Technical indicators i use | Brief explanation
Every trading system based on technical analysis uses moving average or hi/lo passband filters. A huge amount of your profit rely on the goodness of your filters or moving averages. There are two key features needed for a good moving average, little lag and smoothness. Lag is the amount of time the moving average need to follow the price action while smoothness represent the lack of noise.
It is always a trade-off beetween lag and smoothness, you can’t have both.
As a moving average i use the “ALMA” moving average, copyright by Arnaud Legoux and Dimitris Kouzis-Loukas. For better info about this moving average download this PDF from the author website.
As a main oscillator i use “THE INVERSE FISHER TRANSFORM” By John Ehlers, more info in this PDF
Basically i like the ALMA moving average because of its kernel that doesnt give too much importance to what happened in the last bar of data, so it filters out very well noise remaining stick to the underlying trend and when it really matters it responds much better then any other know moving averages.
I like the RSI-based inverse Fisher Transform because it help clearly define trigger points. First, a specified length RSI is computed and adjusted so that the values are centered around zero. The inverse transform is then applied to these values that will range from -1 to +1; when the oscillator will cross above -0.5 it’s a buy signal, viceversa when it will cross below +0.5 it’s a sell signal, as confirmation tool the signals generated by the inverse fisher transform RSI could be filtered out looking the direction of the ALMA moving average.
For computing the daily range estimates i publish on twitter i use the Hodrick-Prescott filter.
The Hodrick–Prescott filter is a mathematical tool used in macroeconomics, especially in real business cycle theory to separate the cyclical component of a time series from raw data. It is used to obtain a smoothed non-linear representation of a time series, one that is more sensitive to long-term than to short-term fluctuations. Once i have the value of the filter (see it as a baricenter) from there i compute the top and low estimate with a quantitative approach.
Quantitative financial analysis | brief overview
One of the prevailing concepts in financial quantitative analysis is that equity prices exhibit “random walk” characteristics. There is a large mathematical infrastructure available for applications of fractal analysis to equity markets, there are interesting implications that can be exploited if equity prices exhibit fractal characteristics:
- It would be expected that an equity’s price would fluctuate, over time, and the range, of these fluctuations (ie., the maximum price minus the minimum price,) would increase with the square root of time.
- It would be expected that the number of equity price transitions in a time interval, (ie., the number of times an equity’s price reaches a local maximum, then reverse direction and decreases to a local minimum,) would increase with the square root of time.
- It would be expected that the zero-free voids in an equity’s price, (ie., the length of time an equity’s price is above average, or below average,) would have a cumulative distribution that decreases with the reciprocal of the square root of time.
- It would be expected that an equity’s price, over time, would be mean reverting, (ie., if an equity’s price is below its average, there would be a propensity for the equity’s price to increase, and vice versa.)
- It would be expected that some equity prices, over time, would exhibit persistence, ie., “price momentum”.
Point 3 is very important and use it extensively trying to understand where a particular swing might end up reversing its direction. While point 5 give us some indication about the probability that a current swing will persist. Point 2 tend to negate the possibility to achieve a good market timing strategy while point 5 may allow it to some extent.
To compute the probability of price momentum you should follow these steps:
- compute natural logarithm of data increments
- compute the mean for all data increment computed in step 1
- compute rms (root mean square) of all data increments, squaring each data increment and sum all togheter
- Compute price momentum probability with the formula P = (((avg / rms) – (1 / sqrt (n))) + 1) / 2
where avg = data computed in step 2, rms = data computed in step 3, n = total samples of your dataset. If the resulting probability is above 0.5 then there is positive momentum, otherwise under 0.5 negative momentum. So for example you might compute the probability for the last fifty days and once this cross above the 0.50 thresold open a long position till it stays above 0.50
For further details and explanations of these formulas i recommend to deeply study the material present at http://www.johncon.com/ntropix/
Leave a comment if you need more explanations about this topic