Stock Market Price Prediction Using Technical Analysis
- 1. Stock Price Prediction Using Technical Data Analysis Asheesh Sahlad Verma M.Sc.IT (2020-2021)
- 2. Outline ➢ Abstract ➢ Introduction ➢ Goal ➢ Literature Review ➢ Method ➢ Workflow and Visualization ➢ Conclusion ➢ Feature work
- 3. Abstract • Stock price forecasting is a popular and important topic in financial and academic studies. • We are aware of Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis for prediction. • In this project we attempt to implement an Artificial Neural Network approach to predict stock market prices. • It’s very effectively implemented in forecasting stock prices, returns, and stock modeling, and the most frequent methodology
- 4. Introduction ➢ A stock market is a platform for trading of a company’s stocks and derivatives at an agreed price. Supply and demand of shares drive the stock market. ➢ Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. ➢ Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price Movement.
- 5. ➢ Analysis of statistics generated by market activity such as past price and volume to come up with reasonable outcome in future using charts as a primary tool. ➢ Should I take a long position? Should I take a short position? What is going to be the price tomorrow, next week or next year?
- 6. • This Project proposes a novel method for the prediction of stock market closing price. • It is an attempt to determine whether the BSE market news in combination with the historical quotes can efficiently help in the calculation of the BSE closing index for a given trading day.
- 7. Trends ➢ The meaning of trend in finance isn't all that different from the general definition of the term - a trend is really nothing more than the general direction. ➢ A trend represents a consistent change in prices (i.e. a change in investor’s expectations). ➢ A trendline is a simple charting technique that adds a line to a chart to represent the trend in the market or a stock.
- 8. Literature Review ➢ In the last few decades forecasting of stock returns has become an important field of research. ➢ In most of the cases the researchers had attempted to establish a linear relationship between the input macroeconomic variables and the stock returns. ➢ In literature, different sets of input variables are used to predict stock returns. In fact, different input variables are used to predict the same set of stock return data.
- 9. ➢ After the discovery of nonlinearity in the stock market index returns, many literatures have come up in nonlinear statistical modeling of the stock returns, most of them required that the nonlinear model be specified before the estimation is done. ➢ Stock market return is noisy, uncertain, chaotic and nonlinear in nature, ANN has evolved out to be better technique in capturing the structural relationship
- 10. Methods Long Short Term Memory (LSTM) ➢ Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. ➢ LSTM networks are well-suited to classifying, processing and making predictions based on time series data, since there can be lags of unknown duration between important events in a time series.
- 12. SVM method ➢ Support Vector Machines SVM is specific to supervised learning machine learning model learns from the past input data and makes future predictions as output so we teach the model. ➢ We found that most recent research has used the SVM model. ➢ SVM algorithm is a simple yet powerful Supervised Machine Learning algorithm that can be used for building both regression and classification models.
- 13. Hardware and Software • Hardware Specifications for Trading systems may sound too complex but requires very few hardware costs, you just need a good computer with a good editor and you’re ready to go, not much requirement of extra hardware specifications. • Software- • Python Idle3.6.5 • NumPy • Scikit-Learn/Sk-Learn • Pandas and Matplotlib
- 15. Process Flow
- 16. Dataset ➢ Recently, the web has rapidly emerged as a great source of financial information ranging from news articles to personal opinions. ➢ Financial information sources on the Web and www.yahoo.finance.com ➢ From stock market (BSE & NSE) and Financial Times (www.ft.com) maintain excellent electronic versions of their daily issues.
- 18. 2). Pie Chart And Bar Graph
- 19. Conclusion ➢ Determining the Stock market forecasts is always been challenging work for business analysts. ➢ Thus, Project applies the data mining technology of neural network to stock price forecast and receives a preferable result, which will provide the research of the stock market development a new thought & We attempted to make use of huge textual data to predict the stock market indices .
- 20. Feature Enhancement ➢ Applying other Techniques and compare the results for better predictions. ➢ Using News feeds and Experts Advices from websites and taking them as datasets for Advanced predictions. ➢ More work on refining key phrases extraction will definitely produce better results.