---
title: "The Top Ten Technical Indicators for \"Timing\" in A-shares"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/271154848.md"
description: "This article explores the technical indicators of the A-share market, aiming to construct effective timing strategies by segmenting market conditions into five dimensions (price, volume, trend, volatility, and crowding). Ten market observation indicators were selected, and a comprehensive scoring system was used to help investors understand market dynamics. Ultimately, the long-short timing strategy based on technical scoring performed well on the A-share broad-based index"
datetime: "2025-12-31T00:55:40.000Z"
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  - [zh-CN](https://longbridge.com/zh-CN/news/271154848.md)
  - [en](https://longbridge.com/en/news/271154848.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/271154848.md)
---

# The Top Ten Technical Indicators for "Timing" in A-shares

## **Fully Exploit Technical Information to Characterize Market Conditions, Form a Scoring System, and Build Timing Strategies**

This article aims to fully exploit technical information to characterize market conditions and form scoring opinions based on the understanding of the current market state. First, the vague concept of "market conditions" is subdivided into several specific dimensions such as price, volume, volatility, trend, and crowding, and then 10 effective market observation indicators are selected within each dimension; secondly, after equal-weight voting of the signals from the 10 indicators, a comprehensive scoring system between **\[-1, +1\]** is formed to help investors intuitively and timely observe and understand the market, with the scoring results integrating the bullish and bearish strength opinions for the subsequent market operation; finally, based on technical scoring, a bullish and bearish timing strategy is constructed, which has good timing capabilities across most broad-based indices in the A-share market.

## **Five Dimensions to Characterize Market Conditions: Price, Volume, Trend, Volatility, Crowding**

The first step in constructing the market scoring system is to clarify the perspective of market observation. This article starts from the overall data of stock indices and derivatives, initially selecting five dimensions: price, volume, trend, crowding, and volatility to characterize market conditions. Price reflects the current stock index price and return level, with specific observation indicators including momentum, deviation rate, and narrowing channels; volume reflects the size of volume and the relationship between volume and price, with specific indicators including volume, volume deviation, and volume-price correlation coefficient; trend reflects the strength of market trends, with specific indicators including ADX, the proportion of new highs and new lows, and the proportion of days with moving average arrangements; volatility reflects market instability, with specific indicators including price volatility, volume volatility, and implied volatility of options; crowding reflects the convergence of investor behavior, with specific indicators including concentration of trading volume, proportion of limit-up stocks, and options open interest PCR.

## **Indicator Selection and Construction of Single Indicator Timing Strategies**

The process of selecting effective market observation indicators from the above five dimensions is as follows: Step 1: For each indicator, traverse multiple calculation cycles such as daily, weekly, monthly, and quarterly to construct factor sequences; Step 2: Use the Wind All A index as the timing target to construct timing strategies and test the timing ability of the factors; Step 3: For factors with good backtesting performance, examine their economic logic and analyze whether they have tracking observation value. Finally, 10 indicators are selected: 20-day price deviation rate, 20-day Bollinger Bands, 20-day turnover rate deviation rate, 60-day turnover rate deviation rate, 20-day ADX, proportion of days with new highs over 20 days, implied volatility of options, 60-day turnover rate volatility, average proportion of limit-up stocks over 5 days, and average options open interest PCR. The single indicator bullish and bearish timing strategies constructed based on the above factors have certain timing effects on the Wind All A index.

## **Construction of Technical Scoring System and Comprehensive Timing Strategies**

Based on the above 10 indicators, a comprehensive technical scoring system is constructed to form scoring opinions for the future: the 10 indicators first issue bullish and bearish signals according to the factor trends, then the bullish and bearish signals are averaged, with the result falling between **\[-1, +1\]**. The closer the score is to +1, the more bullish the current state is; the closer it is to -1, the more bearish the current state is The technical scoring system helps investors intuitively understand the market and sensitively grasp marginal changes in the market. The scoring results are converted into three timing signals: long, short, and flat, using a threshold of 0.33. The comprehensive timing strategy from January 4, 2010, to December 19, 2025, optimizes the timing points for the Wind All A index by 21%, with a coverage ratio of 1.2 and a win rate of 53%. The Sharpe ratio is 3 times, fully reflecting the ability of technical information to capture major trends. When applied to major broad-based A-shares, all except the Shanghai Stock Exchange 50 show good timing performance.

**Main Text:**

This article's guide: Exploring technical information to depict market conditions.

To depict the stock market conditions, one can approach from multiple angles, including macroeconomics, micro-enterprises, policy guidance, investor sentiment, capital flow, and technical analysis. Among these, technical analysis is widely used; this method predicts future price trends by studying historical market data (mainly price data), assuming that all known and unknown information is reflected in the prices, that price movements follow certain patterns, and that historical patterns will repeat in the future.

Timing models mostly include technical dimensions, but simply using technical indicators often yields poor results. The reasons are as follows: first, the volume-price information synchronizes with the market, making it difficult to make left-side judgments; second, the market often has multiple nested operating modes, which may lead to overfitting or underfitting in depicting historical patterns; third, historical patterns may be broken in the future. Nevertheless, technical information is the most intuitive, immediate, and accurate, making it a good entry point for constructing a market observation system.

This article aims to fully explore technical information to depict market conditions and build a comprehensive analysis system for market technical states. First, the vague concept of "market state" is subdivided into several specific dimensions: price, volume, volatility, trend, and crowding. Then, preliminary observation indicators are selected within each dimension to examine their timing effects. If there are clear and stable operating patterns in these dimensions historically, the relevant indicators can serve as valuable market observation windows and be included in the market state analysis system; if the patterns are unstable or unclear, they will not be included for now.

After a series of single indicator tests, this article selects 10 specific indicators and issues long and short signals based on each single indicator. Furthermore, we conduct equal-weight voting on the long and short signals of the 10 indicators to form a comprehensive analysis system. The final scoring results are represented as values between \[-1,1\]; the closer to +1, the stronger the bullish view on the market; the closer to -1, the weaker the market or the higher the risk, indicating a stronger bearish view. The distribution of multiple indicators is relatively more robust than that of single indicators, and the observation perspective is more comprehensive, allowing for a more sensitive response when the market experiences fluctuations.

## Five Dimensions for Observing Market Conditions: Price, Volume, Trend, Volatility, Crowding

The first step in constructing a market analysis system is to clarify the angles of market observation. This article starts from the volume and price data of the stock and derivatives markets and preliminarily selects five dimensions to depict market conditions: price, volume, trend, crowding, and volatility, and within each dimension, preliminary selections are made based on subdivided indicators: 
-   Price: Reflects the current stock index price and yield level, specific observation indicators include cumulative returns (momentum), price deviation relative to the past period, whether a narrowing channel has formed, etc.;
-   Volume: Reflects the volume situation and the relationship between volume and price, specific indicators include the absolute size of volume, volume deviation relative to the past, volume-price correlation coefficient, etc.;
-   Trend: Reflects the strength of the current market trend, specific indicators include ADX average trend index, the proportion of days with new highs/lows over the past period, the proportion of days with bullish/bearish moving average arrangements, etc.;
-   Volatility: Reflects the market volatility over the past period, specific indicators include actual price and volume fluctuations, implied volatility derived from option prices, etc.;
-   Congestion: Reflects the convergence of investor behavior, specific indicators include the concentration of trading volume in leading stocks, the proportion of stocks hitting the daily limit, the distribution of long and short positions in option contracts, etc.

## Indicator Selection and Single Indicator Timing Effectiveness Testing

Screening method for market observation indicators

Testing the preliminary selection of the aforementioned indicators to filter effective market observation indicators, the process for indicator testing is as follows:

**Step 1: Construct Factor Series**

For each indicator, iterate over N values such as 1 day, 5 days, 20 days, 60 days, 120 days, etc., to obtain specific factor series, such as the volatility of turnover rate over 60 days, ADX over 20 days, etc.;

**Step 2: Timing of Broad-Based Index**

Using the Wind All A index as the timing target, apply this indicator to construct a timing strategy and test the timing ability of the factors. The specific method is: iterate through different factor smoothing windows, strategy lookback windows, and strategy types (including 4 forward strategies and 4 reverse strategies), issue target long and short timing signals, calculate timing net value and statistics; then draw box plots covering the changes in signal types, observing whether the timing coverage is significantly binned under both forward and reverse logic. If the binning is significant and the box distribution is skewed upwards, it indicates that the factor has certain historical timing value and is not sensitive to parameters; if the distinction between forward and reverse strategies is not obvious, or most boxes do not outperform the buy-and-hold index itself, it indicates that the factor does not provide timing gains or is highly parameter-dependent.

**Step 3: Evaluate Strategy Logic**

Examine the economic logic of the indicators and analyze whether they have tracking observation value. Below, we illustrate this with two examples: The proportion of days with new highs within 20 days factor: the forward strategy outperforms the reverse strategy, with clear differentiation. This factor intuitively depicts the market trend continuation characteristics—when the index breaks upward and momentum strengthens, it is suitable to buy; when momentum weakens, it is suitable to sell. Although the overall return distribution of the indicator is not high, the logic is clear and intuitive, possessing tracking value.

The 5% stock turnover ratio factor: the forward strategy outperforms the reverse strategy, with even clearer differentiation, but the strategy reflects the timing logic of "buying when crowded, selling when dispersed," which aligns with certain market phases (such as the short squeeze of popular stocks), but may reverse after the market overheats; during the bull market expansion phase, the rotation of hotspots and dispersed turnover does not necessarily indicate risk, and shorting based on this may miss the market. Therefore, we judge that the timing logic of this indicator is not robust enough, and its in-sample performance may be overfitted to extreme market conditions, so it will not be included in the tracking system for now.

Selection results of market observation indicators

Based on the screening of the above three steps, we finally selected 10 indicators, as follows:

For the above 10 indicators, we constructed timing strategies respectively, and the applicable strategies and the timing logic reflected are summarized in the table below. Most sub-strategies depict market trend continuation characteristics, while a few depict market trend reversal characteristics, which is consistent with our previous testing results of technical indicators—returns generated in trends are higher than the wear and tear generated in sideways markets. Among them, the "proportion of days with new highs within 20 days" and "60-day turnover rate volatility" have significantly higher win rates and odds on the bullish side than on the bearish side, thus optimizing them into a unilateral long strategy, only going long and not shorting. For stocks, which have a certain income-generating ability, asset prices have a long-term upward trend, and shorting should be approached with more caution.

## Single Indicator Timing Effect on Wind All A

This article mainly considers the directional timing of technical indicators on the overall A-share market, thus using the Wind All A Index, which represents the overall market operation, as the main timing target. The above 10 indicators are used to time the Wind All A Index, with the backtesting framework following "Revisiting A-share Timing: Multi-dimensional Integration - 20250529," specifically designed as follows:

-   Backtesting period: 2010/1/1-2025/12/19, some single indicator data may have a shorter availability period, and the starting time will be adjusted accordingly based on the data availability of that indicator.
-   Rebalancing frequency: daily closing price rebalancing, signals sent on day T, using the closing price on day T+1 for rebalancing
-   Signal Rules: Both long and short trading, 1 represents going long (buy), -1 represents going short (sell), and 0 represents a neutral view.
-   Fee Settings: Trading fee rate is 0.05% for both sides, without considering slippage or capital costs.

Next, we will introduce these indicators one by one.

**20-Day Price Deviation Rate**

The factor itself represents the deviation of the closing price from the past 20-day moving average. The strategy adopts a positive trend strategy, buying when the deviation rate rises (1) and selling when it falls (-1). This is equivalent to tracking the second-order change of the price.

**20-Day Bollinger Bands**

Directly applies the Bollinger Bands strategy to the index closing price. A buy signal (+1) is issued when the closing price breaks above the past 20-day average +2 standard deviations, and a sell signal (-1) is issued when it breaks below the past 20-day average -2 standard deviations. The previous day's view is maintained at other times. Bollinger Bands are a classic channel strategy; although they often have a low win rate when used alone, they can sensitively identify price anomalies and grasp major trends within a comprehensive system, providing tracking value.

**20-Day Turnover Rate Deviation Rate**

The factor represents the deviation of the current day's turnover rate from the past 20-day turnover rate average; it adopts a positive trend strategy, buying when the deviation rises and selling when it falls. This is equivalent to tracking the second-order change of the turnover rate.

**60-Day Turnover Rate Deviation Rate**

The current day's turnover rate deviation from the past 60-day turnover rate average adopts a positive trend strategy, buying when the deviation rate rises and selling when it falls. It similarly tracks the second-order change of trading volume.

**20-Day ADX**

First, introduce the factor: ADX stands for Average Directional Movement Index, proposed by technical analyst Welles Wilder The ADX indicator consists of three lines: +DI (Positive Directional Indicator), -DI (Negative Directional Indicator), and ADX. The first two measure the strength of upward and downward trends over a certain period (in this article, the value is set to 20 days), and together they determine the directional index DX, while ADX is the moving average of DX (also set to 20 days in this article).

DX = |(+DI - -DI)| / (+DI + -DI)

ADX itself only describes the strength of the trend without reflecting the direction. When observing the market, we can further construct positive ADX and negative ADX to clarify whether the current trend is upward or downward. In strategy construction, we uniformly adopt a trend strategy that tracks the 20-day ADX, buying when trend strength increases and selling when it decreases.

**Proportion of Days with New 20-Day Highs**

This factor represents the proportion of days within the past 20 days that have reached a new high compared to the previous 20 days. Using a positive trend strategy, we buy when the proportion of new high days increases and sell when it decreases. This is equivalent to tracking the momentum of upward breakthroughs. This indicator performs well on the bullish side, only going long and not short.

**Implied Volatility of Options**

Track the trend of implied volatility of options; buy when it rises and sell when it falls. Although this factor uses a positive trend strategy, implied volatility itself reflects market instability. An increase in implied volatility indicates panic in sentiment; buying at this time aims to capture rebounds after overselling. Essentially, this is a reversal signal that takes advantage of irrational pricing in the options market.

**60-Day Turnover Rate Volatility**

In the previous section's factor selection, we found that the volatility of turnover rate and trading volume significantly outperforms the volatility of prices themselves in timing ability. Therefore, in this dimension of volatility, we did not use the volatility of index prices but rather the volatility of trading volume and the implied volatility inferred from options.

The 60-day turnover rate volatility factor represents the standard deviation of the turnover rate over the past 60 days, depicting the volatility of trading volume. In backtesting, it performs better on the bullish side, only going long and not short. Analyzing its timing logic: trading volume generally leads price; before price movements (especially significant increases), volume may react in advance. Observing the volatility of turnover rate can capture changes in trading volume, thus seizing market opportunities 
**Percentage of Limit-Up Stocks in Index Components 5-Day Average**

In the past 5 days, the average percentage of limit-up stocks among index components indicates strong stock performance, trading overflow, and market liquidity that cannot meet the buying demand for these stocks, creating a certain degree of crowding. From another perspective, an increase in the proportion of limit-up stocks also signifies enhanced market momentum. Tests have shown that limit-up stocks have better timing ability than limit-down stocks; therefore, we discard information related to limit-down stocks and focus solely on the upward side, adopting a positive trend-following strategy for this indicator.

**5-Day Average of PCR for Options Open Interest**

The 5-day average of the put-call ratio (PCR) for 50ETF options shows that historically, this factor has a negative correlation with market trends. During market uptrends, call options are consumed while put open interest accumulates, leading to an upward movement in this stock ratio indicator. Backtesting suggests that this indicator should adopt a contrarian strategy, meaning the higher the stock ratio indicator, the more bearish the outlook.

## Market Scoring System and Comprehensive Timing Strategy Construction

Technical scoring system for reviewing market trends over the past two years

The above 10 indicators first depict the current operational state of the market (whether prices are high or low, whether trading volume is large or small, volatility levels, trend strength, etc.); secondly, through timing strategies, they grasp market operational rules, converting "factors" into "signals," which represent future bullish or bearish views. Ultimately, we construct a comprehensive technical scoring system based on these 10 indicators, which scores future performance based on the understanding of the current state. The specific synthesis method is to average the bullish and bearish signals of the 10 indicators, resulting in a score that falls between \[-1, +1\]. The closer the score is to +1, the more bullish the current state; the closer it is to -1, the more bearish the current state.

Since most of the data used is price and volume data, this technical scoring system primarily focuses on trend following. When the market is strong, it tends to continue to go long; when weak, it tends to continue to go short. However, a small number of indicators also depict reversal characteristics, used in conjunction with trend characteristics. After combining multiple signals, this scoring system is not merely a depiction of the current market state but also contains views on the strength of future bullish or bearish trends. An advantage of the technical scoring system over other dimensions such as fundamentals and sentiment is that it mainly uses price and volume data, which is easy and efficient to obtain, with rich sub-dimensions that closely follow the market, allowing for a sensitive grasp of marginal changes in the market Help investors intuitively understand the market.

As shown in the figure below: Since 2024, the market has declined at the beginning of the year, but the technical score has gradually increased, indicating that the market may be oversold, with subsequent reversal momentum; in February, the market bottomed out and rebounded, with scores first accumulating in line with the trend, then declining against the trend in late February, signaling a potential peak; from March to May, the market fluctuated, with scores hovering between -0.4 and 0.4; from May to July, the market declined, and the technical score surged during the July fluctuation phase, suggesting a buying opportunity, but the market did not cooperate, and subsequently, from July to September, the market continued to weaken, with scores decreasing accordingly, but again surged at the end of August and early September, indicating a buying window; after "924", the market score reached an extreme value but quickly declined in early November, reflecting a temporary slowdown in upward momentum. From January to May 2025, the market fluctuated, with scores hovering between -0.4 and 0.6, with signal changes primarily following short-term trends; from June to August, the market established an upward trend, with scores surging, reaching an extreme value again in late August, which was also the smoothest phase of the market's upward trend, and defensive indicators such as valuation had not yet issued overheating signals; at the end of August and early September, the market adjusted, and the technical score quickly declined, shifting from optimism to caution (meanwhile, multi-dimensional timing models' indicators for valuation, sentiment, etc., also issued phase defensive signals); after mid-October, scores turned negative, and the market shifted from an upward trend to fluctuations, with prices remaining high, but the changes in scores clearly observed the switching of market states.

## Build a comprehensive timing strategy and extend it to mainstream broad-based A-shares

Convert the above technical scoring results into long-short timing signals, with the following rules:

-   When the score is greater than +0.33, bullish;
-   When the score is between -0.33 and +0.33, neutral;
-   When the score is less than -0.33, bearish;

The above scoring scheme has three values: 0, 1, and -1, representing bullish, neutral, and bearish views, respectively, with the probabilities of the three views being roughly balanced (since the two indicators only favor bullish, the probability of being bullish will be slightly higher than bearish), setting a neutral value range that balances the continuity and sensitivity of the signals, and is more in line with investors' perceptions of the market (upward, downward, fluctuation).

Applying the comprehensive timing signals to the main broad-based indices of A-shares, backtesting results show that the comprehensive timing signals have significant timing effects on most broad-based indices, covering above 0.7; for the Wind All A, CSI 300, CSI 500, CSI 800, CSI 2000, and ChiNext Index, the timing coverage exceeds 1; however, for large-cap indices such as the SSE 50, the effect is not good. Overall, this system has good generalization ability for most broad-based indices except for the SSE 50, but the timing returns still rely heavily on capturing major trend movements, which is a common issue in technical analysis Due to the fact that factor calculations are primarily based on the price and volume information of the timing targets themselves, there are slight differences in scores among different broad indices at the same point in time. This not only allows for the characterization of market strength and weakness over time but also enables horizontal comparisons among different broad indices. 

Reviewing the aforementioned timing system, we find that the model captures large trend markets well (such as in 2014-2015, September 2024, and June-August 2025), while its ability to generate profits in volatile markets is relatively poor. The reasons for this are twofold: first, the two dimensions of volume and price in the sub-indicators directly track trends, with the Bollinger Bands being a typical representative. Such strategies often focus more on odds rather than win rates, exhibiting characteristics of "small losses in volatile markets and large gains in trend markets"; second, the threshold settings are relatively strict, issuing signals only when the scoring results show a clear tendency. In volatile markets, positions are not easily opened, which avoids risks but also misses out on some profits.

Therefore, we consider signal version two: removing the four factors of volume and price dimensions, retaining only the six factors of trend, volatility, and crowding; and relaxing the signal threshold to 0. The performance is as follows: Signal 2 shows an improvement in win rates compared to Signal 1, while the odds have decreased. Due to the increased opportunities to open positions, returns, volatility, and drawdowns have all amplified, leading to an overall improvement in Sharpe and Calmar ratios.  From the net value trend and annual returns, Signal 2 shows improvements over Signal 1 in negative return years such as 2012, 2017, 2021, and 2023, indicating an increase in return stability. In comparison, Signal 1 is a typical technical indicator system that focuses more on capturing large trends; Signal 2 sacrifices some trend returns to reduce wear in volatile markets. 
## Summary

The main work of this article is to fully explore technical information to characterize market conditions. First, the vague concept of "market conditions" is subdivided into several specific dimensions such as price, volume, volatility, trend, and crowding, with a total of 10 specific observation indicators selected within each dimension; second, a technical scoring system is constructed to help investors intuitively and comprehensively understand and observe the market; third, a comprehensive timing strategy is developed, which has good adaptability across most broad indices in the A-share market. However, this timing strategy overall emphasizes technical dimensions and relies on capturing large trend markets. In practical use, it is recommended to combine it with fundamental, valuation, sentiment, and capital dimensions Risk Warning and Disclaimer

The market has risks, and investment should be cautious. This article does not constitute personal investment advice and does not take into account the specific investment goals, financial situation, or needs of individual users. Users should consider whether any opinions, views, or conclusions in this article are suitable for their specific circumstances. Investment based on this is at one's own risk

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