Real-Time Risk Management in the Trading Arena

Real-Time Risk Management in the Trading Arena

Vardaan CHAWLA

In this article, Vardaan CHAWLA (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2020-2023) shares a case study on real-time risk management in the trading arena.

As an individual investor venturing into the dynamic world of financial markets, it’s crucial to understand and implement effective risk management strategies. The following article, explores the key principles and techniques to safeguard your investments and navigate the potential risks.

Financial markets are very dynamic, interesting, and filled with opportunities and risks. Learning to manage risks in the always-changing world of financial markets is crucial. In the following article I discuss the effective methods to manage, navigate, and avoid risk while dealing in financial markets to help you make informed decisions and safeguard your money.

Understanding Your Risk Tolerance

The first principle of effective risk management is self-awareness. Before diving into financial markets one must assess one’s own risk tolerance meaning the amount of losses you are able to manage comfortably.

Ask yourself critical questions:

  • How much capital can I realistically afford to lose?
  • How would a significant loss impact my financial well-being?
  • Am I prone to emotional decision-making during market fluctuations?

After answering these questions you can start making your trading and risk management strategies and techniques. A very aggressive investor will be open to taking a high amount of risk with more potential results while a conservative investor will be the opposite, low risk with less potential returns. One must invest based on their own loss tolerance.

Core Risk Management Strategies

Once you understand your risk tolerance, equip yourself with these key risk management strategies:

  • Position Sizing: This describes how much capital is devoted to a specific deal. Starting small is a vital notion, particularly for novices. A typical place to start is with 1% to 2% of your entire portfolio for each deal. With a diversified portfolio, you can progressively raise position size as your experience and risk tolerance permits.
  • Stop-Loss: Stop orders are vital instruments for safeguarding your investment. To limit potential losses if the market swings against your position, a stop-loss order automatically sells an asset when the price hits a predefined level (lower than the current market price). It’s critical to create stop-loss levels that balance possible asset recovery with risk minimization.
  • Take Profit: Limit orders work similarly to stop-loss orders in that they automatically lock in profits by selling an asset when the price hits a predefined level (higher than the current market price). This lessens the chance of losing gains if the market turns south. To safeguard your earnings and resist the need to cling to a winning position for too long, use take-profit orders wisely.
  • Diversification: Avoid putting all of your money in one place. Distribute your investments throughout several industries, sectors, and asset classes. This lessens the effect that a fall in one asset will have on the value of your entire portfolio. Diversification makes your portfolio more stable and less vulnerable to changes in the market.
  • Risk-Reward Ratio: This measure contrasts the possible gain with the possible loss on a certain transaction. Seek for transactions where the possible profit margin outweighs the potential loss margin. A better risk profile is indicated by a greater ratio. Prior to making a trade, evaluating the risk-reward ratio will help you make well-informed judgments regarding potential gain vs downside.

The figures below illustrate how take-profit and stop-loss can be implemented for a given stock (Meta around August 15,2024). Two orders are sent to the market (at the same time): a sell limit order with a limit price of $290 and a stop order with a trigger price of $280. Note that it is not always possible to place both a limit order an stop order at the same time (it depends on the brokers or trading platforms).

In Figure 1, the stock price stays below the limit price and above the trigger price.

Figure 1. No order execution.
No order execution
Source: computation by the author.

In Figure 2, the sell limit order is executed as the market price reaches the limit price of the order; the transaction price is $290.

Figure 2. Take profit: execution of the limit order.
Take profit: execution of the limit order
Source: computation by the author.

In Figure 3, the sell stop order is executed as the market price reaches the trigger price of the order; the transaction price is $280 (or lower if the market is not very liquid).

Figure 3. Stop loss: execution of the stop order.
Stop loss: execution of the stop order
Source: computation by the author.

Advanced Risk Management Techniques

As you gain experience, consider incorporating these advanced techniques:

  • Hedging: This is the process of offsetting possible losses in your underlying holdings by employing derivative instruments, such as option contracts. Before putting hedging methods into practice, careful thought and comprehension are necessary because they can be complicated.
  • Volatility Targeting: This strategy modifies the overall risk exposure of your portfolio in response to fluctuations in the market. You may lower the sizes of your positions or devote more capital to less volatile assets during times of high volatility. On the other hand, you may decide to take on larger positions or invest in riskier assets during times of low volatility.

Disciplined Execution: The Key to Success

Risk management is not just about having the right tools; it’s about disciplined execution. Here are some essential practices to cultivate:

  • Trading Plan: One must work meticulously in developing a comprehensive trading plan that clearly defines your entry, exit, risk management strategies, and what you aim to achieve from trading and avoid emotional and impulsive decision-making.
  • Monitoring and Adjustment: You must also regularly monitor your portfolio and be updated on financial news in order to prepare for potential future losses or opportunities. To maximize your gains utilize Stop loss orders and take profit orders and adjust your trades and position as and when needed.
  • Emotional Control: When we receive surprise losses or surprise gains we are inclined to make emotional and impulsive decisions that can lead to further future losses. The trader must always make decisions with a calm composed mind to make sound decisions.

By adopting these risk management principles and maintaining disciplined execution, you can navigate the real-time financial markets with greater confidence and minimize the possibility of significant losses. Remember, risk management is an ongoing process that requires constant evaluation and adaptation.

Related posts on the SimTrade blog

   ▶ Federico DE ROSSI Understanding the Order Book: How It Impacts Trading

   ▶ Jayati WALIA Quantitative risk management

   ▶ Ziqian ZONG My experience as a Quantitative Investment Intern in Fortune Sg Fund Management

   ▶ Michel VERHASSELT Risk comes from not knowing what you are doing

Useful resources

SimTrade course Trade orders

Justin Kuepper (June 12, 2023) Risk Management Techniques for Active Traders

Amir Samimi & Alireza Bozorgian (2022) An Analysis of Risk Management in Financial Markets and Its Effects, Jounrnal of Engineering in Industrial Research, 3(1): 1-7

About the author

The article was written in December 2024 by Vardaan CHAWLA (ESSEC Business School, Master in Strategy and Management of International Business (SMIB), 2020-2023).

Trading strategies based on market profiles and volume profiles

Trading strategies based on market profiles and volume profiles

Michel Henry VERHASSELT

In this third article on a series on market profiles, Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025) explains trading strategies based on market profiles and volume profiles.

Introduction

We have defined and seen illustrations of all the key concepts related to both market profiles and volume profiles. Let us now look at their practical applications and trading strategies that may be applied.

These techniques apply to both market profiles and volume profiles.

Mean reversion

A mean reversion strategy is a trading approach based on the idea that prices tend to revert to their historical average or mean over time. Traders employing this strategy look for opportunities to enter trades when prices deviate significantly from their historical average, anticipating a return to the mean.

Market profiles naturally fit this kind of strategy, as their whole point is to show where participants have deemed the price to be fair. For example, a trader could consider that when the price is trading below a high-volume area, that area will act as a magnet to pull the price up. The prices in that region were indeed considered fairer, and the current low price would be an anomaly to be corrected by market participants. Therefore, the trader would buy at the current price and sell around the POC or at least within the value area.

Resistance and support

Conversely, a different interpretation within the same framework involves viewing these highly-traded areas as potential resistance or support zones. Support is a crucial level preventing an asset from further decline, often due to an upsurge in buying interest. In contrast, resistance is a pivotal level inhibiting an asset from rising higher, typically caused by intensified selling activity.

For a trader emphasizing resistance and support concepts, consider a rising price nearing a heavily traded zone encountering resistance, similar to reaching a ceiling. The outcome may lead to either a breakout to new highs or a reversal downward. In this context, the value area is not seen as a magnetic force drawing prices toward fair value; instead, it functions as a testing ground. The result hinges on whether the attempt to breach resistance is rejected, leading to a lower price, or successful, resulting in an upward move past this pivotal point. This dynamic interaction adds layers of complexity to mean reversion and support/resistance strategies within the realm of market profiles.

Entries and exits

More generally, traders employ various tools to make well-informed decisions about when to enter or exit market positions. One such powerful tool is the market profile. Even if a trader’s primary strategy relies on other triggers to look at a trade, say for example macro events, they can still leverage market profiles. These profiles help determine optimal entry or exit points, considering factors like obtaining liquidity with minimal market impact and identifying levels for stop losses and target profits based on perceived resistance and support.

Breakouts

As mentioned above, breakout trading is a strategy employed in financial markets where traders capitalize on significant price movements beyond established levels of support or resistance. In a breakout, the price surpasses a predefined range or pattern, triggering potential buying or selling signals. Traders often interpret breakouts as indicators of strong momentum, with the expectation that the price will continue moving in the breakout direction. The aim of breakout trading is to enter positions early in a new trend and ride the momentum for profitable gains.

Market profile can help identify breakout opportunities. For example, when a market exhibits confined trading within a narrow range and the profile reveals an accumulation of TPOs (Time Price Opportunities) near the boundaries of this range, a breakout surpassing these levels could indicate a potential trading opportunity.

False breakout strategy

The false breakout trading strategy relies on discerning instances where the price briefly moves beyond a trading range but subsequently retraces, indicating potential weaknesses in the current trend. In a false bullish breakout, signaling buyers’ weakness, traders might opt for short positions. Conversely, in retraced bearish breakouts, suggesting sellers’ uncertainty, opportunities for long positions may emerge. The effectiveness of this strategy lies in recognizing imbalances in supply and demand, a task facilitated by market profiles.

Market profiles offer a nuanced visual representation of price movements over time, highlighting areas of significant trading activity and the distribution of volume at different price levels. This information aids traders in identifying potential entry and exit points more precisely. By integrating market profiles into the false breakout strategy, traders gain insights into the dynamics of supply and demand within specific price ranges. This, in turn, enhances their ability to navigate market sentiment shifts and make informed decisions, contributing to the overall effectiveness of the false breakout trading strategy.

Single prints

The Market Profile Single Print strategy is a dynamic approach leveraging the unique concept of single prints within the Market Profile chart to identify potential breakout opportunities.

The strategy’s foundation lies in identifying single prints—instances where a price level remains untouched throughout the trading session, creating a gap in the Market Profile chart. Price can often revisit these areas to test these inefficiencies. These single prints therefore act as crucial markers, indicating potential areas of support or resistance. The significance of this lies in the ability to pinpoint breakout levels: a break above a single print suggests a bullish breakout, while a break below indicates a bearish breakout.

Crucially, market profiles assist in managing risk effectively by providing a visual representation of potential areas of support or resistance. Continual monitoring of the trade is emphasized, with adjustments made based on evolving market conditions. Trailing stop-loss orders are recommended to protect profits as the trade progresses favorably.

Related posts on the SimTrade blog

   ▶ Michel VERHASSELT Market profiles

   ▶ Michel VERHASSELT Difference between market profiles and volume profiles

   ▶ Theo SCHWERTLE Can technical analysis actually help to make better trading decisions?

   ▶ Theo SCHWERTLE The Psychology of Trading

   ▶ Clara PINTO Strategy and Tactics: From military to trading

Useful resources

Steidlmayer P.J. and S.B. Hawkins (2003) Steidlmayer on Markets: Trading with Market Profile, John Wiley & Sons, Second Edition;

Steidlmayer P.J. and K. Koy (1986) Markets and Market Logic: Trading and Investing with a Sound Understanding and Approach, Porcupine Press.

About the author

The article was written in December 2023 by Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025).

Difference between market profiles and volume profiles

Difference between market profiles and volume profiles

Michel Henry VERHASSELT

In this second article on a series on market profiles, Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025) explains the difference between market profiles and volume profiles.

Comparison

Both Market Profiles and Volume Profiles follow the auction theory of markets. According to this theory, price, time and volume are the three processes through which trading takes place.

More exactly:

  • Price advertises all opportunities. It lets the participants know that they can buy or sell an asset at a given price; it tells them what their opportunities are.
  • Time regulates all opportunities. Indeed, the opportunities given by price are limited in time; they are ephemeral and depend on the liquidity and volatility of an asset, in other words, how much time it takes for the price to change and the opportunity to vanish.
  • Volume measures the success or failure of advertised opportunities. Volume reflects the degree of market participation and validates the relevance of the opportunities presented. If an opportunity is advertised and becomes successful that means many participants agree on the fairness of this opportunity and a relatively significant amount of trading activity (volume) takes place at this price. A price that is not accepted over time is, in fact, rejected: the advertisement has failed.

All traders feel the pressure of time ticking away during a trade. When a trade stalls and doesn’t go as expected, it can create doubts, especially the longer it remains stagnant. The constant tick of the clock forces traders to ponder what might be going wrong. For instance, the late liquidation or short-covering rally in the pit session may be due to day traders running out of time rather than a lack of trading volume. In that sense, volume must take place within a given time range to validate the price advertisement.

Now when it comes to Volume Profiles, the chart shows the distribution of volume at different price levels, kind of like a visual map of where the action is happening. It uses a vertical histogram to make it easy for traders to see where the most trading activity is concentrated. This charting tool is all about giving traders a closer look at how much trading is going on at different price points over time.

Comparing Volume Profile to Market Profile, we find three key areas of differences: analytical focus, representation of data, and time and price dynamics.

Analytical Focus

Volume Profile: As the name suggests, Volume Profile places a paramount emphasis on volume, aiming to dissect the distribution of trading activity at different price levels over a designated timeframe.

Market Profile: In contrast, Market Profile combines time and price to create a graphical representation of market behavior. It divides price movements into designated time segments, typically 30-minute intervals, offering a nuanced perspective on the interplay between time and price.

Representation of Data

Volume Profile: The chart generated by Volume Profile provides a clear visualization of how volume is distributed across various price levels, offering insights into where significant buying or selling activity is concentrated.

Market Profile: While also representing volume, Market Profile charts use letters (TPOs) to signify the time spent at specific price levels, creating a distinctive visual pattern resembling a probability distribution.

Time and Price Dynamics

Volume Profile: Its primary concern is the interrelation of volume and price, with a focus on understanding the significance of different price levels based on the amount of trading activity.

Market Profile: Integrates time as a crucial factor, providing traders with a holistic view of market behavior over specific time intervals. This temporal dimension aids in identifying periods of heightened activity and potential areas of interest.

Let’s now look at Market and Volume profiles graphs.

Illustration

The figure below is taken from Steidlmayer’s main work: “Steidlmayer on Markets, Trading with Market Profile”. Each letter (A, B, C, D, etc.) corresponds to a single timeframe of 30 minutes. The condensed triangle-shaped figure shows where price has moved throughout the entire time period according to the trading activity.

Market profile.
Market profile
Source: Steidlmayer’s book “Steidlmayer on Markets, Trading with Market Profile”.

If we rotate the figure, we get a bell-shaped pattern that looks like a normal distribution.

Market profile (reversed presentation).
Market profile
Source: Steidlmayer’s book “Steidlmayer on Markets, Trading with Market Profile”.

The price distribution in a Market Profile tends to exhibit a bell-shaped pattern due to the nature of market dynamics and participant behavior. In a well-functioning and liquid market, prices are subject to constant fluctuations driven by the interplay of buying and selling activities and the bell-shaped distribution is simply a reflection of the statistical tendency of prices to cluster around a central point. The majority of trading activity should in theory occur around a fair or equilibrium price. As you move away from this central point, the occurrences of extreme price levels decrease, forming the characteristic bell curve. It is a visual representation of the market’s natural inclination to spend more time around prices that are deemed fair.

The figure below represents the volume profiles of the BTC/USDT pair on Binance’s futures market from December 8 until December 15, 2023.

Volume profile.
Volume profile
Source: exocharts.com.

We see the point of control (POC) that corresponds to the most traded price as a red line extending through the volume profile of each day. The value area is marked both by a whiter grey and dotted lines. The current price is a green line on the far left. On the far right, we find the volume profile for the whole timeframe displayed on the screen, with its own value area and point of control.

While the two profiles are very similar, however instead of looking at price and time as in a market profile, the volume profile focuses on volume. First, the volume profile is indifferent to when exactly a given trade took place within the same timeframe, here a day. Second, the volume profile uses true volume data rather than simply whether or not a trade took place. The length of each bar within a volume profile is directly proportionate to the volume of the trades at that price. In contrast, the market profile does not show the size of the trades but simply shows whether or not a price was traded during a 30-minute period, and then aggregates (or “collapses”) the data to form one profile, as we saw in the bell-shaped curve above.

Why should I be interested in this post?

Students of finance interested in financial markets and trading would be the target audience of this post. I believe this technique to be relatively obscure despite its long history. We rarely see asset charts displayed as histograms as an effort to understand market behavior and participant psychology. I believe it is fundamental to consider that the market is made up of human actors, that these actors have their biases on price and value, and in turn that these biases’ success is represented as a function of volume. Even if a student does not subscribe to this understanding of markets, it would broaden his/her perspective and allow him/her to understand trading more generally.

Related posts on the SimTrade blog

   ▶ Michel VERHASSELT Market profiles

   ▶ Michel VERHASSELT Trading strategies based on market profiles and volume profile

   ▶ Theo SCHWERTLE Can technical analysis actually help to make better trading decisions?

   ▶ Theo SCHWERTLE The Psychology of Trading

   ▶ Clara PINTO Strategy and Tactics: From military to trading

Useful resources

Steidlmayer P.J. and S.B. Hawkins (2003) Steidlmayer on Markets: Trading with Market Profile, John Wiley & Sons, Second Edition;

Steidlmayer P.J. and K. Koy (1986) Markets and Market Logic: Trading and Investing with a Sound Understanding and Approach, Porcupine Press.

TPO versus Volume Profiles

Trader Dale Volume Profile vs. Market Profile – What Is The Difference? YouTube video

About the author

The article was written in December 2023 by Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025).

Market profiles

Market profiles

Michel Henry VERHASSELT

In this first article on a series on market profiles, Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025) explains the history behind this concept and defines its central themes.

Introduction

The concept of Market Profiles emerged as a response to the dynamic nature of financial markets, where prices are in constant flux due to the continuous flow of information. Peter Steidlmayer, a trader at the Chicago Board of Trade during the 1960s and 1970s, sought to develop a charting method that could capture the interplay between price and volume, reflecting the idea that, despite the constant price changes, there should be a fair value around which prices revolve at any given time.

In traditional charting methods like bar charts and candle charts, the emphasis is typically on plotting price against time. Steidlmayer, however, wanted to make volume immediately apparent on the chart. This emphasis on volume is crucial because it provides insights into the level of participation and conviction among market participants.

The development of Market Profile was influenced by various theories and disciplines. In particular, it drew inspiration from the concept of value investing articulated by Benjamin Graham and David Dodd, the statistical bell curve, and John Schultz’s work on minimum trend. By combining these influences, Steidlmayer aimed to create a charting technique that would not only reveal price movements but also offer a visual representation of the market’s perception of value.

Market Profile, as a charting technique, differs significantly from traditional methods. Instead of using standard bar charts with prices plotted against time, Market Profile organizes data in a way that reflects the distribution of prices at different levels. Each time period is represented by a separate column, with prices displayed in ascending order on the vertical axis. This organization provides a visual representation of how much time the market spent at different price levels, creating a histogram-like structure.

The resulting chart, with letters (A, B, C, D, etc.) representing Time Price Opportunities (TPO), helps traders identify key areas such as the Value Area (where the majority of trading activity occurred), the Point of Control (the most traded price level), and Single Prints (indicating areas of price discovery). These elements collectively contribute to a comprehensive understanding of market dynamics and help traders make more informed decisions.

Definitions

We define below the key terms to understand Market Profile: Volume, Value Area, and Point of Control.

Volume

Volume in the context of financial markets refers to the number of contracts or shares traded at during a specific time period. Volume is a crucial component in Market Profile analysis because it provides insights into the level of participation and conviction among market participants. High volume at a particular price level suggests a significant level of interest or agreement on the value of the asset at that point.

Volume helps us shape the Time Price Opportunities. A TPO represents a unit of time and price on a Market Profile chart. Each 30-minute period (or another specified time frame) is represented by a letter, forming a vertical histogram on the price axis. TPOs help visualize the distribution of trading activity at different price levels over time. By organizing price data into these time brackets, traders can identify patterns, trends, and areas of importance, contributing to a better understanding of market behavior.

Value Area

The Value Area represents the range of price levels that contain a specific percentage of the total traded volume (usually 70% of the day’s trading activity). Traders also use the Upper Value Area (where 15% of the volume is located above) and the Lower Value Area (where 15% of the volume is below), with the area in between considered the “fair value” zone. It helps traders identify the price levels that are deemed fair by the market. It provides insights into where the majority of trading activity occurred, offering potential support and resistance zones for future price movements.

Point of Control

Within the value area, we find the Point of Control. The Point of Control is the price level at which the most TPOs occurred during a specific time period. It is considered a point of balance and represents the price where the market found the most acceptance. It indicates the price level that had the most trading activity, suggesting a level of equilibrium where buyers and sellers found agreement. Traders often monitor the POC for potential shifts in market sentiment.

By understanding the interplay between these elements, traders can gain valuable insights into market dynamics, identify key support and resistance zones, and make more informed decisions in their trading strategies.

With this background and definitions, we can look further into the practice of market profiles and its closely related concept, volume profiles.

Why should I be interested in this post?

Students of finance interested in financial markets and trading would be the target audience of this post. I believe this technique to be relatively obscure despite its long history. We rarely see asset charts displayed as histograms as an effort to understand market behavior and participant psychology. I believe it is fundamental to consider that the market is made up of human actors, that these actors have their biases on price and value, and in turn that these biases’ success is represented as a function of volume. Even if a student does not subscribe to this understanding of markets, it would broaden his/her perspective and allow him/her to understand trading more generally.

Related posts on the SimTrade blog

   ▶ Michel VERHASSELT Difference between market profiles and volume profiles

   ▶ Michel VERHASSELT Trading strategies based on market profiles and volume profile

   ▶ Theo SCHWERTLE Can technical analysis actually help to make better trading decisions?

   ▶ Theo SCHWERTLE The Psychology of Trading

   ▶ Clara PINTO Strategy and Tactics: From military to trading

Useful resources

Steidlmayer P.J. and S.B. Hawkins (2003) Steidlmayer on Markets: Trading with Market Profile, John Wiley & Sons, Second Edition;

Steidlmayer P.J. and K. Koy (1986) Markets and Market Logic: Trading and Investing with a Sound Understanding and Approach, Porcupine Press.

Letian Wang (2020) Using Python for Market Profiles

About the author

The article was written in December 2023 by Michel Henry VERHASSELT (ESSEC Business School – Master in Finance, 2023-2025).

Volume-Weighted Average Price (VWAP)

Volume-Weighted Average Price (VWAP)

Raphael TRAEN

In this article , Raphael TRAEN (ESSEC Business School, Global BBA, 2023-2024) explains about the Volume-Weighted Average Price (abbreviated as VWAP), a statistic used by traders to determine the average trading taking into account transaction volume.

Definition

The volume-weighted average price (VWAP) is a measurement that shows the average price of a security, adjusted for its volume. It is calculated during a specific trading session by taking the total dollar value of trading in the security (sum of the products of the price by the quantity of each trade during the trading session) and dividing it by the total volume of trades (sum of the quantities of each trade during the trading session). The formula for calculating VWAP is given by

Formula VWAP

Where N is the number of transactions during the trading session (trading day).

VWAP can also be computed for consecutive time intervals during the trading sessions.

Sometimes, the price is replaced by a “typical price” computed as the average of the minimal price, maximal price, and closing price observe over a time interval.

Typical price

Interpreting the VWAP indicator / Key takeaways

Volume-weighted average price (VWAP) is a popular technical indicator used by traders and investors to identify trends, support and resistance levels, and potential entry and exit points. It can also be used for example to assess the liquidity and market depth of a security. If the VWAP is closely clustered around the current price, it suggests that there is a lot of liquidity and that the market is well-balanced. If the VWAP is spread out over a wide range of prices, it suggests that the market is less liquid and that there is a higher risk of wide price swings.

Breakout above the VWAP line suggests a bullish trend

A breakout above VWAP suggests that the price has momentum and is moving upwards. This could be due to increased buying pressure from investors, indicating a shift in sentiment towards the security. Once the price breaks above VWAP, it can act as a support level, making it more difficult for the price to fall below that level.

This could be an opportunity to enter a long position, anticipating the price to continue rising.

Breakdown below the VWAP line suggests a bearish trend

If the price of a security breaks below the VWAP line, it may signal a potential bearish trend. This could be an opportunity to enter a short position, anticipating the price to continue falling.

VWAP line can act as support or resistance level

The VWAP line can also function as a support or resistance level, representing a price range where the price of the security may tend to bounce off.

VWAP to identify trends

If the VWAP line is trending upwards, it suggests an overall upward trend in the price of the security. This could indicate favorable conditions for long-term investments. Conversely, if the VWAP line is trending downwards, it suggests an overall downward trend in the price of the security. This could indicate caution for long-term investments.

Conclusion

It is important to note that VWAP is just one indicator, and it should not be used in isolation. It is always a good idea to consider other technical indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI), before making any trading decisions.

Often, multiple interpretations are possible and because of this, it is important to use the VWAP in combination with other indicators.

As I said, a breakdown below the VWAP may suggest a bearish trend. But it can also be interpreted as the following: Stocks with prices below the VWAP are considered as undervalued and those with prices above it, overvalued.

So while some institutions may prefer to buy when the price of the security is below the VWAP or sell when it is above, VWAP is not the only factor to consider. In strong uptrends, the price may continue to move higher for many days without dropping below the VWAP at all. Therefore, waiting for the price to fall below the VWAP could mean a missed opportunity if prices are rising quickly.

Why should I be interested in this post?

This article will provide students interested in business and finance a comprehensive overview of VWAP and how it is used by traders and investors. By understanding this fundamental concept in technical analysis, students will gain a valuable tool for making informed investment decisions.

Related posts on the SimTrade blog

   ▶ Shruti CHAND Technical analysis

   ▶ Shruti CHAND Technical Analysis, Moving Averages

   ▶ Theo SCHWERTLE Can technical analysis actually help to make better trading decisions

   ▶ Giovanni PAGLIARDI Tail relation between return and volume

Useful resources

Academic articles

Menkhoff, L. (2010) The use of technical analysis by fund managers: International evidence, Journal of Banking & Finance 34(11): 2573-2586.

Kirkpatrick II, C. D., and J.R. Dahlquist (2010) Technical Analysis: The Complete Resource for Financial Market Technicians. FT press.

Videos

Humbled Trader VWAP Trading Strategy Crash Course (YouTube video)

MHFIN VWAP Explained For Beginners In Under 5 Minutes (YouTube video)

About the author

The article was written in December 2023 by Raphael TRAEN (ESSEC Business School, Global BBA, 2023-2024).

Strategy and Tactics: From military to trading

Strategy and Tactics: From military to trading

Clara PINTO

In this article, Clara PINTO (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2020-2023) shares her insights as a former military analyst on strategy and tactics applied to trading.

IRSEM: The Institute for Strategic Research at the Military School

Created in 2009, IRSEM is the strategic research institute of the French Ministry of Armed Forces and operates under the supervision of the Directorate General for International Relations and Strategy (DGRIS) operating under the umbrella of the Ministry for the Armed Forces. IRSEM is home to a staff of about forty civilian and military permanent researchers. The Institute seeks to foster the emergence of a new generation of researchers specialized in security and defense issues.

Logo of IRSEM.
Logo of IRSEM
Source: IRSEM.

From military to Finance: Strategy and tactics applied to trading

My experience as an analyst in a military Think tank required me to learn the basics of military strategy and tactics. The distinction between strategy and tactics is frequently stated as “strategy is long-term, whereas tactics are short-term.” While the two terms may exhibit similar qualities at times, it is an erroneous and partial explanation of their definitions.

Chinese General Sun Tzu wrote the difference this way: “All the men can see the tactics I use to conquer, but what none can see is the strategy out of which great victory is evolved.”

However, even if strategy and tactics are also used in business, their principles still apply in the trading context. Indeed, successful trading requires a solid understanding of both strategy and tactics. In this article, we will explore the differences between these two concepts and how they can be applied to trading.

Strategy refers to a long-term plan that outlines how you will achieve your trading goals. It involves identifying your objectives, assessing the risks and opportunities in the market, and deciding on a plan of action. A good trading strategy should be flexible enough to adapt to changing market conditions, but also structured enough to provide a clear path forward. One key aspect of a trading strategy is risk management. This involves identifying the potential risks associated with a particular trade and taking steps to mitigate them. This might involve setting stop-loss orders to limit potential losses or diversifying your portfolio to reduce the overall risk. A good trading strategy should also take into account the amount of capital you have available to trade with, as well as your risk tolerance and investment goals.

Tactic, on the other hand, refers to the specific actions you take to implement your trading strategy. These might include analyzing technical indicators to identify trends and patterns to assess the value of a particular asset. A successful trading tactic will depend on a number of factors, including the specific asset you are trading, and the current market conditions.

Ultimately, the success of your trading will depend on how well you are able to combine strategy and tactics. A strong strategy will provide a clear framework for making decisions and managing risk, while effective tactics will allow you to execute that strategy in a way that maximizes your returns. In order to develop a successful trading strategy, it is important to conduct thorough research and analysis of the markets you are interested in. This might involve studying historical market trends, analyzing economic and political factors that could impact the markets, or keeping up to date with news and events that could affect the value of specific assets. It is also important to remember that trading involves a degree of risk, and no strategy or tactic can guarantee success. However, by developing a strong strategy and using effective tactics to execute that strategy, you can improve your chances of making profitable trades over the long term.

In conclusion, strategy and tactics are both essential components of successful trading. A strong trading strategy provides a clear framework for decision-making and risk management, while effective tactics allow you to execute that strategy in a way that maximizes your returns. By combining careful research and analysis with disciplined execution, you can increase your chances of success in the complex and ever-changing world of trading.

Why should I be interested in this post?

Unfortunately, the concepts of strategy and tactics are often mixed up and not entirely understood. However, they provide a good framework to trade in the long term and structure your choices in the decision-making process.

Related posts on the SimTrade blog

   ▶ Momentum Trading Strategy

Useful resources

IRSEM – The Institute for Strategic Research

About the author

The article was written in March 2023 by Clara PINTO (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2022-2023).

Quantitative equity investing

Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) elaborates on the concept of quantitative equity investing, a type of investment approach in the equity trading space.

This article follows the following structure: we introduce the quantitative equity investing. We present a review of the major types of quantitative equity strategies and we finish with a conclusion.

Introduction

Quantitative equity investing refers to funds that uses model-driven decision making when trading in the equity space. Quantitative analysts program their trading rules into computer systems and use algorithmic trading, which is overseen by humans.

Quantitative investing has several advantages and disadvantages over discretionary trading. The disadvantages are that the trading rule cannot be as personalized to each unique case and cannot be dependent on “soft” information such human judgment. These disadvantages may be lessened as processing power and complexity improve. For example, quantitative models may use textual analysis to examine transcripts of a firm’s conference calls with equity analysts, determining whether certain phrases are commonly used or performing more advanced analysis.

The advantages of quantitative investing include the fact that it may be applied to a diverse group of stocks, resulting in great diversification. When a quantitative analyst builds an advanced investment model, it can be applied to thousands of stocks all around the world at the same time. Second, the quantitative modeling rigor may be able to overcome many of the behavioral biases that commonly impact human judgment, including those that produce trading opportunities in the first place. Third, using past data, the quant’s trading principles can be backtested (Pedersen, 2015).

Types of quantitative equity strategies

There are three types of quantitative equity strategies: fundamental quantitative investing, statistical arbitrage, and high-frequency trading (HFT). These three types of quantitative investing differ in various ways, including their conceptual base, turnover, capacity, how trades are determined, and their ability to be backtested.

Fundamental quantitative investing

Fundamental quantitative investing, like discretionary trading, tries to use fundamental analysis in a systematic manner. Fundamental quantitative investing is thus founded on economic and financial theory, as well as statistical data analysis. Given that prices and fundamentals only fluctuate gradually, fundamental quantitative investing typically has a turnover of days to months and a high capacity (meaning that a large amount of money can be invested in the strategy), owing to extensive diversification.

Statistical arbitrage

Statistical arbitrage aims to capitalize on price differences between closely linked stocks. As a result, it is founded on a grasp of arbitrage relations and statistics, and its turnover is often faster than that of fundamental quants. Statistical arbitrage has a lower capacity due to faster trading (and possibly fewer stocks having arbitrage spreads).

High Frequency Trading (HFT)

HFT is based on statistics, information processing, and engineering, as the success of an HFT is determined in part by the speed with which they can trade. HFTs focus on having superfast computers and computer programs, as well as co-locating their computers at exchanges, actually trying to get their computer as close to the exchange server as possible, using fast cables, and so on. HFTs have the fastest trading turnover and, as a result, the lowest capacity.

The three types of quants also differ in how they make trades: Fundamental quants typically make their deals ex ante, statistical arbitrage traders make their trades gradually, and high-frequency traders let the market make their transactions. A fundamental quantitative model, for example, identifies high-expected-return stocks and then buys them, almost always having their orders filled; a statistical arbitrage model seeks to buy a mispriced stock but may terminate the trading scheme before completion if prices have moved adversely; and, finally, an HFT model may submit limit orders to both buy and sell to several exchanges, allowing the market to determine which ones are hit. Because of this trading structure, fundamental quant investing can be simulated with some reliability via a backtest; statistical arbitrage backtests rely heavily on assumptions on execution times, transaction costs, and fill rates; and HFT strategies are frequently difficult to simulate reliably, so HFTs must rely on experiments.

Table 1. Quantitative equity investing main categories and characteristics.
 Quantitative equity investing
Source: Source: Pedersen, 2015.

Conclusion

Quants run their models on hundreds, if not thousands, of stocks. Because diversification eliminates most idiosyncratic risk, firm-specific shocks tend to wash out at the portfolio level, and any single position is too tiny to make a major impact in performance.

An equity market neutral portfolio eliminates total stock market risk by being equally long and short. Some quants attempt to establish market neutrality by ensuring that the long side’s dollar exposure equals the dollar worth of all short bets. This technique, however, is only effective if the longs and shorts are both equally risky. As a result, quants attempt to balance market beta on both the long and short sides. Some quants attempt to be both dollar and beta neutral.

Why should I be interested in this post?

It may provide an opportunity for investors to diversify their global portfolios. Including hedge funds in a portfolio can help investors obtain absolute returns that are uncorrelated with typical bond/equity returns.

For practitioners, learning how to incorporate hedge funds into a standard portfolio and understanding the risks associated with hedge fund investing can be beneficial.

Understanding if hedge funds are truly providing “excess returns” and deconstructing the sources of return can be beneficial to academics. Another challenge is determining whether there is any “performance persistence” in hedge fund returns.

Getting a job at a hedge fund might be a profitable career path for students. Understanding the market, the players, the strategies, and the industry’s current trends can help you gain a job as a hedge fund analyst or simply enhance your knowledge of another asset class.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Introduction to Hedge Funds

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Long-short strategy

Useful resources

Academic research

Pedersen, L. H., 2015. Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined. Chapter 9 : 133 – 164. Princeton University Press.

About the author

The article was written in December 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Moving averages

Moving averages

Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains the concept of moving averages and its implementation in financial markets as an indicator in technical analysis of stock price movements.

What is a moving average?

A moving average is a technique to analyze a time-series of data points by taking subsets of data and computing their averages. The subsets of data can explicitly be of a fixed size like simple moving averages or implicitly take into account all past points like exponential moving averages. These averages computed on rolling windows constitute a new time series. The aim of this exercise is essentially to filter noise and smoothen out the data in order to identify an overall trend in the data.

In financial markets, moving averages are one of the most popular indicators used in technical analysis. A moving average is used to interpret the current trend of a stock price (or any asset). It basically shows the price fluctuations in a stock as a single curve and is calculated using previous prices. Hence, a moving average is a lagging indicator.

Moving averages can be computed for different time periods such as 10 days, 20 days or 200 days. The greater the length of the time period (the lag in the trend), the greater the degree of smoothness in the moving average, however, the lower the price sensitivity of the moving average.

To measure the direction and strength of a trend, moving averages involve price averaging to establish a baseline. For instance, if the price moves above the average, the indicated trend is bullish and if it moves below the average, the trend is bearish. Moving average crossovers are also used commonly in trading strategies to identify trends. It then involves two moving averages: one computed on a short-term period and another one computed over a long-term period. When a shorter period moving average crosses above a longer period moving average, the trend is identified as bullish and indicates a buy signal. When a shorter period moving average crosses below a longer period moving average, the trend is identified as bearish and indicates a sell signal.

Moving averages are also used in development of other indicators such as Bollinger’s bands and Moving Average Convergence Divergence (MACD).

Types of moving averages

The moving average indicator can be of many types. Two basic types of moving averages and their interpretation are explained below: simple moving average and exponential-weighted moving average.

Simple moving average

Simple moving average (SMA) is the easiest type of moving average to compute. An n-period SMA is simply calculated by taking the sum of the closing prices of an asset for the past ‘n’ time-periods divided by ‘n’.

The formula to compute the SMA at time t is given by:

Simple moving average formula

Where Pi represents the asset price at time i (i indicating any time between the interval [t-n, t]).

If the current asset price is greater than the SMA value, the viewpoint for trend is established as bullish and similarly, if the current asset price is less than the SMA value, the viewpoint for trend is established as bearish.

Figure 1 below illustrates the 20-day and 50-day SMA for Amazon stock price.

Figure 1. 20-day and 50-day simple moving averages for Amazon stock price.
20-day and 50-day SMA for Amazon stock price Source: Computation by author.

We can observe from the above figure that when the price is going down, the SMA also is going downwards (as expected from the formula). It can also be seen that the movement of the SMA curve lags the change in price movements. The greater is the chosen time-period for SMA, the greater is the lag observed. Thus, while a 50-day SMA maybe smoother compared to a 20-day SMA, the lag observed will also be greater.

Exponential-weighted moving average

Exponential-weighted moving average (EWMA), also known as exponential moving average (EMA) is an improvisation of moving average over the SMA. It assigns weights to moving averages such that the recent data points are assigned greater weight factors than older data points. Thus, EWMA is more sensitive to recent price changes and the line is smoother than that of SMA.

The formula to compute the value of the EWMA at time t is given by:

Exponential-weighted moving average formula

Where Pt represents the stock price at time t, and α is a smoothing (or weighting) factor.

The series is initialized as: EWMA0 = P0.

The smoothing factor, α, is a constant value which lies between 0 and 1. The higher the value of α, the greater the weight assigned to the recent data, and the less smooth the EWMA curve.

How to set alpha for an exponential-weighted moving average?

α can be varied by a trader using EWMA based on how heavily he or she wants the recent data to be weighted. If a single EWMA is being considered, an optimal value for alpha can be chosen by minimizing the mean-squared errors (MSE).

A rule of thumb sometimes by traders is specified as:
Alpha for EWMA

For instance, for a short-term EWMA with the lookback period, n = 20, and alpha is equal to 2/21 = 0.095. For a long-term EWMA with n = 50, and alpha is equal to 0.039. Note that n is not related to a meaningful number of days like for the SMA.

When α=2/(n+1), the weights of an SMA and EWMA have the same center of mass.

A more sophisticated method is to relate alpha to the ‘half-life’ concept, meaning how long it takes for the weight to become half of the weight of the most recent data.

If the formula of EWMA is expanded for k days, we get the following:

EWMA formula expanded

For α=2/(n+1), the idea is that for a sufficiently large value of n, the sum of weights assigned to last n days is around 86%.

Figure 2 below illustrates the weights of each day for a EWMA with α equal to 3.92% (corresponding to n equal to 50 with the rule of thumb used by traders). It can be observed that the weights are decreasing in an exponential fashion and lower values are assigned as weights to the least recent days. The sum of the weights assigned to the first 10 days is 35.60 %, the first 50 days 86.47%, and the first 100 days 98.24%.

Figure 2. Weights of each day for an EWMA
EWMA day weights
Source: Computation by author.

Crossovers

EWMA is typically used in crossovers, which is a common strategy used by traders wherein two or more moving averages can help determine a more long-term trend. Basically, if a short-term EWMA crosses above a long-term EWMA, the crossover indicates an uptrend and similarly, if a short-term EWMA crosses below a long-term EWMA, the crossover indicates a downtrend. Traders can utilize it to establish their position in the stock.

Figure 3. below illustrates short-term and long-term EWMA curves for Amazon stock prices.

Figure 3. Short-term and long-term EWMA for Amazon stock price.
img_SimTrade_EWMA_Amazon_stock
Source: Computation by author.

We can observe in the figure above that the short-term EWMA follows the price movements in Amazon stock more closely than the long-term EWMA does. We can also see that a crossover of the two EWMA curves is followed by a change in trend. For instance, in April 2022, the short-term EWMA crosses below the long-term EWMA and there is an evident downtrend observed post the crossover.

You can also download below the Excel file for computation of SMA and EWMA for Amazon stock price and visualize the above graphs.

Download the Excel file to compute SMA and EWMA for Amazon stock price

Related posts on the SimTrade blog

   ▶ Jayati WALIA Trend analysis and trading signals

   ▶ Jayati WALIA Bollinger bands

   ▶ Akshit GUPTA Momentum trading strategy

Useful resources

Hunter, J. S. (1986). The exponentially weighted moving average. Journal of Quality Technology, 18:203–210.

Wikipedia Moving averages

National Institute of Standards and Technology (NIST) US Department of Commerce Single Exponential Smoothing

About the author

The article was written in August 2022 by Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).