Technical analysis of stocks is an approach to evaluating securities that involves analyzing market activity statistics like past prices and volumes. Technical analysis is based on the idea that market dynamics, as shown by charts and some other technical indicators, could predict future activity. Charts and other methods are used by technical analysts to identify patterns and make investment choices. Technical analysis of stocks is really a trading practice that employs trends and patterns obtained from market activity, such as price action and volume, to evaluate investments but rather identify trading strategies. Apart from fundamental analysis, which aims to assess a security’s value based on the corporate result obtained such as revenue and earnings. Fundamental Analysis V/S Technical Analysis Fundamental analysis is a way of evaluating securities that attempts to calculate a stock’s intrinsic value. Fundamental analysts research everything from the overall economy and industry dynamics to a company’s financial condition and management. Revenues, expenditures, investments, and debts are all crucial to fundamental analysis. Technical analysis of stocks differs from fundamental analysis because the only inputs are the stock price and volume. The fundamental assumption is that all known fundamentals are priced in, so there is no need to pay particular attention to them. Analysts do not attempt to calculate a security’s intrinsic value, but rather use stock charts to recognize trends and patterns that indicate how a stock will perform in the future. Fundamental analysis is a method of evaluating securities that attempts to calculate a stock’s intrinsic value. The core assumption of technical indicators, on the other hand, is that all known fundamentals are factored into the price and thus do not require close attention. Technical analysts do not try to calculate a security’s intrinsic value; instead, they use charts to recognize trends and patterns that may indicate how the security will perform in the future. Why Technical analysis of stocks? Help you choose between the three options at any given time: 1. Create a new position 2. Exit a position 3. Take no action What are the 4 basics of technical analysis? Introduction to Technical analysis of stocks attempts to identify trading opportunities and identify entry and exit points based on price and volume data. The four core parts of technical analysis are as follows. Trend Analysis: entails determining the overall trend of the economy, whether up, down, or sideways, and then determining whether a specific security is moving in a similar direction. Chart Analysis: Technical analysts frequently use charts and graphs to visualize market trends and recognize potential trading opportunities. This includes the use of chart patterns like the head and shoulders, triangles, and flags. Indicator Analysis: Indicators are numerical calculations based on a security’s price and/or volume. Indicators include moving averages, momentum indicators, and oscillators used by technical analysts to help identify potential trading signals and trends. Volume analysis means analyzing the trading volumes in security to help verify trend direction and market strength. Volume indicators can also help technical analysts spot possible trend reversals. Technical analysts use these four basic elements to make trading decisions based on previous market data and also to forecast future trends in markets. Using Technical analysis Professional analysts frequently combine technical charts with other types of research. Retail traders may base their decisions solely on a security’s price charts and similar statistics, but practicing equity analysts rarely confine their research to fundamental or technical analysis alone. Technical analysts believe that any security with historical trading data can be subjected to technical analysis tools. This includes stocks, futures, commodities, fixed-income securities, currencies, and other financial instruments. Indeed, analysis is far more common in commodities and forex markets, where traders are concerned with short-term price movements rather than the future price. What is Dow theory? Charles H. Dow, the co-founder of Dow Jones & Company and The Wall Street Journal, developed Dow Theory, a market analysis approach. It is regarded as one of the first forms of technical analysis, and it gives a framework for comprehending market patterns and making financial decisions. While the stock market and the economy have changed dramatically since the theory’s inception, its principles are still widely employed by technical analysts today. Nowadays, the technical analysis includes hundreds of patterns and signals developed over many years of research. Fundamental assumptions of Dow Theory Dow Theory’s fundamental assumptions are as follows: Dow Theory holds that the stock price discounts everything publicly available information, including economic, political, and financial data, in the prices of securities. This means that the market reflects all market participants’ collective opinions and expectations and that stock prices will move in anticipation of future events. There are three market trends: Dow Theory holds that there are three distinct trends in the stock market: primary, secondary, and minor. Volume Confirms Price Movement: According to Dow Theory, volume is an important tool for confirming the validity of market price move. Increased volume during an uptrend indicates market strength, while increased volume during a downward trend indicates market weakness. These Dow Theory fundamental assumptions provide a framework for understanding market trends and making investment decisions based on publicly available data. What is a Trend? Trend: An upper trend is a series of successively higher peaks and troughs; a the downtrend is just the opposite, while horizontal peaks and troughs would identify a sideways price trend (trendless).  Remark: It is the direction of those peaks and troughs that constitute a market trend. What Are Technical Analysts’ Assumptions? Professional technical managers typically accept three broad assumptions about their field. The first is that the market, such as the efficient market hypothesis, discounts everything. Second, they anticipate that price levels, even in random market volatility, will exhibit trend lines regardless of the time frame. Finally, they believe that history has a habit of repeating itself. The repetitive nature of price changes is attributed largely to market psychology, which is based on emotions such as fear or excitement. What is meant by Support and Resistance? How can they be used to

Meta des – Here is a comparison of the FD interest rates offered by some large banks: PNB, SBI, and ICICI Bank. There is a chance that banks will raise the FD rate soon. On February 8, 2023, during its Monetary Policy Committee (MPC) meeting, the Reserve Bank of India (RBI) raised the repo rate by 25 basis points (bps). While some banks take their time boosting the interest rates on fixed deposits, several banks have already increased the rates on loan lending, passing the expense on to customers. With effect from February 15, interest rates for fixed deposits under 2 crore have increased by 5 basis points to 25 basis points at India’s largest public sector lender, State Bank of India (SBI). Additionally, the bank offered a special 400-day tenure plan with a 7.10% rate. Senior citizens benefit most since they receive a higher premium than average rates. SBI increased FD rates from 6.75% to 7% in terms of two years to under three years, a 25 basis point increase. On tenures of three years to ten years, a further 25 bps increase was allowed, bringing the rate from 6.25% to 6.50%. The lowest hike of 5 bps was awarded on a term of less than 2 years, going from 6.75% to 6.80%. For FDs with tenures of less than a year, interest rates are constant. SBI is still offering 5.75% on terms of 211 days or less to less than a year, 5.25% on terms of 180 days to 210 days, 4.50% on terms of 46 days to 179 days, and a 3% rate on terms of 7 days to 45 days. Senior Citizens’ FD rates Similar to the general category, senior citizens’ interest rates on fixed-term deposits under Rs. 2 crore have increased for terms starting at 1 year and higher. However, compared to the general category, seniors typically receive greater interest rates on FDs. SBI increased the FD rates for senior persons by 25 bps annually during the course of two years, from 7.25% to 7.50%. On tenure of five years or more and up to ten years, a comparable increase was made to 7.50% from the prior 7.25%. The highest interest rate that SBI has ever provided to older persons is this one. Additionally, SBI increased rates from the prior 6.75% to 7% on 3 years to less than 5 years of tenure by another 25 basis points. The 1 year to less than 2 year rate is raised by 5 basis points to 7.30% from the previous 7.25%. Below a one-year term, rates remain the same. An elderly person will still be paid 6.25% on tenures of 211 days or fewer, 5.75% on tenures of 180 days to 210 days, 5% on tenures of 46 days to 179 days, and 3.50% on tenures of 7 days to 45 days. A new tenure structure SBI has also developed a unique “400 days” tenor arrangement, under which it would begin offering a 7.10% APR on February 15. The deadline for this programme is March 31, 2023. Currently, SBI offers a special “SBI Wecare” deposit programme for senior citizens in the retail TD segment. Under this programme, senior citizens will receive an additional premium of 50 basis points (bps) on top of the existing 50 bps, or 100 bps over the public card rate (as shown in the above table), on their retail TD for a “5 Years and Above” tenure only. The “SBI Wecare” deposit plan is still in effect till March 31, 2023. How much interest would you earn on SBI Fixed Deposits under Rs 2 crore? The bank increased the interest rate on deposits with maturities of one year or less to 6.80 percent from 6.75 percent, which is a 5 basis point increase for average people. SBI increased the interest rate on loans with a term of two years or less by 25 basis points, from 6.75 percent to 7 percent. The interest rate for FDs with maturities between three and fewer than ten years has increased from 6.25 to 6.50 percent. SBI raises the interest rates on house loans; find out how much your EMI will increase. The State Bank of India (SBI) is the most recent bank to boost the interest rates on house loans following the Reserve Bank of India’s (RBI) 25 basis point increase to 6.50 percent in the repo rate. With effect from February 15, 2023, SBI raised the interest rates on MCLR, EBLR, and RLLR loans. According to the SBI website, the MCLR for one month, three months, and six months has increased from 8% to 8.10%; the MCLR for one year has increased from 8.40% to 8.50%. The MCLR is a factor in the majority of consumer loans, including auto, home, and personal loans. The two-year MCLR was increased from 8.50% to 8.60%, and the three-year MCLR was also increased from 8.60% to 8.70% SBI EBLR From 8.90%+CRP+BSP to 9.15%+CRP+BSP, SBI’s external benchmark-based lending rate (EBLR) increased. SBI RLLR From 8.50%+CRP to 8.75%+CRP, SBI Repo Linked Loan Rate (RLLR). SBI home loan lending rates The regular house loan interest rate has a minimum of 9.15% (EBR+0%) for consumers with credit scores of 800 or higher, according to the SBI website. In this case, no risk premium is imposed. For borrowers with credit scores between 750 and 799, the risk premium in this situation is 10 basis points (EBR+0.10%), translating to an interest rate of 9.25%. The interest rate is 9.35% (EBR+0.20%) with a risk premium of 20 basis points for a CIBIL score between 700 and 749. The interest rate is 9.45% plus a risk premium of 30 basis points (EBR+0.30%) for scores between 650 and 699. Please take note that these loans will have a 0.05% decrease for female borrowers. SBI is extending its concession rates through March 31, 2023, it should be emphasised. When a concessional rate is used, the loan rates will be altered (lower). Axis Bank Increases FD Interest Rates In

Select stocks for Intraday trading is the practise of buying and selling a security on the same day in order to benefit. It is a market order in which you do not intend to take delivery to demat or fulfil it. In other words, if you place an intraday order to buy shares, you don’t want to buy them but are expecting for the share price to rise so that you can sell them before the trading day ends. These orders also allow you to sell shares even if you don’t possess them first and then buy them later in the day to complete the transaction. Because you only have a few hours before closing your position, success in intraday trading is strongly reliant on selecting the appropriate stocks. As a result, selecting the right stock is critical. This blog will teach you about Stock Selection for Intraday Trading. Let’s go over some ideas to assist you fine-tune your stock selection strategy. Please keep in mind that each person has their own profile and investing goals. You can use this blog as a starting point to further build your strategies. Before selecting a stock, please examine your risk tolerance and complete the appropriate due research. Before we get into how to choose a company for intraday trading, it’s crucial to remember that as a day trader, you don’t have the luxury of holding on to the stock, so one bad pick can lead to big losses. If you wish to buy and sell a commodity in one day, it should have specific characteristics. In high demand (liquidity)Price changes allow you to buy low and sell high (volatility)Market TrendsTrends in the industryStocks on the riseTechnical Evaluation Here’s an example to help you understand: Assume you wish to buy and sell onions for a profit. You will succeed if there is continuous trading in onions throughout the day, with prices fluctuating according to supply and demand (onions). Furthermore, there should be sufficient facts to support the claim that the general market is doing well, that business and the onion sector are thriving. Furthermore, current trend should favour onion trade. If these conditions are met, you will be in a strong position to benefit. The similar method is used to select intraday stocks for trading. How to Select Right Stocks for Intraday Trading Here are some things to think about before picking stocks for intraday trading and figuring out how to pick stocks for intraday trading the day before. LiquidityAs stated in the preceding example, the liquidity of the said liquid stock should be high to ensure that you can buy and sell stocks at any time. Another essential component of purchasing high liquidity stocks is that they typically have large volumes. As a result, you can buy and sell big amounts without affecting the stock price. When selecting highly liquid equities, make careful to consider liquidity at various price levels. While some stocks may have significant liquidity at low prices, high volumes can drop dramatically once a particular level is reached. Understanding this can assist you in purchasing them at the appropriate moment. Volatility (Medium-to-High) Day traders profit only when the price moves in the direction they expect. Traders may incur losses if the price moves contrary to expectations. If the stock price is erratic, they can place more intraday orders and profit from price fluctuations. However, buying highly volatile stocks can be detrimental if the drop/rise is too rapid. While there are no hard and fast rules, most intraday traders prefer stocks that move between 3-5% on either side. Market TrendsMarkets tend to move higher or downward based on economic, political, social, and other reasons. Stocks and markets have a positive or negative association. This means that if the markets rise, stock prices will rise or fall. As a result, keeping this correlation in mind is critical when purchasing stocks for intraday trading. Trends in the IndustryThe market is separated into industries such as technology, pharmaceuticals, automobiles, oil and gas, FMCG, and banking. As an intraday trader, you must be aware of the performance of all areas of the economy. If you discover a sector or sectors that have been consolidating for months and are about to break out, you can hunt for companies in that area to invest in. In addition, if you identify any stocks that have achieved significant gains but you did not capitalize on the surge, you can look for peer stocks in the same sector that are in the possible-breakout zone. Stocks’ MomentumThe stock’s price movement is defined as the rate at which its price changes over time. This might assist you in determining the strength of an upward or negative trend in the stock’s price. If the stock price swings in tandem with the strength of the momentum, the stock is referred to be a momentum stock. These stocks are used by day traders to benefit by going long (upward trend) or short (downward trend). Technical AnalysisAside from stock momentum, there are several methodologies that can assist you in conducting technical analysis of stocks to find buy or sell signals. You might look for equities that have price gaps in either way. These gaps might be profitable for day traders. Technical analysis can also be used to identify equities that are breaching their support and resistance levels. Your Take to Choose Stock For Day Trading  Remember that buying the appropriate stocks is critical to making money in intraday trading. While the following trading strategy can assist you in locating them, you will eventually learn how stock selection works best for you. To make money through intraday trading, stay objective and data-driven. Avoid making emotional decisions. Good luck with your investments!

Red herring prospectus(rhp) is a document submitted by a securities company for investors of an public offering(IPO) to the securities and exchange board

You need to know stocks and how they work. The following basic concepts will help new investors grasp how does stock market work.

Monte Carlo simulation is a statistical method is used to evaluate the probability of a specific outcome by modeling various scenarios and observing the results. This technique is named after the Monte Carlo Casino in Monaco and was originally developed for use in the field of physics. Today, the Monte Carlo experiment is in a wide range of industries, including finance, engineering, and computer science. What is meant by Monte Carlo simulations. The core concept of Monte Carlo simulation software is simple: simulate a large number of scenarios to estimate the likelihood of different outcomes. To do this, a model is created that contains all of the variables and relationships that are deterministic to the outcome being studied. Random samples are then generated for each variable, and the model is run to see what happens. This process is repeated many times to build up a large sample of results, which can then be analyzed to use numbers in the estimation of the probability of various outcomes. Can linear systems be modeled via Monte Carlo simulations? One of the key benefits of Monte Carlo simulation is that it allows for the modeling of complex and non-linear systems. This is because the simulation can take into account a wide range of variables and their inter-relationships, including interactions between variables and the presence of uncertainty. This makes it a valuable tool for decision-makers who need to make informed choices in the face of uncertainty and risk using a mathematical technique. Which domains use Monte Carlo simulations?  Monte Carlo simulation is widely used in finance, where it evaluates the potential returns and risks associated with investment portfolios. For example, a financial analyst might use Monte Carlo computation to estimate the probability that a particular portfolio will meet a specific return target, taking into account various factors such as market conditions, investment strategy, and the performance of individual assets. Another application of Monte Carlo simulation is in engineering, where it models the behavior of complex systems. For example, an engineer might use Monte Carlo numerical to determine the reliability of a new design, by modeling various scenarios that could impact its performance. This information can then be used to improve the design and reduce the risk of failure and for optimization purposes. In computer science, Monte Carlo simulation helps to model a wide range of systems and processes, including algorithms, artificial intelligence, and machine learning. For example, a computer scientist might use Monte Carlo simulation to evaluate the performance of a new algorithm by modeling its behavior in various scenarios and observing the results. This information can then be used to refine and improve the algorithm by making it uniformly distributed. What are random numbers? How are they used in Monte Carlo simulations? Random numbers are figures produced by an unpredictable process. They have a wide variety of applications, including descriptive statistics, cryptology, gaming, and simulation. There are algorithms in computer programming that generate sequences of figures that meet certain statistical prerequisites for unpredictability. These numbers are typically generated using a seed value and a set of conditions and can be utilized to simulate real-world occurrences such as stock prices and weather patterns. These numbers have the important property of not being predictable, as such, they can be employed to design ambiguity and offer a way to analyze the outcomes of different scenarios. Random variable play a crucial role in the Monte Carlo simulation method. Monte Carlo simulations are a type of modeling that relies on sampling to simulate real-world situations. In these simulations, they are used to model uncertainty and variability. By generating multiple sets of numerical values, Monte Carlo simulations can produce a range of possible outcomes for a given scenario, allowing analysts to understand the potential impact of different variables. For example, in financial simulations, these numbers can be used to model stock prices or interest rates. By generating multiple sets of random numbers based on historical data, analysts can estimate the potential return on investment over time. Similarly, in engineering simulations, they can be used to model variables such as material strength or manufacturing tolerance, allowing engineers to evaluate the performance of a design under a range of conditions. The random numbers in Monte Carlo methods are used by typically generated using a generator algorithm, which produces a sequence of numbers that meet certain requirements for randomness. The key property of these random numbers is that they should not be predictable, so they can be used to model uncertainty and provide a way to analyze the outcomes of various scenarios. Limitations of Monte Carlo simulation? Despite its many benefits, Monte Carlo integration is not without its limitations. One of the main challenges is the complexity of the models used in the simulation, which can be difficult to build and validate. Additionally, Monte Carlo simulation can have some computational intensive issues like requiring large amounts of computing power and memory to run the simulations. Another challenge is the interpretation of the results. Since Monte Carlo simulation models are based on fvalues the results can be difficult to interpret and understand, particularly for those without a strong background in mathematics or experiment who might not conceptually understand the concept of monte carlo methods. Finally, Monte Carlo method is not a substitute for actual data and real-world experience. While it provides a valuable tool for decision-makers, it should not be relied upon as the sole source of information when making important decisions and forecasts. In conclusion, Monte Carlo simulation is a powerful method that can be used to evaluate the probability of various outcomes in a wide range of industries and applications. Despite its limitations, it is a valuable tool for decision-makers who need to make informed choices in the face of uncertainty and risk. Whether you are a financial analyst, engineer, or computer scientist, Markov chain Monte Carlo simulation can provide valuable insights and help you make better decisions. What are the 5 steps in a Monte Carlo Simulation? The five steps

Markov chains are an important data science tool because they can be used to model and analyze complex systems as well as predict future states. They are particularly useful for analyzing data sequences such as text, video, audio, or other digital data. Markov chains can also be used to simulate a system’s behavior over time, which can help with decision-making, forecasting, and optimization. What is the Markov chain used for in real life? Markov chains are used to model a system’s behavior over time. They are used to forecast a system’s future states based on its recurrent state and the probabilities of state transitions. Markov chains can be used in a variety of fields, including economics, biology, genetics, computer science, and finance. They are also used in natural language processing to model languages, recognize speech, and generate text. Natural language processing, speech recognition, financial analysis, predictive modeling, and bioinformatics are just a few of the real-world applications for Markov chains. Markov chains, for example, are commonly used in speech recognition to predict the likelihood of one word following another. They can be used in natural language processing to analyze large amounts of text data and identify patterns or relationships between words. Markov chains are frequently used in financial analysis to forecast stock market behavior based on past trends. Finally, Markov chains can be used in bioinformatics to analyze protein sequences and identify patterns that may indicate the function or evolutionary relationships. What are Markov chains examples? 1. Text Generation: Markov chains can be used to generate new text by taking the existing text as input and generating a probability distribution of what words will appear next. 2. Image Captioning: Captions for images can be generated using Markov chains. The chain can determine what objects are in the image and suggest possible captions based on that information by analyzing the image. 3. Music Composition: Markov chains can also be used to compose music by generating a series of notes based on probabilities derived from existing music patterns. 4. Weather Forecasting: By analyzing past trends and creating probability distributions of potential outcomes, Markov chains can be used to forecast future weather events. Is Markov chain irreducible? Yes, Markov chains are irreducible. That is, given any two states in the chain, it is possible to transition between them in a finite number of steps. Do Markov chains always converge? Contrary to popular belief, they do not always converge. Markov property can either converge to a steady state or cycle between different states depending on the initial conditions. When is a Markov chain periodic? It is periodic if there is an integer n>1 such that after n steps, all states in the chain return to their original state matrix. What is the Markov chain in probability? They are a type of stochastic process model system transition between states. They are commonly used to describe random processes in probability and statistics. The probability of transitioning from one state to another in a stochastic is determined solely by the current state and not by any prior states. As a result, it is useful for modeling processes with no memory of the past, such as stock prices or weather patterns. How to make a Markov chain model? 1. Determine which system you want to model.2. Determine the system’s states and assign a numerical value to each.3. Select transition probabilities that describe the likelihood of changing from one state to another.4. Determine the system’s stationary distribution of states, which is a measure of the likelihood that any given state will be reached after a large number of possible state spaces.5. Using this stationary distribution, multiply the initial probability by the stochastic matrix to calculate the probability of transitioning from one state of the Markov chain to a two-state markov.6. Use these probabilities to simulate random state transitions and observe how they affect the system’s long-term behavior over time, such as expected values for certain variables. How to simulate the Markov chain? 1. Define your Markov chain’s states and transitions: The states are the possible values of a random variable, and the transitions are the methods for moving from the present state. 2. Determine the transition probabilities: This is accomplished by constructing a transition probability matrix, which contains the probabilities of transitioning from the previous state. 3. Generate an initial state: This is usually done at random or based on prior knowledge. 4. Create a Markov chain trajectory by using a loop that generates the next state based on the recurrent state and its associated probabilistic. Record the current state and any other relevant information, such as time elapsed or other parameters, at each step. 5. Analyze the results: Depending on your goals, this could entail calculating statistics (e.g., expected time in certain states) or plotting visualizations (e.g., heatmaps and mathematical matrix). Are Markov chains memoryless? Yes, they have no memory. This means that the transition probabilities of a Markov chain monte carlo are determined solely by the current state and not by the preceding sequence of events. How to plot Markov chain in python? The NetworkX library can be used to plot a Markov chain in Python. NetworkX is a Python package for creating, manipulating, and studying complex networks’ structure, dynamics, and functions. You can use NetworkX to create a graph object, add nodes and edges, and then draw the graph with matplotlib. To plot a Markov process chain, you must first define the transition matrix, which is a matrix that describes the probability of changing state of the process. The graph can then be plotted using the NetworkX draw() function diagram matrix. What is an irreducible Markov chain? An irreducible Markov model is one in which any state can be reached from any other state in a finite number of steps in use of continuous time markov matrix. This means that it is not possible to be “trapped” in a specific state, and that any other state can be reached from any other state in a