We are an exciting new cryptocurrency that is evolving from day one. Our projects include an AI stock trading uses robo-advisors to analyze millions of data points and execute trades at the optimal price.
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price prediction

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Historical Data

Run a job to collect historical data — a course of action for assembling data from various sources and then enriching that data so as to create valuable. The amount of historical data determines the accuracy of the predictions; the more data the better. For stocks and commodities, we have over 10 years data

Time Series

Time series models are used to forecast events based on verified historical data. We use ARIMA, Prophet and LSMT. Not all models will yield the same results for the same dataset, but prediction result can help understand future trends.


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The price forecasting algorithm is an important tool in predicting future prices based on historical data. However, it may also be limited by some factors such as changes in market behavior or correlations between factors. Therefore, to improve the accuracy of the price forecasting algorithm, we may need to update the algorithm to reflect changes.

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Multiple Technology

Our Price Forecasting system is the ultimate tool for investors who want to make informed decisions. With advanced features such as Data Collection System, Artificial Intelligence, and Mobile App, users can easily access real-time data and make smart investment decisions.

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Machine Learning In Finance

Application of AI will change trading in many ways and this is already happening. Investors may find out soon that medium-term returns will be much below expectations after the current trend caused by QE expires. If this scenario materializes, then investors will have to return to the old way of finding a good financial adviser that can suggest a portfolio mix and pick securities that will appreciate in value. In some cases, the adviser will be an AI program and this process will be executed online.
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Despite the volatility, stock prices aren’t just randomly generated numbers. So, they can be analyzed as a sequence of discrete-time data; in other words, time-series observations taken at successive points in time (usually on a daily basis). Time series forecasting (predicting future values based on historical values) applies well to stock forecasting.
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Predictive analytics is a branch of advanced analytics that makes predictions about future outcomes using historical data combined with statistical modeling, data mining techniques and machine learning. Companies employ predictive analytics to find patterns in this data to identify risks and opportunities. Predictive analytics is often associated with big data and data science.
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Until recently, only the hedge funds were the primary users of AI and ML in Finance, but the last few years have seen the applications of ML spreading to various other areas, including banks, fintech, regulators, and insurance firms, to name a few. Right from speeding up the underwriting process, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, to offering alternative credit reporting methods, the different use cases of AI and Machine Learning In Finance are having a significant impact on this sector.
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Maruti Techlabs
There is a new wave of machine learning and data science in finance, and the related applications will transform the industry over the next few decades. Currently, most financial firms, including hedge funds, investment and retail banks, and fintech firms, are adopting and investing heavily in machine learning. Going forward, financial institutions will need a growing number of machine learning and data science experts. Machine learning in finance has become more prominent recently due to the availability of vast amounts of data and more affordable computing power. The use of data science and machine learning is exploding exponentially across all areas of finance.
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Forecasting whether to lend a firm money or not and whether it will default are possibly one of the key questions that analysts attempt to solve in a financial institution. This crucial information then drives the business in regards to whether to save or to take more risks to generate more revenue. Machine learning techniques can be used to model it accurately and efficiently. This article focuses on explaining how we can measure the risk of a party defaulting using the techniques of machine learning. I will be attempting to explain the concepts from the very basics.
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Farhad Malik