GoldenCross by PuffyThis is a simple trading strategy that seeks the Golden Cross and Death Cross on the 4HR chart. The fast moving indicator in this strategy is the EMA 50 and the slow moving indicator is the EMA 200. When the EMA 50 crosses over the EMA 200 the strategy indicates a buy. When the EMA 50 crosses below the EMA 200 the strategy indicates a sell. This strategy averages trades in the 40 - 50 day range and as such should not be used with heavy leverage.
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Exponential Moving Average (Set of 3) [Krypt] + 13/34 EMAsI took Krypt's script and essentially added on to it.
the 20/50/100/200 EMAs should be used together as support and resistance as normal.
Wait for price to break 200 EMA
Wait for 50 EMA to cross 200 EMA
Wait for pullback to 50 EMA to open position
20 and 100 EMAs are for extra information about moving support and resistance
and 13/34 EMAs should be used in conjunction
When 13 EMA crosses 34 EMA, open position
When price gets far from 13/34, close position (because price will attempt to revert back to mean)
This is better for scalping and swing trades than the 20/50/100/200 setup.
Twitter: @AzorAhai06
MTF EMAExponential Moving Average indicator that can be configured to display different timeframe EMA's.
Timeframe is set in minutes. Max timeframe currently is the daily (1440 minutes). Any value higher than 1440 will result in no plot.
Examples:
Daily 50 EMA plotted on 4H chart
4H 50 EMA and Daily 50 EMA plotted on 1H chart
Can also work in reverse if needed.
Example, Daily 50 EMA plotted on Weekly Chart
Price vs VolImproved version of OBV/price (this one actually works)
Both lines show where price is going relative to volume metrics (one line uses OBV, the other uses accumulation/distribution).
Green and above 50 means price is rising faster then buying volume
Red and below 50 means price is falling faster then selling volume
you can add smoothing in the controls and color will go according to raw (even if smoothing goes above/below 50)
under the hood: changes price, OBV and AD to RSI for comparability, calculates the difference between price and the others, then an RSI on the result to create an <50< style indicator.
this script replaces the previouse from:
Trend-Fib-Pivot Sweep [JopAlgo]Trend-Fib-Pivot Sweep — trend rails + Fib touch rules + sweep logic
Core idea
This tool blends two trend MAs, a rolling Fibonacci grid, and pivot sweep tags so you can do three things quickly:
Trend → MA1 vs MA2 stack and slope
Location → Fib touch/bounce/reject rules
Triggers → sweep → reclaim or trend pullback → continuation
Use the MAs for bias, the Fib levels for where price should react, and the sweeps to spot traps and entries after liquidity grabs.
What you’ll see
MA 1 (default 21, purple) and MA 2 (default 50, gray)
Fib lines from the highest/lowest of your lookback: 0.236 (light blue), 0.382 (green), 0.5 (white), 0.618 (orange), 0.786 (red)
Sweep markers: triangle above = high sweep; triangle below = low sweep
Background: soft green when MA1 > MA2, soft red when MA1 < MA2
Read it fast → Trend (background + MA stack)? Which Fib are we near? Any sweep and reclaim?
How the Fib levels work (and what to do at each)
0.236 → shallow pullback in a strong trend
→ Expect quick bounce continuation.
→ If price closes through 0.236 and stalls, momentum may be cooling; look to 0.382.
0.382 → standard trend pullback
→ In a bullish trend, tests here often bounce and continue.
→ Entry idea: touch/bounce at 0.382 with MA1 above MA2 and rising, then a higher-low and push back above 0.382 → enter.
0.5 → midline / fair value
→ Often the “decision” level.
→ Clean continuation if 0.5 holds; deeper rotation if we accept below (for longs).
0.618 (“golden”) → deep pullback / last line for trend
→ Best risk-defined continuation entries come from rejects/reclaims here.
→ For longs: wick below 0.618, then reclaim 0.618 → long with stop under the sweep low.
0.786 → exhaustive pullback / trap zone
→ If trend is truly alive, 0.786 rejects and snaps back.
→ If we accept beyond 0.786 (closes), expect a full range rotation or trend change.
Touch/bounce rule of thumb
You want to see price interact: touch → reject (wick) → reclaim the level.
A close back above the Fib after a downside probe (or below after an upside probe) is a stronger confirmation than intrabar wicks.
What the MAs do (and how to use them)
MA1 (fast) vs MA2 (slow) define bias and momentum.
MA1 above MA2 and both rising (↗) → bullish regime.
MA1 below MA2 and both falling (↘) → bearish regime.
Flat / crossing often → balance; lean on sweeps and the deeper Fibs (0.5/0.618/0.786).
Interaction with Fibs
Highest quality: Fib level + MA confluence (e.g., 0.382 near MA1).
When MA1 = dynamic trigger: reclaim MA1 at a Fib → continuation signal.
When MA2 = last defense: lose MA2 at 0.5/0.618 → expect deeper rotation.
Sweep logic (why it matters and how to execute)
High sweep = current bar’s high takes out the recent high then fails → liquidity grab above.
Low sweep = current bar’s low takes the recent low then fails → liquidity grab below.
Execution idea
Longs: low sweep into 0.5/0.618/0.786, then reclaim the Fib and, ideally, MA1 → enter; stop under sweep low.
Shorts: high sweep into 0.5/0.382/0.236, then reclaim below the Fib and MA1 → enter; stop above sweep high.
Repaint note
If you enable Lag-Confirmed Pivot Mode, sweep labels are stricter and may “finalize” later (can appear as repaint).
For signals/alerts, prefer non-repaint mode; for review/training, lag-confirmed is fine.
How to trade it (simple playbook)
Direction filter (use MAs first)
Bullish bias → MA1 > MA2 and not flat → look for longs at 0.236/0.382/0.5.
Bearish bias → MA1 < MA2 → look for shorts at 0.236/0.382/0.5 from above.
Entries (two clean templates)
Trend pullback → continuation
→ In bull regime: price pulls to 0.382 or 0.5, shows rejection wick, then reclaims level and MA1 → enter long.
→ In bear regime: mirror with short from above.
Sweep → reclaim
→ Downside sweep through 0.618/0.786, then close back above the Fib and through MA1 → enter long.
→ Upside sweep through 0.382/0.236, then close back below and under MA1 → enter short.
Risk & targets
Stops → beyond the sweep extreme or below/above the reclaimed Fib (structure-based).
Targets → next Fib ladder (e.g., long from 0.5 → target 0.382 → 0.236), or obvious POC/HVNs if you use Volume Profile.
Settings that matter (and how to tune)
MA Types/Lengths
EMA (default fast) = responsive trend read.
SMA/HMA = smoother backbone.
21/50 is a solid default; swing traders can run 34/89.
Fib Lookback
Shorter lookback = tighter range, more sensitive levels;
Longer = broader swing map, fewer interactions but stronger signals.
Sweeps
Sweep Detection Range controls how “recent” the pivot must be (default 10).
Lag-Confirmed mode reduces false sweeps but can finalize later.
Starter presets
Intraday (15m–1H) → MA1 21 EMA, MA2 50 SMA, Fib lookback 100–150, Sweeps 10
Swing (4H) → MA1 34 EMA, MA2 89 SMA, Fib lookback 150–250, Sweeps 10–14
Pattern cheat sheet
0.382 kiss & go (trend day) → quick tag and bounce in bull regime → continuation.
0.5 decision → hold = trend resumes; failure = rotate to 0.618.
0.618 sweep + reclaim → high-quality continuation with tight risk.
0.786 trap → deep flush then snapback; if acceptance persists, expect full rotation.
MA pinch → break → MA1 and MA2 compress, then price breaks and holds a Fib → expansion leg.
Best combos (kept simple)
Volume Profile v3.2 → use VAH/VAL/POC/LVNs as concrete targets; look for Fib + VP confluence.
Anchored VWAP → reclaims/rejections at anchored lines with Fib reaction and MA agreement improve timing.
Common mistakes this helps you avoid
Buying into 0.618/0.786 without a reclaim (catching falling knives).
Fading a 0.236 pullback when MAs are strongly ↗ (fighting trend).
Taking sweeps without a reclaim/confirmation.
Ignoring the MA stack when choosing direction.
Disclaimer
This indicator and write-up are for education only, not financial advice. Trading involves risk; results vary by market, venue, and settings. Test first, act at defined levels, and manage risk. No guarantees or warranties are provided.
Triple VWAP [JopAlgo]Triple VWAP — three volume-weighted rails for trend, pullback, and reversion
Core idea
This is three rolling VWAPs (VWMA-style) with user-set lengths. Together they show:
Trend structure → stack & slope of the three lines
Pullback zones → dynamic VWAP supports/resistances
Reversion risk → distance from the fastest VWAP
Use the stack (fast/medium/slow) for bias, slope for momentum, and distance to avoid chasing.
What you’ll see
VWAP 1 (fast), VWAP 2 (medium), VWAP 3 (slow)
Colors match inputs; each line can be toggled on/off
No bands or extras—just three clean volume-weighted rails
Read it fast → Which line is on top? Are they fanning out or braiding? How far is price from the fast VWAP?
How to use it (simple playbook)
Direction filter
Bullish bias → fast above medium above slow and slopes ↗
Bearish bias → fast below medium below slow and slopes ↘
Entry timing
Trend pullback (with level): In a bullish stack, wait for price to retest fast/medium VWAP at a real level → look for the first higher-low and continuation.
Reclaim / reject: Long when price reclaims fast → medium with holds (mirror for shorts on rejects).
Don’t chase: If price is far above the fast VWAP, wait for a revert toward fast before engaging.
Location first (always)
Act at real references → Volume Profile v3.2 (VAH/VAL/POC/LVNs) and Anchored VWAP
No level → no trade
Quality check (optional)
CVDv1 → prefer Alignment OK, avoid entries when Absorption reads against your side
Entries, exits, risk
Continuation long: Bullish stack ↗, pullback into fast/medium at VAL / AVWAP / LVN, hold → enter
Stop → below structure/last swing • Targets → POC/HVNs or prior swing
Break + retest: Price crosses medium and holds above it, lines begin to fan out ↗ → enter on the retest
Fade to value (advanced): Extended move into VAH with price stretched far from fast VWAP → look for reject and revert toward POC/fast
Trim/Avoid: Into HVNs with lines flattening or braiding → take profits / stand down
Settings that matter (and how to tune)
VWAP Length 1 / 2 / 3 → choose a fast / medium / slow ladder
Shorter = more reactive, more noise
Longer = steadier bias, more lag
Visibility toggles → hide one line if cluttered; many traders keep fast & slow only
Starter presets
Scalp (1–5m) → 20 / 50 / 100
Intraday (15m–1H) → 50 / 100 / 200
Swing (2H–4H) → 50 / 150 / 300
High-vol pairs → 30 / 60 / 120
Pattern cheat sheet
Stack flip: Fast crosses medium, then slow, and all slopes turn ↗ / ↘ → regime change
Triple pinch → expansion: Lines braid tight, then fan out with price holding a level → expansion leg
Kiss & go: Pullback tags fast VWAP in trend and bounces → add/enter with structure
Mean-revert tag: Stretch away from fast into VP edge → revert toward fast/POC
Best combos (kept simple)
Volume Profile v3.2 → entries at VAH/VAL/LVNs, targets at POC/HVNs
Anchored VWAP → session/weekly/event anchors for major reclaims/rejections; use Triple VWAP for day-to-day timing
CVDv1 (optional) → take VWAP-aligned setups with flow; skip when Absorption is against you
Common mistakes this helps you avoid
Trading against the VWAP stack
Chasing far from the fast VWAP
Acting mid-range while lines braid (do less; wait for expansion or edges)
Disclaimer
This indicator and write-up are for education only, not financial advice. Trading involves risk; results vary by market, venue, and settings. Test first, trade at defined levels, and manage risk. No guarantees or warranties are provided.
Multi-Timeframe MACD with Color Mix (Nikko)Multi-Timeframe MACD with Color Mix (Nikko) Indicator
This documentation explains the benefits of the "Multi-Timeframe MACD with Color Mix (Nikko)" indicator for traders and provides easy-to-follow steps on how to use it. Written as of 05:06 AM +07 on Saturday, October 04, 2025, this guide focuses on helping you, as a trader, get the most out of this tool with clear, practical advice before diving into the technical details.
Benefits for Traders
1. Multi-Timeframe Insight
This indicator lets you see momentum trends across 15-minute, 1-hour, 1-day, and 1-week timeframes all on one chart. This big-picture view helps you catch both quick market moves and long-term trends without flipping between charts, saving you time and giving you a fuller understanding of the market.
2. Visual Momentum Representation
The background changes from red to green based on short-term (15m) momentum, giving you a quick, easy-to-see signal—red means bearish (prices might drop), and green means bullish (prices might rise). The histogram uses a mix of red, green, and blue colors to show the combined strength of the 1-hour, 1-day, and 1-week timeframes, helping you spot strong trends at a glance (e.g., a bright mix for strong momentum, darker for weaker).
3. Enhanced Decision-Making
The background and histogram colors work together to confirm trends across different timeframes, making it less likely you’ll act on a false signal. This helps you feel more confident when deciding when to buy, sell, or hold.
4. Proactive Alert System
You can set alerts to notify you when the percentage of bullish timeframes hits your chosen levels (e.g., below 10% for bearish, above 90% for bullish). This keeps you in the loop on big momentum shifts without needing to watch the chart all day—perfect for when you’re busy.
5. Flexibility and Efficiency
You can turn timeframes on or off, adjust settings like speed of the moving averages, and tweak transparency to fit your trading style—whether you’re a fast scalper or a patient swing trader. Everything is shown on one chart, saving you effort, and the colors make it simple to read, even if you’re new to trading.
How to Use It
Getting Started
Add the Indicator: Load the "Multi-Timeframe MACD with Color Mix (Nikko)" onto your TradingView chart using the Pine Script editor or indicator library.
Pick Your Timeframes: Turn on the timeframes that match your trading—use 15m and 1h for quick trades, or 1d and 1w for longer holds—using the enable_15m, enable_1h, enable_1d, enable_1w, and enable_background options.
Reading the Colors
Background Gradient: Watch for red to signal bearish 15m momentum and green for bullish momentum. Adjust the Background_transparency (default 75%, or 25% opacity) if the chart feels too busy—try lowering it to 50 for clearer candlesticks in fast markets.
Histogram and EMA Colors:
The histogram and its Exponential Moving Average (EMA) line show a mix of red (1-week), green (1-day), and blue (1-hour) based on how strong the momentum is in each timeframe.
Brighter colors mean stronger momentum—white (all bright) shows all timeframes are pushing up hard, while darker shades (like gray or black) mean weaker or mixed momentum.
Turn off a timeframe (e.g., enable_1h = false) to see how it changes the color mix and focus on what matters to you.
Setting Alerts
Set Your Levels: Choose a threshold_low (default 10%) and threshold_high (default 90%) based on your comfort zone or past market patterns to catch big turns.
Get Notifications: Use TradingView alerts to get pings when the market hits your set levels, so you can act without staring at the screen.
Practical Tips
Pair with Other Tools: Use it with support/resistance lines or the RSI to double-check your moves and build a solid plan.
Tweak Settings: Adjust fast_length, slow_length, and signal_smoothing to match your asset’s speed, and bump up the lookback (default 50) for steadier trends in wild markets.
Practice First: Test different timeframe combos on a demo account to find what works best for you.
Understanding the Colors (Simple Explanation)
How Colors Work
The histogram and its EMA line use a color mix based on a simple idea from color theory, like mixing paints with red, green, and blue (RGB):
Red comes from the 1-week timeframe, green from 1-day, and blue from 1-hour.
When all three timeframes show strong upward momentum, they blend into bright white—the brightest color, like a super-bright light telling you the market’s roaring up.
If some timeframes are weak or pulling down, the mix gets darker (like gray or black), warning you the momentum might not be solid.
Brighter is Better
Bright Colors = Strong Opportunity: The brighter the histogram and EMA (closer to white), the more all your chosen timeframes are in agreement that prices are rising. This is your signal to think about buying or holding, as it points to a powerful trend you can ride.
Dark Colors = Caution: A darker mix (toward black) means some timeframes are lagging or bearish, suggesting you might wait or consider selling. It’s like a dim light saying, “Hold on, check again.”
Benefit in Practice: Watching the brightness helps you jump on the best trades fast. For example, a bright white histogram on a green background is like a green traffic light—go for it! A dark gray on red is like a red light—pause and rethink. This quick color check can save you from bad moves and boost your profits when the trend is strong.
Why It Helps
These colors are your fast friend in trading. A bright histogram means all your timeframes are cheering for an uptrend, giving you the confidence to act. A dull one tells you to be careful, helping you avoid traps. It’s like having a color-coded guide to pick the hottest market moments!
Technical Details
Input Parameters
Fast Length (default: 12): Short-term moving average speed.
Slow Length (default: 26): Long-term moving average speed.
Source (default: close): Price data used.
Signal Smoothing (default: 9): Smooths the signal line.
MA Type (default: EMA): Choose EMA or SMA.
Timeframe and Scaling
Timeframes: 15m, 1h, 1d, 1w, with on/off switches.
Lookback Period (default: 50): Sets the data window for trends.
Background Transparency (default: 75%): Controls background see-through level.
MACD Calculation
Per Timeframe: Uses request.security():
MACD Line: ta.ema(src, fast_length) - ta.ema(src, slow_length).
Signal Line: ta.ema(MACD, signal_length).
Histogram: (macd - signal) / 3.0.
Background Gradient
15m Normalization: norm_value = (hist_15m - hist_15m_min) / max(hist_15m_range, 1e-10), limited to 0-1.
RGB Mix: Red drops from 255 to 0, green rises from 0 to 255, blue stays 0.
Apply: color.new(color.rgb(r_val, g_val, b_val), Background_transparency).
Histogram and EMA Colors
Color Assignment:
1h: Blue (#0000FF) if hist_1h >= 0, else black.
1d: Green (#00FF00) if hist_1d >= 0, else black.
1w: Red (#FF0000) if hist_1w >= 0, else black.
Final Color: final_color = color.rgb(min(r, 255), min(g, 255), min(b, 255)).
Plotting: Histogram and EMA use final_color; MACD (#2962FF), signal (#FF6D00).
Alerts
Bullish Percentage: bullish_pct = (bullish_count / bullish_total) * 100, counting hist >= 0.
Triggers: Below threshold_low or above threshold_high.
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Conclusion
The "Multi-Timeframe MACD with Color Mix (Nikko)" is your all-in-one tool to spot trends, confirm moves, and trade smarter with its bright, easy-to-read colors. By using it wisely, you can sharpen your market edge and trade with more confidence.
This README is tailored for traders and reflects the indicator's practical value as of 05:06 AM +07 on October 04, 2025.
Multiple Moving Averages [JopAlgo]Multiple Moving Averages — read trend, timing, and strength at a glance
What it does:
Mark up to 5 moving averages (you pick type + length + color). Watch how they stack, slope, braid, and fan out to judge trend direction, pullback timing, and breakout quality on any timeframe.
Read it in 5 seconds
Stack order:
Bullish: fast MAs on top of slow MAs.
Bearish: fast MAs below slow MAs.
Slope: up = trend has a tailwind; down = headwind.
Spacing: wide = strong trend; tight/braided = balance/chop.
If you remember only one rule: trade with the stack and slope, enter at levels.
High-probability plays (simple and repeatable)
Trend pullback (with level)
Stack is bullish, slopes up.
Price pulls back to the MA cluster (or AVWAP/VAL), holds, fast MAs curl back up.
Long. Stop: below structure/slowest MA. Target: POC/HVNs or next swing.
(Mirror for shorts in a bearish stack.)
Reclaim + recurl
After a down phase, price closes above fast MAs (MA1–MA2), they turn up, and you’re at a real level (AVWAP/VA edge).
Take the first higher-low with the stack starting to flip.
Squeeze → expansion
MAs braid tight = energy building.
Break at a level, then the lines fan out in your direction.
Enter on the first retest that holds.
Skip trades when the lines are braided mid-range and you’re not at a level.
Timeframe guide (what usually works)
1–5m (scalps): EMA heavy (e.g., 5/9/21/34/55). Expect more signals; filter with levels + CVD.
15m–1H (intraday): 9/21/34/50/200 (mix EMA for fast, SMA for slow).
2H–4H (swing): 10/20/50/100/200 or 8/21/34/55/89 (smoother read).
1D+ (position): 20/50/100/200 (bias) and enter on lower TF.
Tip: Don’t set all five to the same length—stagger them so the stack tells a story.
Settings that matter (and what they mean)
MA types (pick the feel you like):
EMA – fastest response (great for timing).
SMA – smoother backbone (great for bias).
WMA / LWMA – responsive but less twitchy than EMA.
VWMA – weights price by volume (good on assets with uneven volume).
SMMA – very smooth (reduces whips).
DEMA – extra fast (can be noisy).
HEMA – in this script behaves like a double-EMA style response (fast).
RVIMA – not implemented here (will plot nothing if chosen).
Length:
Shorter = earlier turns, more noise.
Longer = slower, cleaner bias.
Keep a sensible spread (e.g., 1:2:3… or Fib-style 9/21/34/55/89).
Colors:
Use consistent colors (e.g., warm = fast, cool = slow) so you can read the stack instantly.
Best combos with other tools
Volume Profile v3.2: take signals at VAH/VAL/LVNs; use POC/HVNs for targets.
Anchored VWAP: reclaims/rejections + MA recurl = clean timing.
CVDv1: execute with flow (Alignment OK, strong Imbalance, no Absorption against you).
Common mistakes this prevents
Shorting into a bullish stack (or buying into a bearish one).
Chasing far from the fast MAs; better to wait for a pullback.
Trading every wiggle in chop—braids tell you to do less.
Quick FAQs
Cluttered chart? Hide 1–2 lines (keep fast, middle, slow) or thin the linewidth.
Which one is “right”? None. Pick a set that fits your tempo and stick to it.
RVIMA option? Not implemented in this version—choose another type.
Starter presets (copy these, then adjust)
Intraday: MA1 EMA9, MA2 EMA21, MA3 SMA34, MA4 SMA50, MA5 SMA200
Swing: MA1 EMA10, MA2 SMA20, MA3 SMA50, MA4 SMA100, MA5 SMA200
Scalp: MA1 EMA5, MA2 EMA9, MA3 EMA21, MA4 EMA34, MA5 EMA55
Mini-disclaimer
Educational tool, not financial advice. Always anchor trades to levels, flow, and risk—this indicator keeps your bias and timing honest; the plan is still yours.
Multi MA Cross [JopAlgo]Multi MA Cross — simple, flexible trend + timing
What it does:
Plots two moving averages (you pick the types and lengths) and marks their crossovers. Use it to read trend direction and time pullbacks/breakouts. Works on any timeframe.
What you’ll see
Short MA (orange)
Long MA (lime)
Cross mark (aqua ✚) when they cross
Green/lime above orange = bullish bias (short MA above long).
Orange above lime = bearish bias.
How to use it (simple playbook)
Trade with the bias
Longs only when short MA > long MA.
Shorts only when short MA < long MA.
Enter at a real level
Use Volume Profile v3.2 (VAH/VAL/POC/LVNs) or Anchored VWAP .
Crosses at or just after a level hold are higher quality.
Quality check (optional, strong)
CVDv1 : take trades when Alignment = OK, Imbalance strong, Absorption ≠ red.
Manage risk
Stop goes beyond the level/structure, not on an MA wiggle.
Trim into POC/HVNs or next structure.
Good entries you’ll recognize
Pullback-to-long MA (trend):
Bias up, price pulls to long MA (or AVWAP/VAL), short MA curls back up → enter long.
Reclaim + cross:
Price reclaims AVWAP/VA edge, then short MA crosses over long → confirmation to join.
Squeeze → break:
MAs converge (tight), then expand after a level break. Enter on retest that holds.
Skip crosses in the middle of nowhere. Cross + location + flow beats cross alone.
Timeframe guidance
1–5m (scalps): EMA/EMA or EMA/WMA. Expect more crosses. Use VP/AVWAP and CVD filters.
15m–1H (intraday): EMA(9) vs SMA(21) is a solid default.
2H–4H (swing): SMA(20–34) vs SMA(50) or EMA(21) vs EMA(55).
1D+ (position): SMA(50) vs SMA(200) for broad bias; entries on lower TF.
Settings that matter (and what they mean)
Short/Long MA Type:
EMA = fast, good for timing.
SMA = smooth, good for bias.
WMA/LWMA = in-between (responsive).
VWMA = weights by volume.
SMMA = very smooth (reduces whips).
HEMA/DEMA = extra responsive.
VWAP = daily session VWAP (anchor), ignores length in practice.
Short/Long Length:
Short = timing sensitivity.
Long = trend backbone.
Keep a ratio ~ 1:2 to 1:3 (e.g., 9/21, 10/30, 20/50).
Note on VWAP option: The script fetches a daily VWAP anchor. It acts like a fair-value line, not a rolling MA. Your Length won’t affect VWAP.
Filters that boost win rate
Slope check: Only take longs when both MAs slope up; shorts when both slope down.
Distance check: Don’t chase if price is far from the short MA; wait for a pullback.
HTF agreement: On 15m, glance at 1H/4H bias; on 4H, glance at 1D. Trade with the higher-TF wind.
Combos that work
Volume Profile v3.2: Use VAH/VAL/POC/LVNs for entries/targets. Cross at those references is meaningful.
Anchored VWAP: Reclaims/rejections first, MA cross second = cleaner timing.
CVDv1: Only act when flow agrees (ALIGN OK, no Absorption against you).
Common mistakes this avoids
Shorting into an up-bias (or vice versa).
Chasing a cross far from value (wait for the pullback).
Trading every cross in chop (use levels + CVD to filter).
Defaults to start with
Short MA: EMA 9
Long MA: SMA 21
Timeframes: 15m–4H
Process: Bias → Level → Cross/Retest → CVD check → Execute
Quick disclaimer
Educational tool, not financial advice. Test first, size sensibly, and always anchor your trades to levels, flow, and risk.
Kairi Relative Index Upgrated v1Kairi Relative Index Upgraded v1 — how far from “fair” are we, right now?
Most oscillators mash together price and momentum in ways that are hard to explain to a new trader. KRI is refreshingly simple: it measures how far price is from its moving average, as a percent of that average.
KRI = 100 × (Price − SMA) / SMA
Above 0 → price is above its average (stretched up).
Below 0 → price is below its average (stretched down).
The farther from 0, the more stretched we are from the mean.
This upgraded version keeps the pane clean (zero line, colored KRI, optional guide rails at +Line Above / Line Below) so you can read extension, reversion pressure, and reclaims at a glance—on any timeframe.
(If you add screenshots: image #1 should label the zero line and ± threshold lines; image #2 should show a textbook “overshoot at VAH/VAL + KRI extreme → rotate back to POC.”)
What you’re seeing (and how to read it fast)
KRI line
Green when KRI ≥ 0 (price above SMA)
Red when KRI < 0 (price below SMA)
Zero line = the moving average itself (no stretch).
Guide lines (default +10/−10) = “This is pretty far for this setting.” Treat these as review-and-decide zones, not auto-trade signals.
Three quick reads:
Magnitude: how far from the mean (size of KRI).
Direction: above/below zero (which side of the mean).
Turn: KRI curling back toward zero (reversion starting) or accelerating away (trend impulse continuing).
What KRI really measures (plain-English)
The SMA(length) is your “fair value” line for this indicator.
KRI tells you the percentage deviation from that fair value—normalized, so you can compare across assets/timeframes with the same length.
Because it’s a pure distance metric, KRI excels at:
spotting over-extensions into VP edges (VAH/VAL) and AVWAP,
timing mean-reversion back to POC/AVWAP in balance,
confirming reclaims (KRI crossing back through zero at a level),
framing pullbacks in trend (healthy dips usually avoid deep negative KRI in strong uptrends).
Using KRI on any timeframe
The workflow is always Location → Flow → KRI:
Location: a real level (Volume Profile v3.2’s VAH/VAL/POC/LVNs or Anchored VWAP).
Flow quality: check CVDv1 (Alignment OK? Absorption not red?).
KRI: are we stretched into/away from the level, and is KRI turning?
Scalping (1–5m)
Fade the stretch (balance): At VAH/VAL or Session AVWAP, an extreme KRI that rolls back toward zero = quick rotation to the middle (POC/AVWAP).
Don’t fade if bands are expanding and flow is strong (CVDv1 says go) — big KRI can stay big in expansion.
Intraday (15m–1H)
Continuation after pullback: In uptrends, look for shallow negative KRI at support (VAL/AVWAP) that turns up → join trend.
Failed breakout tell: Price pokes above VAH but KRI barely increases or rolls over quickly → likely a reclaim back inside value.
Swing (2H–4H)
Edge-to-mean rotations: At composite VAH/VAL, KRI extremes are great context: fade back to POC/HVNs if flow doesn’t confirm a breakout.
Reclaim confirmation: After a flush below Weekly AVWAP, KRI crossing back up through zero on the reclaim bar is a clean green light.
Position (1D–1W)
Regime posture: Multi-day runs with sustained positive KRI (and shallow dips) = constructive; mirror for downtrends. Use KRI pullbacks to ~0 at Weekly AVWAP for adds.
Entries, exits, and risk (simple rules)
Mean-reversion entry: At VAH/VAL or AVWAP, wait for KRI extreme at/through your guide line and a turn back toward zero.
Stop: just beyond the level; Target: POC/HVN or the zero line on KRI.
Trend-continuation entry: In a trend, take pullbacks where KRI stays modest (doesn’t blow through your lower/upper guide) and turns back with the trend at the level.
Avoid: chasing breakouts where KRI is already extreme and still climbing unless CVDv1 says Alignment OK + no Absorption and you have a clean retest.
Settings that matter (and how to tune them)
Length (default 50): defines the moving average “fair value.”
Shorter (20–34): faster, more signals, more noise—good for intraday.
Longer (50–100): steadier, better for swings/position.
Source (default close): keep it simple; hlc3 or close both work.
Line Above / Below (defaults +10/−10): your review zones. Tune them to the asset/timeframe:
Scroll back 6–12 months and eyeball typical |KRI| spikes. Set your lines around the 80th–90th percentile of |KRI| for that market and length.
Majors often need smaller thresholds than thin alts on the same timeframe.
Tip: If your KRI is always beyond the lines, increase length or widen the thresholds. If it never touches them, shorten length or tighten thresholds.
What to look for (pattern cheat sheet)
Stretch into level → curl: KRI tags an extreme right at VAH/VAL/AVWAP, then turns back → classic rotation.
Shallow pullback in trend: KRI dips toward zero but doesn’t hit your lower guide, then turns up at support → continuation.
No-juice break: New price high with weaker KRI (smaller positive % vs prior leg) → breakout lacks extension; plan for retest or reclaim.
Zero-line reclaims: After a washout, KRI crosses zero as price reclaims AVWAP/VAL → clean confirmation.
Combining KRI with other tools
Cumulative Volume Delta v1 (CVDv1):
Use KRI for stretch/turn, CVDv1 for quality.
A KRI extreme at VAH with CVDv1 Absorption (red) is a do-not-chase; look for the fail/reclaim.
A KRI pullback toward zero at VAL with Alignment OK + strong Imbalance + no Absorption = high-quality continuation.
Volume Profile v3.2:
KRI’s best signals happen at VAH/VAL/POC/LVNs.
LVN traversals with rising KRI often run quickly to the next HVN—use VP for targets.
Anchored VWAP :
Treat AVWAP as fair-value rails. KRI zero cross on an AVWAP reclaim is your green flag; KRI extreme + failure to accept beyond AVWAP warns of a fake break.
Common pitfalls KRI helps you avoid
Buying high into a tired move: KRI already very positive at VAH and rolling over = likely rotation; wait.
Fading true expansion: In strong trends with confirmed flow, KRI can remain extreme; don’t automatically fade just because it’s “far.”
Wrong thresholds: Copy-pasting ±10 to every market/timeframe can mislead. Calibrate to the market you trade.
Practical defaults to start with
Length: 50
Lines: +10 / −10 as placeholders—calibrate later.
Timeframes: great out of the box on 15m–4H; for 1–5m try Length 34 and tighter lines; for daily swings try Length 100 and broader lines.
Process: Level → CVDv1 quality → KRI stretch/turn. If any of the three disagree, wait for the retest.
Disclaimer & Licensing
This indicator and its description are provided for educational purposes only and do not constitute financial or investment advice. Trading involves risk, including the possible loss of capital. makes no warranties and assumes no responsibility for any decisions or outcomes resulting from the use of this script. Past performance is not indicative of future results. Use at your own risk.
Licensing & Attribution:
Copyright (c) 2018–present, Alex Orekhov (everget). Modified and upgraded by .
The original “Kairi Relative Index” is released under the MIT License, and this derivative is distributed under the MIT License as well. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files to deal in the Software without restriction, subject to the conditions of the MIT License, including the above copyright notice and this permission notice. The Software is provided “AS IS,” without warranty of any kind, express or implied.
Cycle Momentum Filter [JopAlgo]Cycle Momentum Filter (CMF) — spot “when” to engage the market, on any timeframe
Markets breathe in cycles (expansion → contraction) while momentum and trend decide which moves actually travel. CMF is a compact filter that blends those ideas so you can answer two questions before you click:
Is this a good moment to take a trade? (cycle position)
If I take it, is there enough force behind the move to carry it? (momentum + trend)
CMF does not replace your levels—use it with your location tools (e.g., Volume Profile v3.2 and Anchored VWAP). It simply keeps you out of entries taken at the wrong part of the swing or against weak momentum.
(When you add screenshots: image #1 should label each sub-line and the green/yellow/red background; image #2 can show CMF turning green at VAL + AVWAP before a rotation back to POC.)
What you’re seeing (and how to read it at a glance)
CMF draws five sub-lines around a zero line, plus a background color:
Cycle Oscillator (blue): where you are in the swing. Above zero ≈ cycle crest side; below zero ≈ trough side.
ROC % (purple): short-term price acceleration. Above zero = positive momentum; below zero = negative.
MACD Histogram (orange): classic impulse measure (fast–slow EMA gap). Above zero = bullish impulse.
EWO (cyan): Elliott Wave Oscillator (EMA fast – EMA slow). Above zero = trend tilt up.
RSI-MA (gray, plotted as RSI−50): smoothed RSI relative to 50. Above zero = buyers have the relative strength.
Background color = the filter result:
Green → bullish window: cycle favors longs and momentum/trend/RS confirm.
Red → bearish window: mirror logic.
Yellow → neutral: at least one piece disagrees—do less, or wait for alignment.
For new traders: Every sub-line crossing above/below zero is a yes/no vote. Green happens only when all bullish checks are true; red when all bearish checks are true.
How CMF is built (plain-English version)
Cycle (DPO-style): CMF subtracts a displaced SMA from price to remove trend and expose the swing. Below 0 = you’re on the dip side of the cycle; above 0 = rally side.
Momentum (ROC): percent change over roc_length bars; tells you if price is actually accelerating.
Impulse (MACD hist): measures push from fast vs slow EMAs.
Trend tilt (EWO): broader drift via two EMAs (fast/slow).
Participation bias (RSI-MA): smoothed RSI relative to 50 (plotted as RSI−50 so its zero line matches the others).
The signal rules are strict AND conditions:
Bullish = cycle < 0 and ROC > 0 and MACD hist > 0 and EWO > 0 and RSI-MA > 0.
Bearish = cycle > 0 and ROC < 0 and MACD hist < 0 and EWO < 0 and RSI-MA < 0.
Otherwise Neutral.
This strictness is deliberate: it cuts a lot of low-quality entries.
Using CMF on any timeframe
The framework is the same—only your anchors/targets change as you zoom.
Scalping (1–5m)
Where: VP v3.2 VAL/VAH/LVNs or Session AVWAP.
When: take longs when CMF turns green on/after a dip to your level; shorts when it turns red on/after a pop into resistance.
Skip: yellow reads in the middle of the range; that’s chop.
Tip: on very fast pairs, require two consecutive green/red bars before entry.
Intraday (15m–1H)
Use CMF green to time pullbacks to AVWAP or VA edges in the trend direction.
In balance days, wait for CMF color + level alignment to fade back to POC.
If CMF flips yellow after entry, tighten risk; if it flips against you, consider exiting early.
Swing (2H–4H)
Treat first green after a higher-timeframe pullback to Weekly AVWAP or composite VAL as your A-setup.
If CMF stays green through the first pullback, consider adding; the opposite for red in downtrends.
Position (1D–1W)
Fewer, bigger decisions: CMF green at Monthly/Quarterly AVWAP or at composite VAL suggests rotation toward POC/HVNs; CMF red at VAH suggests mean-reversion lower.
If CMF can’t turn green/red at key retests, that’s valuable: the level likely won’t hold.
Entries, exits, and risk (simple rules)
Entry: trade at a level when CMF just flips to your side (green for longs / red for shorts).
Invalidation: if CMF reverts to yellow immediately, it’s a warning; if it flips to the opposite color, that’s your soft stop condition—tighten or exit unless higher-timeframe context argues otherwise.
Targets: use Volume Profile v3.2 (POC/HVNs) and AVWAP (mean) for logical destinations.
Don’t use CMF alone for stops; place them beyond the level or structure.
Settings that actually matter (and how to tune them)
Cycle Length (default 20): swing detection.
Shorter (10–14): quicker flips, better for scalps.
Longer (30–40): steadier cycle for swings/position.
ROC Length (default 10): momentum lookback.
Shorter: earlier yes/no, more noise.
Longer: slower, more selective.
MACD Fast/Slow (5/13) & EWO Fast/Slow (5/35): impulse and drift.
Increase slow values to calm false flips; decrease fast to react sooner.
RSI Length (14) & Smoothing (5): participation tilt.
Reduce smoothing for faster confirmation; increase to avoid whips.
Background on/off: keep it on while learning; once you’re comfortable, you can hide the background and read the lines against zero.
Tuning tip: If you trade only a few coins, optimize Cycle and ROC first; leave MACD/EWO defaults. Then decide how strict you want RSI (try RSI smoothing = 3 for faster reads).
What to look for (pattern cheatsheet)
Green at a dip-level (VAL/AVWAP) → rotate toward POC/HVN.
Red at a pop-level (VAH/AVWAP) → rotate down toward POC/HVN.
Color holds through the retest → continuation is more likely.
Color flips against the breakout → watch for failed break and reclaim.
Only one line disagrees (e.g., ROC < 0 while others > 0) → expect slower follow-through; consider waiting one bar.
Combining CMF with other tools
Volume Profile v3.2 :
Use VAH/VAL/POC/LVNs for where. CMF answers when.
Green at VAL → mean-reversion long to POC.
Red at VAH → fade to POC.
LVN breaks with green often travel quickly to the next HVN.
Anchored VWAP :
Reclaim of AVWAP + CMF turns green → higher-quality long; rejection + red → cleaner short.
Weekly AVWAP + CMF color is a reliable swing compass.
Cumulative Volume Delta v1 (CVDv1):
CMF says “now”, CVDv1 says “how good”.
Prefer CMF green when CVDv1 Alignment = OK, Imbalance strong, Absorption ≠ red.
If CMF flips green but CVDv1 shows Absorption (red), do not chase; look for a reclaim instead.
Common pitfalls CMF helps you avoid
Buying high in the cycle: CMF keeps longs to when the cycle is on the dip side and momentum/trend agree.
Forcing trades on yellow: yellow is your do-less mode—wait for alignment.
Ignoring flow at levels: CMF gives the window, but quality still matters; confirm with CVDv1.
Practical defaults to start with
Cycle 20 | ROC 10 | MACD 5/13 | EWO 5/35 | RSI 14 (smooth 5)
Works out of the box on 15m–4H.
For scalps, try Cycle 14 / ROC 7–9 / RSI smooth 3.
For daily swings, Cycle 30–34 / ROC 12–14.
Alerts (what they tell you)
Bullish Signal: CMF turned green (all bullish checks passed). Use it as a heads-up; still anchor the entry to VP/AVWAP.
Bearish Signal: CMF turned red. Same rule: wait for the level.
Open source & disclaimer
This indicator is published open source so traders can learn, tweak, and build rules they trust. Tools guide decisions; risk management decides outcomes.
Disclaimer — Not Financial Advice.
The “Cycle Momentum Filter ” 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.
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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Advanced Directional Stoch RSIAdvanced Directional Stochastic RSI
Overview
The Advanced Directional Stochastic RSI (Adv Stoch RSI Dir) is a powerful oscillator that combines the classic Stochastic RSI with John Ehlers' SuperSmoother filter for ultra-smooth signals and reduced noise. Unlike traditional Stoch RSI, this indicator incorporates directional coloring based on price action relative to a smoothed trend line, helping traders quickly spot bullish or bearish momentum. It's designed for swing traders and scalpers looking for clearer overbought/oversold conditions in volatile markets.
Key Features
Directional Coloring: %K line turns green when price is above the trend MA (bullish) and red when below (bearish), providing instant visual bias.
Multi-Pass SuperSmoothing: Apply Ehlers' SuperSmoother filter up to 5 times for customizable noise reduction—dial in passes (default: 2) to balance responsiveness and smoothness.
Trend-Aware Baseline: Uses a cascaded smoothed moving average (default length: 20) to gauge overall direction, making the oscillator more context-aware.
Classic Stoch RSI Core: Built on RSI (default: 14) and Stochastic (default: 14), with SMA smoothing for %K (3) and %D (3).
Visual Aids: Includes overbought (80), oversold (20), and midline (50) levels, plus a subtle blue fill between OB/OS zones for easy reference.
How It Works
Source Smoothing: The input source (default: close) is passed through the SuperSmoother filter multiple times to create a trend MA.
Stoch RSI Calculation: Computes RSI on the source, then applies Stochastic to the RSI values, followed by SMA smoothing for base %K and %D.
Advanced Smoothing: Extra SuperSmoother layers are applied to %K and %D based on your chosen passes, minimizing whipsaws.
Directional Logic: Compares current close to the trend MA to color %K dynamically.
Plotting: %K (thick line, colored) and %D (thin orange) oscillate between 0-100, highlighting crossovers and divergences.
Usage Tips
Buy Signal: Green %K crosses above %D below 50, or bounces off oversold (20) in uptrends.
Sell Signal: Red %K crosses below %D above 50, or rejects overbought (80) in downtrends.
Customization: Increase smoothing passes (3-5) for choppy markets; reduce for faster signals. Pair with volume or support/resistance for confirmation.
Timeframes: Best on 1H-4H charts for stocks/crypto; adjust lengths for forex.
This open-source script is licensed under Mozilla Public License 2.0. Backtest thoroughly—past performance isn't indicative of future results. Enjoy trading smarter with less noise! 🚀
© HighlanderOne
Relative Performance Indicator - TrendSpider StyleRelative Performance Indicator - TrendSpider Style
📈 Overview
This Relative Performance (RP) indicator measures how your stock is performing compared to a benchmark index, displayed as a percentile ranking from 0-100. Based on TrendSpider's methodology, it answers the critical question: "Is this stock a leader or a laggard?"
Unlike simple ratio charts, this indicator uses percentile ranking to normalize relative performance, making it easy to identify when a stock is showing exceptional strength (>80) or concerning weakness (<20) compared to its historical relationship with the benchmark.
✨ Key Features
Three Calculation Modes:
Quarterly: 3-month relative performance for swing trading
Yearly: Weighted 4-quarter performance for position trading
TechRank: Composite of 6 technical indicators for multi-factor analysis
Clean Visual Design:
Green fills above 80 (strong outperformance)
Red fills below 20 (significant underperformance)
Dotted median line at 50 for quick reference
Current value label for instant reading
Flexible Benchmarks:
Compare against major indices (SPY, QQQ, IWM)
Sector ETFs for within-sector analysis
Custom symbols for specialized comparisons
Built-in Alerts:
Strong performance zone entry (>80)
Weak performance zone entry (<20)
Median crossovers (50 level)
📊 How To Use
Buy Signals:
RP crosses above 80: Stock entering leadership status
RP holding above 60: Maintaining relative strength
RP rising while price consolidating: Accumulation phase
Sell/Avoid Signals:
RP drops below 50: Losing relative strength
RP below 20: Significant underperformance
RP falling while price rising: Bearish divergence
Sector Rotation:
Compare multiple assets to find strongest sectors
Rotate into high RP assets (>70)
Exit low RP positions (<30)
🎯 Reading The Values
80-100: Exceptional outperformance - Strong buy/hold
60-80: Moderate outperformance - Hold positions
40-60: Market perform - No edge
20-40: Underperformance - Caution/reduce
0-20: Severe underperformance - Avoid/exit
⚙️ Calculation Method
Calculates percentage performance of both your stock and the benchmark
Finds the performance differential
Ranks this differential against historical values using percentile analysis
Normalizes to 0-100 scale for easy interpretation
This percentile approach adapts to different market conditions and volatility regimes, providing consistent signals whether in trending or choppy markets.
💡 Pro Tips
For Growth Stocks: Use quarterly mode with QQQ as benchmark
For Value Stocks: Use yearly mode with SPY as benchmark
For Small Caps: Compare against IWM, not SPY
For Sector Analysis: Use sector ETFs (XLK, XLF, XLE, etc.)
Combine with Price Action: High RP + price breakout = powerful signal
⚠️ Important Notes
RP is relative, not absolute - stocks can fall with high RP if the market falls harder
Choose appropriate benchmarks for meaningful comparisons
Best used in conjunction with price action and volume analysis
Historical lookback period affects sensitivity (adjustable in settings)
🔧 Customization
Fully customizable visual settings, thresholds, calculation periods, and smoothing options. Adjust the normalization lookback period (default 252 days) to fine-tune sensitivity to your trading timeframe.
📌 Credit
Inspired by TrendSpider's Relative Performance implementation, adapted for TradingView with enhanced customization options and Pine Script v6 optimization.
Tags to include: relativeperformance, relativestrength, percentile, ranking, sectorrotation, benchmark, outperformance, trendspider, marketbreadth, strengthindicator
Category: Momentum Indicators / Trend Analysis
Feel free to modify this description to match your style or add any specific points you want to emphasize!
AI Agent PRIMEFLOW v1AI Agent PRIMEFLOW v1 — Trend + Breakout + Smart Stops
*By AI Agent Community*
## Overview
PRIMEFLOW v1 is a clean, rules-based signal tool that fires only when **trend + regime + market structure** align.
It combines a **baseline trend**, a **volatility regime filter** (ATR z-score), and **Donchian breakouts**, with **ATR bands** and **Chandelier-style stops** for risk control. Optional **HTF confirmation** keeps entries in sync with higher-timeframe bias.
> Built from public trading concepts (EMA/KAMA/HMA baselines, Donchian breakout, ATR trailing). No proprietary code used.
---
## What it does (3-Layer Confirmation)
1. **Trend** – EMA50/200 relationship + user-selectable baseline (EMA/HMA/KAMA).
2. **Regime** – ATR% z-score filter reduces chop; “Conservative/Balanced/Aggressive” modes adjust threshold.
3. **Structure** – Donchian breakout confirms momentum beyond recent range.
Only when all three align do BUY/SELL labels appear. ATR bands and dynamic stops are plotted for exits and trailing.
---
## Signals & Risk
* **Long**: Trend up (EMA50>EMA200), regime trending, price crosses above baseline **and** breaks the prior Donchian high.
* **Short**: Mirror conditions to the downside.
* **Stops**: Auto-plotted **Long/Short Stop** (ATR-based, Chandelier-style).
* **Targets**: Consider 1.5–2× ATR or ATR bands; keep a runner with trailing stop.
---
## Inputs (key)
* **Signal Mode**: Conservative / Balanced / Aggressive (regime threshold).
* **Use Heikin Ashi Source** (optional smoothing).
* **Structure Lookback (Donchian)**.
* **Volatility Lookback** (for ATR z-score).
* **Baseline Type & Length**: EMA / HMA / KAMA.
* **Trend Filter EMAs**: Fast (default 50) vs Slow (default 200).
* **HTF Confirmation**: set a higher TF (blank = off).
* **ATR Length & Multiplier** (bands & stops).
* **Style toggles**: Bands, regime background, labels.
---
## Recommended Presets
**XAUUSD – M15 (scalping/intraday)**
* Mode: *Balanced* · Baseline: *EMA 50* · Donchian: *20* · ATR: *10 × 2.5* · HTF: *H1*.
**XAUUSD – H1 (intraday)**
* Baseline: *KAMA 50* · Donchian: *25* · ATR: *14 × 2.5* · HTF: *H4*.
**BTCUSDT – H1 (crypto)**
* Baseline: *EMA 100* · Donchian: *30* · ATR: *14 × 2.0* · HTF: *H4* · Mode: *Conservative* in chop.
---
## Alerts (ready)
Create alerts **Once Per Bar Close**:
* **PRIMEFLOW Long** – long entry condition met.
* **PRIMEFLOW Short** – short entry condition met.
* **Trail Flip (Long)** – long trailing stop flips (exit/trim).
* **Trail Flip (Short)** – short trailing stop flips.
Tip: Route alerts to your bot/Telegram/WA webhook. Include placeholders (e.g., `{{ticker}} | {{interval}} | {{close}} | LONG/SHORT | SL: {{plot("Long Stop")}}`).
---
## Best Practices
* Avoid taking breakouts that are **>1.5× ATR** away from baseline (overextended).
* Re-enter on pullbacks while trend & regime remain valid.
* Around high-impact news (NFP/FOMC), wait 15–30 minutes after release.
* Use **HTF 4×** your chart TF (e.g., M15→H1, H1→H4).
---
## Who it’s for
Swing/scalp traders who want higher-quality trend entries with **built-in structure confirmation** and **clear risk lines**, especially on **XAUUSD** and **BTC**.
---
## Notes
* This is an **indicator** (not a strategy). A strategy/backtest version can be provided.
* Educational purposes only. Not financial advice. Trading involves risk.
**Tags:** trend, breakout, ATR, Donchian, chandelier stop, regime filter, XAUUSD, BTC, scalping, intraday, multi-timeframe, heikin ashi
**Changelog**
v1.0 – Initial release: 3-Layer Confirmation, ATR bands/stops, HTF bias, 4 alerts.
KD The ScalperWe have to take the trade when all three EMAs are pointing in the same direction (no criss-cross, no up/down, sideways). All 3 EMAs should be cleanly separated from each other with strong spacing between them; they are not tangled, sideways, or messy. This is our first filter before entering the trade. Are the EMAs stacked neatly, and is the price outside of the 25 EMA? If price pulls back and closes near or below the 25 or 50 EMA and breaks the 100 EMA, we don't trade. Use the 100 EMA as a safety net and refrain from trading if the price touches or falls below the 100 EMA.
1. Confirm the trend- All 3 EMAs must align, and they must spread
2. Watch price pull back to the 25th or the 50 EMA
3. Wait for the price to bounce - And re-approach the 25 EMA
Why is this powerful?
Removes 80% of the low-probability Trades
It keeps you out of choppy markets
Avoids Reversal Traps
Anchors us to momentum
We take the entry when the price moves up again and touches the 25 EMA from below, and then when it breaks above the 25 EMA, or even better, when a lovely green bullish candle forms. A bullish candle indicates good momentum. When a bullish candle closes in green, it means the momentum has increased significantly. This is when we enter a long trade, with the stop-loss just below the 50 EMA and the profit target being 1.5 times the stop-loss.
The same rule applies to the bearish trade.
Anchored EMA/VWAP### Anchored EMA/VWAP Indicator
**Description:**
The **Anchored EMA/VWAP Indicator** is a powerful and versatile tool designed for traders seeking to analyze price trends and momentum from a user-defined anchor point in time. Built for TradingView using Pine Script v6, this indicator calculates and displays multiple **Exponential Moving Averages (EMAs)**, **Volume-Weighted Exponential Moving Averages (VWEMAs)**, and a **Volume-Weighted Average Price (VWAP)**, all anchored to a specific date and time chosen by the user. By anchoring these calculations, traders can focus on price action relative to significant market events, such as news releases, earnings reports, or key support/resistance levels.
The indicator supports multi-timeframe (MTF) analysis, allowing users to compute EMAs, VWEMAs, and VWAP on a higher or custom timeframe (e.g., 5-minute, 1-hour, daily) while overlaying the results on the current chart. It also includes customizable cross signals for EMA and VWEMA pairs, marked with distinct shapes (circles, diamonds, squares) to highlight potential trend changes or reversals. These features make the indicator ideal for trend-following, momentum trading, and identifying key price levels across various markets, including stocks, forex, cryptocurrencies, and commodities.
**Key Features:**
- **Anchored Calculations**: EMAs, VWEMAs, and VWAP start calculations from a user-specified anchor time, enabling analysis relative to significant market moments.
- **Multi-Timeframe Support**: Compute indicators on any timeframe (e.g., 60-minute, daily) and display them on the chart’s timeframe for flexible analysis.
- **Customizable EMAs and VWEMAs**: Four EMAs and four VWEMAs with adjustable lengths (default: 9, 21, 50, 100) and colors, with options to show or hide each.
- **Volume-Weighted Metrics**: VWAP and VWEMAs incorporate volume data, providing a more robust representation of market activity compared to standard EMAs.
- **Cross Signals**: Visual markers (circles, diamonds, squares) for crossovers between EMA and VWEMA pairs, with customizable visibility to highlight bullish (up) or bearish (down) signals.
- **User-Friendly Interface**: Organized input groups for General, EMA, VWEMA, VWAP, Arrow Settings, and Cross Visibility, with intuitive inline inputs for length and color customization.
- **Visual Clarity**: Overlaid on the price chart with distinct colors and line styles (dotted for EMAs, dashed for VWEMAs, solid for VWAP) to ensure easy interpretation.
**How to Use:**
1. **Set the Anchor Time**: Click a specific bar or enter a date/time (default: June 1, 2025) to start calculations from a significant market event.
2. **Select Timeframe**: Choose a timeframe (e.g., "5" for 5-minute, "D" for daily) to compute the indicators, allowing alignment with your trading strategy.
3. **Customize EMAs and VWEMAs**: Adjust lengths and colors for up to four EMAs and VWEMAs, and toggle their visibility to focus on relevant lines.
4. **Enable VWAP**: Display the anchored VWAP to identify volume-weighted price levels, useful as dynamic support/resistance.
5. **Monitor Cross Signals**: Enable cross visibility for specific EMA or VWEMA pairs to spot potential trend changes. Bullish crosses (e.g., shorter EMA crossing above longer EMA) are marked with green shapes below the bar, while bearish crosses are marked with red shapes above the bar.
6. **Interpret Signals**: Use EMA/VWEMA crossovers for trend confirmation, VWAP as a mean-reversion level, and volume-weighted VWEMAs for momentum analysis in high-volume markets.
**Use Cases:**
- **Trend Trading**: Identify trend direction using EMA and VWEMA crossovers, with shorter lengths (e.g., 9, 21) for faster signals and longer lengths (e.g., 50, 100) for trend confirmation.
- **Mean Reversion**: Use the anchored VWAP as a dynamic support/resistance level to trade pullbacks or breakouts.
- **Event-Based Analysis**: Anchor the indicator to significant events (e.g., earnings, economic data releases) to analyze price behavior post-event.
- **Multi-Timeframe Strategies**: Combine higher timeframe EMAs/VWAPs with lower timeframe price action for high-probability setups.
**Settings:**
- **Anchor Time**: Set the starting point for calculations (default: June 1, 2025).
- **Timeframe**: Choose the timeframe for calculations (default: 5-minute).
- **EMA/VWEMA Lengths**: Default lengths of 9, 21, 50, and 100 for both EMAs and VWEMAs, adjustable per user preference.
- **Colors**: Customizable colors with slight transparency for visual clarity.
- **Cross Visibility**: Toggle specific EMA and VWEMA cross signals (e.g., EMA1/EMA2, VWEMA1/VWEMA3) to reduce chart clutter.
- **Arrow Colors**: Green for bullish crosses, red for bearish crosses.
**Notes:**
- The indicator is overlaid on the price chart, ensuring seamless integration with price action analysis.
- VWEMAs and VWAP are volume-sensitive, making them particularly effective in markets with significant volume fluctuations.
- Ensure the anchor time is set to a valid historical or future bar to avoid calculation errors.
- Cross signals are conditional on non-NA values to prevent false positives during initialization.
**Author**: NEPOLIX
**Version**: 6 (Pine Script v6)
**Published**: For TradingView Community
This indicator is a must-have for traders looking to combine anchored, volume-weighted, and multi-timeframe analysis into a single, customizable tool. Whether you're a day trader, swing trader, or long-term investor, the Anchored EMA/VWAP Indicator provides actionable insights for informed trading decisions.
Sols Day Trading Signals (5m / 10m)This indicator is designed for day trading on the 5-minute and 10-minute charts.
Includes:
EMA 9 & EMA 21 crossover signals
MACD momentum confirmation
RSI trend filter (50+)
Buy/Sell labels directly on the chart
💡 How to Use:
Go long when EMA 9 crosses above EMA 21, MACD is positive, and RSI is above 50
Go short when EMA 9 crosses below EMA 21, MACD is negative, and RSI is below 50
Best used with proper risk management (1-2% per trade)
⚠️ Disclaimer: This is for educational purposes only — always backtest and trade responsibly.
BIST30 % Above Moving Average (Breadth)
BIST30 % Above Moving Average (Breadth)
This indicator shows the percentage of BIST30 stocks trading above a selected moving average.
It is a market breadth tool, designed to measure the overall health and participation of the market.
How it works
By default, it uses the 50-day SMA.
You can switch between SMA/EMA and choose different periods (5 / 20 / 50 / 200).
The script checks each BIST30 stock individually and counts how many are closing above the chosen MA.
Interpretation
Above 80% → Overbought zone (short-term correction likely).
Below 20% → Oversold zone (potential rebound).
Around 50% → Neutral / indecisive market.
If the index (BIST:XU030) rises while this indicator falls → the rally is narrow-based, led by only a few stocks (a warning sign).
Use cases
Short-term traders → Use MA=5 or 20 for momentum signals.
Swing / Medium-term investors → Use MA=50 for market health.
Long-term investors → Use MA=200 to track bull/bear market cycles.
Notes
This script covers only BIST30 stocks by default.
The list can be updated for BIST100 or specific sectors (e.g., banks, industrials).
Breadth indicators should not be used as standalone buy/sell signals — combine them with price action, volume, and other technical tools for confirmation.
Multi-Indicator Panel (RSI, Stoch, MACD, VIX Fix, MFI)A versatile single-pane oscillator panel combining RSI, Stochastic, MACD (scaled to 0–100), Williams VIX Fix (normalized & inverted: low value = high fear), and MFI. Each module is toggleable, with reference levels, background highlights, and ready-made alerts.
Key features
Per-indicator toggles: RSI, Stoch %K/%D, MACD (lines + optional histogram), inverted 0–100 VIX Fix, and MFI.
Standard levels & center line at 50; adjustable overbought/oversold thresholds.
Contextual background coloring (optional) for extreme conditions.
Built-in alerts: RSI/Stoch OB/OS, MACD–Signal cross, VIX Fix “High Fear/Low Fear,” and MFI OB/OS.
Unified scale: MACD mapped around 50 to align with other oscillators; VIX Fix normalized to 0–100.
How to use (quick)
Add the indicator → enable needed modules via “Indicator Toggles.”
Tune periods & levels (e.g., RSI 14, Stoch 14/3, MACD 12-26-9, VIX Fix 22/252, MFI 14).
(Optional) Turn on MACD histogram.
Create alerts from “Add alert on…” using the provided conditions.
Interpretation notes
Inverted VIX Fix: low values ⇒ high fear/volatility (potential bounces); high values ⇒ complacency.
Scaled MACD: lines around 50 ≈ MACD zero; line crosses remain valid despite scaling.
Disclaimer
Analysis tool, not financial advice. Test across timeframes/instruments and pair with risk management.
SATHYA SMA SignalThis indicator overlays 20, 50, and 200 Simple Moving Averages (SMAs) on the chart. It generates bullish signals when the 20 SMA crosses above the 200 SMA before the 50 SMA, with both above 200 SMA. Bearish signals occur when the 20 SMA crosses below the 200 SMA before the 50 SMA, with both below 200 SMA. Signals appear as distinct triangles on the chart, helping traders identify trend reversals based on systematic SMA crossovers and order of crossing.
SATHYA SMA Signal)This indicator overlays 20, 50, and 200 Simple Moving Averages (SMAs) on the chart. It generates bullish signals when the 20 SMA crosses above the 200 SMA before the 50 SMA, with both above 200 SMA. Bearish signals occur when the 20 SMA crosses below the 200 SMA before the 50 SMA, with both below 200 SMA. Signals appear as distinct triangles on the chart, helping traders identify trend reversals based on systematic SMA crossovers and order of crossing.
IV Rank (tasty-style) — VIXFix / HV ProxyIV Rank (tasty-style) — VIXFix / HV Proxy
Overview
This indicator replicates tastytrade’s IV Rank calculation—but built entirely inside TradingView.
Because TradingView does not expose live option-chain implied volatility, the script lets you choose between two widely used price-based IV proxies:
VIXFix (Williams VIX Fix): a fast-reacting volatility estimate derived from price extremes.
HV(30): 30-day annualized historical volatility of daily log returns.
The goal is to approximate the “rich vs. cheap” option volatility environment that traders use to decide whether to sell or buy premium.
Formula
IV Rank answers the question: Where is current implied volatility relative to its own 1-year range?
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IVR=
IV
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IV
current
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×100
IVcurrent: Current value of the chosen IV proxy.
IV1yHigh/Low: Highest and lowest proxy values over the user-defined lookback (default 252 trading days ≈ 1 year).
IVR = 0 → Current IV equals its 1-year low
IVR = 100 → Current IV equals its 1-year high
IVR ≈ 50 → Current IV sits mid-range
How to Use
High IV Rank (≥50–60%)
Options are relatively expensive → short-premium strategies (credit spreads, iron condors, straddles) may be more attractive.
Low IV Rank (≤20%)
Options are relatively cheap → long-premium strategies (debit spreads, calendars, diagonals) may offer better risk/reward.
Combine with your own analysis, liquidity checks, and risk management.
Inputs & Customization
IV Source: Choose “VIXFix” or “HV(30)” as the volatility proxy.
IVR Lookback: Rolling window for 1-year high/low (default 252 trading days).
VIXFix Parameters: Length and stdev multiplier to fine-tune sensitivity.
Info Label: Optional on-chart label displays current IV proxy, 1-year high/low, and IV Rank.
Alerts: Optional alerts when IVR crosses 50, falls below 20, or rises above 80.
Notes & Limitations
This indicator does not pull real option-chain IV.
It provides a close structural analogue to tastytrade’s IV Rank using price-derived proxies for markets where options data is not directly available.
For live option IV, use broker platforms or third-party data feeds alongside this script.
Tags: IV Rank, Implied Volatility, Tastytrade, VIXFix, Historical Volatility, Options, Premium Selling, Debit Spreads, Market Volatility
