Introduction
Indicators can be misleading, and moving averages serve as a popular trend-following tool in trading Our experience shows that utilizing the 200-day moving average (MA) effectively identifies trend direction By focusing on buy signals only when the price is above the 200-day MA, we have observed enhanced profitability across various trading strategies.
Recent research indicates that moving averages can effectively identify short-term mean reversion trading opportunities, which may surprise some traders Although moving averages are typically associated with trend-following strategies, their application in mean reversion approaches can yield valuable insights for short-term trading.
In this strategy, moving averages (MAs) are utilized, but not in their conventional manner As emphasized in the 2004 book "How Markets Really Work," gaining unique insights into price behavior is crucial for effective market analysis.
In "How Markets Really Work," we challenged conventional market wisdom and found that it is often more advantageous to invest during short-term downturns Our research indicated that selective buying during periods of poor market breadth yields greater profits than purchasing when market breadth indicators are strong Additionally, we revealed that changes in trading volume do not play a crucial role in buy and sell decisions, contradicting the common belief among traders that volume is essential for confirming an uptrend.
We have continued that type of research and we always look at data rather than widely accepted truths
In doing so, we found that moving averages (MAs) can be used as short‐term timing tools
Moving averages (MAs) are commonly employed as trend-following indicators, generating buy signals when prices close above the MA and sell signals when they close below it Although this method can be effective for trading, it also presents several challenges and limitations that traders should be aware of.
In a range-bound market, traders often face numerous whipsaw trades as they anticipate the next trend These whipsaw trades involve quick reversals in positions, which can lead to significant commissions and trading costs As prices fluctuate around the moving average, these expenses can diminish overall profits for traders.
Moving averages (MAs) inherently provide delayed signals, as they trail the market, which can result in missed opportunities during significant price movements For instance, the SPDR S&P 500 ETF (NYSE: SPY) surged over 30% after reaching its low in March 2009, but long-term MAs only generated buy signals after this substantial increase.
Trading systems reliant on moving averages (MAs) typically exhibit low win rates, with most profits generated from a small number of trades The majority of trades tend to yield minimal gains or losses, often due to market whipsaws.
Moving averages (MAs) present challenges for traders due to their delayed signals and high frequency of losing trades, which often results in traders abandoning the strategy despite its apparent profitability in long-term back-testing.
Whipsaws occur due to the binary nature of moving average (MA) systems, which are either in or out of the market, or consistently long or short, depending on price interactions To mitigate this issue, it is essential to establish rules that focus on high-probability trades Many markets are untradeable most of the time, but by designing rules that identify extreme market conditions, traders can execute trades only when the circumstances are favorable.
Moving Average (MA) systems often face the challenge of giving back substantial profits after a trend reversal or experiencing delays that can result in missed opportunities before entering trades This issue arises because prices can move significantly away from the MA during trending markets Some traders attempt to mitigate this by closing trades when prices deviate excessively from the MA; however, this approach can lead to missing out on strong trends and ultimately reduce the system's profitability To tackle this problem, we utilize a dual MA strategy, which minimizes delays at turning points and enhances trading effectiveness.
All of the strategy rules are fully detailed in the next section This is a powerful new way to use MAs that can deliver profits in any market.
Strategy Rules
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Moving averages (MAs) are essential tools for trend analysis in trading, helping to identify overbought or oversold market conditions Traders often assess when prices deviate significantly from the MA, utilizing channels based on percentages or standard deviations to gauge these movements However, it is important to note that channels can be ineffective in measuring market strength and may provide misleading signals during significant market advances or declines.
The Quantified Moving Average Strategy employs two moving averages to minimize the risk of errors during significant market shifts By tracking price movements, the interplay between these averages reveals oversold market conditions, enhancing trading decisions.
This strategy executes trades using a simple three‐step process consisting of Setup, Entry and Exit The rules for each of these steps are detailed below
A Quantified Moving Average Strategy Setup occurs when all of the following conditions are true:
1 The stock’s price must be above $5
2 The stock’s average daily volume over the past 21 trading days (approximately one month) must be at least 250,000 shares
3 The historical volatility over the past 100 days, or HV(100), must be greater than 30 (See the Appendix for a definition of historical volatility)
4 Today’s close must be above the 200‐day moving average, or MA(200)
5 The fast MA is at least Y% below the slow MA where Y = 2.5, 5.0, 7.5, or 10.0% The following MA scenarios will be tested:
Scenario Fast MA Slow MA
If the previous day was a Setup, then we Enter a trade by:
6 Submitting a limit order to buy the stock at a price X% below yesterday’s close, where X is 2, 4, 6, 8 or 10%
After we’ve entered the trade, we Exit using one of the following methods, selected in advance:
7a The closing price of the stock is higher than the previous day’s close We typically refer to this exit as the First Up Close
7b The stock closes with a ConnorsRSI value greater than 50
7c The stock closes with a ConnorsRSI value greater than 70
7d The closing price of the stock is greater than the 3‐day moving average, or MA(3)
7e The closing price of the stock is greater than the 5‐day moving average, or MA(5)
Let’s look at each rule in a little more depth, and explain why it’s included in the strategy
Rules 1 & 2 assure that we’re in highly liquid stocks which can be readily bought and sold with tight bid/ask spreads that reduce trading costs
Rule 3 assures that the stock has enough volatility to allow for large moves
Rule 4 identifies the direction of the long‐term trend By requiring the close to be above the 200‐day
MA, we are finding stocks that are oversold but remain in a long‐term uptrend
Rule 5 identifies short‐term oversold extremes
Rule 6 enables traders to enter positions at the best possible price by utilizing Setup rules to identify oversold stocks The entry strategy focuses on waiting for these stocks to become even more oversold on an intraday level, optimizing the trading opportunity.
Rule 7 outlines a clear and structured exit strategy, a rarity among trading strategies It specifies precise exit parameters supported by over 12.75 years of historical testing Consistency is key; we pre-select the type of exit method to be utilized and adhere to this rule throughout our trading activities.
Rules 7b and 7c utilize ConnorsRSI for exit strategies, replacing the previously used 2-day RSI (RSI(2)) for identifying overbought and oversold conditions Recent research indicates that ConnorsRSI is a more effective indicator for these purposes For those unfamiliar with ConnorsRSI, additional details are available.
In our testing, we found that closing all trades at the end of the trading day when the Exit signal appears yields optimal results However, if this approach isn't feasible for you, our research indicates that exiting positions at or near the market open the following morning can produce similar outcomes.
Now let’s see how a typical trade looks on a chart
In this strategy variation, the 5-day moving average (MA) must be at least 10% lower than the 20-day MA on the Setup day A limit order is set at 6% below the closing price of the Setup day, and the position is exited when the ConnorsRSI exceeds 70, following the guidelines outlined in Rule 7c.
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Chart created in TradingView Reprinted courtesy of TradingVew.com
Figure 1: Smith & Wesson Corp (SWHC) Trade
The chart for Smith & Wesson Holding Corp (SWHC) displays price bars in black, with a 5-day moving average (MA) represented in blue and a 20-day moving average (MA) in green A green arrow indicates the entry point for the trade, while a red arrow marks the day when the exit rule is activated.
Rule 1 is satisfied because the stock’s closing price is $7.96 on August 22, 2012, well above the minimum value of $5
Rule 2 is met because the average daily volume on the day the Setup is completed is more than 1.9 million, above the minimum of 250,000
Rule 3 mandates that the historical volatility (HV) over the last 100 days must exceed 30 upon the completion of the Setup On that specific day, the HV(100) recorded an impressive value of 67.64.
Rule 4 is satisfied because SWHC closed at $7.96, above the 200‐day MA which was $6.43 on that day
Rule 5 requires the fast MA is at least Y% below the slow MA where Y = 2.5, 5.0, 7.5, or 10.0% We are using 5‐days for the fast MA and 20‐days for the slow MA with Y = 10.0%
On August 22, the 5-day moving average (MA) was recorded at $8.09, while the 20-day MA stood at $9.24, indicating that the shorter-term MA was over 12% lower than the longer-term MA This relationship between the two moving averages can be analyzed using a specific formula.
Percent above/below = ((Fast MA / Slow MA) – 1) * 100
If the fast MA is above the slow MA, this value would be positive
Having met all five Setup rules, we will place a limit order for the upcoming trading day, August 23rd According to our chosen strategy variation, this limit order should be set at 6% below the closing price of the Setup day.
(Rule 6), so we would use a limit price of:
On August 23 rd the price of SWHC dropped as low as $7.40, so our limit order gets filled and we buy the stock at the limit price of $7.48
On August 24th, SWHC's stock price closed at $8.05, while the ConnorsRSI rose to 72.22, surpassing the 70 threshold This signals an exit according to Rule 7c, prompting us to close our position at or near the closing price.
$8.05, which gives us a profit on the trade of 7.6% before commissions and fees:
Profit = Gain (or Loss) / Cost Basis
In this example, we set specific trade parameters where the 5-day moving average (MA) must be at least 5% lower than the 20-day MA on the Setup day A limit order will be executed at 8% below the closing price of the Setup day, and we will exit the trade when the price closes above the 5-day MA, following the exit strategy outlined in Rule 7e.
The chart below is for Spreadtrum Communications (SPRD), and uses the same conventions as the previous chart
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Chart created in TradingView Reprinted courtesy of TradingVew.com
Figure 2: Spreadtrum Communications Inc (SPRD) Trade
The Setup day for this trade was December 13, 2011 As per Rule 1, the closing price is above $5 at
The stock price of SPRD closed at $20.74, meeting Rule 2 with an average daily volume exceeding 1.9 million shares, well above the minimum requirement of 250,000 Additionally, Rule 3 is fulfilled as the historical volatility (HV) over 100 days stands at 77.60 Furthermore, Rule 4 is satisfied since the closing price is above the 200-day moving average of $19.50.
Rule 5 requires the fast MA is at least Y% below the slow MA where Y = 2.5, 5.0, 7.5, or 10.0% We are using 5‐days for the fast MA and 20‐days for the slow MA with Y = 5.0%
On December 13th, the 5-day moving average (MA) was $21.82, while the 20-day MA stood at $24.39, indicating that the fast MA was approximately 11% lower than the slow MA This relationship between the two moving averages can be analyzed using a specific formula.
Percent above/below = ((Fast MA / Slow MA) – 1) * 100
Test Results
While it’s impossible to predict the future performance of a trading strategy, a fully quantified strategy can be assessed through its historical performance This evaluation method, known as back-testing, allows us to analyze how the strategy has fared in the past.
To conduct a back-test, we begin by choosing a selection of securities, often referred to as a watchlist, on which to evaluate our strategy In this instance, our watchlist includes non-leveraged stocks.
To conduct effective back-testing, it is essential to select an appropriate timeframe, as a longer duration yields more meaningful and informative results In this Guidebook, back-tests are conducted from January 2001 to September 2013, utilizing the most recent data available at the time of writing.
We implement our entry and exit rules on each stock in the watchlist throughout the test period, meticulously documenting data for every trade executed and consolidating all trade information based on a specific strategy variation.
The Average % Profit/Loss, often referred to as the Average Gain per Trade or simply the edge, is a crucial statistic derived from back-tested results This metric is calculated by summing all gains and losses (expressed as percentages) and dividing by the total number of trades Understanding this statistic can significantly enhance a trader's strategy and performance.
Trade No % Gain or Loss
The Average % P/L would be calculated as:
Average % P/L is the average gain based on invested capital, i.e the amount of money that we actually spent to enter each trade
For short-term trades lasting between three to ten days, traders typically aim for an Average % P/L of 0.5% to 2.5% across all their trades Generally, a higher Average % P/L indicates a greater potential return on investment.
To maximize profits, it is advisable to invest the same amount of capital in each trade, as making an average profit of 4% across ten trades will yield greater returns than a single trade with a 10% profit.
The Winning Percentage, or Win Rate, is a crucial metric that measures trading success by dividing the number of profitable trades by the total number of trades executed For instance, in a scenario where 7 out of 10 trades yield positive returns, the Winning Percentage is calculated as 7 divided by 10, resulting in a 70% win rate.
Understanding the significance of Win Rate is crucial, even when maintaining a high Average % P/L A higher Win Rate typically results in more stable portfolio growth, as losing trades tend to cluster together, leading to drawdowns that can negatively impact your portfolio's value These drawdowns can cause stress and may even prompt traders to reconsider their strategies Conversely, a higher Winning Percentage reduces the likelihood of clustered losses, promoting a smoother upward trajectory for your portfolio and minimizing erratic fluctuations.
Let’s turn our attention to the test results for the different variations of the Quantified Moving Average
The table below sorts the test results to show the 20 variations that produced the highest
Average % P/L All variations that generated fewer than 100 trade signals during the 12+ year testing period have been filtered out to avoid skewing the results
Top 20 Variations Based on Average Gain
Win % MA Scenario MA Stretch Limit
267 4.24% 4.7 69.29% MA(5)/MA(10) 10.0 8 CRSI > 70 1,125 4.23% 4.1 70.76% MA(5)/MA(20) 7.5 10 Close > MA(5)
273 4.17% 3.9 68.86% MA(10)/MA(20) 10.0 8 Close > MA(5) 1,074 4.16% 4.7 70.86% MA(5)/MA(10) 5.0 10 CRSI > 70
395 4.08% 4.4 69.87% MA(10)/MA(20) 10.0 8 CRSI > 70 1,731 4.04% 3.8 73.43% MA(5)/MA(10) 5.0 8 Close > MA(5)
Below is an explanation of each column
# Trades is the number of times this variation triggered from January 1, 2001 – September 30, 2013
The Average Percentage Profit/Loss (Avg % P/L) reflects the overall performance of all trades, factoring in both gains and losses relative to the invested capital Over a testing period exceeding 12 years, the top 20 variations achieved impressive returns, with gains varying between 3.93% and 5.51%.
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The win percentage (Win %) represents the proportion of simulated trades that ended in profit, with many of the top 20 trading strategies achieving win rates in the low 70s This indicates a significant level of successful trades, especially compared to the common target of 50-60% sought by numerous traders.
The MA Scenario outlines the two moving averages utilized in the test, aligning with Rule 5, and presents the values for both the fast and slow moving averages Various MA scenarios were evaluated to assess their performance.
Scenario Fast MA Slow MA
The MA stretch reflects the value of Y in Rule 5 of the strategy, indicating that the fast MA must be at least Y% below the slow MA The possible values for Y are 2.5%, 5.0%, 7.5%, or 10.0%.
The Limit % is associated with Rule 6 of the trading strategy, establishing the limit price for trade entry We evaluated limit percentages of 2%, 4%, 6%, 8%, and 10% below the closing price of the Setup day.
Exit Method is the rule that was used to exit trades in this strategy variation, as described in Rule 7
Next, let’s look at the strategy variations that have historically had the highest frequency of profitable trades or Win Rate
Top 20 Variations Based on Highest Win Rate
Win % MA Scenario MA Stretch Limit
1,502 3.14% 1.6 71.17% MA(5)/MA(10) 5.0 8 First Up Close
The top 20 trading variations have consistently generated profits in over 70% of identified trades, demonstrating their reliability Notably, there is significant overlap with the previously mentioned list of Average % P/L, highlighting that several strategies have historically achieved consistent wins while offering strong advantages.
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Selecting Strategy Parameters
In earlier chapters, we explored various values for strategy parameters, including moving averages, the distance between the fast and short moving averages, entry limit percentages, and exit methods This section will address additional factors to consider when selecting the most suitable variations for your trading strategy.
When considering entry and exit rules in trading, it's essential to understand their strictness, which reflects how easily they can be met This strictness can also indicate the frequency of the conditions required for these rules For instance, in oscillators like ConnorsRSI, values near the extremes of 0 and 100 represent stricter conditions that are less likely to occur, while values in the middle range are more frequent and easier to achieve.
Stricter entry rules lead to fewer trades compared to more lenient rules, as they are satisfied less frequently While a strategy based on strict criteria may generate fewer trades, it often results in higher gains per trade on average For instance, purchasing a slightly oversold stock typically yields moderate gains, whereas waiting for a stock to become extremely oversold significantly increases the likelihood of a substantial price surge and greater profits.
Stricter exit rules have a minimal impact on the number of trades generated by a trading strategy, but they often lead to higher average profits This occurs because such rules allow trades to remain open longer, enabling stocks to exhibit mean reversion, which the strategy aims to capitalize on Therefore, the tradeoff for entry rules involves balancing the number of trades with potential gains, while for exit rules, it revolves around the duration of trades versus the profitability per trade.
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This Guidebook outlines a strategy that utilizes a consistent moving average framework, specifically a 5-day fast moving average and a 10-day slow moving average In the following table, we compare four variations of this strategy, all of which implement a 6% limit entry and employ the ConnorsRSI for exit methods.
> 70) Only the value of the MA Stretch for the entry threshold differs between the variations shown below
The Effect of MA Stretch Entry Threshold for Quantified MA Strategy
Win % MA Scenario MA Stretch Limit
The analysis reveals that the entry rule with an MA Stretch of 2.5% produced the highest number of trade signals but yielded the lowest average gain per trade Conversely, as the MA Stretch threshold increases, the frequency of trade signals decreases while the average gains per trade rise significantly Notably, an entry threshold of 10% enhances the Average % P/L by approximately 75% compared to the initial threshold, albeit with less than 1/20th the number of trades executed.
When analyzing stock performance while maintaining consistent parameters, it becomes evident that varying the Limit % for entry prices significantly influences outcomes Specifically, if the Setup conditions remain unchanged, a greater number of stocks will likely experience a pullback of 2% or more the following day compared to those that pull back by at least 10%.
Variations with Different Limit % Entries for Quantified MA Strategy
Win % MA Scenario MA Stretch Limit
Stricter entry rules lead to a reduction in the number of trades while increasing the average gains In this analysis, we maintain consistent Setup and entry criteria but explore different exit methods to evaluate their impact on trading outcomes.
Variations with Different Exit Methods for Quantified MA Strategy
Win % MA Scenario MA Stretch Limit
420 2.19% 1.9 65.95% MA(10)/MA(20) 7.5 10 First Up Close
The five variations produced a comparable number of trade signals, ranging from 379 to 525 trades Notably, the lenient exit method, which closes positions on the first day the stock price rises, yields an average gain that is approximately half of that achieved with stricter exit strategies A comparison of the different moving average (MA) and ConnorsRSI exit methods reveals that stricter exits enhance both average gains and win rates Specifically, MA(3) offers a less stringent exit requirement than MA(5), resulting in lower average profitability despite a higher number of trades This trend is similarly observed with the ConnorsRSI exit rule.
With this knowledge, you can effectively choose strategy parameters that are likely to generate optimal trade signals, maximize average gains, and align with your desired trade duration, ultimately enhancing your overall trading strategy.
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Using Options
Options trading has experienced significant growth in recent years due to tighter spreads, increased liquidity, and the simplicity of trading complex options.
In this section, we will explore how to implement options trading based on the short-term market movements we've discussed As with all strategies outlined in this Guidebook, there are clear guidelines for executing an options trade once a strategy signal is activated.
Before we go on, it will be helpful to review a few terms and concepts related to options
A call option grants the holder the right, but not the obligation, to buy the underlying security at a predetermined strike price before the option's expiration date Typically, the value of a call option increases as the price of the underlying asset rises, making it a valuable tool for investors looking to capitalize on potential price gains.
In options trading, a call option is considered In-The-Money (ITM) when its strike price is below the current price of the underlying security, while it is classified as Out-of-The-Money (OTM) when the strike price is above that price For instance, with SPY options having a $1 increment between strike prices and SPY priced at $162.35, the nearest ITM call option has a strike price of $162, whereas the first OTM call option is priced at $163.
A put option grants the holder the right, but not the obligation, to sell an underlying security at a predetermined strike price before the option's expiration date Typically, the value of a put option increases as the underlying security's price decreases A put option is classified as In-The-Money (ITM) when its strike price exceeds the underlying security's price, while it is Out-of-The-Money (OTM) when the strike price is lower For instance, if SPY is priced at $166.55, the nearest ITM put option is the $167 strike, and the nearest OTM put option is the $166 strike.
This guidebook outlines a strategy for purchasing oversold stocks using quantified moving average rules, while employing call options for implementation Additionally, put options will be utilized to execute short position strategies detailed in other guidebooks.
Most option contracts represent 100 shares of the underlying stock, but trading platforms usually quote the price on a per-share basis Consequently, the total cost of acquiring an option contract is generally 100 times the quoted price per share, in addition to any commissions For example, if a SPY call option is priced at a certain amount per share, the overall expense will reflect that multiplied by 100.
$1.27, then it will cost you $127.00 plus commissions to purchase the call option contract Sometimes you will hear the price of an option referred to as the option’s premium
All option contracts have an expiration date, after which the contract is no longer valid The three most common types of option expirations are:
Weekly: Contract expires on the last trading day of the week, typically a Friday
Monthly: Contract expires on the Saturday following the third Friday of the month, which means that the last day for trading the option is the third Friday
This Guidebook focuses on option contracts with monthly expirations, specifically identifying the contract with the nearest expiration date as the front month For instance, if today is June 10th, the front month contract is set to expire in the third week of June, while the next available expiration in July is referred to as the second month Following the June expiration, July will take on the role of the front month, and August will become the second month.
Strategies in the Guidebook generally follow certain patterns:
1 The majority of the moves from entry to exit have been held a very short period of time (2‐12 trading days)
2 The average gains per trade have been large – well beyond the normal distribution of prices over that short period of time
3 A high percentage of the moves have been directionally correct
Analyzing this behavior reveals various strategies, but one particularly effective approach, endorsed by professional traders, is to purchase in-the-money call options for the front month.
Front month, in-the-money long options are preferred because they closely track the movement of the underlying stock This correlation means that when the stock moves in the anticipated direction, the percentage gains on these options can be significantly higher.
2 Buy the front month in‐the‐money call If you would normally trade 500 shares of the stock buy 5 call contracts (every 100 shares should equal one call option contract)
3 Exit the options when the signal triggers an exit on the stock
1 What does in‐the‐money exactly mean here?
When selecting call options, aim for one to two strike prices that are in the money, which means they should be below the current market price For example, if a stock is priced at $48 and the interval between option contracts is $5, consider purchasing a call option with a strike price of either $45 or $40.
2 What does front month mean here?
To optimize your trading strategy, focus on options with the nearest monthly expiration, especially when it is eight trading days or fewer from the front month’s expiration date If the closest month falls within this timeframe, consider trading the following month's options instead.
3 What happens if I’m in the position and it expires, yet the signal for the stock is still valid?
In this case, roll to the next month You’re trading the stock signals so you want to have exposure to that signal
4 What about liquidity and spreads?
Liquidity in options trading is somewhat subjective, as there are no strict definitions Traders often assess liquidity by examining minimum volume and open interest levels.
Assuming there is active volume in the options, look at the spreads If the option is trading $3.00 bid /
$3.30 offer, the spread is 10% Can you really overcome a 10% spread? Not likely Now compare this to an option that’s trading at $3.25 bid / $3.30 offer This is far more acceptable and tradable
5 What are the advantages of buying call options instead of the stock?
Assuming the spreads and liquidity are there, the advantages are large:
1 Greater potential ROI on capital invested
Investing in options presents a lower risk compared to purchasing stocks directly For instance, if you buy a stock at $50, it could potentially drop to zero, resulting in a loss of $50 per share In contrast, when you buy an option, your maximum loss is limited to the premium paid For example, if you purchase a $45 call option for $5.50, your total risk is confined to that premium of $5.50, making options a safer investment choice.
Options trading offers greater flexibility for investors For instance, if a stock triggers a buy signal at $50 and you purchase $45 calls for $5.50, a subsequent rise in the stock price to $56 presents you with choices You can choose to exit your position or roll into a $55 call, allowing you to recoup most of your initial investment This strategy can effectively transform your trade into a nearly cost-free opportunity if you anticipate further price increases.
Additional Thoughts
1 As you have seen throughout this Guidebook, the Quantified Moving Average Strategy has had large quantified edges when applied in a systematic manner
You can explore countless variations to tailor your trading strategy by adjusting the input variables outlined in the rules If you're seeking more trades, consider using faster moving averages or smaller MA stretch values for entry For higher average returns, focus on variations with strict entry criteria, such as high MA stretch values and elevated Limit % entry rules, along with longer durations using the ConnorsRSI 70 exit method Alternatively, if you want to execute trades more rapidly to minimize overnight risk and optimize your capital for additional trades, experiment with variations that implement the First Up Close exit method.
3 What about stops (and we include the answer to this in all our Strategy Guidebooks)?
We have published research on stops in other publications including in our book Short Term Trading
Research indicates that using stop-loss orders can negatively impact trading performance, often leading to significant losses While it may feel reassuring to exit a position as a stock declines, data from two decades of testing short-term trading strategies shows that stops are frequently triggered, resulting in accumulated losses that most strategies struggle to recover from.
For many traders, implementing stop-loss orders is essential as it helps them manage risk and make difficult trading decisions with greater confidence Ultimately, the choice to use stops is a personal one, as successful traders can be found both utilizing and forgoing this strategy However, it is important to note that applying stops may reduce the potential advantages of certain short-term trading strategies.
When trading, it's essential to consider slippage and commission, even though they weren't included in the testing Since entries are made at limit prices, slippage isn't a concern, but ensuring you trade at the lowest possible costs is crucial Many brokerage firms now offer trading for less than 1 cent per share, so it's wise to compare options, especially for active traders, as online brokers are eager for your business.
Thank you for exploring our latest installment in the Connors Research Trading Strategy Series For any inquiries regarding this strategy, please don't hesitate to reach out to us at info@connorsresearch.com.
Since the mid-1990s, Larry Connors and Connors Research have been dedicated to developing and publishing quantified trading strategies, thoroughly evaluating various technical indicators for their predictive effectiveness They have now advanced their research by creating their own indicator, ConnorsRSI, which will be detailed in this chapter along with its calculation methods.
ConnorsRSI is a composite indicator made up of three components, with two based on the Relative Strength Index (RSI) calculations created by Welles Wilder in the 1970s The third component ranks the latest price change on a scale from 0 to 100 Together, these elements create a momentum oscillator that ranges between 0 and 100, signaling whether a security is overbought (high values) or oversold (low values).
Before calculating ConnorsRSI, it's important to understand Wilder's RSI, a widely used momentum oscillator that assesses the relationship between a stock's gains and losses over a specified look-back period Wilder recommended a 14-period look-back, commonly referred to as RSI(14) The following formula is utilized to compute RSI(14) based on a series of price changes.
To calculate the Relative Strength Index (RSI) for a different number of periods (N), simply substitute 14 in the formula with N and 13 with N-1 The RSI will always yield a value between 0 and 100, regardless of the period used Traders commonly utilize RSI(14) to identify overbought conditions when the value exceeds 70 and oversold conditions when it falls below 30 Additionally, variations like RSI(3) and RSI(4) exist, with different period settings impacting the levels that signify overbought and oversold scenarios For instance, an RSI(2) reading below 10 often indicates an oversold condition, while a value above 90 suggests an overbought condition.
ConnorsRSI is a unique indicator that integrates three key components, all of which have demonstrated substantial predictive power according to our research.
Price momentum can be effectively measured using the Relative Strength Index (RSI), which identifies overbought and oversold conditions in the market The ConnorsRSI, by default, utilizes a 3-period RSI calculation based on the daily closing prices of a security, referred to as RSI(Close,3).
The duration of an up or down trend in a security's closing price provides valuable insights into potential price movements A "closed down" day occurs when today's closing price is lower than yesterday's, and a consecutive decline creates a "streak" of down days Research indicates that longer down streaks often lead to significant price rebounds when the stock reverts to its mean, while extended up streaks can result in more substantial declines upon mean reversion Therefore, the duration of these streaks serves as an effective overbought/oversold indicator.
The duration of a streak can theoretically extend indefinitely, but practical limits based on historical data suggest that up or down streaks rarely exceed 20 days However, this observation does not align with the typical values of oscillators, which generally fluctuate between 0 and 100.
To effectively track streaks, we will represent positive numbers for up streaks and negative numbers for down streaks, allowing for a clear and concise count of each streak's duration.
Day Closing Price Streak Duration
On Day 2, the closing price surpasses that of Day 1, marking the beginning of a one-day up streak This upward trend continues on Day 3 with another higher closing price, resulting in a two-day up streak, indicating a Streak Duration value of 2.