• Feature: Reducing Uncertainty — A Valuable Practice by RJ Hixson
• Workshops: Workshop Dates Set for the rest of 2017, including London, England this October!
• Tips: A View from the Bear Side — The First Chink in the Armor? by D.R. Barton, Jr.
• FREE BOOK!: Trading Beyond the Matrix

Feature Article

• Fish Population — To estimate the number of fish in the pond, catch a bunch of fish, tag them and release them. Then capture more fish in the same exact way as the first bunch and by using the proportion of tagged to untagged fish in the second catch, you can come up with a reliable estimate for the number of fish in a body of water.
• German Tank Production During World War II — Allied intelligence estimated how many tanks the Germans were producing but really, they had no idea. Posed with the problem, a group of statisticians developed a new sampling method using the only real information they had — serial numbers from tanks that had been captured. The statisticians’ estimates for tank production turned out to be very different than the intelligence service numbers and they also turned out to be remarkably close to the actual production figures (confirmed after the war).
• Forecasting Fuel for US Marine Corps — Hubbard helped the US Marine Corps improve their battle planning process. Previously, the service had used a very safe (large) number for the fuel units needed and the related resources to provide that fuel. By measuring fuel consumption on trucks over various paved and off-road conditions (which had never been done), Hubbard’s consulting team came up with a fuel consumption model that allowed the Marine Corps to cut its fuel usage estimates dramatically yet safely, save money, reallocate resources, and in the end save lives.

• When you have a lot of uncertainty, you don’t need tons of data to reduce that uncertainty. Even a small sample can be highly valuable if that’s all that’s available or affordable.
• Labeling risk as high, medium, and low is ambiguous and probably harmful to decision making processes. Providing very simple quantifications to those terms is actually a huge improvement.
• Confidence intervals are close enough to probabilities that practitioners (traders) can use them interchangeably. For example, some figure at a 95% confidence interval effectively says that figure has a probability of 95%.
• Monte Carlo modeling is a very useful and typically underutilized decision tool.

1. You have “good” objectives. Good here would include: written down, quantifiable, complete/robust, and they fit you. Those are all about reducing uncertainty about what you are trying to accomplish, about why you are trading.
2. You know how your system performs. You can reduce uncertainty about your system’s performance greatly in two main ways — following rules and measuring performance by market type. Obviously, gathering a large sample size of R multiples in a particular market type is very useful. Also useful — calculating your system’s SQN score. These practices reduce uncertainty about what a trading system will generate when you trade it in the future.
3. You understand position sizing strategies. They answer a quantification question, “How much to risk in a trade?” They are modified for each system’s R multiple distribution. One of their primary functions is to provide a solid level of confidence that you will not blow up your account.

1. You need well-crafted position sizing strategies to help you reach your objectives. Well-crafted position sizing strategies help you achieve the returns you seek and avoid the drawdown level you are unwilling to tolerate. Well-crafted position sizing strategies cannot guarantee some result (imperfect information) but they can greatly reduce the uncertainty about the range of ending results you can expect in your equity.
2. You need processes to define good objectives and craft effective position sizing strategies. Again, the processes will involve some measurement and will greatly reduce the uncertainty around each item. These processes might include some modeling and at least for the position sizing strategy development, Monte Carlo simulations can be very useful

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1. A lump sum one-time payment of $50,000 (less applicable discounts**) will save people $10,000 over the new price.
2. Three installment payments six months apart for the current price of $52,500 (less applicable discounts**) will save people $7,500 over the new price. This payment option spreads out the cost over a year and a half period — which I don’t do normally.

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