Massive Multiple Spread Trading on Banks

Some examples of Multiple Spread Trading using 4 or 5 correlated and cointegrated underlying assets have been shown.

Actually Quantitative Algorithms of Multiple Spread Trading have no limitation on the maximum number of underlying assets to be considered.

This produces an enormous potential that this Strategy can reach if applied systematically and extensively on even large subsets that can be formed using thousands of shares belonging to US market only.

Sometimes it is better not to inflate the number of underlying assets because amplitude of oscillations around basket average synthetic value (which are at the origin of our profits) decreases as assets number increases. Furthermore, commission fees could increase if a minimum commission threshold is set for each asset.

On the contrary sometimes it is useful to increase basket size when, for example, particularly volatile assets are considered. In this way basket volatility is reduced as the number of assets increases, yielding more regular fluctuations around average value.

In this example we consider just an application of Multiple Spread Trading using 10 assets belonging to Bank sector.

On July 23rd Algorithms had found the following linear combination (10K$ margin):

Buy    23       TREE            (LendingTree Inc.)

Buy    183     EWBC          (East West Bancorp)

Sell     112     SNV             (Synovus Finl Corp)

Sell     385     STL              (Sterling Bancorp)

Sell     320     C                  (Citigrp Inc)

Buy    52       SBNY           (Signature Bank)

Sell     171     WFC            (Wells Fargo & Company)

Sell     97       CMA            (Comerica Inc.)

Buy    993     KEY             (KeyCorp)

Buy    368     NYCB          (New York Community Bancorp)

Two hours before market close, a short Trade was suggested and opened a few minutes later.

On July 24th Trade has been closed with a overall net profit of 2.9%, staying always in a safe market neutrality condition.