Advanced Trading Strategies
- A Simple, Efficient and Convenient Universal System
- Stock Trend Forecaster: online backtesting, Excel template
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Data Shaping Solutions will soon co-brand its proprietary stock market forecaster on popular
financial portals. Our forecaster provides real daily performance
of the associated strategy for any stock over the last 365 days, in just
a few clicks on our member page.
This innovative technology, performing online computation of real
historical return in a snap, brings a new dimension in trading
A Simple, Efficient and Convenient Universal System
We propose an original system that provides
reliable daily index and stock trending signals. The non-parametric statistical techniques
described in this article have several advantages: simplicity, efficiency, convenience and
There are no advanced mathematics involved, only basic algebra. The algorithms do not
require sophisticated programming techniques. They rely on data that is easy to obtain.
Daily predictions were correct 60% of the time in our tests. This good performance can be
improved using techniques described in this article.
The non-parametric system does not require parameter estimation. It automatically adapts
to new market conditions.
Additionally, the algorithms are very light in terms of computation, providing
forecasts in a snap even on very slow machines.
The system works with any stock or index with a large enough volume, at any given time, in
the absence of major events impacting the price. The same algorithm applies to all stocks
The algorithm computes the probability, for a particular stock or index, that tomorrow's
close will be higher than tomorrow's open by at least a specified percentage. The
algorithm can easily be adapted to compare today's close with tomorrow's close instead.
The estimated probabilities are based on at most the last 100 days of historical data for
the stock (or index) in question.
The first step consists of selecting a few price cross-ratios that have an average value
of 1. The variables in the ratios can be selected so as to optimize the forecasts. In one
of our applications, we have chosen the following three cross-ratios:
Then each day in the historical data set is assigned to one of eight possible price
configurations. The configurations are defined as follows:
- Ratio A = ( today's high / today's low ) /
( yesterday's high / yesterday's low )
- Ratio B = ( today's close / today's open ) /
( yesterday's close / yesterday's open )
- Ratio C = today's volume / yesterday's volume
Now, to compute the probability that close tomorrow will be at least 1.25% higher than
tomorrow open, we first compute today's price configuration. Then we check all past days
in our historical dataset that have that configuration. We count these days. Let N be the
number of such days. Then, let M be the number of such days further satisfying the
- Ratio A > 1, Ratio B > 1, Ratio C > 1
- Ratio A > 1, Ratio B > 1, Ratio C <= 1
- Ratio A > 1, Ratio B <= 1, Ratio C > 1
- Ratio A > 1, Ratio B <= 1, Ratio C <= 1
- Ratio A <= 1, Ratio B > 1, Ratio C > 1
- Ratio A <= 1, Ratio B > 1, Ratio C <= 1
- Ratio A <= 1, Ratio B <= 1, Ratio C > 1
- Ratio A <= 1, Ratio B <= 1, Ratio C <= 1
Next day close is at least 1.25% higher than next day open.
The probability that we want
to compute is simply M/N. This is the
probability, based on past data, that close tomorrow will be at least 1.25% higher than
tomorrow's open. Of course, the 1.25 figure can be substituted by any arbitrary
There are different ways of assessing the performance of our stock trend predictor. We
have investigated two approaches:
Our tests showed a success rate between 54% and 65% in predicting the Nasdaq trend. The
strategy associated with the forecaster has been analysed on our web site. Check our
section on universal keys.
- computing the proportion of successful daily predictions, using a threshold of 0%
instead of 1.25%, over a period of at least 200
- using the predicted trends (with threshold set to 0% as above) in a strategy: buy at
open, sell at close or the other way around based on the prediction
Even with a 56% success rate in predicting the trend, the long-term (non compound) yearly
return before costs is above 40% in many instances. Note that we provide similar
strategies that do not rely on the open price to interested clients. As with many trading
strategies, the system sometimes exhibits oscillations in performance. It is possible to
substantially attenuate these oscillations, using a technique described on our website.
In its simplest form, the technique consists of using the same system tomorrow if it
worked today. If the system fails to correctly predict today's trend, then use the reverse
system for tomorrow.
Universal Trend Forecaster is the full name of our implementation of this system. It is
You can check out the real past performance (last 365 days) online, for any stock or
index, by entering the stock symbol in the trading box and clicking on the submit button.
Additionally, we provide an Excel template containing all the formulas to
perform the required computations.
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Vincent Granville, Ph.D., Editor
Data Shaping Solutions