def update_ma(range1, range2, stock_picker):
#start = datetime.strptime(start_date[:10], '%Y-%m-%d')
#end = datetime.strptime(end_date[:10], '%Y-%m-%d')
df = StooqDailyReader(stock_picker, start="2020-01-01").read()
df = df.sort_values(by='Date')
rolling_mean1 = df['Close'].rolling(window=range1).mean()
rolling_mean2 = df['Close'].rolling(window=range2).mean()
trace1 = go.Scatter(x=df.index,
y=df['Close'],
mode='lines',
name=stock_picker)
trace_a = go.Scatter(x=df.index,
y=rolling_mean1,
mode='lines',
yaxis='y',
name=f'SMA {range1}')
trace_b = go.Scatter(x=df.index,
y=rolling_mean2,
mode='lines',
yaxis='y',
name=f'SMA {range2}')
layout1 = go.Layout({
'title': 'Stock Price With Moving Average',
"legend": {
"orientation": "h",
"xanchor": "left"
},
"xaxis": {
"rangeselector": {
"buttons": [{
"count": 6,
"label": "6M",
"step": "month",
"stepmode": "backward"
}, {
"count": 1,
"label": "1Y",
"step": "year",
"stepmode": "backward"
}, {
"count": 1,
"label": "YTD",
"step": "year",
"stepmode": "todate"
}]
}
}
})
fig = {'data': [trace1], 'layout': layout1}
fig['data'].append(trace_a)
fig['data'].append(trace_b)
return fig
def _update_data_from_web(self, ticker, source) -> None:
"""
download and merge data into database
start date is the last updated date + 1 and end date is today
"""
# check if source is available for the ticker
if self._get_number_of_trials(ticker, source) > 3:
print(f'{ticker} from {source} is skipped', flush=True)
return
suffix, relative_limit, processor = source_map[source]
# configure start, end dates
today = pd.Timestamp.today() - rdate("1b")
start_date = self._get_last_date_from_db(ticker, source) + rdate("1b")
if relative_limit is not None and start_date < today - relative_limit:
start_date = today - relative_limit
end_date = today
# check date
if start_date > end_date:
return
# download data and pre-process if needed
if source == "stooq":
data = StooqDailyReader(ticker + suffix, start_date,
end_date).read()
if data.shape[0] == 0:
self._increment_trial_count(ticker, source)
return
else:
try:
data = web.DataReader(ticker + suffix, source, start_date,
end_date)
except KeyError:
self._increment_trial_count(ticker, source)
return
if processor is not None:
processor(data)
data.index = data.index.astype(
"datetime64[ns]") # for some sources, string can be return
sdate, edate = parse_time(data.index.min()), parse_time(
data.index.max())
print(
f"Downloaded {ticker} from {sdate.date()} to {edate.date()} totally {data.shape[0]:d} bars via {source}",
flush=True)
data["source"] = source
data.to_sql(ticker,
con=self.conn,
index=True,
index_label="date",
if_exists="append")
def get_rsi(
start=datetime(2020, 1, 1),
end=datetime(2020, 12, 31),
brand='1305.JP',
n=14,
):
stooq = StooqDailyReader(brand, start=start, end=end)
data = stooq.read()
#input data must be pandas dataframe
diff = data["Close"].diff()
up = diff.copy()
up[up < 0] = 0
down = diff.copy()
down[down > 0] = 0
up_sma_14 = up.rolling(window=n, center=False).mean()
down_sma_14 = down.abs().rolling(window=n, center=False).mean()
RSI = (up_sma_14) / (down_sma_14 + up_sma_14)
return RSI * 100, data["Close"]
def update_graph(stock_ticker):
df = StooqDailyReader(stock_ticker, start="2020-01-01").read()
df_box = df[["Close"]]
fig = {
"data": px.box(df_box, y=df_box.columns),
'layout': {
'title': {
'text': 'DISPLAY ME!'
}
}
}
return fig
def update_graph(n_clicks, start_date, end_date, stock_ticker):
#Convert passed date variables to date type since they change to string when they are paased to this function.
start = dt.strptime(start_date[:10], '%Y-%m-%d')
end = dt.strptime(end_date[:10], '%Y-%m-%d')
#Create session variable for StooqDailyReader just in case if you got stuck behind company firewall.
session = requests.Session()
session.verify = False
#Query each selected tick from Nasdaq and append its trace to traces list
traces = []
for tic in stock_ticker:
data = StooqDailyReader(tic, start=start, end=end, session=session)
df = data.read()
traces.append({
'x': df.index,
'y': df['Close'],
'name': tic,
'hoverinfo': 'text',
'text': df['Close']
})
#Create figure object to pass to main layout
stocks = ['<b>' + stock + '</b>' for stock in stock_ticker]
fig = {
'data': traces,
'layout': {
'title': ', '.join(stocks) + ' Closing Prices',
'hovermode': 'closest',
'xaxis': {
'title': 'Time',
'showline': True
},
'yaxis': {
'title': 'Price (USD)',
'showline': True
}
}
}
return fig
def update_graph(n_clicks, stock_ticker, start_date, end_date):
start = datetime.strptime(start_date[:10], '%Y-%m-%d')
end = datetime.strptime(end_date[:10], '%Y-%m-%d')
traces = []
df = pd.DataFrame()
for tic in stock_ticker:
#df = yf.Ticker(tic).history(start = start, end = end)
df[tic] = StooqDailyReader(tic, start=start, end=end).read()["Close"]
corr = df.corr()
fig = px.imshow(
corr,
color_continuous_scale="Greens",
title='Korelacje cen zamknięcia wybranych spółek notowanych na WIG')
return fig
def update_graph(n_clicks, stock_ticker, start_date, end_date):
start = datetime.strptime(start_date[:10], '%Y-%m-%d')
end = datetime.strptime(end_date[:10], '%Y-%m-%d')
traces = []
df = pd.DataFrame()
for tic in stock_ticker:
#df = yf.Ticker(tic).history(start = start, end = end)
df[tic] = StooqDailyReader(tic, start=start, end=end).read()["Close"]
column_maxes = df.max()
df_max = column_maxes.max()
column_mins = df.min()
df_min = column_mins.min()
normalized_df = (df - df_min) / (df_max - df_min)
fig = px.line(normalized_df,
x=normalized_df.index,
y=normalized_df.columns,
title='Ceny zamknięcia wybranych spółek notowanych na WIG')
return fig
def update_graph(stock_ticker):
df = StooqDailyReader(stock_ticker, start="2020-01-01").read()
fig = {
"data":
go.Figure(data=[
go.Candlestick(x=df.index,
open=df['Open'],
high=df['High'],
low=df['Low'],
close=df['Close'])
]),
'layout': {
'xaxis': {
'title': 'Data'
},
'yaxis': {
'title': 'Cena zamknięcia'
}
}
}
return fig
def update_return(stock_picker):
#start = datetime.strptime(start_date[:10], '%Y-%m-%d')
#end = datetime.strptime(end_date[:10], '%Y-%m-%d')
df = StooqDailyReader(stock_picker, start="2020-01-01").read()
df_wig = StooqDailyReader("WIG.PL", start="2020-01-01").read()
df = df.sort_values(by='Date')
df_wig = df_wig.sort_values(by='Date')
stocks = pd.DataFrame({
"Date": df.index,
str(stock_picker): df["Close"],
"WIG": df_wig["Close"]
})
stocks = stocks.set_index('Date')
stock_return = stocks.apply(lambda x: ((x - x[0]) / x[0]) * 100)
trace2 = go.Scatter(x=stock_return.index,
y=stock_return[str(stock_picker)],
mode='lines',
name=str(stock_picker))
trace3 = go.Scatter(x=stock_return.index,
y=stock_return['WIG'],
mode='lines',
name="WIG")
fig = {
'data': [trace2],
'layout': {
'xaxis': {
'title': 'Data'
},
'yaxis': {
'title': 'Cena zamknięcia'
}
}
}
fig['data'].append(trace3)
return fig
def DataReader(
name,
data_source=None,
start=None,
end=None,
retry_count=3,
pause=0.1,
session=None,
api_key=None,
):
"""
Imports data from a number of online sources.
Currently supports Google Finance, St. Louis FED (FRED),
and Kenneth French's data library, among others.
Parameters
----------
name : str or list of strs
the name of the dataset. Some data sources (IEX, fred) will
accept a list of names.
data_source: {str, None}
the data source ("iex", "fred", "ff")
start : {datetime, None}
left boundary for range (defaults to 1/1/2010)
end : {datetime, None}
right boundary for range (defaults to today)
retry_count : {int, 3}
Number of times to retry query request.
pause : {numeric, 0.001}
Time, in seconds, to pause between consecutive queries of chunks. If
single value given for symbol, represents the pause between retries.
session : Session, default None
requests.sessions.Session instance to be used
api_key : (str, None)
Optional parameter to specify an API key for certain data sources.
Examples
----------
# Data from Google Finance
aapl = DataReader("AAPL", "iex")
# Price and volume data from IEX
tops = DataReader(["GS", "AAPL"], "iex-tops")
# Top of book executions from IEX
gs = DataReader("GS", "iex-last")
# Real-time depth of book data from IEX
gs = DataReader("GS", "iex-book")
# Data from FRED
vix = DataReader("VIXCLS", "fred")
# Data from Fama/French
ff = DataReader("F-F_Research_Data_Factors", "famafrench")
ff = DataReader("F-F_Research_Data_Factors_weekly", "famafrench")
ff = DataReader("6_Portfolios_2x3", "famafrench")
ff = DataReader("F-F_ST_Reversal_Factor", "famafrench")
"""
expected_source = [
"yahoo",
"iex",
"iex-tops",
"iex-last",
"iex-last",
"bankofcanada",
"stooq",
"iex-book",
"enigma",
"fred",
"famafrench",
"oecd",
"eurostat",
"nasdaq",
"quandl",
"moex",
"robinhood",
"tiingo",
"yahoo-actions",
"yahoo-dividends",
"av-forex",
"av-daily",
"av-daily-adjusted",
"av-weekly",
"av-weekly-adjusted",
"av-monthly",
"av-monthly-adjusted",
"av-intraday",
"econdb",
]
if data_source not in expected_source:
msg = "data_source=%r is not implemented" % data_source
raise NotImplementedError(msg)
if data_source == "yahoo":
return YahooDailyReader(
symbols=name,
start=start,
end=end,
adjust_price=False,
chunksize=25,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "iex":
return IEXDailyReader(
symbols=name,
start=start,
end=end,
chunksize=25,
api_key=api_key,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "iex-tops":
return IEXTops(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "iex-last":
return IEXLasts(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "bankofcanada":
return BankOfCanadaReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "stooq":
return StooqDailyReader(
symbols=name,
chunksize=25,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "iex-book":
return IEXDeep(
symbols=name,
service="book",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "enigma":
return EnigmaReader(dataset_id=name, api_key=api_key).read()
elif data_source == "fred":
return FredReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "famafrench":
return FamaFrenchReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "oecd":
return OECDReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "eurostat":
return EurostatReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "nasdaq":
if name != "symbols":
raise ValueError("Only the string 'symbols' is supported for "
"Nasdaq, not %r" % (name, ))
return get_nasdaq_symbols(retry_count=retry_count, pause=pause)
elif data_source == "quandl":
return QuandlReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "moex":
return MoexReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "robinhood":
return RobinhoodHistoricalReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "tiingo":
return TiingoDailyReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "yahoo-actions":
return YahooActionReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
elif data_source == "yahoo-dividends":
return YahooDivReader(
symbols=name,
start=start,
end=end,
adjust_price=False,
chunksize=25,
retry_count=retry_count,
pause=pause,
session=session,
interval="d",
).read()
elif data_source == "av-forex":
return AVForexReader(
symbols=name,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "av-daily":
return AVTimeSeriesReader(
symbols=name,
function="TIME_SERIES_DAILY",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "av-daily-adjusted":
return AVTimeSeriesReader(
symbols=name,
function="TIME_SERIES_DAILY_ADJUSTED",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "av-weekly":
return AVTimeSeriesReader(
symbols=name,
function="TIME_SERIES_WEEKLY",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "av-weekly-adjusted":
return AVTimeSeriesReader(
symbols=name,
function="TIME_SERIES_WEEKLY_ADJUSTED",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "av-monthly":
return AVTimeSeriesReader(
symbols=name,
function="TIME_SERIES_MONTHLY",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "av-monthly-adjusted":
return AVTimeSeriesReader(
symbols=name,
function="TIME_SERIES_MONTHLY_ADJUSTED",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "av-intraday":
return AVTimeSeriesReader(
symbols=name,
function="TIME_SERIES_INTRADAY",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=api_key,
).read()
elif data_source == "econdb":
return EcondbReader(
symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
).read()
else:
msg = "data_source=%r is not implemented" % data_source
raise NotImplementedError(msg)
def get_data_stooq(*args, **kwargs):
return StooqDailyReader(*args, **kwargs).read()
def download_data(ticker):
df = StooqDailyReader(symbols=(ticker)).read()
cols = {col: col + '_' + ticker for col in df.columns}
df = df.rename(columns=cols)
return df
avg = np.mean(array)
low = avg - 2*stdev
high = avg + 2*stdev
return {
# 'date': data.index,
'low': low,
'mean': avg,
'high': high,
'pos': (data[0]-low) / (high-low)
}
wig40 = "MWIG40TR.PL"
dax = "^DAX"
sp500 = "^SPX"
dax_data = StooqDailyReader(dax).read()['Close']
mwig40_data = StooqDailyReader(wig40).read()['Close']
spx_data = StooqDailyReader(sp500).read()['Close']
# variance
print('dax')
print(bollinger(dax_data, 50, 0))
print('mwig40')
print(bollinger(mwig40_data, 50, 0))
print('sp500')
print(bollinger(spx_data, 50, 0))
def DataReader(name,
data_source=None,
start=None,
end=None,
retry_count=3,
pause=0.001,
session=None,
access_key=None):
"""
Imports data from a number of online sources.
Currently supports Google Finance, St. Louis FED (FRED),
and Kenneth French's data library, among others.
Parameters
----------
name : str or list of strs
the name of the dataset. Some data sources (google, fred) will
accept a list of names.
data_source: {str, None}
the data source ("google", "fred", "ff")
start : {datetime, None}
left boundary for range (defaults to 1/1/2010)
end : {datetime, None}
right boundary for range (defaults to today)
retry_count : {int, 3}
Number of times to retry query request.
pause : {numeric, 0.001}
Time, in seconds, to pause between consecutive queries of chunks. If
single value given for symbol, represents the pause between retries.
session : Session, default None
requests.sessions.Session instance to be used
access_key : (str, None)
Optional parameter to specify an API key for certain data sources.
Examples
----------
# Data from Google Finance
aapl = DataReader("AAPL", "google")
# Price and volume data from IEX
tops = DataReader(["GS", "AAPL"], "iex-tops")
# Top of book executions from IEX
gs = DataReader("GS", "iex-last")
# Real-time depth of book data from IEX
gs = DataReader("GS", "iex-book")
# Data from FRED
vix = DataReader("VIXCLS", "fred")
# Data from Fama/French
ff = DataReader("F-F_Research_Data_Factors", "famafrench")
ff = DataReader("F-F_Research_Data_Factors_weekly", "famafrench")
ff = DataReader("6_Portfolios_2x3", "famafrench")
ff = DataReader("F-F_ST_Reversal_Factor", "famafrench")
"""
if data_source == "yahoo":
raise ImmediateDeprecationError(DEP_ERROR_MSG.format('Yahoo Daily'))
return YahooDailyReader(symbols=name,
start=start,
end=end,
adjust_price=False,
chunksize=25,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "yahoo-actions":
raise ImmediateDeprecationError(DEP_ERROR_MSG.format('Yahoo Actions'))
return YahooActionReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "yahoo-dividends":
comp = 'Yahoo Dividends'
raise ImmediateDeprecationError(DEP_ERROR_MSG.format(comp))
return YahooDivReader(symbols=name,
start=start,
end=end,
adjust_price=False,
chunksize=25,
retry_count=retry_count,
pause=pause,
session=session,
interval='d').read()
elif data_source == "google":
return GoogleDailyReader(symbols=name,
start=start,
end=end,
chunksize=25,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "iex":
return IEXDailyReader(symbols=name,
start=start,
end=end,
chunksize=25,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "iex-tops":
return IEXTops(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "iex-last":
return IEXLasts(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "bankofcanada":
return BankOfCanadaReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "stooq":
return StooqDailyReader(symbols=name,
chunksize=25,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "iex-book":
return IEXDeep(symbols=name,
service="book",
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "enigma":
return EnigmaReader(dataset_id=name, api_key=access_key).read()
elif data_source == "fred":
return FredReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "famafrench":
return FamaFrenchReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "oecd":
return OECDReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "eurostat":
return EurostatReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "edgar-index":
raise ImmediateDeprecationError(DEP_ERROR_MSG.format('EDGAR'))
return EdgarIndexReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == 'nasdaq':
if name != 'symbols':
raise ValueError("Only the string 'symbols' is supported for "
"Nasdaq, not %r" % (name, ))
return get_nasdaq_symbols(retry_count=retry_count, pause=pause)
elif data_source == "quandl":
return QuandlReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "moex":
return MoexReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == "morningstar":
return MorningstarDailyReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
interval="d").read()
elif data_source == 'robinhood':
return RobinhoodHistoricalReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session).read()
elif data_source == 'tiingo':
return TiingoDailyReader(symbols=name,
start=start,
end=end,
retry_count=retry_count,
pause=pause,
session=session,
api_key=access_key).read()
else:
msg = "data_source=%r is not implemented" % data_source
raise NotImplementedError(msg)
from pandas_datareader.stooq import StooqDailyReader
import pandas as pd
import numpy as np
df = StooqDailyReader(symbols='FB.US').read()
# print(df)
df['Open_group'] = pd.cut(x=df['Open'], bins=[80, 100, 200, df['Open'].max()])
print(
df.groupby(['Open_group']).agg({
'Open': ['sum', 'mean']
}).apply(np.round))
import pandas_datareader as web
import fix_yahoo_finance as yf
yf.pdr_override() # <== that's all it takes :-)
#Stocks
ticker_list = [
"HSBA.L", "BARC.L", 'LLOY.L', "RBS.L", "BP.L", "rdsa.l", "RIO.L", "AAL.L"
]
data = web.data.get_data_yahoo(ticker_list,
start="2009-07-02",
end="2012-07-01")['Close']
data = data.interpolate(method='time', axis=0)
print(data)
print(data.isna().sum())
data.to_csv('stock_prices.csv')
#FX
from pandas_datareader.stooq import StooqDailyReader
import datetime as dt
start_date = dt.date(2009, 7, 1)
end_date = dt.date(2012, 7, 1)
fx = StooqDailyReader(symbols='rubgbp', start="2009-07-01",
end="2012-07-01").read()
fx = fx.sort_index()
fx = fx[start_date:end_date]['Close']
print(fx)
fx.to_csv('rubgbp.csv', header=['Close'])