Financial Astrology Sun Longitude

Financial astrology is a branch of mundane astrology that research the correlations of planet cycles with market prices, this indicator developed by the Financial Astrology Research Group provides the visualization of the Sun Tropical Zodiac Longitude to support that astrology traders can study multiple markets within the powerful Trading View UI to detect potential cyclical patterns in price action that are connected with the cosmic rhythm of the Sun.

The Sun have been very relevant cycle among all ancient civilizations such as Maya, Aztec, Inca, this cyclical move is the fundamental frequency of our life's due to the fact that our calendar year is a model from this cycle. Chinese astrologers and W.D. Gann was aware of the powerful predictive power of the solar terms which is a representation of the most relevant weather transitions within the Sun longitude path.

With this indicator we try to ease the research work of the amazing community of astro-traders that prior to this indicators needed to create hundreds of manual annotations on the markets price charts to visualize the Sun zodiac position within a long period of time in order to research potential cycles. That manual work is over. Let's move faster in our cycles research!

We encourage all traders using astrology to continue their research, please share your ideas of astro cycles trading strategies and contribute your experiments at our Github exploration projects:

Note: The Sun longitude is based on an ephemeris array that covers years 2010 to 2030, prior or after this years the longitude is not available, this daily ephemeris are based on UTC time so in order to align properly with the price bars times you should set UTC as your chart reference timezone.
Informacje o Wersji: Increase max_labels_count to 500.
Informacje o Wersji: Improved the representation of longitude zodiac sign boundaries with horizontal lines and symbols icons and add two digits precision longitude.

Join the financial astrology research discussions at Telegram:
Contribute to our statistical analysis and machine learning models at Github:
Skrypt open-source

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