currency', 'matchScore'
]
x[0]
return x[0]
def searchPage(request):
try:
api_key = 'EADU97JO5Z1TCYPV'
input_symbol = request.POST['s']
ts = TimeSeries(key=api_key, output_format='pandas')
tz_NY = pytz.timezone('US/Pacific')
datetime_NY = datetime.now(tz_NY)
current_time = datetime_NY.strftime("%a %b %d %H:%M:%S %Z %Y")
# r = requests.get('https://www.alphavantage.co/query?function=SYMBOL_SEARCH&keywords='+value_A & 'apikey='+api_key)
r = ts.get_symbol_search(keywords=input_symbol)
data, meta_data1 = ts.get_daily(symbol=input_symbol, outputsize='full')
# result = r.json()
# full_name = result['bestMatches'][0]['2. name']
full_name = r[0]['2. name'][0]
close_data = data['4. close']
num_change = round(close_data[0] - close_data[1], 2)
percentage_change = round(num_change / close_data[1] * 100, 2)
if num_change > 0:
num_change = '+' + str(num_change)
else:
num_change = '-' + str(num_change)
if percentage_change > 0:
percentage_change = '+' + str(percentage_change) + '%'
else:
percentage_change = '-' + str(percentage_change) + '%'
return render(request,
'search.html',
context={
'data1': current_time,
'data2': full_name,
'data3': close_data[0],
'data4': num_change,
'data5': percentage_change
})
except Exception:
return render(request, 'err.html')
class MarketData:
""" Class for holding market data """
def __init__(self):
""" Initialize the MarketData object
"""
# Create storage for each stock. Each stock is represented as an entry
# in the following dictionary. This allows caching each stock that is
# queried so we don't need to look it up a second time. Each stock id
# references a pandas dataframe storing open, close, high, low, and
# volume trading data for the past 20 years
self.stock_data = dict()
# Keep track of some information associated with the stock
self.stock_info = dict()
# Update the column names
self.data_cols = {
'1. open': 'open',
'2. high': 'high',
'3. low': 'low',
'4. close': 'close',
'5. adjusted close': 'closeAdj',
'6. volume': 'volume',
'7. dividend amount': 'dividend',
'8. split coefficient': 'splitcoeff'
}
# Stock info column names
self.info_cols = {'1. symbol': 'symbol', '2. name': 'name'}
# Set the bar colors
self.line_colors = ['black', 'red', 'blue', 'green', 'orange']
self.setcolors(False)
# Setup the query objects
self.timeseries = TimeSeries(os.environ['AVTOKEN'],
output_format='pandas')
def setcolors(self, cbf=False):
""" Sets the bar colors for candlestick plot
Parameters
----------
cbf : bool
True if color-blind friendly colors should be used
"""
# Colorblind friendly colors?
if cbf:
self.colors = {'inc': 'blue', 'dec': 'orange'}
# Red/green are fine
else:
self.colors = {'inc': 'lightgreen', 'dec': 'red'}
def queryStocks(self, symbols):
""" Extracts the latest stock information and stores it
Parameters
----------
symbols : list(str)
List of symbols to query for
Returns
-------
Error message string if an error occurs.
"""
# Track whether there was an error
errMsg = None
# Loop on each symbol
for sym in symbols:
if sym not in self.stock_data.keys():
try:
# Get info regarding this symbol
if sym not in ['DJI', 'SPX']:
try:
# Download information about the stock symbol
info = self.timeseries.get_symbol_search(sym)
info_df = pd.DataFrame(info[0])
info_df.index = info_df.index.astype(int)
info_df['9. matchScore'] = info_df[
'9. matchScore'].astype(float)
except IndexError as err:
errMsg = f'Symbol not found: {sym}'
break
# If the first symbol does not return a 100% value
if (info_df['9. matchScore'][0] < 1.0):
raise ValueError(
self.format_symbol_opts(sym, info_df))
break
# Handle stock indexes for Dow and S&P
else:
info_df = pd.DataFrame(
[[sym, sym]], columns=['1. symbol', '2. name'])
# Get the data for this symbol
dat = self.timeseries.get_daily_adjusted(symbol=sym,
outputsize='full')
except ValueError as err:
errMsg = err
break
# Store the data in a dataframe
market_df = pd.DataFrame(dat[0])
market_df['datetime'] = pd.to_datetime(market_df.index)
# Store the dataframe
self.stock_data[sym] = market_df.rename(columns=self.data_cols)
self.stock_info[sym] = info_df.rename(columns=self.info_cols)
return errMsg
def format_symbol_opts(self, sym, df):
""" Format returned symbol information for easy to read info
Parameters
----------
df : pandas.DataFrame
Represents the returned information
Returns
-------
Formatted list of similar sybols
"""
n = '<br/>'
# Setup the message
message = f"No symbol matching \"{sym}\" found.{n}Try one of the following:{n}{n}"
message += f" Symbol (<i>Name, Region</i>){n}"
message += f"------------------------{n}"
# Append each possible match
for row in df.itertuples():
new_sym = row[1]
new_name = row[2]
region = row[4]
message += f" {new_sym: <8} (<i>{new_name}, {region}</i>){n}"
return message
def moving_avg(self, values, window=5):
""" Compute the moving average for a distribution
Parameters
----------
values : array
Array object of values to compute the moving avarage of
window : int
Number of values to compute the average for
Returns
-------
Array representing the moving average
"""
# Create a return array
size = len(values)
mvavg = np.zeros(size)
# Loop through the list of values
w2 = int(window / 2)
for i in range(size):
# Get the range for summing
start = i - w2 if i >= w2 else 0
stop = i + w2 if i < size - w2 else size - 1
# Compute moving average
mvavg[i] = np.sum(values[start:stop + 1]) / (stop - start + 1)
return mvavg
def plotCandlesticks(self, symbol, days2show=100):
""" Plots the stock information for all symbols in 'symbols'.
Parameters
----------
symbols : list(str)
Python list of symbols to be plotted
days2show : int
Initial number of days to show data for when plot first loads
Returns
-------
bokeh.plotting.figure object
"""
# Get the data associated with this symbol
df = self.stock_data[symbol]
info = self.stock_info[symbol]
# Get indices for increasing/decresing market days
inc = df.close > df.open
dec = df.open > df.close
# Specify the width of the bars
w = 20 * 60 * 60 * 1000 # half day in ms
# Define over tooltips
hover = HoverTool(tooltips=[('Date', '@datetime{%F}'),
('Open', '$@open{0.2f}'),
('Adj. Close', '$@closeAdj{0.2f}'),
('High', '$@high{0.2f}'),
('Low', '$@low{0.2f}'),
('Volume', '@volume{0.00 a}')],
formatters={'@datetime': 'datetime'},
names=['segments'])
# Define the tools for user manipulation of figure
TOOLS = ['pan', 'wheel_zoom', 'box_zoom', 'reset', 'save', hover]
# Specify the range of the x,y axes for visibility
xstop = df.datetime[0] + relativedelta(days=3)
xstart = xstop - relativedelta(days=days2show)
ystart = 0.95 * np.min(df.low[:days2show])
ystop = 1.05 * np.max(df.high[:days2show])
# Setup the figure for plotting
p1 = figure(
x_axis_type='datetime',
tools=TOOLS,
x_range=(xstart, xstop),
y_range=(ystart, ystop),
x_axis_location='above',
y_axis_label='Price ($USD)',
plot_width=750,
plot_height=300,
title=f"Stock Price Data: {info['name'][0]} ({info['symbol'][0]})")
p1.grid.grid_line_alpha = 0.3
# Generate the plots
p1.segment('datetime',
'high',
'datetime',
'low',
source=ColumnDataSource(df),
color="black",
line_width=2,
name='segments')
p1.vbar(df.datetime[inc],
w,
df.open[inc],
df.close[inc],
name='incbar',
fill_color=self.colors['inc'],
line_color="black",
line_width=0.5)
p1.vbar(df.datetime[dec],
w,
df.open[dec],
df.close[dec],
name='decbar',
fill_color=self.colors['dec'],
line_color="black",
line_width=0.5)
# Get moving average of high and low
mvavg_lo = self.moving_avg(df.low.to_numpy())
mvavg_hi = self.moving_avg(df.high.to_numpy())
# p.line(df.datetime, mvavg_hi, line_color='blue')
# p.line(df.datetime, mvavg_lo, line_color='red')
p2 = figure(x_axis_type="datetime",
x_range=p1.x_range,
x_axis_label='Date',
y_axis_label=r'Volume (millions)',
plot_width=750,
plot_height=140)
p2.grid.grid_line_alpha = 0.3
p2.vbar(df.datetime[inc],
w,
0,
df.volume[inc] * 1e-6,
fill_color=self.colors['inc'],
line_color='black',
line_width=0.5)
p2.vbar(df.datetime[dec],
w,
0,
df.volume[dec] * 1e-6,
fill_color=self.colors['dec'],
line_color='black',
line_width=0.5)
# Combine all the plots
p = gridplot([p1, p2], ncols=1)
return p
def plotStocks(self, symbols, param, days2show=100):
""" Plots the stock information for all symbols in 'symbols'.
Parameters
----------
symbols : list(str)
Python list of symbols to be plotted
param : str
Parameter to plot (open, close, high, low, volume)
days2show : int
Initial number of days to show data for when plot first loads
Returns
-------
bokeh.plotting.figure object
"""
# Define the tools for user manipulation of figure
TOOLS = ['pan', 'wheel_zoom', 'box_zoom', 'reset', 'save']
# Define the starting range to be the last 100 days
xstop = datetime.datetime.now() + relativedelta(days=3)
xstart = xstop - relativedelta(days=days2show)
ystart = 1e30
ystop = -1e30
# Setup the figure for plotting
p = figure(x_axis_type="datetime",
tools=TOOLS,
x_range=(xstart, xstop),
plot_width=750,
plot_height=400,
x_axis_label='Date',
y_axis_label=param,
title=f"{param.upper()} Data: {', '.join(symbols)}")
for s, sym in enumerate(symbols):
# Get the data associated with this symbol
df = self.stock_data[sym]
info = self.stock_info[sym]
# Specify the range of the x,y axes
ystart = min(ystart, np.min(df[param][:days2show]))
ystop = max(ystop, np.max(df[param][:days2show]))
# Add the next line plot
color_indx = s % len(self.line_colors)
p.line(df.datetime,
df[param],
line_color=self.line_colors[color_indx],
legend_label=info.name[0])
# Update the range of x,y
p.y_range = Range1d(0.95 * ystart, 1.05 * ystop)
# Setup the legend
p.legend.location = "top_left"
return p
def plot(self, symbols, param='closeAdj', cbf=False):
"""
Parameters
----------
symbols : list()
Python list of symbols to be plotted
param : str
Parameter to be plotted for multi-symbol plots
cbf : bool
Whether color blind friendly colors should be used
Returns
-------
(script,div) for showing the plots in the webpage
"""
# Update colors if colorblind friendly
self.setcolors(cbf)
# Get the actual figure to be plotted
p = None
if len(symbols) == 1:
p = self.plotCandlesticks(symbols[0])
else:
p = self.plotStocks(symbols, param)
return components(p)
def timeSeries(API_key, ticker):
from alpha_vantage.timeseries import TimeSeries
import matplotlib.pyplot as plt
import mplfinance as mpf
ts=TimeSeries(key=API_key, output_format='pandas')
option=input('1. Intraday\n2. Daily\n3. Weekly\n4. Monthly\n5. Quote Endpoint\n6. Search Endpoint\n').lower()
if option=='intraday' or option=='1':
interval=int(input("Enter the interval:\n\t1. 1 minute\n\t2. 5 minutes\n\t3. 15 minutes\n\t4. 30 minutes\n\t5. 60 minutes\n"))
inter=['','1min','5min','15min','30min','60min']
data=ts.get_intraday(symbol=ticker, interval=inter[interval])[0]
data.columns = ['Open', 'High', 'Low', 'Close', 'Volume']
data.index.name = "Date"
mpf.plot(data,
type='candle',
title=f"Intraday Time series for the {ticker} stock {inter[interval]}",
mav=(20),
volume=True,
tight_layout=True,
style='yahoo')
return data
elif option=='daily' or option=='2':
data=ts.get_daily_adjusted(symbol=ticker)[0]
data.columns = ['Open', 'High', 'Low', 'Close', 'Adjusted_close','Volume','Dividend Amount','Split Coefficient']
data.index.name = "Date"
mpf.plot(data,
type='candle',
title=f'Daily Time series for the {ticker} stock',
mav=(20),
volume=True,
tight_layout=True,
style='yahoo')
return data
elif option=='weekly' or option=='3':
data=ts.get_weekly_adjusted(symbol=ticker)[0]
data.columns = ['Open', 'High', 'Low', 'Close', 'Adjusted_close','Volume','Dividend Amount']
data.index.name = "Date"
mpf.plot(data,
type='candle',
title=f'Weekly Time series for the {ticker} stock',
mav=(20),
volume=True,
tight_layout=True,
style='yahoo')
return data
elif option=='monthly' or option=='4':
data=ts.get_monthly_adjusted(symbol=ticker)[0]
data.columns = ['Open', 'High', 'Low', 'Close', 'Adjusted_close','Volume','Dividend Amount']
data.index.name = "Date"
mpf.plot(data,
type='candle',
title=f'Monthly Time series for the {ticker} stock',
mav=(20),
volume=True,
tight_layout=True,
style='yahoo')
return data
elif option=='quote endpoint' or option=='5':
data=ts.get_quote_endpoint(symbol=ticker)[0]
return data
elif option=='search endpoint' or option=='6':
keywords=input('Enter the Name of the Stock:\n')
data=ts.get_symbol_search(keywords=keywords)[0]
return data
else:
print("CANNOT RECOGNIZE")
