run.py
import requests
import numpy as np
import pandas as pd
from bokeh.io import curdoc
from bokeh.layouts import column
from bokeh.layouts import widgetbox
from stockstats import StockDataFrame
from bokeh.models import ColumnDataSource
from visualization import plot_stock_price, plot_sma, plot_macd, plot_rsi, plot_obv, widget_symbols, widget_show_text
intro_text = "Data is current, courtesy of https://iextrading.com/"
warning_text = "Dropbox is slow to load, please be patience (7000+ stocks)."
stock = ColumnDataSource(
data=dict(date=[], open=[], close=[], high=[], low=[], OBV=[], SMA_5=[], SMA_10=[], SMA_50=[], SMA_100=[], macd=[],
macd_histogram=[], macd_signal=[], close_line=[], rsi_15=[], zeros=[]))
def dropdown_on_change(attrname, old, new):
update()
dropdown = widget_symbols()
dropdown.on_change('value', dropdown_on_change)
def update(selected=None):
symbol = dropdown.value
# Test getting 6m chart
url = 'https://api.iextrading.com/1.0/stock/' + symbol + '/chart/6m'
response = requests.get(url)
respon = response.json()
data_temp = pd.DataFrame(respon)
data_temp["date"] = pd.to_datetime(data_temp["date"])
stock_data = StockDataFrame(data_temp.copy())
data = pd.DataFrame(data_temp)
# Adding indicators
# Smoothed close price line
# data['close_line'] = savgol_filter(stock.data['close'], 9, 3)
data['rsi_15'] = stock_data['rsi_15']
data['bolling_upper'] = stock_data['boll_ub']
data['bolling_lower'] = stock_data['boll_lb']
data['macd'] = stock_data['macd']
data['macd_signal'] = stock_data['macds']
data['macd_histogram'] = stock_data['macdh']
# Moving averages
data['SMA_5'] = stock_data['close_5_sma']
data['SMA_10'] = stock_data['close_10_sma']
data['SMA_50'] = stock_data['close_50_sma']
data['SMA_100'] = stock_data['close_100_sma']
# On-Balance Volume (OBV)
obv = list()
for today in range(0, len(stock_data)):
if today == 0:
obv.append(0)
continue
# Today price higher than yesterday
if stock_data.close.iloc[today] > stock_data.close.iloc[today - 1]:
obv.append(obv[today - 1] + stock_data.volume.iloc[today])
continue
# Today price lower than yesterday
if stock_data.close.iloc[today] < stock_data.close.iloc[today - 1]:
obv.append(obv[today - 1] - stock_data.volume.iloc[today])
continue
# Today price same as yesterday
obv.append(obv[today - 1])
data['OBV'] = np.array(obv)
data['zeros'] = np.zeros(len(obv))
stock.data = stock.from_df(data)
elements = list()
update(dropdown.value)
p_stock = plot_stock_price(stock)
p_sma = plot_sma(stock)
p_sma.x_range = p_stock.x_range
p_macd = plot_macd(stock)
p_macd.x_range = p_stock.x_range
p_rsi = plot_rsi(stock)
p_rsi.x_range = p_stock.x_range
p_obv = plot_obv(stock)
p_obv.x_range = p_stock.x_range
elements.append(widgetbox(widget_show_text(intro_text)))
elements.append(widgetbox(widget_show_text(warning_text)))
elements.append(widgetbox(dropdown))
elements.append(p_stock)
elements.append(p_sma)
elements.append(p_macd)
elements.append(p_rsi)
elements.append(p_obv)
curdoc().add_root(column(elements))
curdoc().title = 'Bokeh stocks data'
