Harvesting Momentum: Let's Kick Tires With AmiBroker [ Part II ]

In Part I of this series the frame work for a basic Tactical Asset Allocation strategy was discussed using two ETF's representing stocks (first SPY, later MDY) and bonds (TLT). Following on the daily signals of the first post, the continuation of this series will be all about the higher time frames, starting with weekly signals.

Engaging a strategy based on weekly signals might be the way to go for anybody who does not want to be in synch with the crowd, since everyone and his uncle is doing momentum on monthly basis these days. And as it happens, the AFL-code provided in the previous post is just as suitable for weekend traders too.

Weekly Periodicity

As a starting point, the weekly equivalent of 85 days for the momentum calculation is used: 17 weeks without any smoothing.

Portfolio Equity SPY - TLT (17 weeks), draw down periods in red

Profit table SPY - TLT (17 weeks)

SPY-TLT (17w)		2004 - 2013
Total Profit		211	%
CAR		12.05	%
MDD (trades)		-16.16	%
MDD (system)		-16.16	%
Calmar (0%)		0.75
Sharpe (0%)		0.76
Trades		43
Winners		58.14	%

Harvesting Momentum: Let's Kick Tires With AmiBroker [ Part I ]

Developing indicators and strategies is great, putting them to a stress test by mr. Market is better. But how? As we speak, thinkDesktop does not have the nuts and bolts to perform proper backtesting. After much consideration I decided to invest nearly $500 by buying the AmiBroker Professional suite at the turn of the year. Next I found myself back in primary school, learning to write again ...

Over the last couple of weeks I have been studying AmiBroker’s Formula Language (AFL) like there is no tomorrow. Luckily the "Introduction to AmiBroker" by Howard Bandy (free download) is a great read and AFL has a huge and generous user base, gathered in the AmiBroker User’s List on Yahoo. Last but not least there is Tomasz Janeczko’s extensive UsersGuide with an overwhelming amount of examples.

So this post is all about backtesting. Actually it probably will be the first in a series for presenting some of the backtesting possibilities AmiBroker has to offer. And while we are discovering the AmiBroker Backtester, chances are we might come across some pretty neat portfolio strategies along the way. Stay tuned!

Harvesting Momentum

As stated in previous posts, the "momentum anomaly" is known for centuries. The gist of the momentum anomaly is that assets often continue their price momentum, defined as the change in price over a given lookback period. Momentum works well across asset classes as well as within them. So harvesting momentum really is all about follow the money.

On Seeking Alpha contributor Marc Cohn has published various strategies involving momentum based trading. One of them, "Return Like A Stock, Risk Like A Bond: 15.5% CAGR With 17% Drawdown" utilizes so called "Paired Switching" (see here for SSRN paper) between two exchange traded funds: SPY and TLT. Marc's comprehensive system actually is a TAA strategy in its simplest appearance.

Flexibile Asset Allocation With C(r)ash Protection

The "Conceptual sketch" posting presented a survey for designing a portfolio that generates stable profits during every type of economic environment the investor is faced with. Stimulating as well as  challenging comments were made providing food for thought on the building blocks for such a model. During our research we came across a paper published in late 2012 by Keller and Van Putten: "Generalized Momentum and Flexible Asset Allocation (FAA), An Heuristic Approach". The interested reader is encouraged to get acquainted with the elements of FAA.

Common asset allocation strategies (like the TAA strategy) are based on the so-called "momentum anomaly", which is known for centuries. The gist of the momentum anomaly is that assets often continue their price momentum, defined as the change in price over a given lookback period. Therefore one should buy assets with the highest momentum and sell assets with the lowest momentum.

FAA incorporates new momentum factors into risk regime determination. Next to the traditional momentum factor (R) based on the Relative returns among assets, Keller and Van Putten introduced Generalized Momentum by adding these new factors: Absolute momentum (A), Volatility momentum (V) and Correlation momentum (C). In their paper Keller and Van Putten demonstrated that by expanding the traditional momentum approach, portfolio performance increases compared to the buy and hold benchmark, both in terms of return as well as risk.

Summarizing FAA, Keller and Van Putten present their strategy with an example universe of 7 index funds. Applying a 4 month lookback, from this universe at the end of each month the top 3 assets are selected through a nested ranking process of these 7 assets based on relative momentum (higher is better), volatility (lower is better) and correlations (lower is better). Last, each of the top 3 assets chosen, has to pass the absolute momentum test: if their absolute momentum is negative, just go into cash. Capital is equally allocated over the top 3 assets or if applicable into cash.

FAA was scrutinized by Empiritrage. In their full report following findings are reached:
FAA has significantly higher risk-adjusted return than an equal weight portfolio. FAA decreases maximum drawdown dramatically. FAA is robust when adjusting look-back periods. Absolute momentum can directly add value on identifying down side risk regimes and decrease maximum drawdown.

Source: Empiritrage

Conceptual sketch for an "All-Weather-Portfolio" by deploying Adaptive Risk Parity

As stated in the title, the nature of this posting is conceptual. It is a survey for designing a portfolio that generates stable profits during every type of economic environment the investor is faced with. What is about to follow is partly a compilation of the information found in several sources* along with suggestions and idea's put forward by co-researcher "Ram". This post is also an open invitation to anybody who is willing to share valuable insights for improving the model.

The goal of this quest is not about generating the highest possible returns. Instead it is about creating a portfolio with a risk profile as close as possible to cash, but with yields much higher than cash. The idea is to reduce the portfolio's overall volatility by investing in assets that naturally move in opposite directions. One of the largest hedge funds in the world, Ray Dalio's Bridgewater, applies this investment philosophy in their "All-Weather-Portfolio".

AWP is based on four premises:

• The future is unknown and impossible to predict.
• Assets are responding to two drivers: economic cycle (expansion/contraction) and inflation (high/low).
• Asset classes thrive not equally during each of the four scenario's.
• Risk is distributed equally over the four scenario's.

Instead of allocating equal amounts of capital to each asset class, the AWP-model assigns equal buckets of risk to the distinctive asset classes. Thus "risk parity" for each quadrant is accomplished to match equal odds for the next economical and inflationary condition. AWP assumes the economy transitions randomly from regime to regime and thus the AWP needs to have one or more asset classes working well in each regime. So AWP seeks for one or more asset classes generating decent returns in each quadrant. For instance:

• Stocks for economic expansion and low inflation.
• Bonds for economic contraction and low inflation.
• Commodities for economic expansion and high inflation.
• Inflation linked bonds for economic contraction and high inflation

	Economic contraction	Economic expansion
High inflation	25% of risk: - inflation linked bonds - precious metals	25% of risk: - stocks - commodities - real estate
Low inflation	25% of risk - government bonds - inflation linked bonds	25% of risk: - stocks - real estate - corporate bonds

    Table I  

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