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STOCK PRICE PREDICTION
USING LSTM
RAJIV GANDHI COLLEGE OF ENGINEERING ,RESEARCH & TECHNOLOGY,CHANDRAPUR
(DR.BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY,LONERE)
(2023-24)
DEPARTMENTOFCOMPUTER SCIENCEAND ENGINEERING
Semester VI Mini project– II
Submitted By
1.Mohit Titarmare
2.Gaurav Wankar
3.Gaurav Thapliyal
4.Yash Raut
Guided By:
Dr. Bireshwar Ganguly
Mini project Incharge :
Prof. Madhavi Sadu
Dr. Nitin Janwe
HOD , CSE
INTRODUCTION
 A market where shares are publicly issued and traded is known as a share market.
 Implementing the concept of algorithmic trading, which uses automated, pre-
programmed trading strategies to predict stock prices.
 Time series forecasting (predicting future values based on historical values) applies
well to stock forecasting.
 Predicting stock prices is a challenging task that blends finance, mathematics, and
computer science. It involves using historical data, market trends, and various
analytical techniques to forecast the future movements of stock prices.
Need of Project
 The stock market is known for being volatile, dynamic,
& nonlinear
 Accurate stock price prediction is extremely challenging
because of multiple factors.
 But, all of this also means that there’s a lot of data to
find patterns in.
 So, we keep exploring analytics techniques to detect
stock market trends.
 So, they can be analyzed as a sequence of discrete-time
data
 Despite the volatility, stock prices aren’t just randomly
generated numbers.
STEPS PERFORMED
1. Importing data
2. Split the Data into training / test sets
3. Creating and Training the Model
4. Making Predictions
5. Evaluating and Improving Predictions
METHODOLOGY
• Data Preprocessing: Normalizing or standardizing stock price data so that the LSTM network
can train more effectively.
• Model Training: Feeding historical stock data into the LSTM, which learns from sequences of
past stock prices, volumes, etc.
• Prediction: Using the trained LSTM to predict future stock prices; these predictions can
be visualized or used to trigger trading actions in the app.
• Backtesting: Using historical data to validate the model’s predictions, which is crucial
for understanding the effectiveness of the LSTM model before it’s used in live trading.
• Deployment: Integrating the LSTM model into a stock trading application where it provides
regular predictions, updates based on new data, and potentially adapts to changing market
conditions.
SOURCE CODE
Algorithm Used
• Long Short-Term Memory (LSTM) networks, a type of recurrent neural network
(RNN), play a crucial role in applications involving sequence prediction
• Handling Time-Series Data: Stock market data is inherently sequential and
time-dependent. LSTM networks are designed to recognize patterns in
sequences of data, making them particularly suitable for modeling stock price
movements
• Avoiding Vanishing Gradient Problem: LSTMs solve the vanishing gradient
problem commonly encountered with standard RNNs through their unique
structure of gates, including input gates, output gates, and forget gates.
• Predictive Performance: In stock prediction apps, LSTM networks can be
trained to predict not just one-step ahead (next day’s price) but also multiple
steps ahead, providing forecasts over a horizon that can be tuned according to
user needs or specific application requirements.
OUTPUT
FUTURE WORK
 Machine learning and Data science is a game changer in this domain so
there is a lot of data to find patterns in for predicting with high degree
of accuracy.
 In future we’ll try to predict the values based on multiple factors such
as politics, global economic conditions, unexpected events like covid,
companies financial performance, and so on.
 Decided to implement a simple User Interface to operate this whole
process for users so to make people engage in Stock market.
CONCLUSION
 Stock price prediction is a challenging task influenced by numerous variables and
uncertainties. While various methodologies, including statistical models and
machine learning algorithms, aim to forecast future prices, no approach can
consistently predict market movements with absolute accuracy.
 The dynamic nature of financial markets, coupled with the complexity of factors
influencing stock prices, makes it difficult to develop models that reliably capture all
relevant information. Additionally, the efficient market hypothesis suggests that
stock prices reflect all available information, further complicating prediction efforts.
THANK YOU

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stock price prediction using machine learning

  • 2. RAJIV GANDHI COLLEGE OF ENGINEERING ,RESEARCH & TECHNOLOGY,CHANDRAPUR (DR.BABASAHEB AMBEDKAR TECHNOLOGICAL UNIVERSITY,LONERE) (2023-24) DEPARTMENTOFCOMPUTER SCIENCEAND ENGINEERING Semester VI Mini project– II Submitted By 1.Mohit Titarmare 2.Gaurav Wankar 3.Gaurav Thapliyal 4.Yash Raut Guided By: Dr. Bireshwar Ganguly Mini project Incharge : Prof. Madhavi Sadu Dr. Nitin Janwe HOD , CSE
  • 3. INTRODUCTION  A market where shares are publicly issued and traded is known as a share market.  Implementing the concept of algorithmic trading, which uses automated, pre- programmed trading strategies to predict stock prices.  Time series forecasting (predicting future values based on historical values) applies well to stock forecasting.  Predicting stock prices is a challenging task that blends finance, mathematics, and computer science. It involves using historical data, market trends, and various analytical techniques to forecast the future movements of stock prices.
  • 4. Need of Project  The stock market is known for being volatile, dynamic, & nonlinear  Accurate stock price prediction is extremely challenging because of multiple factors.  But, all of this also means that there’s a lot of data to find patterns in.  So, we keep exploring analytics techniques to detect stock market trends.  So, they can be analyzed as a sequence of discrete-time data  Despite the volatility, stock prices aren’t just randomly generated numbers.
  • 5. STEPS PERFORMED 1. Importing data 2. Split the Data into training / test sets 3. Creating and Training the Model 4. Making Predictions 5. Evaluating and Improving Predictions
  • 6. METHODOLOGY • Data Preprocessing: Normalizing or standardizing stock price data so that the LSTM network can train more effectively. • Model Training: Feeding historical stock data into the LSTM, which learns from sequences of past stock prices, volumes, etc. • Prediction: Using the trained LSTM to predict future stock prices; these predictions can be visualized or used to trigger trading actions in the app. • Backtesting: Using historical data to validate the model’s predictions, which is crucial for understanding the effectiveness of the LSTM model before it’s used in live trading. • Deployment: Integrating the LSTM model into a stock trading application where it provides regular predictions, updates based on new data, and potentially adapts to changing market conditions.
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  • 10. Algorithm Used • Long Short-Term Memory (LSTM) networks, a type of recurrent neural network (RNN), play a crucial role in applications involving sequence prediction • Handling Time-Series Data: Stock market data is inherently sequential and time-dependent. LSTM networks are designed to recognize patterns in sequences of data, making them particularly suitable for modeling stock price movements • Avoiding Vanishing Gradient Problem: LSTMs solve the vanishing gradient problem commonly encountered with standard RNNs through their unique structure of gates, including input gates, output gates, and forget gates. • Predictive Performance: In stock prediction apps, LSTM networks can be trained to predict not just one-step ahead (next day’s price) but also multiple steps ahead, providing forecasts over a horizon that can be tuned according to user needs or specific application requirements.
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  • 13. FUTURE WORK  Machine learning and Data science is a game changer in this domain so there is a lot of data to find patterns in for predicting with high degree of accuracy.  In future we’ll try to predict the values based on multiple factors such as politics, global economic conditions, unexpected events like covid, companies financial performance, and so on.  Decided to implement a simple User Interface to operate this whole process for users so to make people engage in Stock market.
  • 14. CONCLUSION  Stock price prediction is a challenging task influenced by numerous variables and uncertainties. While various methodologies, including statistical models and machine learning algorithms, aim to forecast future prices, no approach can consistently predict market movements with absolute accuracy.  The dynamic nature of financial markets, coupled with the complexity of factors influencing stock prices, makes it difficult to develop models that reliably capture all relevant information. Additionally, the efficient market hypothesis suggests that stock prices reflect all available information, further complicating prediction efforts.