Futures trading is a dynamic and potentially lucrative arena for investors seeking exposure to various financial instruments, including commodities, currencies, and stock indices. While market analysis, timing, and risk management are essential components of a successful trading strategy, one crucial aspect often overlooked is position sizing. Determining the appropriate size of a futures contract is a critical decision that can significantly impact profitability and risk exposure. In this article, we will delve into the importance of position sizing and explore various methodologies that can help traders optimize their risk-reward balance. 

Position sizing refers to the process of determining the number of futures contracts to trade based on factors such as account size, risk tolerance, and market conditions. It involves striking a balance between the potential for profit and the potential for loss. In essence, position sizing provides a framework for managing risk and capital allocation within a trading strategy. 

Effective position sizing begins with a clear understanding of risk management. Before entering a trade, traders must define their risk tolerance and establish a predetermined stop-loss level. The position size should be adjusted accordingly to limit potential losses within an acceptable range. 

Volatility is a critical factor in futures trading, as it determines the potential price swings and associated risks. Highly volatile markets require smaller position sizes to account for larger price fluctuations, reducing the risk of significant losses. Conversely, less volatile markets may allow for larger position sizes to capitalize on smaller price movements. 

The size of an individual’s trading account plays a vital role in position sizing. It is generally advisable to limit the exposure of each trade to a small percentage of the total account balance. This ensures diversification and protects against catastrophic losses. As the account grows, position sizes can be adjusted accordingly to maintain an appropriate risk-to-reward ratio. 

Different trading strategies may require varying position sizes. For example, a day trader who aims to profit from short-term price fluctuations may take larger positions with tighter stop-loss levels. On the other hand, a swing trader focusing on capturing more significant market moves over a longer time horizon may opt for smaller positions with wider stop-loss levels. 

One of the most common approaches to position sizing in futures trading is the fixed fractional method. This strategy involves determining the percentage of trading capital that traders are willing to risk on any given trade. For example, if a trader has Rs. 1,00,000 in trading capital and decides to risk 2% on each trade, they would be willing to risk Rs. 2,000 on a single trade. 

The fixed fractional method ensures that traders are not risking too much of their capital on any single trade, which can help protect their account from significant losses. By keeping their risk exposure in check, traders can also avoid the emotional highs and lows that can come with large losses or gains. 

Another approach to position sizing in futures trading is the volatility-based method. This strategy involves adjusting the size of the trade based on the market’s volatility. When markets are more volatile, traders may opt to reduce their position size to limit their risk exposure. Conversely, when markets are less volatile, traders may choose to increase their position size to maximize their potential returns. 

The volatility-based method can be highly effective when trading in highly volatile markets like commodities or currencies. However, it requires traders to have a good understanding of market volatility and to adjust their position sizes accordingly. 

Position sizing is a vital component of a comprehensive futures trading strategy. By carefully considering factors such as risk management, volatility, account size, and trading strategy, traders can determine the appropriate position size for each trade. Whether using fixed-ratio position sizing, volatility-based approaches, or the Kelly Criterion, the goal is to strike a balance between maximizing potential gains and minimizing potential losses. By adopting a disciplined and calculated approach to position sizing, traders can enhance their overall profitability and achieve long-term success in the challenging world of futures trading. 

I had the honour to attend, and present, at the 50th annual conference of the CMT: Chartered Market Technicians. While everyone in finance uses charts, Market Technicians are the pinnacle of professional financial chart users: charts are core to their analyses, recommendations, actions and trading strategies.

Here’s a few comments on some of the sessions. There’s a few snapshots too: visualization people who aren’t financial professionals may be interested to see some differences and perhaps a few insights.

To start, technical analysis is the notion that price patterns in markets result from changes in supply and demand. But demand isn’t necessarily rational: people have fear of losses, fear of missing out, fear of not following trends that all their neighbours are talking about at dinner parties. We’re social, we tend to have herd behaviour in markets, whether bitcoin, meme stocks, houses (2007), railroads (1840), tulips (1634), etc. While these examples are market bubbles, there are other similar patterns in stock prices, bond prices, house prices and so forth. Patterns may similarly exist in inflation and other economic data too. The notion of trends and patterns are a feature of markets, and these trends and patterns can be observed, analyzed, communicated and traded. The Chartered Market Technicians (CMT) is the association that formalizes technical analysis and provides training and certification for professionals.

Many timeseries

David Keller – chief market strategist at Stockcharts.com – hosts a daily TV show on technical analysis. I’ve had the opportunity to work with Dave in the past, and one thing of note is that timeseries analysis is rarely done in isolation: timeseries are compared to other timeseries, events, derivations and so on. Here’s one of Dave’s charts from his show which he broadcast live at the CMT event:

Dave’s chart with many different timeseries indicators to analyze the current level of the S&P500.

Hand-drawn charts

David had an opportunity to interview legends of technical analysis Ralph Acampora (who painted a 70 foot chart of the Dow Jones Industrial Average on his barn), and Louise Yamada (who pulled out a paper chart during Dave’s interview). Both talked about their early careers when they had to manually calculate averages, plot their charts by hand and learned to get a sense of the markets from the physical charts.

Ralph Acampora and Louise Yamada both spoke about physical charts.

Annotated charts

Many technical analysts annotate charts: drawing trend lines, support and resistance lines, indicating peaks and troughs (sharp or gradual), other patterns (head and shoulders, wedges) and textual notes such as prices or key events. Whereas the visualization community is questioning whether or not 2-4 annotations are too many, the technical analysis community is well inclined to have 10 or more annotations (such as Ralph’s barn, or Louise’s markup on her charts).

Paul Ciana – chief of fixed income, commodity and currency technical analysis at Bank of America – presented some work analyzing trends in unemployment. Here’s a fantastic chart looking at employment filled with annotations explaining unemployment trends:

Many annotated explanations on an unemployment rate chart by Paul Ciana.

Statistical analysis

Many technical analyses use statistical approaches to assess data. A simple example is Ned Davis use of overlaying a mean or regression line on a timeseries chart. In a trending chart, whether economics data or price data, it can be clearly seen when a series is above below the regression. A significant divergence from the regression line indicates at a minimum a warning. Here’s a sample of one of Ned’s charts, from his book:

Ned Davis, and charts with mean overlay and regression overlays.

John Bollinger – inventor of Bollinger bands – provided a historical perspective on bands in timeseries charts going back to the 1800’s. John made a special point that these financial calculations are no longer technically difficult to implement – he provided Python code for each historic example, and commented on ChatGPT’s ability to generate code. Here’s one of the charts, with Python code. More importantly, John stressed how these technical analysis studies are used, for example, a trivial understanding of a Bollinger band is to derive a signal when the raw data (price), crosses the band. John explained that a signal on a Bollinger band is created when the trend weakens, the price crosses to the opposite side of the band, touches the band, then fails to break back across the moving average line (I’m paraphrasing, my notes might not be exactly right on that).

John Bollinger explains the code, the bands, their use.

Trading

Technical analysis can be used to understand markets, and also to underpin trading strategies used to make money in the markets. To use technical approaches to trade markets requires backtesting the technical analysis model against historic data to validate the model’s effectiveness. In addition to making a profit and other metrics, one key metric is max drawdown, an indication of how much money a model might lose at any time. In at least two presentations I saw models that had drawdowns of greater than 50% — which will require a significant commitment on the part of the trader to believe in the model and have confidence that the original model was not overfit to the test data.

There were a couple of great interviews interviews, including Jerry Parker. Parker’s story goes beyond backtesting to one of the gutsiest validation tests ever done. Parker was one of 20 people without a trading background, trained in a trading system, given a $1million to trade, and succeeded. It’s an incredible story, here’s a basic outline.