Ticker IXJ
From 2018-05-02 00:00:00
Security Type Equity
Market USA
Resolution Daily
Status Waiting Review

When requesting historical data for this ticker, the closing price on May 2nd 2018 is equal to 112.41 . On May 3rd 2018 the close price drops to 56.16.

Jupyter Notebook code: (I cannot attach the notebook, because it stays "Loading Notebooks..." in the drop-down menu)

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

qb = QuantBook()

assets = [
# Equity ETFs
"IXJ", # iShares Global Healthcare ETF
"KXI" # iShares Global Consumer Staples ETF
]

symbols = [qb.AddEquity(asset) for asset in assets]
for symbol in symbols:
symbol.SetDataNormalizationMode(DataNormalizationMode.TotalReturn)

def PlotCummulativeReturn(assets):

# Get historical data.
histdata = pd.DataFrame()
for symbol in symbols:
if str(symbol) in assets:
histdata = histdata.append(qb.History(symbol.Symbol, timedelta(days=12*31*4), Resolution.Daily).loc[:, "close"].unstack())

histdata = histdata.T
histdata = histdata.loc[((histdata.index + timedelta(days=2)).year >= 2018)
& (histdata.index.year <= 2020)]

# Normalise the data for plotting.
plotdata = histdata.apply(lambda x: x/x[0])
# Plot data.
plt.figure(figsize=(18,6))

for symbol in plotdata.columns:
plt.plot(plotdata.loc[:, symbol], label=str(symbol))

xlabels = pd.date_range(min(plotdata.index), max(plotdata.index), freq="M")
plt.xticks(xlabels, rotation=30)
plt.margins(x=0)
legend_labels = [symbol.split()[0] for symbol in plotdata.columns]
plt.legend(legend_labels)
plt.axhline(y=1, color='black', linestyle='--')
plt.axvline(x="2019-12-31", color='black', linestyle='--')
plt.show()

return histdata

PlotCummulativeReturn(assets)

histdata = pd.DataFrame()#dict()
for symbol in symbols:
histdata = histdata.append(qb.History(symbol.Symbol, timedelta(days=12*31*4), Resolution.Daily).loc[:, "close"].unstack())

histdata = histdata.T
histdata = histdata.loc[histdata.index.year == 2018]
histdata = histdata.loc[histdata.index.month >= 4]
histdata = histdata.loc[histdata.index.month <= 5]
returns = histdata.apply(lambda x: x)#/x[0])
returns.loc[:, ["IXJ", "KXI"]]