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EOD_charts.py
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EOD_charts.py
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'''
This script produces EOD graphs from Yahoo adjusted close prices
No adjustment for closing prices is needed.
This script has RadioItems that can select type of graphs to be shown
The graphs plotted are Price-Volume graphs (Main graph on top)
Choices:
RSI, Swing, Ichimokucloud, Bollinger bands.
'''
from get_yahoo import download_quotes
import pandas as pd
import numpy as np
import re
import requests
from datetime import timedelta
import dash
import dash_html_components as html
import dash_core_components as dcc
from dash.dependencies import Output, Input
import plotly
import plotly.tools as tls
from subprocess import run, PIPE
from io import StringIO
colist = requests.get('https://www.nseindia.com/content/equities/EQUITY_L.csv').text
colist = pd.read_csv(StringIO(colist))
colist = list(colist.SYMBOL)
app = dash.Dash(__name__)
app.layout = html.Div([
html.Div([
html.Div([html.B('Stock to plot : ')],style={'padding':10}),
html.Div([dcc.Dropdown(id = 'stock_input',
options = [ {'label':i,'value':i} for i in colist] ,
value = 'INFY')
],style = {'width': '40%','margin': 2})
], style = {'display':'flex'}),
dcc.Graph(id = 'price_volume'),
dcc.RadioItems(id = 'selector',
options=[{'label':'Bollinger Bands','value':1},
{'label':'Swing','value':2},
{'label':'RSI','value':3},
{'label':'Ichimoku Cloud','value':4}
], value=4, labelStyle = {'display': 'inline-block', 'padding-left':'10%'}),
dcc.Graph(id = 'graph2'),
html.Div(id = 'store', style = {'display': 'none'})
])
# The hidden div to store our stock dataframe in JSON format
# for sharing between callbacks.
@app.callback(
Output('store', 'children'),
[Input('stock_input', 'value')]
)
def cache(stock):
# The '.NS' is for NSE stocks, for other exchange stocks just input the correct extension ,
# that is available from Yahoo Finance.
df = download_quotes(stock+'.NS', write_to_file = False)
df = df.dropna()
df = df.reset_index(drop = True)
return df.to_json(date_format = 'iso', orient = 'split')
# Main graph (the top one)
@app.callback(
Output('price_volume', 'figure'),
[Input('store', 'children')]
)
def graph(json_data):
df = pd.read_json(json_data, orient = 'split')
df['20 DMA'] = df['Adj Close'].rolling(window= 20).mean()
df['50 DMA'] = df['Adj Close'].rolling(window= 50).mean()
df['100 DMA'] = df['Adj Close'].rolling(window=100).mean()
df['200 DMA'] = df['Adj Close'].rolling(window=200).mean()
# to determine the monitor's height, width for graph's dimension
output = run(['xrandr'], stdout=PIPE).stdout.decode()
result = re.search(r'current (\d+) x (\d+)', output)
width, height = map(int, result.groups()) if result else (800, 600)
fig = tls.make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.09)
fig.append_trace({'x' : df.DateTime, 'y' : df['Adj Close'], 'type':'scatter', 'name':'Price'}, 1,1)
fig.append_trace({'x' : df.DateTime, 'y' : df['20 DMA'], 'type':'scatter', 'name':'20 DMA'}, 1,1)
fig.append_trace({'x' : df.DateTime, 'y' : df['50 DMA'], 'type':'scatter', 'name':'50 DMA'}, 1,1)
fig.append_trace({'x' : df.DateTime, 'y' : df['100 DMA'], 'type':'scatter', 'name':'100 DMA'}, 1,1)
fig.append_trace({'x' : df.DateTime, 'y' : df['200 DMA'], 'type':'scatter', 'name':'200 DMA'}, 1,1)
fig.append_trace({'x' : df.DateTime, 'y' : df.Volume, 'type':'bar', 'name':'Volume'}, 2,1)
if height<800: # for laptops.
fig['layout'].update(height=600, legend = dict(font=dict(size=22)))
if 1000<height<1500: # for 1080p monitors
fig['layout'].update(height=800, legend = dict(font=dict(size=22)))
if 1600<height: # for 4k TV
fig['layout'].update(height=1000, legend = dict(font=dict(size=22)))
fig['layout']['yaxis1'].update(tickfont=dict(size= 15), title= 'Price', titlefont=dict(size=18))
fig['layout']['yaxis2'].update(tickfont=dict(size= 15), title= 'Volume', titlefont=dict(size=18))
fig['layout']['xaxis1'].update(tickfont=dict(size= 20), tickformat='%Y-%b-%d')
return fig
# The choice graphs (The graph below the main one)
@app.callback(
Output('graph2', 'figure'),
[Input('store', 'children'),
Input('selector', 'value')])
def gr2(json_data,graphtype):
df = pd.read_json(json_data, orient='split')
df['20 DMA'] = df['Adj Close'].rolling(window=20).mean()
df['UpperBand'] = df['20 DMA']+(df['Adj Close'].rolling(window=20).std()*2)
df['LowerBand'] = df['20 DMA']-(df['Adj Close'].rolling(window=20).std()*2)
if graphtype==1: # Bollinger bands indicator
traces1 = []
traces1.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df['Adj Close'],
name = 'Close',mode = 'lines'))
traces1.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df.UpperBand,
fill = None,line = dict(color='rgb(143, 19, 13)'), name = 'UB',mode = 'lines'))
traces1.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df.LowerBand,
fill = 'tonexty',line = dict(color='rgb(143, 19, 13)'),name = 'LB',mode = 'lines'))
layout1 = plotly.graph_objs.Layout(title = 'Bollinger Band',titlefont=dict(size=25),
yaxis = dict(title='Price',tickfont=dict(size=20),titlefont=dict(size=18)),
xaxis = dict(tickfont=dict(size=20), showspikes=True,tickformat='%Y-%b-%d'),
margin=dict(r=10))
return {'data': traces1, 'layout':layout1}
elif graphtype==2: # Swing level indicator
# This indicator is got from http://www.vfmdirect.com/kplswing/index.html
# The souce code for this - http://www.vfmdirect.com/kplswing/kpl_swing.afl
#
# In simple words this is a break-out trading strategy.
res=df['High'].rolling(window= 20).max()
sup=df['Low'].rolling(window= 20).min()
avd = np.where(df['Adj Close'] > res.shift(1), 1,np.where(df['Adj Close']<sup.shift(1),-1,0))
df1=pd.concat([res,sup],axis=1)
df1['s']=avd
df['swing']=np.nan
df['swing']=np.where(df1.s ==1 ,df1.Low, np.where(df1.s == -1 ,df1.High, df['swing'].fillna(method='ffill')))
df.swing=df['swing'].fillna(method='ffill')
traces2=[]
traces2.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df['Adj Close'], name = 'Close',mode = 'lines'))
traces2.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df.swing, name = 'Swing',mode = 'lines'))
layout2 = plotly.graph_objs.Layout(title = 'Swing levels')
return {'data': traces2, 'layout':layout2}
elif graphtype==3: # RSI
traces3 = []
window_length = 14
close = df['Adj Close']
delta = close.diff()
delta = delta[1:]
up, down = delta.copy(), delta.copy()
up[up < 0] = 0
down[down > 0] = 0
# Calculate the RSI based on EWMA
# roll_up1 = pd.stats.moments.ewma(up, window_length)
# roll_down1 = pd.stats.moments.ewma(down.abs(), window_length)
# RS1 = roll_up1 / roll_down1
# RSI1 = 100.0 - (100.0 / (1.0 + RS1))
# Calculate the RSI based on SMA
roll_up2 = up.rolling(window= window_length).mean()
roll_down2 = down.abs().rolling(window= window_length).mean()
RS2 = roll_up2 / roll_down2
RSI2 = 100.0 - (100.0 / (1.0 + RS2))
traces3.append(plotly.graph_objs.Scatter(x = df.DateTime, y = RSI2, name = 'RSI',mode = 'lines'))
layout3 = plotly.graph_objs.Layout(title = 'RSI')
return {'data': traces3,'layout':layout3}
elif graphtype==4 : # ichimoku cloud
high_9 = df['High'].rolling(window= 9).max()
low_9 = df['Low'].rolling(window= 9).min()
df['tenkan_sen'] = (high_9 + low_9) /2
high_26 = df['High'].rolling(window= 26).max()
low_26 = df['Low'].rolling(window= 26).min()
df['kijun_sen'] = (high_26 + low_26) /2
last_index=df.iloc[-1:].index[0]
last_date=df['DateTime'].iloc[-1].date()
for i in range(26):
df.loc[last_index+1+i,'DateTime']=last_date+timedelta(days=i)
df['senkou_span_a'] = ((df['tenkan_sen'] + df['kijun_sen']) / 2).shift(26)
high_52 = df['High'].rolling(window= 52).max()
low_52 = df['Low'].rolling(window= 52).min()
df['senkou_span_b'] = ((high_52 + low_52) /2).shift(26)
df['chikou_span'] = df['Adj Close'].shift(-22)
traces4=[]
traces4.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df['Adj Close'], name = 'Close',mode = 'lines'))
traces4.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df['tenkan_sen'], name = 'tenkan_sen',mode = 'lines'))
traces4.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df['kijun_sen'], name = 'kijun_sen',mode = 'lines'))
traces4.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df['senkou_span_a'],fill = 'none', name = 'senkou_span_a',mode = 'lines'))
traces4.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df['senkou_span_b'],fill = 'tonexty' ,name = 'senkou_span_b',mode = 'lines'))
traces4.append(plotly.graph_objs.Scatter(x = df.DateTime, y = df['chikou_span'],name = 'chikou_span',mode = 'lines'))
layout4 = plotly.graph_objs.Layout(title = 'Ichimoku Cloud')
return {'data': traces4,'layout':layout4}
if __name__ == '__main__':
app.run_server(debug=True)