Testing Across Markets, Trigger Signals, Signal Breakdown [New Update]

I wanted to give this latest version of BuildAlpha its own blog post as there are a few features that deserve a more thorough explanation to fully understand their power. I will highlight three of these new features below.

1. Testing Across Markets

The latest version allows the trader/money manager to develop strategies across a basket of instruments as opposed to just one symbol. For example, one can select Gold, Oil, SP500 and 30 Year Bonds (as well as thousands of signals at once) and the software will find the best strategies based on the combined performance across the selected instruments. You can also build trading strategies that are trained on various timeframes at once. For example, a basket can consist of 30 minute data, 240 minute data and daily data and the software will find the strategies that work best across all these timeframes. I first mentioned this technique of testing across markets to reduce the chances of curve-fitting in this blog here: https://www.buildalpha.com/3-simple-ways-to-reduce-the-risk-of-curve-fitting/

Testing across markets (and/or timeframes) is a great way to find robust strategies that generalize well and help to combat over-fitting. If you don’t believe me, take Jaffray Woodriff’s word for it. Woodriff, the founder of $3 Billion Quantitative Investment Management, was recently in the news for his $120M (yes, million) donation to the University of Virginia’s data department. In the book Hedge Fund Market Wizards, Woodriff was asked what changed from his early years when he was slightly down for two straight years to when he made over 80% in just the first six months of the following year. Here is his response straight from the book

“I started to figure out that the more data I used to train the models, the better the performance. I found that using the same models across multiple markets provided a far more robust approach. So the big change that occurred during this period was moving from separate models for each market to common models applied across all markets.” Pg. 146

Furthermore, if (when) the market changes then strategies built on multiple (and even better, diverse) markets should stand a better chance of surviving as they’ve been built across different data sets.

Build Alpha now makes it incredibly simple to build and test ideas and strategies across multiple markets at once. Just select one or as many markets as you prefer from the drop down menu and Build Alpha will return the results of the strongest strategies built across the entire basket. The ability to analyze the aggregated results as a basket as well as analyze the individual components is extremely simple.

The top row is the aggregate performance of the entire basket denoted by the symbol ‘BSK’. If you click the top left arrow it will drop down the basket’s components with their respective indiviudal performance.

Note: I am not saying that single market models do not work! They most certainly do, and there are certainly players that only participate in specific markets thus creating idiosyncratic patterns and opportunities. Of course the BuildAlpha trader can still search for single market models in BA as well. Flexibility is always key!

2. Trigger Signals

A trigger signal is the combination of two rules or signals occurring within a specified time window. We assume the main signal is occurring right now and then require that the second signal – the trigger signal – had to occur at least once in the last N bars for the full combined signal to be valid.

A simple example is to consider these two signals

  1. Close crosses above the 20 period SMA (main)
  2. Close is below the lower Bollinger Band at any time in the last 5 bars (trigger)

If we cross above the 20 SMA today and at any point in the last 5 bars we also closed below the lower Bollinger Band then we have an active signal.

Adding a ‘trigger’ to a signal can greatly improve the edge of the main signal. I ran a simple test using the above Trigger Signal on a basket of securities (ES,NQ,US,SPY,QQQ,TLT) and viewed the combined E-Ratio.  The original main signal (with no trigger signal) is seemingly random with an aggregated E-Ratio of only 1.02. However, adding the trigger signal jumped the E-Ratio to 1.39 – a 36.27% improvement.

Additionally, the E-Ratio for the main signal when just considering the 30 Year Bond Futures (symbol US) improved about 100% from 1.05 to 1.92 with the addition of the trigger signal.

To create trigger signals simply go to File -> Custom Indicators and set type to Trigger. Here is an example of the above Trigger Signal:

And here is a visual (pink dot is a true signal with trigger and blue dot is a true signal without trigger)

More on these in future upgrades..

3. Signal Breakdown

The final new feature I want to discuss allows us to view EVERY signal Build Alpha has (currently 5000+) and the likelihood it occurs on our winning trades as well as the likelihood it occurs on our losing trades even if it is not included in our original strategy’s rules. Why is this helpful? Well this can give us insights into what signals, rules or filters we can add (or exclude) to improve a strategy.

For example, if you knew that a specific signal occurred on 75.32% of your winning trades and on only 14.75% of your losing trades would you want to include this rule in your strategy to see if it improves results? Of course.

Additionally, if you knew that a specific signal occurred on only 18.37% of your winning trades but on 84.69% of your losing trades would this not be a rule you’d want to exclude from your strategy – in other words, avoid trading when this rule is true as it appears only true on losing trades!

The Signal Breakdown can be a powerful tool to help improve, tweak and understand strategies. However, caution is advised to still follow proper testing procedures such as in and out of sample testing, etc. There are other posts regarding this matter.

Why doesn’t Build Alpha find these rules initially? It does, but the user also has the ability to limit the software to generate the best strategies only using a user specified maximum number of rules. If the user wants to later add an additional rule then using the Signal Breakdown can lend insights into which additional rule(s) to add.

As always, thanks for reading and your support. It means a lot.



Ensemble Strategies [Reduce Overfitting By Combining Strategies]

“In statistics and machine learning, ensemble methods use multiple learning algorithms (trading strategies in our case) to obtain better predictive performance than could be obtained from any of the constituent (individual strategies) learning algorithms.”

A simpler example would be think of it as a voting system. Imagine 3 SPY strategies. In theory (and every individual case needs tested), if one strategy says long and the other two say flat (or short) then long is not a very confident position. On the other hand, if two or all three strategies say long then it is a more confident long entry.

It is also important to point out or imagine the market as a living breathing organism that is constantly evolving and dynamic in nature. So imagine having a strategy on Oil, Gold, Bonds and Stocks. Now you can get a better picture of what is happening in individual markets and how their combination or interaction can help predict what to do next in a stock index or the US dollar strategy, for example.

This is slightly different than intermarket strategies because intermarket strategies simply include ‘signals’ from secondary markets. Ensemble strategies are actually using the strategies themselves which undoubtedly carries more information than just the signals themselves.

Ensemble Strategies in the real-world and Finance

The first example, is about the Netflix challenge where Netflix offered a million dollar prize for the “best suggested movies to watch” algorithm. Jaffray Woodriff, founder and CEO of Quantitative Investment Management ($3B in AUM), competed in the contest and took 3rd! Woodriff is a big proponent of ensemble methods and mentions such in his Hedge Fund Market Wizards chapter of Jack Schwager’s great book. The team that actually won the contest was in a close race with the second place team and wound up running away with the first place prize by using an ensemble method applied to the other top 30 competitors’ models. I am trying to convey financially incentivized practicality here; the story comes from Ensemble Methods in Data Mining by Giovanni Seni and John Elder with a foreword by Jaffray Woodriff.

Ensemble Strategies and Trading

The two main things that plague trading strategies are

  1. Underfitting (bias)
  2. Overfitting (variance)

In the latest book sweeping the Quant world: Advances in Financial Machine Learning,  Ensemble Methods have their own dedicated chapter! In this chapter, the author Marcos Lopez de Prado (head of AQR’s machine learning division and formerly founded and led Guggenheim’s Quantitative Investment Strategies that had over $13B in assets) states, “An ensemble method is a method that combines a set of [weak] learners… in order to create a [stronger] learner that performs better than any of the indiviudal ones. Ensemble methods help reduce bias AND/OR variance”.

So ensemble methods can help reduce overfitting? That sounds desirable for every system trader I know!

Quick tips to keep in mind:

-Ensemble strategies will be better the more diverse the indiviudal strategies. For example, use one strategy that looks at volume, one that looks at price action, and one that looks at spreads or intermarket signals.

-Do not try and ensemble very poor individual strategies and expect miracle improvements. Simple ensemble methods such as bagging, cannot improve the accuracy of such poor systems. So you still have to put effort in designing the indiviudal strategies! Ensembling is not a shortcut that can turn dirt into gold; however, it can polish up some things very nicely.

-Finally, ensembling (with bagging) is more likely to be successful in reducing variance (overfitting) than reducing bias (underfitting). This attacks the BIGGEST problem we have as system traders and developers.

Ensemble Strategies and Trading

We can now select indiviudal strategies that we want to use in the overall simulation. This means we can build strategies that only take a position when another strategy has an existing position. This is a great way to create hedging strategies (more on this in a later post/video).

The above ‘signal’ (if used) would only enter a trade when our strategy ‘SPY Strategy3 Trend’ has an existing position. This is a simple way to build hedgers, or use confirmation from other strong systems.

If you mark strategies in your Build Alpha portfolio as ‘Active’ then they can be used in ensemble strategies. The below selected signal would only enter a position when at least 2 (any 2 of your active strategies) already has a position. You can also select all the ‘at least’ signals and have the software find the best combinations of your existing strategies to use in an ensemble strategy.

This new feature in Build Alpha gives system traders an unique advantage never before offered. We can now create ensemble strategies at the click of a few buttons and completely automate them.

How Can I Make Ensemble Strategies Only Using The SPY Strategies?

If you have a few diverse strategies in your portfolio and want to create an ensemble only using a few of them then make sure the strategies you want to include in the ensemble are the only ones marked active in your portfolio. Below I’ve only marked the SPY strategies as active in my portfolio (left photo). Then when I select at least 2 on the right photo it means it will trade whenever at least 2 of the SPY strategies are in a position.

How Can I Make Ensemble Strategies Only Using The Non-SPY Strategies?

Simply change the active strategies in your portfolio by unselecting the SPY strategies and selecting the others.

How Can I Make Ensemble Strategies Using All The Strategies From My Portfolio?

You guessed it… just mark all as active. Now all strategies in our portfolio will be considered for ensemble strategies in Build Alpha’s strategy building process.

Note the above green box with the 1004 + 1000 numbers. This means that I have selected 1000 technical signals to use in the strategy building process plus 4 ensemble signals. I have also selected 1000 technical exit signals to include in the strategy building process. Now Build Alpha will create the best strategies it can given your inputs. You can also right click to ‘require’ any signal (including ensemble signals) to force the software to use that signal in the strategy building process.

Small Accounts

Ensemble strategies are great for smaller accounts too. If you cannot afford to trade multiple strategies, but have created a few then you can still capture the information of all the strategies by ensembling them into one ensemble strategy. Then you can trade this ‘master’ strategy with a smaller account than trading all the indiviudal components. So better use of capital and less overfitting? Sounds good.

Ensembles: A Case Study

This is a simple case study showing the RSI2 strategy outlined in Larry Connors’s book Short-term Trading Strategies That Work. I have applied the strategy to the indiviudal equity index ETFs and then shown the results of two different ensemble strategies of these four strategies applied to SPY.

The first ensemble strategy takes a position in SPY whenever two of the four indiviudal RSI2 strategies have an existing position. It does not matter which two markets are trading at the moment, but it will trigger a SPY trade. This first ensemble strategy does better than three of the four indiviudal components on most metrics, but specifically profit and loss to drawdown ratio.

Furthermore, if we look at the second ensemble strategy that only trades SPY when the RSI2 strategy has an active trade in all four of the equity indexes AND SPY is trading above its 200SMA then we can improve the profit and loss to drawdown ratio to over 10! Significantly better than any of the indiviudal strategies.

In Summary...

Ensemble strategies combine multiple strategies to create a more powerful ‘master’ strategy. Ensemble strategies help reduce overfitting (curve fitting) which is the biggest plague us system traders and quant traders face. They have been shown in real-life trading financially motivated contests as well as with large hedge funds to be superior to simpler trading strategies and machine learning models. BuildAlpha can now help you build, employ and automate ensemble strategies with a few clicks. Private videos for users now up!

2018 Year End Update

It is becoming more and more commonplace for large quantitative firms to use ensemble methods.

The future (and even the present) of quantitative finance is the discovery and deployment of ensembles of many marginal edges – even weak predictors. These smaller edges will not attract academic or practitioner interest because they may not be sufficiently profitable on their own, but combined in an ensemble can be very powerful. It is even believed Renaissance Technologies had this breakthrough a while ago and that has manifested/propelled them into the greatest Hedge Fund of all time.

A small and anecdotal example is the year end performance of this popularized RSI2 trading strategy. The indiviudal strategy ran on the single markets resulted in

SPY -$17.5 per share traded

DIA +$0.24 per share traded

QQQ -$1.07 per share traded

IWM -$20.50 per share traded


Ensemble +$2.79 per share traded

An overall weak year for this strategy, but improved by the ensembling described above. Ensembles are just another tool in our arsenal with Build Alpha to build the trading portfolio we need! Cheers guys

A Strategy For Each Day of the Week [Seriously?]

A different strategy for each day of the week? Sounds crazy, but is it?

I have wrote about “Turnaround Tuesday” before: https://www.seeitmarket.com/turnaround-tuesday-wall-street-cliche-media-fiction-17013/

This simple strategy is actually at new highs since this was published over 10 months ago!

But what about other markets? Is it possible to just trade one market per day of the week (from open to next day’s open) on a specific day of the week and still do well? If you would have followed this simple “portfolio” below for the past 15+ years then yes.. you would have.  You wouldn’t even of had to sit in front of the screens like the rest of us degenerates..

Strategy 1: Short Crude Oil on Mondays

Simple strategy dating back to 2003. Short Oil on Monday’s open and cover on Tuesday’s open. Results based on a 1 lot.

Strategy 2: Turn Around Tuesday

If Monday was a down day (Monday’s close less than or equal to Monday’s open) then buy Tuesday’s open and sell on Wednesday’s open. Results based on 1 lot.

Strategy 3: Long Gold on Friday's. Weekend Risk.

Maybe in lieu or anticipation of bad weekend news releases, buying Gold on Friday’s open and holding over the weekend to exit on Monday’s open has produced the following based on 1 lot.

Not a bad portfolio equity curve, right?! One of the hidden ‘holy grails’ in trading is combining systems together to smooth the equity growth of the account. No one single super star out of any of these 3 strategies but combined their curve starts to look rather nice.

Sure these might not be alpha generators on their own but maybe they can be combined with some other price patterns, filters or targets and stops to become full strategies. They can certainly act as intermarket or multi-time frame filters for more complicated strategies.

For example, if you have a 60 minute mean reverting system that buys oil dips then maybe it should be turned OFF on Mondays! At the very least it is worth a look and this is all made very simple with Build Alpha – just point, click, test.

For example, did you know S&P500 has performed much better on even days than odd days? (April 6 is an even day).

Anyway, I just wanted to show some simple things that can be tested in Build Alpha and how sometimes the most simple ideas turn out to work the longest. Maybe because people think they’re too simple and that they can’t work? I don’t know.

Either way I have put some python code (and TradeStation code) in the private Build Alpha users’ forum to specify day of the week as I know a lot of you are trying to learn python so I figured I’d help out a bit. But day of week is all point and click options pre-built into Build Alpha’s signal library for those of you who could care less about learning to code! Happy Friday.

Cheers fellas,


Three Trading "Truths" Quantified

I want to discuss three trading “truths” that I often heard but when I finally got into testing ideas found them to be myths. These discoveries were instrumental in turning my trading around.

For those that know my story, it was not all roses and rainbows – what trading story is?!? I actually “learned” like a lot of traders from online sources, chat rooms, webinars, and eventually found the right circles to roam in after a LOT of trial and error. I was then lucky enough to land a job with a high frequency trading firm. I was quickly made to realized that much of what I thought trading “was” was most certainly false.

In an attempt to show me the light, a few of the quick trading axioms they wanted to disprove to me were simple “well known” trading ideas that have been around for years but were in fact large falsehoods. I quickly realized beyond this list of three examples there must have been many, many more “trading truths” that were causing my account harm and doing my trading aspirations a disservice.

I then realized the value in testing everything and quantifying my entire approach. It wasn’t until this point in my journey did I get out of the “rat race” of trading… the ups and downs, the barely consistent, always doing slightly better but not really getting anywhere trading most of the traders I meet today are going through.

I cannot stress the importance of testing everything and quantifying your edge. Investigate these trading axioms. Otherwise you will be stuck in randomness until your account random walks to 0. For more on my journey check out this podcast I did chatwithtraders.com/103 Below you will find my three favorite trading “truths” quantified.

Trading “Truth” #1 - Bearish Engulfing Candle

This one is great because many are familiar with Japanese candle stick patterns and the name implies such a negative move in price that one cannot help but to shift their bias to the downside. These candlestick patterns are often traders first introduction to technical analysis.

The pattern is defined as a bearish candle that “engulfs” the range of the previous day. I also like to look for a negative close and preferably a close below the previous session’s low. Here are a few pictures of bearish engulfing candles.

The truth is that this pattern has been one of the most bullish (not bearish) one day signals for the SP 500 over the past 15+ years. Did you know that the day following a bearish engulfing candle actually closes higher 61.72% of the time in the SP500 futures and 65.33% of the time in SPY ETF? The day following a bullish engulfing candle only closes higher 54.05% of the time in the SP500 futures and 51.70% of the time in SPY. You tell me why they’re named how they are!

Trading Truth #2 – Above a moving average is bullish and below is bearish

Most of the time this is true but it depends on the moving average, moving average length, and the market. I recently came across a few blogs that mentioned using a short term moving average as a sign to exit long market exposure and wait for sunnier days. In reality, this sounds great; however, analyzing the data this can be an extremely misleading “truth”.

Below is a chart plotting the equity curve if you would buy every close when the SP500 is BELOW the 8 period simple moving average (8SMA) and sell the next bar (repeating until above 8SMA). The second chart is if you were to buy every close when the SP500 is ABOVE the 8SMA and exit the next bar (repeating until below 8SMA). Yes, being below this moving average is actually better for long returns.

Of course this is not true for all markets and moving averages, as mentioned. I pointed this out in another post on SeeItMarket where I dissected popular stocks and different moving average lengths here: https://www.seeitmarket.com/testing-moving-averages-popular-stocks-etfs-16809/

The point is that these trading truths like “above a moving average is bullish” and “below a moving average is bearish” need to be quantified and tested. It is important to stop thinking trading truths can be generalized to every market, timeframe, indicator value, etc. and just verify them yourself and you’ll be much better off!

Trading Truth #3 – Overnight Exposure is Risky

Sure earnings announcements and large unexpected news announcements typically happen after market close. Does this mean we should avoid trading overnight, if possible? So many want to day trade and be flat on the close that they miss a lot of gains from the overnight session (sometimes all of them).

Below you will see 30 top stocks where I breakdown their returns in the day session compared to the overnight session. The blue line signifies if you bought the open and sold the close the same day (day session) and the orange line signifies buying the close and selling on the next day’s open (simulating overnight exposure). As expected, there are no generalities in trading truths. Some stocks exhibit that most, if not all, returns come from the overnight.

Don’t get me wrong there are certainly edges to be had from avoiding overnight exposure and there can also be value added by increasing overnight exposure. Again, I am just making the point to dig deep into the data and understand where the edge(s) actually live.

Quick note: You can easily test overnight holds in Build Alpha by setting max hold to 1 bar and setting entry to this bar close and exit to next bar open in the settings menu.

In conclusion

Not all trading truths are “truths”. There are many that still have value! The edge is not in these truths but in determining the difference between a truth and a “truth”. Most do not take the effort to investigate these trading axioms found in trading books, blog sites, chat rooms, etc. If I have one goal then it is to make traders realize the value in testing everything – it is something I wish I would have started earlier. It is a super power to quickly test these ideas and trade/act accordingly!

I think many traders shy away from digging into the data because it requires effort and they think it will be a hard and arduous journey before they find some gold. But now I think it is increasingly easier than many may think.

That’s why Build Alpha’s main goal was (and still is) to make this type of testing easier for those with no programming skills or those tired of crunching endlessly in excel. The game is changing and things like this can be easily tested now. Now trading just comes down to those who will put in the work and who won’t: this should greatly increase your odds for success if you’re part of the former group.

Using Python in Build Alpha [Creating Custom Signals]

Build Alpha and Python

As 2017 comes to an end, I want to introduce one more upgrade to BuildAlpha in its first year. As you know Build Alpha allows users to create, stress test, and even generate tradable code without ANY programming at all.

It also allows traders to use a custom drag and drop signal builder to create unique rules and signals to test alongside the pre-built Build Alpha signal library of nearly 5,000 signals – and growing (below is a picture of the drag and drop custom signal builder).

However, I have now added a python environment to give traders even more freedom. Traders now have the ability to code their own signals in python and test these signals in the Build Alpha strategy creation engine.

Again, you do NOT need ANY programming skills to use Build Alpha but if you WANT to you can now use python to create signals, but you do NOT have to as Build Alpha will work without. This is just an upgrade for the more sophisticated traders out there.

Python Example in Build Alpha

Below is a simple, 5-step walkthrough of how to create a signal using python and test it with other pre-built signals in Build Alpha. From the file menu, create a new custom signal which will open the window below. Set the type to ‘Python’.

Build Alpha then produces a simple to use template and we just need to set the variable file_name_base to the file we wish to do analysis on. Build Alpha will change this to whatever symbol(s) you have selected from the main interface at runtime. I have used the built-in Build Alpha data in the example below.

*Note* You can find the file path by right-clicking on your data file, selecting properties, and copy/pasting the path inside two quotes.

The only requirement is that we return a Signal list with the same length as data rows in our input file. This ensures there is a signal for every bar. Then simply save and access your custom signal from the main interface.

This new feature opens the door for what is possible in Build Alpha. Traders can leverage the power of python as well as BuildAlpha in extremely easy to use ways.

Moving Average Trading Strategy Python

Build Alpha’s python environment is a full python environment which means we can import external libraries. Let’s import the famous technical analysis library, talib, to create a moving average trading strategy with python.

Create a new indicator and add `import talib` at the top of the python file.

Python Import talib for SMA strategy

Install Python Library for Technical Analysis Indicators

If you do not have talib installed, then open a command terminal and navigate to your python directory. You can type ‘cmd’ into the start menu of most Windows devices. If you used the Build Alpha installer then talib is included. However, you can navigate to your python directory like this: 

Command Prompt for Python Install

Then install talib using the following prompt. Notice the hyphen in ta-lib.

Command Prompt for Python talib install

Next let’s calculate an 8-period moving average and create a signal that is true when the close price is below the 8-period SMA. Both code and screenshot are below.

def GetCustomSignal():


## Write signal calculation here


        global df1

        df1['SMA'] = talib.SMA(df1['Close'].values,8)

        Signal = [1 if c < sma else 0 for c,sma in zip(df1['Close'],df1['SMA'])]

`Moving Average Calculation Python Code talib

Then you can access this signal in the Custom list of the main Strategy Builder screen.

Custom Signals Build Alpha Interface

You can then combine it with any other Build Alpha signal or feature as if it were a built-in signal. This one of course is a built-in signal but made for a simple talib example.

Algorithmic Trading Python

Python is the fastest growing and most versatile programming language making it extremely attractive for quantitative traders and developers. Many algo traders prefer python due to its easy-to-read code and simple syntax. Python does not require code block brackets or semi-colons to end statements, but still is object oriented providing a great mix of ease and flexibility.

Python for algorithmic trading is growing every day with new libraries and popular libraries being updated. TALIB is the most popular library, but many more advanced libraries continue to pop up. Algo trading with python has never been easier.

However, connecting to exchanges, handling live price data, and coming up with trading ideas can be a daunting task. The best part about coding is coming up with new signals. Build Alpha takes care of the heavy lifting enabling python traders to simply do the fun part: signal creation.


Build Alpha now supports the ability to import custom signals via python. This is additional functionality for programmers that want to leverage the speed and ease of Build Alpha. Reminder that this is extra and NO programming is needed to use Build Alpha’s built-in signals or drag and drop custom signal builder.

It has been one heck of a year for Build Alpha’s development and there is still so much to do in 2018! Thanks for all those that support the software; I am looking forward to next year.


David Bergstrom Author Photo Trading Edge

David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.

Mean Variance Optimization [Portfolio Construction]

What is Mean Variance Optimization?

Here is the definition from Investopedia.com“Mean – Variance analysis is the process of weighting risk (variance) against expected return. By looking at the expected return and variance of an asset, investors attempt to make more efficient investment choices – seeking the lowest variance for a given expected return or seeking the highest return for a given variance level.”

It also assumes investors are rational and would choose the asset that had lower variance if the expected return of two assets were equal. I will assume you all are!

In layman terms, this test shows two things. First, if adding a strategy increases the overall risk: reward of your portfolio or not. Second, what are the appropriate weights each strategy in my portfolio should be assigned? This is certainly a crude explanation of mean-variance optimization, but this isn’t an academic blog.

Testing Mean Variance Optimization

This test can be done with either historical or “predicted” returns. We will assign random weights to each of the strategies in the portfolio; the sum of all the weights shall equal 1 (or 100% of allocated capital). After assigning weights to each of the strategies we can generate a risk-adjusted return value or Sharpe Ratio. This is the Sharpe Ratio had we traded each of these strategies with the weight we just assigned to each.

After thousands of tests assigning random weights, we generate a plot of the thousands of Sharpe Ratios. We are trying to find the best weightings to lower the variance in our portfolio while keeping the return as high as possible.  The graph’s legend or color bar shows the highest Sharpe Ratio possible. This portfolio contains the three “checked” strategies on the far left of the photo below and the highest Sharpe from this portfolio is 2.17.

We can then hover over the graph and view the weights that produced a specific Sharpe value or dot on the chart.  The idea here is we can see how to weight the strategies in our portfolio to give us the “optimal” or best risk-adjusted reward – which is our ultimate goal as trading system developers.

The red dot shows equal weights or how our portfolio would have performed had we traded each strategy with the same position size.

Does this strategy improve my portfolio?

Now to answer the question “does adding this strategy to my portfolio help or hurt expected performance?”.  In Build Alpha’s portfolio mode you can simply include or remove strategies and quickly see if the Sharpe Ratio increases or decreases. Below I have selected one additional strategy (see checks on the far left of the photo below) and you can see a Sharpe Ratio improvement from our original 2.17 to 2.50.

Adding new individual strategies will almost certainly “smooth” out the portfolio’s equity curve, but does it actually increase risk: reward? This test gives a simple, quick way to quantify the addition (or subtraction) of a strategy.

Here I have changed the ES (S&P500 strategy) in the portfolio. I unselected the top ES strategy and selected the other ES strategy. You can see the Sharpe Ratio worsening as it appears this portfolio only achieved 2.28 (vs. previous portfolio configuration of 2.50). This is a good strategy but just does not work as well with the other strategies in the portfolio as the original ES strategy we had included.

Mean Variance Optimization Summary

We want our money to be as efficient as possible and this test gives us another check if it is or not.  No point in trading a strategy that is not expected to increase our overall risk reward.

It is now much simpler to compare strategies or to decide whether or not to include a strategy into a portfolio or not. Much of trading comes down to making informed decisions that maximize risk:reward and running this type of analysis certainly increases the information at your disposal. It is now only one click away in portfolio mode.


PS: Build Alpha allows traders to import systems built outside Build Alpha for analysis and portfolio construction. So you can do all of this analysis on Build Alpha strategies, your own strategies, or any combination between.


David Bergstrom Author Photo Trading Edge

David Bergstrom – the guy behind Build Alpha. I have spent a decade-plus in the professional trading world working as a market maker and quantitative strategy developer at a high frequency trading firm with a Chicago Mercantile Exchange (CME) seat, consulting for Hedge Funds, Commodity Trading Advisors (CTAs), Family Offices and Registered Investment Advisors (RIAs). I am a self-taught programmer utilizing C++, C# and python with a statistics background specializing in data science, machine learning and trading strategy development. I have been featured on Chatwithtraders.com, Bettersystemtrader.com, Desiretotrade.com, Quantocracy, Traderlife.com, Seeitmarket.com, Benzinga, TradeStation, NinjaTrader and more. Most of my experience has led me to a series of repeatable processes to find, create, test and implement algorithmic trading ideas in a robust manner. Build Alpha is the culmination of this process from start to finish. Please reach out to me directly at any time.

Free Friday #20 – Time Windows

There has been a recent popularity regarding time windows and it is one I completely agree with! There are certain structural changes that happen throughout the 24 hour session and as a trader it is important to take note of these when designing a system or strategy (or just placing trades). For example, how is my strategy’s performance when Asia closes? How about when the US opens?

There are also some blatant market anomalies that still exist regarding time windows. The most obvious one, and often popularized by Eric Hunsader of Nanex (and others), is the S&P500 futures performance from 2am to 3am. This little one hour window looks like quite the edge – and has been persistent despite being publicly known for quite some time.

There are plenty of ways to use the time of the day to your advantage. For instance, only find trades/patterns that exist during a window of time where you might be at your desk. Another simple approach is to filter out unfavorable periods of the day to improve your performance (obviously proper testing methods important here).

My main point is… if you start paying attention to time windows you may be surprised as to what simple edges you’ll discover. It has certainly been a staple in my research for quite a while and knows many other successful traders who put a lot of emphasis on this.

I’ve recently upgraded Build Alpha to allow for intraday strategy creation and time window filtering (among other things). Inside BA you can now select (and create your own) time windows to use as input in the strategy creation engine. There is also a time of day test which allows you to select certain windows to see if it improves strategy performance.

Below is a simple strategy that shorts EURUSD (I used the futures contract) at 5:00 am EST every Wednesday, Thursday, and Friday morning. This idea simply holds the short trade for 3 hours.

These might not be standalone strategies, but can certainly give you a head start for continued research, act as a filter for other strategies or be paired with other ideas to create fully operational strategies.

Trading is extremely competitive. Looking at price alone is may be too naïve in today’s market; there is simply too much computing power looking at it. If you’re struggling to find quantified edges then maybe it is time (pun intended) to start pairing price action with time filters.

As always, thanks for reading. And please stay safe with all these hurricanes!


Thanks for reading,

Intraday Checks and Hidden Edges

What is an intraday check? It is a phrase I am trying to coin, but I will explain the concept here..

There are certain rules (mainly for intraday strategies) that I believe should be applied to each strategy to check if there are any “hidden edges” in the underlying strategy.

So what are these rules? And how can we test them? Well, Build Alpha now offers a test that allows users to test any intraday strategy to find out if it is as efficient as it can be or if there are any “hidden edges” within the strategy as I like to call it. Here are the additional intraday rules or checks.

  1. Time windows. Is the strategy improved by only trading between certain times of the day?
  2. A maximum number of trades per day. Is the strategy improved by limiting the number of trades per day it makes?
  3. Minimum P&L per day. Is the strategy improved by limiting the amount of money it is allowed to lose per day? For example, automatically turn the strategy off for the day once you’ve lost $500 today.
  4. Maximum P&L per day. Is the strategy improved by stopping the trading when an amount of money is earned per day? For example, automatically stop trading for the day once you’ve made $2,500 today.

Obviously, these tests are common and obvious ideas to look for hidden edges and strategy improvements – and of course, with all things, we need to use proper data analysis techniques (in vs out of sample, sample size, etc.) which I am not going to get into in this post as I have other posts regarding that.

The Intraday Checks test can be easily configured by clicking on the “Test Settings” button and configuring the right-hand side of the pop-up window seen below:

The coolest feature of this addition to the software is the ability to add any of the newly found rules to the generated code for any of the supported Build Alpha platforms. For example, if you find a rule (or few rules) that improves the original strategy then you can double-click the test result and BuildAlpha will automatically incorporate those rules when generating trading code for TradeStation, MultiCharts, Ninjatrader or MetaTrader.

Here is a photo after selecting (double-clicking) on a test result which would then add this test’s rules to the exportable code. The highlighted red (selected) strategy’s rules can be seen at the bottom of the graph.

Trading System

This selected strategy would add the rules

-Max Trades per day: 2
-Only take entries between 2:00 am and 12:00pm
-Stop trading each day after losses reach $1,000 per day

Finally, the histograms below the graph show the distribution of all the tests on the intraday rules. This is important to make sure you’re not just selecting an outlier but that the underlying distribution/simulation itself was strong. Again, this post is not meant to get into selection bias and other potential pitfalls as I have other content about that.

All in all, it is important to search every nook and cranny to make sure we’re not leaving money on the table or missing an easy edge or strategy filter. This test aims to help in that regard.

Thanks for reading


Improving Strategies

A crazy cool way to use Build Alpha. I have to admit that I did not come up with this idea, but it was suggested to me by a potential Build Alpha user.

He was wondering if Build Alpha could help come up with some rules of when he should avoid trading his existing strategy or even when to fade his existing strategy. Heck any improvement is a plus, right?

**Please note Build Alpha now accepts data in this format:  mm/dd/yyyy, hh:mm, open, high, low, close, volume, OI. Please refer to buildalpha.com/demo page for adding own data instructions**

*I say we found one strategy but we actually found tons that would be an improvement to his original strategy. Him and I only spoke specifically about one so that’s why in the video I slip and say we found one strategy. Did not feel like making a new video to clarify this minor point.*

He had a day trading system and compiled profit and loss results for that system in the following (Build Alpha accepted) format. Date, time, open, high, low, close, volume.  (*note BuildAlpha now accepts the time column as intraday capabilities are becoming fully operational*).

Below is his sample file. We purposely left the open (high and low) columns as all 0’s. The close column contains the end of day p&l from his original strategy.

We then set Build Alpha to have a maximum one bar holding period and to ONLY enter on the next bar’s open and to the ONLY exit on the next bar’s close. I will explain why this is in a minute.

We then chose the underlying symbol the original strategy was built on as market2. So for example, his original strategy trades ES (S&P500 Emini futures) so we only select Build Alpha signals calculated on Market2 which is set for ES.

So now if Build Alpha calculates a rule on ES-like close[0] <= square root(high[0] * low[0]) then we would “buy” the next bar’s open of market1 (again his results – which are 0) and “sell” the next bar’s close of his results which is the original strategy’s p&l for that day. This would essentially say that if this rule is true then go ahead with a green light to trade the original strategy the next day. If the rules are not true, then don’t trade the original strategy the next day. Ideally, we can find rules that increase risk-adjusted returns for the original strategy (which we did).

Now, what is even cooler is if we set Build Alpha to find short strategies we would essentially be “fading” his original strategy or finding rules of when to go opposite his original strategy.

Build Alpha found some good short/ “fade” rules to use as well. Here is an example that did quite well fading his original strategy (even out of sample – highlighted section).

After emailing him the results here is what he had to say in his email response:

“There are 2028 negative periods in my data with a gross loss of -1,217,880.26. That’s the theoretical maximum a short rule can achieve, if it were to find all losses. Your graph seems to show 380,000 short rule profits. That’s already 31% of all losses. If I don’t trade on these days, my net profit would go up by 380,000, a 46% increase.”

I thought this was a really unique way to use Build Alpha and I wanted to share. I think the same analysis can be done on strategies with longer holding periods too. I would just import daily marked to market results of the original strategy and Build Alpha would essentially find rules of when to hedge your strategy or fade it for a day or two. I think this is certainly a unique approach to add some alpha to performance.

Anyways, thanks for reading as always and keep a lookout for some MAJOR upgrades coming to Build Alpha very soon!



Thanks for reading,

$70,000 in profits in just 3 months. A Build Alpha testimonial

This is hands down the best email I have received since launching the BuildAlpha software a little over six months ago. It is a thank you note sent from a Build Alpha user, Madhur, who licensed the software back in March 2017 and has grown his account about $70,000 in that span (or about +55.92%).

Below is a photo of the email he sent and his account statements verifying his amazing first three months of trading while using the software.

What is amazing is that Madhur is/was a discretionary or hand trader! That’s right, he’s found a way to combine what he was already doing and the systematic edges that the Build Alpha software can find to further increase his OVERALL edge in the markets.

I love this story for so many reasons. First, it shows that finding a trading edge is vital regardless if you choose to automate your execution or not. Second, Build Alpha is a trading tool and not necessarily a system trading tool (albeit geared toward system traders no doubt). Third, Madhur found a unique way to incorporate the old with the new to make something better – a lesson for all traders (myself included).

I have received tons of emails of Build Alpha success and thank you notes, but none as specific as this one. It is hard to market user success without the proof otherwise you all would have your doubts (rightfully so) – but after receiving this I cannot help but feel proud and share.

Congrats Madhur, who can be found on twitter here: @MadhuryAlba, and the other successful Build Alpha users out there! Thanks for pushing my development of the software and continuing to support me with this pursuit.

Also, please read all the disclaimers. I am not guaranteeing that if you license the software and poof 3 months later you are up big. No one can promise anything in this game – I just wanted to share a story that put a smile on my face and makes all the development hours worth it!

Thanks for reading


Free Friday #19 – Long/Short Small Caps and June Update

This Free Friday, Free Friday #19, is a user submission! It is a long/short strategy for $IWM – the Russell 2000 ETF. Both the long and the short strategy only have two rules each and only hold for 1 day. Below I’ve posted the long strategy on the left and the short strategy on the right. Short edges have certainly been difficult to find over the past few years in the US equity indexes on a daily time frame, but one hopes they’ll pay for the effort when/if things turn south!

Both strategies were tested from 2002 to 2017 using 35% out of sample data. All performance is based on only a simple 100 shares per trade. *1 S&P500 futures contract is equivalent to about 500 $SPY shares for reference*

There is also $SPY (green plot) and $TLT (gold plot) plotted to see how the strategies would have performed on these markets as well; the strategy maintains profitability in both cases.

The long strategy rules are simple and all trades exit at the next day’s open.

  1. Day number is greater than 5. Today is June 30, 2017. Today’s day number is 30.
  2. High[3] <= Low[7]

The short strategy rules are simple as well and all trades exit at the next day’s open.

  1. Close[3] > Low[6]
  2. Close[0] > 8 Period Simple Moving Average

Below there is a photo of the long/short equity performance for this simple portfolio.

I also want to add an update to some of the Free Friday strategies. Things were pretty quiet for most of the futures strategies other than the equity index strategies this month.

Strategies #5, #6, #16 were the only futures strategies that traded so I wanted to show their June performance below.

Nasdaq #5: +$1,640.00
Russell Futures #6: +680.00
S&P500 Futures #16: +862.50

Again, all are just trading 1 contract for demonstration purposes and were posted publicly months ago. You can see the strategies on twitter here: @dburgh

Thanks as always and have a Happy Fourth of July,


Thanks for reading,

Free Friday #18 – Building a Strategy with Open Interest

What is Open Interest?

Open Interest is just the total number of outstanding contracts that are held by market participants at the end of each day. All derivatives have Open Interest. That is, both futures and options have their own Open Interest.

For example, the March SP500 e-mini futures contract can have its own Open Interest while the March 4000 Strike Call option can also have an its own Open Interest.

Open Interest is a proxy for cash flowing into the market. If Open Interest increases, then more money is moving into the market whereas Open Interest declining could be seen as cash leaving the market.

How is Open Interest Calculated?

Open Interest is calculated by adding all the contracts from opened trades and subtracting the contracts when a trade is closed.

For example, Steve, Chris, and Clay are trading the same futures contract. If Steve buys two contracts to open a long trade, open interest increases by two. Chris also longs four contracts, thus increasing open interest to six. If Clay puts on a short trade of three contracts, open interest again increases to a new total of nine.

Open Interest would remain at nine until any traders exit positions, which would cause a decline in open interest. If Steve sells both of his contracts and Chris exits two of his four contracts, then open interest would decrease by four from nine to five.

What is the difference between Open Interest and Volume?

Both are similar as they count total contracts traded. However, volume is a count of all traded contracts and open interest is a total of the contracts that remain open.

If a trader buys one contract, then both volume and open interest are one. If the trader sells the contract, then volume is two and open interest is zero. Volume is a running total of all transactions and Open Interest is the total of all open positions.

Is higher or lower Open Interest better?

Better is relative to your current market view or position. However, typically higher open interest is good because it signals more interest in a particular contract or strike price and may also signify that there is more available liquidity (i.e., exiting shall be easier and you may experience less slippage).

On the other hand, lower open interest may be a positive too. If the market moves or news comes out in favor of your existing position, then many traders may need to pile into your same trade pushing price in your direction.

When in doubt, test your ideas on whether higher or lower Open Interest is better. That is what Build Alpha is for! Stay tuned for an example strategy later.

What is an Open Interest strategy? What is an Open Interest trading strategy?

A trading strategy that relies on Open Interest as an input signal or filter can be considered an Open Interest trading strategy. For example, if Open Interest rises by x% then buy or if Open Interest is greater than the N-day average, etc.

There are a ton of creative ways to incorporate Open Interest data into your algo trading strategy development process. Creativity is often a source of alpha!

Open Interest Strategy Example

As always, happy Friday!

This week I was asked by a Build Alpha user if he could build strategies using a contract’s Open Interest. Open Interest is just the total number of outstanding contracts that are held by market participants at the end of each day. So, it is intuitive that as more contracts are opened or closed then it might be telling of how traders are positioning.

This is a detailed and advanced post. Build Alpha is all point and click, but this is certainly a way more advanced blog post showing how a more sophisticated user can utilize the software.

I have to admit this is not something I have looked at previously so I was quite intrigued, but I pulled some open interest data from TradeStation and saved it.

Structing Open Interest Data

I then went on to create columns I – M below. Columns I-L are momentum measures (N period change) of Open Interest. For example, column J holds the Open Interest change over the past 5 days. Column K holds the Open Interest change over the past 10 days. Column N just holds the 3-bar sum of the 1-period momentum of Open Interest. The data can be seen below opened in Excel (I know who uses Excel anymore).

Build Alpha Custom Open Interest Data Import in Excel

In order to use the above data in BuildAlpha, we need to format two separate files. First, we need to create a date, open, high, low, close, volume file of the actual S&P500 futures data (columns A, C, D, E, F, G). I copy and pasted those columns to a new sheet and then reformatted the date to YYYY/MM/DD, removed the headers, and saved it as a .csv file. Pictured below…

Excel Open Interest Data formatted for Build Alpha import

I then copy and pasted our dates and custom Open Interest data to a new excel sheet. This time instead of having the date, open, high, low, close, the volume we’ll use (copy) the date, 1-period OI change, 5-period OI change, 10-period OI change, 20-period OI change, 3 bar sum of OI as our six columns.

Open Interest Data in Excel formatted for Build Alpha

We can now pass this data into Build Alpha and build strategies using the Intermarket capabilities. However, in this case, our intermarket or Market 2 will be this custom open interest data and not some other asset.

Build Alpha Import Custom Data
Build Alpha custom symbol selection

Video Examples

The two videos below show exactly how I did this process in case you didn’t follow my Thursday night, two glasses of scotch deep, blog writing.

Build Alpha Open Interest Strategy Example

There is a different strategy displayed in the video above, but I promised some guys on twitter I’d share the strategy I posted Thursday. So below is the actual Free Friday #18. It holds for one day and trades when these conditions are true:

  1. Momentum(OpenInterest,20)[0] <= Momentum(OpenInterest,20)[5]
  2. Momentum(OpenInterest,20)[0] > Momentum(OpenInterest,5)[1]
  3. Momentum(OpenInterest,10)[0] > Momentum(OpenInterest,10)[1]
Open Interest Strategy Equity Curve in Build Alpha

**S&P500 Futures strategy built on open interest data only and tested across Nasdaq, Dow Jones, and Russell Futures. Results just based on 1 contract**

So the last rule in Build Alpha would appear as Low2[0] > Low2[1] or translated as the low of Market 2 is greater than the low of Market 2 one bar ago. However, if you remember we created a custom data series for Market 2 and in the low column, we inserted the 10-period momentum of open interest!

Like I said this is a confusing post, but a really neat idea of how creative you can be with this software. The possibility of things we can test are immense.

Furthermore, when Build Alpha calculates RSI or Hurst, for example, using the close price of Market 2 (our intermarket selected) it will actually calculate RSI or Hurst on 20 bar momentum of the Open Interest (what we passed in for the close column)! You can also use the custom indicator/rule builder on these custom data columns.

You can also run strategies built on custom data like this through all the robustness and validation tests as well.

All in all, thanks for reading. I thought this was a cool idea taking system development to a whole new level.



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Thanks for reading,