The candlestick plot in the current form is a bit bland. Traders usually overlay stock indicators such as Average True Range (ATR), Bollinger band, Commodity Channel Index (CCI), Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), and various other stats for technical analysis.

Stockstats (https://github.com/jealous/stockstats) is a great package for calculating these indicators/stats and many more. It wraps around pandas DataFrames and generate the stats on the fly when they are accessed. To use stockstats, we simply install it via PyPI: pip install stockstats.

Next, we can convert a pandas DataFrame to a stockstats DataFrame via stockstats.StockDataFrame.retype(). A plethora of stock indicators can then be accessed by following the pattern StockDataFrame["variable_timeWindow_indicator"]. For example, StockDataFrame['open_2_sma'] would give us the 2-day simple moving average on the opening price. Shortcuts may be available for some indicators, so please consult the official documentation for more information:

from stockstats import StockDataFrame


# Convert to StockDataFrame
# Need to pass a copy of candlestick_data to StockDataFrame.retype
# Otherwise the original candlestick_data will be modified
stockstats = StockDataFrame.retype(candlestick_data.copy())

# 5-day exponential moving average on closing price
ema_5 = stockstats["close_5_ema"]
# 20-day exponential moving average on closing price
ema_20 = stockstats["close_20_ema"]
# 50-day exponential moving average on closing price
ema_50 = stockstats["close_50_ema"]
# Upper Bollinger band
boll_ub = stockstats["boll_ub"]
# Lower Bollinger band
boll_lb = stockstats["boll_lb"]
# 7-day Relative Strength Index
rsi_7 = stockstats['rsi_7']
# 14-day Relative Strength Index
rsi_14 = stockstats['rsi_14']

With the stock indicators ready, we can overlay them on the same candlestick chart:

import datetime
import matplotlib.pyplot as plt
from matplotlib.dates import date2num, WeekdayLocator, DayLocator, DateFormatter, MONDAY
from matplotlib.finance import candlestick_ohlc


# Create a new Matplotlib figure
fig, ax = plt.subplots()

# Prepare a candlestick plot
candlestick_ohlc(ax, candlestick_data.values, width=0.6)

# Plot stock indicators in the same plot
ax.plot(candlestick_data["Datetime"], ema_5, lw=1, label='EMA (5)')
ax.plot(candlestick_data["Datetime"], ema_20, lw=1, label='EMA (20)')
ax.plot(candlestick_data["Datetime"], ema_50, lw=1, label='EMA (50)')
ax.plot(candlestick_data["Datetime"], boll_ub, lw=2, linestyle="--", label='Bollinger upper')
ax.plot(candlestick_data["Datetime"], boll_lb, lw=2, linestyle="--", label='Bollinger lower')

ax.xaxis.set_major_locator(WeekdayLocator(MONDAY)) # major ticks on 
# the mondays
ax.xaxis.set_minor_locator(DayLocator()) # minor ticks on the days
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d'))
ax.xaxis_date() # treat the x data as dates
# rotate all ticks to vertical
plt.setp(ax.get_xticklabels(), rotation=90, horizontalalignment='right')

ax.set_ylabel('Price (US $)') # Set y-axis label

# Limit the x-axis range from 2017-4-23 to 2017-7-1
datemin = datetime.date(2017, 4, 23)
datemax = datetime.date(2017, 7, 1)
ax.set_xlim(datemin, datemax)

plt.legend() # Show figure legend
plt.tight_layout()
plt.show()