Artificial Intelligence Based Stock Market Prediction Model using Technical Indicators

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 7 Issue 2, March-April 2023 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD53854 | Volume – 7 | Issue – 2 | March-April 2023 Page 1026
Artificial Intelligence Based Stock Market
Prediction Model using Technical Indicators
Mr. Ketan Ashok Bagade1
, Yogini Bagade2
1
Vidyalankar Polytechnic, Faculty of Information Technology, Mumbai, Maharashtra, India
2
Vidya Prasarak Mandal's Polytechnic, Faculty of Electrical Engineering, Mumbai, Maharashtra, India
ABSTRACT
The stock market is highly volatile and complex in nature. However,
notion of stock price predictability is typical, many researchers
suggest that the Buy & Sell prices are predictable and investor can
make above-average profits using efficient Technical Analysis
(TA).Most of the earlier prediction models predict individual stocks
and the results are mostly influenced by company’s reputation, news,
sentiments and other fundamental issues while stock indices are less
affected by these issues. In this work, an effort is made to predict the
Buy & Sell decisions of stocks, trends of stock by utilizing Stock
Technical Indicators (STIs) As a part of prediction model the Long
Short-Term Memory (LSTM), Support Virtual Machine (SVM)
Artificial intelligence algorithms will be used with (Stock Technical
Indicators) STIs. The project will be carried on National Stock
Exchange (NSE) Stocks of India.
KEYWORDS: Stock Technical Indicators (STIs), Long Short-Term
memory (LSTM), Support Vector Machine (SVM), Moving Averages
(MA), Exponential Moving Average (EMA), Moving Average
Convergence Divergence (MACD), Relative Strength Index (RSI),
Stochastic Oscillator (SO), William %R(WPR), Rate of Change
(ROC), Commodity Channel Index (CCI), Momentum (MOM)
How to cite this paper: Mr. Ketan
Ashok Bagade | Yogini Bagade
"Artificial Intelligence Based Stock
Market Prediction Model using
Technical Indicators" Published in
International Journal
of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-7 |
Issue-2, April 2023,
pp.1026-1033, URL:
www.ijtsrd.com/papers/ijtsrd53854.pdf
Copyright © 2023 by author (s) and
International Journal of Trend in
Scientific Research and Development
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Open Access article
distributed under the
terms of the Creative Commons
Attribution License (CC BY 4.0)
(http://creativecommons.org/licenses/by/4.0)
1. INTRODUCTION
The analysis and prediction of stock market data have
got a significant role in today’s economy. The
prediction models are based on various algorithms and
can be categorized into linear models such as Auto-
Regressive Integrated Moving Average (ARIMA) and
non-linear models like Machine learning, Neural
Network (NN) and Deep Learning.
Numerous researchers have attempted to construct an
efficient model for prediction of Stock market for the
individual stocks and indices.
The approaches, for stock price prediction, are
generally classified into four categories
Fundamental Analysis(FA): Utilizes news,
earnings, profits and other economic factors for
forecasting.
Technical Analysis (TA) : Utilizes technical
indicators like Moving Averages (MA),
Exponential Moving Average (EMA), Moving
Average Convergence Divergence (MACD),
Relative Strength Index (RSI), Stochastic
Oscillator (SO), Average Directional Index with
DMI (ADX), William (WPR), Rate of Change
(ROC), Commodity Channel Index (CCI),
Momentum
(MOM) for Stock Market Prediction.
Hybrid Method: Utilizes combination of both of
the above methods.
Time series analysis: Utilizes analysis of time
series data.
The stock indices are generally not affected by
fundamental issues, so technical analysis is a better
option for indices prediction. STIs are statistical
calculations based on the price, volume or significance
for a share, security or contract.
STIs are independent of fundamentals of a business,
like earnings, revenue, or profit margins. The TA is
useful while predicting the future prices of assets so it
can also be integrated into automated trading systems.
The TA anticipates what is likely to happen to prices
over time, while the Artificial intelligence give
strength to such anticipations by improving accuracy.
IJTSRD53854

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
2. Literature Review:
STIs are statistical calculations based on the price,
volume, or significance for a share, security or
contract. These does not depend on fundamentals of a
business, like earnings, revenue, or profit margins.
The active stock traders and technical analysts
commonly use STI’s to analyze short-term and long-
term price movements and to identify entry and exit
points. Technical indicators can be useful while
predicting the Buy & Sell decisions of stocks, trends
of stock.
M. Nabipour [1] Collected 10 years historical data of
stocks. The value predictions are created for 1, 2, 5,
10, 15, 20, and 30 days in advance. Various machine
learning algorithms were utilized for prediction of
future values of stock market groups. He employed
decision tree, bagging, random forest, adaptive
boosting (Adaboost), gradient boosting, and eXtreme
gradient boosting (XGBoost), and artificial neural
networks (ANN), recurrent neural network (RNN) and
long short-term memory (LSTM). Technical
indicators were selected as the inputs into each of the
prediction models. The results of the predictions were
presented for each technique based on four metrics.
Among all algorithms used in this paper, LSTM
shows more accurate results with the highest model
fitting ability.
Can Yang [2] presents a deep learning framework to
predict price movement direction based on historical
information in financial time series. The framework
combines a convolutional neural network (CNN) for
feature extraction and a long short-term memory
(LSTM) network for prediction. He specifically use a
three-dimensional CNN for data input in the
framework, including the information on time series,
technical indicators, and the correlation between stock
indices. And in the three-dimensional input tensor, the
technical indicators are converted into deterministic
trend signals and the stock indices are ranked by
Pearson product-moment correlation coefficient
(PPMCC). When training, a fully connected network
is used to drive the CNN to learn a feature vector,
which acts as the input of concatenated LSTM. After
both the CNN and the LSTM are trained well, they are
finally used for prediction in the testing set.
Manish Agrawal [3] effort is made to predict the
prices of stock indices by utilizing Stock Technical
Indicators (STIs) which in turn helps to take buy-sell
decision over long and short term. Two different
models are built, one for future price trend prediction
of indices and other for taking Buy-Sell decision at the
end of day. As a part of prediction model the
optimized Long Short Term Memory (LSTM) model
is combined with highly correlated STIs.
Dongdong Lv[4], analysed large-scale stock datasets.
He synthetically evaluate various ML algorithms and
observe the daily trading performance of stocks under
transaction cost and no transaction cost. Particularly,
he used two large datasets of 424 S&P 500 index
component stocks (SPICS) and 185 CSI 300 index
component stocks (CSICS) from 2010 to 2017 and
compare six traditional ML algorithms and six
advanced deep neural network (DNN) models on
these two datasets, respectively. According to this
paper ML algorithm has better performance for
technical indicators.
Hyun Sik Sim[5] propose a stock price prediction
model based on convolutional neural network (CNN)
to validate the applicability of new learning methods
in stock markets. When applying CNN, technical
indicators were chosen as predictors of the forecasting
model, and the technical indicators were converted to
images of the time series graph. This study addresses
two critical issues regarding the use of CNN for stock
price prediction: how to use CNN and how to
optimize them.
Mojtaba Nabipour[6] study compares nine machine
learning models (Decision Tree, Random Forest,
Adaptive Boosting (Adaboost), eXtreme Gradient
Boosting (XGBoost), Support Vector Classifier
(SVC), Naïve Bayes, K-Nearest Neighbors (KNN),
Logistic Regression and Artificial Neural Network
(ANN)) and two powerful deep learning methods
(Recurrent Neural Network (RNN) and Long short-
term memory (LSTM). Technical indicators from ten
years of historical data are our input values, and two
ways are supposed for employing them. Firstly,
calculating the indicators by stock trading values as
continues data, and secondly converting indicators to
binary data before using. Each prediction model is
evaluated by three metrics based on the input ways.
The evaluation results indicate that for the continues
data, RNN and LSTM outperform other prediction
models with a considerable difference. Also, results
show that in the binary data evaluation, those deep
learning methods are the best. however, the difference
becomes less because of the noticeable improvement
of models’ performance in the second way.
3. Stock Technical Indicators
STIs are statistical calculations based on the price,
volume, or significance for a share, security or
contract. These does not depends on fundamentals of
a business, like earnings, revenue, or profit margins.
The active stock traders and technical analysts
commonly use STIs to analyze short-term and long
term price movements and to identify entry and exit
points. Technical indicators can be useful while
predicting the future prices of assets so they can be

integrated into automated trading systems. There are
two basic types of technical indicators: Overlays and
Oscillators. In this work, we use SMA as it is one
among the most widely used STI. It filters out the
noise which occurs due to random price variations
and helps to smooth out price. It is said as trend
following indicator or simplylagging as it depends on
past prices. Formulae for calculating the most
prevailing Stock Technical Indicators (STIs) is
presented in Table 1.
Stock Technical Indicators (STIs) Mathematical Formula
Moving Averages (MA)
Exponential Moving Average (EMA)
Moving Average Convergence Divergence (MACD) 13 Period EMA – 26 Period EMA
Relative Strength Index (RSI)
Stochastic Oscillator (%K)(SO)
William %R(WPR)
Rate of Change (ROC)
Commodity Channel Index(CCI)
Momentum(MOM) Momentum = C- Cx
Table 1: Stock Technical Indicators
Details of Technical Indicators
Moving Average (MA)
Moving Average (MA) are average values for a given
time frame and they reflect mood of market. It’s a
simple average of the past closing.eg., 50 day SMA is
nothing but average of previous 50 days closing
prices.
Formula for Moving Average is
Cn = Closing price of an stock at period n.
n = The number of total periods.
Exponential Moving Average (EMA)
Exponential Moving Average (EMA) is a type of
moving average that is similar to a simple moving
average, except that more weight is given to the latest
data. The exponential moving average is also known
as exponentially weighted moving average. EMA is
used to produce buy and sell signals based on
crossovers. Important EMA are 5,13,26,50,100,200
Formula for Exponential Moving Average is
Where Smoothing = 2
Moving Average Convergence Divergence
(MACD)
Moving Average Convergence Divergence (MACD)
is a trading indicator used in technical analysis.It is
called as Trend indicator. MACD indicator has 3
components in it. MACD Line is blue line in the
MACD indicators. It is calculation result of
subtracting 26-period EMA from 12-Period
EMA.Signal Line is the Red line which is plotted on
the top of the MACD line. It is basically 9-Period
EMA of the MACD lineWhen MACD line crosses
the signal line in upward direction it triggers a Buy
signal When MACD line crossing the signal line in
downward direction triggers a SELL Signal.
Histogram are vertical lines/bars.
Formula for Moving Average Convergence
Divergence is
12 Period EMA − 26 Period EMA

Relative Strength Index (RSI)
Relative Strength Index (RSI) is a popular momentum
oscillator. The RSI provides technical traders signals
about bullish and bearish price momentum, and it is
often plotted beneath the graph of an asset's price. An
stock is usually considered overbought when the RSI
is above 70% and oversold when it is below 30%
Formula for Relative Strength Index is
Stochastic Oscillator(SO)
Stochastic Oscillator (SO) is a popular technical
indicator for generating overbought and oversold
signals. Readings over 80 are considered in the
overbought range, and readings under 20 are
considered oversold. It is a popular momentum
indicator. %K is referred to sometimes as the fast
stochastic indicator.(Blue wave). The slow stochastic
indicator is taken as %D = 3-period moving average
of %K. (Red Wave)
Formula for Relative Strength Index is
where:
C = The most recent closing price
L14 = The lowest price traded of the 14 previous
trading sessions.
H14 = The highest price traded during the same14
day period
%K = The current value of the stochastic indicator
%D = 3-period moving average of %K.
Avg.Directional Index with DMI(ADX)
Directional Movement Index (DMI) is made of “+DI”,
“-DI” and ADX where +DI and –DI are directional
Indicators & ADX is a strength in a given trend.
If +DI > -DI - Bullish direction. ADX shows strength
in uptrend.
If -DI > +DI - Bearish direction. ADX shows strength
in downtrend.
ADX should be > 12
Formula for Avg.Directional Index with DMI is
William % Range (WPR)
Williams %R (WPR) moves between zero and -100. A
reading above -20 is overbought. A reading below -80
is oversold. An overbought or oversold reading
doesn't mean the price will reverse. Overbought
simply means the price is near the highs of its recent
range. Oversold means the price is in the lower end of
its recent range. Can be used to generate trade signals
when the price and the indicator move out of
overbought or oversold territory.
Formula for Moving Average is
where:
Highest High=Highest price in the lookback
period, typically 14 days.
Close=Most recent closing price.
Lowest Low=Lowest price in the lookback
period, typically 14 days.
Rate of Change(ROC)
Rate of Change (ROC) Measures the percentage
change between the most recent price and the price
“n” Periods in the past. ROC is classed as a price
momentum oscillator or a velocityIndicator because it
measures the rate of change or the strength of
momentum of change. It is set against a zero-level
midpoint. A rising ROC above zero typicallyconfirms
an uptrend while a falling ROC below zero indicates a
downtrend. When the price is consolidating, the ROC
will hover near zero.
Formula for Rate of Change is
where:
B=price at current time
A=price at previous time
Commodity Change Index(CCI)
Commodity Change Index (CCI) is a market indicator
used to track market movements that may indicate
buying or selling. The CCI compares current price to
average price over a specific time period. The
indicator fluctuates above or below zero, moving into
positive or negative territory. When the CCI moves
above +100, a new, strong uptrend is beginning,
signaling a buy. When the CCI moves below −100, a
new, strong downtrend is beginning, signaling a
sell. Look for overbought levels above +100 and
oversold levels below -100.
Formula for Commodity Change Index is

Where
Momentum(MOM)
Momentum (MOM) is the speed or velocity of price
changes in a stock, security, or tradable instrument.
Momentum shows the rate of change in price
movement over a period of time to help investors
determine the strength of a trend. Investors use
momentum to trade stocks whereby a stock can
exhibit bullish momentum–the price is rising–or
bearish momentum–the price is falling.
For UpTrend
MOM > 0 = Upward Bias
MOM > 0 + Price Increase = Strong UpTrend
MOM > 0 + Price Decrease = Weak UpTrend
For DownTrend
MOM < 0 = Downward Bias
MOM < 0 + Price Decrease = Strong DownTrend
MOM < 0 + Price Increase = Weak DownTrend
Formula for Momentum is
Momentum = C- Cx
where:
C =Latest price
Cx =closing price
x=Number of days ago
4. Problem Statement
The investors usually take the decisions of buying or
selling the stock by evaluating a company’s
performance and other unexpected global, national &
social events. Although, such events eventually affect
stock prices instantaneously in a negative or positive
way, these effects are not permanent most of the time.
So, it is not viable to predict the stock prices and
trends on the basis of Fundamental Analysis.
Investors are familiar with the saying, “buy low, sell
high” but this does not provide enough context to
make proper investment decisions. Before an investor
invests in any stock, he needs to be aware how the
stock market behaves. Investing in a good stock but at
a bad time can have disastrous results, while
investment in a mediocre stock at the right time can
bear profits. Financial investors of today are facing
this problem of trading as they do not properly
understand as to which stocks to buy or which stocks
to sell in order to get optimum profits. Predicting long
term value of the stock is relatively easy than
predicting on day-to-day basis as the stocks fluctuate
rapidly every hour based on world events.
4.1. Problem Definition
The objective of our project is to develop a Artificial
Intelligence System Using Technical indicators to
predict Stock trends. There are various technical
indicators like Moving Averages (MA), Exponential
Moving Average(EMA), Moving Average
Convergence Divergence (MACD), Relative Strength
Index (RSI),Stochastic Oscillator, ADX –
Avg.Directional Index with DMI, William %R
(WPR), Rate of Change(ROC), Commodity Channel
Index(CCI), Momentum(MOM) are calculated on
basis of closing price, opening price, High price, low
price.
We will use this technical indicators as input to our AI
algorithms i.e., support vector machine (SVM) &
Long Short Term Memory(LSTM).Our AIalgorithms
will give accuracy, buy & sell decisions, trends of the
Stock. On this basis trader can take decision of buying
and selling of stock.
4.2. Objectives
The system must be able to access a list of
historical prices. It must calculate the STI based
on the historical data. It must also provide an
instantaneous visualization of the market index.
As a consequence, an automated system or model,
to analyses the stock market and upcoming stock
trends based on historical prices and STIs, is
needed.
Two versions of prediction system will be
implemented; one using Support Vector Machines
and other using Long Short Term Memory
(LSTM). The experimental objective will be to
compare the forecasting ability of SVM with
LSTM. We will test and evaluate both the systems
with same test data to find their prediction
accuracy.
Applying traditional machine learning and deep
learning approaches yields average results as the
stock market follows random walk motion.
Applying AI Learning algorithm and adaptive
STIs can make an effective forecast.
4.3. Suggested System Architecture
we are proposing new model for the Stock Market
Prediction. Proposed system consists of different
modules working together to achieve robust and more
accurate system than its predecessors.

Fig.1 System Architecture of Stock Price Prediction
The stock data is captured through APIs. The values of Stock i.e., Date, Time, stock-name, Volume, Open, High,
Low and Close (OHLC) prices are extracted from the dataset. We will build the new input features, which are
known as STIs, by applying technical analysis. The STI will be given to AI algorithms LSTM,SVM. This AI
algorithm will give the output i.e., predicted Buy & sell decision, Identifies up-trend & down-trend, comparison
between SVM & LSTM output.
4.4. Block Diagram of the System.
Input to the system are date, symbol, close price, open price, high price, low price, previous close price.
SMA,EMA,MACD,RSI,SO,ADX,WPR,ROC,CCI,MOM are Stock Technical Indicators(STI’s) values are
calculated from input values.
Fig.2 Block Diagram of the System
Calculated STI values are send to Prediction Model of LSTM (Long Short Term Memory), SVM(Support Vector
Machine).
Output from Prediction model will be BUY/SELL Signals.

4.5. Flowchart:
Fig.3 Flowchart of Stock Market Prediction
System
4.6. System Requirements:
Hardware Software
Processor:
Pentium-i5
Operating System:Windows10
RAM: 4GB (min) Anaconda Navigator
Hard disk: 20GB Jupyter Notebook
Monitor: VGA
Python Programming.AI
Algorithm & Libraries,
Python Libraries.
5. Artificial Intelligence ALGORITHM
Support Vector Machine (SVM) and Long Short Term
Memory (LSTM) will be used for prediction. SVM is
a supervised machine learning algorithm and LSTM is
a deep learning algorithm.
5.1. Support vector machine(svm)
Support Vector Machine (SVM) is a supervised
machine learning algorithm which can be used for
both classification or regression challenges. However,
it is mostly used in classification problems. In the
SVM algorithm, we plot each data item as a point in
n-dimensional space (where n is number of features
you have) with the value of each feature being the
value of a particular coordinate. Then, we perform
classification by finding the hyper-plane that
differentiates the two classes very well.
Fig.4 Support Vector Machine
Support Vectors are simply the co-ordinates of
individual observation. The SVM classifier is a
frontier which best segregates the two classes (hyper-
plane/ line).
5.2. Long Short Term Memory(LSTM)
LSTM (Long short-termMemory) is a type of RNN
(Recurrent neural network), which is a famous deep
learning algorithm that is well suited for making
predictions and classification with a flavour of the
time. Unlike standard feed-forward neural networks,
LSTM has feedback connections.
Fig.5 Long short-term Memory(LSTM) Cell
A general LSTM unit is composed of a cell, an input
gate, an output gate, and a forget gate. The cell
remembers values over arbitrary time intervals, and
three gates regulate the flow of information into and
out of the cell. LSTM is well-suited to classify,
process, and predict the time series given of unknown
duration.
Conclusion
We studied the existing Stock Prediction System. To
predict and present Stock Prediction System we will
use Long Short Term Memory (LSTM), Support
Virtual Machine(SVM) AI models. The models
proposed in this Work will helps us to decide the stock
trends as well as decision of selling or buying the
stock. The proposed models thus decreasing the risk
while increasing there returns on investments.

References
[1] M. Nabipour, P. Nayyeri, H. Jabani, A. Mosavi,
E. Salwana and Shahab S. “Deep Learning for
Stock Market Prediction” Published in National
Center for Biotechnology Information Volume
22(8) 2020 Aug
[2] Can Yang, Junjie Zhai, and Guihua Tao “Deep
Learning for Price Movement Prediction Using
Convolutional Neural Network and Long Short-
Term Memory” Published in Hindawi
Publication Volume 2020, Article ID 274
[3] Manish Agrawal, Asif Ullah Khan and Piyush
Kumar Shukla “Stock Indices Price Prediction
Based on Technical Indicators using Deep
Learning Model” published in International
Journal on Emerging Technologies Volume
10(2) 2019.
[4] Dongdong Lv, Shuhan Yuan, Meizi Li and
Yang Xiang “An Empirical Study of Machine
Learning Algorithms for Stock Daily Trading
Strategy” Published in Hindawi Publication
Volume 2019, Article ID 7816154
[5] Hyun Sik Sim, Hae In Kim,and Jae Joon Ahn
“Is Deep Learning for Image Recognition
Applicable to Stock Market Prediction”
Published in Hindawi Publication Volume2019,
Article ID 4324878
[6] Mojtaba Nabipour, Pooyan Nayyeri, Hamed
Jabani, Shahab S., Amir Mosavi “Predicting
stock market trends using machine learning and
deep learning algorithms via continuous and
binary data; a comparative analysis on the
Tehran stock exchange” Published in IEEE
Volume 8 2017

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