Taya 365
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Project Description of Online Casino
The task at hand involves predicting multiplier values for a popular game called “crash” on TAYA 365. In this game, multiple users bet money on a rocket that starts with a multiplier of 0x and can increase to a very high multiplier but can also instantly crash to 0x in any round. The goal is to develop a method for predicting the next multiplier values and automate the betting process.
Data can be extracted from TAYA 365/crash using the hash of a specific rocket launch. By utilizing the hash of any launch, it is possible to obtain the multiplier value of the preceding hash. Since the first rocket’s hash is provided by Roobet, hashes and consequently multiplier counts of all rocket launches can be extracted.
An existing dataset containing over 1 million points, which displays the crash history of all launches with a minimum crash value of 1.00x, has been provided.
Our Solution
Several tasks were undertaken to find an optimal way of predicting results:
Utilizing the existing dataset, a deep learning model (LSTM) was created to identify potential patterns that could be used for predicting future values. However, despite the extensive dataset, the model failed to identify any significant patterns.
An external dataset was later acquired and analyzed, and models were developed based on this data. Although the second model performed well, it was discovered that the website itself is biased, making it challenging to obtain reliable data for predicting values.
Project Deliverables
Generated multiple machine/deep learning models using the provided dataset.
Utilized an external dataset to demonstrate the bias of the TAYA 365 website.
Attempted automation using Optical Character Recognition (OCR), but the results were found to be very unreliable due to the website’s use of dark text animations on a dark background. OCR is inherently not completely accurate in this context.
Documentation and reports related to the tasks performed.
Tools Used for Online Casino
Python: Used for data analysis and machine/deep learning.
JavaScript: Inspected the website’s JavaScript code and used it for automation with Python.
Google’s OCR tool: Employed for text recognition.
BeautifulSoup: Utilized for web scraping.
Selenium: Used for automation.
Language/Techniques Used
Python for coding.
JavaScript and Google’s OCR tool integrated with Python for specific tasks.
