Tag: quant

We are big fans on using distance measures while prospecting for investment strategies. Previously:

• Euclidean Distance for Pattern Matching
• Hamming Distance
• Euclid vs. Hamming Distance
• Beta vs. Hamming

Recently, we came across an interesting paper, Skulls, Financial Turbulence, and Risk Management, Mark Kritzman, CFA, and Yuanzhen Li, that uses the Mahalanobis distance to construct a turbulence index. The basic idea is that the more asset returns break from the past, the more “significant” a market event.

We took the basic intuition behind this and constructed a portfolio that switches between equities and bonds based on the Mahalanobis distance between them.

The out-of-sample results, factoring in transaction costs, look promising but doesn’t really stand out compared to other, more dumber, strategies that avoid steep drawdowns. However, two points over the Midcap buy & hold cannot be dismissed outright.

The code, charts and paper are on github.

Hypothesis: If a price segment is similar to a set of historical segments, then there should be a correlation between historical and future returns.

Setup: Take 60-minute segments of 1-minute bars. Match them with 200 of the most recent days’ with times aligned. Take the top 40 days with the closest match and calculate their median next 30-minute returns. Plot the median vs. actual.

Simplifications:

1. Non-overlapping segments. Its can be argued that patterns don’t start and stop and perfectly aligned boundaries.
2. Most-recent 200 day look-back period. What if that’s not enough? Or too much?
3. 30-minute prediction. Too short? Too long?

Verdict: There is no discernable relationship.

Code: github

Related: Euclidean Distance for Pattern Matching

Our previous post showed how a chrono-clock frequency can be translated into a volume-clock frequency. The aggregated High-Low price data looks very similar to a candle-stick chart.

The difference between the high and the low on each “candle” of the longest living contract doesn’t vary a lot between the two clocks.

The distribution is based on a month’s worth of streaming data. As we accumulate more data points, it is likely that the divergence between the Chrono-Clock and the Volume-Clock increases.

In our brief intro to the volume clock, we showed how you use a volume based aggregation to match the sampling frequency with information flow frequency. To further expand on that intuition, lets say that as a trader, you are used to watch the 5-minute chart. However, when markets turn volatile, you reach for the 1-minute or sometimes even the 30-second chart. Why is that?

In liquid futures markets like the NIFTY, volatility is associated with increased volume. A 5-minute window captures quite a wide range of volume.

While, on average, 10000 contracts get traded in a 5-minute interval, there are times when the market goes “berserk.” This is where the volume-clock begins to make sense. If you fix the volume at 10000, then your aggregation automatically tracks information flow without you having to zoom in and out.

Notice how the width of the window automatically expands and shrinks?

You can choose the volume interval to aggregate based on the current clock frequency that you use to trade.