Tunç ŞatıroğluTunç Şatıroğlu's Technical Analysis Suite
Description:
This comprehensive Pine Script indicator, inspired by the technical analysis teachings of Tunç Şatıroğlu, integrates six powerful TradingView indicators into a single, user-friendly suite for robust trend, momentum, and divergence analysis. Each component has been carefully selected and enhanced by beytun to improve functionality, performance, and visual clarity, aligning with Şatıroğlu's approach to technical analysis. The default configuration is meticulously set to match the exact settings of the individual indicators as used by Tunç Şatıroğlu in his training, ensuring authenticity and ease of use for followers of his methodology. Whether you're a beginner or an experienced trader, this suite provides a versatile toolkit for analyzing markets across multiple timeframes.
Included Indicators:
1. WaveTrend with Crosses (by LazyBear, modified): A momentum oscillator that identifies overbought/oversold conditions and trend reversals with clear buy/sell signals via crosses and bar color highlights.
2. Kaufman Adaptive Moving Average (KAMA) (by HPotter, modified): A dynamic moving average that adapts to market volatility, offering a smoother trend-following signal.
3. SuperTrend (by Alex Orekhov, modified): A trend-following indicator that plots dynamic support/resistance levels with buy/sell signals and optional wicks for enhanced accuracy.
4. Nadaraya-Watson Envelope (by LuxAlgo, modified): A non-linear envelope that highlights potential reversals with customizable repainting options for smoother outputs.
5. Divergence for Many Indicators v4 (by LonesomeTheBlue, modified): Detects regular and hidden divergences across multiple indicators (MACD, RSI, Stochastic, CCI, Momentum, OBV, VWMA, CMF, MFI, and more) for early reversal signals.
6. Ichimoku Cloud (TradingView built-in, modified): A multi-faceted indicator for trend direction, support/resistance, and momentum, with enhanced visuals for the Kumo Cloud.
Key Features:
- Authentic Default Settings : Pre-configured to mirror the exact parameters used by Tunç Şatıroğlu for each indicator, ensuring alignment with his proven technical analysis approach.
- Customizable Settings : Enable/disable individual indicators and fine-tune parameters to suit your trading style while retaining the option to revert to Şatıroğlu’s defaults.
- Enhanced User Experience : Modifications improve visual clarity, performance, and usability, with options like repainting smoothing for Nadaraya-Watson and adjustable Ichimoku projection periods.
- Multi-Timeframe Analysis : Combines trend-following, momentum, and divergence tools for a holistic view of market dynamics.
- Alert Conditions : Built-in alerts for SuperTrend direction changes, buy/sell signals, and divergence detections to keep you informed.
- Visual Clarity : Overlays (KAMA, SuperTrend, Nadaraya-Watson, Ichimoku) and pane-based indicators (WaveTrend, Divergences) are clearly distinguished, with customizable colors and styles.
Notes:
- The Nadaraya-Watson Envelope and Ichimoku Cloud may repaint in their default modes. Use the "Repainting Smoothing" option for Nadaraya-Watson or adjust Ichimoku settings to mitigate repainting if preferred.
- Published under the MIT License, with components licensed under GPL-3.0 (SuperTrend), CC BY-NC-SA 4.0 (Nadaraya-Watson), MPL 2.0 (Divergence), and TradingView's terms (Ichimoku Cloud).
Usage:
Add this indicator to your TradingView chart to leverage Tunç Şatıroğlu’s exact indicator configurations out of the box. Customize settings as needed to align with your strategy, and use the combined signals to identify trends, reversals, and divergences. Ideal for traders following Şatıroğlu’s methodologies or anyone seeking a powerful, all-in-one technical analysis tool.
Credits:
Original authors: LazyBear, HPotter, Alex Orekhov, LuxAlgo, LonesomeTheBlue, and TradingView.
Modifications and integration by beytun .
License:
Published under the MIT License, incorporating code under GPL-3.0, CC BY-NC-SA 4.0, MPL 2.0, and TradingView’s terms where applicable.
אינדיקטורים ואסטרטגיות
Keltner Channels v1 [JopAlgo]Keltner Channels v1 — a clean volatility envelope for timing pullbacks, breakouts, and risk
Keltner Channels are a moving-average centerline with volatility-based bands above and below. They give you a live “speed limit” for price: when the market is calm, bands are tight (expect mean reversion); when volatility expands, bands widen (trend moves can breathe). KC v1 keeps the classic idea but adds a small twist that traders appreciate in crypto: an adaptive centerline that switches between EMA and SMA based on trendiness, plus a choice of how you measure volatility for the bands.
This makes KC v1 useful for any timeframe—from fast scalps to multi-day swings—because it answers three practical questions on every chart:
Where’s the “middle” of price right now? (the centerline)
How far is “far” for current volatility? (the bands)
Should I fade back to the middle or ride with the expansion? (context from band width + slope)
If you attach screenshots to your script page, show one image labeling Upper / Middle / Lower bands with a classic pullback-to-middle entry, and another showing a band expansion where price hugs the outer band in trend.
What you’re seeing (and how it’s computed)
Middle band (MA):
KC v5 computes both an EMA and an SMA of your source (default close) with the same length, then auto-selects the middle band:
If ATR > SMA(ATR) over length, KC marks the market as trending and uses the EMA (faster, responsive).
Otherwise, it uses the SMA (steadier) in balance.
Result: you get a centerline that’s calm in chop and snappier in trend, without touching settings.
Upper / Lower bands:
upper = middle + (mult × volatility)
lower = middle - (mult × volatility)
You choose the volatility measure via Bands Style:
Average True Range (default): smooth, robust; uses ATR(atrlength). Best all-around choice.
True Range: raw TR each bar (more jumpy; reacts to gaps and spikes quickly).
Range: RMA of (high - low) over length (gentler; good for tight mean-reversion regimes).
Colors & fill:
Upper = red, Lower = green, Middle = white, with muted fill between bands so you can still read candles.
How to use Keltner Channels on any timeframe
Same framework everywhere: trade with the envelope when expanding, fade back to the middle when contracting—but only at objective locations and with healthy flow.
Scalping (1–5m)
Pullback-to-middle entry: In a micro-trend, wait for price to retrace to the middle band and print a hold. Enter with the trend, stop just beyond the opposite side of the middle or below minor structure; first target is the near band.
Band tap fades (only in contraction): When bands are tightening and the middle is flat, quick fades from upper → middle or lower → middle are high-probability if your volume/flow read doesn’t show aggressive pressure against you.
Avoid: Fading when bands expand and middle slopes—expect continuation instead.
Intraday (15m–1H)
Continuation rides: When bands open up (volatility expansion) and the middle slopes, price often walks the outer band. Enter on minor pullbacks that hold above the middle (for longs) and trail using the middle band or a structure stop.
Squeeze to break: A period of narrowing bands often precedes a move. Let price close outside the channel with good flow, then buy the retest toward the middle that holds.
Swing (2H–4H)
Trend participation: In established trends, treat pullbacks to the middle band as your primary entry. The upper/lower band is not a take-profit by itself—use it with Volume Profile targets (POC/HVNs) or key swing levels.
Mean reversion in balance: When the middle is flat and bands are tight over many bars, fade outer band → middle at Volume Profile edges, provided your flow read isn’t showing absorption against your idea.
Position (1D–1W)
Context: Use KC to judge regime (wide bands + slope = trend; tight/flat = balance). Position entries come from pullbacks to middle that coincide with Weekly AVWAP / VP value edges.
Entries, exits, and risk (simple rules)
Trend entry (with expansion):
Wait for band expansion + sloping middle in your direction. Enter on the first clean pullback to middle (or shallow pullback that can’t even tag middle).
Stop: below the middle band or just beyond local swing.
Trail: by the middle band in trend, or step-trail under pivots.
Targets: next Volume Profile HVN/POC or structural levels; the far Keltner band is a context line, not a hard TP.
Mean-reversion entry (in contraction):
Bands tight + flat middle → fade outer band back to middle at a Volume Profile VA edge.
Stop: just beyond the band.
Target: middle band (first), opposite band if flow remains weak.
Breakout confirmation:
A strong close outside the band by itself can be a trap. Treat it as signal only when your flow read confirms (see “Combining with other tools”).
Settings that actually matter (and how to tune them)
MA Length (default 20): controls both middle smoothness and the trending test (ATR vs SMA(ATR)).
Shorter (10–14) reacts faster, more whips in chop.
Longer (30–50) steadier middle, better for swings/position.
Multiplier (default 2.0): scales band distance.
Crypto majors: 1.8–2.2 is a good starting range on 15m–4H.
Volatile alts: 2.2–2.6 to avoid over-triggering.
If you keep getting faked out on fades: increase the multiplier.
If the channel rarely contains price for long stretches: decrease slightly.
Bands Style:
ATR for most use cases;
TR when you want maximum responsiveness to spikes;
Range for calmer envelopes in slow, balanced markets.
ATR Length (default 10): only applies if you choose ATR for band style.
Shorter = quicker band changes, good for scalps;
Longer = steadier bands for swings.
Note: KC v1 auto-selects EMA vs SMA for the middle band using the ATR trend test. That’s intentional, so you don’t have to toggle it manually.
What to look for (pattern cheatsheet)
Walk-the-band: In expansion, price hugs the outer band and barely returns to the middle—ride, don’t fade.
First touch of middle in trend: Often the cleanest add or first entry after a breakout.
Band pinch (“squeeze”): A long, narrow channel with flat middle sets up a breakout. Wait for acceptance (close outside + hold on retest).
False break tell: Price pokes outside band but closes back inside quickly—watch for reversion to middle, especially if your flow read shows Absorption against the poke.
Combining KC v1 with other tools
like the Cumulative Volume Delta v1 (CVDv1):
Do not chase an outside-band move if CVDv1 shows Absorption—that’s a classic failed break.
Prefer pullbacks to the middle band when Alignment = OK and Imbalance % is strong in your direction.
Reclaim setups: after a poke outside the band, a CVD divergence on the return through the middle often precedes a mean-reversion run.
Volume Profile v3.2 :
Use VAH/VAL/LVNs for location. A pullback-to-middle that coincides with VA boundary is A-tier.
Breakouts through LVNs with expanding bands tend to travel fast toward the next HVN/POC—good for continuation targets.
(A great screenshot: KC middle kiss at VAL with CVDv1 Efficient, then a move to POC.)
Common pitfalls KC v1 helps you avoid
Fading expansion: Trying to short the upper band when bands are widening and middle slopes up is how you get steamrolled. KC tells you it’s not that kind of day.
Chasing inside contraction: Buying every tiny outside poke while bands are pinched leads to whips. Let acceptance form; buy the retest to middle that holds.
Stops too tight: In trend, volatility is elevated; stops need to live beyond the middle or behind structure, not right at the band.
Practical defaults to start with
Length: 20
Multiplier: 2.0 (adjust ±0.2–0.4 per asset)
Bands Style: ATR
ATR Length: 10
Timeframes: works out of the box on 15m–4H; for 1–5m scalps, consider length=14; for daily swings, length=30.
Open source & disclaimer
This indicator is provided open source so traders can study, test, and adapt it to their workflow. No tool guarantees outcomes; risk management is essential.
Disclaimer — Not Financial Advice.
The “Keltner Channels v1 ” indicator and this description are provided for educational purposes only and do not constitute financial or investment advice. Trading involves risk, including possible loss of capital. makes no warranties and assumes no responsibility for any trading decisions or outcomes resulting from the use of this script. Past performance is not indicative of future results.
RTH & ETH High/Low (Today & Prev)RTH and ETH High Low Indicator. This draws a line for the ETH and RTH highs and lows for todays session and yesterdays session. it allows you to toggle off any of the 8 potential levels as well as change the colour of the 8 levels. WIP
Live Position SizerThis position calculator locks onto the live price in real time and calculates your lot and quantity size for you. Best for scalping if you don't want to open a limit order. You input all the necessary data (Account size, risk, SL placement, LONG/SHORT position, etc...) It also has a nifty feature of allowing you the ability to see TP brackets (+1R, +2R, +3R).
The best way I have used it is seeing where my potential SL will go before I consider opening a position and inputting that. Then when I'm ready to open a position, I already have it calculated for me.
Anchored VWAP Bands v3.3 [JopAlgo]Anchored VWAP — a fair-value compass you can trust on any timeframe
If Volume Profile shows where business concentrated, Anchored VWAP (AVWAP) shows what the crowd has paid on average since a moment that matters. It’s a running average of price weighted by traded volume, reset at a point you choose (the “anchor”). That makes AVWAP a reliable fair-value line: above it, the average participant since the anchor is in profit; below it, they’re under water. Markets naturally react around that line.
This version focuses on the parts that make AVWAP practical for real trading:
Clear, event-anchored VWAP so you can ask “since this moment, where is fair value?”
Optional higher-timeframe anchors (e.g., Weekly AVWAP) to define regime
Simple visuals so newer traders can read it instantly, and advanced traders can layer multiple anchors without clutter
(When you add screenshots: image #1 should point to the main AVWAP line with a label “fair value since anchor”, and show a bounce/reject. Image #2 can show confluence: AVWAP kissing VAL/VAH from Volume Profile v3.2 or a clean reclaim through AVWAP.)
What you’re seeing (and why price cares)
The AVWAP line: the volume-weighted average price since your anchor time.
Price above AVWAP → average long (since anchor) is in profit → pullbacks to AVWAP tend to support.
Price below AVWAP → average long is losing → rallies to AVWAP tend to resist.
Multiple anchors (optional): you can plot more than one AVWAP (e.g., Weekly AVWAP and an Event AVWAP) to separate regime (weekly) from tactical timing (event/session).
AVWAP works because it ties “fair value” to time and participation, not just price. When price departs far from AVWAP and then returns, participants frequently defend the line. When price accepts on the other side (closes and holds), that’s often a regime change relative to the anchor.
Anchors: how to pick them (and what changes when you do)
An anchor is simply the timestamp where you start the calculation. Changing it changes both context and expectations:
Session anchors (intraday) — e.g., session open, London/NY open.
Use: scalps/intraday plays.
Behavior: frequent tests; strong for fade-to-mean trades and quick reclaims.
Event anchors — listing, major news, ETF approval, earnings, a swing high/low.
Use: track how price behaves since the catalyst.
Behavior: excellent for measured pullbacks and “is the market digesting this event yet?”
Weekly/Monthly anchors — the Weekly AVWAP is a regime line.
Use: swing/position bias.
Behavior: clean “reclaim” and “rejection” signals; great with Volume Profile’s VAH/VAL.
Rule of thumb:
Choose the slowest anchor that defines your bias (e.g., Weekly AVWAP for swings) and one faster anchor for timing (e.g., Session/Event AVWAP). Too many lines → hesitation.
How to use AVWAP on any timeframe
The framework doesn’t change—only your anchor choices and expectations do.
Scalping (1–5m charts)
Anchors: Session open, London/NY open, or the prior swing low/high.
Setup: If price trends away from the session AVWAP, fade back to AVWAP only when flow isn’t showing absorption against you (pair with CVDv1). If price reclaims AVWAP after a push below, look for inside-back retests at the line.
Intraday (15m–1H)
Anchors: Session open + important event AVWAP (FOMC-like news, ETF day, etc.).
Setup: Use pullbacks to AVWAP to join trend; require acceptance above/below (close and hold) before flipping bias. Confluence of AVWAP with VP v3.2’s VAH/VAL = high-quality location.
Swing (2H–4H)
Anchors: Weekly AVWAP for regime + event AVWAP for timing.
Setup: Reclaim of Weekly AVWAP → prefer longs on pullbacks to that line; rejection → fade rallies into Weekly AVWAP (target POC/HVNs from VP). The best entries are AVWAP + VP edge with CVDv1 not flashing Absorption.
Position (1D–1W)
Anchors: Monthly/Quarterly/Cycle AVWAP.
Setup: Treat the higher-timeframe AVWAP as the mean. Acceptance through it (and hold) often marks cycle transitions. Add on pullbacks to the line that hold.
Reading reclaims, rejections, and “acceptance”
Reclaim: price trades below AVWAP, then closes back above and holds on a retest → bullish signal since the anchor.
Rejection: price pops above AVWAP, prints rejection wick and closes back under → bearish.
Acceptance: multiple bars closing and holding beyond AVWAP, ideally with CVDv1 Alignment OK and no Absorption → higher odds the move persists.
With Volume Profile v3.2, treat AVWAP at VAL/VAH as A-tier locations:
VAL + AVWAP reclaim → mean-reversion long to POC is common.
VAH + AVWAP rejection → fade back to POC or to the next HVN.
Settings that matter (and simple defaults)
Names may vary by version, but these are the ideas you’ll see.
Anchor Time — pick a timestamp (session open, event, week start). Newer traders: start with Session AVWAP intraday; add Weekly AVWAP for swings.
Multiple anchors — if enabled, you can show Weekly AVWAP alongside your custom anchor. Keep it to two lines to stay decisive.
Smoothing / Display — most traders use raw AVWAP (no smoothing). Make sure the line is visible across zoom levels.
Theme & Colors — use distinct colors for each anchor (e.g., white for Weekly, aqua for Session/Event) so you don’t mix them up.
How AVWAP pairs with other tools
Cumulative Volume Delta v1 (CVDv1) — confirms flow quality at AVWAP.
Don’t chase a tag through AVWAP if CVD Absorption is red (typical failed break conditions).
Do prefer reclaims when Alignment = OK and Imbalance % is strong for your anchor.
Volume Profile v3.2 — gives you objective levels (POC/VAH/VAL/HVNs).
AVWAP + VAH/VAL confluence is where you plan trades.
Passing through an LVN toward AVWAP often travels fast; use that to manage risk.
(Add a screenshot that highlights AVWAP touching VAL with CVDv1 “Efficient” → clean bounce to POC.)
A simple, durable playbook
Pick one slow anchor (e.g., Weekly) for bias and one fast anchor (Session/Event) for timing.
Trade at the line, not mid-air: reclaims and rejections at AVWAP are your signals.
Require confirmation from flow: CVDv1 Alignment OK, Imbalance strong, Absorption ≠ red on the trigger bar.
Add Volume Profile v3.2 for targets (POC/HVNs) and edges (VAH/VAL).
If price accepts beyond AVWAP (closes and holds), stop fading and instead join pullbacks to the line.
Common mistakes AVWAP solves
“Mean keeps moving, my MA lies.” AVWAP weights actual traded volume, so fair value adapts to where business was done, not just where price wandered.
“It broke the line and reversed.” That’s no acceptance (or CVDv1 flagged Absorption). Wait for the retest/hold.
“Too many lines, can’t decide.” Keep two anchors max: one for bias, one for timing.
Practical defaults to start with
Intraday: Session AVWAP only. Add an Event AVWAP on special days.
Swing: Weekly AVWAP + one Event AVWAP (start of move or weekly open).
Colors: Distinct but readable (e.g., white for Weekly, aqua for Session/Event).
No smoothing. Let the line be honest—your eyes adjust quickly.
Open source & disclaimer
This indicator is provided open source so you can learn, test, and adapt it to your workflow. Trading involves risk; tools guide decisions but don’t remove uncertainty.
Disclaimer — Not Financial Advice.
The “Anchored VWAP ” indicator and this description are for educational purposes only and do not constitute financial or investment advice. Markets involve risk, including possible loss of capital. makes no warranties and assumes no responsibility for any trading decisions or outcomes resulting from the use of this script. Past performance is not indicative of future results.
Use Anchored VWAP for the where and when around fair value; let CVDv1 judge who’s pushing, and let Volume Profile v3.2 define targets and edges. That trio stays reliable across any timeframe.
SM OTC style Supply/Demand Zones Lite+//@version=6
indicator("OTC SD MTF Lite+", "OTCSDmtf+", overlay=true, max_boxes_count=200, max_labels_count=200)
// ================= Inputs =================
useH4 = input.bool(true, "Show 4H zones")
useD1 = input.bool(true, "Show 1D zones")
useW1 = input.bool(true, "Show 1W zones")
useM1 = input.bool(false, "Show 1M zones")
baseLen = input.int(2, "Base length (HTF bars)", 1, 5)
wickPctMax = input.float(35.0, "Max wick % in base", 0, 100)
impulseX = input.float(1.5, "Departure body vs ATR (x)", 0.5, 5.0)
atrLen = input.int(14, "ATR length (HTF)")
extendBars = input.int(2000, "Extend bars on chart", 200, 10000)
maxPerTF = input.int(12, "Max zones per TF", 3, 30)
showLegend = input.bool(true, "Show tiny legend (4H/1D/1W/1M)")
onlyNearest = input.bool(false, "Show ONLY nearest zone above/below")
hideOverlapTF = input.bool(true, "Hide overlapping zones within each TF (keep newest)")
showNearestLabels = input.bool(false, "Show distance labels to nearest above/below")
// --- Hard cap for future drawing with xloc.bar_index ---
FUTURE_CAP = 500
// Colors (Demand hues per TF). Supply uses red for contrast.
colH4 = color.new(color.teal, 78)
colD1 = color.new(color.blue, 78)
colW1 = color.new(color.orange, 78)
colM1 = color.new(color.purple, 78)
colSup= color.new(color.red, 78)
// ================= Helpers =================
wickiness(h, l, o, c) =>
rng = math.max(h - l, syminfo.mintick)
topW = h - math.max(o, c)
botW = math.min(o, c) - l
100.0 * (topW + botW) / rng
// Returns: (dTrig, dProx, dDist, sTrig, sProx, sDist)
f_htfSignals(baseBars, wickMax, xImpulse, aLen) =>
float _o = open
float _h = high
float _l = low
float _c = close
float _atr = ta.atr(aLen)
bool ok = true
for i = 1 to baseBars
ok := ok and (wickiness(_h , _l , _o , _c ) <= wickMax)
bool bullDepart = _c > _o and (_c - _o) > xImpulse * _atr
bool bearDepart = _c < _o and (_o - _c) > xImpulse * _atr
float dTrig = 0.0
float dProx = na
float dDist = na
float sTrig = 0.0
float sProx = na
float sDist = na
if ok and bullDepart
float hi = ta.highest(_h, baseBars)
float lo = ta.lowest(_l, baseBars)
dTrig := 1.0
dProx := lo
dDist := hi
if ok and bearDepart
float hi2 = ta.highest(_h, baseBars)
float lo2 = ta.lowest(_l, baseBars)
sTrig := 1.0
sProx := hi2
sDist := lo2
// ================= Pull HTF signals =================
= request.security(syminfo.tickerid, "240", f_htfSignals(baseLen, wickPctMax, impulseX, atrLen))
= request.security(syminfo.tickerid, "D", f_htfSignals(baseLen, wickPctMax, impulseX, atrLen))
= request.security(syminfo.tickerid, "W", f_htfSignals(baseLen, wickPctMax, impulseX, atrLen))
= request.security(syminfo.tickerid, "M", f_htfSignals(baseLen, wickPctMax, impulseX, atrLen))
// ================= Storage per TF =================
var zH4 = array.new_box()
var aH4 = array.new_bool()
var lH4 = array.new_label()
var sH4 = array.new_int() // 1 = Demand, -1 = Supply
var zD1 = array.new_box()
var aD1 = array.new_bool()
var lD1 = array.new_label()
var sD1 = array.new_int()
var zW1 = array.new_box()
var aW1 = array.new_bool()
var lW1 = array.new_label()
var sW1 = array.new_int()
var zM1 = array.new_box()
var aM1 = array.new_bool()
var lM1 = array.new_label()
var sM1 = array.new_int()
// ================= Overlap utils =================
overlap(topA, botA, topB, botB) =>
not (topA < botB or botA > topB)
purgeOverlaps(arrB, arrA, arrL, newTop, newBot) =>
if hideOverlapTF and array.size(arrB) > 0
for i = 0 to array.size(arrB) - 1
if array.get(arrA, i)
box bOld = array.get(arrB, i)
float t = box.get_top(bOld)
float btm = box.get_bottom(bOld)
if overlap(newTop, newBot, t, btm)
box.delete(bOld)
label.delete(array.get(arrL, i))
array.set(arrA, i, false)
// ================= Add zone =================
addZone(arrB, arrA, arrL, arrS, topV, botV, baseColor, isDemand) =>
purgeOverlaps(arrB, arrA, arrL, topV, botV)
int leftX = bar_index - 1
int rightX = bar_index + math.min(extendBars, FUTURE_CAP) // respect +500 cap
box b = box.new(leftX, topV, rightX, botV, xloc=xloc.bar_index, bgcolor=baseColor, border_color=color.new(color.black, 0))
float ly = isDemand == 1 ? topV : botV
st = isDemand == 1 ? label.style_label_down : label.style_label_up
string tagTxt = isDemand == 1 ? "Demand" : "Supply"
label l = label.new(leftX, ly, tagTxt, xloc=xloc.bar_index, style=st, textcolor=color.white, color=color.new(color.black, 0), size=size.tiny)
array.push(arrB, b)
array.push(arrA, true)
array.push(arrL, l)
array.push(arrS, isDemand)
if array.size(arrB) > maxPerTF
box.delete(array.shift(arrB))
array.shift(arrA)
label.delete(array.shift(arrL))
array.shift(arrS)
// ================= Maintain / Invalidate =================
extendAll(arrB, arrA) =>
if array.size(arrB) > 0
for i = 0 to array.size(arrB) - 1
if array.get(arrA, i)
box.set_right(array.get(arrB, i), bar_index + math.min(extendBars, FUTURE_CAP)) // respect +500 cap
invalidate(arrB, arrA, arrL) =>
if array.size(arrB) > 0
for i = 0 to array.size(arrB) - 1
if array.get(arrA, i)
box b = array.get(arrB, i)
float t = box.get_top(b)
float btm = box.get_bottom(b)
// Close outside band → remove
if close > t or close < btm
box.delete(b)
label.delete(array.get(arrL, i))
array.set(arrA, i, false)
// ================= New HTF bar flags (strict booleans) =================
int chH4 = ta.change(time("240"))
int chD1 = ta.change(time("D"))
int chW1 = ta.change(time("W"))
int chM1 = ta.change(time("M"))
bool newBarH4 = useH4 and (not na(chH4)) and (chH4 != 0)
bool newBarD1 = useD1 and (not na(chD1)) and (chD1 != 0)
bool newBarW1 = useW1 and (not na(chW1)) and (chW1 != 0)
bool newBarM1 = useM1 and (not na(chM1)) and (chM1 != 0)
// ================= Create zones on new HTF bar =================
if newBarH4
if d4t > 0 and not na(d4p) and not na(d4d)
addZone(zH4, aH4, lH4, sH4, d4d, d4p, colH4, 1)
if s4t > 0 and not na(s4p) and not na(s4d)
addZone(zH4, aH4, lH4, sH4, s4p, s4d, colSup, -1)
if newBarD1
if d1t > 0 and not na(d1p) and not na(d1d)
addZone(zD1, aD1, lD1, sD1, d1d, d1p, colD1, 1)
if s1t > 0 and not na(s1p) and not na(s1d)
addZone(zD1, aD1, lD1, sD1, s1p, s1d, colSup, -1)
if newBarW1
if w1t > 0 and not na(w1p) and not na(w1d)
addZone(zW1, aW1, lW1, sW1, w1d, w1p, colW1, 1)
if swt > 0 and not na(swp) and not na(swd)
addZone(zW1, aW1, lW1, sW1, swp, swd, colSup, -1)
if newBarM1
if m1t > 0 and not na(m1p) and not na(m1d)
addZone(zM1, aM1, lM1, sM1, m1d, m1p, colM1, 1)
if smt > 0 and not na(smp) and not na(smd)
addZone(zM1, aM1, lM1, sM1, smp, smd, colSup, -1)
// ================= Maintain & Invalidate (every bar) =================
extendAll(zH4, aH4)
extendAll(zD1, aD1)
extendAll(zW1, aW1)
extendAll(zM1, aM1)
invalidate(zH4, aH4, lH4)
invalidate(zD1, aD1, lD1)
invalidate(zW1, aW1, lW1)
invalidate(zM1, aM1, lM1)
// ================= Nearest across all TFs =================
tfNearest(arrB, arrA) =>
int upIdx = -1
int dnIdx = -1
float upDist = 1e10
float dnDist = 1e10
float upBtm = na
float dnTop = na
if array.size(arrB) > 0
for i = 0 to array.size(arrB) - 1
if array.get(arrA, i)
box b = array.get(arrB, i)
float t = box.get_top(b)
float btm = box.get_bottom(b)
if btm >= close
float d = btm - close
if d < upDist
upDist := d
upIdx := i
upBtm := btm
if t <= close
float d2 = close - t
if d2 < dnDist
dnDist := d2
dnIdx := i
dnTop := t
= tfNearest(zH4, aH4)
= tfNearest(zD1, aD1)
= tfNearest(zW1, aW1)
= tfNearest(zM1, aM1)
float upBest = 1e10, dnBest = 1e10
int upArr = -1, upIdxSel = -1, dnArr = -1, dnIdxSel = -1
color upColor = color.new(color.white, 100), dnColor = color.new(color.white, 100)
if (not na(uh4y)) and uh4d < upBest
upBest := uh4d, upArr := 0, upIdxSel := uh4i, upColor := colH4
if (not na(ud1y)) and ud1d < upBest
upBest := ud1d, upArr := 1, upIdxSel := ud1i, upColor := colD1
if (not na(uw1y)) and uw1d < upBest
upBest := uw1d, upArr := 2, upIdxSel := uw1i, upColor := colW1
if (not na(um1y)) and um1d < upBest
upBest := um1d, upArr := 3, upIdxSel := um1i, upColor := colM1
if (not na(dh4y)) and dh4d < dnBest
dnBest := dh4d, dnArr := 0, dnIdxSel := dh4i, dnColor := colH4
if (not na(dd1y)) and dd1d < dnBest
dnBest := dd1d, dnArr := 1, dnIdxSel := dd1i, dnColor := colD1
if (not na(dw1y)) and dw1d < dnBest
dnBest := dw1d, dnArr := 2, dnIdxSel := dw1i, dnColor := colW1
if (not na(dm1y)) and dm1d < dnBest
dnBest := dm1d, dnArr := 3, dnIdxSel := dm1i, dnColor := colM1
// ================= Nearest-only visibility (optional) =================
hideAll(arrB, arrA) =>
if array.size(arrB) > 0
for i = 0 to array.size(arrB) - 1
if array.get(arrA, i)
box.set_bgcolor(array.get(arrB, i), color.new(color.white, 100))
box.set_border_color(array.get(arrB, i), color.new(color.white, 100))
showOne(arrB, arrA, arrS, idx, demColor) =>
if idx >= 0 and idx < array.size(arrB)
if array.get(arrA, idx)
bool isDemand = array.get(arrS, idx) == 1
color c = isDemand ? demColor : colSup
box.set_bgcolor(array.get(arrB, idx), c)
box.set_border_color(array.get(arrB, idx), color.new(color.black, 0))
if onlyNearest
hideAll(zH4, aH4), hideAll(zD1, aD1), hideAll(zW1, aW1), hideAll(zM1, aM1)
if upArr == 0
showOne(zH4, aH4, sH4, upIdxSel, upColor)
if upArr == 1
showOne(zD1, aD1, sD1, upIdxSel, upColor)
if upArr == 2
showOne(zW1, aW1, sW1, upIdxSel, upColor)
if upArr == 3
showOne(zM1, aM1, sM1, upIdxSel, upColor)
if dnArr == 0
showOne(zH4, aH4, sH4, dnIdxSel, dnColor)
if dnArr == 1
showOne(zD1, aD1, sD1, dnIdxSel, dnColor)
if dnArr == 2
showOne(zW1, aW1, sW1, dnIdxSel, dnColor)
if dnArr == 3
showOne(zM1, aM1, sM1, dnIdxSel, dnColor)
// ================= Nearest distance labels (optional) =================
var label nearUp = na
var label nearDn = na
makeNearLabel(y, txt) =>
label.new(bar_index, y, txt, xloc=xloc.bar_index, style=label.style_label_left, color=color.new(color.black, 0), textcolor=color.white, size=size.tiny)
if showNearestLabels
if not na(nearUp)
label.delete(nearUp)
if not na(nearDn)
label.delete(nearDn)
if upArr != -1
box bUp = upArr == 0 ? array.get(zH4, upIdxSel) : upArr == 1 ? array.get(zD1, upIdxSel) : upArr == 2 ? array.get(zW1, upIdxSel) : array.get(zM1, upIdxSel)
float upBtm = box.get_bottom(bUp)
float pctUp = math.round(10000.0 * (upBtm - close) / close) / 100.0
nearUp := makeNearLabel(upBtm, "Nearest Above ~ " + str.tostring(pctUp) + "%")
if dnArr != -1
box bDn = dnArr == 0 ? array.get(zH4, dnIdxSel) : dnArr == 1 ? array.get(zD1, dnIdxSel) : dnArr == 2 ? array.get(zW1, dnIdxSel) : array.get(zM1, dnIdxSel)
float dnTop = box.get_top(bDn)
float pctDn = math.round(10000.0 * (close - dnTop) / close) / 100.0
nearDn := makeNearLabel(dnTop, "Nearest Below ~ " + str.tostring(pctDn) + "%")
// ================= Tiny legend (dots) =================
var table legend = na
if showLegend and na(legend)
legend := table.new(position.top_left, 4, 1)
if showLegend and not na(legend)
table.cell(legend, 0, 0, "● 4H", text_color=color.white, bgcolor=color.new(color.black, 0))
table.cell(legend, 1, 0, "● 1D", text_color=color.white, bgcolor=color.new(color.black, 0))
table.cell(legend, 2, 0, "● 1W", text_color=color.white, bgcolor=color.new(color.black, 0))
table.cell(legend, 3, 0, "● 1M", text_color=color.white, bgcolor=color.new(color.black, 0))
table.cell_set_bgcolor(legend, 0, 0, color.new(color.teal, 70))
table.cell_set_bgcolor(legend, 1, 0, color.new(color.blue, 70))
table.cell_set_bgcolor(legend, 2, 0, color.new(color.orange, 70))
table.cell_set_bgcolor(legend, 3, 0, color.new(color.purple, 70))
DTM 444 BANDS 🚀DTM 444 BANDS 🚀:
The DTM 444 BANDS 🚀 is a powerful, multi-purpose trading indicator combining Supertrend, Dynamic Band Levels, Breakout Signals, and Volume Confirmation to help traders identify high-probability trade setups across different timeframes.
🔧 Key Features
✅ Multi-Timeframe Support
Analyze price action across any timeframe using the Timeframe input.
All band calculations (High, Low, Midline, and Supertrend) are pulled from a higher timeframe for clearer context.
✅ Dynamic Bands Based on Supertrend
High Band: Rolling highest of Supertrend over hiLen period.
Low Band: Rolling lowest of Supertrend over loLen period.
Midline: Midpoint of the above.
Acts like dynamic support/resistance, ideal for trend-following and breakout strategies.
✅ Dual Signal System
Breakout Signals (Buy and Sell): Triggered when price breaks the bands with volume confirmation.
Supertrend Crossover Signals (Buy1 and Sell1): Classic momentum entries with a confirmation twist.
Exit Signals: Optional take-profit/neutral indicators when price reverses.
✅ Volume Confirmation Filter (Optional)
Only triggers signals if the volume exceeds its 20-period SMA.
Helps filter out false breakouts and weak trends in low-liquidity periods.
✅ Visual Enhancements
Color-coded candles based on band positioning (e.g., red = weak, green = strong, etc.)
On-chart labels for each signal for quick reference.
Real-time Signal Dashboard using Pine Script tables showing:
Current signal
Volume filter status
Live volume vs volume SMA
🧪 Practical Use Cases
Trend Traders: Use the Supertrend cross and band breakouts to ride trends early.
Breakout Traders: Catch high-probability moves outside established ranges.
Swing Traders: Time entries and exits using color-coded bars and exit labels.
Volume-Sensitive Traders: Focus on trades with strong volume backing.
📊 Backtest Snapshot
Based on the example chart for Reliance Industries (RELIANCE.NS) on the weekly timeframe:
Several profitable buy and breakout signals during uptrends.
Timely exits and breakdown alerts before reversals.
Volume filter keeps trades clean and avoids noise.
⚙️ Customizable Parameters
High Length and Low Length (default: 19)
Supertrend Multiplier and ATR Length
Volume Filter: Toggle ON/OFF
Volume SMA Length: Default 20
Custom Timeframe: Choose any higher timeframe for multi-timeframe analysis
📢 Alerts Ready
Fully integrated with TradingView alerts:
Breakout & Breakdown
Supertrend crossovers
All alerts respect the volume filter setting
🏁 Final Thoughts
DTM 444 BANDS 🚀 is a versatile and adaptive trading system that blends trend analysis, volatility bands, and volume validation. Whether you're a trend trader, breakout hunter, or swing trader — this tool gives you a structured edge with clear visual cues and real-time alerts.
MARA / mNAV=1 (x)What it does
This script overlays two signals on the MARA chart:
mNAV=1 fair-value line — the MARA price implied by Bitcoin NAV:
mNAV1 = (BTC price × BTC holdings) / MARA shares
Premium/Discount ratio — how far MARA trades vs. its NAV fair value:
Ratio = Close / mNAV1 (1.00 = fair; >1 = premium; <1 = discount)
Inputs
Shares outstanding (default: 370,460,000)
BTC holdings (official or estimated; you can roll forward +25 BTC/day if you want)
BTC symbol used for pricing (e.g., BTCUSD, BTCUSDT, BTCUSDTPERP)
How to use
When Price < mNAV=1 and Ratio < 1.00 → MARA trades at a discount to BTC NAV (potential mean-reversion if BTC is stable).
When Price > mNAV=1 and Ratio > 1.00 → premium (premium often compresses during BTC chop/weakness).
Rule of thumb (with ~53k BTC and 370.46M shares): +$1,000 BTC ≈ +$0.14 on the mNAV=1 line.
Visuals
Blue line = mNAV=1 (fair value) plotted directly on the MARA chart.
Purple line = Ratio (×) on a separate right-hand scale centered around 1.00.
Optional shading: green when Ratio > 1.05 (+5% premium), red when Ratio < 0.95 (−5% discount).
Alerts (suggested)
Premium > +5%: Ratio > 1.05
Discount < −5%: Ratio < 0.95
Notes
This is a proxy for NAV parity; it assumes your BTC holdings input is correct (official last report or your estimate).
Choice of BTC symbol matters; use the feed that best matches your workflow (spot, perp, or index).
The ratio is most informative when BTC is range-bound; during fast BTC moves MARA can overshoot temporarily.
Current Price (Customizable) by DRtradeCurrent Price Line & Dynamic Label (Fully Customizable)
The ultimate tool for clear, real-time price visualization.
This powerful, lightweight indicator draws a clean horizontal line at the current market price, updating instantly with every price tick. Unlike other current price line scripts, this tool ensures you always see where the price is right now and provides full control over every visual element.
Key Features:
- Real-Time Tracking: The line moves dynamically with price ticks within the current candle, eliminating lag and providing true current market price awareness.
- Line Extension Control: Choose to extend: Left, Right, or Both. Helpful for scalpers and options traders
- Visual Customizations: Color, Style, Size, Width, etc.
- Label Positioning: Left of Candle, Above Candle, or Right of Candle
All customization options are available in the indicator's settings menu.
Ping me with feature reqeusts.
Ultra Clean Support / Resistance LevelsThis provides an Ultra Clean look for Support and Resistance levels
Ultra Clean Support / Resistance LevelsThis Provides a very clean Support and Resistance level on any timeframe
Monthly Melodies by Bryan Ramirez Three months make up a quarter. My indicator highlights each month in a distinct color: the first month in green, the second in yellow, and the third in orange. Each month functions as its own time window, where the algorithm may lean toward one side of the market more than the other. I like pairing this with Steve Moore’s seasonal tendencies for added context. This approach applies across Futures, Forex, and Crypto.
Impulse Range Compression & Expansion (IRCE)📌 Impulse Range Compression & Expansion (IRCE) – Visualizing Price Traps Before Breakouts
📖 Overview
The IRCE Indicator is a precision breakout detection tool designed to identify consolidation traps and price coil zones before expansion moves occur. Unlike traditional volatility indicators that rely solely on statistical thresholds (e.g., Bollinger Bands or ATR), IRCE focuses on behavioral price compression, detecting tight-range candle clusters and validating breakouts through body expansion and/or volume surges.
This makes it ideal for traders looking to:
• Catch breakouts from range traps
• Avoid choppy and premature signals
• Spot early-stage momentum moves based on clean price behavior
⸻
⚙️ How It Works
1. Impulse Range Compression Detection
• Measures the high-low range of each candle
• Compares it to a user-defined average range (default 7 bars)
• Flags candles where the range is significantly smaller (e.g., <60% of average)
• Groups these into tight clusters, indicating compression zones or potential “trap ranges”
2. Cluster Box Construction
• When a valid cluster (e.g., 3 or more tight candles) is detected, the indicator:
• Marks the high and low of the cluster
• Draws a shaded box over this “trap zone”
• This helps visually track where price has coiled before a breakout
3. Breakout Confirmation Logic
A breakout from the trap zone is only validated when:
• Price closes above the cluster high (bullish) or below the cluster low (bearish)
• One or both of the following confirm strength:
• Body Expansion: Current candle body is 120%+ of recent average
• Volume Expansion: Volume exceeds recent volume average
4. Optional Trend Filter
• An optional EMA filter (default: 50 EMA) ensures breakout signals align with trend direction
• Helps filter out countertrend noise in ranging markets
5. Signal Cooldown
• Prevents repeated signals by enforcing a cooldown period (e.g., 10 bars) between entries
⸻
🖥️ Visual Elements
• 📦 Yellow compression boxes represent tight price traps
• 🟢 Buy labels appear when price breaks above the trap with confirmation
• 🔴 Sell labels appear when price breaks below with confirmation
• All visuals are non-repainting and updated in real-time
🧠 How to Use
1. Wait for a yellow trap box to appear
2. Watch for a confirmed breakout from the trap zone
3. Take the trade in the direction of the breakout:
• Only if it satisfies body or volume confirmation
• And if trend alignment is enabled, it must match EMA direction
4. Place stops just outside the opposite end of the trap zone
5. Use risk/reward ratios or structure levels for exits
This logic works great on:
• Lower timeframes (scalping breakouts)
• Higher timeframes (detecting price coiling before major moves)
• Any market: Stocks, Crypto, FX, Commodities
⸻
🔒 Technical Notes
• ✅ No repainting
• ✅ No future-looking logic
• ✅ Suitable for both discretionary and systematic traders
• ✅ Built in Pine Script v6
Dynamic Equity Allocation Model"Cash is Trash"? Not Always. Here's Why Science Beats Guesswork.
Every retail trader knows the frustration: you draw support and resistance lines, you spot patterns, you follow market gurus on social media—and still, when the next bear market hits, your portfolio bleeds red. Meanwhile, institutional investors seem to navigate market turbulence with ease, preserving capital when markets crash and participating when they rally. What's their secret?
The answer isn't insider information or access to exotic derivatives. It's systematic, scientifically validated decision-making. While most retail traders rely on subjective chart analysis and emotional reactions, professional portfolio managers use quantitative models that remove emotion from the equation and process multiple streams of market information simultaneously.
This document presents exactly such a system—not a proprietary black box available only to hedge funds, but a fully transparent, academically grounded framework that any serious investor can understand and apply. The Dynamic Equity Allocation Model (DEAM) synthesizes decades of financial research from Nobel laureates and leading academics into a practical tool for tactical asset allocation.
Stop drawing colorful lines on your chart and start thinking like a quant. This isn't about predicting where the market goes next week—it's about systematically adjusting your risk exposure based on what the data actually tells you. When valuations scream danger, when volatility spikes, when credit markets freeze, when multiple warning signals align—that's when cash isn't trash. That's when cash saves your portfolio.
The irony of "cash is trash" rhetoric is that it ignores timing. Yes, being 100% cash for decades would be disastrous. But being 100% equities through every crisis is equally foolish. The sophisticated approach is dynamic: aggressive when conditions favor risk-taking, defensive when they don't. This model shows you how to make that decision systematically, not emotionally.
Whether you're managing your own retirement portfolio or seeking to understand how institutional allocation strategies work, this comprehensive analysis provides the theoretical foundation, mathematical implementation, and practical guidance to elevate your investment approach from amateur to professional.
The choice is yours: keep hoping your chart patterns work out, or start using the same quantitative methods that professionals rely on. The tools are here. The research is cited. The methodology is explained. All you need to do is read, understand, and apply.
The Dynamic Equity Allocation Model (DEAM) is a quantitative framework for systematic allocation between equities and cash, grounded in modern portfolio theory and empirical market research. The model integrates five scientifically validated dimensions of market analysis—market regime, risk metrics, valuation, sentiment, and macroeconomic conditions—to generate dynamic allocation recommendations ranging from 0% to 100% equity exposure. This work documents the theoretical foundations, mathematical implementation, and practical application of this multi-factor approach.
1. Introduction and Theoretical Background
1.1 The Limitations of Static Portfolio Allocation
Traditional portfolio theory, as formulated by Markowitz (1952) in his seminal work "Portfolio Selection," assumes an optimal static allocation where investors distribute their wealth across asset classes according to their risk aversion. This approach rests on the assumption that returns and risks remain constant over time. However, empirical research demonstrates that this assumption does not hold in reality. Fama and French (1989) showed that expected returns vary over time and correlate with macroeconomic variables such as the spread between long-term and short-term interest rates. Campbell and Shiller (1988) demonstrated that the price-earnings ratio possesses predictive power for future stock returns, providing a foundation for dynamic allocation strategies.
The academic literature on tactical asset allocation has evolved considerably over recent decades. Ilmanen (2011) argues in "Expected Returns" that investors can improve their risk-adjusted returns by considering valuation levels, business cycles, and market sentiment. The Dynamic Equity Allocation Model presented here builds on this research tradition and operationalizes these insights into a practically applicable allocation framework.
1.2 Multi-Factor Approaches in Asset Allocation
Modern financial research has shown that different factors capture distinct aspects of market dynamics and together provide a more robust picture of market conditions than individual indicators. Ross (1976) developed the Arbitrage Pricing Theory, a model that employs multiple factors to explain security returns. Following this multi-factor philosophy, DEAM integrates five complementary analytical dimensions, each tapping different information sources and collectively enabling comprehensive market understanding.
2. Data Foundation and Data Quality
2.1 Data Sources Used
The model draws its data exclusively from publicly available market data via the TradingView platform. This transparency and accessibility is a significant advantage over proprietary models that rely on non-public data. The data foundation encompasses several categories of market information, each capturing specific aspects of market dynamics.
First, price data for the S&P 500 Index is obtained through the SPDR S&P 500 ETF (ticker: SPY). The use of a highly liquid ETF instead of the index itself has practical reasons, as ETF data is available in real-time and reflects actual tradability. In addition to closing prices, high, low, and volume data are captured, which are required for calculating advanced volatility measures.
Fundamental corporate metrics are retrieved via TradingView's Financial Data API. These include earnings per share, price-to-earnings ratio, return on equity, debt-to-equity ratio, dividend yield, and share buyback yield. Cochrane (2011) emphasizes in "Presidential Address: Discount Rates" the central importance of valuation metrics for forecasting future returns, making these fundamental data a cornerstone of the model.
Volatility indicators are represented by the CBOE Volatility Index (VIX) and related metrics. The VIX, often referred to as the market's "fear gauge," measures the implied volatility of S&P 500 index options and serves as a proxy for market participants' risk perception. Whaley (2000) describes in "The Investor Fear Gauge" the construction and interpretation of the VIX and its use as a sentiment indicator.
Macroeconomic data includes yield curve information through US Treasury bonds of various maturities and credit risk premiums through the spread between high-yield bonds and risk-free government bonds. These variables capture the macroeconomic conditions and financing conditions relevant for equity valuation. Estrella and Hardouvelis (1991) showed that the shape of the yield curve has predictive power for future economic activity, justifying the inclusion of these data.
2.2 Handling Missing Data
A practical problem when working with financial data is dealing with missing or unavailable values. The model implements a fallback system where a plausible historical average value is stored for each fundamental metric. When current data is unavailable for a specific point in time, this fallback value is used. This approach ensures that the model remains functional even during temporary data outages and avoids systematic biases from missing data. The use of average values as fallback is conservative, as it generates neither overly optimistic nor pessimistic signals.
3. Component 1: Market Regime Detection
3.1 The Concept of Market Regimes
The idea that financial markets exist in different "regimes" or states that differ in their statistical properties has a long tradition in financial science. Hamilton (1989) developed regime-switching models that allow distinguishing between different market states with different return and volatility characteristics. The practical application of this theory consists of identifying the current market state and adjusting portfolio allocation accordingly.
DEAM classifies market regimes using a scoring system that considers three main dimensions: trend strength, volatility level, and drawdown depth. This multidimensional view is more robust than focusing on individual indicators, as it captures various facets of market dynamics. Classification occurs into six distinct regimes: Strong Bull, Bull Market, Neutral, Correction, Bear Market, and Crisis.
3.2 Trend Analysis Through Moving Averages
Moving averages are among the oldest and most widely used technical indicators and have also received attention in academic literature. Brock, Lakonishok, and LeBaron (1992) examined in "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns" the profitability of trading rules based on moving averages and found evidence for their predictive power, although later studies questioned the robustness of these results when considering transaction costs.
The model calculates three moving averages with different time windows: a 20-day average (approximately one trading month), a 50-day average (approximately one quarter), and a 200-day average (approximately one trading year). The relationship of the current price to these averages and the relationship of the averages to each other provide information about trend strength and direction. When the price trades above all three averages and the short-term average is above the long-term, this indicates an established uptrend. The model assigns points based on these constellations, with longer-term trends weighted more heavily as they are considered more persistent.
3.3 Volatility Regimes
Volatility, understood as the standard deviation of returns, is a central concept of financial theory and serves as the primary risk measure. However, research has shown that volatility is not constant but changes over time and occurs in clusters—a phenomenon first documented by Mandelbrot (1963) and later formalized through ARCH and GARCH models (Engle, 1982; Bollerslev, 1986).
DEAM calculates volatility not only through the classic method of return standard deviation but also uses more advanced estimators such as the Parkinson estimator and the Garman-Klass estimator. These methods utilize intraday information (high and low prices) and are more efficient than simple close-to-close volatility estimators. The Parkinson estimator (Parkinson, 1980) uses the range between high and low of a trading day and is based on the recognition that this information reveals more about true volatility than just the closing price difference. The Garman-Klass estimator (Garman and Klass, 1980) extends this approach by additionally considering opening and closing prices.
The calculated volatility is annualized by multiplying it by the square root of 252 (the average number of trading days per year), enabling standardized comparability. The model compares current volatility with the VIX, the implied volatility from option prices. A low VIX (below 15) signals market comfort and increases the regime score, while a high VIX (above 35) indicates market stress and reduces the score. This interpretation follows the empirical observation that elevated volatility is typically associated with falling markets (Schwert, 1989).
3.4 Drawdown Analysis
A drawdown refers to the percentage decline from the highest point (peak) to the lowest point (trough) during a specific period. This metric is psychologically significant for investors as it represents the maximum loss experienced. Calmar (1991) developed the Calmar Ratio, which relates return to maximum drawdown, underscoring the practical relevance of this metric.
The model calculates current drawdown as the percentage distance from the highest price of the last 252 trading days (one year). A drawdown below 3% is considered negligible and maximally increases the regime score. As drawdown increases, the score decreases progressively, with drawdowns above 20% classified as severe and indicating a crisis or bear market regime. These thresholds are empirically motivated by historical market cycles, in which corrections typically encompassed 5-10% drawdowns, bear markets 20-30%, and crises over 30%.
3.5 Regime Classification
Final regime classification occurs through aggregation of scores from trend (40% weight), volatility (30%), and drawdown (30%). The higher weighting of trend reflects the empirical observation that trend-following strategies have historically delivered robust results (Moskowitz, Ooi, and Pedersen, 2012). A total score above 80 signals a strong bull market with established uptrend, low volatility, and minimal losses. At a score below 10, a crisis situation exists requiring defensive positioning. The six regime categories enable a differentiated allocation strategy that not only distinguishes binarily between bullish and bearish but allows gradual gradations.
4. Component 2: Risk-Based Allocation
4.1 Volatility Targeting as Risk Management Approach
The concept of volatility targeting is based on the idea that investors should maximize not returns but risk-adjusted returns. Sharpe (1966, 1994) defined with the Sharpe Ratio the fundamental concept of return per unit of risk, measured as volatility. Volatility targeting goes a step further and adjusts portfolio allocation to achieve constant target volatility. This means that in times of low market volatility, equity allocation is increased, and in times of high volatility, it is reduced.
Moreira and Muir (2017) showed in "Volatility-Managed Portfolios" that strategies that adjust their exposure based on volatility forecasts achieve higher Sharpe Ratios than passive buy-and-hold strategies. DEAM implements this principle by defining a target portfolio volatility (default 12% annualized) and adjusting equity allocation to achieve it. The mathematical foundation is simple: if market volatility is 20% and target volatility is 12%, equity allocation should be 60% (12/20 = 0.6), with the remaining 40% held in cash with zero volatility.
4.2 Market Volatility Calculation
Estimating current market volatility is central to the risk-based allocation approach. The model uses several volatility estimators in parallel and selects the higher value between traditional close-to-close volatility and the Parkinson estimator. This conservative choice ensures the model does not underestimate true volatility, which could lead to excessive risk exposure.
Traditional volatility calculation uses logarithmic returns, as these have mathematically advantageous properties (additive linkage over multiple periods). The logarithmic return is calculated as ln(P_t / P_{t-1}), where P_t is the price at time t. The standard deviation of these returns over a rolling 20-trading-day window is then multiplied by √252 to obtain annualized volatility. This annualization is based on the assumption of independently identically distributed returns, which is an idealization but widely accepted in practice.
The Parkinson estimator uses additional information from the trading range (High minus Low) of each day. The formula is: σ_P = (1/√(4ln2)) × √(1/n × Σln²(H_i/L_i)) × √252, where H_i and L_i are high and low prices. Under ideal conditions, this estimator is approximately five times more efficient than the close-to-close estimator (Parkinson, 1980), as it uses more information per observation.
4.3 Drawdown-Based Position Size Adjustment
In addition to volatility targeting, the model implements drawdown-based risk control. The logic is that deep market declines often signal further losses and therefore justify exposure reduction. This behavior corresponds with the concept of path-dependent risk tolerance: investors who have already suffered losses are typically less willing to take additional risk (Kahneman and Tversky, 1979).
The model defines a maximum portfolio drawdown as a target parameter (default 15%). Since portfolio volatility and portfolio drawdown are proportional to equity allocation (assuming cash has neither volatility nor drawdown), allocation-based control is possible. For example, if the market exhibits a 25% drawdown and target portfolio drawdown is 15%, equity allocation should be at most 60% (15/25).
4.4 Dynamic Risk Adjustment
An advanced feature of DEAM is dynamic adjustment of risk-based allocation through a feedback mechanism. The model continuously estimates what actual portfolio volatility and portfolio drawdown would result at the current allocation. If risk utilization (ratio of actual to target risk) exceeds 1.0, allocation is reduced by an adjustment factor that grows exponentially with overutilization. This implements a form of dynamic feedback that avoids overexposure.
Mathematically, a risk adjustment factor r_adjust is calculated: if risk utilization u > 1, then r_adjust = exp(-0.5 × (u - 1)). This exponential function ensures that moderate overutilization is gently corrected, while strong overutilization triggers drastic reductions. The factor 0.5 in the exponent was empirically calibrated to achieve a balanced ratio between sensitivity and stability.
5. Component 3: Valuation Analysis
5.1 Theoretical Foundations of Fundamental Valuation
DEAM's valuation component is based on the fundamental premise that the intrinsic value of a security is determined by its future cash flows and that deviations between market price and intrinsic value are eventually corrected. Graham and Dodd (1934) established in "Security Analysis" the basic principles of fundamental analysis that remain relevant today. Translated into modern portfolio context, this means that markets with high valuation metrics (high price-earnings ratios) should have lower expected returns than cheaply valued markets.
Campbell and Shiller (1988) developed the Cyclically Adjusted P/E Ratio (CAPE), which smooths earnings over a full business cycle. Their empirical analysis showed that this ratio has significant predictive power for 10-year returns. Asness, Moskowitz, and Pedersen (2013) demonstrated in "Value and Momentum Everywhere" that value effects exist not only in individual stocks but also in asset classes and markets.
5.2 Equity Risk Premium as Central Valuation Metric
The Equity Risk Premium (ERP) is defined as the expected excess return of stocks over risk-free government bonds. It is the theoretical heart of valuation analysis, as it represents the compensation investors demand for bearing equity risk. Damodaran (2012) discusses in "Equity Risk Premiums: Determinants, Estimation and Implications" various methods for ERP estimation.
DEAM calculates ERP not through a single method but combines four complementary approaches with different weights. This multi-method strategy increases estimation robustness and avoids dependence on single, potentially erroneous inputs.
The first method (35% weight) uses earnings yield, calculated as 1/P/E or directly from operating earnings data, and subtracts the 10-year Treasury yield. This method follows Fed Model logic (Yardeni, 2003), although this model has theoretical weaknesses as it does not consistently treat inflation (Asness, 2003).
The second method (30% weight) extends earnings yield by share buyback yield. Share buybacks are a form of capital return to shareholders and increase value per share. Boudoukh et al. (2007) showed in "The Total Shareholder Yield" that the sum of dividend yield and buyback yield is a better predictor of future returns than dividend yield alone.
The third method (20% weight) implements the Gordon Growth Model (Gordon, 1962), which models stock value as the sum of discounted future dividends. Under constant growth g assumption: Expected Return = Dividend Yield + g. The model estimates sustainable growth as g = ROE × (1 - Payout Ratio), where ROE is return on equity and payout ratio is the ratio of dividends to earnings. This formula follows from equity theory: unretained earnings are reinvested at ROE and generate additional earnings growth.
The fourth method (15% weight) combines total shareholder yield (Dividend + Buybacks) with implied growth derived from revenue growth. This method considers that companies with strong revenue growth should generate higher future earnings, even if current valuations do not yet fully reflect this.
The final ERP is the weighted average of these four methods. A high ERP (above 4%) signals attractive valuations and increases the valuation score to 95 out of 100 possible points. A negative ERP, where stocks have lower expected returns than bonds, results in a minimal score of 10.
5.3 Quality Adjustments to Valuation
Valuation metrics alone can be misleading if not interpreted in the context of company quality. A company with a low P/E may be cheap or fundamentally problematic. The model therefore implements quality adjustments based on growth, profitability, and capital structure.
Revenue growth above 10% annually adds 10 points to the valuation score, moderate growth above 5% adds 5 points. This adjustment reflects that growth has independent value (Modigliani and Miller, 1961, extended by later growth theory). Net margin above 15% signals pricing power and operational efficiency and increases the score by 5 points, while low margins below 8% indicate competitive pressure and subtract 5 points.
Return on equity (ROE) above 20% characterizes outstanding capital efficiency and increases the score by 5 points. Piotroski (2000) showed in "Value Investing: The Use of Historical Financial Statement Information" that fundamental quality signals such as high ROE can improve the performance of value strategies.
Capital structure is evaluated through the debt-to-equity ratio. A conservative ratio below 1.0 multiplies the valuation score by 1.2, while high leverage above 2.0 applies a multiplier of 0.8. This adjustment reflects that high debt constrains financial flexibility and can become problematic in crisis times (Korteweg, 2010).
6. Component 4: Sentiment Analysis
6.1 The Role of Sentiment in Financial Markets
Investor sentiment, defined as the collective psychological attitude of market participants, influences asset prices independently of fundamental data. Baker and Wurgler (2006, 2007) developed a sentiment index and showed that periods of high sentiment are followed by overvaluations that later correct. This insight justifies integrating a sentiment component into allocation decisions.
Sentiment is difficult to measure directly but can be proxied through market indicators. The VIX is the most widely used sentiment indicator, as it aggregates implied volatility from option prices. High VIX values reflect elevated uncertainty and risk aversion, while low values signal market comfort. Whaley (2009) refers to the VIX as the "Investor Fear Gauge" and documents its role as a contrarian indicator: extremely high values typically occur at market bottoms, while low values occur at tops.
6.2 VIX-Based Sentiment Assessment
DEAM uses statistical normalization of the VIX by calculating the Z-score: z = (VIX_current - VIX_average) / VIX_standard_deviation. The Z-score indicates how many standard deviations the current VIX is from the historical average. This approach is more robust than absolute thresholds, as it adapts to the average volatility level, which can vary over longer periods.
A Z-score below -1.5 (VIX is 1.5 standard deviations below average) signals exceptionally low risk perception and adds 40 points to the sentiment score. This may seem counterintuitive—shouldn't low fear be bullish? However, the logic follows the contrarian principle: when no one is afraid, everyone is already invested, and there is limited further upside potential (Zweig, 1973). Conversely, a Z-score above 1.5 (extreme fear) adds -40 points, reflecting market panic but simultaneously suggesting potential buying opportunities.
6.3 VIX Term Structure as Sentiment Signal
The VIX term structure provides additional sentiment information. Normally, the VIX trades in contango, meaning longer-term VIX futures have higher prices than short-term. This reflects that short-term volatility is currently known, while long-term volatility is more uncertain and carries a risk premium. The model compares the VIX with VIX9D (9-day volatility) and identifies backwardation (VIX > 1.05 × VIX9D) and steep backwardation (VIX > 1.15 × VIX9D).
Backwardation occurs when short-term implied volatility is higher than longer-term, which typically happens during market stress. Investors anticipate immediate turbulence but expect calming. Psychologically, this reflects acute fear. The model subtracts 15 points for backwardation and 30 for steep backwardation, as these constellations signal elevated risk. Simon and Wiggins (2001) analyzed the VIX futures curve and showed that backwardation is associated with market declines.
6.4 Safe-Haven Flows
During crisis times, investors flee from risky assets into safe havens: gold, US dollar, and Japanese yen. This "flight to quality" is a sentiment signal. The model calculates the performance of these assets relative to stocks over the last 20 trading days. When gold or the dollar strongly rise while stocks fall, this indicates elevated risk aversion.
The safe-haven component is calculated as the difference between safe-haven performance and stock performance. Positive values (safe havens outperform) subtract up to 20 points from the sentiment score, negative values (stocks outperform) add up to 10 points. The asymmetric treatment (larger deduction for risk-off than bonus for risk-on) reflects that risk-off movements are typically sharper and more informative than risk-on phases.
Baur and Lucey (2010) examined safe-haven properties of gold and showed that gold indeed exhibits negative correlation with stocks during extreme market movements, confirming its role as crisis protection.
7. Component 5: Macroeconomic Analysis
7.1 The Yield Curve as Economic Indicator
The yield curve, represented as yields of government bonds of various maturities, contains aggregated expectations about future interest rates, inflation, and economic growth. The slope of the yield curve has remarkable predictive power for recessions. Estrella and Mishkin (1998) showed that an inverted yield curve (short-term rates higher than long-term) predicts recessions with high reliability. This is because inverted curves reflect restrictive monetary policy: the central bank raises short-term rates to combat inflation, dampening economic activity.
DEAM calculates two spread measures: the 2-year-minus-10-year spread and the 3-month-minus-10-year spread. A steep, positive curve (spreads above 1.5% and 2% respectively) signals healthy growth expectations and generates the maximum yield curve score of 40 points. A flat curve (spreads near zero) reduces the score to 20 points. An inverted curve (negative spreads) is particularly alarming and results in only 10 points.
The choice of two different spreads increases analysis robustness. The 2-10 spread is most established in academic literature, while the 3M-10Y spread is often considered more sensitive, as the 3-month rate directly reflects current monetary policy (Ang, Piazzesi, and Wei, 2006).
7.2 Credit Conditions and Spreads
Credit spreads—the yield difference between risky corporate bonds and safe government bonds—reflect risk perception in the credit market. Gilchrist and Zakrajšek (2012) constructed an "Excess Bond Premium" that measures the component of credit spreads not explained by fundamentals and showed this is a predictor of future economic activity and stock returns.
The model approximates credit spread by comparing the yield of high-yield bond ETFs (HYG) with investment-grade bond ETFs (LQD). A narrow spread below 200 basis points signals healthy credit conditions and risk appetite, contributing 30 points to the macro score. Very wide spreads above 1000 basis points (as during the 2008 financial crisis) signal credit crunch and generate zero points.
Additionally, the model evaluates whether "flight to quality" is occurring, identified through strong performance of Treasury bonds (TLT) with simultaneous weakness in high-yield bonds. This constellation indicates elevated risk aversion and reduces the credit conditions score.
7.3 Financial Stability at Corporate Level
While the yield curve and credit spreads reflect macroeconomic conditions, financial stability evaluates the health of companies themselves. The model uses the aggregated debt-to-equity ratio and return on equity of the S&P 500 as proxies for corporate health.
A low leverage level below 0.5 combined with high ROE above 15% signals robust corporate balance sheets and generates 20 points. This combination is particularly valuable as it represents both defensive strength (low debt means crisis resistance) and offensive strength (high ROE means earnings power). High leverage above 1.5 generates only 5 points, as it implies vulnerability to interest rate increases and recessions.
Korteweg (2010) showed in "The Net Benefits to Leverage" that optimal debt maximizes firm value, but excessive debt increases distress costs. At the aggregated market level, high debt indicates fragilities that can become problematic during stress phases.
8. Component 6: Crisis Detection
8.1 The Need for Systematic Crisis Detection
Financial crises are rare but extremely impactful events that suspend normal statistical relationships. During normal market volatility, diversified portfolios and traditional risk management approaches function, but during systemic crises, seemingly independent assets suddenly correlate strongly, and losses exceed historical expectations (Longin and Solnik, 2001). This justifies a separate crisis detection mechanism that operates independently of regular allocation components.
Reinhart and Rogoff (2009) documented in "This Time Is Different: Eight Centuries of Financial Folly" recurring patterns in financial crises: extreme volatility, massive drawdowns, credit market dysfunction, and asset price collapse. DEAM operationalizes these patterns into quantifiable crisis indicators.
8.2 Multi-Signal Crisis Identification
The model uses a counter-based approach where various stress signals are identified and aggregated. This methodology is more robust than relying on a single indicator, as true crises typically occur simultaneously across multiple dimensions. A single signal may be a false alarm, but the simultaneous presence of multiple signals increases confidence.
The first indicator is a VIX above the crisis threshold (default 40), adding one point. A VIX above 60 (as in 2008 and March 2020) adds two additional points, as such extreme values are historically very rare. This tiered approach captures the intensity of volatility.
The second indicator is market drawdown. A drawdown above 15% adds one point, as corrections of this magnitude can be potential harbingers of larger crises. A drawdown above 25% adds another point, as historical bear markets typically encompass 25-40% drawdowns.
The third indicator is credit market spreads above 500 basis points, adding one point. Such wide spreads occur only during significant credit market disruptions, as in 2008 during the Lehman crisis.
The fourth indicator identifies simultaneous losses in stocks and bonds. Normally, Treasury bonds act as a hedge against equity risk (negative correlation), but when both fall simultaneously, this indicates systemic liquidity problems or inflation/stagflation fears. The model checks whether both SPY and TLT have fallen more than 10% and 5% respectively over 5 trading days, adding two points.
The fifth indicator is a volume spike combined with negative returns. Extreme trading volumes (above twice the 20-day average) with falling prices signal panic selling. This adds one point.
A crisis situation is diagnosed when at least 3 indicators trigger, a severe crisis at 5 or more indicators. These thresholds were calibrated through historical backtesting to identify true crises (2008, 2020) without generating excessive false alarms.
8.3 Crisis-Based Allocation Override
When a crisis is detected, the system overrides the normal allocation recommendation and caps equity allocation at maximum 25%. In a severe crisis, the cap is set at 10%. This drastic defensive posture follows the empirical observation that crises typically require time to develop and that early reduction can avoid substantial losses (Faber, 2007).
This override logic implements a "safety first" principle: in situations of existential danger to the portfolio, capital preservation becomes the top priority. Roy (1952) formalized this approach in "Safety First and the Holding of Assets," arguing that investors should primarily minimize ruin probability.
9. Integration and Final Allocation Calculation
9.1 Component Weighting
The final allocation recommendation emerges through weighted aggregation of the five components. The standard weighting is: Market Regime 35%, Risk Management 25%, Valuation 20%, Sentiment 15%, Macro 5%. These weights reflect both theoretical considerations and empirical backtesting results.
The highest weighting of market regime is based on evidence that trend-following and momentum strategies have delivered robust results across various asset classes and time periods (Moskowitz, Ooi, and Pedersen, 2012). Current market momentum is highly informative for the near future, although it provides no information about long-term expectations.
The substantial weighting of risk management (25%) follows from the central importance of risk control. Wealth preservation is the foundation of long-term wealth creation, and systematic risk management is demonstrably value-creating (Moreira and Muir, 2017).
The valuation component receives 20% weight, based on the long-term mean reversion of valuation metrics. While valuation has limited short-term predictive power (bull and bear markets can begin at any valuation), the long-term relationship between valuation and returns is robustly documented (Campbell and Shiller, 1988).
Sentiment (15%) and Macro (5%) receive lower weights, as these factors are subtler and harder to measure. Sentiment is valuable as a contrarian indicator at extremes but less informative in normal ranges. Macro variables such as the yield curve have strong predictive power for recessions, but the transmission from recessions to stock market performance is complex and temporally variable.
9.2 Model Type Adjustments
DEAM allows users to choose between four model types: Conservative, Balanced, Aggressive, and Adaptive. This choice modifies the final allocation through additive adjustments.
Conservative mode subtracts 10 percentage points from allocation, resulting in consistently more cautious positioning. This is suitable for risk-averse investors or those with limited investment horizons. Aggressive mode adds 10 percentage points, suitable for risk-tolerant investors with long horizons.
Adaptive mode implements procyclical adjustment based on short-term momentum: if the market has risen more than 5% in the last 20 days, 5 percentage points are added; if it has declined more than 5%, 5 points are subtracted. This logic follows the observation that short-term momentum persists (Jegadeesh and Titman, 1993), but the moderate size of adjustment avoids excessive timing bets.
Balanced mode makes no adjustment and uses raw model output. This neutral setting is suitable for investors who wish to trust model recommendations unchanged.
9.3 Smoothing and Stability
The allocation resulting from aggregation undergoes final smoothing through a simple moving average over 3 periods. This smoothing is crucial for model practicality, as it reduces frequent trading and thus transaction costs. Without smoothing, the model could fluctuate between adjacent allocations with every small input change.
The choice of 3 periods as smoothing window is a compromise between responsiveness and stability. Longer smoothing would excessively delay signals and impede response to true regime changes. Shorter or no smoothing would allow too much noise. Empirical tests showed that 3-period smoothing offers an optimal ratio between these goals.
10. Visualization and Interpretation
10.1 Main Output: Equity Allocation
DEAM's primary output is a time series from 0 to 100 representing the recommended percentage allocation to equities. This representation is intuitive: 100% means full investment in stocks (specifically: an S&P 500 ETF), 0% means complete cash position, and intermediate values correspond to mixed portfolios. A value of 60% means, for example: invest 60% of wealth in SPY, hold 40% in money market instruments or cash.
The time series is color-coded to enable quick visual interpretation. Green shades represent high allocations (above 80%, bullish), red shades low allocations (below 20%, bearish), and neutral colors middle allocations. The chart background is dynamically colored based on the signal, enhancing readability in different market phases.
10.2 Dashboard Metrics
A tabular dashboard presents key metrics compactly. This includes current allocation, cash allocation (complement), an aggregated signal (BULLISH/NEUTRAL/BEARISH), current market regime, VIX level, market drawdown, and crisis status.
Additionally, fundamental metrics are displayed: P/E Ratio, Equity Risk Premium, Return on Equity, Debt-to-Equity Ratio, and Total Shareholder Yield. This transparency allows users to understand model decisions and form their own assessments.
Component scores (Regime, Risk, Valuation, Sentiment, Macro) are also displayed, each normalized on a 0-100 scale. This shows which factors primarily drive the current recommendation. If, for example, the Risk score is very low (20) while other scores are moderate (50-60), this indicates that risk management considerations are pulling allocation down.
10.3 Component Breakdown (Optional)
Advanced users can display individual components as separate lines in the chart. This enables analysis of component dynamics: do all components move synchronously, or are there divergences? Divergences can be particularly informative. If, for example, the market regime is bullish (high score) but the valuation component is very negative, this signals an overbought market not fundamentally supported—a classic "bubble warning."
This feature is disabled by default to keep the chart clean but can be activated for deeper analysis.
10.4 Confidence Bands
The model optionally displays uncertainty bands around the main allocation line. These are calculated as ±1 standard deviation of allocation over a rolling 20-period window. Wide bands indicate high volatility of model recommendations, suggesting uncertain market conditions. Narrow bands indicate stable recommendations.
This visualization implements a concept of epistemic uncertainty—uncertainty about the model estimate itself, not just market volatility. In phases where various indicators send conflicting signals, the allocation recommendation becomes more volatile, manifesting in wider bands. Users can understand this as a warning to act more cautiously or consult alternative information sources.
11. Alert System
11.1 Allocation Alerts
DEAM implements an alert system that notifies users of significant events. Allocation alerts trigger when smoothed allocation crosses certain thresholds. An alert is generated when allocation reaches 80% (from below), signaling strong bullish conditions. Another alert triggers when allocation falls to 20%, indicating defensive positioning.
These thresholds are not arbitrary but correspond with boundaries between model regimes. An allocation of 80% roughly corresponds to a clear bull market regime, while 20% corresponds to a bear market regime. Alerts at these points are therefore informative about fundamental regime shifts.
11.2 Crisis Alerts
Separate alerts trigger upon detection of crisis and severe crisis. These alerts have highest priority as they signal large risks. A crisis alert should prompt investors to review their portfolio and potentially take defensive measures beyond the automatic model recommendation (e.g., hedging through put options, rebalancing to more defensive sectors).
11.3 Regime Change Alerts
An alert triggers upon change of market regime (e.g., from Neutral to Correction, or from Bull Market to Strong Bull). Regime changes are highly informative events that typically entail substantial allocation changes. These alerts enable investors to proactively respond to changes in market dynamics.
11.4 Risk Breach Alerts
A specialized alert triggers when actual portfolio risk utilization exceeds target parameters by 20%. This is a warning signal that the risk management system is reaching its limits, possibly because market volatility is rising faster than allocation can be reduced. In such situations, investors should consider manual interventions.
12. Practical Application and Limitations
12.1 Portfolio Implementation
DEAM generates a recommendation for allocation between equities (S&P 500) and cash. Implementation by an investor can take various forms. The most direct method is using an S&P 500 ETF (e.g., SPY, VOO) for equity allocation and a money market fund or savings account for cash allocation.
A rebalancing strategy is required to synchronize actual allocation with model recommendation. Two approaches are possible: (1) rule-based rebalancing at every 10% deviation between actual and target, or (2) time-based monthly rebalancing. Both have trade-offs between responsiveness and transaction costs. Empirical evidence (Jaconetti, Kinniry, and Zilbering, 2010) suggests rebalancing frequency has moderate impact on performance, and investors should optimize based on their transaction costs.
12.2 Adaptation to Individual Preferences
The model offers numerous adjustment parameters. Component weights can be modified if investors place more or less belief in certain factors. A fundamentally-oriented investor might increase valuation weight, while a technical trader might increase regime weight.
Risk target parameters (target volatility, max drawdown) should be adapted to individual risk tolerance. Younger investors with long investment horizons can choose higher target volatility (15-18%), while retirees may prefer lower volatility (8-10%). This adjustment systematically shifts average equity allocation.
Crisis thresholds can be adjusted based on preference for sensitivity versus specificity of crisis detection. Lower thresholds (e.g., VIX > 35 instead of 40) increase sensitivity (more crises are detected) but reduce specificity (more false alarms). Higher thresholds have the reverse effect.
12.3 Limitations and Disclaimers
DEAM is based on historical relationships between indicators and market performance. There is no guarantee these relationships will persist in the future. Structural changes in markets (e.g., through regulation, technology, or central bank policy) can break established patterns. This is the fundamental problem of induction in financial science (Taleb, 2007).
The model is optimized for US equities (S&P 500). Application to other markets (international stocks, bonds, commodities) would require recalibration. The indicators and thresholds are specific to the statistical properties of the US equity market.
The model cannot eliminate losses. Even with perfect crisis prediction, an investor following the model would lose money in bear markets—just less than a buy-and-hold investor. The goal is risk-adjusted performance improvement, not risk elimination.
Transaction costs are not modeled. In practice, spreads, commissions, and taxes reduce net returns. Frequent trading can cause substantial costs. Model smoothing helps minimize this, but users should consider their specific cost situation.
The model reacts to information; it does not anticipate it. During sudden shocks (e.g., 9/11, COVID-19 lockdowns), the model can only react after price movements, not before. This limitation is inherent to all reactive systems.
12.4 Relationship to Other Strategies
DEAM is a tactical asset allocation approach and should be viewed as a complement, not replacement, for strategic asset allocation. Brinson, Hood, and Beebower (1986) showed in their influential study "Determinants of Portfolio Performance" that strategic asset allocation (long-term policy allocation) explains the majority of portfolio performance, but this leaves room for tactical adjustments based on market timing.
The model can be combined with value and momentum strategies at the individual stock level. While DEAM controls overall market exposure, within-equity decisions can be optimized through stock-picking models. This separation between strategic (market exposure) and tactical (stock selection) levels follows classical portfolio theory.
The model does not replace diversification across asset classes. A complete portfolio should also include bonds, international stocks, real estate, and alternative investments. DEAM addresses only the US equity allocation decision within a broader portfolio.
13. Scientific Foundation and Evaluation
13.1 Theoretical Consistency
DEAM's components are based on established financial theory and empirical evidence. The market regime component follows from regime-switching models (Hamilton, 1989) and trend-following literature. The risk management component implements volatility targeting (Moreira and Muir, 2017) and modern portfolio theory (Markowitz, 1952). The valuation component is based on discounted cash flow theory and empirical value research (Campbell and Shiller, 1988; Fama and French, 1992). The sentiment component integrates behavioral finance (Baker and Wurgler, 2006). The macro component uses established business cycle indicators (Estrella and Mishkin, 1998).
This theoretical grounding distinguishes DEAM from purely data-mining-based approaches that identify patterns without causal theory. Theory-guided models have greater probability of functioning out-of-sample, as they are based on fundamental mechanisms, not random correlations (Lo and MacKinlay, 1990).
13.2 Empirical Validation
While this document does not present detailed backtest analysis, it should be noted that rigorous validation of a tactical asset allocation model should include several elements:
In-sample testing establishes whether the model functions at all in the data on which it was calibrated. Out-of-sample testing is crucial: the model should be tested in time periods not used for development. Walk-forward analysis, where the model is successively trained on rolling windows and tested in the next window, approximates real implementation.
Performance metrics should be risk-adjusted. Pure return consideration is misleading, as higher returns often only compensate for higher risk. Sharpe Ratio, Sortino Ratio, Calmar Ratio, and Maximum Drawdown are relevant metrics. Comparison with benchmarks (Buy-and-Hold S&P 500, 60/40 Stock/Bond portfolio) contextualizes performance.
Robustness checks test sensitivity to parameter variation. If the model only functions at specific parameter settings, this indicates overfitting. Robust models show consistent performance over a range of plausible parameters.
13.3 Comparison with Existing Literature
DEAM fits into the broader literature on tactical asset allocation. Faber (2007) presented a simple momentum-based timing system that goes long when the market is above its 10-month average, otherwise cash. This simple system avoided large drawdowns in bear markets. DEAM can be understood as a sophistication of this approach that integrates multiple information sources.
Ilmanen (2011) discusses various timing factors in "Expected Returns" and argues for multi-factor approaches. DEAM operationalizes this philosophy. Asness, Moskowitz, and Pedersen (2013) showed that value and momentum effects work across asset classes, justifying cross-asset application of regime and valuation signals.
Ang (2014) emphasizes in "Asset Management: A Systematic Approach to Factor Investing" the importance of systematic, rule-based approaches over discretionary decisions. DEAM is fully systematic and eliminates emotional biases that plague individual investors (overconfidence, hindsight bias, loss aversion).
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Intraday Key OpensIntraday Key Opens plots the key session and cycle opening prices: 90-minute cycles opens, New York open, Asia open, and 9:30 US market open. Each line is labeled, color-coded, and can be toggled on/off independently. Designed for intraday traders to quickly identify important price levels and session pivots.
Equinivesh : TR, ATR, DATR Combined BY ANUPAM ANAND Equinivesh: TR, ATR, DATR Combined BY ANUPAM ANAND
Regular Trading Hours Opening Range Gap (RTH ORG)### Regular Trading Hours (RTH) Gap Indicator with Quartile Levels
**Overview**
Discover overnight gaps in index futures like ES, YM, and NQ, or stocks like SPY, with this enhanced Pine Script v6 indicator. It visualizes the critical gap between the previous RTH close (4:15 PM ET for futures, 4:00 PM for SPY) and the next RTH open (9:30 AM ET), helping traders spot potential price sensitivity formed during after-hours trading.
**Key Features**
- **Standard Gap Boxes**: Semi-transparent boxes highlight the gap range, with optional text labels showing day-of-week and "RTH" identifier.
- **Midpoint Line**: A customizable dashed line at the 50% level, with price labels for quick reference.
- **New: Quartile Lines (25% & 75%)**: Dotted lines (default width 1) mark the quarter and three-quarter points within the gap, ideal for finer intraday analysis. Toggle on/off, adjust style/color/width, and add labels.
- **High-Low Gap Variant**: Optional boxes and midlines for gaps between the prior close's high/low and the open's high/low—perfect for wick-based overlaps on lower timeframes (5-min or below recommended).
- **RTH Close Lines**: Extend previous close levels with dotted lines and price tags.
- **Customization Galore**: Extend elements right, limit historical displays (default: 3 gaps), no-plot sessions (e.g., avoid weekends), and time offsets for non-US indices.
**How to Use**
Apply to 15-min or lower charts for best results. Toggle "extend right" for ongoing levels. SPY auto-adjusts for its 4 PM close.
Tested on major indices—enhance your gap trading strategy today! Questions? Drop a comment.
Thanks to twingall for supplying the original code.
Thanks to The Inner Circle Trader (ICT) for the logical and systematic application.
Needle XRThe Didi Index with Full Validation is a technical indicator developed for the TradingView platform, based on the concept of the Didi Index, created by Odir Aguiar (Didi). It uses the relationship between three exponential moving averages (EMAs) of different periods to identify trend reversal or continuation points, known as "needle points." To increase signal reliability, the indicator incorporates validations from four widely used technical indicators: MACD, TRIX, DMI/ADX, and Stochastic. Buy and sell signals are displayed only when all validation conditions are met, ensuring greater accuracy.
The indicator is plotted in a separate panel below the price chart, displaying the Didi Index lines (positive and negative), a central reference line, and clear buy (green triangles) and sell (red triangles) signals.
3 Red Heikin Ashi with Higher Lows3 Red Heikin Ashi with Higher Lows will give Buy signal when 3 Red Heikin Ashi with Higher Lows is formed
Doji Buy Signal (3-min, Body ≤ 6%)Doji Buy Signal (3-min, Body ≤ 6%) will give a Buy Signal when Doji Candle is formed
Doji Buy Signal (3-min, Body ≤ 6%)Doji Buy Signal (3-min, Body ≤ 6%) will give a buy signal when dojo candle is formed
3 Red Heikin Ashi with Higher Lows3 Red Heikin Ashi with Higher Lows. Will Give a Buy Signal when 3 Red Heikin Ashi with Higher Lows is formed
