QuantDesk® Machine Learning Forecast

for the Week of December 11th, 2017

Putting Your Strategy To The Test Before Risking Capital

Erez Katz writes about Reducing Turnover Through Quantitative Research

by Erez Katz, CEO and Co-founder of Lucena Research.

The process of designing a machine learning algorithmic strategy is predicated on defining, testing and refining an investment hypothesis from the past with the assumption that some knowledge acquired historically can be carried forward. Ideally, when you wish to test your strategy one last time before risking capital you want to test your strategy by paper trading it on a roll forward basis. Why not rely solely on your backtests? Well, backtesting is an important validation tool but it has a few pitfalls that could present unrealistically favorable outcomes.

How Does Forward Trading Differ From Backtesting?

The advantage of backtesting is its ability to roll back time and rapidly simulate your strategy over a prolonged timeframe spanning a wide array of market conditions. However, there are quite a few inherent challenges with backtesting. Admittedly, many can be mitigated with discipline and good practices.

  1. Survivor-bias free data - Dead stocks which existed in the past should still be considered when the backtest spans the periods in which they were alive. Rolling back time based only on the surviving constituents that are alive today could unfairly present an unrealistic, rosy outcome.
  2. Transaction costs and slippage – It’s important to consider the various transaction costs that are associated with buying or selling a stock. In particular brokerage fees, short borrowing cost and most importantly, slippage. Slippage is the negative price impact a trader would experience on the price of a security. Large (and in many cases smaller) transactions would most likely negatively affect the actual purchase or sell price of an asset. Ignoring transaction costs and market impact would present a deceptively favorable outcome.
  3. Overfitting, curve fitting and selection bias – It’s important to note how easy it is to present a compelling yet unsustainable backtest due to a lack of a well-defined research process. A quant needs to separate the periods in which a strategy is formed, trained, modeled, validated and ultimately backtested. Separating the various timeframes ensures that preexisting knowledge does not carry over between research periods. For example, by creating 1000’s of brute-force backtests on the same period, a quant will most likely end up with a wonderful backtest, but without validating it against a new timeframe (holdout period) the strategy is most likely destined to fail.
  4. Prior knowledge bias – This is the most difficult one to mitigate because a quant can unintentionally insert prior knowledge into the strategy’s hypothesis. For example, knowing that the market has been rolling higher with low volatility since the 2008 financial crisis, your strategy could erroneously be tested only for high-risk long only positions.

By rolling your strategy live with a small amount of money or via paper trading simulation, many of the challenges above would be dramatically mitigated or completely eliminated.

How Does QuantDesk® Support Forward Trading?

On QuantDesk, the migration from backtesting to forward trading is intuitive and natural. Pressing one button from a backtest result screen will ready the execution of the backtest rules on a roll forward basis. All that’s required is to supplement the date from which you wish to establish your forward trading simulation.

Image 1: Create a paper trading roll forward strategy.

At Lucena, we’ve put significant effort into building a paper trading execution platform. We support all common transaction types, including scheduled transactions, trailing stop loss/target gain, order cancel order (OCO) and more.

Image 2: QuantDesk Portfolio’s Trade Blotter – notice a mix of two types of order status – executed and scheduled orders.

More importantly, we provide a complete audit trail of all transactions (including dividends, split and reverse splits, as well as other special cash treatments) that impacted the portfolio historically and on a roll forward basis.

Image 3: Full report showcasing a complete audit trail of positions per day and all transactions per day.


Forward trading is an important step a serious investor should consider before ultimately deploying capital. Many unfortunately skip this phase as they find it difficult to implement given the complexities of machine learning-based investment strategies. At Lucena, we have gone to great lengths to make the transformation from a backtest into a paper trading simulation simple. Moving one step further to a fully automated deployment is as simple as connecting to a broker-dealer via FIX interface.

I welcome you to try it yourself and take advantage of our unique discount offered to subscribers who sign before year-end.

For more details please click here: QuantDesk® Sale

Strategies Update

As in past weeks, I want to briefly update you on how the model portfolios and the theme-based strategies we covered recently are performing.

Tiebreaker – Lucena’s Long/Short Equity Strategy - YTD return of 15.23% vs. benchmark of -4.70%
Image 1: Tiebreaker YTD– benchmark is VMNIX (Vanguard Market Neutral Fund Institutional Shares)
Past performance is no guarantee of future returns.

Tiebreaker has been forward traded since 2014 and to date it has enjoyed remarkably low volatility and boasts an impressive return of 53.80%, low volatility as expressed by its max-drawdown of only 6.16%, and a Sharpe of 2.00! (You can see a more detailed view of Tiebreaker’s performance below in this newsletter.)

BlackDog – Lucena’s Risk Parity - YYTD return of 22.01 % vs. benchmark of 13.92%

We have recently developed a sophisticated multi-sleeve optimization engine set to provide the most suitable asset allocation for a given risk profile, while respecting multi-level allocation restriction rules.

Essentially, we strive to obtain an optimal decision while taking into consideration the trade-offs between two or more conflicting objectives. For example, if you consider a wide universe of constituents, we can find a subset selection and their respective allocations to satisfy the following:

  • Maximizing Sharpe
  • Widely diversified portfolio with certain allocation restrictions across certain asset classes, market sectors and growth/value classifications
  • Restricting volatility
  • Minimizing turnover

We can also determine the proper rebalance frequency and validate the recommended methodology with a comprehensive backtest.

Image 2: BlackDog YTD– benchmark is AQR’s Risk Parity Fund Class B
Past performance is no guarantee of future returns.

Utilities - Large-Cap Based Actively Managed - 17.74% of the benchmark!!!

I wrote about utilities last year in an attempt to demonstrate how Lucena’s technology can be deployed to identify fixed income alternatives. Since November 2016 we have been tracking our utilities portfolio, and it has been performing exceptionally well in both total return and low volatility -- well ahead of the S&P and its benchmark, the XLU.

Image 3: Utilities based strategy– captured since November of 2016. Benchmark is XLU – Utilities select sector SPDR
Past performance is no guarantee of future returns.

Industrials - Large-Cap Based Actively Managed - YTD Return of 25.58% vs. benchmark of 17.34%

I wrote about an industrial-centric portfolio in January this year. This portfolio was designed to anticipate the administration’s strong desire to invest in infrastructure. The portfolio identifies a well-diversified industrial stock set to track and outperform the XLI (its benchmark).

Image 4: Industrials-based strategy– captured since January 27, 2017 (covered during that week’s newsletter). Benchmark is XLI – Industrials select sector SPDR ETF.
Past performance is no guarantee of future returns.

Forecasting the Top 10 Positions in the S&P

Lucena’s Forecaster uses a predetermined set of 10 factors that are selected from a large set of over 500. Self-adjusting to the most recent data, we apply a genetic algorithm (GA) process that runs over the weekend to identify the most predictive set of factors based on which our price forecasts are assessed. These factors (together called a “model”) are used to forecast the price and its corresponding confidence score of every stock in the S&P. Our machine-learning algorithm travels back in time over a look-back period (or a training period) and searches for historical states in which the underlying equities were similar to their current state. By assessing how prices moved forward in the past, we anticipate their projected price change and forecast their volatility.

The charts below represent the new model and the top 10 positions assessed by Lucena’s Price Forecaster.

Image 5: Default model for the coming week.

The top 10 forecast chart below delineates the ten positions in the S&P with the highest projected market-relative return combined with their highest confidence score.

Image 6: Forecasting the top 10 position in the S&P 500 for the coming week. The yellow stars (0 stars meaning poorest and 5 stars meaning strongest) represent the confidence score based on the forecasted volatility, while the blue stars represent backtest scoring as to how successful the machine was in forecasting the underlying asset over the lookback period -- in our case, the last 3 months.

To view a brief video of all the major functions of QuantDesk, please click on the following link:
QuantDesk Overview


The table below presents the trailing 12-month performance and a YTD comparison between the two model strategies we cover in this newsletter (BlackDog and Tiebreaker), as well as the two ETFs representing the major US indexes (the DOW and the S&P).

12 Month Performance BlackDog and Tiebreaker
Image 8: Last week’s changes, trailing 12 months, and year-to-date gains/losses.
Past performance is no guarantee of future returns.

Model Tiebreaker, Lucena's Active Long/Short US Equities Strategy:

Active Long/Short US Equities Strategy
Tiebreaker: Paper trading model portfolio performance compared to Vanguard Market Neutral Fund since 9/1/2014. Past performance is no guarantee of future returns.

Model BlackDog 2X: Lucena's Tactical Asset Allocation Strategy:

model portfolio performance compared to the SPY and Vanguard Balanced Index Fund
BlackDog: Paper trading model portfolio performance compared to the SPY and Vanguard Balanced Index Fund since 4/1/2014.
Past performance is no guarantee of future returns.


For those of you unfamiliar with BlackDog and Tiebreaker, here is a brief overview: BlackDog and Tiebreaker are two out of an assortment of model strategies that we offer our clients. Our team of quants is constantly on the hunt for innovative investment ideas. Lucena’s model portfolios are a byproduct of some of our best research, packaged into consumable model-portfolios. The performance stats and charts presented here are a reflection of paper traded portfolios on our platform, QuantDesk®. Actual performance of our clients’ portfolios may vary as it is subject to slippage and the manager’s discretionary implementation. We will be happy to facilitate an introduction with one of our clients for those of you interested in reviewing live brokerage accounts that track our model portfolios.

Tiebreaker: Tiebreaker is an actively managed long/short equity strategy. It invests in equities from the S&P 500 and Russell 1000 and is rebalanced bi-weekly using Lucena’s Forecaster, Optimizer and Hedger. Tiebreaker splits its cash evenly between its core and hedge holdings, and its hedge positions consist of long and short equities. Tiebreaker has been able to avoid major market drawdowns while still taking full advantage of subsequent run-ups. Tiebreaker is able to adjust its long/short exposure based on idiosyncratic volatility and risk. Lucena’s Hedge Finder is primarily responsible for driving this long/short exposure tilt.

Tiebreaker Model Portfolio Performance Calculation Methodology Tiebreaker's model portfolio’s performance is a paper trading simulation and it assumes opening account balance of $1,000,000 cash. Tiebreaker started to paper trade on April 28, 2014 as a cash neutral and Bata neutral strategy. However, it was substantially modified to its current dynamic mode on 9/1/2014. Trade execution and return figures assume positions are opened at the 11:00AM EST price quoted by the primary exchange on which the security is traded and unless a stop is triggered, the positions are closed at the 4:00PM EST price quoted by the primary exchange on which the security is traded. In the case of a stop loss, a trailing 5% stop loss is imposed and is measured from the intra-week high (in the case of longs) and low (in the case of shorts). If the stop loss was triggered, an exit from the position 5% below, in the case of longs, and 5% above, in the case of shorts. Tiebreaker assesses the price at which the position is exited with the following modification: prior to March 1st, 2016, at times but not at all times, if, in consultation with a client executing the strategy, it is found that the client received a less favorable price in closing out a position when a stop loss is triggered, the less favorable price is used in determining the exit price. On September 28, 2016 we have applied new allocation algorithms to Tiebreaker and modified its rebalancing sequence to be every two weeks (10 trading days). Since March 1st, 2016, all trades are conducted automatically with no modifications based on the guidelines outlined herein. No manual modifications have been made to the gain stop prices. In instances where a position gaps through the trigger price, the initial open gapped trading price is utilized. Transaction costs are calculated as the larger of 6.95 per trade or $0.0035 * number of shares trades.

BlackDog: BlackDog is a paper trading simulation of a tactical asset allocation strategy that utilizes highly liquid ETFs of large cap and fixed income instruments. The portfolio is adjusted approximately once per month based on Lucena’s Optimizer in conjunction with Lucena’s macroeconomic ensemble voting model. Due to BlackDog’s low volatility (half the market in backtesting) we leveraged it 2X. By exposing twice its original cash assets, we take full advantage of its potential returns while maintaining market-relative low volatility and risk. As evidenced by the chart below, BlackDog 2X is substantially ahead of its benchmark (S&P 500).

In the past year, we covered QuantDesk's Forecaster, Back-tester, Optimizer, Hedger and our Event Study. In future briefings, we will keep you up-to-date on how our live portfolios are executing. We will also showcase new technologies and capabilities that we intend to deploy and make available through our premium strategies and QuantDesk® our flagship cloud-based software.
My hope is that those of you who will be following us closely will gain a good understanding of Machine Learning techniques in statistical forecasting and will gain expertise in our suite of offerings and services.


  • Forecaster - Pattern recognition price prediction
  • Optimizer - Portfolio allocation based on risk profile
  • Hedger - Hedge positions to reduce volatility and maximize risk adjusted return
  • Event Analyzer - Identify predictable behavior following a meaningful event
  • Back Tester - Assess an investment strategy through a historical test drive before risking capital

Your comments and questions are important to us and help to drive the content of this weekly briefing. I encourage you to continue to send us your feedback, your portfolios for analysis, or any questions you wish for us to showcase in future briefings.
Send your emails to: info@lucenaresearch.com and we will do our best to address each email received.

Please remember: This sample portfolio and the content delivered in this newsletter are for educational purposes only and NOT as the basis for one's investment strategy. Beyond discounting market impact and not counting transaction costs, there are additional factors that can impact success. Hence, additional professional due diligence and investors' insights should be considered prior to risking capital.

If you have any questions or comments on the above, feel free to contact me: erez@lucenaresearch.com

Have a great week!

Erez Katz Signature


Disclaimer Pertaining to Content Delivered & Investment Advice

This information has been prepared by Lucena Research Inc. and is intended for informational purposes only. This information should not be construed as investment, legal and/or tax advice. Additionally, this content is not intended as an offer to sell or a solicitation of any investment product or service.

Please note: Lucena is a technology company and neither manages funds nor functions as an investment advisor. Do not take the opinions expressed explicitly or implicitly in this communication as investment advice. The opinions expressed are of the author and are based on statistical forecasting on historical data analysis.
Past performance does not guarantee future success. In addition, the assumptions and the historical data based on which opinions are made could be faulty. All results and analyses expressed are hypothetical and are NOT guaranteed. All Trading involves substantial risk. Leverage Trading has large potential reward but also large potential risk. Never trade with money you cannot afford to lose. If you are neither a registered nor a certified investment professional this information is not intended for you. Please consult a registered or a certified investment advisor before risking any capital.
The performance results for active portfolios following the screen presented here will differ from the performance contained in this report for a variety of reasons, including differences related to incurring transaction costs and/or investment advisory fees, as well as differences in the time and price that securities were acquired and disposed of, and differences in the weighting of such securities. The performance results for individuals following the strategy could also differ based on differences in treatment of dividends received, including the amount received and whether and when such dividends were reinvested. Historical performance can be revisited to correct errors or anomalies and ensure it most accurately reflects the performance of the strategy.