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Upgrade & Enhancemant of C# Indicator Interface & Launch Pad
/in General News, Knowledge & Academy, WTT News/by LvTThe C# indicator interface has been upgraded
Features:
* Install package from menu without the need to copy & paste files into special folders
* Direct edit of scripts from menu
* Direct launch of C# scripts via indicator launch pad without the need for “custom indicator” scripting
New Function: Map Indicator Turns to Price Chart
/in General News, Knowledge & Academy, Software Updates, WTT News/by LvTStreamline your cycle research by mapping dominant cycle indicator turns to price charts quickly
When dealing with cycle analysis, you often analyse cycles on sentiment sources which are different than the price chart itself. You would have to map the cycles found on a senitment vehicle to the price chart manually to check wheter the cycle turns match to price turns or not. The new function will do this automatically as it helps to map any indicator turn from any chart to another chart of your choise.
So you will see just with a mouse-click wheter detected cycles on different sources match to price turns without the need to manually map from one chart to the other.
In addition to that, it could for sure be used to quickly visualize turns on any indicator directly on the price series. So with the indicator-on-indicator function you can combine different indicators to create individual one just with some moues clicks. You can now check the “performance” of your indicators visually by plotting the turns on the price chart without the need to code any script.
Video on How-To Use the Indicator Mapping Function
On top of that, you could review the detailed trade statistics by selecting the “Show Trade Statistics” checkbox in the indicator mapping window. And voila the performance results by trading the indicator on the destination chart will show you each detail you would expect from an performance report.
This feature offers the ability to plot indicators cross-charts and cross-timeframes. So you can monitor a 1min VIX cycle and map the trade signals to a 4min ES futures chart. Or you can track the cycle swing indicator on a ES volumne chart and map the signals on a 5min ES futures chart. All done via some mouseclicks without the need to code any line.
Show detailed trade performance statistics based on the indicator mapping
All of this without the need to code one line of script or wihtout any manual mapping from on chart to the other.
Non-Linear Indicators – Cyclic Time Part 2
/in Frontpage Article, Knowledge & Academy/by LvTSetup Non-linear Indicators – The Concept of Cyclic Time in Technical Charting
Today, according to modern understanding, time is considered to progress in a linear manner. Many theories exist on the nature, origin, end and other matters related to time. Ancient societies also dwelled on questions related to time and formulated concepts and proposed various models to describe their naked eye observations and deductions based on logic.
Most of the ancient societies believed that time progressed in a cyclical manner. They observed the cyclical nature of the day and night, witnessed similar repetitive patterns of seasons year after year, the monthly cycles of the moon and its changing shape, etc., and applied the same concept for larger time scales as well.
In our “western thinking” and “left-brained” world, we always use the linear concept of time. It has become so “normal” in today’s world that no one questions it. However, awareness of the consequences can have a tremendous impact when building technical indicators and trading systems.
Today and in every charting application, technical analysis is placed in the logic of linear time. Every TA indicator or study uses the last n-bars period to judge the current condition and to derive trading rules and logics, e.g., RSI(13).
Why are the current last n-bars important for judging the current condition? Nobody questions this since we believe that there is only one rule set for time: linear. Hence, all our indicators are based on the linear time concept of our “modern” world.
However, perhaps the bars n-days/years in the past related to a given cycle are more important to rate the present condition than the current last n-bars. This would correspond to the cyclical concept of time. Unfortunately, this idea is not available in financial charting applications for building technical indicators.
Figure 1: Linear vs. cyclical time in technical analysis (TA)
Combination of linear and cyclical time to build predictive indicators
We need to consider the following to convert linear time-based indiccators into cyclical time:
We will assess the current market condition based on the distant past and not on the current past. So, we are using the same indicator calculation, but are putting in bars from the past instead of from the current linear past. To do so, we need to define a point in time from when to use the data, e.g., 365 bars back instead of 1 bar back.
I have already introduced 3 important cycles which reflect a recurring pattern that impacts life on Earth: The Metonic cycles introduced and explained in my book “Decoding The Hidden Market Rhyhtm – Part 2: Metonic Cycles”. ( Book Link )
The Metonic cycles represent a classical real example of cyclical time since they provide us with a cyclical pattern for the same position of the planets Earth, Moon and Sun before our stars. These interactions influence the operation of our electrochemical nervous system which is linked to the limbic system which in the end controls our emotions.
To rate the cyclic sentiment situation today – or to build the indicator score for today based on metonic cycles – we will use the bar 8 years, 11 years, and 19 years ago. Not the current last n-bars – they are completely useless if we follow the cyclical time concept.
Based on this concept, we can already build the indicator into the future for as long as the shortest chosen cycle is. In this case, we can calculate the indicator 8 years into the future. To transfer this concept to an indicator calculation is quite easy. For example, to build a cyclic RSI indicator, we do the following:
a) Calculate the linear-time indicator for the past:
e.g., using a standard RSI(n); (n: length)
b) To build the cyclic-time indicator score, build another indicator that simply uses the outcome from the linear-time indicator and build a composite from the cycles used.
For our Metonic RSI indicator, the pseudo code would look like this:
RSI(n) |today, cyclic-time| = ( RSI(n)[6939] + RSI(n)[4016] RSI(n)[2922] ) / 3
Patterns and cycles other than Metonic cycles certainly exist. However, I have already outlined the strengths of Metonic cycles in my book. You can replace the RSI indicator with any one of your favorite indicators to be transformed into cyclical time based version.
The following chart is a real-case example that was used in private trading seminars and represent one possible way to interpret a non-linear indicator plot. In these instances, the PercentageR time-based indicator was used to build the cyclic model via the Metonic cycles pattern, keeping to the formula used in the pseudo code presented above. The indicator has been plotted in advance into the future.
Example Crude oil futures contract – CL
This example shows the crude oil futures contract at the end of 2011 with a full predictive non-linear indicator plot for the course of 2012. One possible way is to look for the maximum and minimum readings of the indicator plot to identify future areas of potential market highs and lows. As the indicator does not provide an exact forecast each day, the first step would be to check the general extremes. Extreme situations are marked with red and green arrows on the chart.
Crude Oil – Cyclic Time Forecast
The second chart illustrates how this forecast was able to predict the major turning points of crude oil for 2012 in advance.
Crude Oil – Cyclic Time Outcome
Building trading systems with cyclical time-based indicators
After witnessing the predictive power of this technique, you can use this concept to build mechanical trading rules based on the created cyclical time-based indicators. For further reading and concrete pseudo code details, I introduce different purely mechanical trading systems in the book “Decoding the Hidden Market Rhythm – Part2: Metonic Cycles” and compare the standard linear time-based model against the cyclical time-based one to demonstrate the predictive advantages of the new non-linear indicators.
Charting platform and the automatic creation of non-linear indicators
Almost no charting platform is able to build non-linear cyclic indicators and transform them into cyclic time for plotting on a trading chart, as the West has a conceptual bias towards linear time. Opening our minds to the concept of cyclical time will present new possibilities and tools for technical analysis. This is part of why we have built the WhenToTrade charting platform, capable of building and plotting non-linear indicators.
A cyclic module for automating the transformation from linear into cyclical time based on any one of a full range of cycles and indicators is available in the WhenToTrade platform. Before transforming a linear indicator into its cyclic pendant, it is crucial to define the pattern to be used for the transformation; for example, an intraday sunrise/noon/sunset pattern might be used as a repetitive pattern during a trading day. There are many more possibilities.
I hope this article helped to broaden your view beyond the linear time concept; it is merely one of many ways to conceive of time, and I have attempted to show you concretely how to conceptualize others.
Let’s finish with reference to a well-known work:
Please also read article “Cyclic Time Part 1“
Genetic Algorithm Toolbox
/in General News, Knowledge & Academy/by LvTGenetic Algorithm explained in 4 minutes
GA System Examples: Daily S&P 500 | eMini Intraday
Browse two Genetic Evolution System examples:
System 1: Daily S&P 500 Index
System 2: eMini Intraday Futures (5 min.)
Realtime intraday live trading recording
This video was recorded during a live intraday trading session. A GA was setup prior to the intraday session and the generated trades are reviewed during the live trading. Watch in realtime how the trades come in. The basic setup was able to trade 12.5 eMini points during the day. A real example – no backtest.
Download WTT GA Magazine Articles
A) Detection of dynamic cycles in financial data with a genetic algorithm (Jan 2014)
Cycle forecasts have been traditionally made based on the current active cycle, where the detected dominant cycle is considered static and extrapolated into the future. However, this assumption oversimplifies the behavior of the market and often results in poorly estimated future cycles. Thus, a successful cycle-based trading approach should allow the user to follow the dynamic component.
Genetic algorithms (GAs) could provide such an approach by tracking market conditions and adapting parameters dynamically over time based on the underlying dataset. The GA approach differs from traditional digital signal processing […]
PDF Link: https://whentotrade.com/content/TW56_Mar2014_TradingGA.pdf
B) Detect intraday volume cycles for the S&P500 futures with a genetic algorithm (May 2014)
One of the ways of analyzing cycles in financial data is to detect cycles in traded volume. In particular, there are interesting correlations between the fixed amount of volume traded and price reversals in intraday trading. However, once you have detected the active correlations between volume cycles and price movements, this behavior will not remain static. It is, therefore, quite difficult to keep pace with the dominant volume cycles as these cycles are dynamic. A genetic algorithm (GA) is a promising way to detect volume cycles and to incorporate the flow of traders on a daily basis. It is a new alternative to using digital signal processing for detecting possible cycles. […]
PDF Link: https://whentotrade.com/wordpress/wp-content/uploads/2014/05/TWMag75.pdf
Sentiment: How Cycles of Financial Stress Pinpoint Market Turns
/in Knowledge & Academy, Sentiment/by LvTUnderstanding the sentiment cycles in financial stress is critical to generating returns in the current market environment. This article demonstrates the power and importance of sentiment cycles.
Sentiment cycles influence the movement of financial markets and are directly related to people’s moods. Getting a handle on sentiment cycles in the market would substantially improve one’s trading ability.
In this article, we highlight the importance of detecting cycles in sentiment to spot turning points in financial data. The “realtime” case study exemplifies the importance of sentiment cycles and the predictive power of the Dynamic Cycle Explorer (DCE).
The Dynamic Cycle Explorer works on the assumption that cycles are static over time, which is misleading for trading purposes. Dominant cycles morph over time because of the inherent nature of the parameters of length and phase. Typically, one dominant cycle will remain active for a longer period and vary around the core parameters compared to other cycles. As real cyclic motions are not perfectly even, the period varies slightly from one cycle to the next because of changing physical environmental factors. This dynamic behavior is valid for financial market cycles as well.
How does the approach work? Every time a new bar appears on the chart, the Dynamic Cycle Explorer reassesses the state of the current dominant cycle in terms of length, strength, and phasing. Subsequently, it updates this cycle by plotting it onto future projections. However, a trader will focus only on the next expected turning point, which is what a market analyst is interested in. The DCE is not used to predict a complete static cycle far into the future. We are interested in determining and monitoring the next turning point based on the detected dominant carrier wave, which is the point in time where we expect the market to turn.
As we move forward in time, every bar signifies an update of the expected turning point by a reassessment of the current state of the dominant cycle length and phase. This dynamic forecast based on the actual state of the dominant cycle provides information about the time and direction of the next turning point. We obtain real-time information about when to expect the next major turning point in the market as we continuously reassess the parameters of the dominant carrier wave. This information is updated every time a new bar appears. This technique was used in the following market report that was published on 8 June 2014 in the publicly available magazine section. (Link: Read original market update ahead of time.)
Financial Stress Index
The St. Louis Fed Financial Stress Index (STLFSI) is a vehicle that can be used to analyze sentiment data. It is created using principal component analysis, a statistical method for extracting the factors responsible for the correlation of a set of variables. Financial stress has been identified as the chief factor influencing the co-movement of its designated market variables; extracting this factor allows St. Louis Fed to create an interpretable index. The index is constructed using weekly data series for a variety of interest rate, credit spread, and volatility measures.
We can apply our dynamic cycle tools to this dataset and see if we can detect important dominant cycles that forecast financial stress extremes. This exercise was performed on 8 June with the DCE. The Dynamic Cycle Explorer can automatically detect the current active dominant cycle and track the current phasing to forecast the next expected turn.
The cycle explorer detected an active “financial stress” cycle with a length of 78 weeks that tracked the latest major market movements. This is shown in the lower panel of the chart where the cycle analysis on the FRED data took place. ( SEE LARGE CHART 1) The source data was accessed via the free Quandl data feed through the symbol FRED/STLFSI and was analyzed with the Dynamic Cycle Explorer. The blue cycle shows the automatically detected dominant cycle; the major turns are indicated with red and green arrows on the price chart to show the correlation with the Dow Jones Index.
Crosscheck signal in VIX “fear” index
The integrated, dynamic phasing analysis projected a current extreme low in the financial stress index at the point of the analysis (8 June). An extreme low in the financial stress index correlates to market highs. The current low in the financial stress index is also spotted as an expected major low of the current dominant “stress” cycle. Therefore, the DCE pointed to a possible market high at the time of the analysis. As is often discussed in our articles, one should always crosscheck for other dominant cycles, especially in other timeframes/vehicles. Another sentiment vehicle that is commonly referred to is the Volatility Index (VIX) — often called the “fear” index. A dominant cycle analysis on the VIX showed another sentiment extreme on the daily timeframe. The daily dominant cycle, which was automatically detected with a length of 152 bars, projected a daily sentiment “fear” low on 8 June. Fear index lows also correlate to market highs.
The VIX and the dominant cycle are shown in the lower chart panel. The green cycle was detected automatically by the DCE on the daily data. This cycle (blue line), together with the long-term weekly cycle from the financial stress index (fuchsia line), were mapped to the price chart in the upper window. At the time of the analysis (on 8 June 2014), these two cycles were in perfect alignment, which is a very important cycles-within-cycles alignment. ( SEE LARGE CHART 2 )
How did it turn out…?
A few month later, a review of the blue chip indexes reveals that the high occurred the very next days after the forecast was published. Markets registered a sharp decline of over 10% after the cycle analysis was published and price never showed up for a long time – as the cycle suggested.
Live Call Review
This example not only proves the ability of the WTT Dynamic Cycle Explorer to predict sentiment cycles ahead of time but also emphasizes the importance of analyzing dominant sentiment cycles as leading indicators of market turns.
Our cycles projection did not only call the top of the year, it represents also the largest drop for the year:
Additional examples and real-time forecasts based on this technique are included in the book Decoding the Hidden Market Rhythm– Part 1: Dynamic Cycles. The original market update post ahead of time can be found here: LINK
Video review of chart analysis
A complete video review (35min.) is available to see how the charts have been setup and the cycle analysis has been done to get this juncture in June.
When Money Dies – Endgame Markers
/in Frontpage Article, General News, Magazine, WTT News/by LvTCompressed timeline wrap up:
“When Money Dies”
A Magazine curated by WhenToTrade
All mentioned headlines can be found in our curated magazine including full article details. We will continously add additional follow-up articles and interesting news on this topic in our Flipboard magazine “When Money Dies”.
December 2014 News Wrap-Up (.pdf):
Attached is a pdf wrap-up of the December 2014 events in the international monetary system titled “2015 – The year money dies.”
Monetary System Dec2014 Events