def quant_figure_tests(): df=cf.datagen.ohlc() qf=cf.QuantFig(df) qf.add_sma() qf.add_atr() qf.add_bollinger_bands() return qf.figure()
def make(self):
qf = cf.QuantFig(self.df,
legend='top',
)
qf.add_bollinger_bands(periods=15, boll_std=2)
# 设置RSI指标线
qf.add_rsi(periods=14, showbands=False)
plyo.plot(qf.iplot(asFigure=True), filename='qf_01.html')
def movement(df):
quotes = df.loc[:, ['Open', 'High', 'Low', 'Close']]
qf = cf.QuantFig(quotes,
title='SPY ETF Data 2020',
legend='top',
name='Share Price')
plyo.iplot(qf.iplot(asFigure=True))
def ta_dashboard(asset, indicator, start_date, end_date,
k, N, MACD_Fast_Periods, MACD_Slow_Periods, MACD_Signal_Periods,
RSI_Periods, RSI_UPPER, RSI_LOWER,
EMA_periods,
SMA_periods,
CCI_periods, CCI_UPPER, CCI_LOWER,
minute_data):
if minute_data is True:
df = data.DataReader(asset,
interval = '1m',
start = end_date - timedelta(days = 1),
end = end_date)
else:
df = data.DataReader(asset,
start = start_date,
end = end_date)
qf = cf.QuantFig(df, title=f'TA Dashboard - {asset}',
legend='right', name=f'{asset}')
qf.add_volume()
if 'Bollinger Bands' in indicator:
qf.add_bollinger_bands(periods=N,
boll_std=k)
if 'MACD' in indicator:
qf.add_macd(fast_period=MACD_Fast_Periods,
slow_period=MACD_Slow_Periods,
signal_period=MACD_Signal_Periods)
if 'RSI' in indicator:
qf.add_rsi(periods=RSI_Periods,
rsi_upper=RSI_UPPER,
rsi_lower=RSI_LOWER,
showbands=True)
if 'EMA' in indicator:
qf.add_ema(EMA_periods)
if 'SMA' in indicator:
qf.add_sma(SMA_periods)
if 'CCI' in indicator:
qf.add_cci(periods = CCI_periods,
cci_upper = CCI_UPPER,
cci_lower = CCI_LOWER)
fig = qf.figure()
iplot(fig)
def ta_dashboard(self,asset,indicator,start_date, end_date,bb_k, bb_n, macd_fast, macd_slow, macd_signal,
rsi_periods, rsi_upper, rsi_lower):
df = yf.download(asset,
start=start_date,
end=end_date,
progress=False,
auto_adjust=True)
qf = cf.QuantFig(df, title=f'TA Dashboard - {asset}', legend='right', name=f'{asset}')
if 'Bollinger Bands' in indicator:
qf.add_bollinger_bands(periods=bb_n,
boll_std=bb_k)
if 'MACD' in indicator:
qf.add_macd(fast_period=macd_fast,
slow_period=macd_slow,
signal_period=macd_signal)
if 'RSI' in indicator:
qf.add_rsi(periods=rsi_periods,
rsi_upper=rsi_upper,
rsi_lower=rsi_lower,
showbands=True)
return qf.iplot()
import cufflinks as cf
import plotly.offline as plyo
raw=pd.read_csv('./Data/601952.SS.csv',index_col=0,parse_dates=True)
raw2=pd.read_csv('./Data/000001.SS.csv',index_col=0,parse_dates=True)
cols=['Open' ,'High','Low', 'Close','Volume']
quotes=raw[cols]
quotes_base=raw2[cols]
q_calc=quotes*500/quotes_base
q_calc.Volume/=300
q_calc.dropna(inplace=True)
q_calc.Volume.replace([np.inf, -np.inf], 229,inplace=True)
q_calc.Volume.replace(np.nan,0,inplace=True)
qf1=cf.QuantFig(quotes,title='Suken',legend='top',name='Suken Stock')
qf2=cf.QuantFig(quotes_base,title='Shanghai',legend='top',name='Shanghai Index')
qf3=cf.QuantFig(q_calc,title='Scale',legend='top',name='Suken/Shanghai')
plyo.iplot(qf1.iplot(asFigure=True),image='png',filename='qf1_1')
qf1.add_bollinger_bands(periods=15,boll_std=2)
plyo.iplot(qf1.iplot(asFigure=True),image='png',filename='qf1_2')
qf1.add_bollinger_bands(periods=15,boll_std=2)
plyo.iplot(qf1.iplot(asFigure=True),image='png',filename='qf1_3')
plyo.iplot(qf2.iplot(asFigure=True),image='png',filename=f'qf2_01')
qf2.add_bollinger_bands(periods=15,boll_std=2)
plyo.iplot(qf2.iplot(asFigure=True),image='png',filename=f'qf2_02')
qf2.add_rsi(periods=14, showbands=False)
plyo.iplot(qf2.iplot(asFigure=True),image='png',filename=f'qf2_03')
import pandas as pd
import numpy as np
import yfinance as yf
import cufflinks as cf
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode()
df_twtr = yf.download('TWTR',
start='2018-01-01',
end='2018-12-31',
progress=False,
auto_adjust=True)
qf = cf.QuantFig(df_twtr,
title="Twitter's Stock Price",
legend='top',
name='TWTR')
qf.add_volume()
qf.add_sma(periods=20, column='Close', color='red')
qf.add_ema(periods=20, color='green')
qf.iplot()
#menu
st.sidebar.subheader('Date Range')
start_date = st.sidebar.date_input('start date', datetime.date(2019, 1, 1))
end_date = st.sidebar.date_input('end date', datetime.date(2021, 1, 31))
#tickerdata
ticker_list = pd.read_csv("s&p500.txt")
tickerSymbol = st.sidebar.selectbox('Stock Ticker', ticker_list)
tickerData = yf.Ticker(tickerSymbol)
tickerDataHistory = tickerData.history(period='1d',
start=start_date,
end=end_date) #make period dynamic
#info
st.header('**INFO**')
st.markdown(tickerData.info['longName'])
st.markdown(tickerData.info['sector'])
st.write(tickerDataHistory)
#graph
st.header('Graphs')
qf = cl.QuantFig(tickerDataHistory,
title='first graph',
legend='top',
name='GS')
qf.add_bollinger_bands()
fig = qf.iplot(asFigure=True)
st.plotly_chart(fig)
def ohlcv(data, **kwargs):
kwargs = CufflinksPlotMap._wrapper(**kwargs)
legend = kwargs.pop('legend', 'top')
return cf.QuantFig(data, legend=legend).iplot()
import pandas as pd
import plotly as py
import cufflinks as cf
import tushare as ts
from copy import deepcopy
cf.set_config_file(offline=True,world_readable=False)
stock = { 'name': '中国平安', 'code': '601318' }
df = ts.get_hist_data(stock['code']).iloc[::-1]
print(df)
qf=cf.QuantFig(df,title=stock['name'],legend='top',name=stock['name'], up_color='red', down_color='green')
boll = deepcopy(qf)
boll.add_bollinger_bands(name="Boll通道")
boll.iplot()
# fig = df.iplot(kind='bar', barmode='stack', asFigure=True)
# py.offline.plot(fig)
# rsi = deepcopy(qf)
# rsi.add_sma()
# rsi.add_macd()
# rsi.add_volume()
# rsi.iplot()
cf.datagen.lines(1,500).ta_plot(study='sma',period=[13,21,55])
from plotly.offline import iplot, init_notebook_mode
init_notebook_mode
## Descargando la data
df_twtr = yf.download('TWTR',
start='2018-01-01',
end='2020-04-16',
progress=False,
auto_adjust=True)
## Creando el gráfico de velas
qf = cf.QuantFig(df_twtr,
title="Gráfico de precios de Twitter",
legend='top',
name='TWTR')
## Añadir volumen
qf.add_volume()
## Añadir promedios móviles
qf.add_ema(periods=20, column='Close', color='red')
qf.add_ema(periods=50, column='Close', color='blue')
qf.add_ema(periods=200, column='Close', color='orange')
qf.iplot()
# In[ ]:
# Backtesting de una estrategia basada en un SMA
data.info()
# In[10]:
import cufflinks as cf
# In[11]:
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
# In[12]:
qf = cf.QuantFig(data,
title='EUR/USD',
legend='top',
name='EUR/USD',
datalegend=True)
# In[13]:
iplot(qf.iplot(asFigure=True))
# In[14]:
qf.add_bollinger_bands(periods=10,
boll_std=2,
colors=['magenta', 'grey'],
fill=True)
qf.data.update()
break
else:
print('Improper date format')
continue
# GET END DATE
while True:
end_date_entry = input('Enter end date in YYYY-MM-DD format: \n')
if validate_date(end_date_entry):
eYear, eMonth, eDay = map(int, end_date_entry.split('-'))
end_date = datetime.date(eYear, eMonth, eDay)
break
else:
print('Improper date format')
continue
return start_date, end_date
ticker_symbol = get_ticker_input()
start_date, end_date = get_dates()
ticker_data = yf.Ticker(ticker_symbol)
ticker_df = ticker_data.history(period='1d', start=start_date, end=end_date)
qf = cf.QuantFig(ticker_df,
title=f"{ticker_data.info['longName']} Price Data",
legend='top',
name='GS')
qf.add_bollinger_bands()
fig = qf.iplot(asFigure=True)
fig.write_image('fig1.png')
st.info(string_summmary)
st.write(tickerData.info['website'])
if task == 'Historical Data':
#Ticker data
st.subheader('**Ticker Data**')
tickerDf.sort_index(axis=0, ascending=False)
st.dataframe(tickerDf, 800, 700)
#st.table(tickerDf)
if task == 'Chart':
#Chart: Bollinger bands
#st.subheader('**Bollinger bands**')
st.subheader(tickerSymbol)
#qf=cf.QuantFig(tickerDf,title='QuantFig',legend='top',name='GS')
qf = cf.QuantFig(tickerDf, legend='top')
qf.add_bollinger_bands()
#fig=qf.iplot(asFigure=True)
#st.plotly_chart(fig)
#qf.add_sma([10,20],width=2,color=['green','lightgreen'],legendgroup=True)
#qf.add_rsi(periods=20,color='java')
#qf.add_bollinger_bands(periods=30,boll_std=2,colors=['magenta','grey'],fill=True)
qf.add_volume()
#qf.add_macd()
fig2 = qf.iplot(asFigure=True)
st.plotly_chart(fig2)
# fig3=plt.plot(tickerDf['Close'],label='Close Price')
# st.plotly_chart(fig3)
st.header("**Data Today**")
today_df = yf.download(tickerSymbol, period='1d', interval='1m')
fig = go.Figure(data=[
go.Candlestick(x=today_df.index,
open=today_df['Open'],
high=today_df['High'],
low=today_df['Low'],
close=today_df['Close'])
])
fig.update_layout(title="Facebook's Live Stock Price Today",
xaxis_title=None,
yaxis_title=None,
height=600,
legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01))
fig.update_xaxes(
rangeslider_visible=False,
rangeselector=dict(buttons=list([
dict(count=15, label="15m", step="minute", stepmode="backward"),
dict(count=30, label="30m", step="minute", stepmode="backward"),
dict(count=1, label="HTD", step="hour", stepmode="todate"),
dict(count=3, label="3h", step="hour", stepmode="backward"),
dict(step="all")
])))
st.plotly_chart(fig, use_container_width=True)
st.subheader('**Bollinger Bands**')
qf = cf.QuantFig(today_df, legend='top', name='GS')
qf.add_bollinger_bands()
fig = qf.iplot(asFigure=True)
st.plotly_chart(fig, use_container_width=True)
def chart(range, tickerSymbol):
tickerDf = si.get_data(tickerSymbol,
start_date=datetime.now() -
timedelta(days=periodDict[range]),
end_date=datetime.now(),
interval=intervalDict[range])
r = requests.get(
f'https://www.alphavantage.co/query?function=OVERVIEW&symbol={tickerSymbol}&apikey=Y4NCX1DFBJCN4I82'
)
data = r.json()
try:
string_name = data['Name']
except:
string_name = tickerSymbol
try:
string_summary = data['Description']
except:
string_summary = "Currently Unavailable"
qf = cf.QuantFig(tickerDf,
name='Price',
title='Stock Chart',
margin=dict(l=0, r=40, b=0, t=0))
qf.add_sma(10, width=2, color='green', name='SMA')
qf.add_bollinger_bands(periods=20,
boll_std=2,
colors=['magenta', 'magenta'],
fill=True,
name='BBands')
qf.add_volume(title='Volume',
up_color='grey',
down_color='lightsteelblue',
margin=dict(l=0, r=40, b=0, t=0))
qf.add_rsi(periods=14,
color='midnightblue',
name='RSI',
showbands=False,
margin=dict(l=0, r=40, b=0, t=0))
qf.add_macd(legendgroup=True,
name='MACD',
colors=['red', 'black'],
margin=dict(l=0, r=40, b=0, t=0))
layout = dict(xaxis=dict(rangeslider=dict(visible=False),
categoryorder="category ascending",
type="category",
visible=False))
config = {
'displaylogo':
False,
"modeBarButtonsToRemove": [
'pan2d', 'zoom2d', 'select2d', 'lasso2d', 'toggleSpikelines',
'autoScale2d'
]
}
fig = qf.iplot(asFigure=True, layout=layout)
fig.update_layout(height=1200,
title_text=string_name,
title_x=0.5,
showlegend=True,
legend=dict(orientation="h",
yanchor="top",
y=1.02,
xanchor="center",
x=0.5),
margin=dict(l=0, r=40, b=20, t=100))
return string_name, string_summary, fig, config
'ask_l':'min',
'ask_c': 'last',
'bid_o':'first',
'bid_h':'max',
'bid_l':'min',
'bid_c': 'last',
'volume': 'sum'
}
df = rows.resample('1H', closed='left', label='left').apply(ohlc_dict).dropna()
df = df[['bid_o','bid_h','bid_l','bid_c','volume']]
df = df.rename(columns={'bid_o':'open','bid_h':'high','bid_l':'low','bid_c':'close'})
qf = cf.QuantFig(df, title= 'USD_TRY')
rangeselector = dict(steps=[ 'Reset', '3M' ], bgcolor=('rgb(150,200,250)',.1),fontsize=12,fontfamily='monospace')
qf.layout.update(rangeselector=rangeselector)
qf.iplot()
from flask import Flask
app = Flask(__name__)
@app.route('/')
def hello_world():
return '<iframe src="https://plot.ly/create/?fid=btarhan7%3A10" width="750px" height="550px"></iframe>'
if __name__ == '__main__':
app.run(port=5555, debug=True)
def stocks_chart(stock_data: pd.DataFrame = None,
settings_dict: Dict = None) -> go.Figure:
"""Generate plotly finance chart.
Args:
stocks_data (pd.DataFrame): stock data derived from yf
settings (Dict): settings derived from the UI
Returns:
go.Figure: final stock chart
"""
# setting default values
data = get_stocks_data() if stock_data is None else stock_data
if settings_dict is None:
settings_dict = {
key + "_state": value
for key, value in session_default.__dict__.items()
}
settings_dict["ticker_dropdown_state"] = settings_dict["ticker"][0]
# extract chart settings from settings_dict
# if UI checklists were deselected settings_dict values are empty lists
add_boll_feature = bool(len(settings_dict["bollinger_check_state"]))
add_macd_feature = bool(len(settings_dict["macd_check_state"]))
add_rsi_feature = bool(len(settings_dict["rsi_check_state"]))
add_sma_feature = bool(len(settings_dict["sma_check_state"]))
# initialize QuantFig chart figure
qf = cf.QuantFig(data,
title=settings_dict["ticker_dropdown_state"],
legend="right")
if add_boll_feature is True:
qf.add_bollinger_bands(
periods=settings_dict["bollinger_periods_state"],
boll_std=settings_dict["boll_std_state"],
)
if add_macd_feature is True:
qf.add_macd(
fast_period=settings_dict["macd_fast_period_state"],
slow_period=settings_dict["macd_slow_period_state"],
signal_period=settings_dict["macd_signal_period_state"],
)
if add_rsi_feature is True:
qf.add_rsi(
periods=settings_dict["rsi_periods_state"],
rsi_lower=settings_dict["rsi_lower_state"],
rsi_upper=settings_dict["rsi_upper_state"],
)
if add_sma_feature is True:
qf.add_sma(periods=settings_dict["sma_periods_state"])
# cast QuantFig object to plotly figure
qf_fig = qf.figure()
# add time slider to figure
slider_dict = dict(
rangeselector=dict(buttons=list([
dict(count=1, label="1m", step="month", stepmode="backward"),
dict(count=6, label="6m", step="month", stepmode="backward"),
dict(count=1, label="YTD", step="year", stepmode="todate"),
dict(count=1, label="1y", step="year", stepmode="backward"),
dict(step="all"),
])),
rangeslider=dict(visible=True),
type="date",
)
qf_fig.update_layout(xaxis=slider_dict)
return qf_fig
st.markdown("""
Stock Price Analysis
Shown are the stock prices of S&P 500
""")
st.sidebar.subheader("Parameters")
start_date=st.sidebar.date_input("Start date", datetime.date(2010, 1, 1))
end_date=st.sidebar.date_input("End date", datetime.date(2021, 2, 28))
ticker_list=pd.read_csv("E:\Projects\Combined Projects\constituents_symbols.txt")
tickerSymbol=st.sidebar.selectbox('Stock ticker', ticker_list)
tickerData=yf.Ticker(tickerSymbol)
tickerDf=tickerData.history(period='1d', start=start_date, end=end_date)
string_logo='<img src=%s>' % tickerData.info['logo_url']
st.markdown(string_logo, unsafe_allow_html=True)
string_name=tickerData.info['longName']
st.header('**%s**' % string_name)
string_summary=tickerData.info['longBusinessSummary']
st.info(string_summary)
st.header("**Prices Range**")
st.write(tickerDf)
st.header("**Bands**")
qf=cf.QuantFig(tickerDf, title='Chart', legend='top', name='GS')
qf.add_bollinger_bands()
fig=qf.iplot(asFigure=True)
st.plotly_chart(fig)
auto_adjust = False,
# download pre/post regular market hours data
# (optional, default is False)
prepost = False,
# use threads for mass downloading? (True/False/Integer)
# (optional, default is True)
threads = True,
# proxy URL scheme use use when downloading?
# (optional, default is None)
proxy = None
)
print(stock)
# visualiz stock data
qf = cf.QuantFig(data)
qf.add_bollinger_bands()
qf.add_rsi(periods=20,color='java')
#qf.add_macd()
fig = qf.iplot(asFigure=True)
#qf.iplot()
#fig1.append_trace(fig['data'][0], count, 1)
py.offline.plot(fig, filename=stock + '.html', auto_open=False)
print('Vado in sleep...')
sleep(1*60)
print('ciao')
# sidebar
st.sidebar.subheader('Query parameters')
start_date = st.sidebar.date_input("Start date", datetime.date(2019, 1, 1))
end_date = st.sidebar.date_input("End date", datetime.date(2021, 1, 31))
# scraping ticker data
ticker_list = pd.read_html('https://en.wikipedia.org/wiki/NIFTY_50')[1].drop(
['Company Name', 'Sector'], axis=1)
tickerSymbol = st.sidebar.selectbox('Stock Ticker', ticker_list)
tickerData = yf.Ticker(tickerSymbol)
tickerDf = tickerData.history(period='1d', start=start_date, end=end_date)
#ticker info
logo = '<img src="%s">' % tickerData.info['logo_url']
st.markdown(logo, unsafe_allow_html=True)
name = tickerData.info['longName']
st.header('**%s**' % name)
summary = tickerData.info['longBusinessSummary']
st.info(summary)
#ticker data
st.header('**Ticker Data**')
st.write(tickerDf)
st.header('**Bollinger Bands**')
qf = cf.QuantFig(tickerDf, title='First Quant Figure', legend='top', name='GS')
qf.add_bollinger_bands()
fig = qf.iplot(asFigure=True)
st.plotly_chart(fig)
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import cufflinks as cf
import plotly.offline as plyo
raw = pd.read_csv('../stock_dfs/AAPL.csv', index_col=0, parse_dates=True)
raw.info()
quotes = raw[['Open', 'High', 'Low', 'Close']]
quotes.iloc[-60:]
qf = cf.QuantFig(quotes, title='AAPL Stock', legend='top', name='AAPL Stock')
plyo.offline.plot(qf.iplot(asFigure=True),
image='png',
filename='./images/qf_01.html')
# Adding Bollinger Bands
qf.add_bollinger_bands(periods=15,
boll_std=2) # Number of standard deviations away
plyo.offline.plot(qf.iplot(asFigure=True),
image='png',
filename='./images/qf_02.html')
# Adding RSI
qf.add_rsi(periods=14, showbands=False)
