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Hello, My name is trading4life. I just joined this forum.

The CBOE was opened in 1973 and is located in Chicago. Presently more than one billion options contracts are traded on the CBOE annually.

On July 2 of this year, we bought Guggenheim Solar Energy ETF ($TAN) in our swing trading newsletter. Three months later, we sold those shares of $TAN for a cool price gain of 44.3%. In this trading strategy article, we detail the top 4 technical tips that prompted us to buy $TAN when we did. Then, we walk you through to the day when we eventually exited the trade to lock in the profits. Here’s a snapshot of how the daily chart of $TAN appeared at the time of our initial trade entry. The 4 reasons we bought this ETF immediately follow: 4 Big Tips For ETF Traders 1.) Sector Relative Strength - As detailed in my first ETF book, one of the first steps of my ETF trading strategy is to identify the industry sector showing the most relative strength to the benchmark S&P 500 Index. In early May, the relative strength of the solar energy sector became very apparent to us, prompting us to add $TAN to our watchlist for potential trade entry. 2.) Uptrend Confirmed - When a stock or ETF breaks out to the upside, we have a basic trend qualifier that we utilize in order to confirm a valid uptrend is in place before tending to buy the stock. Specifically, the 20-day exponential moving average must be above the 50-day moving average, and 50-day MA must be above the 200-day MA. Additionally, all three moving averages must be trending upwards. Although $TAN initially pushed above its 50-day MA back on April 8 (the big green bar accompanied by the volume spike), it wasn’t until mid-May that $TAN met our trend qualifier requirement. 3.) Big Volume Breakout - The best breakouts are always accompanied by increasing volume in which turnover spikes to 2 to 3 times its average daily level. After mid-May, when bullish momentum really started pushing tan higher, notice how volume picked up as well. Such volume spikes are like stepping on the gas pedal for a breakout, and help to confirm the legitimacy of a breakout as well. Unlike other technical indicators that frequently give false readings, volume is the one indicator that never lies. 4.) First pullback to 50-day MA - Because of the relative strength in the solar energy sector, the high volume breakout, and the trend qualifier requirement being fulfilled, we knew we had to buy $TAN. It then became a matter of simply waiting for a proper, low-risk entry point. Rather than chasing the price of the ETF after the initial breakout, we simply waited for a pullback that would give us a low-risk buy entry point. Specifically, we were looking for an “undercut” of the 20-day EMA, or even a pullback to more significant support of the 50-day MA. After zooming to the $28 area, $TAN entered into a 4-week base of consolidation, then dipped to “undercut” key support of its 50-day MA for one day before heading right back up. Whenever a stock or ETF breaks out on big volume and leads the market, the first touch of the 50-day MA usually leads to a resumption of the new uptrend because many institutions (“smart money”) use the 50-day MA as an indicator for when to begin accumulating leading stocks and ETFs on a pullback. After $TAN successfully tested support of its 50-day MA, it would’ve been a valid buy entry the following day, when the price moved above that day’s high. However, we prefered to wait for the confirmation of the break of the 6-week downtrend line that formed off the highs of May. That downtrend line breakout occurred on July 1, and we bought the following day at a price of $24.20. So, What Happened Next? Below is a snapshot of the price action that followed our July 2 buy entry into $TAN: After our initial buy entry on July 2, $TAN acted as anticipated by subsequently cruising to a new high less than two weeks later. Thereafter, $TAN appeared to be forming a bull flag chart pattern, which prompted us to add to the position on July 22 (at $27.91). However, since the bull flag pattern did not follow-through to the upside, we maintained a very tight stop on the additional shares, which we closed for a tiny loss of 1.6% on August 2. After chopping around in a range for a few weeks, and again coming into support of its 50-day moving average several times, $TAN eventually broke out to new highs again. As we frequently remind traders, one important psychological aspect of profitable trading is having the discipline and willingness to quickly close out losing trades when you’re wrong, while still not being afraid to re-enter the trade if it still looks good. As such, we again bought additional shares of $TAN when it broke out on September 6 (bear in mind that we still held the initial position from our July 2 entry because those shares never went against us). After buying the breakout to new highs in early September, $TAN consolidated for a few more weeks, then ripped higher as volume began surging higher again. Rather than attempting to guess when a powerful rally will end, we often close winning trades by trailing protective stops tighter and tighter, until a pullback eventually causes us to lock in the profits. But in the case of $TAN, we instead made the decision to sell into strength of the rally due to prior resistance from back in February 2012 (visible on a weekly chart). Upon selling $TAN on October 1, the final tally was a 44.3% share price gain from our initial July 2 entry, and a 15.4% gain from our September 6 buy entry. Is 44% A Big Gain For An ETF Trade? When trading individual stocks, we typically shoot for an average price gain of 20 to 30% for short to intermediate-term momentum trades. Sometimes, bullish momentum propels stocks with massive relative strength 40 to 50% higher before we eventually sell and take profits. For example, in our Wagner Daily ETF and stock picking portfolio, we are presently sitting on unrealized gains of 49% in Silica ($SLCA) and 35% in Yelp ($YELP). On October 8, we also closed a swing trade in Bitauto ($BITA) for a price gain of 36.7% with just a 1-month holding period. However, because they are comprised of a basket of actual stocks, ETFs are generally much less volatile than the individual small to mid-cap growth stocks we trade in bull markets. As such, we consider a solid gain for an ETF swing trade to be in the neighborhood of 10 to 15%, rather than 20 to 30%. In addition to the various leveraged ETFs, $TAN is one of the few non-leveraged ETFs that trades with the volatility of a typical small to mid-cap stock. That’s why we managed to snag a 44.3% gain by trading $TAN, despite it being an ETF. In between, there was just a tiny 1.6% loss from our bull flag entry attempt on July 22. You can screw up a lot of things in trading, but still be profitable if you consistently get just one thing right: Let the profits ride when you’re right, but get the hell outta’ Dodge when you’re wrong!

As you may recall from my August 18 blog post (How To Profit From Oil And Silver ETFs In This Stock Market Downturn), I have been bullish on both Oil and Silver ETFs (and, to a lesser degree, Gold) for the past week. Today, my patience is paying off because crude oil ($USO is the main ETF) has convincingly broken out above key resistance of an 8-week base of consolidation. Take a look: When the main stock market indexes are down sharply (as they are so far today), the benefits of ETF trading really become clear. Unlike stocks, most of which are correlated to the direction of the broad market, ETFs enables traders and investors to still profit in a down market because many types of ETFs have low to zero correlation to the overall stock market direction. Commodity ETFs such as $USO and $AGQ are two great examples of the above. Our current position in $USO is now showing an unrealized price gain of 7.7% since the swing trade buy entry in our nightly newsletter. Also, the position in our leveraged Silver ETF ($AGQ) is now up more 10% since our August 21 buy entry. If you have not yet added $USO to your portfolio, a secondary buy entry point into $USO would be a slight pullback to new support of the breakout level (consider a buy limit order around the $38.50 to $38.75 area).

Hey guys. Forum newbie here. I'm coming to you guys to find the answer to a question that has been bothering me. I've been getting into volatility index ETF based options lately, and have come across something weird that I need to figure out. It appears that for some ETF's, forsome expiry months of those ETF's, there are two unique call and two unique put options at each strike price. As an example, here are links to the January expiration options for VXX: http://www.nasdaq.com/symbol/vxx/option-chain?money=all&dateindex=4 http://finance.yahoo.com/q/op?s=VXX&m=2014-01 Looking at the January 2014 options, we have both of these options symbols, with their associated trading values from yahoo finance as of 8/14/2013: VXX140118C00009000 0.10 VXX1140118C00009000 5.65 (*) I would like what the difference between these two options actually is, that makes them worth different prices on the market. I have tried looking through options chain data on yahoo, nasdaq, etc. to find this information, but to no avail. I've tried googling this information, but have not been able to find any info. that would help:angry:. So here I come to you to ask- what gives? Why are there two symbols different, and why do they both exist only some of the time? If it helps for identification purposes, the second option(with the *) has an extra 1 inserted into the symbol, and the other option has a symbol that matches the usual format. So far, all of my research has led to nothing- so I need your help. I'm much obliged! P.S. It may interest you that I'm working on a python program to estimate the value of volatility ETF options at expiry. The program scrapes options value data, as well as the historical prices of the underlying ETF, in order to generate a probability distribution function of the value of each option at expiry, assuming that the underlying ETF has similar price movement behavior as it has shown in the past. See the program on github: https://github.com/CompelTechnic/QuickieStockAndOptionsScraper

We have been holding Guggenheim Solar ETF ($TAN) as an intermediate-term swing trade since July 2, when we bought in anticipation of another breakout to new highs. This momentum trade has been working out well so far, as this ETF swing trade is presently showing an unrealized share price gain of 13.8% (based on our July 2 entry price of $24.20). Over the past four days, $TAN has been consolidating a tight, sideways range near its all-time high. This is healthy price action and has led to the formation of a “bull flag” type pattern on its daily chart. This is shown on the chart of $TAN below: Because of the bullish pattern that has formed, odds now favor another breakout to new highs for $TAN in the coming days. Since we presently have only 50% of our maximum share size in this trade, we will be adding an additional 25% exposure if the ETF rallies above the July 19 high. Additionally, traders who missed our original entry point for any reason may now also consider establishing a new position in $TAN, based on our same entry and stop price criteria. However, in this case, no more than 25% to 50% of maximum position size would be recommended because the average entry price on this trade would be more than 13% above our original July 2 entry price. Another ETF we are already holding is Market Vectors Semiconductor ETF ($SMH), which we bought one week ago when it broke out above resistance of its prior highs. Since then, the ETF has pulled back and is trading slightly below our entry price, but the current retracement from the highs now provides a low-risk buy entry point for traders who missed our initial entry point. The pullback is also an ideal level to add additional shares for traders who are looking to increase their position size: Notice that $SMH gapped down last Friday (July 19), but found support at its 50-day moving average, which neatly coincided with the intraday low of the session. Furthermore, the ETF formed a bullish “hammer” candlestick after bouncing off key support of its 50-day MA. Because of the hammer candlestick that coincided with a pullback to the 50-day MA, the actual entry point to establish a new position in $SMH (or to add to existing shares) is just above the July 19 high of $38.58. A protective stop could be placed just below major support of the June 24 swing low of $36.08. Alternatively, momentum traders with a shorter-term trading timeframe could place a tight stop just below the July 19 low, which would put $SMH back below its 50-day MA if the stop is triggered. We are already at 75% maximum position size with $SMH, so we are NOT looking to add additional shares at this time. Nevertheless, we wanted to give you a heads-up to this low-risk buying opportunity in case you missed our original entry or are too light in share size.

Like other market funds, Yen ETF are not used to pursue share price stability. Investors look derive current income from fund’s interests bearing as well as by performance of yen versus United States Dollar. Japanese yen is one of the most traded currencies in the world and Yen ETFs are available for many investment strategies calculated by an investor on the relationship between YEN to US Dollar.

The Perfect R Portfolio I was recently watching a short video hosted by Market Club. This particular video was a presentation on their “Perfect R Portfolio”. The Perfect R Portfolio is a portfolio of four ETFs (SPY, USO, GLD, and FXE) that are traded based upon Market Club’s “Trade Triangles” technology. The system rules are simple and clear. For each trade you dedicate 25% of your trading capital. Go long when you see a green Trade Triangle and close the position on the red Trade Triangle. These green and red signals are actually price levels that allow you to place your buy stop and sell stop orders and wait for the market to fill your orders. These values are updated weekly. It does not get any easier than that. Such a simple greenlight/redlight system can be very appealing. In short, the Perfect R Portfolio is a complete trading system that provides you exact entry and exit levels. Because the portfolio contains ETFs, does not trade very often and only takes long positions (there is no shorting in the Perfect Portfolio) it seems suitable for trading in retirement accounts such as a 401K. In fact, I do believe this is what the creators had in mind when developing the system. How Do They Do It? I enjoy attempting to figure out what is going on when I see a trading system demonstrated on-line. It’s a challenge and great fun to reverse engineer signals. Market Club’s Trade Triangles were no exception. Don’t get me wrong, I have nothing against Market Club and I do believe they provide a valuable service. However, how they generate signals became an interest for me and in the end, the concept they are using is well known, simple and totally free. Market Club does provide a nice looking chart where buy/sell signals (Trade Triangles) are nicely displayed on-screen. When I examined the entry and exit signals over time I came to the conclusion that the Trade Triangles are nothing more than a classic breakout indicator. That is, they simply take the highest high over the past N days to determine when to go long and then determine the lowest low over the past N days to determine when to close that same long position. More specifically in the case for the Perfect R Portfolio they use a three month channel of price extremes to determine market direction (trend) and use a three week channel to determine entry/exit price levels. Trend trading based upon price channels is well documented and continues to be a valid trading method. Trend: Three month price extreme. Signal: Three week price extreme. The trend component of the system is used to filter out bearish market conditions since the system only goes long. So, during bearish times we are in cash or cash equivalents waiting for a trend change to bullish. For example, given an ETF we first determine the overall trend. This is done by determining the price extremes based on a monthly chart of the last three bars. Price touching these extreme levels on a daily chart would determine the trend either bullish or bearish. Once the trend is determined a three bar price extreme based on a weekly chart is used to determine when to exit and when to initiate new trades. When the trend changes from bullish to bearish all trades are closed and we don’t open new long positions until the trend becomes bullish. It’s that simple. Below is a trade example. Cloning The System Logic But how well has the Perfect R Portfolio done? Well, the portfolio is rather new so they don’t provide much backtesting data. Market Club does provide a short PDF report demonstrating how well the system performed during the 2008 market crash. However, Market Clubs price channel breakout concept can be programmed into TradeStation rather easily. TradeStations ability to access several timeframes on a single chart will be required to make this trading system. First, all trades are executed on a daily chart, buy/sell price levels are determined on a weekly chart and trend is determined on a monthly chart. All three of these timeframes can be placed within one chart and accessed by a single TradeStation strategy. Programmer speaking coming up so be warned. First I’ll create a workspace with a chart of one of the ETFs used in the Perfect R Portfolio. I’ll select GLD. I will want to place trades on a daily chart so I set my GLD chart to daily price data. Next I want to generate buy/sell signals based upon a weekly chart. To do this I create a sub-chart of GLD to hold weekly price data within my chart. I can then access this data programmatically by referencing “data2″ in my Easy Language code. I do the same thing for the monthly timeframe of GLD and can access that data by referencing “data3″. Data1 = Daily chart Data2 = Weekly chart Data3 = Monthly chart I created a clone of the system and tested the system with the four ETFs over the life of each ETF. Unfortunately TradeStation does not have the ability to test a portfolio of ETFs given a single strategy. This weakness is rumored to be fixed in version nine of TradeStation. Until then we’ll have to test each ETF individually. So how did it do? Not bad for such a simple system. The results are in the table in the section below. You will see that over the life of the system it is profitable. The life of the system is only from 2004 - October 31, 2010. Most of the ETF data only goes back that far! Modified R Portfolio With Risk Management The most obvious drawback I see with the Perfect R Portfolio is the lack of a position sizing algorithm based upon the risk per trade. That is, the dollar amount you’re willing to lose based upon the stop level. I might be inclined to use the Percent Risk Model to calculate the number of shares to purchase based upon a 2% risk-per-trade. This would help normalize risk by reducing the number of shares when the market conditions are volatile and increase the number of shares when volatility is waning. Instead the Perfect R Portfolio uses a fixed percentage (25%) of equity for each new trade regardless of risk. In a future post I will add a position sizing algorithm to see if we can improve the results. If you can't wait check out this blog post where I already have posted the updated version. There is also a short video that explains the inputs to the system. Download I was having trouble uploading the TradeStation Workspace to this post but the EasyLanguage code should be attached. You can also download a copy of the Workspace or the EasyLanguage code at my blog. This code is for TradeStation 8.8. If anyone finds an errors in the code or would like to make a suggestion please let me know. Thanks, Jeff PERFECT_R_PORTFOLIO_CLONE.ELD