Technical Analysis Trading Strategy (Rules, Backtest And Example)

Technical analysis strategy is a popular way of analyzing and forecasting price movements of financial assets such as currencies, stocks, commodities, bonds, and cryptocurrencies. While this strategy is as old as modern financial markets, not many know what it is and how it works. What is a technical analysis strategy?

Technical analysis strategy is the use of past and present price data to analyze a financial market and predict the likely future movement. It can be done by analyzing the price movement themselves or with the help of technical indicators, which are mathematical representations of the price data. This strategy may involve the use of trend-following tools like moving averages, and momentum-based tools like stochastic to identify entries and exits in the market.

In this post, we answer some questions about the technical analysis and we end the post with a backtest.

What is a technical analysis trading strategy?

Technical analysis strategy is a method of analyzing and forecasting the price movement of an asset using past and current price and volume data. It involves the study of past prices and volume data, together with different technical indicators to identify trends and patterns that can be used to make trading decisions.

However, there is no precise definition, and trader A can define it differently than Trader B.

The strategy might be based on the concept that price patterns, trends, and technical indicators. The main idea is to provide valuable information into market psychology and help traders predict future price movements.

One of the theories of technical analysis is that the price of an asset tends to trend, and another is that the price has a mean-reversion tendency. Thus, technical analysis strategies can mainly be categorized into trend-following and mean-reversion strategies.

• Trend Following Trading Strategies and Systems Explained (Including Backtest and Statistics)
• Do Trend Following Trading Strategies Work?

Trend following and mean reversion complements each other well, and hence can be used in a portfolio of trading strategies.

Trend-following strategies involve the identification of a trend in the price of an asset and then buying or selling to profit from the trend. For instance, a trader might employ the use of a moving average to identify an uptrend and then buy when the price is above the moving average or sell when the price is in a downtrend and below the moving average. The 200-day moving average strategy is an example of a trend following strategy.

Mean-reversion strategies involve identifying a particular level that an asset tends to pull towards and then buying or selling when the price wanders too far from that level. This is based on the concept that price tends to revert to its mean after an explosive move. A buy the dip strategy is an example of a mean reversion strategy.

It is important to note that technical analysis does not measure the intrinsic value of an asset, but instead, uses charts and other tools to identify patterns that can forecast future price movements. Fundamental analysis is mostly ignored, however, it can be paired with fundamental analysis, which focuses on the current economic outlook that may affect the future price of an asset.

What types of technical analysis strategies exist for trading?

There are several technical analysis strategies that you can employ. Below we list the most common types of strategies.

• Trend-following. This strategy involves the identification of the overall trend in a given market and then using this trend to make buying and selling decisions.
• Mean-reversion strategies. This involves identifying a particular level that an asset tends to pull towards and then buying or selling when the price wanders too far from that level. This is based on the concept that price tends to revert to its mean after an explosive move.
• Momentum strategy. This uses momentum indicators, such as MACD, RSI, stochastic, and co to measure the price momentum and trade along that direction.
• Breakout strategy. This involves trading when the price breaks out of support or resistance level.
• Chart patterns. This involves identifying specific patterns on the price chart and using them to predict future price movements. An example of a chart pattern is the heads and shoulders pattern.

How can I identify a profitable trading strategy using technical analysis?

There are a few steps you need to make in order to trade successfully. Here are a few important steps you can follow:

• Define your trading goals and risk tolerance. You can use this to decide what kind of plan is best for you. For instance, if you have a high-risk tolerance, you might feel at ease using a technique that has a higher chance of loss but the possibility for bigger rewards. However, most traders have a lower risk tolerance than they realize. When you get losses and drawdowns, you are much more exposed to trading biases in trading.
• Use technical indicators. Technical indicators can aid in the identification of prospective trade setups by providing information on the strength and direction of a trend, as well as potential reversal points. Moving averages, the relative strength index (RSI), and the stochastic oscillator are all popular technical indicators.
• Backtest your strategy. Once you’ve identified a suitable trading technique, you should put it to the test to check if it’s realistic. Backtesting the approach using historical data to evaluate how it works is a necessary step in finding a profitable strategy. We have written a guide in how to backtest a strategy.
• Monitor and optimize your strategy. As market conditions change, you may need to modify your technique to continue making profitable trades. It is critical to examine and monitor your plan frequently to verify that it is still effective. Trading is all about feedback loops and learn from the past. One of the best tools to learn trading is to use a trading journal.

Related reading: Does Technical analysis work?

What are the advantages of using technical analysis for trading?

Some potential advantages of using technical analysis for trading include:

• It is easy to code into a trading algorithm. Technical analysis is often based on price and volume data and mathematical formulae, which makes them easy to be converted into a trading algo. Coding into a trading platform is not as daunting as it may sound. We have been able to do it, and that means most others also can! It just requires a few days of learning to get started, and from then you learn gradually.
• It can be applied to any security. You can use it on any security that has historical price data, such as stocks, bonds, currencies, and commodities. We recommend trading many instruments.
• It can be used to make both short-term and long-term trades. Depending on the period of the chart being reviewed, technical analysis can be used to make both short-term and long-term trades.

• Long-Term Trading Strategy example
• Short-Term Trading Strategy example

How can I create a comprehensive trading strategy based on technical analysis?

Let’s look at how you can develop a trading strategy:

• Determine the market in which you want to trade. Think about your level of experience, risk tolerance, and the time you have available to commit to trading.
• Determine your time frame. Technical analysis may be performed on different time frames. Choose a time range that corresponds to your style of trading and risk tolerance. We believe the daily time frame is best for trading.
• Select the technical indicators you like: Choose from a variety of technical indicators, including moving averages, the relative strength index (RSI), and the Moving Average Convergence Divergence (MACD). Experiment with several indicators to determine which ones work best for your plan.
• Establish trade entry and exit points. Use technical analysis to discover probable trade entry and exit points. Look for chart patterns such as head and shoulders or triangles, or utilize indicators such as the RSI to identify overbought or oversold circumstances.
• Backtest your plan. Test your strategy using historical data to see how it might have fared in the past. This will allow you to spot any flaws and make necessary improvements before trading with real money. If a strategy has not worked in the past, it’s unlikely it will work in the future.
• Implement risk management. Risk management is an integral component of every trading strategy. To assist minimize risk, consider elements like position sizing and stop-loss orders.
• Examine and improve your strategy. Review and evaluate the effectiveness of your approach regularly. Be ready to make changes as needed in response to changing market conditions or your own evolving trading style.

Related reading: Technical Indicators Strategy

What indicators should I use to identify trading opportunities?

There are different trading indicators you can use to discover trading opportunities.

However, the type of indicator you use is determined by the approach you are employing. Moving averages, for example, can be useful if you are a trend trader.

Oscillators are widely employed by short-term traders to identify market extremes such as overbought and oversold conditions. Other indicators include On-balance volume, Williams’ Percent R, Alligator, Ichimoku cloud, etc. Williams %R is a very good indicator.

How should I backtest a trading strategy based on technical analysis?

• Determine how much data you need. You must choose how far back in time you want to test your plan. It could take days, weeks, months, or even years, depending on the strategy. An intraday strategy may require less than a year’s data to get a good sample size, while a position trading strategy may require more than 10 years of data. Perhaps more important, is to include at least one bear market.
• Source your price data. For the assets you intend to trade, you will need to gather historical pricing information. This is frequently available for free from several sources, including Google Finance and Yahoo Finance. But be aware of bad quotes. In trading, garbage in equals garbage out!
• Write the trading algorithm. Use the right programming language for the platform you are using to write the trading algo, specifying the entry and exit rules. Please read our guide about algorithmic trading strategies.
• Execute the backtest. To simulate past trades, use your pricing data and strategy execution. If you have to tweak and optimize your strategy, you will need to divide your data into in-sample and out-of-sample groups.
• Analyze the results. You can examine the backtest data after it is finished to determine how well your strategy performed. Evaluate the performance indicators, such as win rate, returns, maximum drawdown, and Sharpe ratio.

What is backtesting technical analysis?

Backtesting technical analysis involves applying trading strategies to historical market data to evaluate their effectiveness. The purpose is to see how a strategy would have performed in the past, providing insights into its potential future performance. This helps traders refine their strategies by identifying strengths and weaknesses, testing various scenarios, and optimizing parameters before risking real capital. Backtesting can reveal whether a strategy is profitable, its risk levels, and the frequency of trades. It is a critical step in developing robust trading systems, ensuring strategies are data-driven and not based on intuition alone.

What are the most reliable entry and exit points for a trading strategy?

Your entry and exit points are determined by your trading strategy and there is no most reliable entry and exit. For example, if you are using the moving average crossover strategy, you buy when the fast moving average crosses above the slow moving average and exit when it crosses below.

When using a technical analysis strategy, it is important to clearly state your entry and exit conditions and make sure to adhere to them. For any strategy, the most reliable entry and exit points would depend on what your backtesting results show.

If you are a mean reversion trader, you might want to consider the QS exit sell signal of when to sell.

What tools should I use to analyze a trading strategy based on technical analysis?

• Charting software. You may visualize price movements and other data using charting software, such as TradingView, TradeStation, and MetaTrader. We prefer Amibroker and TradeStation. Please have a look at our Amibroker review and Amibroker course.
• Backtesting software. This can be used to find out how the plan would have performed in the past.
• Your trading journal. This is where you maintain a record of your trades, including the reasons you entered and exited them, if you are using a manual strategy. For an automated strategy, the system takes a record of the trades and gives you the necessary data for your assessment.

How can I optimize a trading strategy based on technical analysis?

• Backtesting. This involves evaluating the performance of the strategy using historical data. This will enable you to spot any strategy flaws or shortcomings and make the appropriate corrections.
• Tweaking the parameters. You can experiment by changing the parameters of the components of the strategy to see if the strategy performs better.
• Trying other timeframes: Test the same technique on other time frames (such as daily, hourly, and 15-minute charts) to determine which one performs the best.
• Including risk management. Risk management should be taken into account. This may involve having stop-loss orders and adjusting position sizing.

Please also read our article that show you how to optimize a trading strategy. We recommend optimizing so you get a better grasp of what is driving the returns.

What types of datasets should I use for backtesting a trading strategy?

You can make use of historical price data to backtest your trading strategy. However, be aware that a strategy may perform well in backtesting and do poorly in live trading due to curve fitting. To avoid this, divide your data into in-sample and out-of-sample data if you need to optimize the parameters of the strategy.

Perhaps better, put your trading strategies on hold or in incubation for many months, perhaps a year, before you start live trading. You can use a demo account for this.

• Out of sample backtesting tutorial

How can I determine if a trading strategy based on technical analysis is profitable?

You may backtest a technical analysis-based trading strategy using historical data to examine how it would have performed and ascertain whether it is profitable.

To check how the strategy operates in actual market conditions, you can also forward-test with a demo account. Some of the performance metrics to assess include profit factor, risk-to-reward ratio, and win rate.

What technical analysis techniques should I use to develop a trading strategy?

Anything that shows an exploitable inefficiency in the market can be the basis of a trading strategy. It could be a specific price action pattern, a time of the day, technical indicators, or any other thing. If you find out that the price moves a particular way if a 2-period RSI reaches a certain level, then that becomes the technique for your strategy.

What are the most important considerations when backtesting a trading strategy?

• Data frequency. The frequency of the data (e.g., daily, hourly) can impact the results of your backtest.
• Starting and ending points. The starting and ending points of your backtest can impact the results. Be sure to choose a representative period to test your strategy. The longer the better, and also make sure you include different market sentiments, for example bull and bear markets.
• Slippage and commissions. Your backtesting result is unlikely to include those factors, so keep them in mind. We made a guide about trading commissions.
• Multiple testing. It can be helpful to test your strategy on multiple markets or periods to ensure that it is robust and has a consistent track record. But don’t expect a strategy to perform well on all assets. Forex is, for example, very different from stocks.

How can I apply technical analysis to evaluate a trading strategy?

You can use technical analysis to assess a trading strategy by looking at past price data to spot patterns and trends and utilizing indicators to gauge how strong these trends are. Your plan can also be back-tested to determine how well it might have worked in the past.

Related Reading: Indicators for Technical Analysis

What is the best way to test the effectiveness of a trading strategy?

The easiest way to determine whether a trading strategy is effective is to backtest it using historical data and then forward-test it using real-time data to determine whether the results are reliable. Additionally, you can forward-test it with a demo account to see how it performs in live market circumstances.

What type of data should I use when testing a trading strategy?

Use high-quality data that applies to the trading technique you are testing when evaluating it. Depending on the sort of approach you are implementing, this may contain price data, volume data, and economic data.

Additionally, it’s critical to employ a significant amount of data to accurately assess the effectiveness of the strategy.

How can I determine the risk associated with a trading strategy?

To evaluate the risk-return tradeoff of the strategy, you may also use risk metrics like the Sharpe ratio, maximum drawdown, Jensen’s alpha, and so on.

We have a written about all the main metrics used in backtesting and trading performance:

• Win ratio in trading – what it is and why it is important (winning ratio)
• Trading strategy and system performance metrics (What is it and how to use it)
• What Is A Good Equity Curve? – Profit & Loss Curves Best Practices
• The Sharpe Ratio Explained (What is a good Sharpe Ratio? Examples)
• The profit factor explained (what is a good profit factor in trading? Examples of profit factors)
• What is K-Ratio?
• Treynor Ratio, how to calculate it: What is it and what is good?
• Jensen Ratio – what is it and how is it calculated? (Jensen’s Performance Index)
• Sortino Ratio – what is it and how do you use it?
• Ulcer Index — What Is It?

Technical analysis strategy backtest

Let’s end the article with a simple backtest of the most popular trading indicator- the Relative Strength Index (RSI).

We make the following trading rules:

• When the 3-day RSI drops below 15, we go long.
• We sell when the close ends higher than yesterday’s high.

This simple strategy has returned the following equity curve for Nasdaq 100 (the ETF that tracks Nasdaq-100 is QQQ):

There are only 201 trades, but the average gain per trade is a solid 1.26%. The win rate is 72% and max drawdown is 19%. 100 000 invested in year 2000 is worth 1.1 million today, which equals 10.8% annual returns despite being invested only 12.8% of the time.

The full performance report looks like this:

Worth noting is the risk-adjusted return, which is the annual return divide by the time spent in the market.

Let’s end the article by looking at the monthly and annual returns:

Even though this is a long-only strategy, it has performed spectacularly during bear markets!

FAQ:

How can I identify a profitable trading strategy using technical analysis?

To identify a profitable trading strategy, traders should define their goals and risk tolerance, use technical indicators for analysis, backtest the strategy with historical data, and continually monitor and optimize the strategy based on changing market conditions.

How can I create a comprehensive trading strategy based on technical analysis?

To create a comprehensive trading strategy, traders should determine the market and timeframe, select relevant technical indicators, establish entry and exit points, backtest the strategy, implement risk management, and regularly examine and optimize the strategy.

How should I backtest a trading strategy based on technical analysis?

Backtesting involves using historical data to simulate past trades and evaluate a strategy’s performance. Traders should determine the data frequency, source high-quality price data, write the trading algorithm, execute the backtest, and analyze the results.