def test_chartDF(self):
from pyEX import chartDF
with patch('requests.get') as mock:
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
chartDF('test')
def result():
if request.method == 'POST':
user = {'name': '','email': '','start_date': '','ticker1': '','amount1': '','ticker2': '','amount2': '','ticker3': '','amount3': '',}
result1 = 0
result2 = 0
result3 = 0
share1 = 0
share2 = 0
share3 = 0
try:
user['name'] = request.form['name']
user['email'] = request.form['email']
user['start_date'] = request.form['start_date']
user['ticker1'] = request.form['ticker1']
user['amount1'] = request.form['amount1']
user['ticker2'] = request.form['ticker2']
user['amount2'] = request.form['amount2']
user['ticker3'] = request.form['ticker3']
user['amount3'] = request.form['amount3']
except:
print('user input fetch error')
try:
df1 = p.chartDF(request.form['ticker1'], '5y').reset_index()
start_price1 = df1.loc[df1['date'] ==
request.form['start_date']]['close'].item()
end_price1 = df1.iloc[-1:, 4].item()
share1 = float(request.form['amount1']) / start_price1
result1 = (end_price1 - start_price1) * share1
except:
print("tikcer1 search fail")
try:
df2 = p.chartDF(request.form['ticker2'], '5y').reset_index()
start_price2 = df2.loc[df2['date'] ==
request.form['start_date']]['close'].item()
end_price2 = df2.iloc[-1:, 4].item()
share2 = float(request.form['amount2']) / start_price2
result2 = (end_price2 - start_price2) * share2
except:
print("tikcer2 search fail")
try:
df3 = p.chartDF(request.form['ticker3'], '5y').reset_index()
start_price3 = df3.loc[df3['date'] ==
request.form['start_date']]['close'].item()
end_price3 = df3.iloc[-1:, 4].item()
share3 = float(request.form['amount3']) / start_price3
result3 = (end_price3 - start_price3) * share3
except:
print("tikcer3 search fail")
result = result1 + result2 + result3
result = round(result, 2)
user['share'] = [share1, share2, share3]
print(result)
print(user)
return render_template("result.html", result = result, user = user)
def test_chartDF(self):
from pyEX import chartDF
with patch('requests.get') as mock, \
patch('pickle.dump'):
mock.return_value = MagicMock()
mock.return_value.status_code = 200
mock.return_value.json = MagicMock(return_value=[])
chartDF(SYMBOL)
def _get_data(symbol, start, end):
df = chartDF(symbol, '5y')
if start:
df = df[df.index > start]
if end:
df = df[df.index < end]
return df
def _get_data(symbol, start, end):
df = chartDF('aapl', '5y')
df = df[['open', 'close', 'high', 'low', 'volume']].set_index(df['date'])
df.index = pd.to_datetime(df.index)
if start:
df = df[df.index > start]
if end:
df = df[df.index < end]
return df
def refetch(field, symbol):
if field == 'TICK':
return p.chartDF(symbol, '1d')
if field == 'FINANCIALS':
return p.financialsDF(symbol)
elif field == 'DAILY':
return p.chartDF(symbol, '5y')
elif field == 'COMPANY':
return p.companyDF(symbol)
elif field == 'EARNINGS':
return p.earningsDF(symbol)
elif field == 'DIVIDENDS':
return p.dividendsDF(symbol)
elif field == 'NEWS':
return p.newsDF(symbol)
elif field == 'STATS':
return p.stockStatsDF(symbol)
elif field == 'COMPOSITION':
return _fetchComposition(symbol)
elif field == 'PEERS':
return p.peersDF(symbol)
raise NotImplementedError('%s - %s' % (field, symbol))
def Predict(ticker, timeframe, N):
ticker = str(ticker)
timeframe = str(timeframe)
#N=int(N)
df = p.chartDF(ticker, timeframe)
df = df[['close']]
df.reset_index(level=0, inplace=True)
df.columns = ['ds', 'y']
m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=N)
forecast = m.predict(future)
forecast['day_week'] = forecast.ds.dt.weekday_name
forecast = forecast[forecast.day_week != 'Sunday']
forecast = forecast[forecast.day_week != 'Saturday']
trend = forecast.to_json()
#print(forecast)
#print(trend)
#print(forecast)
#m.plot(forecast,xlabel='Date', ylabel='Price')
#m.plot_components(forecast)
#plt.show()
return trend
def test_chartDF(self):
from pyEX import chartDF
chartDF(SYMBOL)
#url=https://towardsdatascience.com/implementing-moving-averages-in-python-1ad28e636f9d import pandas as pd import numpy as np from datetime import datetime import matplotlib.pyplot as plt import pyEX as p ticker='AMD' timeframe='1y' df=p.chartDF(ticker,timeframe) df=df[['close']] df.reset_index(level=0, inplace=True) df.columns=['ds','y'] plt.plot(df.ds,df.y) plt.show() rolling_mean=df.y.rolling(window=20).mean() rolling_mean2=df.y.rolling(window=50).mean() plt.plot(df.ds, df.y,label='AMD') plt.plot(df.ds,rolling_mean,label='AMD 20 day SMA',color='orange') plt.plot(df.ds,rolling_mean2,label='AMD 50 day SMA',color='magenta') plt.legend(loc='upper_left') plt.show() exp1=df.y.ewm(span=20,adjust=False).mean() exp2=df.y.ewm(span=50,adjust=False).mean()
companies = []
for company in topCompanies:
companies.append(company['name'])
companies = {company: {} for company in companies}
for company in topCompanies:
companies[company['name']]['symbol'] = company['symbol']
companies[company['name']]['industry_id'] = company['industry_id']
arbSymbol = 'AAPL'
# arbitrarily create df object
# get stock history for last month
arbChart = p.chartDF(arbSymbol, timeframe='1m')
# add stock symbol to df to act as a foreign key for company table
arbChart['symbol'] = "Arbitrary"
arbChart['industry_id'] = 0
# get company info
arbCompany = p.companyDF(arbSymbol)
# join the 2 tables and add to rest of stocks df
stocks_df = pd.merge(arbChart, arbCompany, how='left', on=['symbol'])
for company in companies.values():
symbol = company['symbol']
industry_id = company['industry_id']
# get stock history for last 1 month
chart_df = p.chartDF(symbol, timeframe='1y')
# add stock symbol to df to act as a foreign key for company table
chart_df['symbol'] = symbol
def update_graph(tickers):
graphs = []
for i, ticker in enumerate(tickers):
try:
df = pyEX.chartDF(str(ticker), '6m').reset_index()
except:
graphs.append(
html.H3(
'Data is not available for {}, please retry later.'.format(
ticker),
style={
'marginTop': 20,
'marginBottom': 20
}))
continue
candlestick = {
'x': df['date'],
'open': df['open'],
'high': df['high'],
'low': df['low'],
'close': df['close'],
'type': 'candlestick',
'name': ticker,
'legendgroup': ticker,
'increasing': {
'line': {
'color': colorscale[0]
}
},
'decreasing': {
'line': {
'color': colorscale[1]
}
}
}
bb_bands = bbands(df.close)
bollinger_traces = [{
'x': df['date'],
'y': y,
'type': 'scatter',
'mode': 'lines',
'line': {
'width': 1,
'color': colorscale[(i * 2) % len(colorscale)]
},
'hoverinfo': 'none',
'legendgroup': ticker,
'showlegend': True if i == 0 else False,
'name': '{} - bollinger bands'.format(ticker)
} for i, y in enumerate(bb_bands)]
graphs.append(
dcc.Graph(id=ticker,
figure={
'data': [candlestick] + bollinger_traces,
'layout': {
'margin': {
'b': 0,
'r': 10,
'l': 60,
't': 0
},
'legend': {
'x': 0
}
}
}))
return graphs
def _minute_dataframe_for_date(
self, ticker: str, start_timestamp: pd.Timestamp) -> pd.DataFrame:
ret_df = pd.DataFrame()
df = pyEX.chartDF(ticker, timeframe='1d', date=start_timestamp)
if df.empty:
return ret_df
df = df.reset_index()
df['volume'] = df['volume'].astype('int')
df['date'] = df['date'].astype('str')
df['minute'] = df['minute'].astype('str')
df['datet'] = df['date'] + ' ' + df['minute']
df['dividend'] = 0.0
df['split'] = 1.0
df.drop([
'date', 'minute', 'average', 'changeOverTime', 'close', 'high',
'label', 'low', 'marketAverage', 'marketChangeOverTime',
'marketNotional', 'marketNumberOfTrades', 'notional',
'numberOfTrades', 'open', 'volume'
],
axis=1,
level=None,
inplace=True,
errors='ignore')
df.rename(columns={
'datet': 'date',
'marketClose': 'close',
'marketHigh': 'high',
'marketLow': 'low',
'marketOpen': 'open',
'marketVolume': 'volume'
},
inplace=True)
df.date = pd.to_datetime(df.date,
errors='coerce',
utc=False,
infer_datetime_format=True)
df = df[~df.date.isnull()]
df.set_index('date',
drop=True,
append=False,
inplace=True,
verify_integrity=True)
utc = pytz.utc
nytz = pytz.timezone('US/Eastern')
df = df.tz_localize(nytz,
axis=0,
level=None,
copy=False,
ambiguous='raise')
df.index = df.index.tz_convert(utc)
if not (pd.Series(['close', 'high', 'low', 'open']).isin(
df.columns).all()):
log.info(
"Skipping {0} for {1}, not all columns ({2}) received".format(
ticker, start_timestamp.date(), str(df.columns)))
return ret_df
df = self._fixna(df, ticker)
df.index = df.index.tz_convert(None)
# Re-arrange them
ret_df = df[self._cols]
return ret_df
def fetchDF(self, key, field, _ret=True):
# tickers always caps
key = key.upper()
# fields always lower
field = field.lower()
if not (self._tickers['symbol'] == key).any():
# FIXME
return pd.DataFrame()
if key not in self._cache:
# initialize cache
self._cache[key] = {}
self._cache[key]['timestamp'] = {}
if field in ('financials', 'all'):
if 'financials' not in self._cache[key] or self._check_timestamp(
key, 'financials'):
try:
self._cache[key]['financials'] = p.financialsDF(key)
except KeyError:
self._cache[key]['financials'] = pd.DataFrame()
self._cache[key]['timestamp']['financials'] = datetime.now()
if field in ('chart', 'all'):
if 'chart' not in self._cache[key] or self._check_timestamp(
key, 'chart'):
try:
self._cache[key]['chart'] = p.chartDF(key, '1y')
except KeyError:
self._cache[key]['chart'] = pd.DataFrame()
self._cache[key]['timestamp']['chart'] = datetime.now()
if field in ('company', 'all'):
if 'company' not in self._cache[key] or self._check_timestamp(
key, 'company'):
self._cache[key]['company'] = p.companyDF(key)
self._cache[key]['timestamp']['company'] = datetime.now()
if field in ('quote', 'all'):
# always update
self._cache[key]['quote'] = p.quoteDF(key)
if field in ('dividends', 'all'):
if 'dividends' not in self._cache[key] or self._check_timestamp(
key, 'dividends'):
try:
self._cache[key]['dividends'] = p.dividendsDF(key)
except KeyError:
self._cache[key]['dividends'] = pd.DataFrame()
self._cache[key]['timestamp']['dividends'] = datetime.now()
if field in ('earnings', 'all'):
if 'earnings' not in self._cache[key] or self._check_timestamp(
key, 'earnings'):
try:
self._cache[key]['earnings'] = p.earningsDF(key)
except KeyError:
self._cache[key]['earnings'] = pd.DataFrame()
self._cache[key]['timestamp']['earnings'] = datetime.now()
if field in ('news', 'all'):
if 'news' not in self._cache[key] or self._check_timestamp(
key, 'news'):
try:
self._cache[key]['news'] = p.newsDF(key)
except KeyError:
self._cache[key]['news'] = pd.DataFrame()
self._cache[key]['timestamp']['news'] = datetime.now()
if field in ('peers', 'all'):
if 'peers' not in self._cache[key] or self._check_timestamp(
key, 'peers'):
try:
peers = p.peersDF(key)
except KeyError:
peers = pd.DataFrame()
if peers is not None and not peers.empty:
peers = peers.replace({np.nan: None})
infos = pd.concat(
[p.companyDF(item) for item in peers['symbol'].values])
self._cache[key]['peers'] = infos
else:
self._cache[key]['peers'] = pd.DataFrame()
self._cache[key]['timestamp']['peers'] = datetime.now()
if field in ('stats', 'all'):
if 'stats' not in self._cache[key] or self._check_timestamp(
key, 'stats'):
try:
self._cache[key]['stats'] = p.stockStatsDF(key)
except KeyError:
self._cache[key]['stats'] = pd.DataFrame()
self._cache[key]['timestamp']['stats'] = datetime.now()
if field in ('composition', 'all'):
if 'company' not in self._cache[key]:
self.fetchDF(key, 'company', _ret=False)
try:
self._cache[key]['composition'] = pd.read_html(
ETF_URL % key, attrs={'id': 'etfs-that-own'})[0]
self._cache[key]['composition']['% of Total'] = self._cache[
key]['composition']['% of Total'].str.rstrip('%').astype(
float) / 100.0
self._cache[key]['composition'].columns = [
'Symbol', 'Name', 'Percent'
]
self._cache[key]['composition'] = self._cache[key][
'composition'][['Symbol', 'Percent', 'Name']]
except (IndexError, requests.HTTPError, ValueError, HTTPError):
self._cache[key]['composition'] = pd.DataFrame()
self._cache[key]['timestamp']['composition'] = datetime.now()
if _ret:
# pull data
if field == 'all':
ret = copy.deepcopy(self._cache[key])
del ret['timestamp']
ret = pd.concat(ret)
elif field in self._cache[key]:
# or i have that field
ret = pd.concat({field: self._cache[key][field]})
else:
raise Exception('No ticker provided!')
return ret
