To analyse stocks and predict the market movement, there are various factors such as economic indicators, technical tools, global events, company financials, etc. Generally, it is difficult for a retail investor to analyse all these factors at once and make an informed decision. This is where machine learning has emerged as a game-changer by offering powerful tools to analyze complex data patterns and enhance market prediction accuracy.
In this blog, we’ll provide an overview of stock market prediction using machine learning, including its complete process, key advantages, and potential risks involved in applying ML techniques to financial markets.
What is Machine Learning in Stock Market Prediction?
In predicting stock market movements, machine learning helps in various ways through the application of data-driven algorithms that learn from past and present market data to predict future stock price movements. In comparison to rule-based systems, machine learning models can identify complex, irregular patterns and improve with additional data. It has the capability to analyze huge amounts of data quickly and execute trades in milliseconds.
How Machine Learning Helps in Predicting the Market Trend?
Machine learning helps in predicting the market trends by following the below-mentioned process:
Enter input data: To develop robust models, it is important to enter reliable data, such as stock price data, sentiment scores, P/E ratios, RSI, and moving averages.
Discovering Non-Linear Relationships: The stock market is not linear by nature. Its actual dynamics are frequently not captured by straightforward linear correlations. Machine learning is excellent at figuring out complex, non-linear relationships between different market variables, which results in predictions that are more precise.
Data Processing: Machine learning collects the market data such as historical prices, volume, financials, news, macroeconomic events, etc. Based on the collected data, ML algorithms process it into meaningful insights and make decisions based on it.
Minimising Human Interference: Fear and greed are two emotions that have a big influence on trading choices. Through machine learning models, emotional biases from investment strategies are removed because they only use data and algorithms.
Real-time Adaptation: ML models can be trained and updated on new data, so that they can instantly respond to changing economic and market conditions.
Benefits of Using Machine Learning for Stock Market Prediction
The significant benefits of using machine learning for stock market prediction are as follows:
Identifying Pattern: Machine learning algorithms are able to spot complex and unusual trends in past stock data that human analysts may fail to recognise.
Data Analysis: In order to make well-informed, unbiased trading decisions, machine learning models analyse vast amounts of financial data, including sentiment, company fundamentals, price history, and macroeconomic indicators.
Efficiency: Traders can take advantage of short-term market opportunities and act more quickly, thanks to machine learning’s ability to process and analyse real-time data in milliseconds.
Flexibility: Machine learning models are more efficient than traditional methods that depend on rules, as they can continuously learn from new data and respond to changes in the market.
Emotional Bias: Machine learning models make decisions based on data, thereby reducing emotional bias and enhancing decision-making.
Risk of Using Machine Learning for Stock Market Prediction
The key risks of using machine learning for stock market prediction are as follows:
Data Quality: If the quality of data used by machine learning algorithms is outdated, the predictions based on them can be incorrect and result in losses.
Lack of Transparency: The machine learning algorithms usually lack transparency, making it difficult for investors to understand the logic behind the executed trades.
Cyber Threats: Any kind of changes in the ML models by cyber attackers can lead to losses.
Limited Human Interference: There can be certain scenarios in which human judgement may be required. Due to a lack of human interference, the outcomes can be unfavourable sometimes.
Conclusion
On a concluding note, machine learning has changed the stock market in a very significant manner. Both institutional and retail investors can benefit greatly from machine learning. It can help in analysing raw data from various sources and make informed decisions based on predefined algorithms. However, using machine learning has some disadvantages, such as a lack of transparency etc. Therefore, it is advisable to consult your investment advisor before making any investment decision.
S.NO.
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How accurate is machine learning in predicting the trend of stock prices?
Machine learning algorithms can identify the pattern of stock price, but they are not 100% accurate because of unforeseen factors and market conditions.
How can machine learning help in portfolio management?
Machine learning helps in managing one’s portfolio by determining asset allocation, portfolio rebalancing, and predicting the estimated returns in one’s portfolio.
Is it necessary to have a finance background to use machine learning?
No, it is not necessary to have an understanding of finance before using machine learning for predicting stock prices.
Is machine learning available for retail investors?
Yes, retail investors can also use machine learning through AI-powered platforms, algorithmic tools, and robo-advisors.
What type of data is used by machine learning to predict the stock price?
The data such as historical prices, financial ratios, trading volumes, etc., are used by machine learning algorithms to predict the future stock prices.
Savy Infra & Logistics Limited is an experienced EPC company founded in January 2006. The company specializes in infrastructure projects such as road construction, foundation work, embankment construction, and surface paving. Savy Infra & Logistics also provides demolition services and is engaged in mechanical excavation, shoring, strutting, slush removal, and debris disposal. The company’s business model is asset-light, that is, it hires trucks, drivers, and machinery to efficiently complete projects. Its EPC and logistics projects have been successfully completed in several states including Gujarat, Maharashtra, Telangana, Odisha. The company has experienced promoters and a strong management team, which is driving its growth.
In this blog, we will give you all the important information related to this IPO like GMP (grey market premium), subscription details, allotment status, and the process to check it.
Savy Infra & Logistics IPO – Key Details
Particulars
Details
IPO Opening Date
July 21, 2025
IPO Closing Date
July 23, 2025
Price Band
₹114 to ₹120 per share
Total Issue Size
₹69.98 crore (58.32 lakh shares)
Listing Platform
NSE SME
Registrar
Maashitla Securities Private Limited
Important Dates for Savy Infra IPO Allotment
Event
Date
Tentative Allotment
July 24, 2025
Initiation of Refunds
July 25, 2025
Credit of Shares to Demat
July 25, 2025
Tentative Listing Date
July 28, 2025
Savy Infra & Logistics IPO Subscription Status
Category
Shares Offered
Shares Bid For
Subscription
Qualified Institutional Buyers (QIB)
11,07,600
10,30,24,800
93.02×
Non-Institutional Investors (NII)
8,31,600
16,33,63,200
196.44×
Retail Individual Investors (RII)
19,39,200
17,76,76,800
91.62×
Total (Public)
38,78,400
44,40,64,800
114.50×
Anchor Investors
16,60,800
16,60,800
1x
Market Makers
2,92,800
2,92,800
1x
How to Check Savy Infra IPO Allotment Status
Savy Infra IPO allotment status can be checked online very easily. There are two main ways for this – the IPO Registrar’s website and the official website of NSE. Note that this IPO is being listed only on the NSE SME platform, so its allotment status will not be available on the BSE site.
Method 1: Check from Registrar’s website
The most reliable way to check Savy Infra IPO allotment status is by visiting the official website of its registrar
Select “Savy Infra and Logistics Ltd.” from the dropdown list.
Enter your PAN number, Application ID or DP/Client ID.
Click on the Submit button.
If you have been allotted shares, the screen will show “Allotted”, else “No Allotment”.
Method 2: Check from NSE’s website
If there is heavy traffic on the registrar’s website or there is a technical issue, then you can also check the allotment status from the official site of NSE.
How to do :
Go to the NSE site
There you will get the option to check the allotment status.
Select the company name i.e. Savy Infra Limited.
Fill in the PAN number and Application Number.
Click on “Submit”.
Your IPO allotment status will be visible on the screen in a few seconds.
The GMP of Savy Infra IPO on 23 July 2025 has been recorded at around ₹25. Since the price band of this IPO has been fixed between ₹114 to ₹120, so if we look at the upper price of ₹120, then the possible listing price can be up to ₹145. That is, investors are expected to get a profit of more than 20% on listing.
What is GMP?
GMP i.e. Grey Market Premium is the unofficial premium of an IPO share at which it is being traded in the off-market before listing. It gives investors an indication of the price range in which the stock may open on the day of listing. However, this is an unofficial market data and it fluctuates very quickly, so it should be seen only as an estimate.
Can GMP be trusted?
GMP does give investors an early indication of listing day trends, but it is entirely based on market sentiment and can change at any time. It is an unregulated market where off-record transactions take place between dealers and traders, so investment decisions should not be made only by looking at GMP.
What to keep in mind before investing?
If you are thinking of investing in Savy Infra IPO, do not rely only on GMP. Evaluate the ground facts like the company’s financial performance, business model, order book and management team. Also, liquidity and volatility are also high in SME IPOs, so investing from a long term perspective will be a more sensible move.
The current GMP of Savy Infra IPO is around ₹25, which indicates that the market is positive about this issue. Nevertheless, this is a provisional indicator and investors should invest wisely keeping in mind the strength of the company and their risk appetite.
What to Do After Allotment?
What to do now if the shares have been allotted?
If you have got shares in the Savy Infra and Logistics IPO, then these shares will be credited to your demat account by July 25, 2025. After this, the listing of the IPO will be done on the NSE SME platform on July 28, 2025. According to the latest gray market reports, the gray market premium of this IPO is running at ₹25. That is, its potential listing price can be around ₹145. If you are a short-term investor, then decide to book profits by looking at the price movement, market sentiment and volume on the listing day. On the other hand, long-term investors can think of holding keeping in mind the basic fundamentals of the company, industry trends and future projects.
What to do next if the shares are not allotted?
If your shares are not allotted, your funds blocked for the IPO will be unblocked by July 25, 2025. This process is automatic and no additional action is required for this.
After getting the funds, you can use them to invest in any other SME or mainboard IPO. Also, in future it will be beneficial to check GMP, retail subscription and company background carefully before investing.
Conclusion
Savy Infra & Logistics IPO is open for subscription from July 21 to July 23, 2025 and it is expected to be listed on NSE SME platform on July 28, 2025. The company is engaged in the construction and logistics sector, and aims to use the funds raised from the IPO for working capital and purchase of equipment. Investors should take a decision keeping in mind the company’s financial position, sector growth and market risks. Post-listing performance will entirely depend on market sentiment.
Frequently Asked Questions (FAQs)
What are the IPO dates of Savy Infra & Logistics?
This IPO will be open from 21 July 2025 to 23 July 2025.
On which exchange will the Savy Infra & Logistics IPO be listed?
This IPO will be listed only on the NSE SME platform.
When will the allotment of this IPO be finalized?
Its allotment will be finalized on 24 July 2025.
What is the refund date for unallotted investors?
Investors who will not get shares, their funds will be unblocked by 25 July 2025.
When will the shares be credited to the demat account?
The shares will come to the demat account of investors on 25 July 2025.
Swastika Castal Limited, incorporated in 1996 and headquartered in Vadodara, Gujarat, is a specialist aluminium casting manufacturer offering sand, gravity die and centrifugal casting processes. With nearly three decades of experience, it produces components ranging from 70 kg to 250 kg for electrical equipment, railways, diesel engines, automotive and industrial applications. The company boasts advanced in-house heat treatment and quality inspection facilities, a skilled metallurgical team and a global vendor network.
In this blog we will tell you how to check the allotment status of this IPO, what is its Grey Market Premium (GMP), and what has been the subscription status so far. It is important for every investor to know about their IPO allotment status; so that they know whether they have got shares or not. Now, let us discuss the key details of the IPO, its GMP and the process to check your allotment status.
The current grey market premium (GMP) of Swastika Castal SME IPO is recorded at ₹0. This simply means that there is neither much demand for this IPO in the grey market nor any special premium is being added to it. The issue price of the IPO is ₹65, and given the current GMP, no listing gain is expected based on the current GMP of ₹0.
1. What does GMP being ₹0 mean for investors?
The GMP being zero indicates that the market sentiment remains neutral about this IPO at the moment. This does not mean that the IPO is bad, but it is an indication that there is neither enthusiasm nor fear about it in the grey market. In such a situation, there is little hope of getting any big listing gains on the first day of the IPO. However, any investment decision should not be made just by looking at the GMP. Many factors like the company’s financials, subscription status and sector situation together present the real picture of an IPO.
2. Is it right to look only at GMP?
GMP is only an informal indicator, which reflects the current sentiment of the market. It can also change rapidly, especially when the market sentiments suddenly turn positive on listing day. Therefore, before investing, it is important to pay attention to the financial statements of the company, the background of the promoters and the long-term growth potential.
The GMP of Swastika Castal IPO is currently at ₹0, which shows that there is no special interest in the market about the IPO at the moment. GMP may change before listing, but it would be wise to take the final decision only after looking at the strengths of the company and the market trend.
How to Check Swastika Castal IPO Allotment Status?
You can easily check the allotment status of Swastika Castal IPO online. There are two official ways for this – Registrar’s website and BSE’s website. Note that this IPO is being listed only on the BSE SME platform, so it is not possible to check allotment from NSE.
Method 1: Via Registrar’s Website
The easiest and most reliable way to check Swastika Castal IPO allotment is through the Registrar’s website.
How to:
1. Visit the official website of the Registrar.
2. Select “Swastika Castal Limited” from the IPO list.
3. Enter your details –
PAN Number,
or Application Number,
or DP/Client ID
4. Click on “Submit”.
You will see your allotment status.
Method 2: Check from BSE website
You can also check the allotment status from the BSE website.
3. Select “Swastika Castal Limited” from the dropdown.
4. Enter your PAN or Application Number.
5. Click on “Search” and view the allotment status.
What to Do After Allotment?
After getting the allotment of Swastika Castal IPO, you should pay attention to some easy steps, so that you can track your investment properly.
If shares are allotted : If you have been allotted shares, they will start appearing in your Demat account by 25 July 2025. You can sell them on the day of listing or hold them for a long term.
If shares are not allotted : If you do not get allotment, your money will be automatically refunded to your bank account in a few days.
Pay attention to SMS and Email notifications : You will get information related to allotment from the registrar or your broker through SMS or email. Keep a regular check on them so that you can catch any discrepancy immediately.
Investors have shown interest in Swastika Castal IPO, but as their overall current GMP is ₹0, no listing gains can be expected as of now. This means that the listing of the stock can happen around the issue price. In such a situation, investors who have got allotment should avoid taking hasty decisions and should decide keeping in mind the market trend on the day of listing. If allotment is not received, then your money will be unblocked. According to current indications, the IPO response has been ordinary, so be cautious before investing.
S.NO.
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Trading today is very different from what it used to be. It’s no longer just humans making decisions—machines are now actively analyzing the markets in real time. AI i.e. Artificial Intelligence has changed trading to a great extent by estimating price movements and reading market sentiments faster than humans.
In this blog, we will understand some types of AI trading strategies, AI models associated with each strategy, and their limitations.
What is AI Trading Strategy?
AI Trading Strategy is a trading system that uses artificial intelligence to understand market data, learn and then take trading decisions based on that. This strategy is different from the traditional rule-based system because in an AI trading strategy, humans do not analyze market conditions, or design rules and type in orders, but the machine itself becomes better through experience and availability of latest information for analysis.
In AI trading strategy, many factors like past data, price movement, volume, news sentiment are analyzed. Then models based on machine learning and deep learning create trading signals from that data. Its biggest advantage is that this system adjusts the strategy according to the changing market conditions. Simply put, AI Trading Strategy is a smart, data-driven and continuously improving way of trading.
Trading with AI is no longer just a tool, but an entire concept. Below, we will take a closer look at some of the key AI trading strategies: how they work, under what circumstances they produce better results, and what types of traders they may benefit from.
Predictive Modeling Strategies
Sentiment Analysis Strategies (NLP)
Reinforcement Learning Strategies
Deep Learning-Based Pattern Recognition
AI-Based Portfolio Optimization
High-Frequency AI Strategies
Multi-Model Hybrid Strategies
A brief overview of the strategies mentioned above has been given below:
1. Predictive Modeling Strategies
Predictive Modeling is considered a basic but very effective strategy in the world of AI trading. Its purpose is to predict future price movement or volatility, that too on the basis of historical data. In this, machine learning algorithms analyze past price data, volume, market trends and patterns to predict the direction in which the stock or index can go in the future.
Which models are used?
Linear Regression : For simple price trend prediction with one dependent variable
Decision Trees & Random Forest : To work with more variables
LSTM (Long Short-Term Memory Networks) : To catch long term patterns in time series data
ARIMA & Prophet : For traditional time series forecasting
For whom is it beneficial?
This strategy is especially useful for day traders, swing traders and those doing statistical arbitrage, as it helps them identify price reversal or momentum shift early.
What are its limitations?
Predictive models are powerful, but prone to overfitting, i.e., relying too much on old data, is a big problem. Apart from this, if a big event suddenly occurs in the market (such as geopolitical tension or an unexpected policy decision of the RBI), then these models can also give false signals.
Tools used:
Python,
Scikit-learn,
TensorFlow / Keras,
Predictive modeling is a great way to make short-term trading decisions based on data but it should always be used after backtesting and with risk management.
2. Sentiment Analysis Strategy
In Sentiment Analysis Strategy, AI tries to understand what people think or feel about a stock, sector or the entire market – i.e. positive, negative or neutral. This strategy helps in taking trading decisions by reading data from news, social media, reports and analyst comments.
How does this strategy work?
AI uses Natural Language Processing (NLP) in this, which scans the text and gives a sentiment score to each statement. For example: If there are continuous positive things being said about Infosys in the news and tweets like better results or a new big contract then the system can consider it as a buy signal.
Which models are used?
FinBERT : A model trained specifically for financial text
VADER : For short texts like tweets and headlines
RoBERTa or GPT-based models : For understanding deep sentiment and context
Custom lexicon models : Fast and lightweight sentiment scoring tools
What are its limitations?
Models sometimes fail to understand sarcastic or ambiguous language correctly
Reinforcement Learning (RL) is an advanced and continuously learning approach in the world of AI. In this, the system learns by itself from the results of trades conducted based on prior trading logic, and then tries to make a better decision next time from that experience.
How does it work?
The RL system works like an agent that takes action (buy, sell, hold) in the trading environment (such as market data). After every action, it gets a reward or penalty from which it refines its trading decisions.
If a trade goes into profit, the system will try to recognize the same pattern again in the future
If there is a loss, it will weaken that decision logic
Gradually the system learns by itself and makes better trading decisions over time.
Which models are used?
Q-Learning : In AI models that learn to make optimal trading decisions based on trial and error.
Deep Q Networks (DQN) : in complex trading scenarios with high number of variables
PPO (Proximal Policy Optimization) : a popular RL model for fast-changing markets
DDPG (Deep Deterministic Policy Gradient) : in continuous action spaces such as portfolio management
Who is this strategy for?
Portfolio managers : for asset allocation and auto-rebalancing
Algo traders : who want a fully automated, learning-based system
Retail traders : who can build their own models with tools like Python and TensorFlow
What are its limitations?
RL takes a lot of data and time to train properly
Wrong reward functions or biased training can lead to overfitting or wrong decisions
If testing is not done properly in live markets, then there is a high chance of loss
Which tools and platforms help?
Python libraries: Stable-Baselines3, OpenAI Gym
Frameworks: TensorFlow, PyTorch
Backtesting tools: Backtrader, Zipline
Indian brokers with APIs: Pocketful api ,Zerodha, Angel One, Fyers
Reinforcement learning is great for long-term strategy, but applying it directly to the live market without solid backtesting and risk control is risky.
4. Pattern Recognition Strategy
The aim of a pattern recognition strategy is to identify hidden price movement patterns in history and infer future possibilities from them. To do this, Deep Learning, especially Convolutional Neural Networks (CNNs), is used. CNN models scan price charts just like humans do with their eyes but with much more accuracy.
How does it work?
CNNs were originally designed to recognize images (such as recognizing faces or number plates). But they are now trained on trading visual data such as candlestick charts, price graphs, and volume maps.
AI automatically recognizes patterns like Head & Shoulders, Cup & Handle, Double Top/Bottom
No manually drawn trendlines or rule-based logic is required
Once trained, the model continuously scans charts in real-time and gives alerts
Major deep learning models used:
CNN (Convolutional Neural Networks) for image-based pattern identification
LSTM + CNN for capturing patterns as well as time series behavior
Autoencoders for anomaly detection from historical patterns
GANs (Generative Adversarial Networks) for creating and training synthetic chart data
Who is this strategy for?
Technical analysts who take decisions from chart patterns
Quant traders who want historical pattern-based entry/exit
Retail investors who want to trade with the help of automation and alerts
What are the limitations?
Not every pattern always gives accurate future signals there can be false signals
CNN needs thousands of images and correct label data to perform well
Lag and delay in live market can reduce the effectiveness of the signal
Tools and platforms:
Python libraries
Chart data sources
Broker APIs
Backtesting tools
5. AI-Based Portfolio Optimization
AI not only gives trading signals, but has also become a big game changer in Portfolio Optimization. In this strategy, machine learning algorithms manage your entire investment in such a way that returns are maximized and risk is minimized.
AI algorithms do a deep analysis of historical data (such as stock returns, volatility, correlation, etc.) and tell you which assets should be there in your portfolio and in what quantity.
How is it being used in India?
Today in India, platforms and many PMS (Portfolio Management Services) are using AI-driven tools in their asset allocation. Large funds and robo-advisors like Cube Wealth or INDmoney also use AI models in asset rebalancing and goal-based investing.
Popular algorithms used in this:
Personalized portfolios are created by enhancing Markowitz Mean-Variance Optimization with AI
Subjective views are factored in through machine learning in the Black-Litterman model
Dynamic rebalancing is done according to market conditions using Deep Reinforcement Learning
If you are a SIP or goal-based investor, AI-powered portfolio rebalancing can help you reduce long-term risk and give you better returns.
6. High-Frequency Trading (HFT)
High-Frequency Trading (HFT) is a strategy where thousands of trades are executed in a matter of seconds or milliseconds. When AI algorithms are added to it, this strategy becomes even faster, accurate and profitable. AI is used here to analyze market microstructure, predict price movement at the millisecond level and make lightning-fast decisions. Speed is the biggest edge here.
How does this strategy work?
AI models such as neural networks read the order book, bid-ask spread and liquidity depth in real-time.
As soon as a profitable pattern or arbitrage opportunity is found, the algorithm immediately places the order.
Co-location servers and ultra-low-latency networks are used for trade execution.
Use in India:
HFT started in India in the 2010s, but regulation in it has become stricter after SEBI guidelines and fair access norms. Nevertheless, large institutional players such as global firms such as Jane Street, Tower Research, and Virtu Financial still use AI-driven HFT models.
Important:
This strategy is not for ordinary retail investors as it has high requirements of infrastructure, capital and regulatory compliance.
SEBI is now working on a new framework to regulate algorithmic trading to minimize unfair advantages.
If you are a retail investor, HFT strategy may be out of your reach, but understanding its principles will definitely help you in long-term strategy planning.
7. Multi-Model Hybrid Strategies
Multi-Model Hybrid Strategies is a technique that combines different types of AI models to make trading decisions more accurate and balanced. This strategy helps in more intelligent and flexible decision-making by covering the limitations of an individual model.
How does it work?
This approach uses machine learning models (such as Decision Trees or SVM), deep learning models (such as LSTM or RNN), and statistical models (such as ARIMA) together.
Different models are trained on different data sets – one reads price action, one analyzes news sentiment, and one understands volume trends.
The AI system then combines the output of all the models and generates a consensus-based final trading signal.
Use in India:
Some modern Indian hedge funds and quant-based PMS providers are now using ensemble models to generate more consistent returns in volatile Indian markets.
Advantages:
Even if a single model fails, the system remains robust due to the other models.
It is adaptable to different market conditions – can handle sideways, bullish, bearish.
Conclusion
AI trading strategies are no longer limited to big hedge funds; they are opening new opportunities for ordinary investors and active traders. Whether it is trend following or portfolio optimization, each strategy can work wonders in different market conditions if used correctly. But remember, AI is a tool, use it wisely and don’t trust blindly. Keep learning, keep testing, and gradually refine your strategy.
S.NO.
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In today’s fast-paced markets, algorithmic trading is more popular than ever and so is the demand for the right books to master it. Gone are the days when trading relied solely on human decisions; now, intelligent computer programs execute trades in milliseconds. If you’re eager to dive into the world of quantitative trading, starting with the right resources is key.
In this blog, we are sharing a list of selected and latest best algorithmic trading books, which will help you in building a strong foundation in this field.
Top 10 Best Algorithmic Trading Books
Book Title
Author
Goodreads Rating
Key Highlights
Algorithmic Trading: Winning Strategies and Their Rationale
Ernest P. Chan
3.85/5
Includes practical Python strategies with backtesting
Quantitative Trading: How to Build Your Own Algorithmic Trading Business
Ernest P. Chan
3.75/5
Focuses on business mindset and systematic execution
Advances in Financial Machine Learning
Marcos López de Prado
4.13/5
Covers machine learning, financial data science, and ML validation
Building Winning Algorithmic Trading Systems
Kevin J. Davey
3.85/5
Includes Monte Carlo simulations and live trading insights
Inside the Black Box: The Simple Truth About Quantitative Trading
Rishi K. Narang
3.73/5
Explains how quantitative systems work without complicated mathematical formulas
The Science of Algorithmic Trading and Portfolio Management
Robert Kissell
3.91/5
Focuses on statistics, market impact, and portfolio optimization
Python for Algorithmic Trading: From Idea to Cloud Deployment
Yves Hilpisch
3.88/5
End-to-end trading system in Python, cloud-ready solutions
Algorithmic Trading and DMA
Barry Johnson
3.87/5
Deep explanation of direct market access and execution
Systematic Trading
Robert Carver
4.26/5
A unique approach to designing repeatable and scalable strategies
Algorithmic and High-Frequency Trading
Álvaro Cartea, Sebastian Jaimungal, José Penalva
3.93/5
Covers HFT architecture, market microstructure, and risk control
Brief Overview of the Best Algorithmic Trading Books
A brief overview of the 10 best algorithmic trading books is given below:
1. Algorithmic Trading: Winning Strategies and Their Rationale
This book by Ernest P. Chan is a solid foundation for beginners and intermediate traders. It explains trading strategies like mean reversion and momentum in detail, including reasons behind why they work, what the potential risks are, and how to backtest them. MATLAB and Python code examples are available, making it easy to implement these strategies in the real world. Chan specifically adopts a simple, rational approach that avoids problems such as over-fitting. There are constructive practical tips on topics such as risk management, stop-loss, etc. The book helps in understanding and applying research-based cardinal strategies, while also taking into account the needs of institutional and retail traders.
2. Quantitative Trading: How to Build Your Own Algorithmic Trading Business
This latest second edition (2021) by Ernest P. Chan covers not just strategies, but how to turn algorithms into a business. It covers key topics like slippage, real-time order execution, risk management, and portfolio design in simple language. Chan offers practical advice based on his own trading experiences on how to put a strategy into practice and build a trading system. The book focuses on key strategies like mean reversion, momentum, statistical arbitrage, along with the trading mindset, cost structure, and performance enhancement. It is ideal for mid-level traders who want to develop their own algo trading system.
3. Advances in Financial Machine Learning
This book by Marcos López de Prado delves deep into financial machine learning. It explains techniques like purged cross-validation, meta-labeling, and Hierarchical Risk Parity (HRP) that avoid common overfitting and produce reliable results.
The author explains that traditional data validation does not capture the reality of financial data hence the need for specially tailored ML techniques. Feature engineering such as fractional differentiation, noise reduction, and alpha generation are included. The book takes a practical approach, not just a technical one, including discussions on super‑computing, backtesting, and trading pipeline construction. It is extremely useful for quants and data scientists.
4. Building Winning Algorithmic Trading Systems
In this book, Kevin J. Davey shares his experience as a trading competition winner, offering a step-by-step guide to building a successful trading strategy, which includes goal setting, entry–exit rules, walk-forward testing, Monte Carlo simulation, and position sizing. Davey focuses on practicality as he explains why some trading strategies work through invaluable templates and case studies. The book teaches readers how to build a systematic trading platform and apply it regularly in live trading. It is ideal for retail traders who want to learn from competition-based models and build their own accurate algorithmic systems.
5. Inside the Black Box: The Simple Truth About Quantitative Trading
This book by Rishi K. Narang explains the workings of quant systems in simple terms; the emphasis is on systems thinking, data processing, model initiation, and risk control, not mathematical models. This book provides an institutional strategic perspective, rather than technical details: how large quant organizations infer from data, create signal pipelines, and make decisions. Readers are helped to understand how teams, data quality, and backtesting structures are important to sustain strategies. This book is perfect for those who want to learn the mindset and structure of quantitative trading, rather than just coding or theory.
6. The Science of Algorithmic Trading and Portfolio Management
This book by Robert Kissell is written from a more institutional perspective and covers high-stakes investment strategies: market impact, execution algorithms, portfolio optimization, statistical analysis, etc. The emphasis of the book is on systematic execution, such as optimal trading trajectories, market impact modeling, and statistical trading costs, as well as portfolio risk-adjusted returns.
It is academically robust but provides practical answers helping traders design market microstructure, order slicing, algorithmic execution. It is a great guide for advanced traders and institutional quant teams.
7. Python for Algorithmic Trading: From Idea to Cloud Deployment
This book by Yves Hilpisch is especially geared towards Python-lovers. It covers steps such as data fetching, backtesting (pandas, NumPy), ML integration, execution platforms (e.g. OANDA, FXCM), and deployment to the cloud.
The book is packed with practical examples, Socket programming, API integration, and real trading system building processes. The focus is on the ‘idea to production’ pipeline that takes a strategy idea to a working cloud-deployed system. This book is especially valuable for developers and DIY traders who want to build a complete trading infrastructure using their Python skills.
8. Algorithmic Trading and DMA
This book by Barry Johnson gives a technical understanding of Direct Market Access (DMA), explaining how traders connect directly to the market, what order book dynamics are, how slippage and spreads affect execution. This book is extremely useful for mid-level traders who want to take their algorithmic strategies straight to the exchange execution level. It describes practical aspects of the CLOB system, order types, liquidity, latency, and DMA infrastructure. This guide offers a clear and technical approach, especially for those interested in HFT and wanting to understand market microstructure.
9. Systematic Trading: A Unique New Method for Designing Trading and Investing Systems
This book by Robert Carver focuses on creating consistent, rule-based trading systems. It takes a modular approach with signals, portfolio diversification, position sizing (Kelly criterion), and psychological discipline. It offers greater reliability than discretionary trading because decisions are based on predefined rules. Techniques such as walk‑forward validation and risk budgeting are included.
The aim of the book is to create a repeatable trading system incorporating both simple rules and mental discipline that is proven and successful over the long term.
10. Algorithmic and High‑Frequency Trading
This book by Álvaro Cartea and co‑authors provides a comprehensive understanding of HFT and market microstructure. This includes algorithmic speed, latency reduction, colocation strategies, order book dynamics, liquidity modelling, and risk control systems. This book is especially valuable for those who want to delve into HFT: how algorithms make decisions in fractions of a second, why slippage and impact modelling are important, and how execution algorithms evolve. It combines technical‑and‑practical approaches to help advanced quant traders and institutional developers create cutting-edge HFT infrastructure.
The right book can fast-track your learning but only if it matches your goals, skill level, and interests. Here’s what to consider before you dive in.
Understand your needs first : Everyone has a different interest in trading, some just want to understand the basics, while others want to create their own trading strategies. So before choosing a book, be clear about your goal: learning coding, understanding theory, or creating a strategy.
Pay attention to the language and style of the book : Some algorithmic trading books are very technical, which can be a bit difficult for beginners. If you are a complete beginner, choose books that explain concepts in simple language.
Does the book contain real-world examples : A good quantitative trading book explains not just theory, but what happens in real trading through practical examples, case studies, and market data. This makes learning even more effective.
Is there a coding-based approach or not : If you want to automate trading by learning a language like Python, R, or C++, then the book must have coding practice. Such books will help you create real-world algo systems.
Choose a book according to your goal : If you are interested in data science and risk analysis, then choose a book that teaches you data interpretation, backtesting and modeling. On the other hand, if your focus is on trading strategies, then a theory and example based book will be more useful.
Choosing the right book is very important for a successful start in fields like algorithmic trading and quantitative trading. The books mentioned above not only help explain the theory but also provide real world applications and coding practice. Some books are written in easy-to-understand language for beginners, while others cover advanced concepts in detail. Every trader has different needs, so while choosing a book, one should keep in mind his expertise level and goals. A good book can show the right direction and give you the confidence to navigate the financial markets effectively.
S.NO.
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Which is the best book to start learning algorithmic trading?
For starters, “Algorithmic Trading” by Ernest P. Chan is a great book that explains practical strategies in easy language.
Are these algo trading books suitable for beginners?
Yes, some books like “Python for Algorithmic Trading: From Idea to Cloud Deployment” and “Algorithmic Trading: Winning Strategies and Their Rationale” are suitable for beginners.
Do I need programming knowledge to understand these books?
Some books may require basic Python or R understanding, but many books are also suitable for those without a coding background.
Are these books helpful for Indian stock market traders?
Yes, most of the concepts are globally applicable and can be used in the Indian market as well.
Can I build trading strategies using these books?
Absolutely, these books teach you the whole process of building, testing and optimizing a strategy.
Have you ever had trouble understanding the difference between gross margin and net margin? While they may sound alike, these two financial metrics serve distinct purposes and provide different insights into a business’s profitability.
In this blog, we will understand in simple language what is net margin, what is gross margin, and why it is important for every business to know the difference between gross margin and net margin.
What is Gross Margin?
Gross margin is an important financial metric that shows how much profit your company makes by selling its products or services, after deducting direct costs (COGS – Cost of Goods Sold) as a percentage of the total revenues. This margin shows only the profit that comes directly from the sale of the product, all other expenses (such as salary, rent, tax etc.) are not included in it. Gross margin helps in understanding how much profit a product or service is generating and whether the decisions related to pricing or production are correct or not.
Gross Margin Formula:
Gross Margin (%) = [(Total Revenue – Cost of Goods Sold) ÷ Total Revenue] × 100
Example : Suppose your company made sales of ₹10,00,000 and the total cost of manufacturing and selling the products (COGS) was ₹6,00,000.
This means that for every ₹100 sold, you are left with ₹40 as gross profit.
What all is included in COGS?
COGS i.e. Cost of Goods Sold includes all the direct expenses that are required to manufacture a product or provide a service:
Raw Materials
Direct Labor Costs
Manufacturing Costs (Factory Overheads)
Packaging and Shipping Costs (if it is related to product delivery)
The lower the COGS, the better the gross margin.
What is the role of Gross Margin in business?
In deciding the pricing strategy : If the gross margin is low, then either you have to increase the price or reduce the cost.
In understanding product performance : By determining products with high gross margins, you can focus more on it.
Help in Inventory Management : You can optimize your inventory on the basis of COGS.
What is Net Margin?
Net margin is a financial metric that shows how much profit your company has earned after subtracting all expenses from total sales as a percentage of total sales. This includes not only product-related costs (COGS) but also operating expenses, marketing, interest, taxes and all other costs.
Net margin is often called the bottom line, because it comes at the end of the report and reflects the overall profitability of the company.
Net Margin Formula :
Net Margin (%) = (Net Profit ÷ Total Revenue) × 100
Example : Suppose the total sales of your company is ₹10,00,000. Out of this, the total expenses including cost of products, operating expenses, salary, rent, interest and tax is ₹9,00,000.
So Net Profit = ₹10,00,000 – ₹9,00,000 = ₹1,00,000
Both gross margin and net margin are important but they are used in different situations. It is wise to use the right metric at the right time.
1. If you are a product manager or sales head
Gross margin is the most important tool for you. Because it shows how much profit is left after making and selling a product. This metric plays a direct role in pricing strategy, discount planning, and supplier negotiation.
Example: If a T-shirt has a gross margin of 60% and another has 30%, then you can immediately understand which product to promote.
2. If you are a business owner or investor
Net margin should be the main metric you look at. It tells how much net profit the entire business has earned, that is, what is the actual return on your investment.
Example: A company with a gross margin of 70% but a net margin of only 5% means that operating expenses or taxes are very high, which can be a red flag in the long term.
Case study: The truth about a D2C brand
A small D2C clothing brand was selling T-shirts for ₹1,000 through Facebook Ads. Their gross margin was 50%, that is, a profit of ₹500. But when ad spend, packaging, returns, and customer service were added the net margin was -10%, that is, signifying an overall loss.
Why Both Margins Matter in Financial Analysis?
It is beneficial to understand gross margin vs net margin separately, but when you analyze both together, then the real health of your business comes to the fore. They do not just tell the profit figure, but tell where the money is coming from and where it is going.
The whole story of profitability : Gross margin shows how much basic earnings are being made on your products or services – that is, how much is left after deducting direct cost. On the other hand, net margin focuses on overall profitability – it also includes all expenses like salaries, rent, tax and interest. If you focus only on gross margin, then it is possible that there is a loss in net and you do not even know.
Deep understanding of expenses : If the gross margin of a company is good but the net margin is weak, then it means indirect expenses can be significant. These are signals that you need to optimize your expenses.
Long-term growth and investor confidence : Net margin tells how sustainable a company is in the long term. Companies with high net margins are not only stable, but investors are also more interested in them because they are considered to be efficient overall.
Both metrics are important for better decisions : Whether you are running a startup or working in an established business – looking at both margins together makes your decision-making smarter. This helps you know which area needs improvement – product pricing, cost control or operations.
Limitations of Gross and Net Margin
Gross margin and net margin help you understand your profitability, but both these metrics have their own limitations. Relying only on these figures and judging the financial health of the entire company can sometimes be misleading.
Just numbers, do not tell the reason : Gross or net margin do not tell why there is income or loss.
Interpretation varies according to the industry : Margin expectations can differ widely across industries. Service-based companies often have higher margins because they usually incur lower overhead and production costs. In contrast, manufacturing businesses typically face higher expenses for materials, labor, and equipment, which can lead to lower profit margins.
One-time expenses/earnings are also included : If any exceptional income or heavy expenditure has happened for one time in the net margin, then it can distort the margin.
The effect of management decisions is not visible immediately : The effect of cost-cutting or growth strategy takes time to reflect in margins.
Many investors make some basic mistakes while interpreting financial data. These mistakes can affect profitability in the long run:
Considering Gross margin and Net margin as the same : The biggest mistake is not understanding the difference between these two margins. Many people think that if the gross margin is good, then the business is profitable. Whereas the difference between gross margin and net margin shows that gross margin is just the production related expenses, but net margin shows the true picture of the entire operation.
Estimating profit by looking at gross margin only : Gross margin may look good, but if operating and indirect expenses are high, then net margin can be very low or negative. This can hide the real profitability of the company.
Relying on only one metric : To understand profitability, it is important to analyze not just gross or net margin, but both together. This reveals both the strengths and weaknesses of the business.
Conclusion
Both gross margin and net margin are important financial metrics for understanding the profitability of a business. But they serve different purposes: gross margin gives you an idea of efficiency of a company’s core operations, while net margin shows the financial health of the entire company. A smart business leader analyzes both of them together to make accurate business decisions. If you learn to read and analyze margins of the companies correctly, then you can make informed investment decisions.
S.NO.
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Understanding the stock market movements has always been a challenge but now it is a task that is being performed by machines as well as humans. Today, more than 60% of trading in developed markets is done through algorithmic trading, and big financial institutions are analyzing thousands of data points in seconds with the help of AI models. So can AI really predict the stock market? Can machines prove to be smarter than humans in trading?
In this blog, we will learn how AI works in the stock market, what are its limitations, and whether it can completely change the world of trading in the future.
How does the Stock Market work – and Why is it so difficult to Predict?
The stock market is a complex system influenced not only by numbers and financial data but also by human emotions, global events, politics, and thousands of other factors. This is why predicting it completely is still one of the most difficult tasks in the world.
Markets are complex and volatile : The stock market prices never move in a straight line. One day a boom and the next day a fall – this type of volatility always requires investors to be alert. Sometimes events, such as RBI changing interest rates or any geopolitical event can shake the entire market.
Effect of Human Emotions : The market does not run only on data, but human emotions (such as fear, greed, hope) also play a big role in it. A rumor or social media trend can also sometimes cause heavy buying or selling.
Random Walk Theory vs Pattern Recognition : Some experts believe that stock prices are completely random and do not exhibit any pattern (Random Walk Theory). On the other hand, some believe that there are patterns in the price which can be identified and forecasting can be done.
Black Swan Events : Black Swan events are those that are sudden and unexpected – such as the COVID-19 pandemic or the 2008 Global Financial Crisis. At such times, neither human analysis nor machine learning models work.
Predicting the stock market is difficult because it depends not only on data, but also on human behavior, world events and uncertainties. This is why even AI has not been completely successful in fully understanding and predicting it yet but efforts are still on.
The purpose of AI or Artificial Intelligence is to understand data, identify patterns in it, and then predict future trends. AI scans millions of data points and predicts whether the price of a stock will rise or fall based on them.
Machine Learning Models : The most commonly used concept in AI is Machine Learning (ML), in which models automatically learn from historical data. These models are trained on old stock price, volume, indicators and technical analysis data. Example : The AI model takes the historical price data of Tata Motors for the last 10 years and learns in which situations its price went up or down. After this, it gives predictions in the future if similar patterns are found.
Identifying Technical Indicators : AI tools analyze technical indicators such as RSI, Moving Averages, Bollinger Bands, MACD to identify overbought or oversold conditions of a stock. AI generates signals far faster than humans and, when well-trained on robust data, can outperform manual screening.
News & Social Sentiment Analysis (NLP) : AI doesn’t just read numbers it now understands language as well, using Natural Language Processing (NLP) technology. AI scans news articles, Twitter, Reddit and other social media posts to determine whether people’s opinion about a company or sector is positive or negative. Example: If the AI model finds out that there is a sudden increase in negative discussion about a stock on Twitter, it can signal a decline in price of the stock.
Macroeconomic & Global Data Integration : AI analyzes not only company data but also macroeconomic information like interest rates, GDP data, crude oil prices, dollar-rupee exchange rate. For example, when global oil prices rise, AI modes may predict the possibility of a decline in the stock prices of auto companies.
Backtesting and Live Simulation : Before using the AI model to trade, it is backtested, i.e., it is tested on old data to see how accurate its prediction or performance was. After this, it is tested in the real market in live simulation. AI models are considered useful to be those that show good returns and fewer errors even in the live market.
AI doesn’t use just one parameter to predict stock prices, but works on all three levels technical, fundamental and sentiment. Its focus is to understand what kind of patterns are repeated again and again, and learn from them to generate future signals. While these techniques are fast and smart, they are also not 100% perfect – their accuracy depends on the quality of the model and the depth of the data.
AI Models in Action
The real magic of AI is seen when we apply it on real-time financial data. There are many such AI models which are being actively used to analyze stock prices, trends and market behavior:
LSTM (Long Short-Term Memory) : This is an advanced deep learning model that works on historical time-series data. LSTM networks are widely used — from intraday tick forecasting to next-day volatility prediction — by quants in India and abroad.
XGBoost (Extreme Gradient Boosting) : This is a highly accurate machine learning model that makes better predictions by understanding large datasets and multiple financial factors. Traders use it in combination with fundamental metrics (such as P/E ratio, ROE, earnings growth) and technical indicators.
Sentiment-based NLP Models : NLP (Natural Language Processing) models such as BERT, RoBERTa and custom models analyze sentiments from news, Twitter, YouTube videos and forums such as Reddit. Many fintech startups such as SentiStock have built custom models that generate signals by converting market news into sentiment scores.
Reinforcement Learning (RL) : This model continuously learns from the environment (market) and improves its strategy just like a pro trader improves over time. Some advanced quant funds and AI startups are doing intraday strategy optimization using RL.
AutoML Models : These are pre-built models that create powerful AI tools even for non-coders. Some Indian analytics companies (like Tredcode or Kuants) are creating backtested models with the help of AutoML tools.
Hybrid Models (AI + Technical Analysis) : Some Indian traders are combining traditional technical indicators (like RSI, MACD) with AI models (like LSTM + XGBoost) to create more accurate signals.
Limitations of AI in Stock Market Forecasting
AI has made trading very smart, but it has its limitations too. In a dynamic and sentiment-driven market like India, there are some challenges that can affect the accuracy of AI.
Data Quality : If the data used for training is incorrect or incomplete, then the prediction can also be unreliable. In India, many times there are gaps in the historical data of small-cap stocks or intraday pricing data, which can misguide the AI models.
Overfitting : AI models often fit the training data so well that they fail in the live markets. Many Indian traders use models without proper walk-forward validation, which can lead to losses instead of real profit.
Limit of Human Sentiment and Unknown Factors : Even though AI can analyze millions of data points, it is still difficult to fully understand factors like elections results, sudden policy of RBI, or geopolitical events. Retail investor sentiment in India is often beyond AI’s understanding.
Flash Crashes due to HFT: AI-based high-frequency trading systems sometimes interpret price completely wrongly, leading to incidents like flash crash within seconds.
Regulatory Boundaries and Lack of Transparency : As per the new SEBI rules, AI-based advisory tools have to operate under strict guidelines. AI-based advisory tools must be transparent, and those providing them must comply with relevant registration and disclosure norms.
Bias and Model Ethics : If a model is trained on biased data or limited data sources, its output will also be biased.
Conclusion
AI has become increasingly capable of understanding stock market trends and predicting future movements. It processes data quickly, recognizes patterns, and helps traders make informed decisions. But still cannot accurately predict uncertainty, emotional market reactions, and sudden events. So use AI as a supportive tool not a replacement for strategy, experience, and market understanding. It is advised to consult a financial advisor before using AI for trading or investing.
S.NO.
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With inflation on the rise and job stability becoming increasingly uncertain, building passive income streams has become more essential than ever. Whether you’re a working professional, student, or homemaker, having an additional income source can offer financial security and peace of mind.
In this blog, we will talk about 50 best passive income ideas in India, which you can easily start in 2025. From digital ventures to investment-based earnings and physical assets, these ideas cater to different needs, time commitments, and capital levels, so you can find what suits you best.
What is Passive Income?
Passive income is income that comes in continuously after the initial setup effort like rent from a property or returns from a mutual fund. But it’s important to understand that no income stream can be 100% passive. Every income source takes time, effort or money to set up. This is not a get-rich-quick scheme. Most passive income sources fall into three categories:
Asset-based: like real estate, mutual funds
Content-based: like blogging, YouTube
System-based: like affiliate funnels or automated tools
With the right perspective, the benefits of passive income can be realized in the long term.
50 Passive Income Ideas in India
S.No
Passive Income Idea
Description
1
Stock Market Investing
Invest in long-term stocks for dividends and capital appreciation.
2
Mutual Fund Distributor
Become a certified mutual fund distributor and earn commissions.
3
Real Estate Rental
Rent out property for steady monthly income.
4
REITs (Real Estate Investment Trusts)
Invest in property via REITs and earn dividends.
5
Digital Gold Investment
Hold gold digitally for long-term appreciation.
6
P2P Lending Platforms
Lend on regulated platforms and earn interest.
7
Dividend Stocks
Build a portfolio focused on dividend-paying companies.
8
Government Bonds
Invest in RBI/SGB bonds for assured returns.
9
Covered Call Writing
Earn premiums by writing calls on your stocks.
10
Arbitrage Mutual Funds
Exploit market inefficiencies via hybrid funds.
11
High-Interest Digital Savings Accounts
Park idle money in high-interest savings apps.
12
YouTube Channel (Automated)
Create evergreen content and monetize passively.
13
Blogging with Affiliate Links
Write SEO blogs and earn through affiliate sales.
14
eBook Publishing
Self-publish eBooks on Amazon or other platforms.
15
Print-on-Demand Products
Sell custom products via platforms like TeeSpring.
16
Instagram Theme Pages
Grow niche pages and monetize via sponsors.
17
Online Course Sales
Build and sell courses on Skillshare, Udemy etc.
18
Dropshipping Store
Automate sales using third-party fulfillment.
19
Subletting Co-Working Desks
Rent larger spaces and sublet desks to freelancers.
20
Domain Name Flipping
Buy trending domains and sell at a profit.
21
Newsletter Sponsorships
Grow email lists and sell direct ad slots.
22
App-Based Cashback & Referral Income
Use cashback apps for deals & referrals.
23
Voiceover Licensing
Upload voice samples to stock platforms.
24
Stock Photography & Video Licensing
Sell high-quality images/videos on stock sites.
25
NFT Royalties
Earn from resales of your digital art/NFTs.
26
Online Data Set Licensing
Sell curated data sets for AI/ML training.
27
Audiobook Narration & Publishing
Narrate or publish audiobooks on Audible.
28
Create & Sell WordPress Themes
Build themes and sell via marketplaces.
29
SaaS Tools with Freemium Model
Build a simple tool and charge for the premium version.
30
PLR Content Reselling
Repackage and resell private-label content.
31
Voice Cloning Software Licensing
Build voice models and license to creators.
32
Used Book Resale Automation
Set up used book resale via Amazon seller tools.
33
ATM/Vending Machine Hosting (Space Owner)
Lease space to ATM or vending machine companies.
34
Build & Sell Financial Tools
Create and sell Budgeting or SIP calculators or embed them
35
Google News Blog on Finance
Monetization by bringing traffic from realtime finance news
36
Automated Investment Bots
Hands-free investment by investing in robo-advisory bots
37
Personal Finance Podcasts
Sponsorship deals and ad revenues from Spotify or YouTube
38
Budget Tour Planning Portal
Revenue from offering low-budget friendly travel plans
39
Local Business Automation (White-Label SaaS)
Help small businesses automate using SaaS tools
40
Online Ad Space Arbitrage
Buy low CPC traffic, send to high-paying affiliate offers.
41
Rent Out Gaming Consoles
Lease PlayStations, VR gear etc. on hourly basis.
42
Language Translation Assets Licensing
Create language assets and license to edtechs.
43
Sell Website Templates or UI Kits
Upload on ThemeForest or Creative Market.
44
Solar Panel Leasing
Lease your rooftop for commercial solar use.
45
Start a Podcast
Monetize via sponsorships, premium episodes
46
Sell Canva Templates
Earn by selling ready-made design templates.
47
Social Media Influencer
Build a niche audience and earn brand income.
48
Online Coaching or Consulting
Increase reach through selling record sessions as courses
49
Rent Out Your Vehicle
Give cars or bikes on lease to gig workers.
50
Rent Out Mini Warehouse
Let others use your storage space.
1. Stock Market Investing
Investing in the stock market is a popular and long term passive income source. In this, you buy shares of companies and earn from their growth or dividends. You have to understand the financial health of the companies, sector analysis and market trends. There is a possibility of higher returns but the risk is also the same. It is wise to invest in SIP or Bluechip Stocks for the long term.
2. Mutual Fund Distributor
You can earn passive income by becoming a registered mutual fund distributor with AMFI. Once registered, you earn commissions whenever clients invest in mutual fund schemes through you. This income can become recurring as long as clients continue their SIPs. It’s an ideal option for those with a network or basic financial knowledge, and the income grows as your client base expands. SIPs themselves are also great for low-risk, long-term investors seeking wealth creation.
If you have a property, then you can earn stable monthly income by renting it out. This income is stable and inflation-proof. With property management facilities, you can make this income almost passive. Commercial properties can give higher returns than residential ones.
4. Real Estate Investment Trusts (REITs)
REITs are for those who want to invest in real estate without buying a property. These are listed on the stock exchange and you can buy them like shares. They give the rental income from the property to investors in the form of dividends. It is a liquid and low-risk option.
5. Digital Gold Investment
Through digital gold, you can start investing from as little as ₹1 and it is stored in a secure vault in your name. It can be purchased from online platforms like PhonePe, Google Pay etc. Digital gold can be converted into physical gold in the future. It is also liquid and is considered a good option for long term wealth.
6. P2P lending platforms
On P2P (Peer-to-Peer) lending platforms like LenDenClub or Faircent, you can earn interest income by lending. Here you become an investor giving personal or business loans and can get good returns (10-15%). However, this option is a bit risky, so invest only after checking the right platform and credit rating.
7. Dividend stocks
Stocks that pay regular dividends are a good source of passive income. The financial performance of these companies remains stable and they distribute a part of their profits among investors. Dividend income is taxable, but with a good portfolio it can give good returns annually. Companies like HDFC, ITC come in this category.
These instruments issued by the government are considered reliable for fixed interest income. They have very low credit risk and predictable returns. In portfolios where safety and stability are given priority, government bonds prove to be a trusted option.
Passive income can be generated by selling call options on owned stocks in the form of option premium. This method provides steady return with limited downside risk. Combined with disciplined investing and good stock selection, this strategy is a smart way to utilize capital.
These funds capitalise on the price gap between the equity and derivatives markets and offer low-risk returns. With arbitrage opportunities, these funds offer relatively stable and tax-efficient returns, suitable for conservative portfolios.
11. High-Interest Digital Savings Accounts
Operated by neo-banks and fintech platforms, these accounts offer higher interest rates than traditional banks. Features like zero maintenance charges, auto-sweep options and instant liquidity make them a practical way to earn passive returns on idle cash.
12. YouTube Channel (Automated)
Once content is created using AI tools or faceless video formats, a YouTube channel can generate recurring income from advertising, affiliate links and sponsorships. Automated channels with niche-based or evergreen topics can create a strong earning stream over time.
13. Blogging with Affiliate Links
Niche-focused blogs can promote affiliate products through informative content. Commission is generated when purchases are made from the links provided by readers. The flow of passive income from SEO optimized evergreen articles continues for years.
14. eBook Publishing
After launching an eBook on self-publishing platforms like Kindle, one can earn continuous income in the form of royalty. An eBook written for a good quality and targeted audience can sell repeatedly for a long time, especially in popular niches like finance, productivity or education.
15. Print-on-Demand Products
Custom designed T-shirts, mugs, or stationery can be uploaded on POD platforms to sell products without handling inventory. As soon as the order comes, print, pack and delivery happens automatically, due to which passive margin starts being received on every sale.
16. Instagram Theme Pages
By building an audience on pages with specific interest (travel, quotes, fitness), steady income can be generated from sponsored posts and affiliate links. When consistency and engagement rate is better, brands themselves contact for outreach.
17. Online Course Sales
Skill-based or subject-specific courses, once created, can be sold on platforms like Udemy, Teachable or personal websites to create automated income. High-quality content and structured modules for learners keep the course sellable for a long time.
18. Dropshipping Store
An eCommerce model where there is no need to stock products yourself. The supplier directly ships to the customer upon receiving an order. Platforms like Shopify and niche-specific targeting can create a passive income stream without any inventory investment.
19. Subletting Co-Working Desks
Unutilized workspaces or extra desk spaces can be rented out to startups or freelancers on short-term rental to generate recurring income. Location advantage and flexible pricing can ensure steady occupancy and good returns.
20. Domain Name Flipping
High-potential domain names can be bought cheaply and later sold at a premium rate. Domains with brandable, SEO-friendly or trending keywords can prove to be valuable assets for future resale.
21. Newsletter Sponsorships
Niche-specific newsletter audiences can be built to generate recurring income through direct brand sponsorships and affiliate promotions. High open-rate and loyal readership increase monetization potential manifold.
22. App-Based Cashback & Referral Income
Using offers on apps like Cred, Magicpin, Paytm and adding friends through referral links earns passive income in the form of cashback and rewards. This income can be increased with consistent usage and smart referrals.
23. Voiceover Licensing
Posting pre-recorded generic voiceovers (welcome messages, explainer scripts, AI prompts) on licensing platforms and selling licenses repeatedly can earn recurring royalty. Good audio quality and clear articulation make it scalable.
24. Stock Photography & Video Licensing :
Professionally clicked images and short videos are uploaded on sites like Shutterstock or Adobe Stock to generate passive income. Once uploaded, these contents are licensed multiple times and bring recurring revenue.
25. NFT Royalties
Digital art or collectibles are minted as NFTs and set embedded royalties upon resale. The creator receives a percentage royalty each time a secondary sale occurs, creating a long-term passive flow.
26. Online Data Set Licensing
Curated datasets (text, image, audio) can be licensed to AI companies or researchers to generate recurring income. Niche-specific or cleaned datasets attract high-value buyers.
27. Audiobook Narration & Publishing
Self-narrated or recorded audiobooks from professional voice artists can be published on platforms like Audible. Royalties are earned per listen, allowing content to be created once and provide a passive return for years.
28. Create & Sell WordPress Themes
Custom-designed WordPress themes can be sold on marketplaces like ThemeForest to generate recurring income. High-speed, SEO-friendly and niche-specific designs ensure better sales and long-term downloads.
29. SaaS Tools with Freemium Model
A useful SaaS tool (such as invoice generator, SEO checker) can be launched in a freemium structure to generate recurring revenue through paid upgrades. Premium offerings such as automation, team features or API access create a stable income flow.
30. PLR Content Reselling
Ready-made PLR (Private Label Rights) ebooks, videos or courses are rebranded and resold. By selecting a high-converting niche, this can be converted into a passive income source through email funnels or affiliate platforms.
31. Voice Cloning Software Licensing
AI-based voice cloning tools can be developed and offered in a monthly or yearly license model. Their demand is growing rapidly in the media, audiobooks and virtual assistants industries.
32. Used Book Resale Automation
Old or second-hand books can be sourced in bulk and sold on Amazon or Flipkart through resale automation systems. With barcode scanners and listing tools, this becomes a completely systemized passive hustle.
33. ATM/Vending Machine Hosting (Space Owner)
Fixed passive rental income can be earned by renting out empty space in a busy location to ATM or vending machine providers. Long-term lease and maintenance-free setup make it hassle-free.
34. Build & Sell Financial Tools
Excel-based or web-based financial calculators, loan planners or SIP trackers can be developed and sold. Affiliate deals and embedded sales target finance bloggers and YouTubers to generate recurring income.
35. Google News Blog on Finance
Create a Google News-approved finance blog to publish trending news, policy updates and insights. AdSense and sponsorship deals generate a reliable passive revenue stream.
36. Automated Investment Bots
Algorithm-based bots (for equity, crypto or mutual funds) are developed and offered on a subscription or performance fee model. Transparency and risk management maintain trust and customer retention.
37. Personal Finance Podcasts
Launch a weekly podcast on finance-related topics and generate income from sponsorships, affiliate promotions and listener donations. Niche topics (tax saving, credit score, retirement) build audience loyalty.
38. Budget Tour Planning Portal :
Launch a website with curated budget travel plans and DIY itineraries for low-cost travel lovers and generate recurring income from affiliate bookings and consultation fees. Easy to scale with SEO and content marketing.
39. Local Business Automation (White-Label SaaS)
Ready-made automation tools (billing, CRM, SMS alerts) for small businesses can be white-labeled and resold. Local resellers or agencies earn monthly recurring revenue by selling it under their brand.
40. Online Ad Space Arbitrage
Arbitrage income is generated by buying traffic from low-CPC countries and redirecting it to high-paying ad networks. Scalable automation tools and precise targeting make this a strong model for digital revenue.
41. Rent Out Gaming Consoles :
Passive income can be generated by renting out gaming consoles like PlayStation, Xbox on an hourly or daily basis. College hostels, birthday events or local gaming cafes are ideal customers.
42. Language Translation Assets Licensing :
Recurring licensing fees are earned by licensing pre-built glossaries, term-banks and translation memory files to companies or freelance translators. It is highly valued in niche areas like legal, medical and finance.
43. Sell Website Templates or UI Kits
Passive design income is generated by selling pre-designed HTML templates, Figma UI kits or WordPress themes on online marketplaces. niche-specific designs (e.g. SaaS, blogs, portfolios) are high-converting.
44. Solar Panel Leasing
By installing solar panels on unused rooftops, electricity can be leased to nearby businesses or homes. Government subsidies and long-term PPAs (Power Purchase Agreements) make this financially viable.
45. Start a Podcast
Start a topic-specific podcast (e.g. finance, health, productivity) to generate revenue from brand partnerships, affiliate ads, and listener donations. Consistency and niche focus build a loyal audience base.
46. Sell Canva Templates
Customized Canva templates (Instagram posts, resumes, planners) can be sold on platforms like Etsy or Gumroad. One-time effort leads to long-term passive income through downloads.
47. Social Media Influencer
Develop a niche audience (fitness, tech, parenting) and generate income from sponsored posts, affiliate links, and product launches. Authentic engagement and micro-niche targeting help in sustainable growth.
48. Online Coaching or Consulting
Skill-specific coaching (e.g. Excel, digital marketing, stock trading) can be delivered in recorded video format to generate recurring access fees. Lead magnets and email funnels provide consistent client flow.
49. Rent Out Your Vehicle
Passive earning can be earned by renting out a car or two-wheeler on an hourly, daily or subscription basis. Self-drive rental platforms like Zoomcar, Revv, or Drivezy allow vehicle owners to monetize unused vehicles securely. With proper insurance coverage and verified users, this can become a steady, low-effort revenue stream.
50. Rent Out Mini Warehouse
Extra garage or vacant property is rented out to small businesses or urban tenants for storage use. Monthly rent and zero operational involvement make this an ideal passive rental income model.
How to Choose the Right Passive Income Idea for You
Choosing the right passive income source is a smart decision that takes into account several factors:
Time Requirement: For those who have less time, investment-based options (like dividend stocks or government bonds) are more beneficial. If time is available, then one can focus on content creation or creating and selling digital services.
Risk bearing capacity: If you want a secure income, it is better to choose low-risk options. Whereas those who want to earn more will have to focus on high-risk-high-reward options.
Interest and skills: Starting with the field in which you have some knowledge or interest will be easy and sustainable.
Scalability and automation: A passive income source that does not demand substantial effort repeatedly is better.
Tax and legal aspects: The tax regulations governing different passive income sources can different. Decisions should be taken only after understanding this.
There are many smart and scalable ways to earn passive income today; whether it is from the stock market, selling digital products, or owning real assets. Once set up with proper planning and a little hard work, these sources keep generating passive income for a long time. Everyone should choose a passive income idea according to their skills, budget, and time. With gradual growth, this side income can become a strong financial backup. The real key is to move in the right direction while constantly learning.
S.NO.
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The way markets are traded have been undergoing a rapid transformation since the past few years. Smart systems based on AI (Artificial Intelligence) and machine learning are now taking decisions instead of humans. These technologies are making trading faster, more accurate and data-driven.
In this blog, we will learn how AI and Machine Learning can take your trading strategies to the next level along with their benefits, risks and real world case studies.
What is AI and Machine Learning in trading?
Now trading is not limited to just looking at charts or reading news and making decisions. In today’s era, tools like AI (Artificial Intelligence) and Machine Learning (ML) are revolutionizing the world of trading. Simply put, AI is the technology that has the ability to think and learn like humans, while Machine Learning is a branch of AI that learns on its own from data and gets better over time as the data points increase.
When these technologies are used in trading, the computer systems read, analyse and millions of market data points such as stock price movement, volume, news headlines, and social media trends to detect patterns, helping traders make accurate buy-sell decisions. In some cases, traders don’t even type in orders as algorithmic systems send buy and sell orders almost instantaneously.
How are AI and ML used in Trading?
Price Prediction : AI analyzes price data from the past several years to predict what the next movement of a stock might be. For this, time-series models like LSTM (Long Short-Term Memory) are used.
Sentiment Analysis : AI systems today use platforms like Twitter, news sites and Reddit to find out what the market participants are thinking about a stock—positive, negative or neutral. This helps the trader understand the crowd’s attitude towards the stock.
Risk Management : AI systems continuously monitor real-time market data and can instantly detect unusual price movements or trading volumes. This allows for early alerts before significant losses occur, enabling faster, data-driven risk management.
Portfolio Optimization : AI gives smart portfolio suggestions by keeping in mind which stock should get how much weightage in your portfolio, which sector has overexposure, or which asset is giving returns.
Which ML models are used in Trading?
Supervised Learning (learning from labeled data) : These models such as Linear Regression, Decision Trees, etc. learn to take future decisions by learning from past data and their results.
Unsupervised Learning (catching patterns from unlabeled data) : Models like K-Means Clustering identify hidden patterns in the data – like which stocks behave similarly or when the market goes into different phases.
Deep Learning (understanding big and complex data) : Advanced models like Neural Networks are built to understand very large and rapidly changing data. These are very useful in HFT (High Frequency Trading) and image-based chart analysis.
From Intuition to AI: Evolution of Trading Strategies
Below is a brief history of how trading, a process traditionally based on chart reading and instincts, is now impacted by AI.
1. Discretionary Trading
In the beginning, trading as a process was completely done by humans. Traders would read news, analyze charts and decide to buy or sell based on their experience. In this process, emotions like greed, fear or overconfidence often had an effect, which sometimes led to losses. The biggest challenge was the slow decision making and the risk of human error.
2. Algorithmic Trading
The trend of algorithmic trading started after 2000. In this, computers themselves placed trades according to pre-coded rules. This made trading faster and also disciplined.
According to the SEBI report in India, by 2024, about 50-55% of orders in the equity and derivatives segment are being processed through algo systems. However, these algorithms have pre-defined rules and may not be able to adjust according to suddenly changing market conditions.
3. AI and Machine Learning
Today we are at a point where AI and ML have made trading really easy. Now the systems do not just follow the given rules, but learn patterns from the data themselves, improve them and also update themselves according to the changing market environment. AI models can process real-time data such as news, social media trends, price movements simultaneously and that too in a few seconds. For example, large institutions such as Two Sigma, Renaissance Technologies and Citadel are now trading substantial capital based on AI driven models.
In today’s trading, rule-based systems alone do not work. Now there is a need for adaptability – that is, a system that improves itself by learning from new information every second; and this is what AI is doing in the trading process.
AI and ML are transforming the trading process in the following ways:
1. Smart stock selection and timing
Models like LSTM and XGBoost read historical price, volume and technical indicators and determine “when should you buy or sell a stock.” LSTM models achieved 92.46% accuracy in forecasting 1-day S&P 500 price movements in 2024.
2. Understanding market sentiment
AI is now scanning financial news to understand whether the discussion about a stock is positive or negative. This text analysis is done using NLP based models like FinBERT or GPT.
3. Smart portfolio rebalancing
Reinforcement learning models automatically rebalance your portfolio over time, taking into account your risk profile and goals. This technology is being used in artificial intelligence systems by Fidelity and BlackRock.
4. Managing Risk
AI-based Anomaly Detection systems spot hidden patterns or sudden changes in a stock. J.P. Morgan’s AI-driven anomaly detection platform slashed average time to detect market anomalies from 40 minutes to under 5 seconds, enhancing real-time risk management.
5. High-Frequency Trading (HFT)
AI is now playing a key role in HFT as well, where orders are executed in milliseconds. The global HFT market size was valued at USD 20.97 billion in 2024 and is forecast to grow to USD 74.35 billion by 2030 (CAGR 15.1%).
6. Ultra‑Low‑Latency Infrastructure
Traders now have servers that are just microseconds away from the exchange. The HFT server market size in 2024 was $637 million and is projected to be around $675 million in 2025.
7. Competition for AI talent in hedge funds
Top hedge funds offer huge base packages to AI engineers as they know that AI-based models will drive increasing alpha.
Real-World Case Studies: How Top Firms Use AI in Trading
1. Renaissance Technologies (Medallion Fund)
Renaissance Technologies is perhaps the world’s most mysterious and successful hedge fund. It was started in 1982 by Jim Simons, a former NSA cryptographer and math genius. Its Medallion Fund returns are legendary, having delivered an average of 66% gross returns over from 1988 to 2018. The difference is that here, not humans, but machines make trading decisions. This fund scans every possible data such as satellite images, shipping logs, even weather trends. By 2025, its AUM was close to $130 billion. But the Medallion Fund is open only to the firm’s employees. Perhaps that is why its strategies remain completely secret even today.
2. Two Sigma
Two Sigma is a New York-based hedge fund focused on pure AI and data science. It was founded in 2001 by David Siegel and John Overdeck, both hardcore computer scientists. Two Sigma analyzes massive volumes of data every day related to social media trends, satellite feeds, even real-time supply chain shifts to refine its trading models. Its AUM in 2025 was around $74.44 billion. Most of the people working here are PhDs in mathematics, machine learning, and statistics. Today, it is considered one of the most advanced quant funds in the world.
3. Citadel
Citadel, founded by Ken Griffin in 1990, today leverages AI and machine learning to power its trading decisions. It is headquartered in Chicago and has an AUM of over $100 billion in 2025. Citadel’s biggest strength is its ultra-fast data processing capability, which analyzes market signals in milliseconds. The firm uses massive alternative data such as consumer transaction data, satellite imagery, and web scraping. Citadel’s AI ecosystem is so robust that it refines its algorithms daily to adapt quickly to market volatility.
4. J.P. Morgan (LOXM AI Platform)
J.P. Morgan has made AI and machine learning core to trading and portfolio management, particularly through its LOXM (Liquidity Optimization Machine) platform. LOXM is an advanced AI system that smartly executes client orders by analyzing market conditions in real-time. The company has increased the use of generative AI and NLP to improve research automation and fraud detection. Moreover, J.P. Morgan AI Research program has also released dedicated frameworks on AI bias and ethics in financial markets in 2024. This shows that the firm equally values responsible AI along with innovation.
The benefits of using AI in trading is given below:
Faster & Accurate Decisions : AI scans millions of data points in real-time and executes trades in fractions of seconds. This also gives them an edge over others to capitalize on short-term volatility and improves market timing.
Big Data Utilization : AI trading tools can analyze both structured and unstructured data such as financial news, tweets, earnings reports to gain broader insights that may be missed by humans.
Self-Learning Models : Machine learning models learn from historical trends and upgrade themselves with every new data input. This allows trading strategies to evolve over time.
Automation & Operational Efficiency : AI automates repetitive tasks such as backtesting, rebalancing, or risk management. This reduces the need for human resources and makes execution more efficient.
Scalability and Diversification : AI can track multiple markets and asset classes simultaneously—be it forex, commodities or crypto. This makes the portfolio more diversified and balanced.
Freedom from human bias : Emotion-driven trading decisions such as selling out of fear or overtrading out of greed do not occur in AI. This maintains rational decision-making.
Challenges and Risks Associated with AI Trading
The challenges and risks associated with using AI in trading is given below:
Data Quality & Reliability : AI relies heavily on historical and real-time data. If the input data is inaccurate or outdated, it can lead to wrong decisions. Availability of reliable financial data is still a big challenge, especially in developing markets like India.
Model Overfitting and Over-Dependency : Machine learning models can sometimes become too finely tuned to historical data, a problem known as overfitting. When this happens, the model struggles to adapt to new market trends or shifts in macroeconomic conditions. This rigidity increases the risk of failure in dynamic or unforeseen scenarios, highlighting the importance of continuous model validation and adjustment.
Unexpected Market Behavior : AI trading systems may react in an unpredictable way to fast-moving markets. The 2010 Flash Crash is a prime example of this, where algorithmic trades caused the U.S. market to crash in minutes.
Black Box Models and Lack of Explainability : Trading logic behind decisions made by AI models like neural networks are often not explainable. This means why and how a trade was initiated is difficult to answer, which is a concern for both investors and regulators.
Data privacy and security risks : AI trading systems process sensitive financial data through APIs, cloud services, and third-party vendors. This increases the risk of data breaches or cyberattacks.
Artificial Intelligence and Machine Learning are no longer just tools for tech experts as they’re now helping traders make faster, smarter, and more accurate investment decisions. But blindly following AI is not the right approach. It is important that you understand its limitations, verify the data source and use it judiciously. The real strength lies in blending human intelligence with smart technology. In today’s markets, staying curious, informed, and questioning the signals generated by trading systems is what truly sets great traders apart.
S.NO.
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AI investing is a process in which machines use historical data and algorithms to make investment decisions.
Can AI guarantee better returns?
No, AI can help you achieve better returns but cannot guarantee better returns.
Is AI trading suitable for beginners?
Using AI for trading may require substantial hardware requirements and technical expertise making it unsuitable for beginners.
Can AI replace human advisors completely?
AI excels at data processing and pattern recognition, but human oversight remains essential for strategic decision-making, risk management, and regulatory compliance
Is AI trading regulated in India?
SEBI introduced comprehensive algorithmic trading regulations in February 2025, requiring algorithm registration, unique identifiers for trades, etc.
India’s mutual fund industry reached a historic AUM (Assets Under Management) of ₹74.79 lakh crore in June 2025, showing an unprecedented growth of about 22–23% over the previous year. With this rapid expansion, choosing the right mutual fund distributor is more critical than ever. Whether you’re searching for the top mutual fund distributors in India, the biggest industry players, or reliable personal advisors and brokers, making an informed choice can significantly impact your investment journey.
In this guide, we will discuss the most trusted and trending mutual fund distributors of 2025 that can help make your investment journey easier.
Who are Mutual Fund Distributors?
Mutual fund distributors are individuals or institutions that help investors invest in different mutual fund schemes. These distributors are registered with AMFI (Association of Mutual Funds in India) and work under an ARN (AMFI Registration Number). Their job is to suggest the right scheme, help with documentation and provide investment related information to their clients.
Types of Mutual Fund Distributors
Institutional Distributors: Such as NJ Wealth, Prudent, ICICI Securities etc. who do sell mutual funds on a large scale.
Bank Distributors: Such as banks like SBI, HDFC, Axis who have the advantage of branch network.
Fintech Platforms: Such as Groww, Zerodha, ET Money which sell mutual funds through digital channels.
Individual ARN Holders: Independent financial advisors who provide personal guidance on a small scale.
Top 10 Mutual Fund Distributors in India 2025
Distributor Name
Distributor Type
AUM (FY 25) (₹ Cr)
Coverage & Highlights
State Bank of India
Bank
1,73,756
Largest network, deep penetration among rural investors
NJ Wealth
National Distributor
1,60,999
33,000+ ARN holders, digital and in-person support
HDFC Bank
Bank
1,19,188
Extensive branch network and reliable customer service
Axis Bank
Bank
74,290
User-friendly app, faster digital growth
Prudent Corporate
Institutional Distributor
69,785
31% growth rate, Pan-India branch network
ICICI Bank
Bank
55,749
Extensive branch network and digital reach
Kotak Mahindra Bank
Bank
46,100
Focus on HNI and mid-level investors
360 ONE Distribution Services
Institutional Distributor
29,905
75% YoY Growth, App-based wealth management
Anand Rathi Wealth
Institutional Distributor
28,342
Targeted advice for NRI and HNI investors
HSBC Bank
Bank
26,546
18% year-on-year growth
Overview of the Top Mutual Fund Distributors in India
An overview of the top 10 mutual fund distributors in India is given below:
1. State Bank of India
SBI leads the mutual fund distribution industry in India with an AUM of ₹1,73,756 crore. It offers equity, debt, hybrid and ETF schemes. SBI’s strength is its extensive branch network, which helps it offer investment schemes to every corner of the country—city and village. Their Balanced Advantage and Bluechip Fund are preferred investment choices among common investors.
Started in 2000, NJ Wealth has today become the country’s largest fund distribution network with 33,000+ ARN holders and an AUM of around ₹1,60,999 crore. Schemes of many AMCs are available at one place on its platform. The mix of tech-enabled and personal dealing makes it a trusted choice among investors.
3. HDFC Bank
HDFC Bank mutual fund distribution segment, with assets under management of ₹1,19,188 crore, maintains a strong presence in the distribution landscape through its extensive banking channels and reputation for reliable customer service. Its schemes—such as Flexi Cap and Balanced Advantage offer predictable returns. Its digital reach along with its branch network has made HDFC a trusted asset among retail investors.
Axis Bank’s mutual fund distribution segment has an AUM of around ₹74,290 crore. This AMC is known as the digital avatar of mutual funds with research-driven equity funds and strong SIP growth campaigns. Bluechip and Midcap are its key identities.
Prudent Corporate, holding ₹69,785 crore in AUM, is noted for its rapid growth rate and robust pan-India branch network. It operates primarily on the IFA network and offers client-customized digital tools. Its presence in Tier‑2/3 cities is steadily growing.
6. ICICI Bank
ICICI Bank’s mutual fund distribution business, with an AUM of ₹55,749 crore, relies on a multi-channel model that combines its extensive branch network with robust digital offerings, delivering mutual fund solutions to a broad spectrum of clients. With funds like Balanced Advantage and Bluechip Equity leading the way, its distribution network offers investors access to a wide range of mutual fund schemes, supported by consistent performance and strong brand trust.
Kotak Mahindra Bank The bank’s mutual fund distribution business has an AUM of ₹46,100 crore. The bank’s mutual fund distribution team utilizes the bank’s brand name, branch network and digital channels to effectively reach its client base.
8. 360ONE Distribution (ET Money)
360 ONE Distribution Services reports an AUM of ₹29,905 crore. Recognized for its substantial year-on-year growth, this institutional mutual fund distributor employs app-based wealth solutions and has developed a reputation for technological innovation in serving affluent and HNI clients.
9. Anand Rathi Wealth
Anand Rathi Wealth, with an AUM of ₹28,342 crore in mutual fund distribution business, focuses predominantly on NRI and HNI clients. The firm is acclaimed for its professional research, personalized advice, and broad spectrum of wealth management services, including mutual funds and portfolio management.
10. HSBC Bank
HSBC Bank is among India’s leading mutual fund distributors, managing approximately ₹26,546 crore in AUM as of FY 2023–24. Leveraging its global expertise and strong digital platform, HSBC delivers curated multi-AMC mutual fund solutions to retail and affluent clients
Top 10 Individual Mutual Fund Distributors in India
As important as it is to invest in mutual funds, it is equally important to choose a reliable mutual fund distributor. In 2025, market competition and the growing number of digital platforms have given investors many options. But the right choice is the one that strikes a balance between your investment goals, convenience, and cost.
Verify ARN registration and validity : All mutual fund distributors must be registered with AMFI (Association of Mutual Funds in India). Verify whether the distributor’s ARN number is valid and active by visiting the AMFI website.
Experience and quality of investment advice : Choose a mutual fund distributor who can provide you additional services like goal-based investing, risk profiling, and periodic portfolio reviews. Years of experience and client retention are important indicators.
Digital platforms : A good mutual fund distributor gives you an easy-to-use digital platform — where you can track your funds, manage SIPs, and easily buy or sell when needed. Also look at the security features and user interface of the app.
Reporting and data transparency : Does the distributor regularly provide you with Consolidated Account Statement (CAS), Portfolio Report and Tax Summary? All these reports help in your financial planning and monitoring.
Client support and response time : A professional distributor should respond to your queries in a timely manner. Check if they have a dedicated support team and whether they provide regular follow-ups and updates.
Variety of product coverage and advice : The distributor should not be limited to just 1–2 AMCs. It is better to find a distributor who provides you access to mutual funds from multiple AMCs.
Mutual fund investing offers a wide range of choices, but not all are aligned with your financial goals, risk profile, or time horizon. That is why it is important to select a reliable mutual fund distributor who understands your needs and helps you make better decisions. Advice, platform features, and tracking tools—all these aspects are now as important as the performance of the mutual fund itself. So, select your mutual fund distributor carefully and begin investing in mutual funds today after consulting your investment advisor.
S.NO.
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