Embedded Files
Industry-Specific PMax Configuration Settings
Industry-Specific PMax Configuration Settings
The PMax (Profit Maximizer) indicator parameters below are tailored to each industry’s typical volatility and price behavior. More volatile sectors use a wider ATR-based stop (longer ATR period or higher multiplier) to avoid whipsaws, while stable sectors use tighter settings to catch smaller moves. We use ATR (Average True Range) to adjust for volatility (common default ATR period is 14
), and an ATR multiplier around 2–3 as a starting point
(higher multipliers for more volatile markets). The moving average (MA) length is crucial: shorter MAs make PMax more sensitive to trend changes, whereas longer MAs filter out “little” price swings
. We also choose the MA type (EMA, SMA, etc.) suited to each sector (e.g. VIDYA for sideways markets
). Timeframes are selected based on trading horizons typical for the sector (daily for active swing trading, weekly for slow-moving defensive sectors). The signal toggles (showing the MA line, MA/PMax crosses, and price/PMax crosses) are enabled or disabled depending on whether early signals are useful or just noise for that industry. All configurations use the default ATR calculation (Wilder’s method) and non-normalized ATR (i.e. absolute ATR, since we calibrate the multiplier per sector instead). Below, we detail each industry’s settings with rationale:
Information Technology (Tech) – High Volatility
Information Technology (Tech) – High Volatility
Timeframe: Daily – Tech stocks are actively traded with rapid swings, so a daily chart captures typical swing trades.
Source: Close – Using closing prices (standard practice) for ATR and MA calculations.
ATR Length: 14 – A moderate ATR period (14 days) balances responsiveness and smoothing, suitable for tech’s ~14.8% volatility
. (14 is a common default ATR length
.)
ATR Multiplier: 3.0 – Tech is a volatile sector, so we use a relatively high multiplier to set a wider stop and reduce false signals. 3×ATR is a typical setting for volatile markets
, giving the PMax line room during tech’s large price swings.
Moving Average Type: EMA – An Exponential MA responds faster to price changes than an SMA. This helps capture tech’s quick trend reversals and momentum shifts.
Moving Average Length: 50 – A 50-day EMA tracks intermediate trends, making PMax sensitive enough for swings but still filtering out day-to-day noise
. Shorter MA lengths increase sensitivity
, which is acceptable given tech’s strong trending moves.
Change ATR Calculation Method: No – Use the standard Wilder’s ATR. Tech volatility can be handled with the normal ATR; no alternative calculation is needed.
Normalize ATR: No – We keep ATR in absolute terms. Tech stocks within this sector are broadly comparable in price behavior, so no normalization is necessary (normalization is generally turned off unless comparing across very different price scales).
Show Moving Average: Yes – Display the 50-day EMA on the chart. It helps visualize trend direction and the points where the EMA crosses the PMax line (critical for signals).
Show Crossing Signals: Yes – Enable signals when the EMA crosses above or below the PMax. These EMA/PMax crossovers are the primary trend-change signals for PMax
, indicating buy/sell points in trending tech markets.
Show Price/PMax Crossing Signals: No – Disable price/PMax cross signals for tech. In a fast-moving sector, price often pierces the trailing stop line intra-trend, which could generate too many false signals. We rely on the more robust EMA cross to confirm trend shifts, reducing noise from momentary price spikes.
Healthcare – Moderate Volatility
Healthcare – Moderate Volatility
Timeframe: Daily – Healthcare stocks (pharmaceuticals, biotech, providers) are traded on daily news and trends; a daily timeframe suits both swing trades and longer trend following.
Source: Close – Use closing prices for calculations (standard approach).
ATR Length: 10 – Slightly shorter ATR period (10 days) to respond to volatility changes more quickly. Healthcare sector volatility (~12.4% standard deviation
) is lower than tech, so a shorter ATR window will pick up any uptick in volatility without excessive noise.
ATR Multiplier: 3.0 – Using ~3×ATR provides a reasonable stop distance. Healthcare can have sudden drug trial news (volatile for biotech) but also many stable big-pharma stocks; 3.0 is a balanced default to accommodate moderate volatility.
Moving Average Type: EMA – An EMA is used to track price closely. This ensures the PMax reacts in a timely manner if healthcare stocks start trending on news (EMAs weight recent prices more heavily).
Moving Average Length: 100 – A 100-day EMA is chosen to emphasize longer-term trend shifts in this sector. Healthcare companies often have stable, defensive characteristics, so a longer MA reduces whipsaws
while still catching major moves (e.g. broad uptrends or downturns in pharma).
Change ATR Calculation Method: No – Use standard ATR. The default Wilder’s ATR smoothing is adequate for healthcare stocks; no need to change to simple averaging of true ranges.
Normalize ATR: No – Not normalizing; the ATR multiplier is tuned for typical healthcare stock price ranges already.
Show Moving Average: Yes – Plot the 100-day EMA for context. Seeing the EMA helps confirm trend direction (price vs. this long average) and is needed for crossover signals.
Show Crossing Signals: Yes – Turn on EMA/PMax cross signals (buy when the 100-day EMA crosses above PMax, sell when it crosses below). These signals are less frequent with a long MA, so they highlight significant trend changes without too much clutter.
Show Price/PMax Crossing Signals: Yes – Enable price/PMax cross signals. Given the longer MA length here, waiting for an EMA cross can lag. A price crossing above/below the PMax line provides earlier warning of a potential trend change
. Since healthcare stocks aren’t as explosively volatile as tech, these price-cross signals are less likely to be false noise and can be useful for timely entries/exits.
Financials – Moderate to High Volatility
Financials – Moderate to High Volatility
Timeframe: Daily – Financial sector stocks (banks, insurance, etc.) react to economic news and earnings on a daily basis. Daily charts capture medium-term swings (though some investors hold these long-term).
Source: Close – Calculations based on daily closing prices.
ATR Length: 14 – Use a 14-day ATR to measure volatility. Financials had about 16.8% volatility in the 2010s
, so a standard 14-period ATR works to gauge their daily price range.
ATR Multiplier: 3.0 – Keep a relatively high multiplier. Banks can be volatile in crises but stable otherwise; 3×ATR provides a buffer during turbulent periods (e.g. rate change fears) while still triggering signals in normal conditions. This multiplier is within the common default range
suitable for average volatility.
Moving Average Type: EMA – Financial stocks can turn quickly on policy news (e.g. interest rate cuts), so an EMA is chosen for its faster responsiveness.
Moving Average Length: 100 – A 100-day EMA smooths out short-term jitters (like minor rate fluctuations) and focuses on the broader trend. Financial sector trends (e.g. a multi-month rally or selloff) will be captured, while brief noise is filtered
.
Change ATR Calculation Method: No – Use the default ATR calculation. This is sufficient for capturing volatility in financial stocks.
Normalize ATR: No – Not needed; we adjust the multiplier for this sector’s typical range instead of normalizing ATR.
Show Moving Average: Yes – We display the 100-day EMA to judge trend direction and support/resistance (many traders watch long MAs on bank stocks).
Show Crossing Signals: Yes – Enable signals on EMA crossing PMax. These highlight when the longer trend likely flips (e.g. a bullish crossover in a bank stock after a prolonged decline).
Show Price/PMax Crossing Signals: Yes – Enable price cross signals for financials. With a 100-day EMA, a price crossing the PMax line can alert us early to trend reversals. The financial sector, while somewhat volatile, doesn’t whip back and forth as rapidly as tech, so price/PMax signals can be useful confirmations rather than noise.
Energy – Very High Volatility
Energy – Very High Volatility
Timeframe: Daily – Energy stocks (oil & gas companies, etc.) are highly volatile and cyclical. A daily timeframe is appropriate to catch big swings driven by commodity price changes.
Source: Close – Use close prices; energy intraday swings are large, but closing price smooths out some intraday noise.
ATR Length: 14 – Stick with 14-day ATR to gauge volatility. The energy sector is extremely volatile (about 20.3% std dev in the 2010s, the highest of all sectors
), but we’ll mainly control sensitivity via the multiplier. A standard period ensures enough smoothing of this wild volatility.
ATR Multiplier: 3.5 – Higher multiplier to suit energy’s volatility. Energy stocks can have huge price ranges on oil price news; a 3.5× ATR sets a wider trailing stop so that the PMax line isn’t hit by normal volatility spikes. (Higher ATR multipliers are recommended for more volatile markets to avoid premature signals
.) For example, using 3.5 instead of 3 helps filter out the frequent large daily moves in this sector.
Moving Average Type: WMA – A Weighted MA gives recent prices more weight than an EMA. This helps PMax adjust quicker when energy prices reverse sharply, without having to shorten the MA length drastically. We choose WMA to capture trend turns promptly while still using a longer window.
Moving Average Length: 100 – Despite the high vol, we use a relatively long 100-period WMA to focus on the primary trend of energy stocks (which often run in multi-month cycles with oil prices). A longer MA reduces false flips during short-lived rallies or pullbacks
, while the WMA nature ensures it’s not too slow to react when the real trend changes.
Change ATR Calculation Method: No – Keep standard ATR. Wilder’s smoothing helps handle energy’s volatile true ranges by not overreacting to one-day price shocks.
Normalize ATR: No – Not needed (all values are in absolute price terms; energy stocks vary in price, but we assume the multiplier can be tweaked per stock if needed).
Show Moving Average: Yes – Show the 100-day WMA line. It’s useful to see how far price is from this longer trend average in such a volatile sector, and it’s needed for crossover signals.
Show Crossing Signals: Yes – Mark WMA/PMax crossovers (buy when the weighted MA crosses above PMax, etc.). These will occur relatively infrequently (only on major trend changes) due to the long MA, which is desirable in a trend-following approach for energy.
Show Price/PMax Crossing Signals: No – Keep price cross signals off. In energy, price frequently pierces any trailing stop during volatile swings. For example, an oil stock in a volatile consolidation might jump above and below the PMax line repeatedly. Disabling price/PMax signals avoids a flood of false alerts. We rely on the WMA cross (or visual inspection of price vs. PMax) for more reliable trend signals in this high-noise environment.
Consumer Discretionary – High Volatility
Consumer Discretionary – High Volatility
Timeframe: Daily – The consumer discretionary sector (retail, automobiles, leisure, etc.) has fairly active trading and cycles aligned with consumer sentiment, so daily charts are used to catch swings (e.g. seasonal retail cycles or trend shifts on consumer confidence data).
Source: Close – Use closing prices for indicator calculations.
ATR Length: 14 – A 14-day ATR to measure volatility. This sector’s volatility (~14.6% std dev
) is comparable to tech. The standard ATR period smooths short-term fluctuations in retail and consumer stock prices.
ATR Multiplier: 3.0 – Use 3× ATR as the stop distance. Discretionary stocks can be volatile (e.g. earnings surprises can move them sharply), so a generous multiplier is needed to avoid frequent false signals. 3.0 is aligned with common practice for higher-volatility equities
and should accommodate typical swings in this sector.
Moving Average Type: EMA – We pick an EMA to track trend, as consumer stocks can move quickly with shifting economic outlooks. The EMA ensures the PMax reacts without excessive lag when a new trend (up or down) begins.
Moving Average Length: 50 – A 50-day EMA makes PMax fairly sensitive to trend changes in discretionary stocks. These companies (e.g. retailers) often have medium-term swings (driven by consumer spending cycles), and a shorter MA catches those moves early
. It might whip a bit in sideways markets, but that is mitigated by our ATR factor.
Change ATR Calculation Method: No – Default ATR calc (Wilder). That’s adequate for capturing volatility in this sector.
Normalize ATR: No – Not required, since we tailor the multiplier to this sector’s volatility.
Show Moving Average: Yes – The 50-day EMA is shown for context and signal generation. It helps traders see if price is holding above or below the key average.
Show Crossing Signals: Yes – EMA/PMax cross signals are on (to indicate confirmed trend changes). With a 50-day EMA, these signals will trigger reasonably early in new trends, which is useful in a cyclically sensitive sector like consumer discretionary.
Show Price/PMax Crossing Signals: No – Price cross signals are off to avoid noise. Discretionary stocks (especially retail) can have choppy periods (e.g. around earnings or news) where price spikes above/below the stop without a sustained move. We prefer to wait for the EMA to also turn and cross PMax as confirmation, rather than trading every price wiggle.
Consumer Staples – Low Volatility (Defensive)
Consumer Staples – Low Volatility (Defensive)
Timeframe: Weekly – The consumer staples sector is known for low volatility and slow, steady moves
. Investors often hold these stocks long-term for stability and dividends
. A weekly timeframe better captures the gradual trends and reduces noise (daily moves are often tiny). Swing trading in staples might involve multi-week moves, which a weekly chart reflects.
Source: Close – Use weekly closing prices to calculate ATR and MA.
ATR Length: 10 – A slightly shorter ATR period (10 weeks) is used since staples have very stable prices. This makes the ATR more responsive to any change in volatility. Because volatility is consistently low for staples stocks
, even a brief uptick (e.g. due to an earnings surprise) should be reflected quickly in the ATR-based stop.
ATR Multiplier: 2.5 – Tighter stop (lower multiplier) for this low-volatility sector. Consumer staples stocks have small trading ranges and “inelastic” demand
, so a 2.5× ATR is sufficient to buffer normal fluctuations yet flip when a real trend change occurs. (In calmer markets, you can use a smaller ATR multiplier since minor moves are meaningful
.) Using 2.5 instead of 3 makes the PMax more reactive to trend changes in these slow-moving stocks.
Moving Average Type: VAR (VIDYA) – We choose a Variable Index Dynamic MA as suggested for sideways or non-trending markets
. Staples often trade in ranges or gentle trends due to their stable demand, so an adaptive MA like VIDYA adjusts its effective length based on volatility. This means during very quiet periods, the MA can be more sensitive, and during rare volatility spikes, it smooths more – ideal for a sector that is usually quiet but occasionally sees movement.
Moving Average Length: 50 – A 50-week VIDYA (roughly one year of data) is our base. This long window aligns with the slow growth pattern of staples stocks
, focusing on significant trend shifts (e.g. a multi-year uptrend rolling over) rather than minor fluctuations. The VIDYA will adapt within that window to volatility changes, offering a balance between a fixed long MA and responsiveness.
Change ATR Calculation Method: No – Keep standard ATR formula. Given the already low volatility, we don’t need an alternative ATR calculation; Wilder’s ATR works fine.
Normalize ATR: No – Not necessary; staples stocks generally trade in comparable percentage ranges, and we adjust the multiplier for their volatility level.
Show Moving Average: Yes – We display the VIDYA line. It helps in visualizing the prevailing trend on the weekly chart (which investors in staples care about) and is needed for the crossover logic.
Show Crossing Signals: Yes – Enable signals when the VIDYA crosses PMax (though on a weekly, 50-period basis, these crossovers are infrequent and signify major trend changes – valuable information for long-term traders).
Show Price/PMax Crossing Signals: Yes – Enable price/PMax signals to get earlier alerts. Because the MA is quite long-term here, waiting for the MA crossover might be too slow. A price crossing the PMax line (which itself is based on the MA and ATR) gives a heads-up that the trend could be turning. In a low-vol sector like staples, such crossovers are relatively reliable since price doesn’t jump erratically. These signals ensure even swing traders can catch changes within the broader slow trend.
Industrials – Moderate Volatility
Industrials – Moderate Volatility
Timeframe: Daily – Industrial stocks (manufacturing, transportation, etc.) move with economic cycles and news (like PMIs, infrastructure spending) on a daily basis, so we use daily charts for swings and trend following.
Source: Close – Use daily close prices.
ATR Length: 14 – Industrials have roughly average market volatility (they’re cyclical but not the most volatile; think ~13% standard deviation historically). A 14-day ATR captures their price range well, smoothing out day-to-day volatility.
ATR Multiplier: 3.0 – We use a medium multiplier since industrials can swing on economic data surprises. 3× ATR gives enough leeway to avoid whipsaw exits on minor data blips, yet it’s tight enough to signal when a real trend change happens. This is aligned with general ATR stop guidance (common starting multiplier 2–3
, using the higher end for a bit of safety in a cyclical sector).
Moving Average Type: EMA – An EMA is suitable as it will follow price a bit more closely. Industrials can gather momentum (e.g. in a recovery, these stocks trend strongly up), and we want the PMax to track those trends without too much lag.
Moving Average Length: 100 – A 100-day EMA smooths out noise but still reflects medium-term trend shifts. Industrial sector trends can last several months (following economic expansions or contractions), and a 100-day MA will highlight those while ignoring short-lived swings. As noted, a longer MA makes the indicator less sensitive to brief moves
, which is appropriate here to avoid reacting to every small fluctuation in cyclicals.
Change ATR Calculation Method: No – Stick with the default ATR calculation. It’s sufficient for capturing volatility in industrial stocks.
Normalize ATR: No – Not needed; we handle volatility with the fixed ATR period and multiplier.
Show Moving Average: Yes – We plot the 100-day EMA to see the overall trend. It also aids in interpreting PMax (trend is bullish when price and EMA are above the PMax line).
Show Crossing Signals: Yes – EMA/PMax cross signals are on. These will mark significant turning points (for example, if industrials enter a downturn, the 100-day EMA crossing below PMax would generate a sell signal).
Show Price/PMax Crossing Signals: Yes – Enable price cross signals. With a longish MA and moderate volatility, a price crossing can be a useful early signal. Industrial stocks don’t typically make sudden fake-outs day after day, so a price cross of PMax often precedes a real trend change. This gives swing traders a chance to act a bit sooner, while the longer EMA cross might come later as confirmation.
Materials – High Volatility
Materials – High Volatility
Timeframe: Daily – The materials sector (miners, chemicals, commodity producers) is quite volatile due to commodity price fluctuations. A daily timeframe lets traders capture the swings tied to moves in metals, lumber, etc.
Source: Close – Use closing prices for calculations.
ATR Length: 14 – We use a 14-day ATR to gauge volatility in materials stocks. This sector’s volatility is on the higher side (commodity-linked stocks can swing widely; note that commodity markets themselves had ~18.6% volatility in the 2010s
). A 14-period ATR provides a stable average of true range, which is useful given the sometimes erratic daily moves in materials.
ATR Multiplier: 3.5 – High multiplier for volatility. Similar to energy, materials stocks require a wider stop. 3.5× ATR helps accommodate big price swings (for example, a mining stock reacting to a jump in gold prices) without flipping the PMax signal too quickly. This reduces false reversals in an inherently volatile, cyclical sector.
Moving Average Type: EMA – We opt for an EMA to maintain relatively quick responsiveness. Commodity-driven stocks can reverse trend sharply when commodity supply/demand shifts (e.g. sudden drop in metal prices); the EMA will enable PMax to adjust faster to such changes than an SMA.
Moving Average Length: 50 – A 50-day EMA is used, shorter than we chose for energy. Materials sector trends, while volatile, often include shorter cyclical upswings/downswings that a 50-day MA can track. This shorter length makes PMax more sensitive
, capturing medium-term swings in commodity-sensitive stocks (e.g. a 2-3 month rally in mining shares). The trade-off is some noise, but we mitigated that with the high ATR multiplier.
Change ATR Calculation Method: No – Use the default ATR (Wilder’s). This smoothly accounts for the big range days without over-reacting to one-day spikes.
Normalize ATR: No – Not applied; the ATR value in actual price terms works fine since we tuned the multiplier for this sector’s typical price volatility.
Show Moving Average: Yes – Show the 50-day EMA to visualize trend direction. Being a key part of the PMax (the baseline for the stop), it’s useful to see it on the chart.
Show Crossing Signals: Yes – Enable EMA crossing signals (these will alert when the 50-day EMA decisively breaks above or below the PMax stop, indicating a sustained trend change).
Show Price/PMax Crossing Signals: No – Disable price cross signals. Materials stocks can be choppy day-to-day; price may frequently test the trailing stop line during consolidations. We avoid “false start” signals by keeping these off – waiting for the EMA cross or a clear break tends to be more reliable in this high-volatility group.
Communication Services – High Volatility
Communication Services – High Volatility
Timeframe: Daily – The communication services sector contains a mix of volatile internet/media companies and steadier telecoms. Overall volatility is relatively high (~14.1% std dev
), so active traders use daily charts to follow these stocks’ swings (e.g. social media stock moves on user growth news).
Source: Close – Indicator based on daily closing prices.
ATR Length: 14 – A 14-day ATR covers volatility well. It smooths out the varied volatility in this sector (from high-flyers like streaming media stocks to stable phone carriers, the ATR will reflect each stock’s range appropriately).
ATR Multiplier: 3.0 – We use roughly 3× ATR. Communication services names like internet companies can swing big, but others are milder; 3.0 is a reasonable middle-ground multiplier to handle the overall sector’s volatility. It’s wide enough for volatile components (e.g. a Netflix or Meta) but not so wide that it never triggers on steadier telecom stocks.
Moving Average Type: EMA – An EMA is chosen to track the trend, given many companies here behave like tech stocks (which benefit from EMA’s faster reaction). It will adapt as soon as these stocks start trending up or down, ensuring PMax isn’t lagging.
Moving Average Length: 60 – We use a 60-day EMA (a bit longer than the 50 used for tech/discretionary). This slightly longer MA smooths out some noise considering the sector’s diversity. It still captures major swings (two to three month trends) while being a tad less jumpy than a 50-day. This helps accommodate the more stable telecom components, preventing too frequent flips due to their lower volatility, yet it’s short enough for the high-growth names in the sector.
Change ATR Calculation Method: No – Keep standard ATR calculation. It’s adequate for this sector’s volatility mix.
Normalize ATR: No – Not used; we rely on the fixed multiplier to adapt per stock’s ATR, rather than normalizing ATR across different price levels.
Show Moving Average: Yes – Plot the 60-day EMA. It’s needed to track the trend and to generate cross signals. One can visually see if price is trending above or below this average, confirming the PMax signals.
Show Crossing Signals: Yes – Turn on EMA/PMax cross signals. These will signal when the medium-term trend likely shifts (for instance, if a leading internet stock in this sector enters a new uptrend, the 60-day EMA crossing above the PMax will flash a buy).
Show Price/PMax Crossing Signals: No – Do not show price cross signals. Given the higher volatility of many communication services stocks (similar to tech), price can whipsaw around the PMax line on short news bursts. We avoid potential false signals and stick to the confirmed EMA crossover signals for trend changes, which are more dependable in this volatile sector.
Utilities – Low Volatility (Defensive)
Utilities – Low Volatility (Defensive)
Timeframe: Weekly – Utilities are low-volatility, defensive stocks
typically held for income. Their price movements are slow and closely tied to interest rates and regulated returns. A weekly timeframe is appropriate to filter out trivial day-to-day moves and focus on the gradual trends that utility stocks exhibit (often spanning months or years).
Source: Close – Use weekly close prices for calculations.
ATR Length: 14 – A 14-week ATR gauges the trading range. Since utilities have the lowest volatility (~11.8% std dev
), ATR values will be small, but using a standard 14-period still provides a stable measure of their range. We don’t need a shorter ATR here because the volatility is consistently low and doesn’t spike often.
ATR Multiplier: 2.5 – Lower multiplier for a slow sector. Utilities rarely have large price swings absent a major interest rate change; a 2.5× ATR stop is sufficiently wide to avoid trivial fluctuations but tight enough to catch true trend reversals. Because this sector is so stable, a smaller multiple (even 2.0–2.5) is often effective – the PMax will flip relatively quickly once a new trend (up or down) begins, which is desirable for timely exits/entries in a low-volatility context.
Moving Average Type: SMA – We use a Simple MA to maximize smoothness. Utilities tend to trend smoothly (or flatline) with minimal volatility; an SMA further dampens short-term noise (even more than an EMA would). This suits long-term investors in utilities who focus on clear, slow trend changes.
Moving Average Length: 50 – A 50-week SMA (approximately one year of data) is chosen to represent the long-term trend. Utilities often trade in prolonged trends due to economic cycles and rate environments. A one-year MA captures these well, aligning with the sector’s slow-and-steady nature
. The trade-off is that it will be less sensitive to quick moves
, but utilities seldom make “quick” moves. When they do (e.g. a sudden drop due to a rate hike), the ATR stop will likely trigger anyway even if the SMA hasn’t crossed yet.
Change ATR Calculation Method: No – Use Wilder’s ATR formula. No change needed, as the default ATR works fine for low-volatility series (and we prefer the smoothing it provides).
Normalize ATR: No – Not needed; all calculations are in absolute terms, and utility stock prices don’t vary drastically like, say, comparing a $10 stock to a $500 stock in the same sector (most are similarly priced relative to their stable earnings).
Show Moving Average: Yes – Show the 50-week SMA on the chart. This helps identify the primary trend (utilities investors often watch long-term moving averages) and is necessary for PMax’s crossover logic.
Show Crossing Signals: Yes – Enable SMA/PMax cross signals. On a weekly chart with a long SMA, a crossover signal indicates a major shift (for example, a bullish cross might indicate utilities entering an uptrend after a bear phase). Such signals are rare but important for strategic shifts.
Show Price/PMax Crossing Signals: Yes – Enable price/PMax signals to catch early changes. Because the SMA is so slow, price will typically cross the PMax line well before the SMA does in a new trend. A weekly price close above the PMax line, for instance, gives an early alert that the downtrend might be ending, even though the 50-week SMA is still above the price. Given the low volatility, these price cross signals in utilities are reliable enough to act on (they won’t occur frequently unless there is a meaningful change). They provide swing traders or long-term investors a timelier indication to re-evaluate positions.
Real Estate – Low to Moderate Volatility
Real Estate – Low to Moderate Volatility
Timeframe: Weekly – The real estate sector (primarily REITs) is generally considered a defensive/income-oriented sector, with relatively low volatility and investment stability (real estate provides steady rental income)
. While REITs can react to interest rate changes (causing medium volatility at times), a weekly timeframe is appropriate to focus on overarching trends and income-driven price moves.
Source: Close – Use weekly closing prices.
ATR Length: 14 – A 14-week ATR is used. Real estate stocks don’t usually experience rapid volatility shifts; a standard period captures their average range. It will smooth out occasional jumps (e.g. due to rate surprises or economic shocks) while representing the normal week-to-week volatility.
ATR Multiplier: 2.5 – We choose a slightly tighter stop (2.5× ATR) for this relatively stable sector. Real estate investment trusts often trade with moderate volatility, somewhat like utilities (many REITs have betas below 1). A 2.5 multiplier means the PMax will track closer to price, which is acceptable because large swings are uncommon
. It ensures that if the trend does reverse, the stop (and signals) will trigger sooner rather than later, preserving gains.
Moving Average Type: EMA – We opt for an EMA here. Although real estate is defensive, it can react faster than staples/utilities to macro changes (like interest rate moves). An EMA will adjust a bit quicker to a new trend in prices than an SMA. For example, if REITs begin falling due to rising rates, the EMA will start dropping and allow PMax to flip sooner. This choice adds a touch of responsiveness for a sector that, while not highly volatile, is sensitive to external factors.
Moving Average Length: 50 – A 50-week EMA tracks roughly a year’s trend. This highlights the major trend (up or down) in real estate stocks, filtering out shorter oscillations (like quarter-to-quarter portfolio adjustments). It’s long enough to represent the slow-moving nature of real estate valuations, yet with the EMA and a slightly lower ATR multiplier, the PMax can still respond within a reasonable time when conditions change. In essence, we’re targeting significant trend moves in REITs (which often correlate with multi-month interest rate trends or economic cycles).
Change ATR Calculation Method: No – Use the default ATR calculation. It’s sufficient for REIT volatility and ensures a steady measure of true range.
Normalize ATR: No – Not applied; we set the parameters specifically for this sector’s volatility profile.
Show Moving Average: Yes – Display the 50-week EMA to gauge trend direction and to use its crossover with PMax for signals.
Show Crossing Signals: Yes – EMA/PMax crossing signals are enabled. Though infrequent on a weekly 50 EMA, when they do occur, they confirm a robust trend change (for instance, a sustained recovery or a bear market in real estate).
Show Price/PMax Crossing Signals: Yes – Enable price/PMax cross signals for early warning. Real estate prices can begin to turn before the long EMA catches up, especially if there’s a sudden shift in interest rate outlook. A weekly price close crossing the PMax line will alert the trader to a potential trend reversal in advance. Since this sector “doesn't have high volatility”
and generally moves in a more controlled fashion, these price cross signals are reliable and not overly frequent. They provide a useful prompt to pay attention, which can be followed by the confirmed EMA crossover later.
<br>
Summary Table (CSV-Format)
Summary Table (CSV-Format)
Below is a compiled table of the PMax settings for each industry, formatted for easy export (each row corresponds to an industry with all its parameters):
Industry
Timeframe
Source
ATR Length
ATR Multiplier
MA Type
MA Length
Change ATR Method
Normalize ATR
Show MA
Show Crossing Signals
Show Price/PMax Signals
Information Technology
Daily
Close
14
3.0
EMA
50
No
No
Yes
Yes
No
Health Care
Daily
Close
10
3.0
EMA
100
No
No
Yes
Yes
Yes
Financials
Daily
Close
14
3.0
EMA
100
No
No
Yes
Yes
Yes
Energy
Daily
Close
14
3.5
WMA
100
No
No
Yes
Yes
No
Consumer Discretionary
Daily
Close
14
3.0
EMA
50
No
No
Yes
Yes
No
Consumer Staples
Weekly
Close
10
2.5
VAR
50
No
No
Yes
Yes
Yes
Industrials
Daily
Close
14
3.0
EMA
100
No
No
Yes
Yes
Yes
Materials
Daily
Close
14
3.5
EMA
50
No
No
Yes
Yes
No
Communication Services
Daily
Close
14
3.0
EMA
60
No
No
Yes
Yes
No
Utilities
Weekly
Close
14
2.5
SMA
50
No
No
Yes
Yes
Yes
Real Estate
Weekly
Close
14
2.5
EMA
50
No
No
Yes
Yes
Yes
Each configuration is tuned to the industry’s profile – for instance, Energy and Materials use a higher ATR multiplier to account for volatility
, while Staples and Utilities use a lower multiplier and longer timeframe due to their stable, low-volatility nature
. These settings serve as a starting point; traders should visually verify and adjust if necessary for specific securities
, as individual stock behavior can vary even within a sector. The goal is to capture swing and long-term trends with the PMax, minimizing false signals in choppy markets and providing timely signals in trending markets, tailored to each industry’s typical trading characteristics.
Sources: Key parameter rationale is supported by volatility data for each sector (Investopedia/other sources) and by the PMax indicator’s author notes on parameter effects
.
Page updated
Google Sites
Report abuse