Build a Profitable Trading Model in 7 Easy Steps (2024)

A trading model is a clearly defined, step-by-step rule-based structure that investors can use for governing trading activities. It is based on an individual trader's needs and goals. Having a model helps investors make more informed decisions while setting boundaries for the amount and types of risk they can tolerate. In this article, we introduce the basic concept of trading models, explain their benefits, and provide instructions on how to build your own trading model.

Building Your Own Trading Model

Trading models set up rules that investors can follow in their trading strategies. But you don't need to be an experienced trader or have any advanced-level trading knowledge to build your own model. But you should understand a few basic principles, including how and why prices move (like world events), where profit opportunities exist, and how to practically capitalize on opportunities.

Novices and moderately experienced traders can start by becoming familiar with a fewtechnical indicators. These offer meaningful insights into trading patterns. Understanding technical indicators also help traders conceptualize trends and make customized strategies and alterations to their models. In this article, we will focus on trading based on technical indicators.

Developing and using a rule-based trading model offers many benefits:

• There are no human emotions that get in the way of investment-related decision-making because trading models are based on a set of proven rules.
• Models can be easily backtested on historical data to check their worth before taking the dive with real money.
• Backtesting allows verification of any associated costs so the trader can see profit potential more realistically. A theoretical $2 profit may look attractive, but a brokerage fee of $2.50 changes the equation.
• Models can be automated to send mobile alerts, pop-up messages, and charts. This can eliminate the need for manual monitoring and action. With a model, a trader can easily track 10 stocks for a 50-day moving average crossing over a 15‑day moving average. Without such automation, manually tracking even one stock's daily moving average can be difficult.
  

Based on the trend reversal principle, some traders act on the assumption that what goes down will come back up (and vice versa). Using this assumption as a strategy, we will build a trading model. In the steps below, we will walk through a series of steps to create a trading model and test if it is profitable.

Flowchart for Building a Trading Model

1. Conceptualize the Trading Model

The first step is to study historical stock movements to identify predictive trends and create a concept. This concept may be a result of extensive data analysis or it could be a hunch based on chance observations.

For this article, we are using trend reversal to build the strategy. In this case, the initial concept is if a stock goes down X percent compared to the previous day’s closing price, expect the trend to reverse in the next few days.

From here, look at past data and ask questions to refine the concept:

• Is the concept true?
• Will this concept apply to only a few selected high-volatility stocks or will it fit any and all stocks?
• What is the duration of the expected trend reversal: oneday, oneweek, or onemonth)?
• What should be set as the down level to enter a trade?
• What is the goal profit level?

An initial concept usually contains many unknowns. A trader needs a few deciding points or numbers to begin. These may be based on certain assumptions. For example, this strategy may apply to moderately volatile stocks having abetavalue between two and three.So buy if the stock goes down by 3% and wait for the next 15 days for trend reversal and expect a 4% return. These numbers are based on a trader’s assumptions and experience. Again, a basic understanding oftechnical indicatorsis important.

2. Identify the Opportunities

Next, identify the right opportunities or stocks to trade. This involves verifying the concept against historical data. In the example concept, we buy on a 3% dip. Start by choosing high‑volatility stocks for the assessment. You can download historical data of commonly traded stocks from exchange websites or financial portals like Yahoo! Finance.

Using spreadsheet formulas, calculate the percentage change from the previous day’s closing price, filter out the results matching the criteria, and observe the pattern for subsequent days. Below is an example spreadsheet.

In this example, the stock’s closing price is going down below 3% on two days (Feb.4 and Feb.7). Careful observation of the following days will reveal if the trend reversal is visible or not. The price on Feb.5 shoots up to a 4.59% change. By Feb.8, the change is below expected at 1.96%.

Are the results conclusive? No. One observation matches the expectation of the concept (4% and above change) while one observation does not.

Now we need to check our concept even further. This requires going over more data points and stocks. Run the test across multiple stocks with daily prices over at least five years. Observe which stocks give positive trend reversals within a defined duration. If the number of positive results is better than negative ones, then continue with the concept. If not, tweak the concept and retest or discard the concept completely and return to step 1.

3.Develop the Trading Model

Here, we fine-tune the trading model and introduce necessary variations based on the assessment results of the concept. We continue to verify across large datasets and look for more variations, including:

• Does the strategy outcome improve if we consider specific weekdays? For example, does the stock price dipping by 3% on a Friday result in a cumulative 5% or more increase within the next week?
• Does the outcome improve if we take high-volatility stocks with beta values above 4?

We can verify these customizations whether or not the original concept shows positive results. You can keep exploring multiple patterns. At this stage, you can also use computer programming to identify profitable trends by letting algorithms and computer programs analyze the data.Overall, the aim is to improve the positive outcomes from our strategy leading to more profitability.

Some traders get stuck in this stage, endlessly analyzing large datasets with slight variations in parameters. There is no perfect trading model. Remember to draw a line on testing and make a decision.

4.Perform a Practicality Study

Our model is now looking great. It shows a positive profit for a majority of trades, such as 70% wins of $2 and 30% losses of $1. We conclude that for every 10 trades, we can make a handsome profit of $11 (7 * $2 – 3 * $1).

This stage requires a practicality study that can be based on the following points:

• Is the brokerage cost-per-trade leaving sufficient room for profit?
• I may have to make up to 20 trades of $500 each to realize a profit, but my available capital is just $8,000. Does my trading model account for capital limits?
• How frequently can I trade? Is the model showing too frequent trades above my capital available, or are too few trades keeping profits very low?
• Does the theoretical outcome match the necessary regulations? Does it require short-selling or long-dated options trades which may be banned, or the holding of simultaneous buy and sell positions which may also not be allowed?

5.Go Live or Abandon and Start a New Model

Considering the results of the above testing, analysis, and adjustment, make a decision. Go live by investing real money using the trading model or abandon the model and start again from step 1. Remember, once you go live with real money, it is important to continue to track, analyze, and assess the result, especially in the beginning. Failure to do so may result in some pretty big losses.

6.Prepare for Failures and Restarts

Trading requires constant attention and improvements to strategy. Even if your trading model has consistently made money for years, market developments can change at any time. Be prepared for failures and losses. Be open to further customizations and improvements. Be ready to trash the model and move on to a new one if you lose money and can find no more customizations.

7. Ensure Risk Management by Building in What-If Scenarios

It may not be possible to include risk management in selected trading models depending on chosen strategies, but it is wise to have a backup plan if things don’t appear to be as expected. What if you buy the stock that went down 3%, but it did not show trend reversal for the next month? Should you dump that stock at a limited loss or keep holding on to that position? What should you do in the case of a corporate action likea rights issue?

The Bottom Line

Hundreds of established trading concepts exist and are growing daily with the customizations of new traders. To successfully build a trading model, the trader must have discipline, knowledge, perseverance,and fair risk assessment.One of the major challenges comes from the trader’s emotional attachment to a self-developed trading strategy. Such blind faith in the model can lead to mounting losses. Model-based trading is about emotional detachment. Dump the model if it is failing and devise a new one, even if it comes at a limited loss and time delay. Trading is about profitability, and loss aversion is built into the rule‑based trading models.