BBBY Swing Strategy

BBBY: Bed, Bath & Beyond

This high expectancy BBBY swing strategy is a Long Only trade. Average Time in Market is just over 8 days per trade.

Trade Statistics

  • Company Name: Bed, Bath & Beyond
  • Symbol: BBBY
  • Time Period Tested: 17 Feb 2009 – 5 June 2014
  • Number of trading days: 1334
  • Number of trades from this signal: 112
  • Number of winning trades: 56
  • Number of losing trades: 56
  • Average Win per winning trade: $2.48
  • Average Loss per losing trade: -$0.87
  • Max Win per Share traded: $3.80
  • Max Loss per share traded: $1.91

Trade Overview

I screened for stocks with a 52 Week Price Range between $10-$100, a Market Cap greater than $500M, and Average Daily Volume greater than 1M shares. I also added a screen for stocks which pay a dividend. One of the benefits of trading equities with dividends is that I can goose my earnings by receiving a dividend payment if I happen to be holding during the ex dividend period. Although the strategy I describe here is not in the slightest dependent on dividends to be profitable, be aware that it is possible to have that bonus apply when trading BBBY using this strategy.

This trade is a Long Only strategy. (I’m working on the Short Only version and hope to publish it next week.) The basic setup is to begin watching the stock after it has closed below its 20 Day Simple Moving Average (20D SMA) and then – once it closes above that price – Buy at the Open on the next day.

I set a Stop-Loss trigger of 2.5% and a profit target of 5%. Once either target was hit, I automatically closed the trade at that price. If I used a trailing stop, I could possibly improve the profitability of this trade, but I did not do any testing or calculations with that in mind.

  • The maximum number of consecutive losing trades is 3; the maximum drawdown percentage during the period tested is 7%.
  • The maximum number of consecutive winning trades is 5.
  • This trade is an excellent illustration of the fact that I don’t have to be right all the time. Even though this trade has winners only 50% of the time, the average winning trade is $2.48 per share, whereas the average losing trade is only $0.87 per share.
  • The expectancy of this trade is 2.84.

Results of Simulations

I ran a simulation using this strategy starting with a $10,000 account. I traded the maximum number of shares allowed for my account size, traded the signal every time it occurred since February 2009, and plowed all profits back into the trading account. By 5 June 2014, the account value was just under $55,000.

This trade has me in the market 934 days out of 1934 possible days. Although the expectancy of this trade is high, the percentage of time in the market balances that out significantly. I prefer trades that keep me out as much as possible, but I don’t turn my nose up at profitable trades, either.

All in all, this is historically a fairly safe and predictable trade. (And of course, just because it worked in the past is absolutely no guarantee it’ll work in the future.) I’m going to spend some more time on this one to try to find a way to be out of the market more but keep the expectancy up.

Counterpoint & Conclusion

The market has been on a 5+ year bull run during the time I ran this simulation, so it would make sense that a long-only strategy would prosper. So is it my strategy that made money, or is it merely the bull market that made money? For the sake of fairness, I compared this Long Only strategy to a “Buy & Hold” strategy over the same period of time.

Over the same time period:

  • “Buy & Hold” would be worth a little more than $29,300, for a profit of $19,322
  • This Long Only would be worth a little more than $54,500, for a profit of $44,500 over the same period
  • “Buy & Hold” was in the market a total of 1895 days
  • Long Only was in the market a total of 934 days
  • “Buy & Hold” had a max drawdown of 9%
  • Long Only had a max drawdown of 7%

Clearly, the success of this strategy is not due to the general market conditions.

Send me a note if you’d like the details on this strategy.

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How to Calculate Expectancy of Your Trading System

Expectancy is a way to measure the quality of your trading system. It measures “how much money should I expect to make per trade executed on my trading system.” The formula I use here is expressed in “dollars per trade”, (assuming you are trading in dollars), so it is pretty easy to understand and to compare the expectancy of one system with another.

The formula is fairly simple. 

(Average Profit per Winning Trade times Ratio of Winning Trades)

minus

(Average Loss per Losing Trade times Ratio of Losing Trades)

Here are the step by step instructions to calculate expectancy for your trading system.

  1. Record each trade with the date and amount of profit.
  2. Count the total number of trades to get Total Trades.
  3. Count the total number of profitable trades to get Winners.
  4. Subtract Winners from Total Trades to get Losers.
  5. Sum your profitable trades to get Total Profits.
  6. Sum you losing trades to get Total Losses.
  7. Divide Winners by Total Trades to get Odds of Winning.
  8. Divide Losers by Total Trades to get Odds of Losing.
  9. Divide Total Profits by Winners to get Average Win.
  10. Divide Total Losses by Losers to get Average Loss.
  11. Multiply Odds of Winning by Average Win to get W.
  12. Multiply Odds of Losing by Average Loss to get L.
  13. Sum W and L to get Expectancy. (L will always be negative, so it will reduce the value of W.)

Download the expectancy calculator which I referenced in the video.

NQ Mobile Day Trade Strategy

Trade 131126 – NQ Mobile

NQ Mobile has been a consistent performer since its IPO in 2011. This day trade strategy takes advantage of that fact.NQ-Mobile-Announces-New-Integration-with-Samsung[1]

Trade Statistics

  • Company Name: NQ Mobile Inc.
  • Symbol: NQ
  • Market Cap: ~$740M
  • 3M Average Daily Volume: ~7M shares
  • Time Period Analyzed: 1 June 2011  – 19 November 2013
  • Number of trading days: 641
  • Number of trades from this signal: 87
  • Number of winning trades: 65
  • Number of losing trades: 22
  • Average Win per winning trade: $0.27
  • Average Loss per losing trade: -$0.29
  • Max Win per Share traded: $1.06
  • Max Loss per share traded: -$0.75
  • Max Drawdown: 13%

Trade Overview

This is a fairly simple trade strategy, but it took a while to find the right combination of entry, Profit Target, Stop-Loss-Trigger and trade filters. All the details are contained in the Trade Strategy Package. I use a variation of the 5 Day Average True Range. To re-create this trade, you need to understand how I compute Average True Range. At the time I did the analysis, the average 5D ATR for the entire time period was 69 cents. You don’t need to know the Average 5DATR to recreate this strategy, but you need to calculate the moving 5D ATR.

Details about the trade:

  • The maximum number of consecutive losing trades is 2.
  • Max drawdown is 13%.  In my simulations, I calculated Max Drawdown by multiplying Max Loss per Share times Shares Traded.  A series of sequential win/lose/win/lose trades in 2012 resulted in a smaller drawdown but it took months to recover from due to the fact that the trade doesn’t occur more than about every 8 days.
  • The maximum number of consecutive winning trades is 14. As I’ve said before, that’s great for the confidence and good for the P&L.
  • Average losses are about the same size as average wins, but since we win three times for every loss, we can make money on this trade.
  • The expectancy of this trade is 2.74 when executed exactly according to my rules.

Like most of the trades I study, this is a day-trade, so I’m not subject to overnight margins. If I haven’t hit the Profit target or the Stop-Loss Trigger by end-of-day, I close the position with a Market-At-Close order. One minor filter I added is to limit my losses from Market-at-Close orders to no more than my stop-loss amount.

Results of Simulations

I ran a simulation using this strategy starting with a $10,000 account. I traded the maximum number of shares allowed for my account size, traded the signal every time it occurred since 1 June 2011, and plowed all profits back into the trading account. By 19 November 2013, the account value was just over $34,500.

Since the price per share was so small, I was able to start trading with blocks of 1500 shares. I eventually got up to almost 4700 shares per trade. Just as a reminder, you can run into unforeseen problems trading large blocks. This is why “Dark Pools” were born, (and is also why I prefer to trade highly liquid instruments.) Since NQ trades seven million shares a day, it’s not difficult to get good fills with minimal slippage.

With 87 trades out of 641 trading days, this strategy can be traded about every 8 days.

Standard disclaimer: just because this trade worked in the past is absolutely no guarantee it will work in the future.

Use This Strategy

My tests showed an average profit of $398.02 per trade. I started with a $10,000 account and plowed the profits back into the account. That allowed me to increase my average trade size as the account grew up to the maximum of 4700 shares per trade. 

If you’d like to see all the details so you can use this strategy yourself, you can purchase a zipped file containing everything you need for $197. Here’s what you will get from me:

  1. Welcome letter describing each file
  2. commentary on the trade, how I trade it, and how I built and tested the strategy
  3. pdf file which contains:
    • The history of every trade generated by this strategy since June 2011 and the results of each trade.
    • Detailed statistics on this trade
    • A chart of the Equity Curve of this trade from beginning to present
  4. A text file with pseudo-code which will allow you to see exactly how I trade it. This detailed and commented code gives you everything you need need to re-create this trade for yourself. Please note – this is pseudo-code which clearly demonstrates the algorithm. To use it on your own trading platform, you must translate my code to the appropriate code for your platform.

I guarantee you will be satisfied. If not, I’ll refund your money. Your purchase is protected by PayPal.

This is trade number 131126.

[purchase_link id=”1824″ style=”button” color=”green” text=”Trade Strategy Package 131126″ direct=”true”]

JDSU Day Trading Strategy

Overview

This week’s trade idea is for JDS Uniphase (JDSU).

  • Symbol: JDSU
  • Date Range for Analysis: 2009/03/02 – 2013/10/30
  • Trade Duration: Day Trade
  • Trade Type: Gap Fill

When we want to execute short-term trades on securities, some of the metrics we examine are Market Cap, Average Daily Volume, Beta and 52 week Price Range. The numbers for JDSU:

  • Market cap is $3.11B – big enough to matter, but not so big as to be boring.
  • 3 month Average Daily Volume is 4.8M – plenty of volume
  • 52 week Price Range is 9.72-16.61 – plenty low enough to allow traders with even small accounts to trade it.
  • Beta is 2.58, which is on the high side, but we’ll live with it.

These are the characteristics of a relatively stable security. The emphasis is on “relatively” because, with a Beta over 2, it’s pretty volatile. However, the bigger market cap and good-sized average volume mitigate some of the more extreme effects of volatility. We want to use a statistically-based trading strategy, so we need some stability. Since we build our statistics based on what happened in the past, we expect the future to be similar to the past.  JDSU meets those criteria.

JDSU Gap Days Summary

NOTE: Because I believe that the single most dominant factor of the current market is the Federal Reserves monthly dump of $85 billion into the market, I take the beginning of QE in March 2009 as the start date for my analysis. However, the numbers are quote below are also valid when the entire history of JDSU is analyzed.

Out of 1177 trading days since the beginning of March 2009:

  • JDSU gapped up 51% of the time and closed that gap 63% of the time.
  • JDSU gapped down 43% of the time and closed that gap 92% of the time.

Clearly, gaps down are a better play than gaps up, but we’re going to dig a little deeper before talking about the way we’d play this.

I use the filters first published by The Gap Guy’s to do my gaps evaluation.  If you know his work, you’ll recognize these filters. If not, check out his work at thegapguy.com. To fully understand these trades, you need to understand some of the terminology:

  • A Large Unfilled Gap is defined as a gap of at least 20% of the 5D ATR.
  • A Large Range Day is defined as a day when the range is greater than 150% of the 5D ATR.
  • A False Down Day is a day that closes Down on the day, but higher than the previous day’s close.
  • A False Up Day is a day that closes Up on the day, but lower than the previous day’s close.
  • Maximum Adverse Excursion (MAE) is the maximum amount a trade moves against your position.

The 5 Day Average True Range during the period studied was $0.52. We’d like a gap of at least 20% of 5D ATR, so we’re going to filter out any gaps less than that. With that criterion in place, we have these figures for gaps:

  • JDSU gapped down 226 times and closed that gap 188 times, or 83% of the time.
  • Average Gap Down was 24 cents
  • Average MAE was 28 cents

The JDSU Gap Down Day Trading Strategy

gap-trading-full-gap-down-short[1]The risk / reward ratio of 8:7 is worse than I would normally play, but because of the high batting average, I think it is worth it.

If we set our Stop Loss Trigger at 28 cents, and our Profit Target at the Gap Fill, the record since March 2009 would look like this:

  • Trades taken: 226
  • Total Winners: 193
  • Average Win: $0.182 ($35.17)
  • Total Losers: 33
  • Average Loss: $0.28 (-9.24)
  • Net Profit/(Loss): 25.93

This is a bit of an unorthodox strategy because the average loser is 50% bigger than the average winner. However, because this strategy has close to 83% winners, we can afford slightly out-sized losses when we are wrong. This strategy has an expectancy of greater than 2.00, which is actually pretty good. The negatives to trading this strategy are:

  1. Not many trading signals
  2. Relatively small profits

The negatives are outweighed by the positives, though, which are:

  1. High probability of success
  2. Relatively small losses

I would open this trade at the Open, set my Stop Loss Trigger at 24 cents, my Profit 1 Target at the Gap Fill, then let the balance ride and move the Stop Loss Trigger up to Profit Target 1 in the hopes of picking up a little gravy. With 226 signals in the last 56 months, this signal occurs almost 4 times a month.

Summary

Obviously, you won’t get rich on this trade. It’s a “singles” trade rather than a “home run” trade. But Ichiro Suzuki is going into the Baseball Hall of Fame because he was such a reliable singles hitter. That’s not a bad tool to have in your trading arsenal.

ES Weekly Gap Down Strategy

This strategy is a little different than what I’ve been working on lately, it’s an ES Weekly Gap Down strategy. I’m still in the middle of it and am working out the details, but it looks promising. Since I haven’t posted in a couple of weeks, I wanted to give you guys an idea of what I am working on.

I am using a 5-day time frame on the ES, and analyzing performance on “Gap Down” from a 5 day standpoint. In other words, I am checking the opening price today against the closing price 5 trading days ago. Of course, there are almost always gaps in such a scenario. (In the 20 years of data I checked, there were no gaps only 38 times in over 6000 trading days.)

I’m teasing out a strategy from Gap Down days that is one of those “Win Big Infrequently / Lose Little Frequently” types of strategies. Here’s some of the details:

  • Number of signals since March 2009: 498
  • Number of profitable trades: 133
  • Number of losing trades: 383
  • Average win per winning trade: 33.89
  • Average loss per losing trade: 5.00
  • Expectancy: 2.35
  • Max losing trades in a row: 16
  • Max drawdown: 92.00

Over the time period analyzed, (March 2009 to the September 2013), this strategy appears to return 2681 ES points per contract. Not great for a 54 month period, but not bad. That’s an average of about 49.5 points per month. If you were trading a single contract, that would be ~$2500 per month in average profit. Of course, that 92 point, 16 losing-streak drawdown would be almost -$5000, so it is not without pain.

This reminds me a bit of the old Turtle Strategy: you have to be able to handle long losing streaks and an ugly drawdown. The good news is that it is hugely profitable when you are right.

I will continue to dig deeper into this strategy to refine it, but for now it is clearly very promising.

I’d appreciate hearing from anyone who feels somewhat competent to discuss ES options. My analysis shows me something else that I think would be profitable with some sort of delta-neutral strategy, but I want to run it by someone with more ES options experience than I have.