Cycle Tools - App Manual

User Manual on how to use the Cycle Tools Application provided at

Data Management

How to manage data in the cycles app for analysis

Data Management

Integrated Datafeeds

The cycle toolbox has integrated external data-sources for end-of-day datasets. It includes major global stocks, market indices, crypto-currencies and forex data.

Stock market data

The market type ID to get major global stocks and indices datasets is YFI. Use Yahoo Finance for symbol search.

For weekly data add -W to the symbol.

Example symbols are:

Symbol Name Cycle Tools Symbol ID Link
^GSPC S&P 500 Index ^GSPC:YFI Open
^GSPC-W S&P 500 Index Weekly ^GSPC-W:YFI Open
CL=F Oil CL=F:YFI Open
ES=F E Mini Futures ES=F:YFI Open
DX-Y.NYB-W US Dollar Index Weekly DX-Y.NYB-W:YFI Open


Crypto-currency datasets

The market type ID to get crypto datasets is CDS.
Generic symbol format is: [FromSymbol]-[ToSymbol]-[Exchange]
Short usage is: [Symbol] – in this case ToSymbol is USD and the exchange is CCCAGG index *.

Example symbols are:

Symbol Name Cycle Tools Symbol ID Link
ETH Ethereum USD ETH:CDS Open
BTC Bitcoin USD BTC:CDS Open
LTC Litecoin USD LTC:CDS Open

Managed Forex datasets

The market type ID to get managed forex currency pairs is FX.
Generic symbol format is: [FromSymbol][ToSymbol]

Symbol Name Cycle Tools Symbol ID Link

Economic datasets (FRED)

Access to the economic data services of the Economic Research Division of the Federal Reserve Bank of St. Louis. The market type ID to get FRED data is FDS.

Online symbol search via FRED:

Example symbols are:

Symbol Name Cycle Tools Symbol ID Link
VIXCLS CBOE Volatility Index VIXCLS:FDS Open
VXDCLS DJIA Volatility Index VXDCLS:FDS Open
T5YIFR 5-Year Forward Inflation Expectation Rate T5YIFR:FDS Open
STLFSI2-W St. Louis Fed Financial Stress Index (Weekly)
More dataset details

Quandl datasets

The market type ID to get free quandl datasets is QDS.
Generic symbol format to load free quandl data via the time series API:
[Quandl database code]-[Quandl dataset code]-[column]-[collapse]

[column] (optional)
Ensure that you pick that correct column number. The column number is different for each dataset.

[collapse] (optional):

Change the sampling frequency of the returned data. Default is none; i.e., data is returned in its original granularity.

Example symbols are:

Database-Symbol Name Cycle Tools Symbol ID Link
CHRIS-EUREX_FDAX1-4 DAX Futures, Continuous Contract #1 (FDAX1) (Front Month), EUREX, Settle CHRIS-EUREX_FDAX1-4:QDS Open
FSE-VOW3_X Volkswagen AG, Stock Price, Frankfurt Stock Exchange FSE-VOW3_X:QDS Open
BSE-Sensex Bombay Stock Exchange – SENSEX Index BSE-Sensex:QDS Open
LBMA-Gold-2 Gold London Fixing USD PM (London Bullion Market Association)
More dataset details
LBMA-Gold-2:QDS Open
LBMA-Gold-2-weekly Gold London Fixing USD PM weekly data LBMA-Gold-2-weekly:QDS Open
LBMA-Gold-4 Gold London Fixing GBP PM (London Bullion Market Association)
More dataset details
LBMA-Gold-4:QDS Open
LBMA-Silver-3 Silver London Fixing EUR (London Bullion Market Association)
More dataset details
LBMA-Silver-3:QDS Open
ECB-EURJPY-1 EUR vs JPY Exchange Rate (European Central Bank)
More dataset details
CHRIS-ICE_CC5-4 Cocoa Futures, Continuous Contract (Settle)
More dataset details
CHRIS-ICE_B1-4 Brent Crude Futures, Continuous Contract (Settle)
More dataset details
CHRIS-ICE_B1-4-weekly Brent Crude Futures, Continuous Contract (Settle) - weekly CHRIS-ICE_B1-4-weekly:QDS Open



*) Data is sourced from CryptoCompare. If no exchange is specified the CCCAGG index data will be returned. The Crypto Coin Comparison Aggregated Index (“CCCAGG”) refers to the real-time index calculation methodology, the purpose of which is to show the best price estimation for crypto traders and investors to value their portfolio at any time. It aggregates transaction data of over 70 exchanges,using 24 hour volume weighted average. The CCCAGG is calculated for each crypto coin in eachcurrency it is trading in. We provide the data from CryptoCompare to the community based on their license without any additional change or charge (for research, software/applicationdevelopment, portfolio valuation, etc.), and is under the Creative Commons Attribution-NonCommercial3.0 Unported (CC BY-NC 3.0) license ( ).

Data Management

Upload your own data

You can upload your own datasets as CSV files and save them in the cycles app. A CSV ("comma-separated values") file is a delimited text file that uses a comma to separate values.

You can always manage (add, remove, update) your own saved data-sets in the settings area:

To upload new data series, go to the Settings -> MyDatasets area and click "Upload New".

You need to ensure the right format of your text file. The first line always needs to include the header "time, value" indicating each following line has a text for the date/time and a numeric value (e.g. close).

 time, value
 2019-04-11, 80.01
 2019-04-12, 82.1

Use the attached file eod_upload_test.csv shown in the navigation panel as template with daily data.

time, value
2019-04-11 13:01, 2301
2019-04-12 13:02, 2320

Please see and test the attached file (eurusd-1h-example.csv) shown in the navigation panel as template with 1h intraday data for the EURUSD.

Generic date formats

Cycle Scanner

How to use the Cycle Scanner or Cycle Finder

Cycle Scanner

Settings Panel

The following video is an How-To walk through on the configuration parameters of the settings panel in the Cycle Scanner window.

This video is based on our webinar series and has special focus on:
* Cycle Skew
* Power Spectrum
* Profile 1 / Profile 2 (P1, P2)

Cycle Scanner

Asymmetric Business Cycles and Cycle Skewness

Cycle analysis and cycle forecasting often imply the use of a symmetric time distribution between high to low and low to high. This is the underlying framework used by anyone applying mathematical signal processing to cycles and producing cycle-based composite cycle forecasts. This technique is now faced with a new challenge that has emerged over the past 30 years based on financial regulations impacting today’s economic business cycle. The following article will highlight the situation and present the reader with a proposed skew factor to account for this behavior in cycle forecasting models.

Business cycles are a fundamental concept in macroeconomics. The economy has been characterized by an increasingly negative cyclical asymmetry over the last three decades. Studies show that recessions have become relatively more severe, while recoveries have become smoother, as recently highlighted by Fatas and Mihov. Finally, recessive episodes have become less frequent, suggesting longer expansions.

As a result, booms are increasingly smoother and longer-lasting than recessions.

These characteristics have led to an increasingly negative distortion of the business cycle in recent decades. Extensive literature has examined in detail the statistical properties of this empirical regularity and confirmed that the extent of contractions tends to be sharper and faster than that of expansions.

In a paper published in the American Economic Journal on Jan. 2020, Jensen et al. summarized:

Booms become progressively smoother and more prolonged than busts. Finally, in line with recent empirical evidence, financially driven expansions lead to deeper contractions, as compared with equally sized nonfinancial expansions.

When recessions become faster and more severe and recoveries softer and longer, standard symmetric cycle models are doomed to fail. This new pattern challenges the existing standard, symmetrical, 2-phase cycle models.

Since 2-phase cycle models are based on a time-symmetric distribution of dominant cycles with mathematical sine-based counting modes from low to low or high to high. However, these models lose their forecasting ability under the assumption that a uniform distribution from high to low and low to high is no longer given.

A new model is needed. A dynamic skew cycle model that includes a skew factor.

Before introducing a new mathematical model to account for the asymmetric behavior, the cycle difference will be visualized and compared with some diagrams. The following illustration shows a classical, symmetrical 2-phase cycle on the left (green) and an asymmetric 3-phase cycle is highlighted on the right (red).

Asymmetric Cycle Model

This following model shown in Chart 1 uses a simplified formula that allows different distortions of the phases with a skew factor, but also keeps the length of the whole cycle, from peak to peak, the same without distortion.


Chart 1: Comparing 2-phase symmetric (green) and 3-phase asymmetric cycle models (red)

The new “skew factor” used in the red model shows that the upswing phase is twice as long as the recession, while ensuring the same total duration and amplitude of the standard, 2-phase cycle model (green, left). This allows us to model identified cycle lengths and strengths in the 3-phase model (red, right).

So, if we add the “skew factor” to the traditional mathematical cycle algorithms, we get cycle models that consider the asymmetric changes mentioned above. And thus, the cycle models can be used again for forecasts.

Example: The skew factor on the S&P 500 index

The next chart 2 shows a detected dominant, symmetric cycle with a length of 175 bars in January 2020 for the S&P 500 index. The light blue price data were not known to the cycle detection algorithm and represent the forecast out-of-sample range. The cycle is shown as a pink overlay. This symmetrical cycle forecast predicts that the peak would occur as early as the end of 2019, and a new low for this cycle to occur in May.


Chart 2: S&P 500 with 175 day symmetric cycle, skew factor: 0.0, date of analysis: 16. Jan 2020

As can be seen, the predicted high was too early than the real market top, and the predicted low was too late compared to the market low. This is a common observation when using symmetric cycle models in today’s markets. On the one hand, the analyst can now anticipate, based on knowledge of asymmetric variation, that the predicted high will be too early and the plotted low too late. However, additional knowledge of the analyst is required without being represented in the model. A better approach would be to include this knowledge already in the modeling of the cycle projection.

Therefore, we now add the skew factor to the detected cycle analysis approach.

In the next graph (Chart 3), a skew factor to the same 175-day cycle is applied. The date of analysis is still January 16, and the light blue is the prediction out-of-sample period. Here the asymmetric cycle forecast projects the peak for late January and the low for March 2020. The real price followed this asymmetric cycle projection more accurately.


Chart 3: S&P 500 with 175 day asymmetric cycle, skew factor: 0.4, date of analysis: 16. Jan 2020

This example demonstrates the importance to adapt traditional cycle prediction models with the addition of a skew factor. The introduction of a skew factor is based on the current scientific knowledge of the changed, asymmetric business cycle behavior.

The next paragraph explains how this asymmetry can be applied to existing, mathematical cycle models by introducing the skew factor formula.

Cycle Skew

The skew factor allows the representation of an asymmetric shape for business cycles in a cyclic model, as shown in the following examples. The green cycle is a standard sine-wave cycle (skew=0.0); the red cycle applies a specific skew factor.




skew = 0.5

skew = 0.75