Overview
In the first article of this mini series we talked about building Continuous Futures which satisfy our requirements for deriving consistent PnL figures for our position keeping, as well as generating trading signals not effected by roll gaps between contracts. A solution of simply splicing contracts after each other clearly didn’t qualify, and we came up with the Panama Method being the closest to an ideal timeseries for our data set of German Government Bonds (Schatz, Bobl, Bund).
In this article, we will take a closer look at how trading might be effected. Therefore, we pick some prominent technical trading measures to represent our signal generation. As a first step we present Exponential Moving Average (EMA), Bollinger Bands, Moving Average Convergence/ Divergence (MACD), and Relative Strength Index (RSI) visually in a Candlestick Chart format with nice hover effects offered by a combination of the Plotly and Cufflinks library in Python. Afterwards, we quantify the signal dates that did change by the introduction of our Continuous Futures timeseries.
Finally, we come to the conclusion that trading signals are heavily influenced by the method chosen and that in a serious approach towards Futures trading, roll gaps should be carefully taken into account.
Some basics about charts and technical analysis
In this section we briefly clarify the terminology around charts and technical analysis which is later used in our analysis. As we can only touch the surface here, some links to further readings will be provided.
Charts
For our data set we will utilize so called Candlestick Charts. The chosen data frequency will be represented by one candlestick (daily data in our example therefore yields one candlestick per trading day). The beauty of this chart form is that we can identify a bunch of details about the events on the respective trading day with visual means: Color will tell, if the market was going up or down. The body of the candlestick shows open and close price, while the wicks (yes, our trading candle burns in two directions) themselves refer to the lowest and highest price on that day. Typically, volume is shown under the candlestick to complete OHLCV (Open, High, Low, Close, Volume) information. This raw form of the chart is then often enriched with visual representation of certain technical analysis studies as an overlay or a subplot of the chart.
Technical analysis
In it’s very basic form, technical analysis makes use of past data (mainly OHLCV) to predict future price movements. There is a vast amount of indicators from which we will pick a small subset which mainly is about the trend of the underlying timeseries and the respective implication for future price movements. As we are more interested in implementation, and the very fact if and how signals are influenced by the composition of our timeseries, we will only point you to some references about the afterwards used approaches here: EMA, Bollinger Bands, MACD, and RSI.
Building charts and technical indicators
Outlining our approach and the parameter settings
Since we have quite a few objects that we want to compute and visualize, here is a summary of what we are about to present:
 Chart type


Candlestick Chart

 Technical Indicators


EMA — Exponential Moving Average

Higher weighting to more recent data

20 periods considered


Bollinger Bands

Upper and lower bands

20 periods considered

2 standard deviations for bands


MACD — Moving Average Convergence/ Divergence

Fast and slow EMA (12 vs. 26 periods)

Signal line is moving average of MACD (9 periods)


RSI — Relative Strength Index

Compares average gain to average loss in a certain period

Range 0100, we consider 70 overbought and 30 oversold

20 periods considered


Python Visualization
In the following we will leverage on a combination of the Plotly and the Cufflinks libraries. To enable nice and fast exploring of the data in a Jupyter Notebook or even here on the web page, we do the imports as below (notice that we use Plotly offline here to circumvent data communication with the Plotly server):
import cufflinks as cf
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
Afterwards we basically start with passing a DataFrame with OHLCV data to the Cufflinks QuantFig class. With this call we instantiate our Candlestick Chart format and are also able to select some nice features like a range selector for some interactive exploring of the data. That first step is done by the following code:
plot_list = ['FGBL', 'FGBL_pan', 'FGBM', 'FGBM_pan', 'FGBS', 'FGBS_pan']
plot_titles = ['Bund Spliced', 'Bund Panama', 'Bobl Spliced',
'Bobl Panama', 'Schatz Spliced', 'Schatz Panama']
qf_list = []
for i, j in enumerate(plot_list):
if i % 2 == 0:
qf_list.append(cf.QuantFig(
data_dict[j]['Front']['201711':'20171117'],
title=plot_titles[i],
legend='top',
name=plot_titles[i],
datalegend=False,
rangeselector=dict(steps=['Reset','6M','3M','1M'],
bgcolor=('rgb(150, 200, 250)',.1),
fontsize=12, fontfamily='monospace', x=0, y=1)
))
else:
qf_list.append(cf.QuantFig(
data_dict[j]['201711':'20171117'],
title=plot_titles[i],
legend='top',
name=plot_titles[i],
datalegend=False,
rangeselector=dict(steps=['Reset','6M','3M','1M'],
bgcolor=('rgb(150, 200, 250)',.1),
fontsize=12, fontfamily='monospace', x=0, y=1)
))
Cufflinks now makes it very easy to add a bunch of technical analysis studies. In former versions this functionality was dependent on the TALib library, but now it can independently create the respective numbers. We will add the above outlined studies by writing a small function:
def cf_objects(qf_list, expiry_dates):
qf_list = qf_list
plt_list = []
expiry_dates = pd.read_excel(expiry_dates, header=None, index_col=0, squeeze=1)
expiry_dates = pd.to_datetime(expiry_dates.values, dayfirst = True)
for i, qf in enumerate(qf_list):
for d in expiry_dates:
qf.add_shapes(shapes=dict(kind='line', x0=d, x1=d, yref='paper', y0=0, y1=1,
color='grey', dash='dot'))
qf.add_volume(colorchange=True)
qf.add_macd(fast_period=12, slow_period=26, signal_period=9, name='MACD')
qf.studies['macd']['display'].update(legendgroup=True)
qf.data.update(showlegend=False)
qf.add_ema(colors='brown', name='EMA')
qf.add_bollinger_bands(periods=20, boll_std=2, colors=['magenta', 'grey'], name='BOLL')
qf.add_rsi(periods=20, rsi_upper=70, rsi_lower=30, name='RSI')
qf.data.update(showlegend=False)
plt_list.append(qf_list[i].iplot(asFigure=True))
dict(plt_list[i])['data'][1]['showlegend'] = False
dict(plt_list[i])['data'][3]['name'] = 'EMA'
dict(plt_list[i])['data'][7]['showlegend'] = False
dict(plt_list[i])['data'][8]['showlegend'] = False
dict(plt_list[i])['data'][9]['showlegend'] = False
dict(plt_list[i])['data'][0]['showlegend'] = False
return plt_list
Basically we now have everything together to directly illustrate the differences for the various trading signals across our Continuous Futures. To make the visualization piece a bit more clear, we do some formatting and further enrich the Candlestick Chart by some annotations. We spare the code details here, as this is just about presenting the data. Probably now the best strategy is to explore the figure below by yourselves as they have nice hover effects as well as zoom in and out capabilities. Please note that generated signals (squares and triangles) as well as Futures expiry dates (dotted line) are directly annotated in the chart).
You may have encountered that around the annotated contract expiries
the roll effects have clearly been overcome by the Panama Method.
Also the Bollinger Bands for example provide less of a "shock" to
the standard deviation by the roll as well as some of the MACD and
RSI signals around expiry have moved, appeared or disappeared.
Quantifying the Signals
Although the chart delivers quite a good feeling for what actually happened to the timeseries and the various signals, we want to be a bit more precise, what actually has changed in terms of counting the signals. So let’s count signals for MACD and RSI for Spliced Contracts first, and then for the Panama Method. Afterwards we check which signal dates are unique in sense that a signal date for Spliced Contracts is not in the list for the Panama Method et vice versa.
Future  Type  MACD Signals  RSI Signals 

Bund 
Spliced 
18 
34 
Panama 
18 
35 

Bobl 
Spliced 
12 
22 
Panama 
16 
19 

Schatz 
Spliced 
21 
18 
Panama 
15 
21 
Future  Study  Unique Date 

Bund 
MACD 
22 
RSI 
17 

Bobl 
MACD 
24 
RSI 
15 

Schatz 
MACD 
28 
RSI 
13 
In the above we find that for the signal counts as a total number there are differences but no clear direction in a sense that a certain contract type does yield significantly less/ more signals.
With regards to occurrence of a signal on a particular trading day uniquely (be it Spliced OR Panama, NOT both), we find that MACD signals are indeed solely occurring for one contract type in about 50% of the cases. The equivalent ratio for RSI — depending on the Futures — is between 25% and close to 50%.
Together these findings clearly indicate that construction of Continuous Futures timeseries needs to be carefully conducted. Our example with German Government Bond Futures may not be fully representative for the whole Futures space but nevertheless advices for sensible research before trading algorithms are set up.
References
This is a post in the
Continuous Futures series.
Other posts in this series:
 May 26, 2018  Continuous Futures — Construction Methods
 May 26, 2018  Continuous Futures — Visualization & Signal Analysis