Tag: algo trading

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Algo Trading

An Overview on SEBI’s New Rules on Algo Trading for Retail Investors

SEBI's new rules on algo trading | marketfeed

Algo trading has transformed the financial markets (stocks, currencies, commodities, derivatives, etc.), enabling faster execution, data-driven decision-making, and improved efficiency.  It’s the method of executing orders using automated or pre-programmed trading instructions. After recognising the risks and challenges faced by retail investors, the Securities and Exchange Board of India (SEBI) has introduced a regulatory framework to ensure safer participation in algo trading.

Effective from August 1, 2025, these new measures aim to enhance transparency, mitigate risks, and promote accountability in the algo trading space in India. In this article, we break down what these changes mean for retail traders and market participants.

Why Has SEBI Introduced These Regulations?

With the growing adoption of algo trading, SEBI aims to address concerns regarding market manipulation, security vulnerabilities, and lack of investor awareness. The key objectives of the latest circular are:

  • Protect Retail Investors: Ensure that individuals who participate in algo trading understand the risks involved.
  • Enhance Transparency: Provide clear guidelines on approval, monitoring, and risk management of trading algorithms.
  • Prevent Market Manipulation: Implement safeguards to detect and prevent unfair trading practices.
  • Establish Accountability: Mandate compliance measures for brokers and algo providers to maintain ethical and fair trading environments.

Key Measures Introduced by SEBI in the Latest Circular

1. Empanelment of Algo Trading Providers

To ensure credibility, SEBI has mandated that all algo trading providers must be empanelled with stock exchanges before brokers can onboard them. This prevents unverified or sketchy service providers/companies from offering algo trading solutions to retail investors.

2. Approval Process for Algorithms

Brokers must obtain prior approval from stock exchanges for each algorithm they wish to offer to retail investors. Additionally, any modifications to approved algorithms also require pre-approval from the exchange. This ensures that all trading strategies comply with SEBI’s guidelines.

3. Monitoring and Risk Management

Brokers are responsible for monitoring investor grievances and ensuring compliance with anti-manipulation measures. They must:

  • Implement strong risk management practices.
  • Maintain oversight of algo trading activities.
  • Report any unusual patterns to SEBI.

4. API Access Restrictions

To prevent unauthorised access, SEBI has banned open APIs. [In the context of algo trading, open APIs enable traders and third-party applications to connect directly to a broker’s trading platform, execute trades, and access market data]. Instead, access will be granted only through unique vendor-client setups, ensuring proper identification and traceability of algo trades.

5. Registration Requirements for Self-Developed Algorithms

Retail investors who develop their own algorithms must register with stock exchanges through their brokers if their trading activity exceeds a specified order-per-second threshold. These self-developed algorithms can only be used for personal accounts, including those of immediate family members.

6. Regulations for Black Box Algorithms

Algorithms that do not disclose their underlying logic, known as black box algorithms, must register as Research Analysts with SEBI. They must:

  • Maintain detailed research reports.
  • Undergo re-registration if there are significant modifications to their trading logic.

7. Unique Identification for Algo Orders

Each algo order must be tagged with a unique identifier (an “algo ID”) provided by the exchange. This will help establish an audit trail, enhancing transparency and accountability in trading activities.

8. Standard Operating Procedures (SOPs)

Stock exchanges will issue SOPs for testing, monitoring, and simulating algo trading. This includes establishing a “kill switch” mechanism, allowing exchanges to disable specific algo IDs in case of irregularities.

9. Transparency in Charges

Brokers and algo providers must clearly disclose all charges related to algorithmic trading. This includes:

  • Subscription fees for algo trading strategies.
  • Brokerage costs associated with executing algo trades.

When Will These Regulations Be Implemented?

SEBI has set the following timeline for the new framework:

  • April 1, 2025: Brokers’ Industry Standards Forum will formulate detailed implementation standards.
  • August 1, 2025: Full implementation of the new regulatory framework.

How Will This Impact Retail Traders and Market Participants?

Retail Investors:

  • Increased security and transparency in algo trading.
  • Better education and awareness through mandated investor education programs.
  • More robust oversight to prevent fraud and manipulation.

Brokers:

  • Need to enhance compliance with SEBI’s risk management requirements.
  • Must obtain approvals for every algorithm they offer to clients.
  • Implement strict monitoring and reporting mechanisms.

Algo Providers:

  • Must be registered and approved by stock exchanges.
  • Ensure algorithms meet predefined risk and compliance standards.
  • Maintain detailed logs and records for transparency.

Conclusion

SEBI’s new regulatory measures aim to strike a balance between innovation and investor protection in algo trading. By ensuring a structured and transparent framework, SEBI seeks to democratise access to automated/algo trading while minimising potential risks.

As a retail trader, staying informed about these regulations is crucial to navigating the evolving algo trading landscape. Whether you’re developing your own trading algorithm or using broker-provided strategies, understanding these compliance requirements will help you trade with confidence and security.

For further details, refer to SEBI’s official circular on the new algo trading regulations.

Also Read: Is Algo Trading Legal in India Now?

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Editorial

Here’s Why You NEED a Trading Plan in 2025

trading plan in 2025 | marketfeed

Trading in financial markets can be thrilling and profitable, but for many, it leads to consistent losses. A key reason for this is the absence of a well-structured trading plan. If you’re serious about making profits and avoiding emotional pitfalls, understanding and implementing a trading plan in 2025 is non-negotiable. In this article, we dive into the step-by-step process of building a robust trading plan.

Why Traders Lose Money Without a Plan

Statistics reveal that a staggering 90% of Indian traders experience losses. Out of this, 70% of traders who lose money don’t have a plan. This highlights the critical role a trading plan plays. While having a plan doesn’t guarantee immunity from losses, it significantly improves the odds of profitability by:

1. Avoiding emotional trading caused by impulsive decisions.
2. Promoting discipline and consistency, key traits for success.

    Key Components of a Trading Plan

    To craft a robust plan, focus on these components:

    1. Set Clear Goals

    Your trading goals should be:

    • Specific: What do you aim to achieve?
    • Measurable: How will you track your progress?
    • Realistic: Are your targets aligned with your trading style?

    Define attainable benchmarks based on your trading approach.

    2. Risk Management

    Here’s how you can protect your trading capital:

    • Follow the 2% rule: Risk only 2% of your capital per trade.
    • Diversify across strategies, sectors, and assets.
    • Use stop-loss and position sizing to control losses.

    Explore our Position Sizing Calculator!

    3. Develop a Strategy

    Your strategy should include:

    • Indicators suited to your trading style: Examples include Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), or multi-Exponential Moving Average (EMA) setups. [Read: What are Technical Indicators?]
    • Rigorous backtesting: For systematic traders, test your strategy on historical data. For discretionary traders, forward test with small capital.
    • Clear entry and exit rules: Define triggers for profitable and loss-making scenarios.

    4. Review and Adapt

    No plan is perfect from the start. Regularly assess and refine your approach by:

    • Maintaining a trading journal to document strategies, trades, and emotional states.
    • Tracking key metrics like win rates, risk-reward ratios, and drawdowns.
    • Adapting to market conditions and learning from both successes and failures.

    A trading plan is only effective if followed with discipline. Without consistency, even the most sophisticated plan will fail. Train your mind to stick to your rules and avoid impulsive deviations.

    Automated Solutions for Modern Traders

    If tracking multiple strategies feels overwhelming, consider algo trading services like those offered by marketfeed. Our automated systems execute pre-defined strategies, eliminating emotional biases and ensuring consistency!

    Conclusion

    Creating and following a trading plan is the cornerstone of successful trading. By setting realistic goals, managing risks, developing effective strategies, and continuously reviewing your performance, you can pave the way for consistent profits. Whether you’re an option buyer, swing trader, or algo trader, the principles of a trading plan apply universally.

    Ready to take your trading to the next level? Start crafting your trading plan today—and watch your performance improve exponentially.

    Watch: Why You Need a Trading Plan in 2025!

    Categories
    Algo Trading

    What Are The Risks of Algo Trading?

    risks of algo trading | marketfeed

    Algo trading has become a popular alternative to traditional trading, which can often be time-consuming and emotionally draining. Traders can now automatically execute trades at lightning speed using computer algorithms. While it offers many advantages—speed, precision, and efficiency—algo trading comes with its own set of risks. It’s crucial to understand these risks before diving into this high-tech approach to trading.

    In this article, we explore some of the key risks involved in algo trading and ways to minimise
    them.

    Let’s Understand Algo Trading!

    Algorithmic (algo) trading involves using automated, pre-programmed instructions to place orders in financial markets— stocks, derivatives (F&O), currencies, and commodities. These algorithms follow specific rules based on factors like price, timing, and quantity to identify trading opportunities and automatically execute orders once set conditions are met. Algos can analyse vast amounts of market data, monitor performance, and execute trades seamlessly, all without human intervention!

    For instance, imagine a trader with a strategy to buy a stock when its price rises above the 50-day moving average and sell when it drops below. An algorithm can track this stock continuously and execute trades the moment the criteria are met. It’s rapid and reduces the time and effort required to trade, compared to traditional methods. This precision and efficiency make algo trading appealing for those who value speed and wish to minimise emotional biases in their trading decisions.

    What are the Risks of Algo Trading?

    1. Technical Glitches

    Algo trading is highly dependent on technology, meaning any technical glitch can have serious consequences. A minor error, like a coding bug or server malfunction, can trigger unintended trades or even cause a series of rapid trades that disrupt the market. Such errors have, in extreme cases, led to flash crashes—sudden, severe market drops driven by automated trading gone wrong.

    For instance, if a server outage stops the algorithm from executing trades, it could miss critical opportunities or fail to exit a position at the right time. To prevent this, traders should implement robust monitoring and backup systems. Having manual oversight and setting up contingency plans, like alert systems or manual intervention protocols, can help address issues as soon as they arise, reducing the impact of technical mishaps.

    2. Data Quality and Integrity

    The success of algo trading largely relies on the quality of data fed into the algorithm. Poor or outdated data can lead to inaccurate decisions, resulting in losses. For example, if an algorithm is backtested using unreliable historical data, it might suggest profitable patterns that don’t hold up in live trading. Real-time data errors, like incorrect prices, can also cause the algorithm to execute faulty trades.

    To avoid this, traders should source data from reputable data providers/vendors and consistently verify its accuracy and integrity. [You can source data directly through exchanges, brokers, or data vendors.] Popular data vendors in India include TrueData, Global Datafeeds, and Accelpix.] Regularly updating data feeds and cross-checking sources can ensure the algorithm is always working with accurate information, minimising the risk of errors based on faulty data.

    Also Read:     How to Source Market Data for Algo Trading?

    3. Overfitting

    Overfitting occurs when an algorithm is overly customised to historical data, making it less effective in real-world trading. In essence, the algorithm “learns” past trends too well, which might limit its adaptability to current and future market conditions. For example, an algorithm might show great performance when tested on past data but struggle to generate profits when market conditions change.

    One way to address overfitting is by designing algorithms that balance specificity with adaptability. Regularly testing the algorithm with new and varied data can also reveal whether it’s overly dependent on past trends. By developing flexible strategies, traders can ensure that their algorithms are capable of adapting to changing market dynamic

    4. Cyber Threats

    In today’s digital age, cybersecurity is crucial, especially for algo trading platforms where valuable trading strategies and sensitive data are stored. Hackers or cyber attackers might attempt to steal proprietary trading algorithms or disrupt trading activities. For instance, a hacker gaining access to a trading account could lead to unauthorised trades, resulting in significant financial losses.

    Traders can mitigate this risk by investing in strong cybersecurity measures, such as multi-factor authentication and encrypted data storage. Regularly updating software and staying informed about the latest cybersecurity threats are essential practices. Ultimately, protecting trading systems against cyber threats is as important as developing profitable trading strategies.

    5. Lack of Human Oversight

    You may be wondering: isn’t algo trading all about eliminating manual aspects of trading? Well, there’s more to it. One critical risk in algo trading is the absence of human supervision, which can lead to unexpected and sometimes severe consequences. Algorithms are typically designed based on historical data and specific rules, meaning they may fail to account for rare or unforeseen market events, known as black swan events. For example, during a sudden market crash or an unexpected geopolitical event, an algorithm might continue trading in ways that amplify losses, as it lacks the judgment to pause or reassess in extreme conditions.

    To mitigate this risk, it’s essential to maintain some level of human oversight. Regularly monitoring the performance of algorithms and having manual override options in place can help traders intervene when markets behave unpredictably. Staying informed about major events and adjusting algorithms accordingly can also reduce the chances of undesirable outcomes.

    Conclusion

    Here’s an important aspect we would like to mention: risks in algo trading can be highly subjective. What might be a significant risk for one trader could be manageable for another, depending on factors like their experience, financial capacity, and trading objectives. An experienced trader with a strong understanding of the market and ample capital might be more willing to take on risks compared to a beginner with limited resources.

    The key to successful algo trading is not just recognising the risks but also knowing how to manage and mitigate them effectively. By implementing strong risk management practices, staying informed, and approaching each risk with a clear strategy, traders can navigate the challenges of algo trading more confidently.

    Remember, no trading strategy is entirely risk-free, but with the right approach, you can maximise the potential rewards while minimising potential pitfalls in algo trading. 

    FAQs

    1. What are the biggest risks in algo trading?

    Algo trading carries several risks, including technical glitches, data quality issues, overfitting, cyber threats, and lack of human oversight. A minor coding error or server failure can lead to unintended trades, while reliance on poor-quality data may result in inaccurate decisions. Cybersecurity threats can also compromise trading systems. Moreover, algorithms may fail to adapt to unpredictable market events without human intervention.

    2. How can technical failures affect algo trading?

    Technical failures like server outages, coding bugs, or software crashes can cause algorithms to malfunction, leading to missed opportunities or unintended trades. In extreme cases, these failures can contribute to market disruptions (such as flash crashes). To mitigate this risk, traders must have backup systems, alert mechanisms, and manual intervention protocols in place.

    3. What is overfitting in algo trading, and why is it a problem?

    Overfitting occurs when an algorithm is overly optimised for historical data, making it less effective in live trading. This happens when a strategy is designed to fit past trends too precisely, limiting its ability to adapt to real-world market changes. To avoid this, traders should test strategies on new and varied data to ensure adaptability.

    4. How can traders protect their algo trading systems from cyber threats?

    To protect algo trading systems from cyber threats, traders should implement multi-factor authentication, encrypted data storage, and regular software updates. Use secure networks, monitor unusual activity, and stay informed about cybersecurity risks to safeguard your trading strategies and sensitive information.

    Categories
    Algo Trading

    Essential Data for Backtesting in Algo Trading: A Simple Guide

    backtesting data | marketfeed

    In the fast-paced world of algo trading, data is everything. Whether you’re an experienced trader or new to the field, the quality and type of data can make or break your algo trading journey. With algorithms executing trades at lightning speed, every decision must be based on accurate and reliable information. While building the “perfect” trading strategy is key, the data driving these strategies is just as crucial.

    Just like a chef needs the freshest ingredients to create a perfect dish, your trading algorithm requires high-quality data to deliver accurate and profitable results. In this article, we’ll explore the essential data you need for backtesting in algo trading.

    But First, What is Backtesting?

    Backtesting is a process that allows traders to evaluate the performance of their trading strategies using historical market data. By doing this exercise, you’ll get invaluable insights and opportunities to refine your trading strategies.

    Let’s consider a real-life example to understand backtesting better. Imagine you’ve created a trading strategy designed to capitalise on short-term price movements in the Nifty 50 index. Backtesting allows you to test your trading strategy with historical Nifty 50 data before risking capital in live markets. By using price data from the past 5-10 years, you can see how your strategy would have performed across different market conditions—bullish, bearish, or highly volatile periods.

    It’s like turning back the clock to see how your trading strategy would have performed during a specific timeframe. So you can refine it before applying it in real trading!

    Read: Why Should You Backtest Algo Trading Strategies?

    Important Data You Would Need for Backtesting Algo Trading Strategies:

    1. Historical Price Data

    The cornerstone of any backtesting process is historical price data. This data captures the prices of stocks, derivatives (futures & options contracts), currencies, commodities, or other financial assets/instruments at different points in time. Think of historical data like a time machine. It allows your algorithm to trade in the past, giving you a sneak peek into how it might perform in the future.

    What does historical price data include?

    • Open price: The price at which an asset starts trading when the market opens in a session/specific period (eg, 1 minute, 5 minutes, 15 minutes, etc).
    • Close price: The last price at which an asset trades during a session/specific period (eg, 1 minute, 5 minutes, 15 minutes, etc).
    • High and low prices: The highest and lowest prices that an asset reaches during a session/specific period (eg, 1 minute, 5 minutes, 15 minutes, etc).

    The National Stock Exchange (NSE) offers free historical index data for indices like Nifty 50, Bank Nifty, and FIN Nifty. Traders can download this data in CSV format for specific timeframes, providing them with the foundational data needed to test their trading strategies.

    Data vendor platforms like TrueData, Global Datafeeds, and Accelpix offer market data services through monthly subscriptions. These platforms can provide traders with additional insights, such as intraday data (price movements within a single trading session) and more detailed financial statistics.

    2. Volume Data

    While price data is vital, understanding how much trading occurred at different prices is equally critical. This is where volume data comes in.

    Volume data refers to the number of shares, contracts, or units traded during a particular period. It helps in assessing market liquidity and interest. These two key components ensure that your trades can be executed smoothly and avoid significant slippage or market impact.

    [A slippage occurs when the price at which your order is executed does not match the price at which it was requested.]

    For example, if you’re testing a momentum-based trading strategy, understanding volume data is essential. In markets with high trading volume, your strategy might quickly capture strong price trends. However, in low-volume markets, price movements can be less reliable, and your strategy may struggle to identify clear trends, leading to false signals or missed opportunities.

    Use Technical Indicators

    Technical indicators are mathematical tools or calculations derived from a financial asset’s (stock, index, etc.) historical price and volume data. It is used to predict market trends or volatility. You can incorporate technical analysis and indicators into your algo trading system to make it more objective and rule-based. Popular indicators include:

    • Moving Averages (MA): A moving average is the average of the closing prices of a security/asset (index, stock, F&O, etc.) over a specified period. It is an indicator that helps traders determine the trend in the market and identify key levels of support and resistance.
    • Supertrend: A supertrend is a simple line used to indicate the market trend. This is one of the most used trend-following indicators in algo trading. It can also act as support or resistance.
    • Average Directional Index (ADX): Traders use the ADX indicator to identify the strength of a trend, making it a valuable tool for avoiding sideways markets and improving trading decisions. During analysis, we can adjust the indicator settings based on time frames and market conditions to maximise its full potential.

    By including these indicators in your backtesting process, your algorithm can better simulate real-world trading conditions. It’s easy to implement technical indicators using various programming languages (like Python or C++) and algo trading platforms.

    Account for Transaction Costs and Slippage

    When you’re backtesting, it’s easy to get excited about hypothetical profits. However, to make your simulations more realistic, it’s essential to factor in transaction costs and slippage.

    • Transaction costs: These are the fees your broker charges for every trade, such as brokerage charges, taxes, etc.
    • Slippage: This is the difference between the expected price of a trade and the price at which the trade is executed. Slippage often occurs during periods of high volatility or low liquidity when prices move quickly.

    Incorporating transaction costs and slippage into your backtesting framework provides more realistic outcomes, helping you avoid over-optimistic results.

    Conclusion

    The importance of data quality in backtesting cannot be emphasised enough. Using poor-quality data can lead to inaccurate assumptions, causing your trading strategy to fail when applied in real-world conditions. To avoid costly mistakes, always ensure your data sources are trustworthy, and take the time to double-check the accuracy of the data you’re using.

    Here are a few key factors to consider when evaluating data quality:

    • Missing Data: Gaps in price or volume data can skew your backtest results, leading to unreliable performance estimates.
    • Incorrect Timestamps: Properly timestamped data is crucial to ensure that trades and market events are sequenced accurately.

    Backtesting in algo trading is a powerful tool that can provide insights into the viability of a trading strategy. However, it’s only as good as the data you feed it. From historical price and volume data to transaction costs, and slippage every piece of information plays a critical role in ensuring the accuracy of your simulations. So, gather your data, eliminate biases, and ensure high-quality inputs to build a trading algorithm that stands a higher chance of success in live markets.

    Also read: How to Source Market Data for Algo Trading?

    Categories
    Algo Trading

    A Simple Framework to Start Algo Trading in India

    start algo trading | marketfeed

    Do you find yourself endlessly staring at trading charts, trying to figure out the unpredictable movements of the stock market? Or does your busy schedule leave very little room for trading nowadays? If manual trading feels difficult now, it might be time for you to consider algo trading!

    Instead of spending countless hours glued to your screen, you have a smart trading algorithm constantly monitoring the markets and executing trades on your behalf. It’s quick, accurate, and never needs a break. Sound too good to be true? With algo trading, this can be your new reality!

    In this article, we’ll walk you through a simple framework to help you get started with algo trading in India.

    What is Algo Trading?

    Algorithmic trading, or algo trading in short, is a method of trading where computer programs or “algorithms” follow predefined rules or criteria to execute trades automatically in the financial markets (stocks, currencies, commodities, derivatives, etc.). These criteria include price, timing, and quantity instructions. Trading algorithms can analyse large amounts of market data, identify opportunities, and place orders at speeds that no human could match!

    By eliminating the emotional aspect of trading, algo trading allows for more disciplined and precise strategies. It’s like having a team of expert traders working around the clock— while you sit back and relax!

    How Can You Start Algo Trading in India?

    You can participate in algo trading in India in four main ways, based on your skills, experience, and preferences:

    1. Do It Yourself (DIY)

    In this approach, you handle everything on your own—from developing the trading strategy to coding the algorithm to executing the trades. If you’re someone who enjoys coding and wants complete control over your trading system, this might be the path for you. You’ll need to learn programming languages like Python or C++ to code the algorithm and test it to ensure it’s effective. While this method requires more time and effort, it offers the most flexibility.

    2. Get Help from Experts

    If you’ve got great trading ideas but lack coding skills, you can collaborate with developers to code your strategies. Alternatively, you can use pre-built algorithms offered by algo trading platforms available in India. These platforms allow you to automate your strategies without needing extensive programming knowledge.

    3. Use Algo Trading Platforms

    A growing number of algo trading platforms in India now allow even beginners to execute trades at lightning speed, offering a competitive edge over traditional traders. Additionally, many of these platforms allow users to backtest their trading strategies using historical data, ensuring they’re fine-tuned before going live in the markets! Tradetron, uTrade Algos, Algo Test, QuantMan, and Algo Bulls are examples of popular algo trading platforms in India.

    Simple Guidelines to Start Algo Trading in India

    Here’s a breakdown of the steps you can follow to kickstart your algo trading journey in India:

    Step 1: Learn the Basics of Algo Trading

    Before jumping in, take time to understand how algo trading works. Learn about different trading strategies, the role of algorithms in executing trades, and how these systems eliminate emotional decisions. Familiarise yourself with terms like backtesting, slippage, and transaction costs to get a well-rounded understanding. It’s also important to understand the regulations surrounding algo trading in India.

    Step 2: Define Your Trading Goals

    Ask yourself:

    • What do you hope to achieve with algo trading?
    • Do you want to maximise profits, reduce errors, or improve risk management?
    • Which markets or asset classes do you plan to trade in?

    Defining these goals early on will help you choose the right strategy and tools for your journey.

    Step 3: Choose an Algo Trading Platform

    You can skip this step if you’ve selected the DIY approach we mentioned earlier!

    If you’re a beginner, you can choose an algo trading platform to use pre-built strategies developed by experts, backtest your existing trading strategy, automate your strategies seamlessly, execute them, and get P&L reports. 

    While choosing a platform, look for:

    • Ease of use
    • Backtesting features
    • Integration with your broker
    • Support for your preferred trading strategies

    Read: Top 5 Algo Trading Platforms for Beginners in India

    Step 4: Pick a Trading Strategy

    You can skip this step if you’ve selected the DIY approach we mentioned earlier!

    If you don’t have a trading strategy, this is the next step. Some common strategies used in algo trading include:

    • Trend Following: By using technical analysis indicators such as moving averages, or trend lines, these strategies aim to recognise price trends. For instance, your algo trading system could automatically buy assets (like stocks, futures, or options) when prices rise and sell them when they fall.
    • Arbitrage: These strategies exploit price differences for identical assets traded on various exchanges (NSE, BSE). To capitalise on price disparities, your trading algorithm will have to place buy and sell orders simultaneously. These strategies demand rapid execution speeds, as arbitrage opportunities often disappear quickly.
    • Mean Reversion: These strategies operate on the principle that asset prices and returns eventually revert to their historical average. Your algo buys when prices are low compared to their historical average and sells when they are high, using statistical tests to identify significant deviations from the mean.
    • Statistical Arbitrage: Use statistical models and machine learning to identify patterns and trade based on small price differences. In this case, your algo trading system has to analyse large amounts of data to find patterns and correlations that can be exploited for trading.

    Each strategy has its own strengths and risks. Choose one that fits your goals and risk tolerance. Always remember to incorporate strong risk management practices like stop-loss orders and position sizing.

    Step 5: Design Your Algorithm

    This step might be challenging for those new to coding or programming!

    Once you’ve chosen a strategy, the next step is to turn it into a functional algorithm. If you’re coding it yourself, Python and C++ are popular languages used for designing trading algorithms in India. You’ll need to account for data inputs, trade execution, risk management, and performance reporting.

    If coding sounds intimidating, platforms like Tradetron or AlgoBulls offer no-code tools to help you backtest simple trading strategies.

    Step 6: Backtest and Optimise

    Before you go live, it’s crucial to test your strategy using historical data to see how it would have performed in the past. This is known as backtesting. By simulating your algorithm on past market data, you can fine-tune your strategy, eliminate any issues, and improve performance.

    After backtesting, adjust key parameters like entry and exit points, position sizing, and stop-loss levels. Most algo trading platforms come with built-in backtesting tools to make this process easier.

    Step 7: Monitor and Adjust Regularly

    Even after your algorithm is live, it’s important to monitor its performance in real-time and make adjustments when necessary. Market conditions change, and your algorithm may need updates to stay effective. Periodically review your strategy, and adjust it to fit new market conditions or regulatory changes.

    Many algo trading platforms provide tools to track real-time performance, monitor risk, and refine strategies based on changing data.

    Additional Tips for Aspiring Algo Traders in India:

    • Start Small: Begin with a small amount of capital and gradually increase your exposure as you gain experience and confidence in your algo trading strategy.
    • Stay Updated: Algo trading is evolving rapidly. Stay informed about new strategies, regulations, and technologies in the market to keep your trading systems sharp.
    • Seek Community Support: There’s a growing community of algo traders in India. Online forums, meetups, and courses can help you learn from experienced professionals and improve your skills.

    Algo trading opens up a world of possibilities for traders in India, offering speed, precision, and the ability to capitalise on market opportunities that would be impossible for human traders to spot. By following the steps outlined in this guide, you can start your algo trading journey confidently. Remember, success in algo trading requires continuous learning, monitoring, and strategy refinement!

    Categories
    Algo Trading

    The Importance of Quantitative Trading: A Beginner’s Guide

    what is quantitative trading? | marketfeed

    If you believe trading needs logic and calculations, you’re right. If you think it requires understanding the market and human behaviour, that’s true too! But if you think only experience leads to success, that’s not the case. Traders used to be those with a loud voice to shout orders. But as technology evolved so did the qualifications of a trader. A trader now can also be someone with coding abilities, and proficient in mathematics and statistics.  Quantitative trading is proof of this. This trading method is becoming popular because it can make quick, data-driven decisions. In this article, we’ll explain quantitative trading and why it’s so important!

    Introduction

    Trading involves buying and selling financial assets like stocks, bonds, or commodities to profit from fluctuations in their prices. While many believe that successful trading requires a ‘gut feeling’ or strong intuition, this is not entirely accurate. The truth is, profitable trading relies on logical decision-making, data analysis, and reliable strategies. This is where quantitative trading comes in. 

    Think of quantitative trading like playing chess against a computer. The computer doesn’t rely on emotions, it calculates every move based on pre-determined strategies. This makes it nearly unbeatable. Similarly, quantitative trading uses data and mathematical models to make decisions, making it extremely factual.

    What is Quantitative Trading?

    Quantitative trading or quant trading is a technique that makes use of mathematical, statistical, and algorithmic models to discover trading opportunities. It eliminates the concept of emotional biases and relies solely on numbers or quants to find data patterns. In this technique, data is organised and analysed more efficiently for quick order placement. 

    Typically, quant traders have expertise in fields like data mining, programming, and finance. They might work at investment banks, asset management companies (AMCs), or hedge funds, or they might trade independently. 

    Algo Trading in Quantitative Trading

    Algo trading is a specialised type of quant trading. While quantitative trading establishes the framework and logic behind trading strategies, algo trading automates these strategies using computer programs or ‘algorithms’. This means trades are executed automatically based on pre-defined rules, such as price, volume, or timing.

    High-Frequency Trading (HFT) is a sub-category of algo trading. It uses superior algorithms to execute trades in microseconds, taking advantage of even the smallest price fluctuations. HFT is commonly used by institutional investors. But with technological advancements, it has become popular among retail traders as well.

    Algo trading is rapidly gaining popularity worldwide, and India is no exception. From large institutions to independent traders, more people are turning to this method to optimise their trading strategies and improve returns. 

    Also read: Future of Algo Trading In India

    By integrating advanced techniques and algorithms, quantitative trading has changed the way markets operate, offering a structured, data-driven approach to gain an edge in the competitive field. Let’s see the advantages of quant trading!

    Advantages of Quant Trading

    1. Speed and Accuracy: Quant trading allows for computer algorithms to place orders in the market. This ensures there is precision in terms of speed. As soon as pre-defined conditions are met, the system places the necessary orders. 

    2. Removing Emotional Biases: By relying purely on data, human biases don’t impact trades, making results more consistent. Human stress, mood fluctuations, and self-doubt aren’t factors a computer program goes through. In the need for quick judgment, computers are more reliable.  

    3. Tested and Optimised: Since the strategies are formed in advance by observing patterns, they can be tested and verified. Backtesting and Forward testing are popular tests that help traders optimise their strategies. It boosts confidence before deploying it in the live market. 

    4. Easy Tracking and Predicting: With a set strategy, traders can predict their potential profits. This helps in smarter capital allocation, better decisions, and creative techniques. Traders can observe their strategy at work and track the shifts. This enhances the scope for improvement. 

    While quant trading has many advantages, the major shortcoming is the inability to adapt to market conditions. Strategies perform solely based on the instructions given They lack dynamic thinking. Apart from this, quantitative trading requires technical knowledge to generate results. So let’s analyse what these requirements are and how quant trading works!

    How Does Quant Trading Work?

    As we have already established, it takes proficiency in mathematics and statistics to work with quantitative trading. Forming strategies, creating algorithms, and understanding the technical terms, are skills that need to be learned. Let’s break down the process of quant trading. 

    1. Form The Strategy: The first step is to develop a theory or hypothesis based on your market understanding. For example, you might notice that when a stock’s 50-day moving average goes above the 200-day moving average, prices tend to rise. This is your hypothesis or idea. Next, you turn this idea into a mathematical rule and program it into a computer, so it can automatically trade when your conditions are met.

    2. Gather Data: Now the trader needs to test whether the strategy works. For this, you need to collect historical data and backtest your strategy. Ensure the period isn’t too far back as your results won’t be accurate representations of success. 

    3. Validate Through Testing: Backtesting and forward testing are done on the data. Backtesting will uncover any major gaps in the strategy. Once that is set, you can forward test the strategy using live data. You can test your strategy with virtual trades (paper trading) or real money, giving you a clear view of your strategy’s real-time performance.

    Also read: Backtesting and Forward Testing in Algo Trading

    4. Execute Orders: Once you have tested the strategy and are satisfied with how it’s performing, you can deploy it in the live market. Sit back and watch the strategy perform, executing orders based on your analysis. 

    5. Continuous Monitoring: The process doesn’t end with placing the order. As a trader, you must monitor the movements and ensure your strategy continues to perform well. This will help you stay relevant in the market. 

    How Can You Learn Quant Trading?

    We have now understood the steps involved in quantitative trading. As a beginner, the way to enter the field is to familiarise yourself with the various strategies, and techniques, and decide if you have the capacity to code using mathematical formulas. If you do not have the abilities, there are many other ways to get involved. Joining communities and teaming up with experts will give you the necessary insights. You can take up courses, gain certifications, and stay up to date with the latest technology! 

    Also read: How to Learn Algo Trading and Algo Trading Without Coding

    Conclusion 

    The advancements in Artificial Intelligence and Machine Learning have created a need for automation. Finance and trading are fields where automation implies consistent profits. As computers are becoming faster, so are trading techniques. To survive in the long run, embracing these changes is the way to go. Keep researching and learning, and grasp new ways to make profits! 

    Categories
    Algo Trading

    What is the Difference Between Backtesting and Forward Testing in Algo Trading?

    backtesting and forward testing | marketfeed

    In the complex world of algo trading, there are a lot of uncertainties and scope for error. Traders turn to two methods to boost their confidence regarding their trading strategies: backtesting and forward testing. By performing these vital tests, traders evaluate their strategy and check for any faults, thereby giving them a certain amount of assurance and comfort. In this article, we will examine what backtesting and forward testing is, how they are performed, and why they are important!

    Understanding the Terms

    Algo trading is a method of trading where a computer program or ‘algorithm’ is used to execute orders automatically in the financial markets (stocks, futures & options, commodities, etc). The algorithm contains pre-defined instructions like price, volume, etc. These algos execute trades on time and without any bias. But how can you judge which algorithm to use when? Let’s find out below!

    Backtesting

    Algorithms in trading are built using complex mathematical and statistical models. Just like in a math exam, where using the wrong formula could lead to incorrect answers, algo trading carries the risk of applying the wrong algorithm to specific market conditions. The solution? Much like practising previous years’ questions in school to avoid mistakes in the exam, traders use backtesting to find gaps in their strategy. 

    Backtesting is a method that allows a trader to test their trading strategy using historical data, and thereby fix potential errors before deploying it in real-world markets. It helps refine the strategy and understand if it could have worked in the past. 

    Forward Testing 

    Now what if the math syllabus is different this year? The past papers are not a reflection of the required knowledge. What can you do to prepare for the exam? You can simply solve practice papers that are available based on current or real-time requirements. This is what forward testing does!

    Forward testing allows you to test your strategy in real time and find out how it performs in the current market. It simulates the real world, so any profits or losses made are just a representation of what would actually happen. 

    Backtesting and forward testing ensure your strategy works and is sustained in the dynamic world of trading. But which test is better suited in algo trading? Let’s see below!

    Importance in Algo Trading and Key Differences

    Now that you understand what each term means, let’s examine why they are important in algo trading. 

    Backtesting

    • Correct Major Gaps: When you check your strategy against previous scenarios, you can identify significant pitfalls (hidden dangers) that might not be obvious in theoretical models. Backtesting helps find major gaps in your strategy, thereby increasing the overall success rate when applied in live trading markets.
    • Proof of Strategy Success: Backtesting is a powerful tool for validating the accuracy of your algorithm. If your strategy performs well in historical data, you can deploy it in live markets with confidence, as you have evidence of its potential effectiveness.
    • Ticks Necessary Metrics: Traders can perform comprehensive tests and analyses of metrics like Sharpe Ratio, Maximum Drawdown, and Win/Loss Ratio during backtesting. This detailed examination gives a clear overview of the strategy’s performance and helps optimise it. 

    Forward Testing 

    • Real-Time Analysis: Past successes don’t guarantee future profits, which is why forward testing is crucial. Like a simulation, forward testing allows you to evaluate your strategy in the current market environment. You can test it with virtual trades (paper trading) or real money, giving you a clear view of your strategy’s real-time performance.
    • Adaptability Check: Forward testing helps traders combat the question of whether the strategy can adapt to movements. It helps in building dynamic strategies and preparing for unforeseen circumstances. 
    • Considers Execution Factors: Your strategy may excel under perfect conditions without any execution troubles. But, in the live market, there are many factors like slippages, (differences between expected and actual prices due to quick market shifts) latency (delay in trade execution), and order fill rates. Forward testing considers all these factors and gives a realistic view. 

    Now that we have established the need for the tests, let’s compare both of them:

    CriteriaBacktesting Forward Testing 
    Type of Data UsedHistorical Real-time
    Time and Duration Quickly performed and processed  Slower, move with live markets
    Purpose Asses major gaps in strategy Simulate real market and check execution efficiency 

    Backtesting and forward testing have their own advantages and necessities. It’s not a question of which test to conduct. As an algo trader, you must test your strategy using both these tests to gain the best results. By using them simultaneously you can thoroughly optimise your strategy! 

    How Are Backtesting and Forward Testing Performed?

    With the importance and key differences in mind, we’ll dive into how you can perform each of these tests on your strategy:

    Backtesting

    1. Outline the Strategy: This means recognising the major moves in the strategy, like entry and exit points, position sizing (the trade size based on your capital and risk appetite), etc. Once you get this information, you can move to the next step. 

    2. Gathering Historical Data: Data can be taken either manually or through software platforms. However, manual data collection can take more time and effort. Backtesting platforms, such as Amibroker, TradeTron, and TradingView, are available to run backtests. You must also determine the timeframe for testing your strategy. Consider relevant market conditions, significant events, data availability, and the type of strategy you’re using.

    3. Run the Strategy: Backtesting is done in two sets of data samples, in-sample and out-of-sample data. This ensures the strategy isn’t over-optimised and over-fitted for one set of data. It checks reliability in different situations. Run the strategy using in-sample data first, then out-of-sample data. 

    (In-sample data is the historical data you use to develop and refine your trading strategy. Out-of-sample data is a different set of historical data that you don’t use when building the strategy. It allows you to test how well your strategy would perform in real market conditions that it hasn’t encountered before.)

    4. Analysis and Improvement: Interpret and analyse the results from the backtest. Use metrics like win/loss ratio, Sharpe Ratio, etc. Based on these, make necessary changes in the strategy.

    Also Read: Ultimate Guide to Backtesting

    Forward Testing

    1. Set up Demo Account: The first step to forward testing is to set up a paper trading or demo account on platforms like AlgoTest and TradeTron. This lets you trade without risking real capital—just make sure to size your account based on your true capacity. But that’s just one way. To truly uncover real execution issues, forward testing with a small amount of real money is key. Only then can you spot any potential hurdles in live market conditions and fine-tune your strategy for success.

    2. Run the Strategy: Once the account is ready, you can start executing the strategy as you would in the live market. 

    3. Monitor and Record the Performance: Now sit back and analyse the performance of your strategy, record where there are major faults or where the strategy fails to provide optimal results. 

    4. Analysis and Improvement: Take the results and see where you can improve the strategy so it performs better in the live market.

    This is how you can backtest and forward-test your strategy before deploying it in the live market! 

    Conclusion 

    Backtesting and forward testing are essential prerequisites to algo trading. They each have different requirements and steps to perform. Both of the tests offer risk-free ways to verify strategies. Backtesting is quicker to conduct, but forward testing uses live market data to recheck your strategy. As an algo trader, you must conduct both tests to maximise your chances of profits! 

    Categories
    Algo Trading

    What are the Advantages of Pre-Built Algorithms in Algo Trading?

    pre-built algorithms | marketfeed

    Algo trading has completely revolutionised financial markets, enabling faster and more advanced trade execution. As it gains global acceptance, algo tools and strategies continue to evolve. One of the most notable advancements in this field is the rise of pre-built algorithms, which benefit both beginner and experienced traders. In this article, we’ll explore the advantages of pre-built algorithms and why they are becoming increasingly popular among traders.

    What are Pre-Built Trading Algorithms?

    Algo trading, short for algorithmic trading, uses automated, pre-programmed instructions (based on factors like price and volume) to execute orders swiftly in financial markets. It has been gaining popularity due to its high speed and precision. These algorithms are built using complex mathematical formulas and coded in languages like Python or C++. However, creating them can be difficult and time-consuming, especially for those who lack the necessary skills and coding expertise. This is where pre-built trading algorithms come in! 

    Pre-built algorithms are ready-to-use strategies designed by experts and proven to be effective. They eliminate the need for traders to code their own algorithms. With minimal adjustments, these strategies can be deployed in live markets.

    Although pre-built algorithms offer an easy entry into algo trading, traders still need market knowledge to choose the right algorithm, customise it, and manage risks effectively.

    The Advantages of Pre-Built Algorithms

    Algo trading has many advantages that give a trader an edge in the fast-moving world of financial markets. Pre-built algorithms have all of those advantages, along with many more! Let’s see them below:

    1. Accessibility:

    Creating a personal algorithm often involves either hiring a programmer or dedicating significant time to learning coding. It could be expensive for non-technical traders. Pre-built algorithms could be more cost-effective than developing your own. Many platforms offer subscription or one-time purchase options, making algorithmic trading more accessible.

    2. Proven Performance:

    Pre-built algorithms often have a proven track record as they have been successful in the past. But it’s not just about past data, pre-built algorithms often provide real, forward-test data. This shows how the strategy has performed in real time, building trust and credibility. This can boost a trader’s confidence in using the strategy for live trading. 

    3. Beginner Friendly:

    Pre-built algorithms simplify the process by offering pre-designed strategies, making it easier for new traders to explore the world of algo trading without the need for extensive coding knowledge

    Many algo trading platforms offer helpful resources and guidance for beginners. This on-the-job learning can be invaluable as it allows new traders to modify and implement strategies effectively.

    4. Flexible:

    Pre-built algorithms don’t have to be a one-size-fits-all. Some algo trading platforms in India allow for customisation, hence traders can tailor them to their individual preferences or goals. This way, a trader can begin with a simple strategy and gradually add complexity by incorporating additional indicators.

    To know more about technical indicators used in algo trading, check out this article.

    You must be thinking, “All this sounds great, pre-built algorithms are the way to go!” However, there are some things to consider before making a decision. Let’s analyse them below.

    The Disadvantages of Pre-Built Algorithms: 

    While pre-built algorithms offer significant advantages, they also come with drawbacks:

    1. Over-Reliance on Past Markets: 

    Most pre-built algorithms exhibit backtested results from historical markets. However, past performance does not guarantee future success, especially in changing conditions. Even with forward testing available, pre-built algorithms are created with a different market condition in mind, which can impact profits. 

    2. Lack of customisation: 

    Some pre-built trading algorithms offer customisation opportunities before deploying them. However, these are very basic changes. By using pre-built algorithms, a trader doesn’t have the ability to create major changes when the market shifts. To have complete control over the strategy, a trader can approach do-it-yourself (DIY) platforms that code your strategy or they can code from scratch. 

    3. Obsolete Strategies Risk: 

    Financial markets are constantly evolving, and so are the strategies used within them. There’s a risk that older algorithms may not be reviewed thoroughly enough to stay updated, which could lead to a decline in their effectiveness over time.

    Where Can You Access Pre-Built Algorithms?

    1. Algo trading platforms: Platforms such as Tradetron, AlgoTest, and StockMock offer pre-existing algorithms that can be adopted or modified by the trader. They provide simple interfaces that allow the trader to select strategies by trading preference. Platforms like TradingView offer marketplaces where developers sell pre-built algorithms. Expert platforms like marketfeed create and execute algorithms for you, eliminating stress and offering a chance at higher returns!

    2. Open Source Communities: There are open-source platforms or communities, like GitHub, that offer free algorithms to be downloaded, customised, or implemented where the flexible individual can tinker with strategies.

    Note: It’s essential to conduct thorough research and due diligence before selecting and using any pre-built algorithm. Always consider your specific needs, risk tolerance, and the potential limitations of pre-built algorithms before making any investment decisions.

    Conclusion

    Pre-built algorithms offer several advantages for traders, particularly those new to algorithmic trading. By eliminating the need for extensive coding, pre-built algorithms simplify the process and make it more accessible to a wider range of traders. These algorithms often come with pre-tested strategies and backtest data, providing valuable insights into their historical performance. Additionally, many pre-built algorithms offer customisation options, allowing traders to tailor them to their specific needs.

    Overall, pre-built algorithms offer a chance for non-coders to enter into algo trading. They are a great resource to start with and can be used as the first step in the field!

    Categories
    Algo Trading

    Discretionary vs Algo Trading: Which Way to Go?

    Discretionary vs algo trading | marketfeed

    When you think of pursuing a career in trading, the first thing that comes to mind is sitting at a desk from 9:15 AM to 3:30 PM, staring at charts and executing trades at your discretion. You have to make decisions on what to buy and sell based on complex chart patterns or technical indicators in various market conditions. But what if I told you that this is not the only option to trade? You don’t have to stare at a screen for long hours and focus on other things that matter the most to you! Doesn’t that sound exciting?

    Well, this is precisely where the demand for algo trading comes from. In this article, we’ll dive into the difference between discretionary trading and algo trading, understand why automation can be a game-changer, and what you should choose. 

    Understanding the Two Main Methods of Trading

    1. Discretionary Trading

    Discretionary trading involves human judgment and experience in analysing market movements and placing orders. Here, traders observe the market, interpret the data, make necessary decisions, execute orders, and monitor them. 

    Let’s explore some of the advantages of discretionary trading: It relies on your experience and allows you to make flexible decisions. It enables traders to come up with creative trading strategies. Although this process is tedious and time-consuming, it’s more adaptable since humans can assess market trends and adjust their plans ahead of time, helping them avoid significant losses.

    However, discretionary trading comes with several drawbacks. Human emotions can be a double-edged sword, clouding judgment and leading to poor decisions in stressful moments. Beyond emotional biases, it’s also difficult to scale, as managing multiple portfolios can be overwhelming for traders. Additionally, it demands significant time and can be mentally draining, often resulting in inconsistent results. In some cases, the annual returns may not even keep pace with the benchmark index (like Nifty).

    2. Algo Trading 

    Algo trading makes use of computer programs or algorithms consisting of instructions relating to price, volume, etc, which are used to “automatically” place orders in the market. The first form of algo trading was seen five decades ago in New York, and it has taken the financial world by storm ever since. 

    Here are some key advantages of algo trading: Its increasing popularity stems from its ability to eliminate emotional biases and human error. It capitalises on minute market fluctuations, profiting from brief timeframes. By relying on software to execute trades, it reduces the pressure on traders to make real-time decisions. This automation also makes algo trading highly scalable, enabling the placement of multiple orders simultaneously.

    Compared to the stock market, the derivatives market can be more challenging to follow due to its rapid movements. With extreme volatility, profits are often generated from small price changes. In such cases, algo trading enables swift trade execution, helping to minimize slippage.

    (Slippage is when a trade executes at a different price than expected, usually due to quick market changes. This can result in paying more or selling for less than intended.)

    But Wait, Are There Any Drawbacks to Algo Trading?

    Algo trading requires significant time and financial investment. Learning the processes, techniques, and strategies is crucial but can be time-consuming. Institutional traders often need costly infrastructure, such as co-location servers and real-time data feeds. Retail traders face decisions like whether to code their own trading strategies, use pre-built algorithms, or rely on experts. Another drawback is the lack of human intuition and limited ability to adapt to unexpected changes as effectively as human traders. Additionally, over-reliance on algorithms without regular monitoring can be risky!

    What Are The Skills Required for Each Method? 

    Now that we’ve explored the key differences between discretionary and algo trading, let’s dive into the skills required to excel in each type:

    Discretionary Trading

    • In-depth Market Knowledge: The most essential skill for discretionary traders is a deep understanding of the stock market. You can gain an edge over others by understanding how markets work, recognising patterns, and knowing about key historical events, all of which can give you a competitive advantage.
    • Chart Analysis and Research Skills: Another vital skill is proficiency in reading charts and conducting both fundamental and technical analyses. This requires traders to keep learning and continuously improve their analytical abilities. 
    • Discipline and Emotional Control: Traders must have emotional discipline and stay logical under pressure. You must avoid impulsive decisions driven by market volatility. Successful traders rely on research-backed choices rather than emotional reactions.
    • Experience and Focus: Finally, experience in the market and the ability to stay focused and attentive make a discretionary trader successful. Years of observing market trends and refining trading strategies can greatly enhance decision-making.

    Algo Trading

    You can approach algo trading in several ways. You can either develop and code your own trading strategies, use pre-built ones, or rely on expert guidance. Each option requires a different skill set. However, if you’re interested in creating and programming your own strategy, you’ll need specific technical expertise. Here are the basic skills you will need if you build your trading strategy from scratch:

    • Programming Knowledge: The primary skill required in order to formulate and backtest algorithms is computer programming. Algo traders need to be proficient in Python or C++ to code their own strategies.
    • Strong Mathematical Abilities: Algorithms are complex mathematical formulas that provide trading instructions. A robust trading strategy is what makes you profitable. An algo trader must also be proficient in mathematics and statistics to create such strategies.
    • Trading Infrastructure: Algo trading needs a solid hardware and software setup. A fast internet connection and reliable network will help improve order placement speed. Institutional traders often use co-location servers for faster execution. Robust security systems are essential to prevent data breaches. 

      [Co-location refers to the service of setting up servers closer to the exchange. This improves connectivity and reduces latency or delays in collecting data.]
    • Market Data Access and Monitoring: Market data access is vital for backtesting and order placement. Platforms like Accelpix, TrueData, and broker platforms like Zerodha, and Angel One offer these services. Traders must also monitor their algorithms to ensure they are performing well in the current market. 

    These are the major skills needed to code and form your strategy. However, if you wish to do algo trading but lack coding skills or do not want to code, there are other ways for you to enter the market! To know how it’s possible, read this: How to Master Algo Trading Without Coding !

    What is the Right Path for You?

    Discretionary and algo trading require distinct skill sets for success. While algo trading may be more efficient in the long term, it demands a higher level of technical knowledge, infrastructure, and investment. Meanwhile, discretionary trading relies entirely on your experience and understanding of the market.

    Choosing the right approach depends on your skills, risk tolerance (the amount of risk you’re comfortable taking), and trading goals. Your goals might vary, so it’s important to assess why you want to trade, how much capital you can invest, and what returns you expect over a certain period.

    Financial markets are dynamic and can change quickly. Traders can achieve significant profits by automating decisions based on patterns and using experience in specific situations. You must have a continuous drive to learn and stay updated on the latest trends. Reflecting on key questions like why you’re trading and how much profit you aim to make in a given timeframe will help determine the right approach!

    Categories
    Algo Trading

    Co-location Servers in Algo Trading: Benefits and Uses Explained

    What are co-location servers? | marketfeed

    Every second counts when it comes to the cutting-edge world of algo trading (or even trading in general). Traders can earn significant profits in a matter of seconds by executing high-speed transactions. In this journey, many ‘big players’ use co-location servers to execute trades faster and gain an edge over competitors. In this article, we’ll explore the role of co-location servers and their importance in algo trading!

    Why is ‘Speed’ a Significant Factor in Algo Trading?

    Algo trading relies on the ability to analyse market data quickly. The market is constantly changing, with prices fluctuating rapidly. Algorithms can scan large amounts of data to find the perfect moment to make trades. In such fast-moving markets, getting data too late can mean missing out on opportunities or taking extra risks. The quicker an algorithm receives real-time data, the more accurately it can execute profitable trades! 

    Fast algorithms can place and execute orders more efficiently, reducing the risk of slippage (the difference between the intended price and the actual price at which the trade is executed). Moreover, when markets are volatile, algorithms can quickly react to changing conditions and adjust positions to minimise risk. Speed is crucial for implementing risk management strategies effectively. That’s why fast, reliable data is essential for success in algo trading!

    What is a Co-Location Server?

    Imagine the stock market as a massive building with walls that block signals. Far away traders, experience delays as the signals take time to reach them. Co-location servers function like powerful Wi-Fi routers, ensuring ultra-fast, lag-free connections for traders by bringing them closer to the data source. Co-location servers allow traders to place their machines close to the infrastructures of an exchange, enabling orders to be executed at faster rates. This gives them an advantage over those who are further away, thereby communicating changes in the market within a short time.

    Before 2009, brokers and proprietary traders used certain machines linked with exchange servers. However, orders wouldn’t be placed during peak trading volumes or for certain connectivity problems. On various occasions, these delays meant huge losses in high-frequency trading (HFT), as every millisecond is crucial. As a result, firms lost out because of the slow pace of placing orders.

    [Proprietary traders are firms or individuals that trade financial instruments using their capital, rather than on behalf of clients.]

    To combat this problem, the National Stock Exchange (NSE) offered co-location services in 2009. Under this service, brokers can place their servers within the exchange premises. The service helped traders to get real-time tick-by-tick price data. This gave an advantage in the HFT game, where algorithms would execute trades under a second. Co-location allowed traders to respond instantaneously with minimal latency to stay ahead of the competition.

    The Bombay Stock Exchange (BSE) started co-location services in 2011. Brokers place their servers inside the BSE data centre through co-location. For traders, this reduced latency, increasing the order execution speed. Being close to the exchange helped them obtain quicker access to real-time data!

    Why Are Co-Location Servers Important in Algo Trading?

    Now that you understand what co-location servers are, let’s look at some important aspects of it:

    1. Low Latency:
    These servers eliminate the need for data to travel across the globe, your trading system is positioned next to the exchange to collect data efficiently. Traders and firms get their orders executed faster giving them an edge over those who are waiting over long data travel times.

    2. Better Data Access:
    Co-location servers placed closer to an exchange have better access to real-time market feeds. This ensures that your algorithms get better access to data to identify market changes and take action, without much lag.

    3. Enhanced Strategy Performance:
    The performance of your strategy depends on latency and data access. With co-location servers, your strategies are the first to act and ensure your trades hit the market at the right moment.

    Can Retail Traders Get Access to Co-Location Servers?

    Retail traders typically cannot get direct access to co-location servers. These servers are primarily designed for high-frequency traders (HFTs) and institutional investors who require extremely low latency for their trading activities. The cost of renting server space is very high, some costing ₹15 lakhs a month, making it unaffordable for most retail traders.

    However, retail traders can use broker/trading platforms like Zerodha and Upstox, and authorised data vendors like TrueData, Global Datafeeds, and Accelpix to source near real-time data access for algo trading. While these platforms may still need to fully utilise the speed advantages of co-location, they allow retail traders to compete in the stock market.

    What are the Costs and Risks of Co-Location Servers?

    1. Significant Expenses: Renting space in an Indian co-location centre will cost around ₹5 lakhs to ₹15 lakhs per month. This price includes power, cooling, and high-speed connectivity charges. These costs for co-location will be a huge financial commitment for small traders or firms with a minimal budget.

    2. Tech Maintenance: Maintaining co-located servers requires a technically skilled team. You need experts to keep everything running smoothly. Any downtime or technical issues can lead to significant losses, so having a reliable support team is crucial.

    3. Security Concerns: Storing your trading algorithms in a third-party data centre brings about security risks. Breaches or unauthorised access can expose your strategies, leading to financial and reputational damage. Keeping your systems secure is a top priority.

    4. Regulatory Analysis: Co-location servers work under tight scrutiny due to fairness-related concerns. Traders must comply with several tight regulatory rules to continue co-location service usage. Regular reviews are made by authorities concerning data access, system performance, and trading behaviour for transparency. In cases of non-compliance, heavy fines, restrictions, or a ban from trading are imposed. 

    Conclusion

    Co-location servers help reduce latency and improve access to real-time data, providing an edge in trading. This technology is mostly utilised by high-frequency traders and large institutions to execute trades efficiently. While it enhances speed and data access, it comes at a very high cost and risk.

    For retail traders, the costs and complexities of co-location servers make them less accessible. While you may not be able to fully leverage this technology, you can still succeed by focusing on strong trading strategies, risk management, and alternative tools to stay competitive!

    Categories
    Algo Trading

    Mastering Algo Trading Without Coding: Explained!

    algo trading without coding | marketfeed

    Algo trading has become very popular in financial markets over the last few decades. While it might seem difficult and confusing at first glance, it’s a skill that any serious, dedicated trader can master with the right approach. Many people think that you have to be a coder to do algo trading, but that’s not true! As a retail trader with no coding skills, you can still take advantage of this technique by using the right tools and strategies. In this article, we’ll explore the different ways and opportunities that allow you to successfully start algo trading, even if you aren’t proficient in programming! 

    Let’s break it down into three major questions:

    1. What is algo trading?
    2. Why is coding important in algo trading?
    3. How can you be an algo trader without coding knowledge?

      What is Algo Trading?

      Before we discuss how to master algo trading, let’s first understand what it is. Algo trading is a method where trades in financial markets (like stocks, currencies, commodities, or derivatives) are carried out or executed automatically using pre-defined instructions based on factors like volume and price. This allows trades to be executed at the right time without human errors.

      Also read: What is Algo Trading?

      Algo traders follow and implement a sequence of steps to be successful:

      • Forming the Strategy and Coding It: The first step would be forming your trading strategy and converting it into a computer language, like Python. 
      • Backtesting: A necessary practice that an algo trader must perform is backtesting their strategy using historical data before deploying it in the live market. While it’s not a 100% guarantee of success, it allows the trader to identify any major flaws in the code.
      • Order Execution Systems: A trader must connect with an exchange to place the order. For this, you would go through a broker API like Zerodha, Upstox, or Fyers. 
      • Monitoring: Executing a strategy doesn’t mean you can leave it to the market and forget about it. A trader must continuously monitor and modify it to ensure profitability in the evolving market. 

      These are the basic steps for an algo trader. But what if you are stuck at the very first stage and don’t know how to code? Does that mean you can’t get into algo trading? Let’s answer that below!

      Coding: Why is it Important in Algo Trading? 

      Coding is the primary step in algo trading and for the right reasons. Let’s explore some of them:

      • Create Your Own Custom Strategy: Coding your own trading strategy implies tailoring it to fit your specific needs and preferences. A famous success story showing this is the company Renaissance Technologies and its Medallion Fund. 
      • Enhanced Flexibility and Adaptability: Coding your trading strategy gives you the freedom to adjust and fine-tune it as market conditions change. This flexibility allows you to incorporate new data, refine parameters, and continuously optimise performance. It’s a great way to ensure your strategy remains competitive, efficient, and profitable over time.
      • Speed and Scalability: Financial markets change rapidly, often within milliseconds. This makes quick execution of trades crucial for success. Coded trading strategies instantly execute orders based on predefined criteria, while also enabling traders to place multiple trades simultaneously across various markets. 

      Also read: Python for Algo Trading 

      The benefits of coding your strategy are immense, however many of us are not well-versed in programming and might not be interested in it. But, you can still be an algo trader! Read on to find out how!

      How is Algo Trading Possible Without Coding?

      We have established it is possible to be an algo trader without using complex computer language and creating mathematical algorithms. But how exactly can you continue without the very first step in algo trading? Let’s see below:

      1. Build a Solid Foundation

      To truly excel in algo trading, you need to understand it as well as you know the back of your hand. This means investing time in educating yourself about the matter. You can take online courses, read books, and immerse yourself in related learning materials.

      There are a variety of courses available on platforms like Udemy, Coursera, or even specialised trading websites. Experts explain complex topics in simple, structured ways through these courses. Similarly, reading books on algo trading and financial markets can give you deeper insights into the strategies, techniques, and market behaviours that influence automated trading systems.

      Also Read: Essential Resources to Learn Algo Trading

      2. Approaching Experienced Algo Developers

      To gain a deeper understanding of algo trading, one of the best approaches is to connect with someone who already has experience in the field. This helps you gather insights and perspectives that you might not have discovered on your own. They can offer practical tips, share their experiences, and help you understand the nuances of algo trading. You might even consider collaborating with them as they can assist with transforming your strategy to a programming language (Python). This kind of partnership can be incredibly beneficial for someone without coding knowledge.

      Another great way to enhance your knowledge is by participating in online communities dedicated to algo trading. Platforms such as forums, social media groups (including Reddit threads), or specialised trading websites are full of traders willing to share their experiences and insights. These communities allow you to ask questions, discuss strategies, and get advice from people who are familiar with the industry. It’s a great tool if you’re looking for feedback on a strategy, technical support, or general guidance. Online communities can be a valuable resource for continuous learning and growth in the world of algo trading.

      3. Using Platforms to Form Strategies or Deploy Pre-Built Strategies 

      As a trader who wants control over the strategy and its execution, you have two options: using platforms that help build your own strategy without coding or using platforms with pre-built ones available. Both methods have their advantages depending on your level of involvement.

      Building your own strategy offers complete control and customisation, allowing you to adapt to market trends and implement innovative ideas. This option is ideal for traders wanting flexibility and hands-on experience. 

      The other option is using pre-built strategies. This offers a simpler approach to algo trading. As a trader, you would only need to identify which strategy works for you and deploy that. Since these strategies are already backtested and proven, they can be deployed after minor adjustments, saving time and effort. 

      Platforms like uTrade Algos, Tradetron, AlgoTest, and AlgoBulls allow traders to use pre-built strategies or form their strategy at certain fees and costs. 

      Also read: Top 5 Algo Trading Platforms for Beginners In India

      4. Leaving It To Experts!

      What if you do not want to form your own strategy or rely on pre-built strategies? There is another option available for you! You can approach expert algo trading companies who could handle all the work. From doing the necessary research to executing orders, they use APIs to execute trades seamlessly. marketfeed is one such service that takes care of the client’s automated trading from the beginning to the end!

      Is it That Easy?

      The trading landscape is constantly evolving, and using pre-built strategies can lead to successful algo trading. However, it’s important to remember that an algorithm effective in one scenario may not perform well in another. 

      Even without coding, you can’t simply rely on a pre-built algorithm to run on autopilot. You must continuously monitor your strategies and make adjustments as needed. This ongoing oversight is crucial for adapting to market shifts. To stay competitive in algo trading, you must keep learning. Track market trends, engage with trading communities, and experiment with your strategies to fine-tune them!

      Categories
      Algo Trading

      How are APIs Used in Algo Trading? Broker APIs Explained

      API in algo trading | marketfeed

      Humans communicate with each other to share information, but have you wondered how computers do it? Application Programming Interfaces or APIs allow computer programs or components to communicate and connect seamlessly with each other. They help improve everyday life and business operations by simplifying processes, improving accessibility, and fostering innovation. 

      In today’s fast-paced financial markets, algo trading has become increasingly popular among traders. At the heart of this revolution lies a powerful tool: APIs. While APIs serve various purposes in the trading ecosystem, Broker APIs are the key drivers behind the rise of retail algo trading. In this article, we dive into the world of Broker APIs, exploring how they function as the gateway to algo trading.

      What are APIs? 

      In the field of web development, an Application Programming Interface (API) is a set of tools that enables multiple software to communicate with each other using a set of protocols. They allow applications to share real-time information and work dynamically with each other, essentially forming a chain for communicating. While a user interface (UI) connects a computer to a person, an API connects different software. Here’s an example:

      Imagine you are in a huge library with thousands of books. Instead of searching through an endless number of books to find the one you want, you can approach the librarian. They would have a database to search where each book is. Now, replace the library with computer programs and the librarian with an API. Therefore, APIs guide developers to what they need in complex sets of programs, highlighting important connections while skipping unnecessary details.

      APIs are integral to many everyday activities, simplifying tasks such as online payments and navigation through services like Google Maps. Due to its efficiency and benefits, APIs have gained popularity in algo trading as well. Let us further explore how APIs are used in the trading world!

      API and Algo Trading

      Algo trading is a method where trades are executed automatically using computer programs or ‘algorithms’. These algorithms consist of instructions relating to time, volume, and price targeted to execute the order. The algorithms function based on the conditions added to them. 

      Algo trading has recently gained popularity among market participants in India over the past few years. Currently, 50-55% of the total trading volume in India is executed using algo trading systems! Meanwhile, in the US, algo trades account for nearly 60-70% of the total trade volume!

      Also read: How to Get Started with Algo Trading in India?  

      APIs play a crucial role in connecting traders, brokers, and markets— ensuring timely data collection and order execution. They are the reason algo trading is possible, especially for retail traders! Whether you code your strategy or use pre-built strategies on third-party platforms, APIs are responsible for connecting the trading logic/algo to the exchange. APIs come in use at every stage of the algo trading process by connecting all components in the trading ecosystem.

      Institutional traders use various APIs like Risk Management API, Portfolio Optimisation API, Sentiment Analysis API, News API, etc. to backtest and execute their strategies.

      Among the various types of APIs used in trading, Broker APIs have emerged as the most important tool for retail algo traders in India. They are particularly advantageous for retail traders, streamlining the integration of various APIs into a single, user-friendly tool. Broker APIs serve as the critical link between a trader’s algorithm and the broker’s trading platform, enabling the seamless execution of trades.

      What are Broker APIs?

      Broker APIs are specific types of APIs provided by brokerage firms (like Zerodha, Upstox, Fyers, etc.) that allow traders to interact with the broker’s trading platform. Unlike general APIs, Broker APIs are tailored for trading activities, offering key features like order execution, portfolio management, and real-time data access. These APIs are designed to facilitate smooth communication between the trader’s algorithm and the broker’s platform.

      Key Features of Broker APIs:

      • Market Data: Retrieving real-time and historical price data.
      • Order Execution: Place, modify, and track buy/sell orders. You can even specify order types (market, limit, stop, etc.), and set parameters such as quantity and price.
      • Portfolio Management: Access account information such as available margins, open positions, and real-time profit/loss.
      • Risk Management: Implement stop-loss, take-profit levels, and position-sizing rules.

      Some Examples of Broker APIs in India:

      1. Zerodha Kite API: Known for its simplicity and comprehensive documentation, this API allows the automation of advanced trading strategies.
      2. Upstox Pro Developer API: Offers tools for creating custom algorithms, appealing to both new and experienced traders.
      3. Angel One Smart API: Accessible for traders looking to integrate algo trading without additional costs.
      4. Fyers API: Designed to facilitate algo trading and integration with various trading platforms.

      Broker APIs offer a comprehensive solution for algo traders, providing access to all essential market data and tools. To select the right API, consider your specific trading needs, the level of customization available, the ease of use, and the associated costs.

      Conclusion 

      APIs have become vital in today’s digital world, powering everything from basic online payments to advanced systems like algo trading. While their role in algo trading has transformed the financial landscape by providing real-time data and fast trade execution, there are challenges. You may need technical skills to use them effectively, and issues like glitches, slippages, bugs, and security risks remain concerns.

      Despite these hurdles, the benefits of APIs in algo trading are undeniable. They empower traders to streamline operations and adapt to the fast-paced financial environment. As algo trading continues to gain traction, especially in the Indian market, APIs will propel this change forward!