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Technical Analysis applied on
Energy Markets
Holger Galuschke, Technical Market Analyst
Düsseldorf, June 15th, 2010
Holger Galuschke
 Technical Analyst Energy Markets
(Power, Oil, Coal, Gas, Carbon, Freight)
 Technical Tools:
Indicators, Trendlines, Support/ Resistance Lines,
Support/Resistance Channels, Fibonacci Relationships,
Analysis of Contraction & Expansion, Dow Theory,
Point & Figure
 Co-Author of „Tradingwelten“, Finanzbuch Verlag
2
We are part of E.ON
E.ON Energy Trading is part of one of the world’s largest investor-owned power and gas companies, with
commercial activities around the globe.
3
E.ON is an integrated energy company
Central Europe
E.ON Energie AG,
Munich
Russia
E.ON Russia Power,
Moscow
Pan-European Gas
E.ON Ruhrgas AG,
Essen
United Kingdom
E.ON UK plc,
Coventry
Nordic
E.ON Nordic AB,
Malmö
Italy
E.ON Italia,
Milan
Spain
E.ON España,
Madrid
Climate & Renewables
E.ON Climate & Renewables GmbH,
Düsseldorf
US Midwest
E.ON U.S. LLC,
Louisville
Energy Trading
E.ON Energy Trading SE
Düsseldorf
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We unite the entire trading expertise of E.ON
 We trade in all major European
markets.
 We are active at all major exchanges.
 We are active in 40 countries.
 All E.ON’s European trading expertise
is united in Düsseldorf.
5
We have a large stake in the international energy markets*





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Power: 1,240 TWh
Gas: 1,498 TWh
CO2 allowances: 501 million t
Oil: 69 million t
Coal: 223 million t
Adjusted EBIT: 949 million €
(*All numbers cited are for 2009)
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Contents
1.
2.
3.
Technical Tools applied on the Energy Markets
Technical Analysis on individual Energy Products
Bringing Energy Market together
1. Indexed Relative Performance Charts
2. Volatility Analysis in the Energy Markets
3. Correlation Analysis in the Energy Markets
4. Beta Factor Analysis in the Energy Markets
4. Oil as the Benchmark in the Energy Markets ?
1. The Impact of EURUSD on the Oil Market
2. Power as the Benchmark in the Energy Markets ?
3. Gas as the Benchmark in the Energy Markets ?
4. Coal as the Benchmark in the Energy Markets ?
5. Carbon as the Benchmark in the Energy Markets ?
5. Technical Analysis in Cross Commodity Trading – Spreads
7
Technical Tools applied on the Energy Markets
Trendchannels
Support/
Resistance Lines
Exponential
Weighted
Averages
Bollinger
Bands
Fibonacci Ratracements
and - Targets
MACD
Expansion /
Contraction
Momentum
Volume
RSI
Stochastics
Source: Thomson Reuters
TradeSignal Enterprise
Open
Interest
Volatility
8
Technical Tools applied on the Energy Markets
Source: Thomson Reuters,
Updata
9
Technical Tools applied on the Energy Markets
Source: Thomson Reuters,
Trayport
10
Technical Tools applied on the Energy Markets
 Bollinger Bands
 Introduced by: John Bollinger in the early 80s
 Category: Envelopes
 based on standard deviation around a moving average
 used for: evaluating medium term volatility
UpperBandt  MidBand 2 *
LowerBandt  MidBand 2 *
n
1
σ
* (Pr ic e i  Pr ic e)2
n  1 i 1
Bollinger Bands -> n = 20 (days) / STD -> n = 2
11
Technical Tools applied on the Energy Markets
 MACD – Moving Average Convergence Divergence
 Introduced by: Gerald Apple in the 60s
 Category: Trend following System
 Based on two Exponential Weighted Averages
 Oscillator Concept
 MACD = Difference between two Exponential Averages
 Study of a Study: Signal = Exponential Weighted Average of the MACD Values
 used for: evaluating medium term impulses
EWAt   * Pricet  (1   ) * EWAt 1
MACDt  EWAt , fast  EWAt ,slow
Signalt   * MACDt  (1   ) * Signalt 1

2
n 1
EWAslow -> n = 20 (days) / EWAfast -> n = 10 (days) / EWASignal -> n = 5 (days)
12
Technical Tools applied on the Energy Markets
 Momentum / Rate of Change
 Introduced by: Welles Wilder in 1978 in his book
“New Concepts in Technical Trading Systems”
 Category: Trend following System
 Based on Difference between two prices
 EWA of the Momentum Values to smooth Momentum study
 used for: evaluating medium term impulses
Momentumt  Pricet  Pricet n1
EWAMomentum , t   * Momentumt  (1   ) * EWAMomentum , t  1
RateOfChanget 
Pricet
Pricet -n 1
Momentum -> n = 20 (days) / EWAMomentum -> n = 5 (days)
13
Technical Tools applied on the Energy Markets
 RSI
 Introduced by: Welles Wilder in 1978 in his book
“New Concepts in Technical Trading Systems”
 Category: Overbought/Oversold System
 based on relationship between up- and down
differences in prices
 EWA of the RSI Values to smooth RSI study
 used for: evaluating short term impulses
RSIt  100
RS 
100
1  RSt
Avg(Upn )
Avg( Downn )
Avg(Up)t
Up


Avg( Down)t 
t 1
* (n  1)
n
 Downt 1 * (n  1)
n
RSI -> n = 10 (days) / EWARSI -> n = 5 (days)
14
Technical Tools applied on the Energy Markets
 Stochastics
 Introduced by: George Lane in the 50s
 Category: Overbought/Oversold System
 based on relationship between current close and
aggregated high-low range
 EWA of the Stochastics Values to smooth Stochastics study
 used for: evaluating short term impulses
K %  100*
Closet  LowestLown
HighestHighn  LowestLown
D %  SMA( K %)3
slowD%  SMA( D %)3
Stochastics -> n = 10 (days) / EWARSI -> n = 5 (days)
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Technical Tools applied on the Energy Markets
 Volatility
 used for: evaluating short term volatility
 ... of statistical trading range of one day
 ... of statistical trading range of two day
(to include gaps)
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Technical Tools applied on the Energy Markets
 Trend channels
 based on corrections
 high points within a downtrend
 low points within an uptrend
 Support / Resistance Lines
 based important lows and high
 importance dependent on cause of appearance
 Fibonacci Relationships
 based on Fibonacci Row of numbers (1...1...2...3...5...8...13...21...34...55...89...144...233...∞)
 Fibonacci Relationships:
55:89 ≈ 0.681 / 55:144 ≈ 0.382 / 55:233 ≈ 0.236 ...
55:34=1.618 / 55:21 ≈ 2.618 / 55:13 ≈ 4.236 ...
 Used to evaluate possible correction targets and possible impulse targets
 Foundation for Elliott Wave Analysis
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Technical Analysis on EURUSD
Source: Thomson Reuters,
TradeSignal Enterprise
18
Technical Analysis on individual Energy products – Oil
Source: Thomson Reuters,
TradeSignal Enterprise
19
Technical Analysis on individual Energy products – Gas
Source: Trayport,
TradeSignal Enterprise
20
Technical Analysis on individual Energy products – Carbon
Source: Thomson Reuters
TradeSignal Enterprise
21
Technical Analysis on individual Energy products – Coal
Source: Trayport,
TradeSignal Enterprise
22
Technical Analysis on individual Energy products – Power
Source: Trayport
TradeSignal Enterprise
23
Technical Analysis on Freight
Source: Thomson Reuters,
TradeSignal Enterprise
24
Technical Analysis on Gas – Fibonacci Retracements & Targets
Source: Thomson Reuters,
TradeSignal Enterprise
25
Technical Analysis on Power – Fibonacci Retracements & Targets
Source: Thomson Reuters,
TradeSignal Enterprise
26
Technical Analysis on Spreads – API2 vs. API4
Source:
Thomson Reuters,
Trayport
TradeSignal Enterprise
27
Technical Analysis on Spreads – API2(€) vs. NBP(€)
Source:
Thomson Reuters,
Trayport
TradeSignal Enterprise
28
Technical Analysis on Spreads – API2(€) vs. NBP(€)
Source:
Thomson Reuters,
Trayport
TradeSignal Enterprise
29
Bringing the Energy Markets together –
From Individual to Integrated Evaluation
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Indexed Relative Performance Charts
Volatility Analysis
Correlation Analysis
Beta Factor Analysis
30
Indexed Relative Performance of the Energy Markets
Source:
Thomson Reuters,
Trayport,
TradeSignal Enterprise
31
Volatility Analysis in the Energy Markets
 What is Volatility ?
A statistical measure of the dispersion of returns for a given security or market index. Volatility can
either be measured by using standard deviation or variance between returns from that security or market
index. Return can either be calculated as absolute or logarithmic relative.
 Standard Deviation
The Standard Deviation is a measure of the variability or dispersion of a data set from its mean.
 Formula
 ind


n
2
1

*  xi  x
n  1 i 1
 closeind 

x  ln

 closeind,t -1 
 dep

n
1

*  yi  y
n  1 i 1

2
 closedep 

y  ln
 close

dep, t -1 

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Correlation Analysis in the Energy Markets
 What is Correlation ?
The Correlation describes the linear relationship between two or more statistical variables. In financial markets, the
question which should be answered is whether there is a dependency between two or more time series and if so,
how distinctive it is. The mathematical figure which answer that question is the Correlation Coefficient.
 Correlation Coefficient
The Correlation Coefficient is the figure to determine the grade of linear relationship. The Correlation Coefficient
can accept values between +1 and -1. A Correlation Coefficient of +1 means a complete positive relationship („the
more ...the more“), a Correlation Coefficient of -1 means a complete negative relationship („the more...the less“)
between two time series. A Correlation Coefficient of 0 means no relationship between two time series.
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Correlation Analysis in the Energy Markets
 Formula
The Correlation Coefficient r according to Pearson is calculated as follow:
n
r
 (x
i 1
ind ,i
 xind ) * ( ydep,i  ydep )
n
n
2
(
x

x
)
*
(
y

y
)
ind
 ind ,i
 dep,i dep
2
i 1
i 1
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Beta Factor Analysis in the Energy Markets
 What is Beta Factor ?
The Correlation describes the linear relationship between two or more statistical variables. If the
independent market moves up and the dependent market moves also up, the Correlation is +1. If the
independent market moves down and the dependent market moves up, the Correlation is -1. If the
independent market moves and the dependent market does not, the Correlation is 0. The Beta Factor
expands the meaning of the Correlation Factor. It is not only a measure of Correlation it is in addition a
measure of risk.
Beta is also referred to as financial elasticity or correlated relative volatility, and can be referred as a
measure of the sensitivity of the return of the dependent market to those of the independent market. It
is the non-diversifiable risk, its systematic risk or market risk.
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Beta Factor Analysis in the Energy Markets
 Variance and Covariance
Variance:
The Variance is closely related to the Standard Deviation. It is simple the square of it. Or, in other
words, the Standard Deviation is the Square Root of the Variance.
Covariance:
It is a measure of how much two variables changes together (Variance is a special case of the covariance,
when the two variables are identical). If two variable tend to vary together, then the covariance between
this two variables is positive. Conversely, if one of them tends to be above its expected value and the
other below, then the covariance between this two variables is negative
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Beta Factor Analysis in the Energy Markets
 Formula
The Beta Factor is calculated as follow:

Covxind , ydep 
Varxind 
 x
n


i 1
ind ,i
Covxind , ydep 
 ind 2

 xind * ydep,i  ydep
 x
n
i 1
ind ,i
 xind


2
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Coal vs. Carbon – Volatility, Correlation and Beta
Source: Thomson Reuters,
Trayport
TradeSignal Enterprise
38
Oil as the Benchmark ? – Oil vs. “All”
Source: Thomson Reuters,
Trayport
TradeSignal Enterprise
39
Excursion: EURUSD as the Benchmark ? – EURUSD vs. “All”
Source: Thomson Reuters,
Trayport,
TradeSignal Enterprise
40
Excursion: S&P 500 as the Benchmark ? – S&P 500 vs. “All”
Source: Thomson Reuters,
Trayport,
TradeSignal Enterprise
41
Gas as the Benchmark – Gas vs. “All”
Source: Thomson Reuters,
Trayport
TradeSignal Enterprise
42
Carbon as the Benchmark – Carbon vs. “All”
Source: Thomson Reuters,
Trayport
TradeSignal Enterprise
43
Coal as the Benchmark – Coal vs. “All”
Source: Thomson Reuters,
Trayport
TradeSignal Enterprise
44
Power as the Benchmark ? – Power vs. “All”
Source: Thomson Reuters,
Trayport,
TradeSignal Enterprise
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Conclusion:
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At a glance, the energy markets seem to move in the same direction in general
But under the magnifying glass there are distinctive differences
Correlation and Beta Factor can help the trader / analyst to benefit from the interrelated energy market
This knowledge can be useful in particular, if trading cross commodity, for example Spreads
Examples:
 In the Power Market: (Clean) Dark Spreads and the (Clean) Spark Spreads
 In the Oil Market: Crack Spreads
 In the Carbon Market: EUA / CER Spreads
 etc.
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