TRADING AND ANALYTICS GUIDE

Transcription

1 Objective information. Sytematic approach TRADING AND ANALYTICS GUIDE February 9, 2016 Part I

2 TRADING AND ANALYTICS GUIDE Overview Page 1 GeckoiCapital Analytics (GiCA) is a research and analysis division of GeckoiCapital, a private investment fund based in the Netherlands. GiCA specializes in natural gas market and provides forecast services for key market fundamentals: supply, demand and storage. This Trading and Analytics Guide will introduce you to GiCA online content to help you better understand how our charts and indicators can improve your trading strategy. Part I of the Trading Guide covers the most important tools: Storage charts, Gravity Models, Cycle Deviations Tracker and Seasonality. In the next part we will describe additional tools: CFTC COT reports, Fair Value Estimator, Weather Anomaly Indicator and NG-Coal Spreads. Both parts will be then combined into a single document, which will be updated on a regular basis. You can always download the latest version of the Guide from the Files section on our website. Disclaimer GeckoiCapital Analytics is not a registered investment adviser and does not provide investment advice. Trading inherently involves substantial risk of loss and is not suitable for all people. GeckoiCapital is not responsible in any way, shape, or form for any loses you may incur while trading. Contacts Headquarters: GeckoiCapital 138 Burgemeester De Monchyplein, The Hague, Netherlands 2585 DG Trading and Analytics Guide

3 Contents TRADING AND ANALYTICS GUIDE... 1 General concepts... 3 Storage Charts... 5 Gravity Models... 8 Cycle Deviations Tracker Seasonality Definitions Trading and Analytics Guide Page 2

4 General concepts Supply and demand are two most important economic concepts. The price of any commodity is a reflection of supply and demand. In other words, the balance between supply and demand determines the price. Usually, when supply is above demand, then the market balance is loose and the price is low. When supply is below demand, then the market balance is tight and the price is high. Table 1 shows the average price of natural gas and key market fundamentals for a number of selected historical periods. Period Date Season Table 1. Market Fundamentals Total Supply Total Demand Implied Balance NG Price (av.) 1 Nov 10 Mar 11 Withdrawal 10,731 12,923-2, Apr 11 Oct 11 Injection 15,397 13,171 +2, Nov 11 Mar 12 Withdrawal 11,312 12,622-1, Apr 12 Oct 12 Injection 15,888 14,398 +1, Nov 12 Mar 13 Withdrawal 11,172 13,376-2, Apr 13 Oct 13 Injection 15,863 13,856 +2, Nov 13 Mar 14 Withdrawal 11,437 14,426-2, Apr 14 Oct 14 Injection 16,625 13,940 +2, Nov 14 Mar 15 Withdrawal 12,366 14,458-2, Apr 15 Oct 15 Injection 17,487 14,925 +2, Total supply dry production, pipeline imports from Canada and Mexico and LNG imports (bcf) Total demand national consumption, pipeline exports to Canada and Mexico and LNG exports (bcf) Implied Balance the difference between total supply and total demand NG price front month futures price (US$ per mmbtu); Z, F, G, H, J for withdrawal season; K, M, N, Q, U, V, X for injection season (see Futures Contract Symbols on p. 16) Source: Energy Information Agency, CME Group, GeckoiCapital calculations The above table shows only absolute figures and does not really indicate if the market was oversupplied or undersupplied in any particular period. Therefore, it is impossible to determine a trading bias. The only way to figure out if the data is bullish or bearish is to look at it in relative terms i.e., compare it to a certain benchmark. Storage figures (stockpiles or inventories) is the single most important piece of fundamental data in natural gas market. It has the strongest impact on the price because it shows the net result of the interaction between supply and demand. Once again, however, it is important to put information into perspective. One of the most common ways to analyze storage data is to compare it to a 5-year average. Table 2 shows changes in the underground storage during injection and withdrawal seasons and their share of a 5-year average change in percentage terms. Trading and Analytics Guide Page 3

5 Period Dates Season Table 2. Storage Analytics Change in Storage 5-year av. % share of 5-year av. NG Price (EOS*) 1 Nov 10 Mar 11 Withdrawal -2,199-1, Apr 11 Oct 11 Injection +2,171 +2, Nov 11 Mar 12 Withdrawal -1,321-2, Apr 12 Oct 12 Injection +1,435 +2, Nov 12 Mar 13 Withdrawal -2,220-1, Apr 13 Oct 13 Injection +2,092 +1, Nov 13 Mar 14 Withdrawal -2,955-1, Apr 14 Oct 14 Injection +2,747 +2, Nov 14 Mar 15 Withdrawal -2,110-2, Apr 15 Oct 15 Injection +2,470 +2, * EOS end of season; J close for withdrawal season; X close for injection season; (see Futures Contract Symbols on p. 16) Source: Energy Information Agency, CME Group, GeckoiCapital calculations Now it is easier to see if the market was oversupplied or undersupplied in a particular period. Withdrawal season example In Period 3, draws were very low compared to the norm (just 64.5% of the 5-year average), which resulted in a much lower price by the end of the season ($2.191 per mmbtu). Likewise, draws in Period 7 were above the 5-year norm (155% share), which not only means that demand was stronger than supply (it is to be expected during the withdrawal season), but also that the subsequent misbalance in the market was quite large compared to history. Therefore, the price by the end of the season was relatively high ($4.584 per mmbtu). Injection season example In Period 4, injections were very weak if compared to history (just 68.2% of the 5-year average), so the price recovered from $2.191 to $3.471 per mmbtu. Following the same logic, in Period 8, the price went down (from $4.584 to $3.728 per mmbtu) due to strong injections (136% share of the 5- year average). Trading and Analytics Guide Page 4

6 Storage Charts GiCA publishes four separate charts to help you understand the underlying picture in the market balance: Withdrawal, Injection, Comparative Analysis, and Flows. Withdrawal and Injection charts show the level of working gas in the underground storage in comparison with previous years. Comparative Analysis chart plots the level of working gas in the underground storage against a five-year average, maximum and minimum stocks. All three charts also display our long-term storage forecast. As a rule, the higher our projected curve is, the lower should be the price and vice versa. For example, on January 6, 2016 (when natural gas was trading in range) our Comparative Analysis chart looked as follows. Figure 1. Comparative Analysis chart (print screen as of Jan 6, 2016) At that time, we said that we still saw strong EOS inventories by the end of March (link to Tweet). Indeed, we expected withdrawal season to finish with around 2,200 bcf in storage, which is well Trading and Analytics Guide Page 5

7 above the 5-year average and not much below a 5-year maximum (see Figure 1 above). Therefore, the projection was bearish, meaning that short positions had a higher probability of success. The subsequent rally on (January 7 and 8) proved to be short-lived and by January 19, natural gas lost 15% of its value. Flows chart shows the actual change in the underground storage compared with previous years. It also displays our long-term forecast. This chart is more useful for short-term trading. The key here is to look for differences between projected flows and a 5-year norm. For the withdrawal season, if near-term draws are projected to be below a 5-year average curve consider going long. If draws are projected to be above the 5-year average curve consider going short. Similar logic works for injection season. If near-term injections are projected to be below the 5-year average curve consider going long. If injections are projected to be above the 5-year average curve consider going short. For example, on January 14 (when natural gas was trading in range) our Flows chart looked as follows. Figure 2. Flows chart (print screen as of Jan 14, 2016) Trading and Analytics Guide Page 6

8 At that time, we were expecting the next three reports (for weeks ending Jan 15, Jan 22, and Jan 29) to show relatively strong draws i.e., below the 5-year average curve (see Figure 2 above; link to Tweet). On January 14, after a bearish report, the market closed at Over the next few weeks, as bullish reports were on the horizon, the price slowly recovered and by January 29, the market closed at (+7.43% from January 14 close). However, it was already clear even on January 14, that any rallies would be hard to sustain because draws for February looked less bullish i.e., above the 5-year average curve (see Figure 2 on p. 6). Predictably, March contract lost 6% on February 1. Please note that all storage charts are updated on a daily basis. Monitor them regularly to stay up to date. Trading and Analytics Guide Page 7

9 Gravity Models GiCA Gravity Models were specifically designed to provide a quick overview of the fundamental trends in the market. Days of Supply Model 1 (DoS-M1) and Market Balance Model 2 (MB-M2) show the same thing the likely direction of the front month futures price. The only difference between them is that DoS-M1 generates its trends based on domestic factors (national consumption and national production), whereas MB-M2 also employs external factors (exports and imports). DoS-M1 may not provide a full picture of the situation in the marketplace, but it still captures the lion share of fundamental developments. That is because the weight of external factors remains negligible. Imports make up just 10% of total US dry gas production, while exports make up less than 7% of total US national gas consumption. Imports and exports, therefore, essentially balance each other. However, the importance of external factors especially, that of exports is projected to increase in the coming years. Therefore, we decided to keep track of them separately in the MB-M2. Please note that Gravity Models do not project the exact price level. They show the direction, the future movement, but not the actual price level. For more information on Gravity Models, see Definitions and read a detailed description of each model in their corresponding sections on our website (you need to log in as a member to see the full description). Figure 3 and Figure 4 (below) show the evolution of DoS-M1 in December Trading and Analytics Guide Page 8

10 Figure 3. Days of Supply Model 1 chart (print screen as of December 15, 2015) DoS curves begin to move up. Bottom is near. On December 15, 2015 (when natural gas was trading in range and a trading week average price (TWA) was around $1.85 per mmbtu), our DoS-M1 was clearly indicating the upcoming upward movement in price (link to Tweet). The market closed at on Friday, December 18 and then rallied more than 35% to close at on December 29. On December 29, 2015, it was already clear from the DoS-M1 that this rally will be short-lived (see Figure 4 below) and that from mid-january the downtrend will resume (link to Tweet). Indeed, the market closed at $2.472 per mmbtu on January 8 and then declined by more than 20%. Trading and Analytics Guide Page 9

11 Figure 4. Days of Supply Model 1 chart (print screen as of December 29, 2015) DoS curves begin to move down again. Top is near. Please note, that we have renamed our curves: DoS Inclination 1 Trend 1 Inclination 2 Trend 2 TREND Also, note that DoS-M1 and MB-M2 show the average price (TWA), not the latest price. The price curve moves further to the right every Friday when a new forecast week is added. The general market theory implies that the price level depends not only on today s supply-demand situation, but also on the balance expected in the future. Therefore, it is very important to keep in mind the commodity forward curve when making trading decisions. If you see that DoS-M1 or MB- M2 curves are going up, do not rush to go long as this trend may be already (at least partially) reflected in price due to the contango in the market. Trading and Analytics Guide Page 10

12 Cycle Deviations Tracker Cycle Deviations Tracker (CDT) is a technical indicator that is aiming to confirm the moments of trend exhaustion. CDT is a contrarian indicator and should be used with caution. It tracks front month futures price. Therefore, it is most useful in the beginning and in the middle of the contract trading. Due to contango effect, CDT becomes less relevant towards the contract. However, CDT is a very simple and straightforward indicator. When the blue bar reaches extreme levels, it means that the preceding trend in price is mature enough and that the price is at least likely to stabilize and, possibly, reverse. Extreme levels of 4,000 (extremely bullish) and - 4,000 (extremely bearish) happen rarely, but create a strong contrarian signal. Less extreme levels of 3,000 and -3,000 happen more often, but create a weaker signal. Still, it warns that the trend is nearing exhaustion. CDT provides only additional guidance for short-term trading. You should always use it together with fundamental analysis. Below is the history of some of the trading signals that CDT generated over Figure 6. Cycle Deviations Tracker (print screen as of February 5, 2016) Trading and Analytics Guide Page 11

13 Table 3. CDT Signals # Date CDT Signal Comment Action 1 May 14, ,699 2 May 28 June 5, 2015 multiple (all below -4,000) 3 June 16, , August 3, 2015 August 12, 2015 September 14, 2015 October 1, 2015 October 26-28, 2015 November 12-16, 2015 November 24, ,984 +1,894 +2,175-2,750 multiple (around -2,900) multiple (close or above +4,000) -2,420 Bearish (strong) Bullish (strong) Bearish (strong) Bullish (weak) Bearish (weak) Bearish (weak) Bullish (weak) Bullish (strong) Bearish (strong) Bullish (weak) 8 sessions to (middle of the contract) + bearish signal 12+ sessions to (beginning of the contract) + bullish signal 8 sessions to (middle of the contract) + bearish signal 18 sessions to (beginning of the contract) + weak bullish signal 11 sessions to (middle of the contract) + weak bearish signal 10 sessions to (middle of the contract) + weak bearish signal 19 sessions to (beginning of the contract) + weak bullish signal less than 4 sessions to (end of the contract) + bullish signal 7-9 sessions to (middle of the contract) + bearish signal 1 session to (end of the contract) + weak bullish signal Possible Result short -6.40% long % +6.90% short -5.80% long / no action short / no action short / no action long / no action long / no action short long / no action +6.70% % +4.20% +5.0% -8.1% -2.4% +1.0% Trading and Analytics Guide Page 12

14 December 7, 2015 December 17, 2015 January 4-8, 2016 January 19, ,914-2,969 multiple (above +3,000 and +4,000) -3,096 Bullish (weak) Bullish (weak) Bearish (strong) Bullish (weak) 15 sessions to (middle of the contract) + weak bullish signal 7 sessions to (middle of the contract) + weak bullish signal sessions to (beginning of the contract) + bearish signal 6 sessions to (middle of the contract) + weak bullish signal long / no action long / no action short long / no action +14.8% +35.0% -15.4% -10.4% +4.7% Clearly, CDT is not a perfect indicator, but it does produce some valuable signals. Weak signals appear more often (yellow color), but even they can have some powerful results. For example, on December 17, 2015, CDT was -2,969, which gave a bullish bias, but not very strong one. Still, a long position initiated on December 17 (and if held until contract ) would have increased in value by a whopping 35%. Similarly, on August 12, 2015, CDT was +1,894, generating a very weak bearish signal, but a short position would have yielded 10%, which is not bad. It is up to a trader to decide whether to act on a weak signal or not, but CDT can definitely serve as an additional indicator to complement fundamental analysis. Trading and Analytics Guide Page 13

15 Seasonality GiCA offers three seasonal charts: Natural Gas Contract, Natural Gas Calendar and EP Sector Consumption. NG Contract Seasonality and Calendar Seasonality charts show the expected level of return for 12 natural gas contracts (in case of contract seasonality) and for 12 months (in case of calendar seasonality). You can use these charts to look for significant deviations between recent price performance and its historical performance. For example, on January 29, 2016, NG contract seasonality chart looked as follows. Figure 5. Natural Gas Contract Seasonality chart (print screen as of January 29, 2016) On January 29, the market closed at 2.298, which at that time represented a 7.13% growth for H (March) contract. This kind of performance contrasted sharply with the historical returns for H contract (see Figure 5 above). On this basis, it seemed logical to go short. Indeed, it would have been a profitable trade, as natural gas lost some 14% over the next four days. Trading and Analytics Guide Page 14

16 Seasonals are certainly not the only basis for a trade and they can be skewed by one-off events or coincidences. Overall, they are just one of the tools and are something to keep in mind every time the calendar turns. EP Sector Consumption chart shows the consumption of natural gas in the Electric Power sector (as a percentage of total gas consumption) in each of the calendar months. This information allows assessing the strength of the effect of NG-Coal spreads on natural gas consumption in a particular period. For example, if ng-coal spread is low in August (when gas consumption in the Electric Power sector is at its peak), then the effect on coal-to-gas switching will be very strong with a possible bullish effect on injections. Likewise, ng-coal spreads (either low or high) play a much smaller role during winter months. Trading and Analytics Guide Page 15

17 Definitions BTU a measure of the energy content. One BTU equals 1,055 joules. Days of supply (DoS) a measure of the adequacy of gas inventories. DoS is one of the most important fundamental indicators. It forms the basis for most of our models and serves as a key measure of market conditions. DoS calculates the number of days it will take to run out of gas in storage should production come to a standstill. While such a scenario is highly unlikely, the key here is not the number itself, but how it compares with history. We measure DoS on a daily basis and incorporate it in our Days of Supply Model 1. There is also a production-adjusted indicator (PaDoS), which we measure on a monthly basis. When DoS and PaDoS are declining, it means that the storage is shrinking and market balance is tightening. DoS and PaDoS that are below historical averages are likely to spur a rise in natural gas prices. Gravity model - a statistical regression model that is used to forecast the direction of natural gas price and show the underlying market environment. We maintain two gravity models: Days of Supply Model 1 and Market Balance Model 2. Both use logarithmic curves to indicate the likely direction for the price of Henry Hub Natural Gas Futures. It has been observed that the price tends to gravitate in the direction of model curves. Hence, the name for the models. MMBTU one million British Thermal Units (BTU). TWA trading week average. The price of Henry Hub Natural Gas Futures averaged over the last five trading days (Friday, Monday, Tuesday, Wednesday and Thursday). Table 4. Futures Contract Symbols Month Code January F February G March H April J May K June M July N August Q September U October V November X December Z Trading and Analytics Guide Page 16