advanced-algorithmic-trading

320
Obtains a pandas DataFrame containing open normalised prices
for high, low and close for a particular equities symbol
from Yahoo Finance. That is, it creates High/Open, Low/Open
and Close/Open columns.
"""
df = web.DataReader(symbol, "yahoo", start, end)
df["H/O"] = df["High"]/df["Open"]
df["L/O"] = df["Low"]/df["Open"]
df["C/O"] = df["Close"]/df["Open"]
df.drop(
[
"Open", "High", "Low",
"Close", "Volume", "Adj Close"
],
axis=1, inplace=True
)
return df
def plot_candlesticks(data, since):
"""
Plot a candlestick chart of the prices,
appropriately formatted for dates
"""
# Copy and reset the index of the dataframe
# to only use a subset of the data for plotting
df = copy.deepcopy(data)
df = df[df.index >= since]
df.reset_index(inplace=True)
df[’date_fmt’] = df[’Date’].apply(
lambda date: mdates.date2num(date.to_pydatetime())
)
# Set the axis formatting correctly for dates
# with Mondays highlighted as a "major" tick
mondays = WeekdayLocator(MONDAY)
alldays = DayLocator()
weekFormatter = DateFormatter(’%b %d’)
fig, ax = plt.subplots(figsize=(16,4))
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
# Plot the candlestick OHLC chart using black for