By Chainika Thakar, Viraj Bhagat & Apoorva Singh

Algorithmic trading Strategies are simply strategies that are coded in a computer language such as Python for executing trade orders. The trader codes these strategies to use the processing capabilities of a computer for taking trades in a more efficient manner with no to minimum intervention.

Let us find out more about algorithmic trading strategies with this blog that covers:

What are algorithmic trading strategies?

“Algorithmic trading” is a term that may sound complicated but can be implemented easily if you have the dedication to learn and the grit for learning.

An algorithm is, basically, a set of instructions or rules for making the computer take a step on behalf of the programmer (the one who creates the algorithm). The programmer, in the trading domain, is the trader having knowledge of at least one of the computer programming languages known as C, C++, Java, Python etc.).

This knowledge of programming language is required since the trader needs to code the set of instructions in the language that computer understands.

In short, in trading, the set of instructions or rules is given to the computer (by the trader) to automate the execution of trade orders via the stock exchange with minimal human intervention. This is called algorithmic trading.

Coming to “algorithmic trading strategies”, trading strategies are devised by a trader having knowledge of the financial Market with regard to

• Entering the market (buying when the prices are favourable)
• Exiting the market (selling when the prices begin going way below expectations)

These strategies are coded as the programmed set of instructions to make way for favourable returns for the trader. The set of instructions to the computer is given in programming languages (such as C, C++, Java, Python). Following which, the computer can generate signals and take the trading position accordingly.

Classification of algorithmic trading strategies, paradigms & modelling ideas

All the algorithmic trading strategies that are being used today can be classified broadly into the following categories:

• Momentum-based strategies or trend-following algorithmic trading strategies
• Arbitrage algorithmic trading strategies
• Market making algorithmic trading strategies
• Machine learning in trading
• Options trading and options trading strategies

We will be throwing some light on the strategy paradigms and modelling ideas pertaining to each algorithmic trading strategy below.

Momentum-based strategies or trend-following algorithmic trading strategies

Assume that there is a particular trend in the market. As an algo trader, you are following that trend.

Further to our assumption, the markets fall within the week. Now, you can use statistics to determine if this trend is going to continue. Or if it will change in the coming weeks. Accordingly, you will make your next move.

You have based your algorithmic trading strategy on the market trends which you determined by using statistics.

This method of following trends is called momentum-based strategy.

There are numerous ways to implement this algorithmic trading strategy and it has been discussed in detail in one of our previous articles called “Methodology of Quantifying News for Automated Trading”.

Strategy paradigms of momentum-based strategies

Momentum strategies seek to profit from the continuance of the existing trend by taking advantage of market swings.

“In simple words, buy high and sell higher and vice versa.”

And how do we achieve this?

• Short-term positions: In this particular algorithmic trading strategy we will take short-term positions in stocks that are going up or down until they show signs of reversal. It is counter-intuitive to almost all other well-known strategies.
• Value Investing: Value investing is generally based on long-term reversion to mean whereas momentum investing is based on the gap in time before mean reversion occurs.
• Momentum: Momentum is chasing performance, but systematically by taking advantage of other performance chasers who are making emotional decisions.

Explanations:

There are usually two explanations given for any strategy that have  proven to work historically,

• Either the strategy has compensated for the extra risk that it takes, or
• There are behavioural factors due to which premium exists.

Why Momentum works?

There is a long list of behavioral biases and emotional mistakes that investors exhibit due to which momentum works.

However, this is easier said than done as trends don’t last forever and can exhibit swift reversals when they peak and come to an end.

Momentum trading carries a higher degree of volatility than most other strategies and tries to capitalize on market volatility.

It is important to time the buys and sells correctly to avoid losses by using proper risk management techniques and stop-losses. Momentum investing requires proper monitoring and appropriate diversification to safeguard against such severe crashes.

Modelling ideas of momentum-based strategies

First of all, you should know how to detect price momentum or trends. As you are already into trading, you know that trends can be detected by following stocks and ETFs that have been continuously going up for days, weeks or even several months in a row.

For instance, identify the stocks trading within 10% of their 52-week high or look at the percentage price change over the last 12 or 24 weeks. Similarly to spot a shorter trend, include a shorter-term price change.

For instance, back in 2008, the oil and energy sector was continuously ranked as one of the top sectors even while it was collapsing.

Types of momentum trading strategies

We can also look at earnings to understand the movements in stock prices. Strategies based on either past returns (price momentum strategies) or earnings surprise (known as earnings momentum strategies) exploit market under-reaction to different pieces of information.

• Earnings Momentum Strategies: An earnings momentum strategy may profit from the under-reaction to information related to short-term earnings.
• Price Momentum Strategies: A price momentum strategy may profit from the market’s slow response to a broader set of information including longer-term profitability.

Take a look at  useful read below:

• Using Quadratic Discriminant Analysis To Optimize An Intraday Momentum Strategy

Arbitrage algorithmic trading strategies

Let's understand arbitrage with an example. If we assume that a pharma corp is to be bought by another company, then the stock price of that corp could go up.

This is triggered by the acquisition which is a corporate event. If you are planning to invest based on the pricing inefficiencies that may happen during a corporate event (before or after), then you are using an event-driven strategy.

Bankruptcy, acquisition, merger, spin-offs etc. could be the event that drives such kind of an investment strategy. These arbitrage trading strategies can be market neutral and used by hedge funds and proprietary traders widely.

Statistical arbitrage

When an arbitrage opportunity arises because of misquoting in prices, it can be very advantageous to the algorithmic trading strategy. Although, such opportunities exist for a very short duration as the prices in the market get adjusted quickly. And that’s why this is the best use of algorithmic trading strategies, as an automated machine can track such changes instantly.

For instance, assume that each time that Apple‘s stock prices fall by $1, Microsoft’s prices too fall by $0.5. Now, given the case that Microsoft has not fallen yet, you can go ahead and sell Microsoft to make a profit.

Strategy paradigms of statistical arbitrage

If market making is the strategy that makes use of the bid-ask spread, statistical arbitrage seeks to profit from the statistical mispricing of one or more assets based on the expected value of these assets.

A more academic way to explain statistical arbitrage is to distribute the risk between a thousand to a few million trades in a very short holding span with the expectation of gaining profit from the law of large numbers. Statistical arbitrage Algorithms are based on the mean reversion hypothesis, mostly as a pair. You can enroll for our course on Statistical Arbitrage Trading.

Modelling ideas of statistical arbitrage

Pairs trading is one of the several strategies collectively referred to as Statistical Arbitrage Strategies. In a pairs trade strategy, stocks that exhibit historical co-movement in prices are paired using fundamental or market-based similarities.

The strategy builds upon the notion that the relative prices in a market are in equilibrium, and that deviations from this equilibrium eventually will be corrected.

When one stock outperforms the other, the outperformer is sold short and the other stock is bought long, with the expectation that the short-term diversion will end in convergence. This often hedges market risk from adverse market movements i.e. makes the strategy beta neutral.

However, the total market risk of a position depends on the amount of capital invested in each stock and the sensitivity of stocks to such risk.

Below, I have mentioned a useful read that you may like:

• Kalman Filter Techniques And Statistical Arbitrage In China’s Futures Market In Python

Market making algorithmic trading strategies

To understand market making, let me first talk about Market Makers.

According to Wikipedia:

“A market maker or liquidity provider is a company, or an individual, that quotes both a buy and sell price in a financial instrument or commodity held in inventory, hoping to make a profit on the bid-offer spread, or turn.”

Market making provides liquidity to securities which are not frequently traded on the stock exchange. The market maker can enhance the demand-supply equation of securities.

Let us see an example:

Assume you have Martin, a market maker, who buys an asset for INR 500 from the market and sell it at INR 505. He will give you a bid-ask quote of INR 505-500. The profit is of INR 5 but during the day, he indulges in a lot of shares to pocket a significant chunk and offset the risk of price movement against him. Also, when Martin takes a higher risk, the profit probability is also higher.

I found Michael Lewis’ book ‘Flash Boys’ in Indian Bull Market pretty interesting and it talks about liquidity, market making and HFT in great detail. You can check it out after you finish reading this article.

Since you will need to be analytical & quantitative while getting into or upgrading to algorithmic trading it is imperative to learn to programme (some if not all) and build foolproof systems and execute right algorithmic trading strategy.

Reading this article on Automated Trading Systems: Architecture, Protocols, Types of Latency will be very beneficial for you.

Strategy paradigms of market making

As I had mentioned earlier, the primary objective of market making is to infuse liquidity in securities that are not traded on stock exchanges.

In order to measure the liquidity, we take the bid-ask spread and trading volumes into consideration.

Some trading algorithms tend to profit from the bid-ask spread.

For instance, we will be referring to our buddy, Martin, again in this section. Martin being a market maker is a liquidity provider who can quote on both the buy as well as the sell side in a financial instrument hoping to profit from the bid-offer spread.

Martin will accept the risk of holding the securities for which he has quoted the price and once the order is received, he will often immediately sell from his own inventory. He might seek an offsetting offer in seconds and vice versa.

When it comes to illiquid securities, the spreads are usually higher and so are the profits.

For instance, Martin will take a higher risk in this case. Several segments in the market lack investor interest due to a lack of liquidity as they are unable to gain exit from several small-cap stocks and mid-cap stocks at any given point in time.

Market makers like Martin are helpful as they are always ready to buy and sell at the price quoted by them. In fact, much of high-frequency trading (HFT) is passive market making. The strategies are present on both sides of the market (often simultaneously) competing with each other to provide liquidity to those who need it.

So, when is this market making strategy most profitable?

This strategy is profitable as long as the model accurately predicts future price variations.

Modelling ideas of market making

The bid-ask spread and trade volume can be modelled together to get the liquidity cost curve which is the fee paid by the liquidity taker.

If the liquidity taker only executes orders at the best bid and ask, the fee will be equal to the bid-ask spread times the volume. When the traders go beyond the best bid and ask taking more volume, the fee becomes a function of the volume as well.

Trade volume is difficult to model as it depends on the liquidity taker’s execution strategy. The objective should be to find a model for trade volumes that is consistent with price dynamics.

Market-making models are usually based on one of the two:

• First model of market making

The first focuses on inventory risk. The model is based on preferred inventory position and prices based on the risk appetite.

• Second model of market making

The second is based on adverse selection which distinguishes between informed and noise trades. Noise trades do not possess any view on the market whereas informed trades do. When the view of the liquidity taker is short-term, it aims to make a short-term profit utilizing the statistical edge.

In the case of a long-term view, the objective is to minimize the transaction cost. The long-term strategies and liquidity constraints can be modelled as the noise around the short-term execution strategies.

Since moving ahead and seizing opportunities as they come is what we must do to be in this domain, we must adapt to evolving sciences like Machine Learning.

Machine learning in trading

In machine learning based trading, one of the applications is to predict the range for very short-term price movements at a certain confidence interval. The advantage of using Artificial Intelligence (AI) is that humans develop the initial software and the AI itself develops the model and improves it over time.

A large number of funds rely on computer models built by data scientists and quants but they’re usually static, i.e. they don’t change with the market. Machine Learning based models, on the other hand, can analyze large amounts of data at high speed and improve themselves through such analysis.

Modelling idea for Machine Learning in Trading

A form of machine learning called “Bayesian networks” can be used to predict market trends while utilizing a couple of machines.

An AI which includes techniques such as 'Evolutionary computation' (which is inspired by genetics) and deep learning might run across hundreds or even thousands of machines.

What can AI do in Trading?

• It can create a large and random collection of digital stock traders and test their performance on historical data.
• It then picks the best performers and uses their style/patterns to create a new of evolved traders.
• This process repeats multiple times and a digital trader that can fully operate on its own is created.

These were some important strategy paradigms and modelling ideas. Next, we will go through the step-by-step procedure to build an algorithmic trading strategy.

You can learn these paradigms in great detail in EPAT by QuantInsti which is the world's first verified algorithmic trading course.

Some important reads:

• Trading Using Machine Learning In Python
• Gold Price Prediction Using Machine Learning In Python
• Free Resources to Learn Machine Learning for Trading
• Machine Learning for Quants and Traders
• Optimal Portfolio Construction Using Machine Learning

Options trading and options trading strategies

Options trading is a type of trading strategy. It is a perfect fit for the style of trading expecting quick results with limited investments for higher returns.

One can create their own options trading strategies, backtest them, and practise them in the markets. You can enroll and learn for our free course on Basic Options Trading Strategies In Python.

Here are some different types of options trading strategies:

• Diagonal Spreads
• Calendar Spread
• Synthetic Long Put
• Long Combo
• Bear Spread
• Bear Call Ladder
• Collar Options
• Bull Call Spread
• Butterfly Spread
• Straddle Options
• Jade Lizard
• Iron Butterfly
• Long Strangle
• Iron Condor
• Broken Wing Butterfly

Building and implementing algorithmic trading strategies

From algorithmic trading strategies to the classification of algorithmic trading strategies, paradigms and modelling ideas and options trading strategies (You can enroll for our course on Advanced options trading strategies), now we come to that section of this article you will learn how to build a basic algorithmic trading strategy.

That is the first question that must have come to your mind, I presume.

The point is that you have already started by knowing the basics of algorithmic trading strategies and paradigms of algorithmic trading strategies while reading this article. Now, that our bandwagon has its engine turned on, it is time to press on the accelerator.

And how exactly does one build an algorithmic trading strategy?

We will explain how an algorithmic trading strategy is built, step-by-step.

Steps to build algorithmic trading strategies

The concise description will give you an idea of the entire process.

• Step 1 - Decide upon the strategy paradigm
• Step 2 - Establish the statistical significance
• Step 3 - Build a trading model
• Step 4 - Quoting or hitting strategy
• Step 5 - Backtesting and optimization
• Step 6 - Risk and performance evaluation

Step 1 - Decide upon the genre or strategy paradigm

The first step is to decide on the strategy paradigm. It can be market making, arbitrage based, alpha generating, hedging or execution based strategy.

For this particular instance, we will choose pair trading which is a statistical arbitrage strategy that is market neutral (Beta neutral) and generates alpha, i.e. makes money irrespective of market movement.

Step 2 - Establish the statistical significance

You can decide on the actual securities you want to trade based on market view or through visual correlation (in the case of pair trading strategy). Establish if the strategy is statistically significant for the selected securities.

For instance, in the case of pair trading, check for the co-integration of the selected pairs.

Step 3 - Build a trading model

Now, code the logic based on which you want to generate buy/sell signals in your strategy. For pair trading check for “mean reversion”; calculate the z-score for the spread of the pair and generate buy/sell signals when you expect it to revert to the mean. Learn mean reversion strategy in detail in the Quantra course.

Decide on the “Stop-Loss” and “Take Profit” conditions.

• Stop Loss – A stop-loss order limits an investor’s loss on a position in a security. It fires an order to square off the existing long or short position to avoid further losses and helps to take emotion out of trading decisions.
• Take Profit – Take-profit orders are used to automatically close out existing positions in order to lock in profits when there is a move in a favourable direction.

Step 4 - Quoting or hitting strategy

It is very important to decide if the strategy will be “quoting” or “hitting”. Execution strategy, to a great extent, decides how aggressive or passive your strategy is going to be.

• Quoting – In pair trading you quote for one security and depending on if that position gets filled or not you send out the order for the other. In this case, the probability of getting a fill is lesser but you save bid-ask on one side.
• Hitting – In this case, you send out simultaneous market orders for both securities. The probability of getting a fill is higher but at the same time slippage is more and you pay bid-ask on both sides.

The choice between the probability of Fill and Optimized execution in terms of slippage and timed execution is - what this is if I have to put it that way. If you choose to quote, then you need to decide what are quoting for, this is how pair trading works.

If you decide to quote for the less liquid security, slippage will be less but the trading volumes will come down liquid securities on the other hand increase the risk of slippage but trading volumes will be high.

Using statistics to check causality is another way of arriving at a decision, i.e. change in which security causes change in the other and which one leads. The causality test will determine the “lead-lag pair"; quote for the leading and cover the lagging security.

Step 5 - Backtesting & optimization

How do you decide if the strategy you chose was good or bad? How do you judge your hypothesis?

This is where backtesting the strategy comes as an essential tool for the estimation of the performance of the designed hypothesis based on historical data.

A strategy can be considered to be good if the backtest results and performance statistics back the hypothesis. Hence, it is important to choose historical data with a sufficient number of data points.

This is to create a sufficient number of sample trades (at least 100+ trades) covering various market scenarios (bullish, bearish etc.). Ensure that you make provisions for brokerage and slippage costs as well. This will get you more realistic results but you might still have to make some approximations while backtesting.

For instance, while backtesting quoting strategies it is difficult to figure out when you get a fill. So, the common practice is to assume that the positions get filled with the last traded price.

What kind of tools should you go for, while backtesting?

Backtesting for algorithmic trading strategies involves a huge amount of data, especially if you are going to use tick-by-tick data. So, you should go for tools which can handle such a mammoth load of data. Blueshift is a free platform which allows you to perform backtesting, investment research and algorithmic trading, using 10+ years of data.

What should you use - R or MATLAB?

R is excellent for dealing with huge amounts of data and has a high computation power as well. Thus, making it one of the better tools for backtesting. Also, R is open source and free of cost. We can use MATLAB as well but it comes with a licensing cost.

Step 6 - Risk and performance evaluation

"With great power comes great responsibility"

Fine, I just ripped off Ben Parker’s famous quotation from the Spiderman movie (not the Amazing one). But trust me, it is 100% true. No matter how confident you seem with your strategy or how successful it might turn out previously, you must go down and evaluate each and everything in detail.

There are several parameters that you would need to monitor when analyzing a strategy’s performance and risk. Some important metrics/ratios are mentioned below:

• Total Returns (CAGR) – Compound Annual Growth Rate (CAGR) is the mean annual growth rate of an investment over a specified period of time longer than one year.
• Hit Ratio – Order to trade ratio.
• Average Profit per Trade – Total profit divided by the total number of trades
• Average Loss per Trade – Total loss divided by the total number of trades
• Maximum Drawdown – Maximum loss in any trade
• The Volatility of Returns – Standard deviation of the “returns”
• Sharpe Ratio – Risk-adjusted returns, i.e. excess returns (over risk-free rate) per unit volatility or total risk.

Where are algo trading strategies used?

Algo trading strategies can be used by any trader having experience in the financial markets armed with coding skills. Such a trader would be termed an algorithmic trader.

An algorithmic trader can code the algorithmic trading computer to take different actions regarding trade orders such as:

• Perform complex mathematical calculations for predicting prices of financial assets
• Forecast market movements
• Generate trading signals
• Risk Management etc.

Algorithmic trading strategies are used by hedge funds, investment banks, pension funds, proprietary traders and broker-dealers for market making and hence, create the world of algorithmic trading.

How to learn algo trading strategies?

​​Going by the number of courses available online on algorithmic trading, there are several on display, but finding the apt one for your individual requirement is most important. Now, it is obviously in your best interest to learn from a group of market experts. To make this happen, your goal and course offered (for gaining knowledge in the domain) should be in complete synchronization so as to not waste even an iota of time on unnecessary information.

Furthermore, there is a well-designed platform for exercising your knowledge, so as to use the same appropriately in the live market.

Recommended Read:

Learn Algorithmic Trading: A Step-By-Step Guide

Courses

Coming to the list of Algo Trading courses, Quantinsti offers the world's first verified algorithmic trading course EPAT (Executive Programme in Algorithmic Trading) alongside a series of self-paced algorithmic trading courses through Quantra.

Algorithmic Learning Track provides you a list of goals to choose from. Each goal presents you with an organized set of such informative courses that should serve your purpose. QuantInsti’s learning track on the web page offers you with courses in descending order starting from basic and ending with advanced knowledge for each goal.

Here, you can see a list of courses available on the Quantra web page for Algorithmic Trading.

EPAT gives you a more elaborative insight on Algorithmic Trading in case you are a beginner and wish to delve deeper into the understanding of each terminology.

Books

There are several books on Algorithmic trading, which are important for understanding the details such as,” how trades/exchanges occur in markets” and you also can delve further on market participants, trading methods, liquidity, price discovery, transaction costs, etc.

Read the blog, Essential Books on Algorithmic Trading, for a detailed synopsis of each of the relevant reads mentioned below:

• Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris ⁽¹⁾.
• Market Microstructure Theory by Maureen O’Hara ⁽²⁾.
• Algorithmic Trading: Winning Strategies and Their Rationale by Dr. Ernest Chan ⁽³⁾.
• Algorithmic Trading and DMA: An introduction to direct access trading strategies by Barry Johnson ⁽⁴⁾.
• Schaum's Outline of Statistics and Econometrics by Dominick Salvatore, Derrick Reagle ⁽⁵⁾.
• Analysis of Financial Time Series by Ruey Tsay ⁽⁶⁾.
• Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications by John J. Murphy ⁽⁷⁾.
• Options, Futures, and Other Derivatives by John C. Hull ⁽⁸⁾.
• Dynamic Hedging: Managing Vanilla and Exotic Options by Nassim Nicholas Taleb ⁽⁹⁾.

The courses and books mentioned above are sure to enhance your knowledge and expertise in different spheres of algorithmic trading field.

FAQs about algorithmic trading strategies

Here are some of the most commonly asked questions about algorithmic trading strategies which we came across during our Ask Me Anything session on Algorithmic Trading.

Question: I am not an engineering graduate or software engineer or programmer. I don’t know anything about writing a programming language. Then how can I make such strategies for trading? I am retired from the job. Will it be helpful for my trading to take certain methodology or follow? Are there any standard strategies which I can use it for my trading?

Reply: I will break it down into two parts one is that if you don’t have programming experience but you do have some idea about statistics or you do have some idea about trading strategies then the best place to start will be to start learning. You can start connecting with the representatives at QuantInsti® and they can share a lot of material which can help you get started, which is also available on our own portal.

We have also launched a new course along with NSE which is a joint certification free course for options basics using Python, by our self-paced learning portal Quantra.

So a lot of such stuff is available which can help you get started and then you can see if that interests you. The good part is that you mentioned that you are retired which means more time at your hand that can be utilized but it is also important to ensure that it is something that actually appeals to you. I do not generally recommend any standard strategies.

There are no standard strategies which will make you a lot of money. Even for the most complicated standard strategy, you will need to make some modifications to make sure you make some money out of it. If it’s standard then it’s standard for a reason which means that it will not be generating any returns. Good idea is to create your own strategy, which is important.

Question: Can we develop MACD divergence using Python?

Reply: Yes, you can. For almost all of the technical indicators-based strategies you can.

Question: What are the best numbers for winning ratio you have seen for algorithmic trading?

Reply: The interesting part about algorithmic trading, especially about high frequency trading is that it’s not about the percentage returns that you can generate.

I have seen strategies which used to give 50,000% returns in a month but the thing is that all these strategies, a lot of them are not scalable. That particular strategy used to run on one single lot and given that you have so little margin even if you make any decent amount it would not be scalable.

If we look at it more from a perspective of the amount of money it’s making versus the huge amount of infrastructure in place then I cannot make a lot of profit considering it runs on only one.

So looking at the winning ratio would not be the right way of looking at it if it is HFT or if it is low or medium frequency trading strategies typically a Sharpe ratio of 1.8 to 2.2 that’s a decent ratio. So again we cannot talk about what the returns are, the returns can be without defining the risk especially if it’s a directional strategy that does not mean much and that’s the reason I gave you the number in Sharpe, if you are scaling it up.

Besides these questions, we have covered a lot many more questions about algorithmic trading strategies in this article.

Check out if your query about algorithmic trading strategies exists over there, or feel free to reach out to us here and we’d be glad to help you.

Bonus Content

Here are some important reads that will help you learn about algorithmic trading strategies and be of guidance in your learning.

• Learn Algorithmic Trading: A Step-by-Step Guide
• COMPLETE | Webinar Recordings on Algorithmic Trading
• PODCASTS | With experts from the industry
• Algorithmic Trading In India: History, Regulations and Future
• FREE RESOURCES to Learn Algorithmic Trading - A Compiled List
• Making A Career In Algorithmic Trading
• The Growth And Future Of Algorithmic Trading
• FREE BOOK | Algorithmic Trading

Conclusion

Algorithmic trading strategies are devised by a trader experienced in financial markets who also have the knowledge of coding with the computer languages such as Python, C, C++, Java etc.

The entire process of algorithmic trading strategies does not end here. What I have provided in this article is just the foot of an endless Everest. In order to conquer this, you must be equipped with the right knowledge and mentored by the right guide.

If you wish to explore algorithmic trading in detail, enrol into our course known as Executive Programme for Algorithmic Trading or EPAT. The Executive Programme in Algorithmic Trading (EPAT®), helps you achieve your learning goal, that is, becoming a professional algorithmic trader. EPAT is a comprehensive course covering a wide range of topics such as risk management, portfolio optimisation, options trading, market microstructure, etc.

Disclaimer: All investments and trading in the stock market involve risk. Any decision to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.