EXPLORING PROFITABLE OPPORTUNITIES: ANALYSING TECHNICAL INDICATORS COMBINATIONS FOR PROFITABLE TRADING

How to cite this paper: Mukund Harsha, A., & Kesava Rao, V. V. S. (2024). Exploring profitable opportunities: Analysing technical indicators combinations for profitable trading. Corporate & Business Strategy Review , 5 (1), 148 – 160.


INTRODUCTION
This research assesses trading strategies using criteria like trade count, return (%), average return per trade, and Sharpe ratio.It offers a thorough evaluation of the effectiveness of SMA, OBV, and CCI indicator combinations.The main contributions include guiding traders and investors in selecting and optimizing indicators for profitable trading, as well as providing insights into the pros and cons of various combinations, empowering more strategic and informed trading decisions in financial markets.
The objectives of the study are as follows: 1) Explore combinations of indicators that offer higher profits with fewer trades; 2) Assess market applicability by analyzing selected technical indicators (SMA, OBV, CCI) in the context of NIFTY 50 companies and comparing returns with undervalued companies.
3) Evaluate long-term performance: assess the sustainability and robustness of indicator combinations over 10 years.
The rest of the paper is structured as follows.Section 2 reviews relevant literature, summarizing key findings on technical indicators and trading strategies.
Section 3 presents the detailed methodology, including data collection, indicator selection, and levels.Section 4 offers comprehensive research results, demonstrating the performance of various trading strategies.In Section 5, discussions interpret findings and explore practical implications.Finally, Section 6 provides a conclusive summary, highlighting the study's main contributions and underscoring the importance of considering riskadjusted returns in evaluating trading strategies.2) Previous studies often concentrate on specific markets or sectors.This research expands the scope to include NIFTY 50 firms and assesses returns in comparison to undervalued companies.

LITERATURE REVIEW
3) While some studies explore short-term performance, this research delves into the long-term effectiveness of trading strategies using technical indicators.

RESEARCH METHODOLOGY
This study employs a methodology to evaluate trading strategies using technical indicators, specifically SMA, OBV, and CCI, chosen for their practical effectiveness in real-world trading scenarios.These indicators consistently generate fewer but more reliable signals with higher returns.Historical data and simulations provide a comprehensive view of indicator performance under various market conditions, with performance metrics like trade count, total returns, and the Sharpe ratio used to measure strategy success and assess risk-adjusted returns.The research aims to guide traders and investors with evidence-based insights into indicator combinations through rigorous analysis, considering trade count, profit/loss, profit percentage, average profit per trade, and the Sharpe ratio.The methodology also analyses performance measure distribution to identify optimal indicator levels, highlighting the importance of risk-adjusted returns.
This research methodology is grounded in established financial research practices and builds upon previous work by Naved and Srivastava (2015b).It extends and deepens the exploration of CCI, SMA, and OBV, providing valuable insights for traders and investors.This methodology adds to the existing knowledge in this field and offers empirically supported conclusions on the effectiveness of these technical indicators for market participants.Table 1 categorizes the most significant technical indicators, facilitating their comprehension and application within this comprehensive approach to assessing the effectiveness of technical indicators in identifying profitable trading opportunities.

Data collection
The aim of data collection is on individual stocks within the NIFTY 50 index of India and selected undervalued companies, rather than stock indices.
To assess the indicator's profitability, back-testing is conducted on these two distinct groups.The 50 undervalued companies are chosen from the authors' prior research (Mukund Harsha et al., 2023) using a random forest machine learning algorithm, based on high valuation scores derived from ten years of fundamental data.The dataset comprises daily stock price data and trading volumes.Trade simulations are executed using Python programming.The initial analysis covers 47 combinations over 365 historic trading days, with further evaluation of the top 5 combinations performed on 10-year historic data involving 100 selected companies.

Indicator selection
The  = (  − ) (0.15 ×  ) (1) where: • Typical Price is the average of the high, low, and close prices.
• SMA is the Simple moving average of the Typical Prices over a specified period 'p'.
• Mean Deviation is the average deviation of the Typical Prices from the SMA over 'p'.
• Specified Period (p), a 3-day period, is considered for a shorter rolling window.
• The value 0.015 is a constant multiplier used to ensure that approximately 70-80% of CCI values fall between -100 and +100, thereby defining overbought and oversold levels.

On-balance volume (OBV)
OBV is a leading volume indicator used to measure the market's cumulative buying and selling pressure based on trading volume.It helps traders identify the strength of a price trend and potential trend reversals.The OBV is a leading volume indicator given in Eq. ( 2): where: •   is the previous value of the On-balance volume.
•  is the trading volume for the current period.

Simple moving average (SMA)
SMA is a lagging trend indicator employed to recognize price trends and potential support and resistance levels.It computes the average price over a defined period, effectively smoothing out shortterm price fluctuations.The study considered two SMAs, namely   given in Eq. ( 3), and  ℎ as given in Eq. ( 4), to identify price trends: where: • m is a rolling window period for a long moving average (21 days in present work).
• n is a rolling window period for a short moving average (9 days in present work).

Indicator levels
The three chosen technical indicators (CCI, OBV, and SMA) have the following levels.
The CCI levels: • If the CCI level is above 100, it indicates an uptrend and is coded as '1'.
• If the CCI is between -100 and 100, indicating a trend-neutral state, it is coded as '0.5'.
• If the CCI level is below -100, it indicates a downtrend and is coded as '0'.
The OBV levels: • Four out of five days with increasing OBV represent an uptrend and are coded as '1'.
• Three out of five days with increasing OBV indicate a trend-neutral state, it is coded as '0.5'.
• Two or fewer days with increasing OBV signal a downtrend and are coded as '0'.
The SMA levels: and is coded as '1'.
• If  ℎ <   it indicates a downtrend and is coded as '0'.

Trade signal generation
Trading signals are generated based on entry conditions.Exit conditions employ a 10% margin Trailing Stop loss strategy, triggering when the price drops to 90% of the entry price or when a book profit signal is generated.This book profit signal is activated when the price falls to 90% of the highest price after reaching new highs due to a trend shift, preventing further capital depreciation.The sequential buy-sell condition, tailored for small-cap investors, is applied.It generates a subsequent entry signal only if an exit condition is met, completing a trade.Sample trades are detailed in Table 3.

Performance evaluation
The performance measures are examined to find combinations that provide higher returns with fewer trades.The graphs shown in Figure 1 and Figure 2 compare the performance metrics of the NIFTY 50 stocks and the undervalued 50 stocks.Trades for a single stock are summarized in Table 4, with 365 trading day's duration.A total of 100 stocks were analysed for 365 trading days and the results are compared to 10-year performance in the following sections.

Trade count
A box plot graph is used to visualize the total number of trades executed by each combination.Figure 1 displays the distribution of total trades over 365 days, while Figure 2 presents the distribution of total trades spanning a period of 10 years.It can be observed from Figure 4 the total returns for a 10-year period for each indicator combination, that in a longer time horizon, the negative returns for undervalued 50 stocks are higher for some indicator combinations while NIFTY 50 companies give less than 25% of instances with negative total returns (%).Here the Indicator Numbers 24, 27, 42, 43, and 44 show fewer negative returns for undervalued 50 stocks.

Average return (%) per trade
The distribution of average return (%) per trade for 365 days is shown in Figures 5 and 6 showing the long-term average return per trade.Figure 6 shows that the indicator combinations that generate fewer trades, in general, have higher average returns per trade.Most of the NIFTY 50 stocks show fewer negative returns.However, undervalued 50 companies show higher positive in comparison to negative returns for a majority of indicator combinations.

Performance analysis of top 5 indicator combinations
From the performance evaluation of the indicator combinations, five indicator combinations are identified to perform better than the rest.The indicator combination numbers 24, 27, 42, 43, and 44 are found to give better returns in fewer trades.It is also identified that these indicators can be used to identify entry points into long-term investing in undervalued stocks.The 10-year performance of the top 5 indicator combinations is analyzed in this section.Figure 7 shows the 10-year total trade count distribution of five best-performing indicator combinations.The total returns of indicator combinations from Figure 8 show that the positive returns of the undervalued 50 stocks are much higher than the positive returns of NIFTY 50 stocks.While the negative returns of NIFTY 50 stocks are lower than undervalued 50 stocks the difference is not as diverse as the positive returns.The average return (%) per trade of the best indicators combination shows that the positive returns of undervalued 50 stocks are much higher than the positive returns of NIFTY 50 stocks.The negative returns of both NIFTY 50 and undervalued 50 companies are both present, but they are comparatively lower.
Exploring alternative technical indicators is of paramount importance to gain a comprehensive understanding of trading strategies.Different indicators have specific advantages based on varying market conditions.Researchers can diversify their methodology by considering alternative indicators such as Bollinger Bands, MACD, or RSI.Furthermore, future studies could enhance their scope by collecting data from a more extensive range of stocks, encompassing both large-cap and small-cap companies.This approach would provide valuable insights into the performance of indicator combinations across diverse market segments.

RESULTS
The combinations 24, 27, 42, 43, and 44 outperformed others in terms of profitability and risk-adjusted returns.The average return per trade distribution is analyzed to understand the trade-offs between positive and negative returns.The following section shows which among them is the best of all 47 combinations.
The Sharpe ratio is a measure of risk-adjusted return that helps investors evaluate the return generated by an investment relative to its volatility.A positive Sharpe ratio indicates that the investment or portfolio has generated returns above the riskfree rate.It implies that the investment has compensated investors for the risk taken.Figure 10 shows the Sharpe ratio distribution for 10 years for the considered 100 stocks.Figure 10 provides a concise visual representation of the Sharpe ratio distribution across various indicator combinations.Notably, Indicator Combination 43 stands out with a notably superior Sharpe ratio distribution, boasting a higher ratio of positive to negative Sharpe ratios compared to the other combinations.This compelling finding highlights combination 43 as an appealing choice for investors seeking a balanced blend of returns and effective risk management.
Complementing the insights from Figure 10, Table 5 offers a comprehensive summary of the top 5 indicator combinations' performance.This summary, based on percentile-wise returns, enables investors to assess the relative return potential of these combinations.Presented in a tabular format, Table 5 simplifies the comparison of the top-performing combinations, further aiding investors in making well-informed decisions when seeking profitable trading opportunities.

DISCUSSIONS
Table 5 provides a comprehensive summary of the performance of the top 5 indicator combinations in this work.Based on percentile-wise returns, this summary offers investors the means to assess the relative return potential of these combinations.Presented in a tabular format, Table 5 simplifies the comparison of the top-performing combinations, further assisting investors in making well-informed decisions when seeking profitable trading opportunities.Employing Python programming, the distribution of returns was meticulously summarized.This analysis involved excluding the top and bottom 1 percentile returns for each indicator combination, with a specific focus on the 98th percentile returns, revealing the following outcomes.
Among the 47 indicator combinations examined, the research identifies five combinations that consistently yield more positive returns than negative returns, with fewer trades.The results highlight five indicator combinations consistently generating more positive returns than negatives, with the best (combination 43) achieving an average return per trade distributed between 0 to 30% (50% of trades), 30 to 70% (25% of trades), and less than 25% of trades incurring negative returns of up to -10%, as illustrated in Figure 9. Furthermore, this study highlights the significance of risk-adjusted returns, quantified by metrics such as the Sharpe Ratio, emphasizing that trading strategies should consider both profitability and risk management.With an extensive analysis spanning a decade and involving a substantial number of trades (8,50,209), the findings exhibit robustness over a significant period.
The findings of the study highlight Indicator Combination 43 as the most promising choice, as it consistently delivers positive results based on trade count, average return per trade, and the Sharpe ratio.This combination offers an attractive balance between profitability and risk, making it an appealing option for traders.Its specific criteria, including a CCI value above 100, declining OBV over the past five days, and an uptrend in SMA with  ℎ positioned above   , provide valuable insights for informed decision-making.
Indicator Combination 42, on the other hand, stands out as a compelling choice for traders seeking profitable opportunities under specific market conditions.Its criteria, such as a CCI value above 100, decreasing OBV over the past five days, and a downtrend in SMA with  ℎ below   , can aid in identifying potential trade entries in relevant scenarios.
Notably, Indicator Combination 24, despite a neutral OBV value when the CCI is above 100, still offers promising returns, suggesting its potential in a broader range of market conditions.Indicator Combination 27, focusing on situations with a CCI above 100 and decreased OBV over the past five days, also demonstrates robust performance, catering to traders who prefer specific technical conditions.
The analysis of Indicator Combination 44 underscores the importance of understanding associated risks, as it performs well in certain instances despite significant negative returns.
This study underscores the significance of using multiple technical indicators and considering various market conditions to develop effective trading strategies.Combining indicators enhances the decision-making process, improving the accuracy of buy and sell signals.While this research provides valuable insights, it is crucial to acknowledge its limitations, including the reliance on historical data and past performance.Future studies may explore real-time applications and adaptability to evolving market conditions.

CONCLUSION
This study has thoroughly examined the effectiveness of three fundamental technical indicators: SMA, OBV, and CCI.By leveraging historical data from 50 undervalued companies and comparing their returns with those of NIFTY 50 companies, the research has effectively identified profitable trading opportunities based on specific indicator conditions.Key findings from this investigation underscore CCI as the most dominant indicator for achieving higher profitability, closely followed by OBV and SMA, confirming their efficacy in recognizing trading opportunities.However, it is important to note that CCI signals are relatively infrequent, particularly when utilizing a 20-day or a 9-day window period, this work used a 3-day window period highlighting the substantial impact of this parameter choice on signal frequency.The study also reveals the strategic importance of specific entry points, emphasizing the value of price momentum and volume trends, particularly when CCI signals an uptrend and OBV shows a neutral or downtrend.SMA, when used for price trend confirmation, provides additional confidence in trading decisions.
These findings hold significant relevance for active investors, offering them a powerful framework to enhance profit retention and minimize losses during market downturns and corrections.By providing insights into the selection and combination of technical indicators to pinpoint profitable trading opportunities, this research not only guides investors but also underscores the paramount importance of risk-adjusted returns.Understanding the precise conditions that lead to successful trades empowers market participants to make exceptionally informed investment decisions that strike a harmonious balance between the allure of potential returns and the prudence of astute risk management.
However, it is critical to recognize some limitations in this study.The ever-changing nature of market dynamics presents a degree of uncertainty, and this study did not take into account a variety of relevant elements, such as economic events and geopolitical developments.Furthermore, it is critical to acknowledge the retrospective aspect of this study, which examines past performance but does not predict future results.
The future perspectives of the research include exploring exit point analysis to identify reliable exit points for profit retention, conducting further analysis on combinations of various technical indicators, and examining the role of volatility indicators in assessing profitability, contributing to a comprehensive understanding of the factors influencing trading strategies' success.

Financial
markets have always fascinated investors and traders with their potential for generating profits.With the advent of advanced technology and the availability of vast amounts of financial data, traders have increasingly relied on technical analysis to make informed decisions.Technical indicators play a crucial role in analyzing historical price and volume data to identify potential trading opportunities.This research paper aims to address the existing literature gap by comparing the effectiveness of various trading strategies based on the combination of technical indicators, specifically the Simple moving average (SMA), On-balance volume (OBV), and Commodity channel index (CCI).The study draws upon insights from esteemed researchers in the field of technical indicators.The work of Naved and Shrivastava (2015a) on moving averages serves as a foundational reference for this research analysis of the SMA.Chio's (2022) research on the Moving average convergence divergence (MACD) indicator and the contributions of Kuzman et al. (2021) regarding the influence of technical indicators, particularly MACD, inform the methodological approach.Despite the abundance of technical indicators, traders and investors often struggle to identify which combination of indicators is most effective for optimizing their investment performance.A significant gap exists in comprehensive research that rigorously evaluates different indicator combinations and their long-term impact.The primary objective of this study is to provide valuable insights into the strengths and weaknesses of various indicator combinations and their influence on trading strategies.It addresses the following research questions: RQ1: How do specific technical indicator combinations, such as SMA, OBV, and CCI, affect financial market trading strategies?RQ2: What criteria can be used to assess the success of these strategies, and how do they compare across different indicator combinations?
Technical indicators are pivotal in financial markets, offering traders and investors valuable insights into price trends, momentum, and potential market reversals.Numerous studies have investigated the development, application, and assessment of technical indicators.This literature review synthesizes and scrutinizes key works on technical indicators, emphasizing their efficacy, limitations, and applications.Investor sentiment and market volatility are key factors in shaping risk profiles and trading strategies.Research by So and Lei (2015) explores the intricate relationship between investor sentiment, the volatility index, and trading volume, shedding light on how market sentiment and volatility directly impact trading strategies.The understanding of this interplay is instrumental in assessing the risk and effectiveness of trading strategies when utilizing technical indicators.Pandey (2012) applied the Markowitz model to analyze risk and return in stock portfolios offering a valuable framework for understanding risk within financial markets.Naved and Shrivastava (2015a) evaluated the performance of various moving averages, including simple, exponential, triangular, variable, and weighted.The results of their study showed that short-term simple moving averages were more profitable with lower drawdown compared to other types.Chio  (2022) sought to validate the effectiveness of the MACD indicator.The study revealed that the MACD had a win rate of less than 50%.However, by incorporating trading volume and daily price volatility, the MACD's win rate significantly improved.Kuzman et al. (2021) employed technical and economic analyses, utilizing the adaptive neurofuzzy inference system (ANFIS) to guide stock trading decisions.This study highlighted the substantial impact of technical indicators, particularly the MACD, on trading choices.Additionally, the relative change after smoothing the 15-day federal rate emerged as a critical economic indicator.Fernando (2014) scrutinized the effectiveness of technical trading strategies compared to a buyand-hold approach, with a focus on forecasting stock prices and generating excess returns in the Colombo Stock Exchange (CSE).Mitra (2011) reported that trading rules based on short-term moving averages were adept at identifying trends but incurred higher transaction costs due to frequent trading.Consequently, minimizing transaction costs is vital for technical traders.The research conducted by Naved and Srivastava (2015b) concluded that CCI oscillators, in combination with indicators like SMA and relative strength index (RSI), exhibited better profitability.However, the CCI outperformed other indicators, offering higher average profit with fewer trades.Lv et al. (2023) underscored the variability of stock movements among different industries.To address this, the authors proposed a multi-criteria decision-making process for recommending industry-specific stock trading models.Chandar (2022) introduced a stock trading model called TI-CNN, which integrates technical indicators (TIs) and convolutional neural networks (CNNs).Bashir and Aslam (2022) examined the influence of input window length and forecast horizon on predictive model performance.In a related context, Ali et al. (2023) presented a smart trading system that incorporated a wide array of technical indicators from leading, lagging, and volatility categories.Furthermore, Pramudya and Ichsani (2020) aimed to identify the most effective indicators, such as MACD, Bollinger Band, and RSI, for generating precise buy and sell signals for the Jakarta Stock Exchange LQ45 Index.Simultaneously, Klados (2013) underscored the enhanced performance achieved through the combination of multiple technical indicators within trading strategies.The research conducted by Metghalchi et al. (2012) observed the predictive power derived from the fusion of technical indicators in the Taiwanese stock market.Additionally, Faijareon and Sornil (2019) introduced a technique for evolving indicator parameters and integrating various technical indicators to develop trading strategies that consistently outperformed other techniques.Optimal stop-loss strategies are crucial for risk management in trading, enhancing profitability, and minimizing potential losses.Several works shed light on optimal stop-loss strategies.The research of Leung and Zhang (2021) delves into the use of trailing stops in overcoming timing issues in asset buying and selling.According to Lundström (2014), optimal loss-stopping should be incorporated into money management to improve trading profitability, particularly in momentum-based returns.Di Graziano (2014) investigates the development of effective trading stops for algorithmic strategies using position Profit and Loss Statement (P&L) models based on Markov modulated diffusion.Zambelli (2016) meticulously assessed stop-loss criteria on 114 assets.While the approach was generally successful, there were certain weaknesses that indicated the need for additional parameter testing.Existing research on technical indicators and trading strategies reveals a significant gap.Previous studies have primarily focused on individual indicators or limited combinations, neglecting comprehensive analyses that harness multiple indicators for effective trading strategies.This research addresses this gap by systematically evaluating combinations of technical indicators, specifically SMA, OBV, and CCI, to explore synergies and trade-offs.It provides insights for traders and investors seeking profitable, risk-adjusted trading strategies.The key literature gaps include: 1) Existing research primarily focuses on individual technical indicators or limited combinations.This research fills this gap by analysing a wide range of technical indicator combinations.

Figures 1 and 2
Figures 1 and 2 reveal an interesting trend: as the rarity of indicator signals increases and the number of indicators used in combination rises, a noticeable reduction in the number of trades becomes evident.

Figure 1 .
Figure 1.Distribution of total trades per company for each indicator (365 days)

Figure 2 .
Figure 2. Distribution of total trades per company for each indicator (10 years)

Figure 3 .
Figure 3. Distribution of total returns of indicator (365 days)

Figure 4 .
Figure 4. Distribution of total returns of indicator (10 years)

Figure 10 .
Figure 10.Sharpe ratio distribution for top 5 indicator combinations

Figure 11
Figure 11 visually summarizes the study's analysis, offering a comparison of various indicator combinations' trade count and average return per trade.The X-axis represents the indicator combinations, while the Y-axis shows the trade count and average return per trade.Each combination is graphically depicted, aiding viewers in identifying those combinations that provide a substantial number of trades and appealing average return per trade.

Figure 11 .
Figure 11.Trade-off graph for total trades and average return (%)/trade

3 .
Combination No. 24: Entry signal occurs when the CCI value is above 100, and the OBV value is in a neutral state.4. Combination No. 27: Entry signal triggers when the CCI value is above 100, and the OBV value has decreased over the past five days.5. Combination No. 45: Entry signal triggers when the CCI value is below -100 (indicating a downtrend), the OBV value is in a neutral state over the past five days, and the SMA value is in a downtrend, with the  ℎ line positioned below the   line.

Table 3 .
Sample trade data for an indicator combination

Table 4 .
Performance report for each indicator combination for single stock (365 days)

Table 5 .
Performance summary of top 5 indicator combinations (Part 1)

Table 5 .
Performance summary of top 5 indicator combinations (Part 2)