Understanding Trend Analysis and Trend Trading Strategies

Understanding Trend Analysis and Trend Trading Strategies


Trend analysis isn’t a perfect science—the past performance of a stock is not a guarantee of future performance. Additionally, never rely on a single trend-analysis method to make your investing decisions. Using trend analysis is an important aspect of technical analysis, but it’s only one of many tools and techniques available. Forex forecasting software provides technical indicators and trading tools to FX traders. The descending triangle is a chart pattern used in technical analysis.

One that traders commonly use is MA-50, the 50-day moving average. I love it when stocks hit all-time highs without any resistance — especially when they break through previous strong resistance levels. Stock trend analysis is using past price action to predict a future trend.

After years of following markets and investing, diligently trying to understand how and why stocks move, it’s time you put aside the questions of ‘how’ and ‘why’. One of the most hyped stocks in recent memory, TSLA slowly uptrended for months. That changed in late January 2021, when the stock broke both trendlines. It sold off over 30% of its market cap after breaking through the trendline. There needs to be a lot of volume for a stock to have a solid trend.

#5 Use Multiple Indicators

The stock market trend analysis average is the average of the closing prices of a security over a specified number of periods. Moving averages are a smoothing technique that gives the technical analyst a view of market trends. A price move above the long-term moving average is a sign of an uptrend. A price move below the long-term moving average is a sign of a downtrend.

  • Developing a style takes time, effort and dedication, but the rewards can be significant.
  • Besides the different result structure, the datasets that previous works researched on are also different from our work.
  • Then we will feed the data with i selected features into the PCA algorithm to reduce the dimension into j features.
  • Though we did not see the novelty of this work, we can still conclude that the genetic programming algorithm is admitted in stock market research domain.
  • Support and resistance levels aren’t perfect — they’re ranges.
  • Stock market-related data are diverse, so we first compared the related works from the survey of financial research works in stock market data analysis to specify the data collection directions.

They also gather data on the overall performance of the stock market and on the company’s industry. The high-level architecture of our proposed solution could be separated into three parts. First is the feature selection part, to guarantee the selected features are highly effective.


History tends to repeat itself as evidenced by price patterns. After the principal component extraction, we will get the scale-reduced matrix, which means i most effective features are converted into j principal components for training the prediction model. We utilized an LSTM model and added a conversion procedure for our stock price dataset.

Max-min (or min-max) scaling is a transformation method often used as an alternative to zero mean and unit variance scaling. Another well-known method used is fluctuation percentage, and we transform the technical indices fluctuation percentage into the range of [− 1, 1]. After performing the data pre-processing part, the last step is to feed the training data into LSTM and evaluate the performance using testing data. As a variant neural network of RNN, even with one LSTM layer, the NN structure is still a deep neural network since it can process sequential data and memorizes its hidden states through time. An LSTM layer is composed of one or more LSTM units, and an LSTM unit consists of cells and gates to perform classification and prediction based on time series data. After performing feature pre-processing, the next step is to feed the processed data with selected i features into the PCA algorithm to reduce the feature matrix scale into j features.

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According to the basics of technical trend analysis, price changes are always subject to certain rules. Support and resistance levels refer to price levels beyond which the price of an asset will not go in a certain direction. On October 11th, you calculate the moving average for the prior 10 trading days, including the 11th. For example, you add up the prices of IBM stock for the first 10 trading days of October, then divide the total by 10. Commodity and historical index data provided by Pinnacle Data Corporation. Unless otherwise indicated, all data is delayed by 15 minutes.


Commonly used calculated statistical indexes are the put/call ratio, the VIX, and margin debt. Continuation patterns indicate that a market trend that was in place prior to the pattern formation will continue once the pattern is completed. Common continuation patterns are triangles , rectangles , flags, and pennants. Explain technical analysis applications to portfolio management. Here’s a sample of one of the many weekly reports that keep you focused on where the market opportunities are.

One of the main weaknesses found in the related works is limited data-preprocessing mechanisms built and used. Technical works mostly tend to focus on building prediction models. When they select the features, they list all the features mentioned in previous works and go through the feature selection algorithm then select the best-voted features. These behaviors often need a pre-processing procedure of standard technical indices and investment experience to recognize.

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Technical analysis — applying technical indicators to the asset’s price chart. Volume analysis — tracking volume increase, decrease and accumulation. If the price closes above the weekly average, the market will continue to move up for quite a long time afterward, in the opposite case — the market will experience a downtrend. Despite its reliability, the Moving Average indicator can be lagging, which makes it less suitable for medium time frames and short-term trading.

Complexity analysis of proposed solution

Ultimately, changes in supply and demand create trends as market participants compete for the best price. One more approach worth mentioning is graphical market trend analysis, which can be regarded as a part of technical research. As long as prices stay above it in an upward direction or below it in a downward direction, the trend continuation is manifested. Market trend means a certain direction of price movement over a particular period. Trends apply to all assets the cost of which is fluctuating, or when there’s a change in the trading volume.

  • The second algorithm is the LSTM procedure block, including time-series data pre-processing, NN constructing, training, and testing.
  • If so, the stock probably won’t uptrend — even if other indicators are showing that it will.
  • Investors analyzed historical stock charts eager to discover new patterns for use when recommending trades.
  • Normalize method preserves the relative frequencies of the terms, and transform the technical indices into the range of .
  • Detailed technical design with algorithms and how the model implemented are also included in this section.
  • Before the open, the number of buy orders exceeded the number of sell orders and the price was raised to attract more sellers.

However, if the stock is able to rise up through a resistance level on heavy volume, it should be looked at as a sign of strength. The amount of demand for the stock may be so high that it overwhelms the psychological impact of the resistance level. Many investors use this behavior as a buy signal, since it is displaying exceptional strength. Furthermore, once a resistance line has been broken, it tends to become an area of support. Since the stock has proven demand at that level, it should not dip below that level while the current trend is intact. Trend trading strategies attempt to isolate and extract profit from trends by combining a variety of technical indicators along with the financial instrument’s price action.

Trends are identified by drawing lines, known as trendlines, that connect price action making higher highs and higher lows for an uptrend, or lower lows and lower highs for a downtrend. Financial ratios of a listed company are used to present the growth ability, earning ability, solvency ability, etc. Each financial ratio consists of a set of technical indices, each time we add a technical index will add another column of data into the data matrix and will result in low training efficiency and redundancy. If non-relevant or less relevant features are included in training data, it will also decrease the precision of classification. The third research question is that which algorithms are we going to model our data?

Stock Trend Analysis: What It Is + Examples

Try learning how volume and moving averages work together with price action, and then add or subtract indicators as you develop your own system. Below is a good example of a daily chart that uses volume and moving averages, support and resistance levels, multiple indicators, and basic breakout patterns along with price action. It shows how traders might determine support and resistance levels . The volume indicator is below the chart; two moving averages (10-day and 30-day) are drawn over the candles inside the chart. Note the crossover between the two moving averages, which may be a sign that momentum has shifted from bullish to bearish . This fact allows successful traders to analyze the predictable price tendencies of the currencies and other market instruments.

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Detailed technical design with algorithms and how the model implemented are also included in this section. “Results” section presents comprehensive results and evaluation of our proposed model, and by comparing it with the models used in most of the related works. “Discussion” section provides a discussion and comparison of the results. The latest work also proposes a similar hybrid neural network architecture, integrating a convolutional neural network with a bidirectional long short-term memory to predict the stock market index . While the researchers frequently proposed different neural network solution architectures, it brought further discussions about the topic if the high cost of training such models is worth the result or not.

You can probably figure out on your own that a “death cross” isn’t considered to bode well for a stock’s future price movement. There are relative advantages and disadvantages to using different chart construction styles and to using different time frames for analysis. What style and time frame will work best for you as an individual analyst or investor is something that you can only discover through actually doing stock chart analysis. You can glean valuable indications of probable stock price movement from any stock chart. You should choose the chart style that makes it easiest for you to read and analyze the chart, and trade profitably.

It is the opposite of https://trading-market.org/, which is a price range in which price stops rising because of selling activity. Earnings per share – The EPS is useful when companies operating in the same industry need to be compared. A company’s EPS indicates its profitability; hence, traders consider an increasing EPS a good sign. The higher the value of EPS, the more the company shares are worth buying. Fundamental analysis uses different factors such as the current economic environment and finances of the company to estimate its stock value.


Second, we look into the data and perform the dimensionality reduction. And the last part, which is the main contribution of our work is to build a prediction model of target stocks. Figure2 depicts a high-level architecture of the proposed solution. Stock charts can be particularly helpful in identifying support and resistance levels for stocks. Support levels are price levels where you usually seeing fresh buying coming in to support a stock’s price and turn it back to the upside.


For example, Acme Company has been a publicly traded stock for 20 years. During that time, they increased sales at an average rate of 15% per year. Fundamental analysis considers the financial performance of a company. The goal of this analysis is to find a company’s intrinsic value. By the time the trend is identified, a substantial portion of the move has already taken place.

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