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Fully Automated
Trading System

by Quant Algorithms LLC

"System automation has been time-tested and proven to provide a strategic edge in any business industry. Auto-trading our five algorithmic trading systems can be a powerful tool to generate profitable returns in any market condition."

- Lead Developer of Quant Algorithms: B2 Breakout,
P1 Push-Pull, T2 Burst, S2 Breakdown (SHORT) and O2 Overnight Gap Trading Systems

Active Trader
System Features Include:
stock 8.67% per month on the ES Active Trader Package:
Designed for the individual looking to outperform the S&P 500 in a significant way. This is on a back-tested basis going back to May 2001.

stock Trades five uncorrelated algorithms concurrently
B2 Breakout, S2 Breakdown SHORT, T2 Burst, O2 Overnight Gap and P2 Push-Pull. Correlation to S&P 500 is .02%.

stock Trade your IRA/Roth IRA with Auto-Execution
Remove your emotions from trading with zero time commitment required. Let our algorithmic trading system do the work!
stock Traded live since August 2013
Anyone can code an algorithm that looks good back-tested. What separates us from others is our ability to deliver since going live!

stock Profitable during Bull & Bear markets
S2 Breakdown SHORT & P2 Push-Pull take advantage of market weakness. The other three during market strength. On the back-testing, our most profitable year was 2008!

stock Full transparency:
On this website you can see prior customer statements, live trading room, reports from Tradestation and entire back-tested trade list going back to May 2001

April 18th , 2015 Uncategorized

P2 Push-Pull, O2 Overnight Gap and S2 Short algo did great! We had a gain of about 5.56% this week. Check out this video blog reviewing each trade (winners and losers) plus commentary on our special topic for the week. Special Topic for this week: How do you determine your targets on trades?  Short answer, we follow our 10+ design […]


What We Do

Quant Algorithms is a leading developer of Hedge Fund quality algorithmic trading systems for both the retail trader and enterprise level clients. Our algorithms are 100% mechanical and trade the E-Mini Futures (NQ, ES & TY). We offer a single package which is auto-executed with best-efforts and trades our five core Algorithms concurrently (T2 Burst, B2 Breakout, O2 Overnight Gap, S2 Breakdown and P2 Push-Pull).

Watch our Automated Trading System Trade Live

Click on the following link for immediate access to the screen share of our five algorithms trading live in an actual real money account. No password needed, no email required. Simply click on the link provided below. Our transparency is unmatched in this industry. Feel free to follow along as the algorithms trade. Note, it usually isn't exciting. We typically place 1-3 trades per day during the hours our algorithms trade (9:30 AM EST - 5:15 PM EST, M-F). You will see the current P/L for the trade we are in (trading 4 contracts on each algo; about a $68,000 account) as well as entry and exit points. This link is provided so that our current customers can follow along as we trade as well as to show potential customers how the algorithms work.

Access Free Live Trading Room:
Alternatively, you can go to and enter in the code "AlgosLive" under the join meeting section.


Summary of "S&P 500 (ES) Active Trader Package"

The following data is taken from back-tested/simulated accounts trading our algorithms. We have added reasonable slippage and commission to each trade, however actual results can vary.

Digging Deeper into Our Automated Trading Systems

At Quant Algorithms, we have a solid design methodology that we strictly follow. Furthermore, we have quality control processes in place to ensure we continue on the right track.
Even if you have no desire to purchase a Trading System, it is our hope that this section will provide guidance to the aspiring Algorithmic Trading System developer and outline common pitfalls, providing a basic methodology they can follow to help ensure their Trading System not only looks good back tested, but also performs well when traded live.

If you’d rather read this in PDF form, click on this link for our White Paper on this subject entitled:
QA Products offered and Design Methodologies

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Automated Trading System Design: It’s an Art & Science

Unfortunately, for most Automated Trading System developers, the following cycle will look all too familiar. They will start with a faulty Trading Strategy, convince themselves it is the Holy Grail, then trade live only to see horrible performance.

Next, they will rationalize away the poor returns. They might say, “If only my stop was X instead of Y, the performance would have been amazing!” They will then modify the design or reoptimized, only to find that they continue to experience bad results.

When it comes to Automated Trading System development, it really is an Art and a Science.  As most developers know, there is a big difference between a Trading System that looks good in back testing and one that also performs well going live. Coding a solid Trading Strategy is the first step for sure, but not all Automated Trading Systems are created equally.

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Overview of our Automated Trading System Offerings

We offer two distinct packages. The investor and Active Trader Package. Both Trading Systems trade the exact same algorithms (T1 Burst ES, O1 Overnight Gap ES, B1 Breakout ES and P1 Push-Pull TY). The difference is in the allocation and number of contracts we authorize you to trade.  By modifying the allocation (1 contract per $17,000 per algo vs. 1 contract per $34,000 per algo), we are able to scale the potential drawdown and expected gain depending on an individual’s tolerance for risk.

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A Complete Trading System

Regardless of the package you choose, our complete Trading System consists of four uncorrelated Trading Strategies traded concurrently. 

While someone could trade only one of the Futures Trading Strategies, it is our strong recommendation that you trade all four for reasons that will be clear as you continue to dive deeper into our Automated Trading System design methodology.

Keep in mind that no one has the holy grail of trading. Trading Futures involves risk and trading is not for the faint of heart.  You will have days where you suffer losses or give back gains. Even with Automated Trading Systems, you will have urges to turn off the algorithms and not let them run. It is our experience that usually those periods produce the best opportunities and we strongly advise our customers to always let them run.  While one algorithm might be in a drawdown, more than likely the others will be breakeven or profitable allowing the combined system to generate positive results.  Of course, there are not guarantees in trading; however, we attempt to put every odd in our favor to ensure maximum probability of success.

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Predicting Market Direction

No one can predict what the market will do at any given time. While we have percentage profitability expectations for every single trade we make, ultimately no one knows for sure what the market will do at any given time. This is the reason why we employ four separate Trading Strategies.  At any given time, what we do know for sure is that the market will either move sideways, higher or lower for any given period under analysis.  For the purpose of system development, we’ve added a fourth category called “rebounding” that represents a strong move higher after a substantial down move; also called a short covering rally. We do everything we can to be market direction agnostic by trading four Trading Strategies concurrently, each with its own strengths, weaknesses and expectations for the 4 different market conditions. The net result is typically a net winning session for every market condition.

The following diagram captures the four different market conditions along with the expectations of positive performance for each algorithm.  Each algorithm has a strongly positive expectation for one of the four conditions along with weaker positive expectations where the overlaps occur.  The ideal conditions for the algorithms are when the algorithms performance overlap since that implies multiple algorithms are performing well. In fact, we have had months where all four algorithms are profitable resulting in exceptional returns for those periods.

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Performance vs. the S&P 500

The following equity curve shows the performance of the merged algorithms as compared to the S&P 500 (Active Trader Package). As you can see, the combined Automated Trading Strategy performance is spectacular during both bull and bear markets.  This chart includes a more aggressive estimate for slippage and commission. As the equity curve shows, there is little correlation between the four merged algorithms and the S&P 500. Our back tested performance is not tied to the performance of the S&P 500.

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Mathematical Proof of Correlation

The correlation coefficient is a percentage that represents how correlated two data sets are.  In Trading Algorithm development, a designer will typically measure the correlation of their algorithms to the S&P 500 to determine how correlated an algorithm is to the broader market performance. Since the goal of most Auto Trading Systems is to outperform this index, it only makes sense to measure the correlation between the Trading Strategy developed and the S&P.
Here is a commonly accepted definition of what different values imply:
+.70 or higher Very strong positive relationship 
+.40 to +.69 Strong positive relationship 
+.30 to +.39 Moderate positive relationship 
+.20 to +.29 weak positive relationship 
+.01 to +.19 No or negligible relationship 
-.01 to -.19 No or negligible relationship 
A value of 100% would imply that the two data sets are equal. A value of 0% would imply two fully random data sets. A negative value would imply an inverse relationship.

Active Trader Package/Investor Package Correlation Coefficient as compared to the S&P 500 = 28%.
What this means is that the algorithms performance is not driven by the S&P 500 performance. As the merged equity curve shows and correlation coefficient confirms, our Automated Trading System has a weak positive relationship that is acceptable and many would consider ideal.

It is our opinion that any correlation below 50% is positive good news. If the goal were to outperform the S&P 500, then anything north of 50% would seem to defeat the purpose of implementing an Algorithmic Trading System since the trader could simply buy and hold the S&P and not waste their time with trading.

Fortunately, as our merged equity curve demonstrates, our expectation is for continued positive returns independent of market conditions.

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Weekly Returns as Compared to the S&P 500 (1/1/03-2/10/15)

Email us or call to get access to our spreadsheet, which shows the weekly returns of each algorithm from 1/1/2003-2/10/15 as well as merged returns for the combined Trading System (active trader package).   It also shows the weekly S&P 500 returns for a strategy that buys at the open of the week and exits at the close that we used to run the correlation study. Lastly, it includes the correlation report using excels correlation coefficient calculations.

To see the entire excel document click on this link entitled:
QA Weekly PL & Correlation Analysis vs SP500

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Example of a Typical Algorithm Development Cycle

Before we go into the details of our trading methodology and design criteria, it might be helpful to discuss how an algorithm is typically created.

Step 1: Examine an Idea

This process begins with a simple idea that is coded and analyzed. It might start as an idea to “Sell or Fade a gap up at the opening bell” but then change to see what happens if you “buy an opening gap”. After running simulations, the idea might be discarded and something else tried.

Step 2: Back Test & Optimize the Algorithm

Once a basic Trading Strategy is coded and looks to be promising, the developer will optimize the algorithms inputs. This might be a stop, target, or some other technical indicator. During this phase simulations will run, changing inputs based on the granularity selected.  They will also cross optimize the inputs to find based on the previous history what the most optimal inputs (stop, target, technical indicator) would have been. Trading platforms will then produce a report indicating what the most optimal inputs would have been. They will also generate back tested performance reports indicating everything from maximum drawdown, percent profitability, profit factors and much more.

Once the optimization is complete, the trader in theory has a Mechanical Trading System that could be auto-executed. However, there is much more to developing a winning trading system than just running the above outlined steps. 

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Quant Algorithms Automated Trading System Design Specifications

As mentioned, there is much more to Automated Trading System development than just coding an algorithm, back testing and optimizing it.  The real work comes in vetting the algorithm that was developed. The goal of any system developer should be to attempt to break the algorithm and try to find reasons why it will not work going live. This process can create very emotional swings for the developer. At first glance a new Trading System might appear to be full-proof, only to find that it is not qualified to trade because it does not adhere to one of the many requirements (too few trades, back tested only 5 years, too small average $ gain per trade, etc.).

A very common question we get from potential clients is “What separates you from all the other Automated Trading System developers pitching their algorithms to the market place?”. The answer is that we adhere to the following guidelines. Typically, for every 100 trading systems we work on, only one will meet the requirements. This is strict set of design rules helps ensure we are providing the best trading strategies we possibly can, not just selling an algorithm for the sake of selling it.

Quant Algorithms LLC is an innovative Algorithmic Trading System design firm and you should only expect the best from us. This includes the following Design Criteria for our complete Automated Trading System.

One of the biggest flaws a designer can make is to cut corners in an attempt to create a winning system.  Everyone wants to code the Holy Grail and will tend to violate key design criteria either consciously or subconsciously. At Quant Algorithms, we take these principles seriously and do not violate them. We understand that the key to successful Auto-Trading is to remove any emotions from the development process and simply let the chips fall as they may.

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Back-Test 10+ Years

We back-test all the way to 2003 when optimizing our algorithms. Many developers will only go back 4 years (or even less), conveniently avoiding the 2008 crash and market periods prior to that. While we back-test to 2003 for our optimizations, during the development stage we back-test as far back as we can go on the eminis with the 2003-2013 optimizations to ensure that performance did not fall apart. To further try and “break” the algo’s, we modified the burst and push pull so that it could be traded on the broader index and tested as far back as 1984.  This also showed very good results. During our back-testing phase, the more we tried to crack the algo’s – the more we began to realize that these algo’s are truly something special. 

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Uncorrelated Algorithms

We prefer to have 3-4 uncorrelated algorithms for any complete Automated Trading Strategy designed.  This can be easily measured using a correlation coefficient. It is our opinion that any value between 0-.50 is sufficient. However, it does depend on the goal of each algorithm. This is measured by comparing the algorithms weekly performance with the S&P 500 to determine how correlated (or uncorrelated in our case) the Trading System is to the broader index.  A final value of over .50 suggests that the system will simply perform as the S&P 500 does in most cases (a strong positive correlation). In that case, why use an Automated Trading System? Simply buy and hold the S&P instead and save the headache.  Furthermore, it is our expectation that the trading systems independent Trading Algorithms are also uncorrelated.  Refer to the previous section above for details on all of our correlation reports.

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Reasonable Profit Factors (1.2-2.6)

The profit factor is simply a ratio of total gain to total loss.  It is broadly accepted that any PF lower than 1.2 is probably not worth your time since it is barely profitable.  Furthermore, it is our experience that any PF above 2.6 is probably not realistic and can only be achieved by violating the other design criteria we mention here (i.e., back testing < 10 years, scalping or having < 200 trades). Our profit factors range from 1.25-2.50 depending on the algorithm.

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Large Average Gain per Trade

If you average all trades in the complete system, winners and losers, you will come up with an average $ gain per trade. It is very important to have room for error, therefore the higher this number the better.  The most common mistake I have seen in new traders is that they will create a scalping algorithm that is in and out multiple times throughout the day. They look good back-tested but once traded live they fall apart. More times than not, this is because their average gain per trade is less than one tick on the index they are trading. While over time, the $12.50 gains per trade will add up and show great equity curves, stable profit factors and seemingly amazing reports with low drawdowns, the reality is that they will probably be at a loss when going live.  They provide little room for error given that if there is on average one bad fill per trade, they have essentially eaten into all of their profit or perhaps gone net negative.  No retail trader should be in and out multiple times during the day, leave that to the HFT firms that have hundreds of thousands of dollars if not millions invested in getting the most optimal locations for servers and dedicated design teams to monitor their  HFT algorithms.

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Use Look-Inside (LIB) and Intrabar Order Generation (if applicable)

Another common mistake when developing algorithms is to turn off the look-inside bar back testing feature.  This is a bit complicated to explain. In essence, if unchecked you will have inaccurate back-testing results that will show winners when in fact the actual trades were losers. This is a bigger problem for algorithms that trade on large candles and/or algorithms that have very tight stops or very tight targets (even worse both).  The problem arises when within a single candle; either the stop or target could have been hit.  With LIB checked, the back-testing optimizations take longer because Tradestation will analyze every tick within the candle to determine which was hit first, the stop or the target.  Tradestation defaults to this being unchecked so that simulations are faster.  With LIB unchecked, Tradestation will use its own proprietary algorithm to determine if the stop was hit first or the target was. Unfortunately, it seems that their algorithm will more often than not err on the side of assuming a target was hit first. This has caused many system developers to feel that they found the Holy Grail only to learn that their back testing was littered with incorrect data. During our development cycle, we always run with LIB on to ensure that the back-tested results are accurate.

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Include Adequate Slippage & Commission in Analysis

Slippage and commissions eat into any profits. Since this can vary from trade to trade, a system designer needs to be realistic with this and try to err on the side of caution. If an algorithm enters at the market and exits at a limit, then you can assume you will have at least one tick of slippage on the buy and potentially some slippage on the sell (even though it is a limit order to exit).  The reason there is also slippage on the sell possible is that at times, the index traded will barely hit the limit price but it will not be filled. It will then reverse. The algorithm thinks you exited the trade, even though in the live account it did not get filled. If this happens, our algorithms are programmed to exit at the market (typically) 15 seconds later to ensure the live account is in synch with the algorithm.  Commission rates should also be factored into the performance. In our case, we add both slippage and commission to all of our reports unless stated otherwise.

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Use Three of Fewer Technical Indicators

Another broadly accepted principle in Trading System development is the fewer the technical indicators the better. We require three or fewer; in fact, our algorithms have only one in some cases.  We do use price action heavily and pattern recognition in our algorithms, which is a different concept. In general, think of an algorithm as a house of cards. The more technical indicators, the more flimsy the house. Usually, algorithms with a large amount of technical indicators will result in over-optimization when back testing is performed. As results are analyzed, the developer will add new indicators to try to avoid losses building a very flimsy algorithm that will more than likely fall apart once traded live.  Our philosophy is that we would rather have a reliable algorithm that works traded live and accept a lower profit factor, than one that looks good on back-testing, but performs horribly going live.

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Perform Monte Carlo Simulation

Results should undergo a Monte Carlo Simulation. This randomizes the back-tested trades to ensure there are no hidden patterns that exist only due to unique market conditions. It is just another way to try and “crack” or “break” the Trading System and evaluate performance with the same trades executed randomly. This is helpful in determining a worst case potential drawdown.
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Modify Inputs +/- 10%, Ensure Minimal Impact

Once optimization is performed, we modify all inputs randomly by +/- 10% to ensure the algorithm still looks acceptable. This is a way of trying to measure how flimsy the algorithm is.  For example, after optimizing an algorithm we might determine that the most optimal target is 10 points. We will then go back and modify the target to be 9 points and 11 points to ensure that the algorithm still looks ok. If it falls apart, then that is a big warning sign that it has been over-optimized.

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At Least 200 Trades in Back-Testing History

In general, the bigger the data set the better when analyzing an algorithm.  Our complete system has over 3,300 trades as of the time of this writing. If an algorithm has less than 200 trades, it is our opinion that there is not enough data to make a case for that algorithms performance going forward.

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Trade Live Prior to Offering to Public

Any algorithm should be traded live prior to making any strong conclusions about it.

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Drawdown Scalable to Meet Various Customer Needs

The drawdown should be scalable to meet individual’s needs. Our algorithms can be scaled by adjusting the number of contracts traded per X amount of dollars in the account. We currently offer two packages to address this. The investor and active trader. The investor package has have the expected drawdown as the active trader. The gains are also cut in half. That is the tradeoff. 

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Do Not Over Optimize

Once an algorithm is coded, it is optimized to determine the best possible values for each input.  These values can be optimized with as much granularity as a developer wants. While we could optimized down to .0001 points or lower for any give input, we choose to use a much higher granularity to help ensure we are not over optimized. We would rather under promise and over deliver when it comes to framing expectations.

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Independent Third Party Evaluation

Ideally, a third party should evaluate any algorithm or complete Trading System prior to a final seal of approval.  In our case, another organization did have an independent design firm evaluate our algorithms. This is done to get one more set of eyes on the product. In our case, we received a report that gave our algorithms very high marks.  In fact, the evaluator spent over 1 month trying to break our algorithms and really could not. In the end, the report was extremely positive and his final recommendation was that we forward test the algorithms. The entity funding the generation of the report decided not to do this final step because we had live returns since the previous optimizations where performed (over 6 months). In our opinion, this is even better than any forward testing that could have been done.

To see the Independent 3rd Party Evaluation of our algorithms click on this link entitled:

Algo Due Diligence Report v1.1 (with Quant Algorithms comments in red)

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Scalable System (can handle volume and account can grow with system performance)

Any successful system should be able to handle a large account size and be able to scale higher with the success of the system (increasing contract size as the system performs). Very simply, the key to this is to only trade markets that have large amounts of liquidity during market hours that have the most liquidity. We trade the Emini S&P 500 Futures (ES) and the 10 Year Note (TY) which are some of the most liquid futures instruments traded. Furthermore, while futures trade 24 hours we ensure the algorithms can handle volume at all hours by limiting our trading to only when the equity markets are open. This helps ensure when a trade is triggered there will be enough liquidity to ensure our slippage is minimized.

According to the CME Group, the average daily volume (ADV) on the ES is almost 2 Million contracts. At an initial margin rate of $5,000 per contract traded this amounts to approximately $10 Billion worth of trades on the S&P every day.  The TY has an ADV of almost 1.5 Million contracts, which is equal to approximately $2.2 Billion worth of shares traded every day. Averaged out over a 24-hour period, this allows for plenty of liquidity to handle our algorithms traded with very large accounts across multiple customers.

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Final Sanity Check

This final step is bit more loosely defined and since it is a bit abstract and difficult to quantify, it is not listed as an actual design requirement.  Simply put the concept or principles behind the Automated Trading Strategy should make sense and pass a basic sanity check.  For example, it is probably not sufficient to just stumble upon a random pattern and justify it as a reliable basis for an Algorithmic Trading System. While it is possible there are exceptions out there, the best algorithms should be able to have reasons behind their expectation for success.

For the breakout, we are capturing short covering rallies and buying when it is difficult (on a gap up for example). When most day traders are shorting the large gap up, expecting it to fill the gap, we will typically buy the breakout. Furthermore, once it has made a large up move from our entry, most day traders will feel it has moved to far and get out. The back-testing data suggests that in fact, you should hold until the end of the day and so that is what we do.

The logic behind the burst is that we buy breakouts within range bound or sideways moving markets (but exit quickly in case they are false breakouts). We will also buy the bottom of the range in sideways trading markets and allow for a larger target, exiting once the futures trade back towards the top of the range.

For the Push-Pull, it is similar to the burst except that we hold longer and typically only buy on dips. 
The principle behind the overnight gap is equally straightforward in that it buys into strength during upward trending markets, attempting to exit the following morning when the equity markets opens. This tendency to gap up is in our opinion due to the ramp in futures that tends to happen in strong markets during the overnight “light volume” trading session.

At Quant Algorithms, our number on principle when designing algorithms is to think in terms of “Why do most day traders fail?” Answer, because they make the comfortable trades instead of the gut wrenching difficult ones. They are reluctant to buy breakouts because they feel it has already moved to far and sit around waiting for the pullback to happen. Once it does happen, they tend to get scared and will not enter thinking that the market will finally crash. If the pullback intensifies, they will finally feel like the market has moved to far down and cannot go further. That is when they buy which is typically the exact wrong time.  They take the comfortable trade instead of the right trade.

At Quant Algorithms, we determine “What’s the hardest trade to make?” and then execute on that trade without our emotions being involved. We simply let the Robotic Trading System run our trading.

We spend a huge amount of time, energy and resources developing the Trading Strategy and then force ourselves to be as dumb as a rock once it goes live and just let the trades play out.

To summarize, for any given Trading System, it is our opinion that the core principle should make sense in general terms and be something that can be justified. This is the final step in Trading System development where you simply exhaust every possible option to try to find holes or fractures in the algorithm.

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Quality Control Processes

At Quant Algorithms, we have implemented the following quality control mechanisms to monitor the performance on the Automated Trading System and ensure no cracks begin to form.

This includes the following cycle that continually repeats itself.

Ensure Live Returns Match Back-Tested Expectations

As time goes on and more and more trades are placed live, we continue to monitor the performance of the algorithms and constantly compare profit factors, drawdowns and equity curves on each of the four algorithms. We do this to ensure that results continue to match back-tested expectations.

Monitor Slippage

Slippage is closely monitored in our live accounts to ensure that our model accurately represents the average slippage seen. We also monitor the liquidity of the S&P to ensure that it can handle our market buy orders and limit sell orders. One way we do this is by tracking the number of contracts trading our algorithm.

Monitor Auto-Execution Service

We closely monitor the live trading accounts that are setup with The Fox Group to ensure trades are executed properly. The Fox Group has our algorithms loaded on their Tradestation platform and auto-execute with best efforts. They have a full time staff member dedicated to ensuring trades are placed properly.  Fortunately, we have not encountered any major issues with this service and are exceptionally pleased with their ability to auto-execute our Automated Trading System.

Ensure Consistent Results for All Customers

Given that our Automated Trading System is auto-executed through The Fox Group, this is done with great ease. The Fox Group places block orders when a buy is generated meaning we all get the same fills and we are all trading the algorithms properly.

Make Adjustments if Needed

Since going live back in August of 2013 on the Nasdaq Active Trader Package, we have traded the same three core algorithms (Burst, Breakout and Overnight Gap). The Push-Pull was added back in November of 2014 when we capped the NASDAQ.  As of the time of this writing, we have only reoptimized once (February 2014) due to a minor bug fix that was required. Since then, we have not had to adjust the algorithms and have had the mentality of “If it ain’t broke, don’t fix it”.  We take great pride in the fact that our algorithms have held up for over a year now and have not required any reoptimizations. Having said that, should the need tweaks we will update the algorithms with The Fox Group and all of our customers will see the fixes seamlessly.  Should this happen, we will alert current customers to the change. This is all included as part of our maintenance package.

Comments on Forward Testing

Forward testing is a method used by some Auto Trading System developers to further evaluate an algorithms expected performance once it goes live. It is also used to constantly update optimizations (targets, stops, etc.) based on changing market conditions. They typically will weigh heavily the most recent market behavior in their optimizations justifying that the patterns seen more recently are probably more likely to continue (as opposed to patterns seen many years prior). This topic is up for debate in our opinion.  It is a good way to test an algorithm while in the development phase, however we do not agree with the idea that you should constantly update your optimizations based on more recent market behavior. 

It is a somewhat common practice in Automated Trading System development, however since we have been trading live for well over a year with results fitting within the back-tested expectations we do not feel a need to continually forward test and provide new optimizations. Should performance lag and there be a need to do this, we will do so as part of our maintenance agreement.
Having said that, it is our opinion that most algorithm developers will do this only to find that they are constantly chasing their tails and in the process over optimizing their algorithms and therefore never seeing consistent positive results.

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Final Word

Quant Algorithms is a leading provider of hedge fund quality Automated Trading Systems to not only CTA’s & Hedge Funds but also to the retail trader. Our customers receive our full attention and we devote and pride ourselves with customer service while sticking to our core competency of developing high quality Algorithmic Trading Systems. We have a well-formed team that is devoted to providing our customers with the best Algorithm Based Trading System we can.

By using our Automated Trading System, our customers are finally able to remove their emotions from trading allowing the algorithms to do their thing and capitalize on short-term market inefficiencies to reap profits.

Contact us today by phone, email or shoot us a quick message!



U.S. Government Required Disclaimer- Commodity Futures Trading Commission Futures trading has large potential rewards, but also large potential risk. You must be aware of the risks and be willing to accept them in order to invest in the futures markets. Don't trade with money you can't afford to lose. This is neither a solicitation nor an offer to Buy/Sell futures. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed on this website or on any reports. The past performance of any trading system or methodology is not necessarily indicative of future results.

With the exception of the statements posted from live accounts on Tradestation and/or Gain Capital, all results, graphs and claims made on this website and in any video blogs and/or newsletter emails are from the result of back-testing our algorithms during the dates indicated. These results are not from live accounts trading our algorithms. They are from simulated accounts which have limitations (see CFTC RULE 4.41 below). Actual results do vary given that simulated results could under-or-over compensate the impact of certain market factors. Furthermore, our algorithms use back-testing to generate trade lists and reports which does have the benefit of hind-sight. Understand that while back-tested results might have spectacular returns, once slippage, commission and our licensing fee is taken into account, actual returns will vary.

CFTC RULE 4.41 - Hypothetical or simulated performance results have certain limitations. Unlike an actual performance record, simulated results do not represent actual trading. Also, since the trades have not been executed, the results may have under-or-over compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight. No representation is being made that any account will or is likely to achieve profit or losses similar to those shown.

Statements posted from our actual customers trading the algo's include slippage and commission (Customer A and Customer B). Statements posted are not fully audited or verified and should be considered as customer testimonials (individual results, results do vary, etc). They are real statements from real people trading our algorithms on auto-pilot and as far as we know, do NOT include any discretionary trades. Tradelists posted on this site also include slippage and commission.

This strictly is for demonstration purposes. and Quant Algorithms LLC do not make buy, sell or hold recommendations. Unique experiences and past performances do not guarantee future results. You should speak with your CPA or financial representative (broker dealer or financial analyst) to ensure that the software / strategy that you utilize are suitable for your investment profile, before trading in a live brokerage account. All advice and/or suggestions given hereto are intended for running automated software in simulation mode only. Trading futures is not for everyone and does carry a high level of risk. Quant Algorithms LLC is NOT registered as an investment adviser (nor any of it's principles). All advice given is impersonal and not tailored to any specific individual.

* Up to 10.06% per month is based on back-tested results (see limitations on back-testing above) using our NQ Active Trader Package. This includes reasonable slippage and commission. Refer to our license agreement for full risk disclosure.