Swing Trading in Stock Market

Due to the highly volatile and fluctuating nature of the Indian stock market which is influenced by a
number of factors including government policies, release of a company’s financial reports, investor’s
sentiment, geopolitical situation, and many others, the prediction of the stock market has been a
daunting task for traders. In this study, a Long Short Term Memory enforced Decision Support System
is developed for swing traders to accurately analyze and predict the future stock values. The Decision
support system generates a report which incorporates the predicted values of the company stock for
the next 30 days and other technical indicators like MFI, relative RSI, the Support and Resistance of the
stock price, five Fibonacci retracement levels, and the MACD and SIGNAL LINE analysis of the company
and NIFTY industry average stock price. The trader can use the investment success score calculated in
the report to augment his investment decisions. The results achieved by the proposed model in terms
of Root Mean Square Error, Mean absolute error, and Mean Absolute Percentage Error are 4.13, 3.24,
and 1.21 % respectively which establishes the efficacy of the proposed technique compared with the
state-of-art techniques.

An LSTM (Long Short-Term Memory) based decision support system can be used for swing trading in the stock market. Swing trading involves holding a stock for a short period, typically a few days to a few weeks, and taking advantage of price fluctuations within that time frame.
Here is a high-level overview of how an LSTM based decision support system could be used for swing trading:
1.Data collection: Collect historical stock price data, including open, high, low, and close prices, as well as trading volume and other relevant information.
2.Preprocessing: Preprocess the data by scaling it and dividing it into training, validation, and testing datasets. You may also want to perform feature engineering to create new features that could help improve the model's accuracy.
3.Model training: Train an LSTM neural network using the training dataset. The LSTM model can be trained to predict future stock prices based on the historical data.
4.Model validation: Validate the LSTM model using the validation dataset to ensure that it is performing well.
5.Model testing: Test the LSTM model using the testing dataset to see how well it performs on unseen data.
6.Decision making: Use the LSTM model to make decisions on when to buy and sell stocks based on predicted price movements. For example, if the LSTM model predicts that the price of a particular stock will increase in the next few days, you could buy that stock and hold it until the model predicts that the price will start to decrease.
7.Re-evaluation: Continuously re-evaluate the LSTM model's performance and update it as necessary to ensure that it continues to make accurate predictions.
It's worth noting that the stock market is highly unpredictable and volatile, and there is no guarantee that an LSTM-based decision support system will always make accurate predictions. Therefore, it is important to exercise caution and to use other sources of information, such as fundamental analysis and market sentiment, to make informed decisions.

https://bit.ly/3mkkqbd