What Are Trading Rules? (Which One Is Important?)

Trading rules are the heart of a mechanical or algorithmic trader. This article helps you understand the importance of specific trading rules, making you better prepared to face the markets. It’s a trading GPS! We have made plenty of trading rules over the years, and you find many among our memberships.

Let’s get straight to it, and we’ll explain to you all about trading rules, even show you a very profitable trading strategy with complete trading rules:

Table of contents:

Key Takeaways:

Trading rules are essential for automating the trading process, reducing behavioral errors, and managing risk.
The most important trading rules involve buy and sell criteria as well as risk and money management.
Automation and backtesting of trading rules can significantly enhance trading performance.
Different markets require unique trading rules; one size does not fit all.
Continuous learning, discipline, and adaptation are crucial for successful trading.

What are trading rules?

Trading rules are specific guidelines or parameters a trader follows to determine whether to buy or sell a stock or any other financial asset. When the conditions are true, a buy or sell order is sent.

The trading rules are preferably backtested and simulated in a trading software. They are part of a trading plan that includes risk and money management.

The main idea of strict trading rules is to automate and mechanize the trading process. This structured and quantified approach to trading is something we strongly recommend for most traders, whether beginner or pro.

What are the most important trading rules?

The most important trading rules are the buy and sell rules and risk and money management rules. You should have strict rules for all these.

Most traders focus on the buy trading rule, and much less on the sell or money management rules. But the sell rule might be even more important, and so might the risk and money management rules. You should stay focused to develop all of them, as you’ll understand as you progress through this article.

Why do traders need rules?

Traders need trading rules to determine whether a trading strategy has a positive expectancy and to automate the trading process to avoid being fooled by behavioral mistakes.

Traders also need trading rules to evaluate their performance better. If a trader has a trading journal and list of all trades, tracking performance and determining what he or she is doing wrong or right is easy.

How to follow the trading rules?

To follow the trading rules, a trader needs to mechanize and automate all parts of the trading process. The more the process is automated, the easier it is to follow any trading rule.

Most traders overrule or skip trades and signal for various reasons, but mainly because of psychological biases, such as loss aversion, anchoring, optimism bias, etc. There are multiple behavioral traps, and it’s not as easy to follow the trading rules as it seems:

For example, can you pull the trigger on the 7th trade after six losses in a row?

After a 25% drawdown, are you still willing to follow the trading rules, or do you start fiddling with them?

The evolution of trading rules – a historical perspective

The evolution of trading rules in trading is short: only in the 1960s did quantitative rules make some headway into finance. For example, trading rules were still widely unknown when Ed Seykota started dabbling with putting trading rules into a computer in the 1970s.

Even when Victor Niederhoffer and Jim Simons understood the power of strict trading rules in the 1980s, so-called quants were still mostly unknown.

It was not until the 2000s that quants, quantified strategies, and trading made some headway. The rise of computing power made it much more attractive, not to mention all the money that could be made by being successful.

Today, even a small retail trader has access to software and computer power unheard of as recently as the early 2000s.

However, access to computer power has made the market more efficient, and completion by someone who is more capitalized and better equipped makes it hard for a retail trader to make money. That said, you don’t need anything complicated to succeed in trading; you mostly need to be street-smart.

Psychology and trading rules – can rules manage emotions?

Using trading rules, you can reduce, limit, or even exclude emotions from trading. The computer can do the work for you when you have made strict trading rules.

Thus, you are making a “layer” between you and the market, and you are less likely to make emotional decisions and override the trading rules.

At least, that’s the theory. In practice, it’s not so easy. It’s very hard to detach from money, so the chances of overriding the trading rules are still there.

The combination of trading smaller position sizes than you prefer and strict trading rules should keep emotions in check for most traders. Trading small is by far the best remedy for ensuring detachment from money.

Types of trading rules – technical vs. fundamental analysis

We can use trading rules for both technical and fundamental analysis. Technical analysis focuses on price action, volume, price patterns, Intermarket analysis, etc.

On the other hand, fundamental analysis looks at value, intrinsic value, financial value, etc.

Trading rules for technical analysis

The assumption behind technical analysis is that price action tends to repeat.

Typically, trading rules are based on indicators such as moving averages and the RSI indicator (there are many indicators). Price patterns like double bottoms and head-and-shoulders are also used, but the two latter are very difficult to quantify.

Here is an example of trading rules based on technical analysis:

Buy rule: Buy when the 2-day RSI breaks below 10; and
Sell rule: sell when the 2-day RSI breaks above 60.

These are trading rules that can be quantified and backtested easily. You will get an exact answer on how the above trading rules have performed in the past on this asset, stock, or whatever you backtested.

The advantage of trading rules using technical analysis is that it’s easily available, and you don’t need to understand the fundamentals of the asset. The weakness is that history never repeats exactly as in the past, and there is no guarantee it will perform well in the future.

Trading rules for fundamental analysis

Fundamental analysis looks mainly at value and intrinsic value. Trading rules are based on value ratios such as price to book (P/B), price to earnings (P/E), and financial ratios like leverage. The assumption is that cheaper stocks eventually reflect the real value, while overpriced stocks do the opposite and perform poorly.

Here is an example of trading rules based on fundamental analysis:

Buy rule: Buy the 10% cheapest stocks based on the current P/E ratio that has at least 10 ROIC% (return on invested capital) over the last ten years; and
Sell rule: Rebalance annually and sell the ones not among the 10% cheapest and buy others that have become among the 10% cheapest.

Such simple trading rules have worked well in the past but not well after the financial crisis of 2008. Historically, value stocks have performed better than growth stocks.

The strengths of using such a quantified approach to fundamental analysis are that it’s logical because you are buying cheap stocks that have good returns on assets. The disadvantage is that it requires a lot of good data. It’s also less suited for short-term trading.

Fundamental vs technical analysis trading rules

If you are a short-term trader, you should use technical or quantified trading rules based on price patterns, indicators, and volume.

Conversely, if you are a long-term investor, you are most likely better off using fundamental trading rules.

Creating your own trading rules: A step-by-step guide

Let’s show you a specific trading rule guide to making your own trading rules in order to make a trading strategy.

We start by showing a trading idea you might get by looking at a price chart of S&P 500:

The chart contains two indicators: the 5-day RSI indicator (on the bottom) and the 200-day moving average, marked as the orange think line (click on the chart to increase the size).

The chart has marked potential oversold levels at three places where S&P 500 went up after it reached oversold levels. Let’s try to backtest such a strategy with strict trading rules:

Trading rules for buy: When the 5-day RSI drops below 35, and the close is above the 200-day moving average.
Trading rule for sell: When the 5-day RSI ends above 50.

The trading rules are simple, and backtesting on Amibroker, the trading platform we used, takes only a few minutes.

When we backtest these trading rules, we get the following equity curve when we invested 100,000 in 1993 and let it compounded until today:

As you can see, you would have had a pretty linear growth without any gut-wrenching drawdowns that would risk you abandoning the strategy.

The annual return is 7.1% with dividends reinvested, but it’s slightly lower than buy and hold of 10.1%. However, the strategy is invested only 15% (!) of the time! A trade lasts an average of less than five days and returns 0.84%; thus, this is a very short-term strategy.

The 200-day moving average acts as a trend filter and ensures that you are only invested when we have a bull market. Because of this, the max drawdown is only 14%, compared to buy-and-hold, which had 55%. This is a major difference. Would you still hold on to S&P after being down 55% from the last peak? Plenty of traders and investors would not.

The risk-adjusted return of the strategy is a massive 46%. We get that number by taking by dividing the exposure by the return (7.1/0.15).

Risk management and trading rules

Some traders swear to implement trading rules for risk management. For example, such a rule could be a stop-loss. The reason for having trading rules for risk management is to preserve your capital. If you lose your capital, you are out of business, and that is not a situation you like to be in.

Does a stop-loss improve the strategy? Unfortunately not, regardless of whether you have a mean reversion or trend-following strategy. We have gone into depth to explain this in a separate article about the pros and cons of a stop loss.

Let’s add a simple risk management trading rule for the strategy we used in the previous section: If the sell signal has not been triggered after ten days, we sell on the tenth day. The longer you stay in a trade, the more risk you trade, thus this additional trading rule.

The strategy performs worse when we include the additional risk management trading rule, with only 0.75% gains per trade and 6.5% annual return.

This shows why you need to backtest. You can’t just include an arbitrary risk measure and hope it works.

Common Trading Rules Used by Professionals

Common trading rules professionals used by professionals include diversification, having a trading plan, and focusing on the process, not the outcome.

The best risk management trading rule is diversifying into many stocks, assets, or trading strategies. If you are a short-term reader, you want to trade many uncorrelated and complementary trading strategies.

Also, a bad plan is most likely better than no plan at all. Professionals stick to their plans, make necessary adjustments as they proceed, and get feedback from live trading.

Above all, professionals focus on the process, not the outcome. They concentrate on making good decisions based on your rules, not chasing specific profit targets. We recommend reading Annie Duke’s Thinking Bets to understand the importance of process vs. outcome better.

Backtesting and Optimization of Trading Rules

Optimization of the trading rules is an important part of backtesting. You don’t want to curve-fit the trading strategy; you want to optimize to better understand how the strategy responds to changes in the parameters’ values.

Let’s return to the RSI strategy we backtested above to show you an example of the optimization of trading rules:

Instead of buying when the 5-day RSI drops below 35, we optimize by buying when it drops below 40 and 30 using intervals of 5. Thus, we make three backtests: when the S&P 500 drops below 40, 35, and 30. We leave the sell signal of 50 unchanged.

When we do the optimization of the buy trading rule, we get the following table:

RSI level – Buy	CAGR %	Avg per trade %	Win rate %
30	5.9	0.98	80
35	7.1	0.84	81
40	6.5	0.6	79

The original RSI level performs pretty well based on the table. However, in trading, beauty is in the eye of the beholder. What criteria you use also depends on your capital allocation and other trading strategies that you trade.

Trading rules for algorithmic trading

Automation gives you power, and a computer can scan thousands of rules in a second.

Thus, you get leverage by algorithmic trading rules. This is what makes algorithmic trading so fascinating.

Can you use the same trading rules in different markets, such as stocks, forex, crypto, and commodities?

No, you can’t use the same trading rules for stocks, forex, crypto, or commodities. Many traders make the mistake of requiring their backtested trading rules to work on all assets, which is a grave mistake (!).

Why should a stock trading strategy for Apple work for gold or Bitcoin? There’s no logic behind this assumption, and many traders reject good trading strategies because of this.

No market behaves the same. The fundamentals and participants are different, and assets might serve different purposes.

Moreover, trading rules must be differentiated even within the same asset class. Apple is a very different stock than Exxon, for example. Exxon is dependent on the price of oil and gas and thus liable to commodity prices. Apple is not.

Famous traders and their legendary trading rules

Throughout history, many famous traders have sworn to certain trading rules- not necessarily buy-and-sell rules, but overall rules that define their trading style.

Let’s look at some of these iconic traders and look at some of their most important trading rules:

Jesse Livermore: There is nothing new in Wall Street. There can’t be, because speculation is as old as the hills. Whatever happens in the stock market today has happened before and will happen again.
Paul Tudor Jones: Don’t lose money.
Jim Simons: I don’t want to have to worry about the market every minute. I want models that will make money while I sleep. A pure system without humans interfering.
Nicholas Nassim Taleb: We underestimate the share of randomness in about anything.
Richard Dennis: The trend is your friend.
Ed Seykota: If you can’t measure it, you probably can’t manage it… Things you measure tend to improve.

What are the common mistakes in developing trading rules?

The most common mistakes in developing trading rules are curve fitting, ignoring risk and money management rules, and not understanding your emotional biases.

Curve fitting is when you apply and curve fit trading rules to past data so that they are unlikely to work on future and unknown data.

Another mistake is to ignore risk management rules, like measuring how the trading rules fit into your overall strategies (if you trade many strategies – as you should).

But the most obvious mistake is to make rules you are unlikely to follow in real trading. It’s far too easy to look at the past and use hindsight bias. For example, can you trade a low win rate strategy that has many losers in a row?

What are the common mistakes in following trading rules?

The common mistakes in following trading rules are lack of discipline, overtrading, ignoring performance reviews, and ignoring the emotional bias of trading.

First, most humans and traders lack discipline. Are you introverted or extroverted? The best traders tend to be introverted because they are most likely to follow the trading rules. Extroverts are much more likely to overtrade.

Another common mistake is not examining performance to determine what you do right or wrong. You need a trading journal, but meticulous record-keeping is boring. However, the trading journal is one of the best tools you have!

Finally, very few can follow the trading rules. When your money is on the line, overriding the system and making new trading rules on the fly is easy.

Should you revise your trading rules?

You should revise your trading rules at certain intervals. However, you must decide the intervals BEFORE you start trading the rules.

Reasons for revising trading rules include consistent underperformance, strategy degradation, change in risk tolerance, increased knowledge and skills, changed goals, and discovery of new strategies.

It is important to keep a long-term mindset when trading. Many traders change the rules and ignore the long-term mindset required.

Let’s look at potential reasons for revising your trading rules:

Consistent underperformance: If the trading rules are not as good as in your backtest over a long time, it’s time to revise and reevaluate. A trading journal comes in handy on such occasions.
Strategy deterioration: All strategies sooner or later die out, and you should make some rules for when to stop trading BEFORE you start trading live.
Changes in risk and pain tolerance: Your risk tolerance might evolve due to personal circumstances. It is natural and is a factor of self-confidence. The more confidence you have in your trading rules, the more risk you might be willing to take.
Increased knowledge and skills: After a while, you might discover other aspects of the trading rules you were unaware of earlier. This might also be a result of your trading log and journal. You discover patterns as you learn more and do live trading.
Changed goals: As you gain more experience, you might develop a different trading style, and hence, you need to change trading rules and strategies.
Discovery of new strategies: Your goals should be to trade multiple trading strategies, and hence you might need to change trading rules to accommodate diversification and overlapping trades and strategies.

Success stories: traders who credit their trading rules for success

Richard Dennis is one of the most obvious success stories that credit their trading rules for success. He was the brain behind the Turtle strategy experiment, where several people with no experience were given the same trading rules. Perhaps needless to say, the results varied greatly even though the traders were given the EXACT same trading rules.

Another strict trading rule follower is Ed Seykota. He was one of the first to use quantified trading rules in the 1960s and 70s, and he emphasized the importance of psychological control and emotional detachment because of trading rules.

DE Shaw is one of the biggest quantified traders, yet quite unknown. Before deploying any model or strategy, DE Shaw rigorously backtests them on historical data to assess their performance and metrics.

How can you build discipline and consistency with trading rules?

The most important factor in building discipline and consistency is having self-confidence. Of course, that is easier said than done.

This is why we believe trading small is smart to ensure you follow the trading rules. As soon as you follow the rules and hopefully see that they work, you can increase size and scale gradually.

What is important is that you always stay within your mental comfort zone.

What are the reasons for deviating from the trading rules? Why do traders stray?

Traders deviate from the trading rules mainly because they trade with position sizes that are too large and have too low self-confidence.

We repeatedly recommend trading small position sizes until you have gained confidence and knowledge. Learning to trade takes time, many years, and you need confidence to pull the trigger when you get a trading signal. It’s not easy to follow trading rules when you have no detachment from money or low confidence.

What is the future of trading rules with AI and machine learning?

The future of trading rules will most likely change dramatically with AI and machine learning. That will lead to more black box strategies and adaptive trading rules.

First, AI might create trading rules on its own, and machine learning and hidden black box rules might strain the markets, not to mention lawmakers.

What are the legal and ethical considerations about trading rules?

Legal and ethical considerations about trading rules evolve around black box strategies. Reduced transparency might prompt lawmakers to address the issue.

With more AI and machine learning, increased reliance on complex algorithms raises concerns about transparency and potential risks due to their “black box” nature. Regulatory frameworks might need to adapt to address these concerns.

Trading rules – conclusion

Trading rules are the cornerstone of any trading strategy, and we believe you should quantify as many of them as you can. If you manage to automate trading rules and strategies, you gain leverage and can trade as many strategies as possible – only your imagination sets the limit.

The more systematic you are, the better you can improve and learn. Trading is all about feedback.

Glossary

There are other terms and expressions related to trading rules. We have summarized all of them and explained them in one sentence:

Trading Rules: A set of guidelines and principles that traders follow to make informed and disciplined trading decisions.
Risk Management: Strategies and rules aimed at minimizing potential losses in trading.
Position Sizing: Determining the amount of capital to allocate to a specific trade based on risk tolerance and account size.
Stop-Loss Order: A predefined price level at which a trader exits a losing position to limit losses.
Take-Profit Order: A preset price level at which a trader closes a winning position to secure profits.
Risk-Reward Ratio: The ratio of potential profit to potential loss in a trade, used to assess the trade’s viability.
Entry Point: The price at which a trader initiates a trade based on analysis or signals.
Exit Strategy: A predefined plan for exiting a trade, which can include profit targets and stop-loss levels.
Technical Analysis: Analyzing historical price and volume data to forecast future price movements.
Fundamental Analysis: Evaluating the intrinsic value of an asset by studying economic, financial, and industry-related factors.
Candlestick Patterns: Visual patterns on price charts that traders use to predict future price movements.
Moving Averages: Calculated averages of an asset’s past prices to identify trends and support/resistance levels.
Volatility: The degree of price fluctuation in an asset, often measured by standard deviation.
Liquidity: The ease with which an asset can be bought or sold without causing significant price changes.
Market Order: An order to buy or sell an asset at the current market price.
Limit Order: An order to buy or sell an asset at a specific price or better.
Trend Trading: A strategy that involves trading in the direction of the prevailing market trend.
Counter-Trend Trading: A strategy that involves trading against the prevailing market trend.
Day Trading: Buying and selling assets within the same trading day to profit from short-term price movements.
Trading Guidelines: Specific instructions and recommendations that traders follow to make informed decisions in financial markets.
Trading Principles: Fundamental beliefs and values that guide traders in their decision-making process and risk management.
Trading Strategies: Detailed plans or methods used by traders to buy or sell assets, often based on technical or fundamental analysis.
Trading Protocols: Established procedures and steps that traders adhere to when executing trades, including order types and execution rules.
Swing Trading: Holding positions for several days or weeks to capture intermediate-term price swings.
Scalping: Making numerous small trades with the goal of profiting from tiny price movements.
Margin Trading: Trading with borrowed funds to amplify potential gains or losses.
Pattern Day Trader (PDT): A trader subject to regulatory restrictions due to frequent day trading.
Trading Plan: A written document outlining a trader’s goals, strategies, and risk management rules.
Backtesting: Evaluating a trading strategy’s historical performance using past data.
Drawdown: The peak-to-trough decline in a trading account’s value during a losing streak.
Algorithmic Trading: Executing trades automatically based on predefined algorithms and rules.
Arbitrage: Simultaneously buying and selling the same asset in different markets to profit from price discrepancies.
Market Sentiment: The overall mood or attitude of traders and investors toward a specific asset or market.
Diversification: Spreading investments across different assets to reduce risk.
Correlation: The degree to which two or more assets move in relation to each other.
Hedging: Using financial instruments to offset potential losses in other investments.
Gap: A price difference between the closing and opening of consecutive trading sessions.
Slippage: The difference between the expected and actual execution price of a trade.
FOMO (Fear of Missing Out): The urge to enter a trade due to the fear of missing potential gains.
Leverage: Borrowed capital used to increase the size of a trading position.
Margin Call: A demand from a broker to deposit additional funds to cover potential losses.
Rollover: Extending a futures or options contract’s expiration date.
Volatility Index (VIX): A measure of market volatility often referred to as the “fear gauge.”
Trading Standards: Criteria and benchmarks used to evaluate the quality and fairness of trading practices and transactions.
Trading Policies: Official rules and regulations set by financial institutions or regulatory bodies to ensure fair and transparent trading practices.
Trading Framework: A structured approach or framework that traders follow to systematically analyze and execute trades.
Trading Practices: Common techniques and habits employed by traders to minimize risk and optimize profitability in the market.
Trading Regulations: Legal and regulatory requirements imposed by government authorities to maintain market integrity and protect investors.
Trading Best Practices: Proven methods and strategies that are considered optimal for achieving success in trading, often based on industry expertise and experience
Market Maker: A trader or firm that provides liquidity by quoting bid and ask prices.
Bid-Ask Spread: The difference between the highest price a buyer is willing to pay (bid) and the lowest price a seller is willing to accept (ask).
Pip: The smallest price move that an exchange rate can make in the forex market.
Limit Up/Limit Down: Price limits imposed by exchanges to prevent extreme volatility.
Short Selling: Borrowing and selling an asset with the intention of buying it back at a lower price.
Reversal Pattern: A chart pattern indicating a potential change in the prevailing trend.
Breakout: A price movement that exceeds a predefined level of support or resistance.
VWAP (Volume Weighted Average Price): A measure of the average price of an asset, weighted by trading volume.
Whipsaw: A situation where a trader is caught in a series of losing trades due to sudden market reversals.
Stochastic Oscillator: A technical indicator used to identify overbought or oversold conditions.
Bull Market: A market characterized by rising prices and investor optimism.