Drafting a Business Plan

The first business plan I wrote was a basic outline for a small residential real-estate venture.  It detailed the property, the company equity in the property, re-curing expenses, estimated value of the property, competitive rental market data and expected cash flow.  This simple, one-page, business plan helped secure a $10,000 private loan, that has hence been repaid.  This business is still operating successfully and profitably.

Putting together a business plan for a start up is a different matter.  The financials are not there yet, and financial forecasting is at best a guess.  Instead the business plan must focus on  ideas and products that serve to fill a gap in the target market.   It must also demonstrate why this company and this product is well-positioned to fill that market need.   Next, I’ll strive to write a start up business plan…

Sigma1 Financial: A Business Plan for Revolutionizing Financial Portfolio Software.

The Market —  Sigma1’s market analysis reveals a stunning gap in the B2C financial software space.  The exists plenty of portfolio analysis software, but nothing that is truly portfolio-optimization software.  I refer the reader to two prime examples:  1) Quicken Premier 2012 (R) and 2) Financial Engines (R).  Both tools help investors manage and analyze investment portfolios.  They help with tracking asset allocations.  Financial Engines goes further by providing portfolio advice on increasing or decreasing risk level and changing allocations between the following asset classes: cash, bonds, large-cap stocks, mid/small-cap stocks, and international stocks.  In some cases Financial Engines partners with other firms and recommended changes can be implemented automatically.

Competing products tend to focus on broad market sectors and have little to no support for individual stocks and non-traditional-asset-class ETFs (such as gold ETFs, sector ETFs, convertible securities ETFs).

Market analysis of the B2B space is more challenging because publicly available product data is scarce.  Nonetheless, in online social media conversations with investment professions, several features of Sigma1 software appear to be unique.  For now market analysis of the B2B space is an ongoing process.

Core Product(s) — The Sigma1 Financial Engine, presently code-named HAL0 (HAL zero), is based, in part, on heuristic modeling, machine learning, and evolutionary algorithms.  HAL0 has gone through rigorous alpha testing and has proven itself to be very robust for alpha code.  Surrounding the HAL0 core are both traditional and proprietary financial heuristic and quantitative investment models.  These models have been transformed into utility functions that plug into the HAL0 optimization engine.  Additionally, there are scripts and add-ons that enable 2-D and 3-D data visualization using standard Open Source tools such as gnuplot.

Beyond the Products:

The software developer:  I have been coding and investing since I was ten years old.  In college (undergrad) I earned a degree in Electrical Engineering with a minor in Computer Science, graduating with a 3.97 GPA.  After my undergrad work I developed electrical engineering software for Hewlett Packard, Agilent Technologies, and Intel Corporation.  I lead software development on a 5-person team that created the “silicon construction” engine used by 200+ engineers in the R&D lab.

While working and HP and Intel, I took graduate-level coursework in both Finance and Electrical Engineering.  During that coursework, my partner and I created software that used EA and heuristic methods to quickly solve difficult non-linear engineering problems.  It was years later, that I realized these methods could be adapted to optimize financial portfolios… using not only classical modern portfolio theory (MPT), but also other methods beyond MPT.

I also manage a proprietary trading fund within Balhiser LLC and have written over 150 investing articles posted at balhiser.com.

Software Infrastructure and Development Model:  There is a crucial difference between undisciplined “coding” and real software development.  Both can create software that works in the present moment.  A structured software development model, however, creates software with a future.

Sigma1’s HAL0 software development environment (SDE) includes a revision control system, software regression tests, unit tests and tailored software testing support  and debug tools.  Some of the regression tests required special effort to work with the (pseudo-random) algorithms used in the software.  Careful use of srand() and rand() calls allows HALo to maintain robust regression testing capability.

Further, a revision history and log, dates back to day 1 of software development.  It charts what bugs were found and how they were fixed.  It explains what tradeoffs were made and why.  The revision history and comments in the software suggest possible improvements.

The HALo SDE makes it much easier to add and test both run-time improvements and new features.  And, it would allow other software developers to more quickly come up to speed on the code.  This would allow new developers to collaborate on or even take over HAL0 software development.

Marketing, Branding, Company Structure:   Currently Sigma1 and HALo IP and assets are held within Balhiser LLC.  Among these assets are approximately 40 registered domain names, about 20 of which are suited towards portfolio optimization software.  My goal is to secure US trademarks on one or more of these names.  Obviously overt disclosure of these would be unwise at this time.

Having learned that building web and social media awareness is not an overnight process, I have begun building that online presence using a variety of avenues, including sigma1.com.  This process in is its early stages, but Googling “Sigma1 Financial Software” or “Financial Software Heuristics” produce page 1 results.

The Business Model — The Sigma1 Financial Software model includes both B2B and B2C components.   The B2B model is centered around leasing the portfolio optimization engine and software add-ons to money managers, institutional investors and/or investment advisers.  The optimization is not limited to portfolios alone, but can also optimize funds and proprietary-trading accounts.  Along with software leasing fees, businesses are likely to require training in the use of software.  Limited training could be negotiated as part of the software lease agreement, however additional training will also be a revenue source.  Finally consulting and custom-feature development may be additional revenue sources to the business.

The B2C component of the business model is currently planned to be completely internet-centric.  A very limited free online version can serve 2 purposes.  1) As a marketing tool to induce users to pay for a full-featured subscription-based model, 2) possibly, as a source of ad revenue.  A full-featured paid-subscription B2C version would be ad-free and feature larger portfolios and greater investment modeling, optimization, and visualization features.


* Product names, logos, brands, and other trademarks featured or referred to within this document are the property of their respective trademark holders.


New Perspectives on Portfolio Optimization

Portfolio Risk/Reward Contours

Risk/Reward Contours for 100 Optimized Portfolios

Building superior investment portfolios is what money managers are paid to do. As a fund manager, I wanted software to help me build superior, positive-alpha portfolios.

Not finding software that did anything like I wanted, I decided to write my own.

When I build or modify a portfolio I start with investment ideas. Ideas like going short BWX (international government debt) and long JNK (US junk bonds). I want some US equity exposure with VTI and some modest buy-write protection through ETB. And I have a few stocks that I believe are likely to outperform the market. What I’d like is portfolio software that will take my list of stocks, ETFs, and other securities and show me the risk/reward trade off for a variety of portfolios comprised of these securities.

Before I get too far ahead of myself, let me explain the above graphic. It uses two measures of risk and a proprietary measure of expected return. The risk measures are 3-year portfolio beta (vs. the S&P500), and sector diversification. This risk measures are transformed into “utility metrics”, which simply means bigger is better. By maximizing utility, risk is minimized.

The risk utility metrics (or heuristics) are set up as follows. 10 is the absolute best score and 0 the worst. In this graph a beta of 1.0 results in a beta “risk metric” of 10. A beta of infinity would result in a beta risk metric of 0. For this simulation, I don’t care about betas less than 1, though they are not excluded. The sector diversification metric measures how closely any portfolio matches sector market-cap weights in the S&P 500. A perfect match scores a 10. The black “X” surrounded by a white circle denotes such a perfectly balanced portfolio. In fact this portfolio is used to seed the construction of the wide range of investment portfolios depicted in the chart.

On thing is immediately clear. Moving away from the relative safety of the 10/10 corner, expected returns increase, from 7.8% up to 15%. Another observation is that the software doesn’t think there is much benefit in increased beta (decreased beta metric) unless sector diversification is also decreased.  [This is the software “talking”, not my opinion, per se.]

The contour lines help visualize the risk tradeoffs (trading beta risk for non-diversification risk) for a particular expected rate of return.  The pink 11% return contour looks almost linear — an outcome I find a bit surprising given the non-linear risk-estimation heuristics used in the modeling.

For all that the graphic shows, there is much it does not.  It does not show the composition or weightings of securities used to build the 100 portfolios whose scores appear.  That data appears in reports produced by the portfolio-tuner software.  The riskiest, but highest expected-return portfolios are heavy in financials and, intriguingly, consumer goods.  More centrally-located portfolios, with expected returns in the 11% range, are over-weighted in the basic materials, services (retail), consumer goods, financial, and technology sectors.

Back to the original theme: desirable features of financial software — particularly portfolio-optimization software.  For discussion, let’s assign the codename HAL0 (HAL zero in homage to HAL 9000) to this portfolio-optimizing software.  I don’t want dime-a-dozen stock/ETF screeners, but I do want software that I can ask “HAL0, help me build a complete portfolio by finding securities that optimally complements this 70% core of securities.”  Or “HAL, let’s create an volatility-optimized portfolio based on this particular list of securities, using my expected rates of return.”  Even, “HAL, forget volatility, standard-deviation, etc, and use my measures of risk and return, and build a choice of portfolios tuned and optimized to these heuristics”.

These are things the alpha version of HAL0 can do today (except for understanding English… you have to speak HAL’s language to pose your requests).  The plot you see was generated from data generated in just under 3 hours on an inexpensive desktop running Linux.  That run used 10,000 iterations of the optimization engine.  However 100 iterations, running in a mere 2 minutes, will produce a solution-space that is nearly identical.

HAL0 supports n-dimensional solution spaces (surfaces, frontiers), though I’ve only tested 2-D and 3-D so far.  The fact that visualizing 4-D data would probably involve an animated 3-D video makes me hesitate.  And preserving “granularity” requires an exponential scaling in time complexity.  Ten data points provides acceptable granularity for a 2-D optimization, 100 data points is acceptable for 3-D, and 1000 data points for 4-D.  Under such conditions the 4-D sim would be a bit more than 10x slower.  If a granularity of 20 is desired, the 3-D sim would be slowed by 4X, and a 4-D optimization by an additional 8X.   I have considered the idea that a 4-D optimization could be used for a short time, say 10 iterations and/or with low granularity (say 8),  and then one of the utility heuristics could be discarded and 3-D optimization (with higher depth and granularity )could continue from there… nothing in the HALo software precludes this.

HAL0 is software built to build portfolios.  It uses algorithms from software my partner and I developed in grad school to solve engineering problems– algorithms that built upon evolutionary algorithms, AI, machine learning and heuristic algorithms.  HAL0 also incorporates ideas and insights that I have had in the intervening 8 years.  Incorporated into its software DNA are features that I find extremely important:  robustness, scalability and extensibility.

Today HAL0 can construct portfolios comprised of stocks, ETFs, and highly-liquid bonds and commodities.   I have not yet figured out a satisfactory way to include options, futures, or assets such as non-negotiable CDs into the optimization engine.  Nor have I implemented multi-threading nor distributed computing, though the software is designed from the ground up to support these scalability features.

HAL0 is in the late alpha-testing phase.  I plan to have a web-based beta-testing model ready by the end of 2012.

Disclaimer:  Do not make adjustments to your investment portfolio without first consulting a registered investment adviser (RIA), CFP or other investment professional.



Financial Software: Heuristics Explained

Software visualization

Abstract Visual of Software

A Baseball Analogy

Imagine you’re the general manager of a Major League ball club.  Your primary job is to construct (and maintain) a team of players  that will win lots of games, while keeping the total player payroll as low as possible.  When considering a hypothetical roster a baseball GM has two primary objectives in mind:

  1. Total annual payroll (plus any associated “luxury tax”)
  2. Expected season wins (and post-season wins)

These objectives can also be called heuristics — rules of thumb to help find solutions to complex problems.   These heuristics can be turned into numbers (quantified) by creating cost functions or utility functions.  Please don’t let all of this jargon disembolden you; we are merely talking a little baseball here.

The cost function function for payroll is just that… the total annual salaries for a proposed roster.  It is called a cost function because cost is something we are trying to minimize.  Expected wins is called a utility function, because utility is good, and we want to maximize it.

Now, accurately predicting number of wins for a hypothetical (or real) roster of players is a real challenge.  Every scout and adviser is going to have his or her own ideas or heuristics.  Just watch Moneyball to see what I mean.  To turn any given roster into a utility score a GM could write a proposed roster on a whiteboard and point-blank ask each advisory “How many wins will this team produce?”  The GM could average these predictions and, boom!, that’s an utility function.  The GM could also hire a computer scientist and statistician to code up a utility function for any proposed roster relying on a chosen set of stats.

Either way, now the GM has can evaluate any proposed roster based on two metrics: cost and wins.  These data can be plotted, and quickly patterns will emerge.  Some proposed rosters will be both more expensive and less “winning” than others.  These rosters are said to be dominated, and they can be removed from consideration.  Once all the dominated rosters are eliminated, what remains is a series of dots that form a curve.  As one moves up that curve, one finds more winning, but more expensive rosters.  Moving the other way, the payroll cost is less, but the expected wins decrease.  This curve resembles what financial folks call an efficient frontier — the expected risk/reward tradeoff for an optimized portfolio selected from a basket of securities.

Back to Portfolio Optimization Software

The baseball analogy above tries to explain mathematical concepts without resorting to math.   OK, I did use a few math words, but no equations!

There are several differences between a baseball roster and an investment portfolio.  Key differences from an investment portfolio are:  1) You can own multiple shares of a stock or ETF (but have only 1 of any player),  2) You can trade stocks/ETFs virtually whenever you want.

Nonetheless, the baseball analogy is useful in illustrating what Sigma1 Software will be able to do for fund managers and investors.  Instead of building a baseball roster, you are building an investment portfolio.  In the classic “CAPM” investing model, the cost function is standard deviation (risk), and the utility function is expected returnHistorical standard deviation is easy to compute, but expected return is much harder to accurately compute.

Now, if you are an active fund manager, you probably have in-house analysts paid to help you pick stocks (just like GM’s have scouts).  But scouting reports from analysts do not a portfolio make… even if your analysts are giving you positive-alpha stock picks. A robust asset allocation strategy is necessary to build a robust portfolio out of your chosen list of securities.

The Vision for Sigma1 Portfolio Software

A Vision for Financial Professionals

It started with the desire to create software that would allow me to build a better portfolio for my proprietary trading fund — Software that could optimize portfolios using heuristics, cost functions, and utility functions of my own choosing.   I wanted to create portfolio software for investment managers that:

  • Allows them to select their own list of securities (or chosen dynamically from all investable securities)
  • Takes advantage of one or more “seed portfolios” if desired
  • Allows proprietary heuristics, cost functions, market models, etc. to plug seamlessly into the optimization engine
  • Isn’t limited to linear or Gaussian risk-analysis measures
  • Runs in minutes or hours, not days
  • Is capable of efficiently utilizing distributed and parallel computing resources — Scalability

A Vision for “Retail” Investors

For retail investors, the general investing public, I envision scaled-down versions of the professional portfolio optimization software.  The retail investor software will run as an application on a web server.  A free version will provide portfolio optimization for a small basket of user-chosen securities, perhaps limiting portfolio size to 10.   A paid-subscription plan will offer more features and allow retail users to build larger portfolios.

To keep the software easy to use, a variety of ready-to-use heuristics will be available.  These are likely to include:

  • Standard deviation
  • Historic best-year and worst-year analysis
  • Beta (versus common indices)
  • Diversification measures (e.g. sector, market-cap)
  • Price-to-earnings
  • Proprietary expected-return predictors

The Business of Financial Business

Personally the easiest part of the financial software business is software development.  I have been involved with sales before and feel reasonably confident about this aspect of the business.  The primary challenge for me is marketing.

Sales is a face-to-face process.  Software development is either a solo process or a collaborative process usually involving a small group of developers.  Marketing is very different.  It is a one-to-many (or few-to-many) situation.  Striking a chord with the “many” is a perpetual challenge because the feedback is indirect and slow.  With marketing, I miss the face-to-face feedback and real-time personal interaction.

Knowing that marketing is not my strongest point, I have put extra effort into SEO, SEM, social media, and web marketing.  Over the past couple weeks I have purchased about 20 new domains.  Market and entrepreneurial research has shown me that a good idea, a good product, and a good domain name are not sufficient to achieve my business goals.  I realize that solid branding and trademarks are also important.

As a holder of 4 U.S. patents, I understand the importance of IP protection.  However, I am ideologically opposed to patents on software, algorithms, and “business processes.”   Therefore I feel that I must focus on branding, trademark protection, trade-secret protection, and copyright protection.

My redoubled marketing efforts have been exhausting and I hope they will pay off.  Next I plan to get back to software creation and refinement.