Stock market provides a platform for people to buy and sell shares for profit. However, stock market is highly volatile and no human can rightly predict the direction of stock market, which may lead to high losses for investors. The paper proposes a system which removes the limitations of the existing systems with the help of different machine learning modules.
To develop a client-adaptive automated stock trading system that uses filters in the initial stage to narrow down scope of the stocks, Predicts stocks prices, performing risk analysis and recommends different trading strategies to the user to select on the basis of the risk score.
The limitations by traditional trading has restricted the stock market penetration to less than five percent of the population. The mere removal of these limitations by the existing automated systems often result in giving rise to additional problems like computation power, inaccurate performance, etc.