MCX Algo Trading: A Path To Financial Freedom

Before we go any further discussion on MCX Algo Trading, it’s important to grasp the fundamentals of algorithmic trading. Later on, this knowledge will aid you in comprehending more sophisticated trading principles. So, let’s get this party started.

When it comes to stock market trading, most investors rely on stock traders who spend their money in stocks based on their knowledge and market trends. One thing to keep in mind is that it is extremely difficult for a human to decipher the intricacy of the stock markets, and stock trader judgments may not always be worthwhile. This is where Algo trading, also known as Automated trading or the meaning of algo trading is computer-based trading, comes into play.

The employment of pre-programmed programs to execute trades is referred to as Algo-trading. When the requirements are satisfied, a sequence of instructions or an algorithm is entered into a computer program, which automatically performs the transaction. The algorithm might be based on a variety of factors, including price, time, quantity, and other parameters.

The importance of technology in this form of stock trading cannot be overstated. MCX Algo trading India has a variety of advantages, including rapid speed, accuracy, and the absence of human interaction. All trading-related chores are handled by the computer, eliminating the need for traders to look for profitable trading chances. The popularity of Algo trading software in India has risen in recent years, implying that it is the way of the future in stock trading.

Algo Trading Benefits

When it comes to selecting the finest category for Algo trading, the commodity sector emerges as a strong contender. This is due to the fact that commodities markets have a high level of liquidity, making them ideal for Algo trading. The usage of MCX Algo trading has expanded dramatically in recent years, with 2015 being the year when trading in the commodities markets also showed a rapid ascent.

ATF-Algo Trading Facility

Algorithmic trading facilities are available in addition to MCX Algo Trading. It includes the automatic production of buy/sell orders on computer systems using algorithms, the entry of orders into the Exchange’s trading system, and the matching of orders by the Exchange’s trading system.

Members of the MCX have the option of purchasing MCX-approved Algo Trading Facility (ATF) software from Exchange-approved suppliers or developing their own Algo Trading software Facility with MCX approval. For the acquisition of the ATF, there is a specific list of exchange-verified providers.

Members who choose to choose their own tactics or build their own software must submit specific documentation to MCX for approval. They must also pay one-time costs of 1,50,000 and yearly charges of 50,000 for these papers, which include software confirmation, network design, strategy write-up, and information of the software development team, among others.

For MCX Algo Trading, MCX has established a set of principles, rules, and regulations that must be followed.

Regulatory Issues

Due to the enormous number of orders, MCX Algo Trading India might have an impact on market volatility. As a result, SEBI and other market authorities must keep an eye on algorithmic trading to ensure that it does not harm the MCX. There are various guidelines that must be observed for MCX Algo Trading India to guarantee that the market is correctly traded and that the trading platform is used fairly by all members of the exchange. Any algorithm or strategy that is used for the first time, as well as any changes made in the future, must be approved by the Exchange. The Algo orders must be marked as automated with a flag so that they can be distinguished from the manual orders. Additionally, only limit orders are permitted. While executing MCX Algo Trading, market orders and immediate or cancel orders are not authorized.

SEBI also requires members to maintain a close eye on their software when participating in MCX Algo Trading. It is to guarantee that the program does not get corrupted, causing the market to become overburdened with orders. An exact daily order-to-trade ratio is also given, as well as the fees associated with it. Over time, SEBI has recognized the importance and effectiveness of algorithmic trading and has relaxed certain of its laws to encourage MCX Algo Trading.

Above all, MCX Algo Trading makes up a significant portion of all deals. It is carried out with a high level of efficiency and participation, and the MCX has implemented strict rules and regulations to guarantee that market prices and volatility are not incorrectly and negatively impacted by algorithmic trading.

In the MCX, there have been several instances of order flooding caused by Algo trading software, but the authorities have handled them expertly.

Evaluating Trading Strategies

The first, and maybe most obvious, question is if you comprehend the plan. Would you be able to communicate the method succinctly, or would it need a long list of cautions and parameters? Does the approach, in addition, have a good, solid foundation in reality? Could you, for example, allude to a behavioral explanation or a fund structure limitation that may be driving the pattern(s) you’re trying to exploit? Would this limitation withstand a regime shift, such as a significant regulatory environment change? Is the plan based on complicated mathematical or statistical rules? Is it general enough to apply to any financial time series, or is it particular to the asset class for which it is said to be profitable? When analyzing new trading tactics, you should keep these aspects in mind at all times; otherwise, you might waste a lot of time backtesting and optimizing unsuccessful strategies.

Once you’ve established that you understand the strategy’s fundamental concepts, you’ll need to assess whether it matches your personality profile. This isn’t a meaningless consideration at all! The performance characteristics of strategies will vary significantly. Certain personality types can withstand longer periods of downturn or are ready to take on more risk in exchange for a higher return. Biases will always come in, despite the fact that we, as quants, aim to minimize as much cognitive bias as possible and should be able to assess a strategy objectively. As a result, we require a method of evaluating strategy performance that is both consistent and objective. Here is a list of the factors I use to evaluate a prospective new strategy:

Methodology – Is the approach momentum-based, mean-reverting, market-neutral, or directional in nature? Is the plan based on advanced (or convoluted!) statistical or machine learning techniques that are difficult to grasp and need a PhD in statistics? Is there a substantial number of parameters introduced by these procedures, which might contribute to optimization bias? Is the plan likely to endure a regime shift (e.g., new financial market regulation)?

Sharpe Ratio – The Sharpe ratio heuristically characterizes the strategy’s reward/risk ratio. It calculates how much profit you can make given the stock curve’s level of volatility. Naturally, the period and frequency over which these returns and volatility (i.e. standard deviation) are measured must be determined. A higher frequency technique, for example, will necessitate a larger sample rate of standard deviation but a shorter total measurement duration.

Leverage – Is large leverage required for the plan to be profitable? Is it necessary to employ leveraged derivatives contracts (futures, options, swaps) to earn a profit with this strategy? These leveraged contracts can have a high level of volatility, which can quickly result in margin calls.

Frequency – The frequency with which you use the method is determined by your technology stack (and hence your technological skill), the Sharpe ratio, and the total degree of transaction expenses. Taking into account all other factors, higher frequency techniques cost more cash, are more complicated, and are more difficult to implement.

Volatility – Volatility is intimately linked to the strategy’s “risk.” This is characterized by the Sharpe ratio. When unhedged, more volatility in the underlying asset classes leads to higher volatility in the equity curve and consequently poorer Sharpe ratios. I’m assuming, of course, that positive volatility is comparable to negative volatility. Some tactics may be more volatile on the negative. You must be aware of these characteristics.

Liquidity – Unless you’re trading in a relatively illiquid item (like a small-cap stock), you won’t have to worry about strategy capacity at the retail level. The scalability of a plan to raise more funds is determined by capacity. As their strategies rise in capital allocation, many of the larger hedge funds have considerable capacity issues.

Algo Trading Strategies

Algorithmic trading is a trading approach in which softwares executes orders using specific strategies or methodologies on their own. Traders and investors are becoming increasingly interested in trading on an algo platform since it includes less manual execution and more technology. Due to the little human interaction, it avoids the possibility of mistakes. Investment banks, pension funds, mutual funds, and hedge funds all employ algorithms to spread out the execution of bigger orders or to execute deals that are too rapid for human traders to react to.

1. Identify the trend

Algorithm techniques can aid in the detection of a trend or an early reversal of a trend. Algorithmic trading strategies are based on price, volume, support, resistance, or any other idea in which the investor has faith and is at ease. Because the algorithm is based on technology and data, it has a better probability of detecting the proper trend. In addition, an investor’s ability to assess vast amounts of data and act on it in a timely manner is unattainable.

Algorithms allow you to utilize many tactics at once and decide on the overall outcome. An investor, for example, can use 20 distinct methods on a single stock. Finally, because the majority of techniques are indicating a buy signal, the algorithm will automatically buy the stock.

2. Strategy related to data neutral

The difference between the price of the derivative and the price of the underlying asset is referred to as the “delta.” Delta neutral implies balancing positive and negative deltas with numerous places. For a given range of market movements, a delta-neutral portfolio evens out the reaction to market movements, bringing the net change of the position to zero. Delta neutral techniques are impossible to control manually.

The constant mobility of an asset makes it much more difficult. It’s simple to manage your position’s delta thanks to algorithms, since it’s computed automatically by the system and you’re informed about your current portfolio or position every second.

3. Size Positioning

Position management is one of the most crucial components of trading. One of the most important distinctions between an average investor and an excellent investor is how well he manages his position under various conditions. Algorithmic trading has made it easier because computers have no emotions and position sizing is determined by system orders.

For example, you can specify that the value of any trade on any share shall not exceed Rs 1 lakh. If a stock costs Rs 100, the system will automatically purchase 1,000 shares, but if the stock costs Rs 2,500, the system will purchase 40 shares.

4. Stopping the loss modification

It’s critical to safeguard your earnings and manage your portfolios properly in the stock market. Modifying stop losses is one of the most effective strategies. Because markets are volatile and maintaining large portfolios is difficult, algorithms provide straightforward risk management options. Systems can be configured to adjust stop losses based on the movement of the portfolio’s equities. Stop losses can be triggered by a variety of technical procedures, price changes, and other factors.

It’s critical to safeguard your earnings and manage your portfolios properly in the stock market. Modifying stop losses is one of the most effective strategies. Because markets are unpredictable and managing huge portfolios is complex, algorithms give simple methods for managing risk. If you’re a swing trader, you’ll establish a 3% stop loss on every position, and the stop loss will adjust every 3% positive movement in the stock. The current stop loss for a certain stock is Rs 97, while the current price is Rs 100. The procedure will continue in the same manner. You may trade in higher amounts using algo since the risk is controlled automatically by a predetermined mechanism.

5. Scalping

Scalping is a trading strategy in which traders buy and sell a certain stock or commodity on a frequent basis. Forward scalping and reverse scalping are the two types of scalping models. Forward scalping is when a trader buys when the market is rising, and reverse scalping is when a trader buys when the market is falling.


Que.1 What is latency in Algo trading?

Ans. Latency is the amount of time you lose when sending out an order. It is the time it takes for an order to arrive at its trading destination or exchange, or how long it takes to process market data, order routing, and other factors.

Que.2 What are the many sorts of algorithms available for automated trading?

Ans. There are a lot of them; once again, you’d have to filter them out depending on low, mid, and high frequency. In the case of high-frequency trading, the emphasis would be on arbitrage and market creation, as well as some directed methods that would need a massive amount of quicker computation.