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People Who Trade Futures

There are two types of futures traders, Hedgers and Speculators. They differ in one primary aspect, the hedger trades in both the cash andPaper or contract markets, where the speculator only trades in the contract market.

What do I mean by cash and contracts?

Cash is the physical market. For example, in agriculture there are farmers that grow corn, harvest it, and then sell the corn to mills using a cash transaction. This is the cash market.

The paper market is the futures contract used as a proxy to control the physical asset or commodity, like corn. The paper market is highly standardized across several asset types, which are organized in the following categories:

  • Agriculture
  • Currencies
  • Energy
  • Equities
  • Interest Rates
  • Metals

So, what’s with hedgers, why are they different from speculators?

Futures contracts were originally designed to help farmers remove uncertainty of future prices of their crops. This helped them manage their business better, and by proxy, the prices at the consumer shelves were stable.

When a farmer start growing a crop, he/she doesn’t know what the future price of that crop is going to be. It could vary wildly, based on all sorts of factors, or which growing season conditions play a major factor, and that’s based largely on the weather, which we all know is unpredictable. But there are many other factors and market dynamics that could also influence price.

So the farmer anticipates that he is going to have lets say 1,000 bushels of corn when harvest time comes to sell to the mill. And at the time he starts to grow, the price might be $5/bushel, so he buys a futures contract that is set to expire sometime after the harvest is ready for approximately $5/contract, as one corn futures contract is a proxy for 1,000 bushels of corn.

If corn prices drop to $3/bushel when harvest time comes, the farmer’s futures contract will increase in value and make up the $2 difference in the corn he is going to sell to the mill. This protects the farmer from what could have been a 40 percent loss if he didn’t have the insurance of the futures contract.

On the flip side is the mill operator. He or she also needs to protect herself from fluctuations in price, so because she buys corn, she will buy a futures contract to protect against rising prices.

This same concept works for all buyers and sellers of commodities.

Ok, so what about the speculator?

The speculator doesn’t work in the cash market, they are only concerned about buying and selling futures contracts with the hope of profiting from price movements in the contract. Speculators provide an important function in that they create liquidity in the futures market. Without this liquidity, there would be no market, or the market would be too volatile for it to work as an effective hedge for the people like the farmer or mill operator.

The speculator takes on a lot of risk, because there’s tremendous leverage built into the futures contract. This isn’t so much a problem for the farmer or mill operator because they have the physical product as collateral. So the speculator generally uses trading strategies to reduce that risk, like arbitrage and spreads, which will cover in other posts.

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2 Trading Myths Debunked

On the face of things, these 2 myths seem logical, but they are not. And once you learn why, you will have a whole new appreciation for volatility and how to use it to your advantage.

MYTH #1 if your strategy consistently loses 70% of the time, all you have to do is switch your orders, and buy, when you would normally sell. Then you would be a 70% winner.
MYTH #2 If markets are random, just flip a coin to decide when to buy or sell, heads you buy, tails you sell. You’ll be right 50% of the time.

Both of these sound correct from a statistical point of view, however each of these myths is not taking into account some very important information that is present with every trade you make.

Ok, let’s suppose you’re trading Apple and your strategy calls for a 10 cent stop loss. We can see from this price chart from yesterday (October 14th) that any trade with a 10 cent stop loss would have been stopped out on a regular basis. That’s because Apple’s price ranges 30-40 cents throughout the day.

We can see this by looking at the Average True Range (ATR) indicator, which measures volatility and is an excellent way to get a handle on setting an appropriate stop. The ATR was very high in the morning, then settled out later in the day…this is quite typical.

Screen SharingScreenSnapz358

The ATR was at a peak of about 33 around 10:30 AM, then finally settled to about 20 for the remainder of the day. The value of the ATR is in cents, measured as an average range using the previous 14 bars.

Now let’s consider the coin flip myth. While it’s true that a trade taken anywhere during the day has approximately a 50% chance of going up or down, when you add in the 10 cent stop, and consider the amount of volatility, that 50% chance goes down to 0%.

Now, if you were to widen your stop to be something close to the ATR, then we would start approaching that 50% theoretical rate of choosing the right direction. And the wider we make it, the closer we get. So, by having a tight stop loss, you may think you are controlling risk, when in fact you are guaranteeing that your trade is going to lose nearly all the time.

Even if we took on myth #2, and switch our buy and sell orders, that pesky volatility, and out tight stop loss, would give us pretty much the same result, a big losing trade, one after the other. Even if the trade was perfectly timed.

So, what’s the solution?

You need to know where volatility is at all times. Not only will it tell you where your stop needs to be, but it will also clue you into an appropriately sized position. In general, higher volatility means wider stops and smaller positions, and low volatility is the opposite, smaller stops and larger positions.

These dynamic adjustments will move you closer to the trading sweet spot.

As far as myth #2 is concerned, the coin flip…volatility is only one of the factors you must consider. You would have to time your trades perfectly to gain an edge, a random trade simply wouldn’t cut it. If you go long at the daily peaks, you’re gonna suffer.

So, one way you can overcome this is to measure momentum, in an attempt to find the peaks and valleys of price action. Of course you’ll never be perfect, pin pointing the exact bottoms and tops of pivots is virtually impossible. So, what do you do?

Let price come to you, and except the fact that you are going to lose some trades.
Use momentum to get you somewhere in the ball park for timing your entry, and ATR to adjust your stop and position size.
You also have to accept that when you settle on some value for momentum, you might not hit an extreme during the day, and thus there will be no trade setups. That can be frustrating, but you have to suck it up and take it. On the other hand, there will be days when the market simply goes nuts, and runs straight up or down, and you never find a spot to enter, or you enter what your indicator says should be a top, but then it just continues going up.

Again, you need to come to grips with the fact that you can’t win every trade. You may not even be able to win 50% of the trades you take. So, long as your winning trades make more money than your losing trades, you’ll make money. But that’s easier said than done.

Is ATR the only way to measure volatility?

The ATR measures volatility in terms of price action, but that’s only part of the available information that makes up the volatility of a security. Let’s face it, using 14 bars before you can determine where volatility is can make your trading choices always behind the gun, lacking real time info.

Another way to assess volatility is through liquidity. You measure liquidity in terms of the size and frequency of limit orders placed by other traders waiting to take your trade. This is a far more complex topic that I will leave for another email. But let’s just say that there’s a direct correlation between the amount of liquidity and the amount of volatility.

At least now, you have a new understanding of volatility, and some ways to use it, and hopefully a new found skepticism. Sometimes things that seem logical, are missing key bits of information. Do your homework, and preserve your capital at all costs, before you chase myths that promise easy profits.

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How Trade Size Impacts Returns

I have often said that if you want to make money trading, then reduce your size. And this is true, as most of us tend to over-leverage. But simply reducing your size isn’t the most effective strategy…how much you reduce it, and the way you size your positions can have significant impact to your returns as well.

So, here I’m going to examine the effect of different trade size strategies that you can employ in your trading that may make your road to riches a bit more enjoyable.


Most books tell you your position size should depend on the amount of risk you want to take, in other words, size your position based on how much you can tolerate losing. And in my experience, that’s probably going to be the end result…losing that amount of money. If we would just step off that band wagon of managing risk, and start thinking more in terms of managing our winners, we would be far better off.

There are lot’s of ways to control the size of you position, I’m going to examine four, and they are:

The trading strategy we will use should be inconsequential, so let’s make it super simplistic, we’ll use a counter trend model employing the Relative Strength Index (RSI) to detect overbought and oversold conditions. This is perhaps the most popular indicator in every chartists heads, and the one most often referred to by the talking heads on CNBC. Of course that’s no ringing endorsement, but we’ll go with it!

In our strategy we’ll only look at long trades, and use the daily timeframe of the SPDR S&P 500 ETF (SPY) going back to October 25th, 1995, that should provide enough data points to make this exercise relevant. We will start with $100k of initial capital to establish a baseline for comparison. I have also included slippage and commission so that the results more closely reflect real life conditions.

Here’s the basic strategy:

Indicator Action Input Value
Buy Rule RSI(2) Crosses below 30
Sell Rule RSI(2) Crosses above 70
Share Size Strategies using a basic RSI trading strategy
Share Size Strategies using a basic RSI trading strategy

Fixed Share Amount
This is a non-risk based method that buys 500 shares of SPY for every trading signal given by the strategy.

fixed shares perf

fixed share stats

fixed shares annual

fixed share equity

Fixed Dollar Amount
This is the second non-risk based method that invests $100,000 of SPY for every trading signal given by the strategy.

fixed dollar perf

fixed dollar stats

fixed dollar annual

fixed dollar equity

Fixed Fractional
The fixed-fractional method is one of two formulas that we examined that incorporates a risk component into the denominator of the trade-size calculation. In this formula, the risk component is decided as a point amount that you would be comfortable risking on a trade. In this test we risked 2 percent of the capital in each trade. Some call this method compounding.

fixed fractional perf

fixed fractional stats

fixed fractional annual

fixed fractional equity

Percent Volatility
The percent-volatility method was our second risk-based trade-size formula. The risk component in this formula was once again in the divisor, but this time it is computed from the security’s (SPYs) average true range. So as the volatility of the security increases, we take a smaller trade size and as volatility decreases we take a larger trade size. We risked 2 percent of the capital in each trade and divided it by the average true range, times an ATR multiple.

percent vol perf

percent vol stats

percent vol annual

percent vol equity

Analysis of Different Strategies

We focused on two types of trade-size methods in this paper. The fixed-fractional and percent-volatility methods each incorporate an element of risk into the divisor. As we mentioned earlier, as the risk element grows (shrinks) in size, the position would be smaller (larger). The other trade-size approaches we analyzed were two non-risk-based techniques: the fixed-share amount and fixed-dollar amount. In the fixed-share approach, we invested 500 shares per trade, and in the fixed-dollar amount, we invested $25,000 per trade. These values did not change over the time period tested.

The main purpose of this paper was to compare trade-size methods that incorporate risk to methods that do not. Risk is a vital concept and can be addressed through different modes of portfolio management. With respect to position size, risk can be a personal dollar-amount preference, as was the case with fixed-fractional formula, or it can be a characteristic (average true range) of the security, as was the case with the percent-volatility formula. One factor that distinguishes the fixed-fractional formula from the percent-volatility formula is that the percent-volatility formula is a dynamic type of position sizing; the trade size adjusts as the volatility of the security increases or decreases. The fixed fractional is dependent on your personal risk preference in terms of dollars per trade. Both the fixed-share and fixed-dollar methods use a static trade size and do not consider any type of risk in the calculations.

The performance impact these trade-size methods had on the RSI strategy positively favored fixed fractional and percent volatility over the fixed-share and fixed-dollar trade sizes.

  • The fixed-fractional and percent-volatility methods had the highest annual returns of 5.51% and 6.05% when compared to the fixed-share and fixed-dollar methods, which only had annual returns of 3.30% and 2.06%.
  • The net profit values of the fixed-fractional formula (Net Profit: $174,414) and percent-volatility formula (Net Profit: $204,017) were also greater than the fixed-share method (Net Profit: $83,600) and fixed-dollar method (Net Profit: $42,217).
  • The fixed-fractional and percent-volatility methods also had the best risk-adjusted returns, with Sharpe ratios of .13 and .17. The fixed-share and fixed-dollar amount methods had negative Sharpe ratios of .09 and .01, although their total return and annual returns were positive for the period tested.


The fixed-fractional and percent-volatility methods only scrape the surface in terms of what is possible in developing trade-size formulas. For example, we could have easily modified the fixed-fractional method to have a dynamic risk amount in the denominator of the formula that adjusts to levels of percentage drawdown that the strategy experiences. Good money management is acknowledged as an important part of strategy trading. Through creative thinking and an understanding of how a risk component can affect the size of a position and, in effect, the performance of the strategy, one can experiment with this area of money management to help improve the risk-and-reward statistics of a trading strategy.

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How To Analyze Gaps for Profitable Trade Strategies – Part 1

Gaps are a popular pattern for market technicians to analyze and from which to profit. The reason is that gaps are easy to identify, and the specific characteristics of a gap are easy to quantify. This means we can apply statistical analysis on the various aspects of a gap to determine if there is opportunity.

But before we get into the strategies around gaps, we must understand exactly what a gap is, and how we can deconstruct them so that we can identify an edge.

Basically there are two types of gaps; gap ups and gap downs. Don’t confuse this with bullish or bearish, because we won’t know whether the market is bullish or bearish until the post gap analysis. And there are odds associated with a post bullish or bearish move, based on the gap characteristics.

We will develop a gap language so that we can easily communicate the type of gap, and that language is very precise. That’s what this video is all about. Once we have the language down, we can then model a strategy that reflects these gap aspects and backtest scenarios, so that we can identify statistically probable trades.

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What Is A Gap?

A Window of Opportunity

Definition: the difference in price between a security’s opening price and it’s prior day closing price. This difference shows up visually on a candlestick chart as a space between candles.

When we do Gap talk, it’s a special language, so we need to be clear on some of the words in that language, so we understand each other.

What Is a Gap?
What Is a Gap?

Myth – Gaps Must be Filled

Many traders have lost their shirt on the cliche that Gaps must be filled, in other words that price needs to retrace back over a recent gap. See the illustration above.

Don’t fall into this Gap, and don’t fall in love with a gap, as if it’s some future certainty, cuz it’s not. Many times gaps are not filled…at least not right away. It might eventually be filled, like next week, next year, or next century…but it has no statistical, and therefore no tradable significance.

Fade The Gap

This is kind of like Fill the Gap, but it’s a circumstance immediately following a gap, where you trade in the opposite direction of the gap. So, if price gaps up. we sell short, and if price gaps down, we go long. So, there are times when betting on a gap getting filled is a really good idea from a statistical point of view. But not always. So, pay attention.

Follow the Gap

The is the most like thing we will do, but only under certain circumstances, like the moon is full, or it’s pay day, or the third Monday of the 2nd month…just kidding. But there are citation conditions that priced a gap, that make a profitable trade in the direction of the gap, very likely.

Gap Size

It all comes down to size…well at least that’s what she said 😉 But really, the bigger the gap, the more likely it means there’s some significant opportunity ahead. In terms of definition, the size of a gap is measured like so…

Gap Up equals today’s open price, minus prior day close
Gap Down equals the prior day close, minus today’s open

So, that’s how to measure the gap, we say a gap is small when this size is less than 40% of the average range in price over the past 5 days. And a gap is big when the range is greater than 40%.

We have a way of measuring this range, it’s called the Average True Range (ATR). This is the distance between the extreme high and low in a particular day, including the prior day close if it extends beyond today’s range.

Generally big gaps are better to trade, but for the sake of referring to a potential trade, we will often say things like, its a large gap up, or a small gap down. So, when we say something like that, you know exactly what we mean.

Gap Zones

We can further describe gaps in relation to the prior day’s price action. So for example, if yesterday was a down day, and we have a gap up, we might say it was a big gap up, following a down day, and that gap opened above the prior days high. We would call the a UH. Check out this gap zone chart.

Gap Zones
Gap Zones

If we were to step back and look at where gaps occurred in terms of market structure, meaning where the gap occurred in terms of it’s location in the current trend, we could further qualify the gap that way.

And there are three basic areas of the trend that are notable, and have statistical significance, they are at the beginning, middle and end of a trend, and they are called Breakaway, Runaway, and Exhaustion.

Well, that’s all for now. Next time we will discuss various strategies and the probabilities that they are worth trading. And from this, we will have developed a very effective trading methodology.

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How To Develop a Trading System Part 6

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PinPoint Strategy – What a Difference a Day Makes

Most people developing trading strategies are stuck in the notion that a strategy should work all the time, that is if the strategy is valid, then it should work. Right? Ummm, no, not necessarily.

The things you trade are generally tied directly to a physical business, a market, or an industry. Each of these entities have unique characteristics and dynamics. The job of the strategy designer is to uncover those characteristics and dynamics and find an edge.

Oil, bubbling crude, Texas Tea, black gold.

Let’s take oil as an example, and here we’ll use the USO ETF (United States Oil Fund). This fund seeks to reflect the performance, less expenses, of the spot price of West Texas Intermediate (WTI) light, sweet crude oil. The fund invests in futures contracts for light, sweet crude oil and other types of crude oil, diesel heating oil, gasoline, natural gas, and other petroleum-based fuels traded on the NYMEX and other exchanges.

Now that’s a lot to take in. USO appears to be well diversified in the energy sector, so how do we find the edge? Well, if you follow the US economic calendar for commodity reports, you would know that the Petroleum Status report is released weekly on Wednesday at 10:30 AM EST. This report can have a major affect on not only oil and other petroleum products, but also on the entire US stock market, as it can be a very good barometer on US market health.

This is the biggie, no other petroleum report has greater affect on USO and by association, the market. But there are several other regularly released reports, that could add to price action.

The challenge is to identify patterns that result from these regular reports, and see if there’s a tradable edge.

The Pin Point and Pivot Strategies

Oil related securities are very volatile, and tend to have strong intra day trends, with volatility and sharp reversals often occurring in the early part of the day, and more so on specific days, particularly when there’s an oil-related report, like the EAI report released.

The Pivot strategy, a strategy that is very good at detecting when a strong traversal has occurred, is a fine fit for oil. In fact, it’s a good overall generic strategy, but particularly good for the price action exhibited by commodities that are pushed into action by economic reports.

The problem is that these reports don’t happen every day, and so we need to limit our strategy to work only on certain days, and possibly between certain periods of that day.

So we combine the PinPoint strategy with the Pivot strategy. The PinPoint strategy is designed to pick a long or short position within specific date-time ranges. A good example of this is our OTW (Over the Weekend_ strategy, which opens a long position in AAPL in the morning on Fridays, and closes it at noon on Tuesdays. This is a remarkable stable strategy, with a definitive edge, that has been extremely profitable for over two decades.

Finding the Edge

The hard part of creating effective strategies is the search for the edge. Sometimes it appears in a particular style of strategy (volatility, momentum, trend, etc), sometimes it’s date and time released (pin point, seasonal, etc). The only way to discover the edge is to develop a hypothesis, do some experiments, refine and repeat, until you have something worth further study.

Having some knowledge of the industry can certainly help in developing the hypothesis and the parameters of your experimentation.

The PinPoint Pivot is my best guess, and based on the knowledge that the EAI report is released on Wednesdays, I’m guessing that Wednesdays we’re going to see an edge appear. The only way to find out is to setup the strategy and experiment, so that’s exactly what I did.

I applied the Pivot strategy to USO, with very basic parameters, using the philosophy of Loose Pants fit More Butts. That means I used very wide ranging parameters to avoid curve fitting and over optimization, which generally lead to good looking past results, that ultimately fail when put into practice.

But by using the Loose Pants theory, and by forward walking the strategy, I can get fairly reasonable results. Basically what I’m looking for is an equity chart that goes up. It might have some draw downs, but if the general direction over a long period of time is a chart with an equity curve that starts in the bottom left and ends in the top right, then I have something to work with.

The next step is to restrict the trading to specific days of the week, and to a lesser extent, to sweet spots during the US market session, like between 10AM and 2PM EST, with a little variance, just to see if there is any correlation to the time an EAI report is released.

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How To Develop a Trading System Part 5

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Evaluating Systems – Commission and Slippage

This is going to be a relatively short post, for two reasons; 1) it’s super important and 2) it’s a super simple concept.

People who don’t declare slippage and commission in their strategies are incompetent, and giving you an incorrect impression of their system performance. Not just incorrect, but potentially account busting information. Virtually all, or I should say the vast majority of forex systems vendors don’t report slippage. That should tell you something.

Under NO circumstance should you put any validity in a system if slippage and commissions, and any other expense, is not computed into the results. If you do, then you are evaluating completely bogus performance data.

And the qualifier…the slippage should be realistic, perhaps overly pessimistic, and the commission should be accurate. In other words, the commission should reflect the actual commission you pay the broker for the types of trades you are evaluating.

These two factors, if not present in the system you are evaluating, could make all the difference between a system that looks wildly profitable, and a system that loses virtually every trade. THAT”S HOW IMPORTANT THIS IS!

CAUTION: Most people who supply automated trading systems, do not include slippage and commission, or they demonstrated there stuff without commission and slippage included, claiming it’s just for illustrative purposes. Look at this with EXTREME PREJUDICE!

Also, you should be asking how they determined slippage…

What did you say? What’s Slippage?

Oh, I’m so sorry…let me explain.

Slippage is the difference in the number of ticks between the Bid and the Ask. You will notice when observing the DOM or Matrix, which shows the Bid and Ask orders, and the current price of a security, that the Bid and Ask limits are one or more ticks apart.

This means that if you were to place a market order, the best possible price you will get filled at, will be where the current Ask is for a Long trade, and for a Short, it will be where the Bid is. It will almost always be 1 or two ticks, or more worse, than where the current price is.

Example TSLA

These few cents add up when you make lots of trades, especially if the security you are trading has a big spread between the Bid and Ask. TSLA is just such a stock, where it routinely has about 5 to 10 cents of spread. If you trade 100 shares of TSLA, then this will mean your trade is cutting your profit short by 5 to 10 per each side of the trade.

In other words, when you figure in slippage on TSLA with 100 shares, you need to overcome 10-20 cents of price movement before you can realize any profit. So, just to see 10 cents of profit, TSLA would have to move 30 cents.

Stocks like Apple have very little slippage, no more than a penny. I usually am pessimistic and account for a penny and a half for each side of the trade. That covers me, and night make my optimization not look as good, but that means the chances my real results are at least that good, will be much more realistic.

Don’t Forget to Figure in Commissions

Same deal with Commissions, the only difference here is depending on what your commission structure is, you may need to be pessimistic with that addition to cost as well, just to be on the safe side.

If you have any questions, please don’t hesitate to reply to this email, or look for my contact info under the support tab on the website:

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How To Develop a Trading System Part 4

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NOTE: This is THE most important step in the entire development process!

I’m not kidding. And it’s the lengthiest, and for some, the most boring. That’s because it is like a factory assembly line, you’re going to do repetitive tasks, over and over, until your eyes bleed!

So, what’s the benefit? You’re going to create a continuous supply of winning algorithms that will make you money, without a lot of the roller coaster emotional rides most traders go on, with severe draw downs, and price shocks and surprise Fed announcements. And the reason why…because you spent the time testing, testing, testing.

Remember, you need multiple non-correlated strategies running concurrently, to get the best possible results. So we need to find great team members, just like the scouts of a major league baseball team do, by going through a huge number of candidate players, and reviewing all their stats. Remember the movie Money Ball, with Brad Pitt and Jonah Hill?

Jonah Hill played the part of the statical, Peter Brand. Now, I don’t expect you to become a super geek, just know some basic stuff, and stick to a plan.

So, what’s involved?

  • Read Equity or P&L charts
  • Evaluate performance statistics
  • Categorize strategies by type and potential

What’s your purpose?

To test a large number and wide variety of strategies, and identify the cream of the crop. And do it continuously…this process has no end.

Why does this process have no end, there must be a conclusion, right?

No. That’s because strategies, no matter how good, won’t last forever. Even the best strategies will either outright fail, and be no longer useful, or will go through extended periods of draw down.

This is because nothing is static in this world, especially in the financial markets. What works today, may not work tomorrow due to a limitless variety of causes, like a product goes out of favor, a law impedes the operations of a company, a CEO is caught with his hand in the cookie jar, the Fed decides to confiscate your 401K money…err, you get the picture. Anything can happen, and it will happen, so you must be prepared by always having strategies ready to go.

This is the essence of Algorithmic trading. But just in case you’re wondering, it’s really not that hard, it’s more boring than anything else, but extremely profitable. The time you spend here will get banked and multiplied. And with compounding, will probably make you rich.

NOTE: if you are a licensed user of my Auto Traders from the Programed Trader, you won’t have to do this stuff, that’s what I do. But, if you wanted to learn, I will teach you as part of the service.

Back Testing and the Forward Walk

One of the most repetitive things you will be doing is back testing a strategy by inputting a range of values to test, and a time period to test over, and then let your platform automatically run through all the data and apply the values and try to find the most profitable combination of configuration of your strategy.

Sometimes, you won’t find ANY combo profitable, so you may decide to scrap that strategy, or try it on some other asset class. Maybe it will work on index funds or commodity ETFs, but not technology stocks, for example.

At the conclusion of the backtest, your system will present the stats and various charts so that you can visualize the performance, like this equity chart.


This is a strategy I call The Code, applied to the Gold ETF GLD in the 10 minute timeframe. All these identifying characteristics are used to categorize the strategy and the individual tests.


Here’s the performance statistics, that tell me even more information about what I can possibly expect from this strategy. I say possibly, because it’s not a certainty. These stats represent how the strategy performed in the past, they are not a guarantee as to how they will perform in the future. This is why we do the forward walk.

Forward Walk Testing

If we back tested 5 years back to the present day, we would get certain results for what happened in the past 5 years. And by continuously changing the parameters and retesting, I could find optimal settings that make the strategy look like a winner. But this is bad, because it’s like being a conspiracy theorist that makes a conclusion, then finds facts that fit the conclusion.

So, I have to do something better. I have to give the strategy data that it has never seen, and see how it performs with my settings. The way we do this is by backtesting a period in the past, say 2001 to 2005. I get my parameters to give me the results I like for that period, then I take those same parameters and back test a period from 2005 to 2008, and see how the strategy performs., then again for 2008 to 2011, and so on.

This is called forward walking. The new tests are using data that has not been used to create an optimized test. This is called “out of sample data.” This is extremely important…because if your strategy can work with out of sample data, the same way it worked with optimized data, then there’s a good chance it will work with future data.

Forward Walk is NOT a Panacea

This is a good way to test, but no guarantee, not until you actually have the strategy running with realtime market conditions, in simulation mode, for a lengthy period of time, say weeks or months. And even this is not perfect, not until you run the strategy with actual money in real time. But, it’s the best we can do. So, we do it.

There are other tests we can do, but they are beyond the scope and time I have to write here, like a Monty Carlo simulation.

I haven’t even scratched the surface here. There’s so much more to know and to evaluate and to categorize, then there’s the whole methodology, which is kind of like the scientific process, and then the continuous improvement process, so that you constantly get better at your testing and eliminate wastes.

I could go on and on, but I’ll spare you.

If you want to learn more about what I do, and how these things make the Programed Trader systems superior to your manual trading, quite frankly, superior to any human’s manual trading, then click here, fill out the form and I’ll give you a demonstration.

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How To Develop a Trading System Part 3

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ABSTRACT When most people embark on a journey, they get a map, they consult an expert, or at least someone that’s been there before, and developed some reasonable outlook. The problem with trading, is that most people think it’s about banking huge profits, taking huge calculated risks, and living the life style of the rich and famous. That’s not it at all. Good trading is routine and can be boring, that is if t’s done right…and that’s what today’s lesson is about, a boring but CRUCIAL concept called Correlation.

Correlation Coefficient

In the previous lesson (Part 2) we learned that strong strategies are ones that exhibit anti-fragile behavior, and are made up of multiple non-correlated strategies. So, what does it mean to be non-correlated?

Let’s first discuss what correlation actually means. It’s a statistical property between 2 things that measures the strength and direction of the relationship between those two things. The relationship can be visualized on a scatter plot as how closely data, that is generated by the two things, comes in line with one another, and is measured as a number between -1 and 1.

This is a plot of trade results by 4 different pairs of trading strategies, mapped with standard run of the mill scatter plots. If you took a statistics course in school, I’m sure you ran across one of these babies.


Plot (a) has a tight correlation with a value of +1.0, notice also the direction of the best fit line, from bottom left to top right. Plot (b) is a correlation of -0.5, (c) is +0.85, and (d) is +0.15.

From a human point of view, +1.0 is perfectly correlated, and plot (c) at +0.85 is considered to be strongly correlated. The other plots simply aren’t in the same ball park.

When we analyze whether two or more strategies are correlated, it’s obvious that we can’t simply look at them and see the correlation. You need special tools to do that. Fortunately those tools are available. You can however make a pretty good guess as to which strategies exhibit a high degree of correlation, and which ones are more likely to not be correlated.

For example, if you had two strategies that were both based on momentum indicators, that determined their respective values by evaluating price with an overbought and oversold zero-based oscillator, like RSI and Stochastic, then you can reasonably assume these two strategies are probably highly correlated. And they are, because both are based on price action, and both measure overbought and oversold.

Highly Correlated Strategies Are Undesirable

Remember the principle of having multiple non-correlated strategies? The first part is “multiple,” you need more than one strategy in your portfolio, three is better, four is slightly better, having five or more starts to get hard to measure how much better, better is.

The 2nd part of the principle, is that the strategies are non-correlated, as measured from a statistical point of view. A correlation of between +0.5 and -0.5 is desirable. If you can get the range even tighter or closer to 0.0 that’s even better. If I saw a correlation like in plot (d), that would make me very happy, plot (b) would also make me happy, nut less so. Plot (b) would represent my limit. I wouldn’t want strategies to be any more correlated than that.

Highly correlated strategies will react the same way to similar market conditions. So if the market suddenly drops, and your strategies are highly correlated, and they are on the wrong side of the trade—which can happen—then all your strategies are going to experience draw down. And that is bad. If however, you have non-correlated strategies, then the odds are that some of those strategies are going to be on the opposite side of the trade, prroviding a natural hedge, and protection against such occurrences.

This is why you need multiple strategies, so that you can have enough strategies that might be hedging a bad choice in direction. But you may think, wouldn’t that work in the positive direction as well. The answer is yes, however if all the strategies have a bias towards being successful—why else would you trade them— then over all you will see positive results.

Strategies that are run simultaneously, with very little correlation to one another, will have a much better chance at handling the infinite variety of market conditions that exist. And these non-correlated strategies, although individually may not always look that great, when combined, they look unbelievably good. And that’s all due to the anti fragile nature of multiple, non-correlated strategies.

I hope this makes sense to you, because it is the principle reason why our auto traders work so well in virtually any market condition, including violent market corrections, price upheavals, price shocks, flash crashes, and even simple choppy or trendy markets. It doesn’t matter, the combination of diverse strategies are fare more powerful than any of them individually.

How Long Does a Correlation or Non-Correlation Last?

This is the big question, because the answer is…I have no friggin idea, nobody does. Some relationships between strategies last a very long time, those are the ones we like to gravitate towards, some don’t last very long at all…those are the one we tend to avoid. There are some definite clues as to which strategies will last, and which will not, the rest is experience.

But none of that is a guarantee. That’s why we must always be developing and testing and evaluating new strategies for possible promotion into our portfolio. This is why we run something I call campaigns, that typically last about 3 months, or one calendar quarter, and reevaluate the portfolio of strategies, and compare it to other potential portfolio mixes.

Strategy Farm System

So, this is the methodology part of the How to Develop Trading Systems. And quite frankly it’s the fun part, if you’re into finding new ways to trade, and evaluating new strategies as potential members in your farm system.

I’ve made the analogy that the farm system is just like the farm system of a major league baseball team, where the major league team is our working portfolio, and the minor league team(s) are the farm system of strategies that we are trying to develop, incubate and cultivate for potential promotion onto the major league team.