Algorithmic Trading Design Methodology

Our approach to algorithmic trading is relatively simple.  We acknowledge that no-one can predict the market direction with 100% accuracy. What we do know is that the market on a month to month basis, will close either strongly up, strongly down or somewhere in between (sideways market). It is our opinion, that the most robust system is one which trades multiple uncorrelated algorithms, each of which targets a specific market condition. This kind of methodology is only viable, if in the contrary market conditions – the algorithms have either small gains or small losses. Therefore, the primary goal of our R&D efforts are to minimize losses during the contrary market conditions. As you review our algorithmic trading design methodology, please consider the risks involved prior to utilizing our algorithmic trading strategies. Trading futures & options is carries significant risk of loss and is not appropriate to all investors.

 

This video, presented by our lead developer – covers in great detail the design methodology used at AlgorithmicTrading.net.

Defining Market States

The first step in running our analysis was to define what it means to be either “strongly up”, “down” or “sideways”. While this analysis could be done daily, weekly or monthly. We decided to run the initial analysis using monthly data.  Our goal was to separate the S&P 500’s monthly performance into three categories, based on an equal distribution of monthly performance. The following table demonstrates how we define each category or market state. This data was taken from a monthly performance report of the S&P 500 which bought on the first day of the month and sold on the last day of the month – for each month beginning in October 2003 through October 2016.

How Do Our Strategies Do in Each Market Condition?

The following table compares each algorithm offered by AlgorithmicTrading.net versus each of the three market conditions as defined in the previous section. The intent of this table is to demonstrate how each algorithm offered performs based on what the market did for that month.  The Monthly P/L Shown, represents the average monthly gain based on a $30,000 account trading 1 unit on each strategy. It includes slippage, commission & protection for our Iron Condor trades.

CFTC RULE 4.41: Results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical 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 profits or losses like these being shown.

The covered call and iron condor strategies trade options on futures. Backtesting an options algorithm poses many challenges due to the unknown estimates for premium collected. Depending on (among other things) market volatility, the premium collected when selling an option can vary greatly. In general, the higher the volatility, the more premium we might expect to collect. In addition, ES Weekly Options were not available to trade for the entire backtested period. To provide our customers with more accurate back-tested data, we have created estimates of premium broken down by Day (Mon-Thu) and used a look-up table for various ranges of the VIX (refer to the Iron Condor product page for details). Please note, these estimates have significant limitations and the corresponding reports which use these estimates should be considered as much less than perfect. All back-testing has limitations, however back-tested options algorithms have even more in our opinion due to the potential inaccuracies used in determining premium collected estimates. 

How to Interpret This Data?

This back-tested data captures how each algorithm does, based on what the S&P 500 did for that month.

For example, in all the back-testing done from Oct 2003-Oct 2016, if the S&P 500 closed down for the month (down), the Treasury Note Strategy actually performed great, by $990/month on average (per 1 Unit Traded).  This suggests to us that the Treasury Note Strategy should continue to do well during months in which the S&P 500 closes down for that month. The Covered Call algorithm and Breakdown Short Day Trade algorithm also do good – with gains of $323 & $280 per month, respectively.

During months where the S&P 500 Closes up by at least $1,500 (Strong Up), the Iron Condor & Momentum algorithms perform good with gains of $1,442 & $1,600 per month on average (per 1 Unit traded).

During markets where the S&P 500 either drifted higher or traded sideways (sideways), the Iron Condor, Covered Calls and Treasury Note algorithm performed well.

 

How Does AlgorithmicTrading.net Use this Data? What’s the Point?

This data is used to create portfolios (collections of trading strategies) that have certain expectations, broken down by market conditions.  It would be great if we knew in advance , with 100% certainty that the market would close higher for any given month. If that data was known, we would simply let the Momentum trading strategy run and turn off all other strategies. Or – simply buy the S&P 500 at the beginning of the month & sell at the end of the month. Sadly, no-one has a crystal ball and so instead, we combine multiple trading strategies, that when traded together – have an expectation to perform well in ALL market conditions. This methodology does not provide guarantees, but in our opinion it does stack the odds better in our favor. Because we have confidence in the complete portfolios ability to handle Strong Up, Sideways & Downward moving markets, we are able to let the complete portfolio run without intervention, no matter what we “think” the market might do.

Case Study: S&P Crusher v2

This is our flagship portfolio, designed to do well in all market conditions. It trades all seven of our trading strategies – in an attempt to better diversify your account. As this graphic demonstrates, when you layer in each trading strategy into one complete trading portfolio, you have what appears to be a robust algorithmic trading system designed to do well whether the market goes Up, Down or Some where in between.

View More Information on S&P Crusher v2

MORE INFO

CFTC RULE 4.41: Results are based on simulated or hypothetical performance results that have certain inherent limitations. Unlike the results shown in an actual performance record, these results do not represent actual trading. Also, because these trades have not actually been executed, these results may have under-or over-compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated or hypothetical 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 profits or losses like these being shown.

The covered call and iron condor strategies trade options on futures. Backtesting an options algorithm poses many challenges due to the unknown estimates for premium collected. Depending on (among other things) market volatility, the premium collected when selling an option can vary greatly. In general, the higher the volatility, the more premium we might expect to collect. In addition, ES Weekly Options were not available to trade for the entire backtested period. To provide our customers with more accurate back-tested data, we have created estimates of premium broken down by Day (Mon-Thu) and used a look-up table for various ranges of the VIX (refer to the Iron Condor product page for details). Please note, these estimates have significant limitations and the corresponding reports which use these estimates should be considered as much less than perfect. All back-testing has limitations, however back-tested options algorithms have even more in our opinion due to the potential inaccuracies used in determining premium collected estimates. 

Is This Methodology Perfect?

It is the opinion of AlgorithmicTrading.net, that no holy grail of trading exists and that there is no such things as a perfect trading strategy. All strategies have flaws and until someone designs a crystal ball – there will be stress & emotions involved with trading.  With that said, it is our experience that this kind of trading methodology – grounded in actual quantitative analysis (not talking heads or loud trading rooms), provides a sense of emotional relief when it comes to active trading.

As all traders know, trading is very difficult and emotions can cause us all to do irrational things.  Our experience is that some of the most stressful trades are ones that go well. Its human nature to want to lock in profits – but traders are all to familiar with getting out too early and watching the market continue higher. They jump back in, wanting to capture more gains only to see the market reverse. They hold onto the loser way too long and end up taking a larger loss than anticipated after moving their stops.  This process repeats itself and is one reason why many day traders fail.

While our methodology is not perfect – we do take losing trades, losing months and even losing quarters at times, the trading of multiple strategies does help with one aspect of trading emotions, namely the fear of “getting the market direction” wrong.  The data does show us that even with our trading methodology, the market can go higher and the best performing “bull market” trading strategy we have (Momentum Trading Strategy) can still take losses. However, this should not be the norm and so we are able to rest a little bit easier knowing that we have a balanced set of strategies, ready to (hopefully) out perform no matter which direction the market decides to head.

As mentioned repeatedly, trading futures and options is not for everyone. You should only trade with Risk Capital. If you are in doubt, discuss our algorithmic trading strategies with a registered CTA or Investment Advisor. As a third party trading system developer, we are not registered with the NFA as Commodity Trading Advisors (claim the self-execution exemption from registration) and can not provide investment advice unique to your personal situation.



AlgorithmicTrading.net provides trading algorithms based on a computerized system, which is also available for use on a personal computer. All customers receive the same signals within any given algorithm package. All advice is impersonal and not tailored to any specific individual's unique situation. AlgorithmicTrading.net, and its principles, are not required to register with the NFA as a CTA and are publicly claiming this exemption. Information posted online or distributed through email has NOT been reviewed by any government agencies — this includes but is not limited to back-tested reports, statements and any other marketing materials. Carefully consider this prior to purchasing our algorithms. For more information on the exemption we are claiming, please visit the NFA website: http://www.nfa.futures.org/nfa-registration/cta/index.html. If you are in need of professional advice unique to your situation, please consult with a licensed broker/CTA.

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.

Unless otherwise noted, all returns posted on this site and in our videos is considered Hypothetical Performance. HYPOTHETICAL PERFORMANCE RESULTS HAVE MANY INHERENT LIMITATIONS, SOME OF WHICH ARE DESCRIBED BELOW. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN. IN FACT, THERE ARE FREQUENTLY SHARP DIFFERENCES BETWEEN HYPOTHETICAL PERFORMANCE RESULTS AND THE ACTUAL RESULTS SUBSEQUENTLY ACHIEVED BY ANY PARTICULAR TRADING PROGRAM. ONE OF THE LIMITATIONS OF HYPOTHETICAL PERFORMANCE RESULTS IS THAT THEY ARE GENERALLY PREPARED WITH THE BENEFIT OF HINDSIGHT. IN ADDITION, HYPOTHETICAL TRADING DOES NOT INVOLVE FINANCIAL RISK, AND NO HYPOTHETICAL TRADING RECORD CAN COMPLETELY ACCOUNT FOR THE IMPACT OF FINANCIAL RISK IN ACTUAL TRADING. FOR EXAMPLE, THE ABILITY TO WITHSTAND LOSSES OR ADHERE TO A PARTICULAR TRADING PROGRAM IN SPITE OF TRADING LOSSES ARE MATERIAL POINTS WHICH CAN ALSO ADVERSELY AFFECT ACTUAL TRADING RESULTS. THERE ARE NUMEROUS OTHER FACTORS RELATED TO THE MARKETS IN GENERAL OR TO THE IMPLEMENTATION OF ANY SPECIFIC TRADING PROGRAM WHICH CANNOT BE FULLY ACCOUNTED FOR IN THE PREPARATION OF HYPOTHETICAL PERFORMANCE RESULTS AND ALL OF WHICH CAN ADVERSELY AFFECT ACTUAL TRADING 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 hypothetical accounts which have limitations (see CFTC RULE 4.14 below and Hypothetical performance disclaimer above). 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. While back-tested results might have spectacular returns, once slippage, commission and licensing fees are taken into account, actual returns will vary. Posted maximum draw downs are measured on a closing month to closing month basis. Furthermore, they are based on back-tested data (refer to limitations of back-testing below). Actual draw downs could exceed these levels when traded on live accounts.

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 algorithms (algos) include slippage and commission. Statements posted are not fully audited or verified and should be considered as customer testimonials. Individual results do vary. 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/educational purposes. AlgorithmicTrading.net does not make buy, sell or hold recommendations. Unique experiences and past performances do not guarantee future results. You should speak with your CTA or financial representative, broker dealer, or financial analyst to ensure that the software/strategy that you utilize is suitable for your investment profile before trading in a live brokerage account. All advice and/or suggestions given here are intended for running automated software in simulation mode only. Trading futures is not for everyone and does carry a high level of risk. AlgorithmicTrading.net, nor any of its principles, is NOT registered as an investment advisor. All advice given is impersonal and not tailored to any specific individual.

* Published percentage per month is based on back-tested results (see limitations on back-testing above) using the corresponding package. This includes reasonable slippage and commission. This does NOT include fees we charge for licensing the algorithms which varies based on account size. Refer to our license agreement for full risk disclosure.